{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'OCR模型免费转Markdown' && linkText !== 'OCR模型免费转Markdown' ) { link.textContent = 'OCR模型免费转Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== '模型下载攻略' ) { link.textContent = '模型下载攻略'; link.href = '/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'OCR模型免费转Markdown'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \n# \n# ```\n\n# ### Output:\n#\n#
\n#\n# \n# \n# My WebPage \n# \n# \n#

Click here for output

\n# \n# \n#
\n\n\n"},"script_size":{"kind":"number","value":7002,"string":"7,002"}}},{"rowIdx":906,"cells":{"path":{"kind":"string","value":"/notebook/str_literal.ipynb"},"content_id":{"kind":"string","value":"6416bebed84c28848996003d87f37db50ee22126"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"nkmk/python-snippets"},"repo_url":{"kind":"string","value":"https://github.com/nkmk/python-snippets"},"star_events_count":{"kind":"number","value":253,"string":"253"},"fork_events_count":{"kind":"number","value":77,"string":"77"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_event_created_at":{"kind":"timestamp","value":"2020-10-25T01:12:53","string":"2020-10-25T01:12:53"},"gha_updated_at":{"kind":"timestamp","value":"2020-10-21T13:42:42","string":"2020-10-21T13:42:42"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6941,"string":"6,941"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\ns = 'abc'\nprint(s)\n\nprint(type(s))\n\ns = \"abc\"\nprint(s)\n\nprint(type(s))\n\ns_sq = 'abc'\ns_dq = \"abc\"\n\nprint(s_sq == s_dq)\n\ns_sq = 'a\\'b\"c'\nprint(s_sq)\n\ns_sq = 'a\\'b\\\"c'\nprint(s_sq)\n\ns_dq = \"a'b\\\"c\"\nprint(s_dq)\n\ns_dq = \"a\\'b\\\"c\"\nprint(s_dq)\n\ns_sq = 'a\\'b\"c'\ns_dq = \"a'b\\\"c\"\n\nprint(s_sq == s_dq)\n\n# +\n# s = 'abc\n# xyz'\n# SyntaxError: EOL while scanning string literal\n# -\n\ns = 'abc\\nxyz'\nprint(s)\n\ns_tq = '''abc\nxyz'''\n\nprint(s_tq)\n\nprint(type(s_tq))\n\ns_tq = '''abc'''\nprint(s_tq)\n\ns_tq_sq = '''\\'abc\\'\n\"xyz\"'''\n\nprint(s_tq_sq)\n\ns_tq_dq = \"\"\"'abc'\n\\\"xyz\\\"\"\"\"\n\nprint(s_tq_dq)\n\nprint(s_tq_sq == s_tq_dq)\n\ns_tq = '''abc\n xyz'''\n\nprint(s_tq)\n\ns_multi = ('abc\\n'\n 'xyz')\n\nprint(s_multi)\n=\"PwKx7HnE9duw\" colab_type=\"code\" colab={}\ntf.global_variables_initializer().run() #init variables\n\n# + id=\"ESCuh_hg9f04\" colab_type=\"code\" colab={}\n#Julia Set\n#Compute the new values of z : z^2 + c\nzs_ = zs*zs + c\n\n# + id=\"8XEmPNlV905h\" colab_type=\"code\" colab={}\n# Have we diverged with this new value?\nnot_diverged = tf.abs(zs_) < 4\n\n# + id=\"y2qWUDkR91bo\" colab_type=\"code\" colab={}\n# Operation to update the zs and the iteration count\n# Note: We keep computing zs after they diverge! This\n# is very wasteful! There are better, if a little\n# less simple, ways to do this.\n#\nstep = tf.group( zs.assign(zs_), ns.assign_add(tf.cast(not_diverged, tf.float32)) )\n\n# + id=\"1VtXhm-m95kF\" colab_type=\"code\" colab={}\n#run\nfor i in range(200):\n step.run()\n\n# + id=\"J5mPwCwR97Rx\" colab_type=\"code\" outputId=\"25cfb1c7-aa43-42a0-b843-a43619d21fdb\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 751}\n#plot\nimport matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(16,10))\n\ndef processFractal(a):\n \"\"\"Display an array of iteration counts as a\n colorful picture of a fractal.\"\"\"\n a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1])\n img = np.concatenate([10+20*np.cos(a_cyclic),\n 30+50*np.sin(a_cyclic),\n 155-80*np.cos(a_cyclic)], 2)\n img[a==a.max()] = 0\n a = img\n a = np.uint8(np.clip(a, 0, 255))\n return a\nplt.imshow(processFractal(ns.eval()))\nplt.tight_layout(pad=0)\nplt.show() \n\n# + id=\"0G1b-Crc99O5\" colab_type=\"code\" colab={}\nsess.close()\n\n# + id=\"9PbmFYLt_QHM\" colab_type=\"code\" colab={}\n\n"},"script_size":{"kind":"number","value":2492,"string":"2,492"}}},{"rowIdx":907,"cells":{"path":{"kind":"string","value":"/machine-learning/HSE-AML-2.ipynb"},"content_id":{"kind":"string","value":"3ea7038b5cbaf714abab88b8f05bda8e19276561"},"detected_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"Kit-Law-42/hse-courses"},"repo_url":{"kind":"string","value":"https://github.com/Kit-Law-42/hse-courses"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":17209,"string":"17,209"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\n# %matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt\nimport sklearn.linear_model\nimport sklearn.model_selection\nimport random\n\nrandom.seed(137)\nrest = random.random()\n\ndef weight(word):\n # overfitted\n if word == 'lerxst@wam.umd.edu':\n return 100.0\n if word == 'car':\n return random.random()\n if word == 'dog':\n return - random.random()\n return random.random()\n\ndef has(word, text):\n return word in text \n\ndef feature(index):\n return 1\n\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Applied Machine Learning\n#\n# ## Linear Models\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Recap\n#\n# - We have some dataset\n# - We identify the problem and define the loss function\n# - Then we minimize the total loss (empirical risk, or objective) using available (training) data\n# - We vary parameters to minimize the objective function\n# - The minimizing parameters are then used to predict unknown values\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### A text classification problem\n#\n# Lets consider the **20 newsgroups** dataset:\n# -\n\nfrom sklearn.datasets import fetch_20newsgroups\ndata = fetch_20newsgroups()\ntext, label = data['data'][0], data['target_names'][data['target'][0]]\nprint(label)\nprint('----')\nprint(text[:300])\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### A linear model for classification\n#\n# Let us consider a function that tells if the `text` comes from `rec.autos`\n# -\n\nweight('car')*has('car', text) + weight('dog')*has('dog', text) + rest\n\n# Alternatively say `car` is `0` and `dog` is `1`:\n\nweight(0)*feature(0) + weight(1)*feature(1) + rest\n\n\n# + [markdown] slideshow={\"slide_type\": \"-\"}\n# How do we find those `weight` ($w$) for all the words?\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Gradient Descent\n#\n# - Last time we used `opt.fmin` and it magically found the solution\n# - The method is simple though\n# - Start with random weights $w_0$\n# - Iterate: $w_{i+1} = w_{i} - \\alpha \\times \\nabla \\mathsf{objective}(w_i)$\n# - All we need to know is the gradient of objective\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Gradient of loss\n#\n# - Last time we considered a regression problem and used $(y-p)^2$\n# - The gradient w.r.t $p$ is obvious: $- 2 (y - p)$\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Gradient check\n#\n# How can we ensure the gradient is correct?\n\n# +\ndef loss(y, p):\n return (y-p)**2\n\ndef gradient(y, p):\n return -2*(y-p)\n\np = 0.1\ny = 0.3\neps = 0.001\ngradient(y, p), (loss(y, p+eps) - loss(y, p-eps)) / (2*eps)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Gradient descent in code\n\n# +\ncurrent_p = random.random()\nalpha = 0.1\n\nfor i in range(5):\n current_p = current_p - alpha*gradient(y, current_p)\n print(i, current_p)\n\n# +\ncurrent_p = random.random()\nalpha = 0.1\n\nxs = list(range(20))\nys = []\nfor _ in xs:\n current_p = current_p - alpha*gradient(y, current_p)\n ys.append(current_p)\n \nplt.plot(xs, ys); plt.hlines(y, xs[0], xs[-1]);\n\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Classification loss\n#\n# - We will use something called **logistic loss**\n\n# +\ndef loss(y, p):\n return np.log2(1.0 + np.exp(-y*p))\n \nloss(-1, -100.0), loss(-1, +100.0)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Logistic Regression in sklearn\n# -\n\nmodel = sklearn.linear_model.SGDClassifier(loss='log', tol=1e-6)\nexample_1 = [1,0]; label_1 = [1]\nexample_2 = [0,1]; label_2 = [0]\nmodel.fit([example_1, example_2], np.ravel([label_1, label_2]))\nmodel.coef_\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Overfitting\n#\n# - We can always come up with a model that fits data perfectly\n# -\n\nweight('lerxst@wam.umd.edu')\n\n# - For some reason that's not what we want. Why?\n# - First, we need to measure if such a thing happens\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Splitting the data\n#\n# - Obviously we should not test what we fit against\n# - We should fit (train) the model on some part of data\n# - Next, we check the model against the rest\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Leave-on-out\n#\n# - Generate as many samples as there are examples\n# - Gives you a good estimate if you don't have a lot of data\n# - Gets impractical on huge datasets\n# -\n\nloo = sklearn.model_selection.LeaveOneOut()\nfor train, test in loo.split([1,2,3,4,5]):\n print(train, test)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Cross validation\n#\n# - Split the dataset into a few (say 5) non-overlapping parts\n# - Four parts go to training data and one part goes to test data\n# - Do the above 5 times to train the model and test it\n# - Makes a decent way to *detect* overfitting\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Cross validation in sklearn\n#\n# Let's consider indices of data\n# -\n\nxval = sklearn.model_selection.KFold(n_splits=3)\nfor train, test in xval.split([1,2,3,4,5,6]):\n print(train, test)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### This thing is an ill-posed problem\n#\n# - A mathematical problem is ill-posed when the solution is not unique\n# - That's exactly the case of regression/classification/...\n# - We need to make the problem well-posed: *regularization*\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Structural risk minimization\n#\n# - Structural risk is empirical risk plus regularizer\n# - Instead of minimizing empirical risk we find some tradeoff\n# - Regularizer is a function of model we get\n# - $\\mathsf{objective} = \\mathsf{loss} + \\mathsf{regularizer}$\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Regularizer\n#\n# - A functions that reflects the complexity of a model\n# - What is the complexity of a set of 'if ... then'?\n# - Not obvious for linear model but easy to invent something\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### $\\ell_1$ regularizer\n#\n# - Derivative is const\n# - Forces weight to be zero if it doesn't hurt performance much \n# - Use if you believe some features are useless\n# -\n\nclassification_model = sklearn.linear_model.SGDClassifier(loss='log', penalty='l1');\nregression_model = sklearn.linear_model.SGDRegressor(penalty='l1');\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### $\\ell_2$ regularizer\n#\n# - Derivative is linear\n# - Forces weights to get *similar* magnitude if it doesn't hurt performance much\n# - Use if you believe all features are more or less important\n# -\n\nclassification_model = sklearn.linear_model.SGDClassifier(loss='log', penalty='l2');\nregression_model = sklearn.linear_model.SGDRegressor(penalty='l2');\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Elastic net\n#\n# - Just a weighted sum of $\\ell_1$ and $\\ell_2$ regularizers\n# - An attempt to get useful properties of both\n# -\n\nregression_model = sklearn.linear_model.SGDRegressor(penalty='elasticnet')\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Limitations of linearity\n#\n# - In low-dimensional spaces linear models are not very 'powerful' (can we define that?)\n# - The higher dimensionality, the more powerful linear model becomes\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Sparse features\n#\n# - We say features are sparse when most of the values are zero\n# - Examples: visited hosts, movies that user liked, ...\n# - Sparse features are efficient in high-dimensional setting\n# -\n\n[0, 0, ..., 1, ..., 0, 0, 1, 0];\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### One hot encoding, hashing trick\n#\n# - One way to encode categorical things like visited hosts\n# - We enumerate all the hosts\n# - We put 1 to position of every host, 0 otherwise\n# - Hashing trick: instead of enumerating them just hash\n# -\n\nprint(hash('hse.ru'))\nprint(hash('hse.ru') % 2**16)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Hashing vectorizer in sklearn\n\n# +\nfrom sklearn.feature_extraction.text import HashingVectorizer\n\nvectorizer = HashingVectorizer(n_features=10, binary=True)\nfeatures = vectorizer.fit_transform(['hello there', 'hey there'])\nprint(features.todense())\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### When do we use linear models?\n#\n# - It is definitely the first thing to try if you have some text data\n# - In general a good choice for any sparse data\n# - This approach is pretty much the fastest one\n# - Even if some method outperforms, you still get a good baseline\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Self-assesment questions\n#\n# 1. You noticed that your linear model learned a weight of **95.3** for the word `the`. *Is there a problem? [Y]/N*\n# 2. The train loss is **0.43** and the test loss is **0.39**. *Is it an example of ..? [a) overfitting] b) underfitting c) I don't know*\n# 3. You've got asically infinite amounts of data. *Do you have to use regularization? Y/N*\n# 4. You believe your dataset is pretty noisy and some features are broken. *You use a) L1 b) L2 c) no regularization* \n# 5. Why do we hash words? *a) it's simpler b) it's faster c) it's more reliable*\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ### Homework 1\n#\n# - No score, just has to be done\n# - Load dataset, create linear model, train, and explain the results\n# - The template is provided: `HSE-AML-HW1.ipynb`\n# - Hint: check the code examples for `KFold`, `HashingVectorizer`, `LogisticRegression`\n"},"script_size":{"kind":"number","value":9598,"string":"9,598"}}},{"rowIdx":908,"cells":{"path":{"kind":"string","value":"/f1_standings_project.ipynb"},"content_id":{"kind":"string","value":"28506a4fe696135d6ef50ce9288b614edef02884"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"andrewsloan/Formula-One-Standings"},"repo_url":{"kind":"string","value":"https://github.com/andrewsloan/Formula-One-Standings"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":14977,"string":"14,977"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ### Importing all the proper tools and retrieving data from API\n\n# +\nimport pandas as pd\nimport requests\nimport json\nfrom IPython.display import clear_output\nimport os\n\nurl = 'http://ergast.com/api/f1/2018/21/driverStandings.json'\njson_data = requests.get(url).json()\ndriver_standings = json_data[\"MRData\"][\"StandingsTable\"][\"StandingsLists\"][0][\"DriverStandings\"]\n#api data is stored in driver_standings\n# -\n\n# ### The data necessary to construct the dataframe is stored in various datatypes within the API. I created a class to organize the data in a manner from which we can create the dataframe with all items.\n\n# +\nclass Driver_info:\n\n def __init__(self, sub_data, category):\n self.list_name = []\n self.sub_data = sub_data\n self.category = category\n\n def construct_list(self):\n if self.sub_data == \"Driver\": \n for i in driver_standings:\n self.list_name.append(i[self.sub_data][self.category])\n # Driver information is a dictionary within DriverStandings\n elif self.sub_data == \"Constructors\": \n for i in driver_standings:\n self.list_name.append(i[self.sub_data][0][self.category])\n # Constructor information is a list within a dictionary within DriverStandings\n else: \n for i in driver_standings:\n self.list_name.append(int(i[self.category]))\n # all else is within the main DriverStandings dictionary\n\nfirst_name = Driver_info('Driver', 'givenName')\nfirst_name.construct_list()\n\nlast_name = Driver_info('Driver', 'familyName')\nlast_name.construct_list()\n\ndob = Driver_info('Driver', 'dateOfBirth')\ndob.construct_list()\n\nnationality = Driver_info('Driver', 'nationality')\nnationality.construct_list()\n\nconstructor = Driver_info('Constructors', 'name')\nconstructor.construct_list()\n\nposition = Driver_info('n/a', 'position')\nposition.construct_list()\n\npoints = Driver_info('n/a', 'points')\npoints.construct_list()\n\nd = {'First Name': first_name.list_name, \n 'Last Name': last_name.list_name,\n 'DOB': dob.list_name,\n 'Nationality': nationality.list_name,\n 'Constructor': constructor.list_name,\n 'Position': position.list_name,\n 'Points': points.list_name\n }\n\n#d represents all the data to be included in the dataframe\n# -\n\n# ### The user will sort the data in a manner of their choice. The sort_standings class assures the data is sorted in a proper manner.\n\nclass Sort_standings:\n\n def sort_action (self):\n sorter = input('How would you like to sort the standings?\\n(First Name, Last Name, DOB, Nationality, Constructor, Points) ')\n os.system('cls' if os.name == 'nt' else 'clear')\n clear_output() \n while sorter not in df.columns:\n print (f\"\\n'{sorter}' is not valid. Please enter term in proper format.\")\n sorter = input('\\nHow would you like to sort the standings?\\n(First Name, Last Name, DOB, Nationality, Constructor, Points) ')\n os.system('cls' if os.name == 'nt' else 'clear')\n clear_output() \n return sorter\n\n\n# ### The Dataframe \n\ndf = pd.DataFrame(data=d)\ndf.set_index(\"Position\", inplace=True)\nsort_standings = Sort_standings()\nsort_by = sort_standings.sort_action()\ndf = df.sort_values(sort_by, ascending=False if sort_by == 'Points' else True)\n#Points will be most to least\n#Oldest to youngest for DOB\n#All others are alphabetical\ndf\n\nboston.feature_names\n\nboston_tensor = torch.from_numpy(boston.data)\nboston_tensor.size()\n\nboston_tensor[:2]\n\nboston_tensor[:10,:5]\n\n# ### 3d- tensor\n\n# +\nfrom PIL import Image\n\npanda = np.array(Image.open('../data/images/panda.jpg').resize((224,224)))\npanda_tensor = torch.from_numpy(panda)\npanda_tensor.size()\n# -\n\nplt.imshow(panda);\n\n# ### Slicing Tensor\n\nsales = torch.FloatTensor([1000.0,323.2,333.4,444.5,1000.0,323.2,333.4,444.5])\n\nsales[:5]\n\nsales[:-5]\n\nplt.imshow(panda_tensor[:,:,0].numpy());\n\nplt.imshow(panda_tensor[25:175,60:130,0].numpy());\n\n# ### Select specific element of tensor\n\n#torch.eye(shape) produces an diagonal matrix with 1 as it diagonal #elements.\nsales = torch.eye(3,3)\nsales[0,1]\n\n# ### 4D Tensor\n\nfrom glob import glob\n#Read cat images from disk\ndata_path='/Users/vishnu/Documents/fastAIPytorch/fastai/courses/dl1/data/dogscats/train/cats/'\ncats = glob(data_path+'*.jpg')\n#Convert images into numpy arrays\ncat_imgs = np.array([np.array(Image.open(cat).resize((224,224))) for cat in\ncats[:64]])\ncat_imgs = cat_imgs.reshape(-1,224,224,3)\ncat_tensors = torch.from_numpy(cat_imgs)\ncat_tensors.size()\n\n# ### Tensor addition and multiplication\n\n# +\n#Various ways you can perform tensor addition\na = torch.rand(2,2) \nb = torch.rand(2,2)\nc = a + b\nd = torch.add(a,b)\n#For in-place addition\na.add_(5)\n\n#Multiplication of different tensors\n\na*b\na.mul(b)\n#For in-place multiplication\na.mul_(b)\n# -\n\n# ### On GPU\n\n# +\na = torch.rand(10000,10000)\nb = torch.rand(10000,10000)\n\na.matmul(b)\n#Time taken : 3.23 s\n# -\n\n#Move the tensors to GPU\na = a.cuda()\nb = b.cuda()\na.matmul(b)\n#Time taken : 11.2 µs \n\n# ### Variables\n\nfrom torch.autograd import Variable\nx = Variable(torch.ones(2,2),requires_grad=True)\ny = x.mean()\ny.backward()\nx.grad\n\nx.grad_fn\n\nx.data\n\ny.grad_fn\n\n\n# ### Create data for our neural network\n\ndef get_data():\n train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n dtype = torch.FloatTensor\n X = Variable(torch.from_numpy(train_X).type(dtype),requires_grad=False).view(17,1)\n y = Variable(torch.from_numpy(train_Y).type(dtype),requires_grad=False)\n return X,y\n\n\n# ### Create learnable parameters\n\ndef get_weights():\n w = Variable(torch.randn(1),requires_grad = True)\n b = Variable(torch.randn(1),requires_grad=True)\n return w,b\n\n\n# ### Implement Neural Network\n\ndef simple_network(x):\n y_pred = torch.matmul(x,w)+b\n return y_pred\n\n\n# ### Implement Neural Network in Pytorch\n\nimport torch.nn as nn\nf = nn.Linear(17,1) # Much simpler.\nf\n\n\n# ### Implementing Loss Function\n\ndef loss_fn(y,y_pred):\n loss = (y_pred-y).pow(2).sum()\n for param in [w,b]:\n if not param.grad is None: param.grad.data.zero_()\n loss.backward()\n return loss.data[0]\n\n\n# +\n### Implementing Optimizer\n# -\n\ndef optimize(learning_rate):\n w.data -= learning_rate * w.grad.data\n b.data -= learning_rate * b.grad.data\n\n\n# ## Loading Data\n\n# ### Defining Dataset\n\nfrom torch.utils.data import Dataset\nclass DogsAndCatsDataset(Dataset):\n def __init__(self,):\n pass\n def __len__(self):\n pass\n def __getitem__(self,idx):\n pass\n\n\nclass DogsAndCatsDataset(Dataset):\n def __init__(self,root_dir,size=(224,224)):\n self.files = glob(root_dir)\n self.size = size\n def __len__(self):\n return len(self.files)\n def __getitem__(self,idx):\n img = np.asarray(Image.open(self.files[idx]).resize(self.size))\n label = self.files[idx].split('/')[-2]\n return img,label\n\n\n# ### Defining DataLoader to iterate over Dogs and Cats Dataset\n\n# +\nfrom torch.utils.data import Dataset, DataLoader\n\ndataloader = DataLoader(DogsAndCatsDataset,batch_size=32,num_workers=2)\nfor imgs , labels in dataloader:\n #Apply your DL on the dataset.\n pass\n# -\n\n\nsubplot(1, 5, 1);\nplt.imshow(train_img[4].reshape(28,28),\n cmap = plt.cm.gray, interpolation='nearest',\n clim=(0, 255));\nplt.xlabel('784 Components', fontsize = 12)\nplt.title('Original Image', fontsize = 14);\n\n# 331 principal components\nplt.subplot(1, 5, 2);\nplt.imshow(explainedVariance(.99, train_img)[4].reshape(28, 28),\n cmap = plt.cm.gray, interpolation='nearest',\n clim=(0, 255));\nplt.xlabel('331 Components', fontsize = 12)\nplt.title('99% of Explained Variance', fontsize = 14);\n\n# 154 principal components\nplt.subplot(1, 5, 3);\nplt.imshow(explainedVariance(.95, train_img)[4].reshape(28, 28),\n cmap = plt.cm.gray, interpolation='nearest',\n clim=(0, 255));\nplt.xlabel('154 Components', fontsize = 12)\nplt.title('95% of Explained Variance', fontsize = 14);\n\n# 87 principal components\nplt.subplot(1, 5, 4);\nplt.imshow(explainedVariance(.90, train_img)[4].reshape(28, 28),\n cmap = plt.cm.gray, interpolation='nearest',\n clim=(0, 255));\nplt.xlabel('87 Components', fontsize = 12)\nplt.title('90% of Explained Variance', fontsize = 14);\n\n# 59 principal components\nplt.subplot(1, 5, 5);\nplt.imshow(explainedVariance(.85, train_img)[4].reshape(28, 28),\n cmap = plt.cm.gray, interpolation='nearest',\n clim=(0, 255));\nplt.xlabel('59 Components', fontsize = 12)\nplt.title('85% of Explained Variance', fontsize = 14);\n# -\n\n# ## PCA to Speed up Machine Learning Algorithms (Logistic Regression)\n\n# Mention how long it takes for me to run classification with 99, 95, 90, 85 (maybe make a table). Go that PCA is not necessary in every data science workflow\n#\n#\n# Need to put the steps for applying PCA for machine learning applications\n# 1. Fit PCA on training set. Note: we are fitting PCA on the training set only\n# 2. Apply the mapping (transform) to both the training set and the test set. \n# 3. Train your machine learning algorithm (in this case logistic regression) on the transformed training set\n# 4. Test your machine learning algorithm on the transformed test set.\n#\n\n# [Logistic Regression Sklearn Documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)
\n# One thing I like to mention is the importance of parameter tuning. While it may not have mattered much for the toy digits dataset, it can make a major difference on larger and more complex datasets you have. Please see the parameter: solver (if you think the algorithm is too slow)\n\n# Step 1: Import the model you want to use\n\n# In sklearn, all machine learning models are implemented as Python classes\n\nfrom sklearn.linear_model import LogisticRegression \n\n# Step 2: Make an instance of the Model\n\n# time it on my computer with and without PCA for viewers benefit\n\n# all parameters not specified are set to their defaults\n# default solver is incredibly slow thats why we change it\n# solver = 'lbfgs'\nlogisticRegr = LogisticRegression()\n\n# Step 3: Training the model on the data, storing the information learned from the data\n\n# Model is learning the relationship between x (digits) and y (labels)\n\nlogisticRegr.fit(train_img_PCA, train_lbl)\n\n# Step 4: Predict the labels of new data (new images)\n\n# Uses the information the model learned during the model training process\n\n# Returns a NumPy Array\n# Predict for One Observation (image)\nlogisticRegr.predict(test_img_PCA[0].reshape(1,-1))\n\n# Predict for Multiple Observations (images) at Once\nlogisticRegr.predict(test_img_PCA[0:10])\n\n# ## Measuring Model Performance\n\n# accuracy (fraction of correct predictions): correct predictions / total number of data points\n\n# Basically, how the model performs on new data (test set)\n\n# (maybe look into F1 score with this just to change it up a bit, dont want viewers to think accuracy is only useful metric)\n\nscore = logisticRegr.score(test_img_PCA, test_lbl)\nprint(score)\n\n# http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html or F1\n\n\n"},"script_size":{"kind":"number","value":11740,"string":"11,740"}}},{"rowIdx":909,"cells":{"path":{"kind":"string","value":"/AppliedDataScienceCapstone - Part2.ipynb"},"content_id":{"kind":"string","value":"4900bf19fef618944ac8731c20a478e54983e144"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"rycooley57/Coursera_Capstone"},"repo_url":{"kind":"string","value":"https://github.com/rycooley57/Coursera_Capstone"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":15646,"string":"15,646"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: myenv\n# language: python\n# name: myenv\n# ---\n\n# +\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport tensorflow as tf\nimport glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport PIL\nfrom scipy import misc\nfrom tensorflow.keras import layers\n\nimport time\nfrom IPython import display\n# To generate GIFs\nimport imageio\nprint(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\ntf.debugging.set_log_device_placement(True)\n\n# +\n#get training data noise to 200, Epoch to 90, kernel initializer\n#(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()\n\nBATCH_SIZE = 256 #256 #64\nIMG_HEIGHT = 76 #32 #76 #152 #218\nIMG_WIDTH = 64 #28 #64 #124 #178\nBUFFER_SIZE = 60000\n\n#NOTE: IN TENSOR FLOW [batch,height,width,channel] height comes before width\n\ndef decode_img(img):\n # convert the compressed string to a 3D uint8 tensor\n img = tf.image.decode_jpeg(img, channels=3)\n # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n #img = tf.image.convert_image_dtype(img, tf.float32)\n img = tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH])\n img = tf.dtypes.cast(img, tf.float32)\n img = (img - 127.5) / 127.5\n # resize the image to the desired size.\n return img#tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH])\n\ndef process_path(file_path):\n # load the raw data from the file as a string\n img = tf.io.read_file(file_path)\n # decode the image\n img = decode_img(img)\n return img\n\nimg_files = glob.glob('training_faces/*.jpg')[:BUFFER_SIZE]\ndataset = tf.data.Dataset.from_tensor_slices(img_files)\n# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\nAUTOTUNE = tf.data.experimental.AUTOTUNE\nprocessed_dataset = dataset.map(process_path, num_parallel_calls=AUTOTUNE)\n\nfor f in processed_dataset.take(1):\n print(f.numpy().shape)\n\n\n# +\n#[batch_size, height, width, color_channels]\n#train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n# Normalize the images to [-1, 1] not sure why, we'll try 0 and 1 and see the difference\n#train_images = (train_images - 127.5) / 127.5\n\n# Batch and shuffle the data\n#train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n\ndef prepare_for_training(ds, cache=True, shuffle_buffer_size=1000):\n # If this is a small dataset, it only loads it once, and keeps it in memory.\n # we use `.cache(filename)` to cache preprocessing work for datasets that don't\n # fit in memory.\n \n if cache:\n if isinstance(cache, str):\n ds = ds.cache(cache)\n else:\n ds = ds.cache()\n\n ds = ds.shuffle(buffer_size=shuffle_buffer_size)\n\n # Repeat forever\n #ds = ds.repeat()\n\n ds = ds.batch(BATCH_SIZE)\n\n # `prefetch` lets the dataset fetch batches in the background while the model\n # is training.\n #ds = ds.prefetch(buffer_size=AUTOTUNE)\n\n return ds\n\ntrain_dataset = prepare_for_training(processed_dataset,shuffle_buffer_size=BUFFER_SIZE)\nimage_batch = next(iter(train_dataset))\nplt.imshow( (image_batch.numpy()[0] + 1) / 2.0 )\n\n# +\nNOISE_SHAPE = 300\ninit = tf.random_normal_initializer(mean=0.0, stddev=0.02)\ndef generator_model():\n model = tf.keras.Sequential()\n model.add(layers.Dense(19*16*256, use_bias=False, input_shape=(NOISE_SHAPE,), kernel_initializer=init))\n model.add(layers.BatchNormalization())\n model.add(layers.LeakyReLU())\n\n model.add(layers.Reshape((19, 16, 256)))\n assert model.output_shape == (None, 19, 16, 256) # Note: None is the batch size\n\n model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False, kernel_initializer=init))\n assert model.output_shape == (None, 19, 16, 128)\n model.add(layers.BatchNormalization())\n model.add(layers.LeakyReLU())\n\n model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False, kernel_initializer=init))\n assert model.output_shape == (None, 38, 32, 64)\n model.add(layers.BatchNormalization())\n model.add(layers.LeakyReLU())\n\n model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh', kernel_initializer=init))\n assert model.output_shape == (None, 76, 64, 3)\n\n return model\n\n\ngenerator = generator_model()\ngenerator.summary()\n\nnoise = tf.random.normal([1, NOISE_SHAPE])\ngenerated_image = generator(noise, training=False)\n\nplt.imshow((generated_image[0] + 1) / 2.0)\n\n# +\ninit = tf.random_normal_initializer(mean=0.0, stddev=0.02)\ndef discriminator_model():\n model = tf.keras.Sequential()\n model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n input_shape=[76, 64, 3], kernel_initializer=init))\n model.add(layers.LeakyReLU())\n model.add(layers.Dropout(0.3))\n\n model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same', kernel_initializer=init))\n model.add(layers.LeakyReLU())\n model.add(layers.Dropout(0.3))\n\n model.add(layers.Flatten())\n model.add(layers.Dense(1))\n\n return model\n\ndiscriminator = discriminator_model()\ndecision = discriminator(generated_image)\nprint (decision)\n\n\n# +\n#smoothing class=1 to [0.7, 1.2]\ndef smooth_positive_labels(label):\n return label - 0.3 + (tf.random.uniform(label.shape) * 0.5)\n#smoothing class=0 to [0.0, 0.3]\ndef smooth_negative_labels(label):\n return label + (tf.random.uniform(label.shape) * 0.3)\n\n# This method returns a helper function to compute cross entropy loss\ncross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n\ndef discriminator_loss(real_output, fake_output):\n real_label = tf.ones_like(real_output)\n fake_label = tf.zeros_like(fake_output)\n \n real_loss = cross_entropy(smooth_positive_labels(real_label), real_output)\n fake_loss = cross_entropy(smooth_negative_labels(fake_label), fake_output)\n total_loss = real_loss + fake_loss\n return total_loss\n\ndef generator_loss(fake_output):\n return cross_entropy(tf.ones_like(fake_output), fake_output)\n\ngenerator_optimizer = tf.keras.optimizers.Adam(1e-4)\ndiscriminator_optimizer = tf.keras.optimizers.Adam(1e-4)\n# -\n\ncheckpoint_dir = './training_checkpoints'\ncheckpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n discriminator_optimizer=discriminator_optimizer,\n generator=generator,\n discriminator=discriminator)\nmanager = tf.train.CheckpointManager(checkpoint,checkpoint_dir, max_to_keep=3, checkpoint_name='ckpt')\n\n# +\nEPOCHS = 190\nnoise_dim = NOISE_SHAPE\nnum_examples_to_generate = 9\n\n# We will reuse this seed overtime (so it's easier)\n# to visualize progress in the animated GIF)\nseed = tf.random.normal([num_examples_to_generate, noise_dim])\nimgs_dir = './epoch_images/'\n\n\n# +\n\n# This annotation causes the function to be \"compiled\".\n@tf.function\ndef train_step(images):\n noise = tf.random.normal([BATCH_SIZE, noise_dim])\n\n with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n generated_images = generator(noise, training=True)\n\n real_output = discriminator(images, training=True)\n fake_output = discriminator(generated_images, training=True)\n\n gen_loss = generator_loss(fake_output)\n disc_loss = discriminator_loss(real_output, fake_output)\n\n gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n \n generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n\ndef train(dataset, epochs):\n for epoch in range(140,epochs):\n start = time.time()\n batch_count = 0\n\n for image_batch in dataset:\n train_step(image_batch)\n batch_count += image_batch.numpy().shape[0]\n display.clear_output(wait=True)\n print('Batches processed {0}'.format(batch_count))\n print('Epoch: {0}'.format(epoch + 1))\n \n\n # Produce images for the GIF as we go\n display.clear_output(wait=True)\n generate_and_save_images(generator, epoch + 1, seed)\n\n # Save the model every 10 epochs\n if (epoch + 1) % 10 == 0:\n manager.save()\n print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n\n # Generate after the final epoch\n display.clear_output(wait=True)\n generate_and_save_images(generator, epochs, seed)\n \ndef generate_and_save_images(model, epoch, test_input):\n # `training` is set to False.\n # This is so all layers run in inference mode (batchnorm).\n predictions = model(test_input, training=False)\n\n fig = plt.figure(figsize=(3,3))\n\n for i in range(predictions.shape[0]):\n plt.subplot(3, 3, i+1)\n plt.imshow( (predictions[i] + 1) / 2.0)\n plt.axis('off')\n\n plt.savefig(imgs_dir+'image_at_epoch_{:04d}.png'.format(epoch))\n plt.show()\n\n\n# +\ncheckpoint.restore(manager.latest_checkpoint)\nif manager.latest_checkpoint:\n print(\"Restored from {}\".format(manager.latest_checkpoint))\nelse:\n print(\"Initializing from scratch.\")\n\ntrain(train_dataset, EPOCHS)\n\n# +\n\nanim_file = 'dcgan.gif'\n\nwith imageio.get_writer(anim_file, mode='I') as writer:\n filenames = glob.glob(imgs_dir+'image*.png')\n filenames = sorted(filenames)\n last = -1\n for i,filename in enumerate(filenames):\n frame = 2*(i**0.5)\n if round(frame) > round(last):\n last = frame\n else:\n continue\n image = imageio.imread(filename)\n writer.append_data(image)\n image = imageio.imread(filename)\n writer.append_data(image)\n\nimport IPython\nif IPython.version_info > (6,2,0,''):\n display.Image(filename=anim_file)\n\n# -\n\ntf.saved_model.save(generator, \"./models\")\n\n\n"},"script_size":{"kind":"number","value":10279,"string":"10,279"}}},{"rowIdx":910,"cells":{"path":{"kind":"string","value":"/AutoGluon+NVDIA_Rapids.ipynb"},"content_id":{"kind":"string","value":"cfd41945d7f37e76b2a65ddc11d835cf3ab84496"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"swarnava-96/NVDIA-RAPIDS"},"repo_url":{"kind":"string","value":"https://github.com/swarnava-96/NVDIA-RAPIDS"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":789210,"string":"789,210"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"scfLT2i0MLyD\"\n# # AutoGluon with NVDIA Rapids\n#\n\n# + id=\"B0C8IV5TQnjN\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"4c8aea0b-95bb-4d0e-a4f8-2fdb31da1ea7\"\n# !nvidia-smi\n\n# + id=\"3Jeh6EJBaBkv\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"ef5c1588-5b84-47bf-e52f-b1f4fb9dae84\"\n# This get the RAPIDS-Colab install files and test check your GPU. Run this and the next cell only.\n# Please read the output of this cell. If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n# !git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n# !python rapidsai-csp-utils/colab/env-check.py\n\n# + id=\"JI7UTXbhaBon\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"c4d0477b-b3f3-43e3-ef2b-9deeab27b8c7\"\n# This will update the Colab environment and restart the kernel. Don't run the next cell until you see the session crash.\n# !bash rapidsai-csp-utils/colab/update_gcc.sh\nimport os\nos._exit(00)\n\n# + id=\"qg2SasWKaBsB\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"051faf4b-cf63-4ba9-b8fc-04e4976c51fd\"\n# This will install CondaColab. This will restart your kernel one last time. Run this cell by itself and only run the next cell once you see the session crash.\nimport condacolab\ncondacolab.install()\n\n# + id=\"fKSMDrN_aB-v\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"5e5dd88f-3b0d-4740-b93f-831584e0e6d3\"\n# you can now run the rest of the cells as normal\nimport condacolab\ncondacolab.check()\n\n# + id=\"m0jdXBRiDSzj\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"4beba58c-9efe-44f7-d5d6-621b432fc346\"\n# Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py '\n# The options are 'stable' and 'nightly'. Leaving it blank or adding any other words will default to stable.\n# The option are default blank or 'core'. By default, we install RAPIDSAI and BlazingSQL. The 'core' option will install only RAPIDSAI and not include BlazingSQL, \n# !python rapidsai-csp-utils/colab/install_rapids.py stable\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"_ks5dh89SCp5\" outputId=\"51ac12e2-20e2-453d-bf3b-f760a9e224dd\"\n# !pip install \"autogluon.tabular[all]==0.1.1b20210312\"\n# !pip install AutoViz\n# !pip install xlrd\n\n# + id=\"Q5wlr-Q7aDf1\"\nimport pandas as pd\ndfe = pd.read_csv('/content/titanic_train.csv')\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"cq9ycjkuf8ge\" outputId=\"8fc4e094-58c6-4d5e-e382-99cf4f820375\"\nfrom autoviz.AutoViz_Class import AutoViz_Class\n\n#Instantiate the AutoViz class\nAV = AutoViz_Class()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 204} id=\"tETBQD9hf_nD\" outputId=\"ec3c9e4f-611d-4c99-9b04-ae2c4757baf6\"\ndfe.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"bp1QydiUgIxy\" outputId=\"02eab9af-3f40-4967-dbbf-0aa4ee3c15ce\"\nftc = AV.AutoViz(filename='', \n sep ='' , \n depVar ='Survived', \n dfte = dfe, \n header = 0, \n verbose = 0, \n lowess = False, \n chart_format ='png', \n max_rows_analyzed = 100000, \n max_cols_analyzed = 30\n)\n\n# + id=\"9z3M248ygVOm\"\nfrom autogluon.tabular import TabularDataset, TabularPredictor\nfrom autogluon.tabular.models.lr.lr_rapids_model import LinearRapidsModel\nfrom autogluon.tabular.models.knn.knn_rapids_model import KNNRapidsModel\n\ntrain_data = TabularDataset('/content/titanic_train.csv')\ntest_data = TabularDataset('/content/titanic_test.csv')\n\nlabel = 'Survived'\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 419} id=\"6f74y47egr_J\" outputId=\"e2b42ff3-2483-4835-cb21-19567deb2615\"\ntrain_data\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"nZIDrl43gxBr\" outputId=\"544b3f65-34bd-4a7a-fb9e-97683b6a4ce8\"\n#using integrated RAPIDS models along with some boosting models\npredictor = TabularPredictor(\n label=label,\n eval_metric='accuracy',\n learner_kwargs={'ignored_columns': ['PassengerId']}\n).fit(\n train_data,\n presets='best_quality',\n hyperparameters={'XGB': {'ag_args_fit': {'num_gpus': 1}},\n 'GBM': [{}, {'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, 'GBMLarge'],\n 'CAT': {'ag_args_fit': {'num_gpus': 1}},\n KNNRapidsModel: {},\n LinearRapidsModel: {},\n \n },\n)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 419} id=\"pbmcEB_6g3My\" outputId=\"d043b5e5-5a89-4d86-9697-0f0635413a25\"\ntest_data\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 204} id=\"z_6UoQ3vhVlm\" outputId=\"da520943-d212-41ff-d0fa-807273efe8a3\"\nimport pandas as pd\nsubmission = test_data[['PassengerId']]\ntest_pred_proba = predictor.predict(test_data)\ntest_pred_proba=pd.DataFrame(test_pred_proba,columns=['Survived'])\nsubmission = pd.concat([submission, test_pred_proba], axis=1)\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 545} id=\"xApjbi9ehkHz\" outputId=\"6d9939ef-e21c-4c60-9059-cd4e4ed8a604\"\npredictor.leaderboard(silent=True)\n"},"script_size":{"kind":"number","value":5385,"string":"5,385"}}},{"rowIdx":911,"cells":{"path":{"kind":"string","value":"/d02_task.ipynb"},"content_id":{"kind":"string","value":"11fe22e72b9018f94277f2c7c009c0e7b43f34e2"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"moscow-dust/Rosalind-C"},"repo_url":{"kind":"string","value":"https://github.com/moscow-dust/Rosalind-C"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":2123561,"string":"2,123,561"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport numpy as np\nimport pandas as pd\n\nfood=pd.read_csv('food.csv')\n\n\nfood.head()\n\nfood.shape\n\nfood.pivot_table(index='City',columns='Gender',values='Spends')\n\nfood.pivot_table(index=['City','Item'],columns=['Gender','Frequency'],values='Spends')\n\n\n"},"script_size":{"kind":"number","value":516,"string":"516"}}},{"rowIdx":912,"cells":{"path":{"kind":"string","value":"/Predict_0mode_curve.ipynb"},"content_id":{"kind":"string","value":"1aeb057b342393851bb173d1c4da398241e48aa7"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"imzhangtianyi/dispersion"},"repo_url":{"kind":"string","value":"https://github.com/imzhangtianyi/dispersion"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":14978,"string":"14,978"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 2\n# language: python\n# name: python2\n# ---\n\n# +\nimport matplotlib.pyplot as plt\nimport cPickle\nfrom dispersion_features import extract_features\n\ncls0 = cPickle.load(open('mode0_classifier.pkl', 'rb'))\nw = []\nl = []\nfor i in range(1,901):\n name = 'a{}'.format(i)\n x = extract_features(name).zetas().values\n\n if cls0.predict(x).any():\n w.append(extract_features(name).properties()['W'].values[0])\n l.append(extract_features(name).properties()['L'].values[0])\nplt.loglog(map(lambda x: x*100, l),w)\nplt.xlabel('Wave number [m$^-$$^1$]')\nplt.ylabel('Frequency [$s^-$$^1$]')\nplt.show()\n# -\n\n\n"},"script_size":{"kind":"number","value":831,"string":"831"}}},{"rowIdx":913,"cells":{"path":{"kind":"string","value":"/src/Ensemble (accuracy 0.74).ipynb"},"content_id":{"kind":"string","value":"16a982332ffa58b13de3a6bfbe1646a483128053"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"yuchen996/Motion_Recognition-BidirectionalLSTM-DATA2040_Final_Project"},"repo_url":{"kind":"string","value":"https://github.com/yuchen996/Motion_Recognition-BidirectionalLSTM-DATA2040_Final_Project"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6287588,"string":"6,287,588"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"YQdMNNpbOM1x\" outputId=\"09180f0f-2821-4780-a732-71026e13e8cc\"\n# import os\n# import numpy as np\n# import shutil\n# import random\n\n# root_dir = 'final2040/HMDB_more/'\n\n# test_ratio = 0.20\n\n# train_dir = 'final2040/HMDB_more/train2/'\n# test_dir = 'final2040/HMDB_more/test2/'\n\n# for cls in classes:\n# os.makedirs(train_dir + cls)\n# os.makedirs(test_dir + cls)\n\n# src = root_dir + cls\n\n# allFileNames = os.listdir(src)\n# np.random.shuffle(allFileNames)\n# train_FileNames, test_FileNames = np.split(np.array(allFileNames),[int(len(allFileNames)* (1 - test_ratio))])\n\n\n# train_FileNames = [src+'/'+ name for name in train_FileNames.tolist()]\n# test_FileNames = [src+'/' + name for name in test_FileNames.tolist()]\n\n# print(\"*****************************\")\n# print('Total files: ', len(allFileNames))\n# print('Training: ', len(train_FileNames))\n# print('Testing: ', len(test_FileNames))\n# print(\"*****************************\")\n\n\n\n# for name in train_FileNames:\n# shutil.copy(name, train_dir + cls)\n\n# for name in test_FileNames:\n# shutil.copy(name, test_dir + cls)\n# print(\"Copying Done!\")\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"_wZMKaF-AmTM\" outputId=\"665afd4d-ef9a-4052-e528-84267c7a360a\"\npip install keras-video-generators\n\n# + id=\"JRTzE66vB5cO\"\nimport os\nimport glob\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow import keras\nfrom keras_video import VideoFrameGenerator\nfrom google.colab import drive\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"PaRw-ktwBYp6\" outputId=\"818992c2-0de3-453b-928b-2ab8adc0b4a6\"\nfrom google.colab import drive\ndrive.mount(\"/content/gdrive\")\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"8n_KhqlcBqvh\" outputId=\"64876877-70eb-4ce8-fa2b-86d10508c955\"\n# cd /content/gdrive/Shareddrives/\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"mmapMt3hEhSH\" outputId=\"6e2bb7bf-f699-4101-a209-cbc0500f22a4\"\nfor i in glob.glob('final2040/HMDB_more/test2/*'):\n print(i.split(os.path.sep)[3])\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"4yONuZy5Asq_\" outputId=\"6c772da6-82c5-4117-dd88-fecfa6833a91\"\n# use sub directories names as classes\nclasses = [i.split(os.path.sep)[3] for i in glob.glob('final2040/HMDB_more/test2/*')]\nclasses.sort()\nprint(classes)\n\n# some global params\nSIZE = 224\nCHANNELS = 3\nNBFRAME = 5\nBS = 10\n\n# pattern to get videos and classes\nglob_pattern='final2040/HMDB_more/test2/{classname}/*.avi'\n\n# for data augmentation\ndata_aug = keras.preprocessing.image.ImageDataGenerator()\n\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"hRiit10lA7ro\" outputId=\"d4c85ed6-d912-44cd-ba0d-25c59444f967\"\n# Create video frame generator\ntrain = VideoFrameGenerator(\n classes=classes, \n glob_pattern=glob_pattern,\n nb_frames=NBFRAME,\n split=0.999, \n shuffle=True,\n batch_size=1,\n target_shape= (SIZE,SIZE),\n nb_channel=CHANNELS,\n transformation=data_aug,\n use_frame_cache=True)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"Sin0zG2BWNrl\" outputId=\"d56fb327-2b49-482e-8654-265cc311941e\"\n#get validation data\nvalid = train.get_validation_generator()\n\n# + id=\"J8jQt89vWaFe\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} outputId=\"0bf69758-631a-4609-815f-c93991703bc9\"\nimport keras_video.utils\nkeras_video.utils.show_sample(train)\n\n# + id=\"cQo7Axy_iwbJ\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 395} outputId=\"eeb9ccb5-7fc0-4959-99cc-4b1b90f627e7\"\nfrom tensorflow.keras.models import load_model\nINSHAPE=(NBFRAME,) + (SIZE, SIZE) + (CHANNELS,)\nnbout = len(classes)\nprint(nbout)\nmodel = load_model(\"final2040/saved_model/51classes_0419_densenet.h5\")\nmodel.summary()\n\n# + id=\"7MA2NsGVmopS\"\nmodel.evaluate(valid)\n\n# + id=\"AfRbkcwIzLsL\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"1ceb3cb6-36df-4154-9fe9-0de95c14096e\"\noptimizer = keras.optimizers.SGD(0.001)\nmodel.compile(\n optimizer,\n 'categorical_crossentropy',\n metrics=['acc'])\nmodel.evaluate(valid)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"WJlhXHPB2C5I\" outputId=\"3c94d458-823a-4943-b2d0-01fd9a240ee4\"\n# a, b = train[2]\n# a.shape\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 35} id=\"X9CDlfMQ2UPs\" outputId=\"ed733518-5dfa-44c6-b982-cfaa935e1082\"\nres = model.predict(a)\nres = np.argmax(res)\nclasses[res]\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"SGjhcQou2dbx\" outputId=\"4d274acf-0092-4420-dc10-cc515b0ff7a5\"\nimport matplotlib.pyplot as plt\n\nfor i in range(10):\n a, b = train[i]\n sequences = a\n labels = b\n rows = len(sequences)\n index = 1\n res = model.predict(a)\n res = np.argmax(res)\n print(\"The predicted catogoy is: \"+classes[res])\n plt.figure(figsize=(10, 22*rows))\n for batchid, sequence in enumerate(sequences):\n classid = np.argmax(labels[batchid])\n classname = train.classes[classid]\n cols = len(sequence)\n for image in sequence:\n plt.subplot(rows, cols, index)\n plt.title(classname)\n plt.imshow(image)\n plt.axis('off')\n index += 1\n plt.show()\n\n# + id=\"_uBNn1Xaq6tF\"\n# import numpy as np\n# res = model.predict(train)\n# res = np.argmax(res, axis=1)\n# cls = []\n# for i in res:\n# cls.append(classes[i])\n# cls\n\n# + id=\"_rsm7P-pzFKP\"\n# keras_video.utils.show_sample(train, index=0, random=False, row_width=22, row_height=5)\n\n# + id=\"SADaYMWdJlEq\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"6cd93cac-a24e-4403-e752-615c2f81d9ce\"\n# save model\nimport os\ndef save_model(model, name):\n model_name = '{}.h5'.format(name)\n save_dir = os.path.join(os.getcwd(), 'final2040/saved_model')\n \n # Save model and weights\n if not os.path.isdir(save_dir):\n os.makedirs(save_dir)\n model_path = os.path.join(save_dir, model_name)\n model.save(model_path)\n print('Saved trained model at %s ' % model_path)\n\nsave_model(modelEns, 'Ensemble1_with2')\n\n# + id=\"V_G-EU_nS5ZM\"\n# import matplotlib.pyplot as plt\n# acc = history.history['acc']\n# val_acc = history.history['val_acc']\n# loss = history.history['loss']\n# val_loss = history.history['val_loss']\n\n# epochs = range(len(acc))\n\n# plt.plot(epochs, loss, label='Training loss')\n# plt.plot(epochs, val_loss, label='Validation loss')\n# plt.title('Training and validation loss')\n# plt.legend(loc=0)\n# #plt.figure()\n# plt.savefig('final2040/output_figures/25classes_tune_loss.png')\n\n# + id=\"z-5ns5UKeV7H\"\n# plt.plot(epochs, acc, label='Training accuracy')\n# plt.plot(epochs, val_acc, label='Validation accuracy')\n# plt.title('Training and validation accuracy')\n# plt.legend(loc=0)\n# #plt.figure()\n# plt.savefig('final2040/output_figures/25classes_tune_acc.png')\n\n# + [markdown] id=\"n4O9fkFlpTtz\"\n# ## Ensemble\n\n# + id=\"LXSWmwMspIWg\"\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.metrics import accuracy_score\nfrom tensorflow.keras.models import load_model\n\ndef get_model(mod):\n if mod == 0:\n model = load_model(\"final2040/saved_model/51classes_0.68_mobilenet.h5\")\n # elif mod == 1:\n # model = load_model(\"final2040/saved_model/alexnet-51class.h5\")\n elif mod == 2:\n model = load_model(\"final2040/saved_model/51classes_0.72.h5\")\n return model\n\n# def get_model():\n# model = load_model(\"../input/resmodel/resmodel_3.h5\")\n# return model\n\nclf1 = tf.keras.wrappers.scikit_learn.KerasClassifier(\n lambda: get_model(0),\n epochs=0,\n verbose=False)\n# res2_clf = tf.keras.wrappers.scikit_learn.KerasClassifier(\n# lambda: get_model(1),\n# epochs=0,\n# verbose=False)\nclf3 = tf.keras.wrappers.scikit_learn.KerasClassifier(\n lambda: get_model(2),\n epochs=0,\n verbose=False)\n\nfor x in [clf1, clf3]:\n x._estimator_type = \"classifier\"\n\nvoting = VotingClassifier(\n estimators=[('1', clf1),\n #('2', clf2),\n ('3', clf3)], \n voting='soft',\n flatten_transform=True)\n\n\n# for clf in (clf1, res2_clf, res3_clf, voting):\n# clf.fit(X_train, y_train)\n# y_pred = clf.predict(X_test)\n# print(clf.__class__.__name__, accuracy_score(y_test, y_pred))\nvoting.fit(train)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"euHBghc9n9jD\" outputId=\"dd70aca2-f944-447e-98fc-de25ddd61605\"\n# save model\nimport os\ndef save_model(model, name):\n model_name = '{}.h5'.format(name)\n save_dir = os.path.join(os.getcwd(), 'final2040/saved_model')\n \n # Save model and weights\n if not os.path.isdir(save_dir):\n os.makedirs(save_dir)\n model_path = os.path.join(save_dir, model_name)\n model.save(model_path)\n print('Saved trained model at %s ' % model_path)\n\nsave_model(model, '16classes_0417_0.8687')\n\n# + id=\"A8gipk2NxCnH\"\nfrom tensorflow.keras.models import load_model\n\ndef ensembleModels(models, model_input):\n # collect outputs of models in a list\n yModels=[model(model_input) for model in models] \n # averaging outputs\n yAvg=tf.keras.layers.average(yModels) \n # build model from same input and avg output\n modelEns = tf.keras.Model(inputs=model_input, outputs=yAvg, name='ensemble') \n \n return modelEns\n\nm1 = load_model(\"final2040/saved_model/51classes_0419_1.h5\")\nm1._name = 'mob1'\nm2 = load_model(\"final2040/saved_model/51classes_0419_densenet.h5\")\nm2._name = 'dense2'\nm3 = load_model(\"final2040/saved_model/51classes_0419_2.h5\")\nm3._name = 'alex3'\nmodels = [m1, m2, m3]\nmodel_input = tf.keras.layers.Input(shape=models[0].input_shape[1:]) \nmodelEns = ensembleModels(models, model_input)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"GqZLdA0wnCJq\" outputId=\"0e193d8e-e017-4cd0-895f-7e9af3687bed\"\nm3.compile(\n tf.keras.optimizers.SGD(0.001),\n 'categorical_crossentropy',\n metrics=['acc'])\nm3.evaluate(valid)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"QOWITMmp-30y\" outputId=\"3b894712-aa6c-414e-c3e2-6eba1803b1e7\"\nmodelEns.compile(\n tf.keras.optimizers.SGD(0.001),\n 'categorical_crossentropy',\n metrics=['acc'])\nmodelEns.evaluate(valid)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 737} id=\"UWyaTe3bFhBj\" outputId=\"0e133739-4ea8-4ac7-f5e8-6c2996053be7\"\nfor i in range(10):\n a, b = valid[i]\n sequences = a\n labels = b\n rows = len(sequences)\n index = 1\n res = modelEns.predict(a)\n res = np.argmax(res)\n print(\"The predicted catogoy is: \"+classes[res])\n plt.figure(figsize=(10, 22*rows))\n for batchid, sequence in enumerate(sequences):\n classid = np.argmax(labels[batchid])\n classname = train.classes[classid]\n cols = len(sequence)\n for image in sequence:\n plt.subplot(rows, cols, index)\n plt.title(classname)\n plt.imshow(image)\n plt.axis('off')\n index += 1\n plt.show()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"fPxgo4TYNAuV\" outputId=\"87a32c1c-e640-4347-ddeb-747882796131\"\nfor i in range(10):\n a, b = valid[i]\n sequences = a\n labels = b\n rows = len(sequences)\n index = 1\n res = model.predict(a)\n res = np.argmax(res)\n print(\"The predicted catogoy is: \"+classes[res])\n plt.figure(figsize=(10, 22*rows))\n for batchid, sequence in enumerate(sequences):\n classid = np.argmax(labels[batchid])\n classname = train.classes[classid]\n cols = len(sequence)\n for image in sequence:\n plt.subplot(rows, cols, index)\n plt.title(classname)\n plt.imshow(image)\n plt.axis('off')\n index += 1\n plt.show()\n"},"script_size":{"kind":"number","value":12034,"string":"12,034"}}},{"rowIdx":914,"cells":{"path":{"kind":"string","value":"/.ipynb_checkpoints/rnn-checkpoint.ipynb"},"content_id":{"kind":"string","value":"d10cad5fcf15e1c1ac58af435dca8b34200734e8"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"RayestGeeta/Stock-Predict"},"repo_url":{"kind":"string","value":"https://github.com/RayestGeeta/Stock-Predict"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":90990,"string":"90,990"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\n# 导入包\nimport pandas as pd\nimport torch\nfrom torch import nn\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import DataLoader,Dataset, TensorDataset\n\n# 读取数据\ndata = pd.read_csv('suning.csv', encoding = 'gbk')\n# 删除空值的行\ndata = data.drop(data[data['涨跌额'].str.contains('None')].index)\n\n\n\n# +\n# 把数据设置成统一的float数据类型\ndata[\"涨跌额\"] = data['涨跌额'].astype(\"float\")\ndata[\"最高价\"] = data['最高价'].astype(\"float\")\ndata[\"最高价\"] = data['最高价'].astype(\"float\")\ndata[\"开盘价\"] = data['开盘价'].astype(\"float\")\ndata[\"涨跌幅\"] = data['涨跌幅'].astype(\"float\")\ndata[\"换手率\"] = data['换手率'].astype(\"float\")\n\n\n# 导入数据和预测值\ndatas = data[data.columns[4:7]].values\nlabels = data[data.columns[3]].values\n\n\n\n\n\n# +\n# 将数据导入 torch的数据集数据类型\ndataset = TensorDataset(torch.tensor(np.array(datas.reshape(-1, 3))[3000:]), torch.tensor(np.array(labels))[3000:])\n# 将dataset导入dataloader(来进行批训练)\ndataloader = DataLoader(dataset, batch_size=len(dataset),shuffle=True, drop_last=False)\n\n\n\nclass rnn(nn.Module):\n def __init__(self):#面向对象中的继承\n super(rnn, self).__init__()\n \n # rnn 网络层\n self.rnn = nn.RNN(1, 2,2)\n # 全连接层\n self.linear = nn.Linear(3, 10)\n self.linear1 = nn.Linear(10,8)\n self.linear3 = nn.Linear(8,2)\n self.linear4 = nn.Linear(2,1)\n\n \n def forward(self,x):\n x1,_ = self.rnn(x.reshape(-1 ,3,1))\n a,b,c = x1.shape\n out = self.linear4(x1.view(-1,c))#因为线性层输入的是个二维数据,所以此处应该将lstm输出的三维数据x1调整成二维数据,最后的特征维度不能变\n out1 = out.view(a,b,-1)#因为是循环神经网络,最后的时候要把二维的out调整成三维数据,下一次循环使用\n # 全连接层\n out = self.linear(x)\n out = self.linear1(out)\n out = self.linear3(out)\n out = self.linear4(out)\n\n \n \n return out\n\n# 构建模型\nrnn = rnn()\nprint(rnn)\n\n# 设定优化器和误差函数\noptimizer = torch.optim.Adam(rnn.parameters(), lr=0.02) # optimize all cnn parameters\nloss_func = nn.MSELoss()\n\n\n# 一共运行20轮\nfor epoch in range(20):\n # 从dataloader读取数据\n for step,(b_x, b_y) in enumerate(dataloader):\n\n\n prediction = rnn(b_x.float()) # rnn的输出\n\n loss = loss_func(prediction, b_y.float()) # 计算误差\n #print(loss.data.numpy())\n optimizer.zero_grad() # 梯度清0\n loss.backward() # 误差反向传播\n optimizer.step() # 误差更新\n\n# 画图\nplt.plot(rnn(b_x.float()).view(-1).data.numpy()[:50])\nplt.plot(b_y[:50].data.numpy())\n# -\n\nplt.plot(rnn(b_x.float()).view(-1).data.numpy()[:50])\nplt.plot(b_y[:50].data.numpy())\nplt.title('Predict')\nplt.xlabel('date')\nplt.ylabel('price')\nplt.savefig('rnn.png')\n\n\n"},"script_size":{"kind":"number","value":2828,"string":"2,828"}}},{"rowIdx":915,"cells":{"path":{"kind":"string","value":"/examples/notebooks/qgis_layer_style_file.ipynb"},"content_id":{"kind":"string","value":"bb8e0f56b3d4a851da0beb7bd575228a208c6542"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"luxizhou/geemap"},"repo_url":{"kind":"string","value":"https://github.com/luxizhou/geemap"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":4048,"string":"4,048"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# \"Open\n#\n# Uncomment the following line to install [geemap](https://geemap.org) if needed.\n\n# +\n# # !pip install geemap\n# -\n\nimport os\nimport geemap\n\n# ## Create a QGIS Layer Style File for NLCD data\n\nout_nlcd_qml = os.path.join(os.path.expanduser('~/Downloads'), 'nlcd_style.qml')\n\ngeemap.create_nlcd_qml(out_nlcd_qml)\n\n# ## Create a QGIS Layer Style File from an Earth Engine color table\n#\n# Separated by Tab\n\nee_class_table = \"\"\"\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n\"\"\"\n\nout_qml = os.path.join(os.path.expanduser('~/Downloads'), 'image_style.qml')\n\ngeemap.vis_to_qml(ee_class_table, out_qml)\n"},"script_size":{"kind":"number","value":1656,"string":"1,656"}}},{"rowIdx":916,"cells":{"path":{"kind":"string","value":"/notebooks/pub/JS_Divergence_B1083_CombinedPopulations-Shuffle_Final.ipynb"},"content_id":{"kind":"string","value":"a22d1901feb121d5cb37e9b52620398db7b1d46e"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"theilmbh/NeuralTDA"},"repo_url":{"kind":"string","value":"https://github.com/theilmbh/NeuralTDA"},"star_events_count":{"kind":"number","value":5,"string":"5"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2020-03-09T16:53:58","string":"2020-03-09T16:53:58"},"gha_updated_at":{"kind":"timestamp","value":"2019-12-28T13:25:18","string":"2019-12-28T13:25:18"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":384287,"string":"384,287"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\n###\n# The goal of this notebook is to:\n# - Take two neural populations\n# - Compute the JS divergence between stimuli pairs for each population (the same stimuli pairs)\n# - Compute the mutual information between the distributions of JS divergences\n\nimport glob\nimport os\nfrom importlib import reload\nimport pickle\nimport datetime\n\nimport numpy as np\nimport scipy as sp\nimport pandas as pd\nimport h5py as h5\nfrom tqdm import tqdm_notebook as tqdm\nimport matplotlib.pyplot as plt\n# %matplotlib inline\n\nimport neuraltda.topology2 as tp2\nimport neuraltda.spectralAnalysis as sa\nimport neuraltda.simpComp as sc\nimport pycuslsa as pyslsa\n\ndaystr = datetime.datetime.now().strftime('%Y%m%d')\nfigsavepth = '/home/brad/DailyLog/'+daystr+'/'\nprint(figsavepth)\n\n# +\n# Set up birds and block_paths\nbirds = ['B1083', 'B1056', 'B1235', 'B1075']\nbps = {'B1083': '/home/brad/krista/B1083/P03S03/', 'B1075': '/home/brad/krista/B1075/P01S03/',\n 'B1235': '/home/brad/krista/B1235/P02S01/', 'B1056': '/home/brad/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/',\n 'B1056': '/home/brad/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/',\n 'B1083-5': '/home/brad/krista/B1083/P03S05/'}\n\n\nlearned_stimuli = {'B1083': ['M_scaled_burung', 'N_scaled_burung', 'O_scaled_burung', 'P_scaled_burung'], 'B1056': ['A_scaled_burung', 'B_scaled_burung', 'C_scaled_burung', 'D_scaled_burung'], 'B1235': [], 'B1075': []}\npeck_stimuli = {'B1083': {'L': ['N_40k','P_40k'], 'R': ['M_40k', 'O_40k']}, 'B1056': {'L': ['B_scaled_burung', 'D_scaled_burung'], 'R': ['A_scaled_burung', 'C_scaled_burung']}, \n 'B1235': {'L': ['F_scaled_burung', 'H_scaled_burung'], 'R': ['E_scaled_burung', 'G_scaled_burung'],}, 'B1075': {'L': ['F_40k', 'H_40k'], 'R': ['E_40k', 'G_40k']},\n 'B1083-5': {'L': ['N_40k','P_40k'], 'R': ['M_40k', 'O_40k']}}\n\nunfamiliar_stimuli = {'B1083': ['I_40k', 'J_40k', 'K_40k', 'L_40k'], \n 'B1083-5': ['I_40k', 'J_40k', 'K_40k', 'L_40k'],\n 'B1235': ['A_scaled_burung', 'B_scaled_burung', 'C_scaled_burung', 'D_scaled_burung'], \n 'B1075': ['A_40k', 'B_40k', 'C_40k', 'D_40k'], \n 'B1056': ['E_scaled_burung', 'F_scaled_burung', 'G_scaled_burung', 'H_scaled_burung']\n }\n\n#bps = {'B1056': '/home/AD/btheilma/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/',\n# 'B1235': '/home/AD/btheilma/krista/B1235/P02S01/'}\n#test_birds = ['B1056', 'B1235']\n#test_birds = ['B1075', 'B1235']\n#test_birds = ['B1056', 'B1235']\n#test_birds =['B1056', 'B1083']\n#test_birds = ['B1083']\n#test_birds = ['B1083', 'B1083-5']\n#test_birds = ['B1056', 'B1235', 'B1083', 'B1083-5']\n#test_birds = ['B1056']\ntest_birds = ['B1083', 'B1083-5']\n# Binning Parameters\nwindt = 10.0 # milliseconds\ndtovr = 0.5*windt # milliseconds\nsegment_info = [0, 0] # use full Trial\ncluster_group = ['Good'] # use just good clusters\ncomment = 'JS_MI_BTWNPOP' # BootStrap Populations\nbdfs = {} # Dictionary to store bdf\n# -\n\n# Loop through each bird in our list and bin the data\nfor bird in test_birds:\n block_path = bps[bird]\n bfdict = tp2.dag_bin(block_path, windt, segment_info, cluster_group=cluster_group, dt_overlap=dtovr, comment=comment)\n bdf = glob.glob(os.path.join(bfdict['raw'], '*.binned'))[0]\n print(bdf)\n bdfs[bird] = bdf\n\n# +\n# extract left vs right stims\n# extract population tensors for the populations of interest\n# Do not sort the stims\npopulation_tensors_familiar = {}\nstimuli = []\n\nfor bird in test_birds:\n stimuli = peck_stimuli[bird]['L'] + peck_stimuli[bird]['R']\n print(stimuli)\n bdf = bdfs[bird]\n population_tensors_familiar[bird] = []\n # open the binned data file\n with h5.File(bdf, 'r') as f:\n #stimuli = f.keys()\n print(list(f.keys()))\n for stim in stimuli:\n poptens = np.array(f[stim]['pop_tens'])\n population_tensors_familiar[bird].append([poptens, stim])\n\n# +\n# extract Unfamiliar stims\n# extract population tensors for the populations of interest\n# Do not sort the stims\npopulation_tensors_unfamiliar = {}\nstimuli = []\n\nfor bird in test_birds:\n stimuli = unfamiliar_stimuli[bird]\n print(stimuli)\n bdf = bdfs[bird]\n population_tensors_unfamiliar[bird] = []\n # open the binned data file\n with h5.File(bdf, 'r') as f:\n #stimuli = f.keys()\n print(list(f.keys()))\n for stim in stimuli:\n poptens = np.array(f[stim]['pop_tens'])\n population_tensors_unfamiliar[bird].append([poptens, stim])\n\n# +\n# flatten the list of population tensors for each population\nthreshold = 6\n\ndef threshold_poptens(tens, thresh):\n ncell, nwins, ntrials = tens.shape\n frs = np.mean(tens, axis=1)\n tfr = thresh*frs\n tfrtens = np.tile(tfr[:, np.newaxis, :], (1, nwins, 1))\n bintens = 1*np.greater(tens, tfrtens)\n return bintens\n\ndef shuffle_binmat(binmat):\n ncells, nwin = binmat.shape\n for i in range(ncells):\n binmat[i, :] = np.random.permutation(binmat[i, :])\n return binmat\n\ndef get_JS(i, j, Li, Lj, speci, specj, beta):\n js = (i, j, sc.sparse_JS_divergence2_fast(Li, Lj, speci, specj, beta))\n print((i, j))\n return js\n\ndef get_Lap(trial_matrix, sh):\n if sh == 'shuffled':\n mat = shuffle_binmat(trial_matrix)\n else:\n mat = trial_matrix\n ms = sc.binarytomaxsimplex(trial_matrix, rDup=True)\n scg1 = sc.simplicialChainGroups(ms)\n L = sc.sparse_laplacian(scg1, dim)\n return L\n\ndef get_M(i, j, L1, L2):\n mspec = sc.compute_M_spec(L1, L2)\n print((i, j))\n return (i, j, mspec)\n\ndef get_JS_spec(i, j, speci, specj, specm, beta):\n js = (i, j, sc.sparse_JS_divergence2_spec(speci, specj, specm, beta))\n return js\n\ndef compute_withins_vs_between(mtx, ntrials, nstim, diag=0):\n stim_per_group = int(nstim//2)\n \n btwn_data = mtx[0:stim_per_group*ntrials, stim_per_group*ntrials:]\n within1 = mtx[0:stim_per_group*ntrials, 0:stim_per_group*ntrials][np.triu_indices(stim_per_group*ntrials, diag)]\n within2 = mtx[stim_per_group*ntrials:, stim_per_group*ntrials:][np.triu_indices(stim_per_group*ntrials, diag)]\n \n within = np.concatenate((within1, within2))\n return (btwn_data, within1, within2)\n\n\n\n# +\n\npoptens = {'familiar': population_tensors_familiar, 'unfamiliar': population_tensors_unfamiliar}\n\n# +\npoptens['familiar']['B1083-5']\n\ncombined_poptens = poptens['familiar']['B1083']\ncombined_poptens.extend(poptens['familiar']['B1083-5'])\nprint(len(combined_poptens))\n\n# +\n# mirroring cuda code\n#Left vs right\nreload(sc)\nfrom joblib import Parallel, delayed\ndim = 1\n\nbetas = [1]\nall_spectra = []\n\nntrials = 20 # Only do half the trials for each stim\nbird_tensors = combined_poptens\nSCG = []\nspectra = []\nlaplacians_save = []\nprint('Computing Laplacians..')\nfor bird_tensor, stim in bird_tensors:\n binmatlist = []\n print(stim)\n ncells, nwin, _ = bird_tensor.shape\n bin_tensor = threshold_poptens(bird_tensor, threshold)\n laps = Parallel(n_jobs=24)(delayed(get_Lap)(bin_tensor[:, :, trial], 'shuffled') for trial in range(ntrials))\n laplacians_save.append((bird, stim, laps))\nlaplacians = sum([s[2] for s in laplacians_save], [])\nN = len(laplacians)\n# compute spectra\nprint('Computing Spectra...')\nspectra = Parallel(n_jobs=24)(delayed(sc.sparse_spectrum)(L) for L in laplacians)\nall_spectra.extend(spectra)\n\n# Precompute M spectra\npairs = [(i, j) for i in range(N) for j in range(i, N)]\nprint('Computing M spectra...')\nM_spec = Parallel(n_jobs=24)(delayed(get_M)(i, j, laplacians[i], laplacians[j]) for (i, j) in pairs)\nM_spec = {(p[0], p[1]): p[2] for p in M_spec}\n\n# Save computed spectra\nwith open(os.path.join(figsavepth, 'Mspectra_{}-{}-{}-{}.pkl'.format('B1083Combined', ntrials, 'shuff', 'fam')), 'wb') as f:\n pickle.dump(M_spec, f)\nwith open(os.path.join(figsavepth, 'Lapspectra_{}-{}-{}-{}.pkl'.format('B1083Combined', ntrials, 'shuff', 'fam')), 'wb') as f:\n pickle.dump(laplacians_save, f)\n\n\n# +\n# compute density matrices\n\nfor beta in betas:\n print('Computing JS Divergences with beta {}...'.format(beta))\n jsmat = np.zeros((N, N))\n\n jsdat = Parallel(n_jobs=24)(delayed(get_JS_spec)(i, j, spectra[i], spectra[j], M_spec[(i,j)], beta) for (i, j) in pairs)\n for d in jsdat:\n jsmat[d[0], d[1]] = d[2]\n\n with open(os.path.join(figsavepth, 'JSpop_fast_B1083Combined-{}-{}-{}_LvsR-fam-shuff.pkl'.format(dim, beta, ntrials)), 'wb') as f:\n pickle.dump(jsmat, f)\n# -\n\nplt.figure(figsize=(12, 12))\nplt.imshow(jsmat + jsmat.T)\nplt.savefig(os.path.join(figsavepth, \"JSDivAltogether_shuffle.pdf\"))\n\nprint(combined_poptens)\n\n\n5,0))\nplt.boxplot(winsorized_Income_Comp_Of_Resources)\nplt.title(\"winsorized_Income_Comp_Of_Resources\")\n\nplt.show()\n\n# +\n# Winsorize Schooling\nfrom scipy.stats.mstats import winsorize\nplt.figure(figsize=(7,4))\n\nplt.subplot(1,2,1)\noriginal_Schooling = df['schooling']\nplt.boxplot(original_Schooling)\nplt.title(\"original_Schooling\")\n\nplt.subplot(1,2,2)\nwinsorized_Schooling = winsorize(df['schooling'],(0.025,0.01))\nplt.boxplot(winsorized_Schooling)\nplt.title(\"winsorized_Schooling\")\n\nplt.show()\n# -\n\nwin_list = [winsorized_Life_Expectancy,winsorized_Adult_Mortality,winsorized_Infant_Deaths,winsorized_Alcohol,\n winsorized_Percentage_Exp,winsorized_HepatitisB,winsorized_Under_Five_Deaths,winsorized_Polio,winsorized_Tot_Exp,winsorized_Diphtheria,winsorized_HIV,winsorized_GDP,winsorized_thinness_1to19_years,winsorized_thinness_5to9_years,winsorized_Income_Comp_Of_Resources,winsorized_Schooling]\nfor variable in win_list:\n q75, q25 = np.percentile(variable, [75 ,25])\n iqr = q75 - q25\n\n min_val = q25 - (iqr*1.5)\n max_val = q75 + (iqr*1.5)\n \n print(\"Number of outliers after winsorization : {}\".format(len(np.where((variable > max_val) | (variable < min_val))[0])))\n\n# Adding winsorized variables to the data frame.\ndf['winsorized_Life_Expectancy'] = winsorized_Life_Expectancy\ndf['winsorized_Adult_Mortality'] = winsorized_Adult_Mortality\ndf['winsorized_Infant_Deaths'] = winsorized_Infant_Deaths\ndf['winsorized_Alcohol'] = winsorized_Alcohol\ndf['winsorized_Percentage_Exp'] = winsorized_Percentage_Exp\ndf['winsorized_HepatitisB'] = winsorized_HepatitisB\ndf['winsorized_Under_Five_Deaths'] = winsorized_Under_Five_Deaths\ndf['winsorized_Polio'] = winsorized_Polio\ndf['winsorized_Tot_Exp'] = winsorized_Tot_Exp\ndf['winsorized_Diphtheria'] = winsorized_Diphtheria\ndf['winsorized_HIV'] = winsorized_HIV\ndf['winsorized_GDP'] = winsorized_GDP\ndf['winsorized_thinness_1to19_years'] = winsorized_thinness_1to19_years\ndf['winsorized_thinness_5to9_years'] = winsorized_thinness_5to9_years\ndf['winsorized_Income_Comp_Of_Resources'] = winsorized_Income_Comp_Of_Resources\ndf['winsorized_Schooling'] = winsorized_Schooling\n\n# +\n # Distribution of each numerical variable.\nall_col = ['life_expectancy','winsorized_Life_Expectancy','adult_mortality','winsorized_Adult_Mortality','infant_deaths',\n 'winsorized_Infant_Deaths','alcohol','winsorized_Alcohol','percentage_expenditure','winsorized_Percentage_Exp','hepatitis_b',\n 'winsorized_HepatitisB','under-five_deaths','winsorized_Under_Five_Deaths','polio','winsorized_Polio','total_expenditure',\n 'winsorized_Tot_Exp','diphtheria','winsorized_Diphtheria','hiv/aids','winsorized_HIV','gdp','winsorized_GDP','thinness_1-19_years','winsorized_thinness_1to19_years','thinness_5-9_years',\n 'winsorized_thinness_5to9_years','income_composition_of_resources','winsorized_Income_Comp_Of_Resources',\n 'schooling','winsorized_Schooling']\n\nplt.figure(figsize=(10,70))\n\nfor i in range(len(all_col)):\n plt.subplot(18,2,i+1)\n plt.hist(df[all_col[i]])\n plt.title(all_col[i])\n\nplt.show()\n# -\n\n# ### Plotting Average Life Expectancy vs Country\n\ndf_country = df.groupby('country')['life_expectancy'].mean()\ndf_country.plot(kind='bar', figsize=(70,20), fontsize=25)\nplt.title(\"Life_Expectancy w.r.t Country\",fontsize=55)\nplt.xlabel(\"Country\",fontsize=50)\nplt.ylabel(\"Avg Life_Expectancy\",fontsize=50)\nplt.show()\n\n# ### Plotting Average Life Expectancy vs Year\n\nplt.figure(figsize=(7,5))\nplt.bar(df.groupby('year')['year'].count().index,df.groupby('year')['life_expectancy'].mean(),color='blue',alpha=0.65)\nplt.xlabel(\"Year\",fontsize=12)\nplt.ylabel(\"Avg Life_Expectancy\",fontsize=12)\nplt.title(\"Life_Expectancy w.r.t Year\")\nplt.show()\n\n# ### Plotting Average Life Expectancy vs Status\n\nplt.figure(figsize=(5,5))\nplt.bar(df.groupby('status')['status'].count().index,df.groupby('status')['life_expectancy'].mean())\nplt.xlabel(\"Status\",fontsize=12)\nplt.ylabel(\"Avg Life_Expectancy\",fontsize=12)\nplt.title(\"Life_Expectancy w.r.t Status\")\nplt.show()\n\n# ### Scatter plot between the target variable(winsorized variables) and all continuous variables.\n\n# +\nplt.figure(figsize=(18,40))\n\nplt.subplot(6,3,1)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Adult_Mortality\"])\nplt.title(\"LifeExpectancy vs AdultMortality\")\n\nplt.subplot(6,3,2)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Infant_Deaths\"])\nplt.title(\"LifeExpectancy vs Infant_Deaths\")\n\nplt.subplot(6,3,3)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Alcohol\"])\nplt.title(\"LifeExpectancy vs Alcohol\")\n\nplt.subplot(6,3,4)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Percentage_Exp\"])\nplt.title(\"LifeExpectancy vs Percentage_Exp\")\n\nplt.subplot(6,3,5)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_HepatitisB\"])\nplt.title(\"LifeExpectancy vs HepatitisB\")\n\nplt.subplot(6,3,6)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Under_Five_Deaths\"])\nplt.title(\"LifeExpectancy vs Under_Five_Deaths\")\n\nplt.subplot(6,3,7)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Polio\"])\nplt.title(\"LifeExpectancy vs Polio\")\n\nplt.subplot(6,3,8)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Tot_Exp\"])\nplt.title(\"LifeExpectancy vs Tot_Exp\")\n\nplt.subplot(6,3,9)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Diphtheria\"])\nplt.title(\"LifeExpectancy vs Diphtheria\")\n\nplt.subplot(6,3,10)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_HIV\"])\nplt.title(\"LifeExpectancy vs HIV\")\n\nplt.subplot(6,3,11)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_GDP\"])\nplt.title(\"LifeExpectancy vs GDP\")\n\nplt.subplot(6,3,12)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_thinness_1to19_years\"])\nplt.title(\"LifeExpectancy vs thinness_1to19_years\")\n\nplt.subplot(6,3,13)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_thinness_5to9_years\"])\nplt.title(\"LifeExpectancy vs thinness_5to9_years\")\n\nplt.subplot(6,3,14)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Income_Comp_Of_Resources\"])\nplt.title(\"LifeExpectancy vs Income_Comp_Of_Resources\")\n\nplt.subplot(6,3,15)\nplt.scatter(df[\"winsorized_Life_Expectancy\"], df[\"winsorized_Schooling\"])\nplt.title(\"LifeExpectancy vs Schooling\")\n\n\nplt.show()\n# -\n\n# #### Conclusions:\n# 1. Nature of scatter plot, we found approx same variation among following pairs,\n# A. income_comp_of_resources and scooling \n# B. thinneess_5to9_years and thinness_1to19_years\n# C. Under_five_death and infant_death\n\n# ### Identifying Correlation and plotting heat map\n\n# Correlation of winsorized variables\ndf_win = df.iloc[:,21:]\ndf_win['country'] = df['country']\ndf_win['year'] = df['year']\ndf_win['status'] = df['status']\ndf_win_num = df_win.iloc[:,:-3]\ncormat = df_win_num.corr()\n\n# Using heatmap to observe correlations\nplt.figure(figsize=(12,12))\nsns.heatmap(cormat, square=True, annot=True, linewidths=.5)\nplt.title(\"Correlation matrix among winsorized variables\")\nplt.show()\n\n# ### Conclusions\n# 1. Based on the heat map above, we found correlation factor of 0.98, 0.94 and 0.88 for under_five_death vs infant_death, thinness_5to9_deaths vs thinness_1to19_deaths and income_comp_of_resources vs scooling respectively.\n# 2. Same behaviour was observed in scatter plot also.\n# 3. We've planned to drop one of the two attributes (based on more null values observed) that are having same behaviour towards target attibute\n# A. under_five_death among under_five_death vs infant_death\n# B. thinness_5to9_deaths among thinness_5to9_deaths vs thinness_1to19_deaths\n# C. income_comp_of_resources among income_comp_of_resources vs scooling\n# 4. Our final dataset will be having 2928 rows and 18 columns\n\n# # Model Part\n\ndf.head()\n\n# ### Dropping three attributes based on their correlation with other three attributes\n\n# +\nX = df[['winsorized_Adult_Mortality',\n 'winsorized_Alcohol', 'winsorized_Percentage_Exp', 'winsorized_HepatitisB',\n 'winsorized_Under_Five_Deaths', 'winsorized_Polio',\n 'winsorized_Tot_Exp', 'winsorized_Diphtheria', 'winsorized_HIV',\n 'winsorized_GDP', 'winsorized_thinness_5to9_years',\n 'winsorized_Income_Comp_Of_Resources','status']]\n\nY = df['winsorized_Life_Expectancy']\n# -\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\nimport matplotlib.pyplot as plt\n\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)\n#we are not using random_state variable to ensure split is being done randomly\n\n# #### Decision Tree Regressor\n\nfrom sklearn.tree import DecisionTreeRegressor\ntree_reg = DecisionTreeRegressor()\ntree_reg.fit(X_train,Y_train)\n\ny_pred = tree_reg.predict(X_test)\ntree_r2_score = r2_score(Y_test,y_pred)\nprint('R2 score for this model is', tree_r2_score)\nprint('MSE',mean_squared_error(Y_test, y_pred))\n\n# #### Linear Regression \n\nlr = LinearRegression()\nlr.fit(X_train, Y_train)\n\nY_pred = lr.predict(X_test)\n\nprint('R2 score for this model is',r2_score(Y_test, Y_pred))\nprint('MSE',mean_squared_error(Y_test, Y_pred))\n\n# #### Random Forest Regressor\n#\n\nfrom sklearn.ensemble import RandomForestRegressor\n\nrf = RandomForestRegressor()\n\nrf.fit(X_train, Y_train)\n\nrf_pred=rf.predict(X_test)\n\nr3 = r2_score(Y_test, rf_pred)\n\nprint('R2 score for this model is', r3)\nprint('MSE',mean_squared_error(Y_test, rf_pred))\n\nrf.feature_importances_\n\nimport seaborn as sns\n# Helper function for plotting feature importance\ndef plot_features(columns, importances, n=10):\n df = (pd.DataFrame({\"features\": columns,\n \"feature_importance\": importances})\n .sort_values(\"feature_importance\", ascending=False)\n .reset_index(drop=True))\n \n sns.barplot(x=\"feature_importance\",\n y=\"features\",\n data=df[:n],\n orient=\"h\")\n\n\nplot_features(X_train.columns, rf.feature_importances_)\n\n# ### Conclusions:\n# 1. We tried to fit our data in three different models namely Decision Tree, Linear Regression and Random Forest Regressor.\n# 2. Among all three models, we achieved r2 value of 0.9633 in random forest regressor with mean square error of 3.25.\n# 3. Feature importance of HIV attribute is highest among all other attributes as target attribute prediction.\n"},"script_size":{"kind":"number","value":19499,"string":"19,499"}}},{"rowIdx":917,"cells":{"path":{"kind":"string","value":"/MVP/MVP_Code/wordnxt.ipynb"},"content_id":{"kind":"string","value":"3ced2a202666157cd807d77f8f432b6371762ed7"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"dhillon1/CAPSTONE-II-WordNXT"},"repo_url":{"kind":"string","value":"https://github.com/dhillon1/CAPSTONE-II-WordNXT"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":7690,"string":"7,690"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ## WORDNXT :: Next Word Predictor\n\n# #### NOTE: This piece of code is first part of the project that is RNN model. This model has been trained on LSTN, a text specific model. There will be more changes in upcoming developments of this code and project as a whole.\n\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.layers import Embedding, LSTM, Dense\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.optimizers import Adam\nimport pickle\nimport numpy as np\nimport os\nimport string\n\n# +\n#pip install tensorflow-gpu --user\n\n# +\n#sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))\n\n# +\n#from tensorflow.python.client import device_lib\n#print(device_lib.list_local_devices())\n\n# +\nfile = open(\"WikiQA-train.txt\", \"r\", encoding = \"utf8\")\nlines = []\n\nfor i in file:\n lines.append(i)\n \ndata = \"\"\n\nfor i in lines:\n data = ' '. join(lines)\n \ndata = data.replace('\\n', '').replace('\\r', '').replace('\\ufeff', '')\n\ntranslator = str.maketrans(string.punctuation, ' '*len(string.punctuation)) #map punctuation to space\nnew_data = data.translate(translator)\n\nz = []\n\nfor i in data.split():\n if i not in z:\n z.append(i)\n \ndata = ' '.join(z)\n\n# +\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts([data])\n\n# saving the tokenizer for predict function.\npickle.dump(tokenizer, open('tokenizer1.pkl', 'wb'))\n\nsequence_data = tokenizer.texts_to_sequences([data])[0]\n\nvocab_size = len(tokenizer.word_index) + 1\n\nsequences = []\n\nfor i in range(1, len(sequence_data)):\n words = sequence_data[i-1:i+1]\n sequences.append(words)\n \nsequences = np.array(sequences)\n\nX = []\ny = []\n\nfor i in sequences:\n X.append(i[0])\n y.append(i[1])\n \nX = np.array(X)\ny = np.array(y)\n\ny = to_categorical(y, num_classes=vocab_size)\n# -\n\nmodel = Sequential()\nmodel.add(Embedding(vocab_size, 10, input_length=1))\nmodel.add(LSTM(1000, return_sequences=True))\nmodel.add(LSTM(1000))\nmodel.add(Dense(1000, activation=\"relu\"))\nmodel.add(Dense(vocab_size, activation=\"softmax\"))\n\n# +\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\nfrom tensorflow.keras.callbacks import TensorBoard\n\ncheckpoint = ModelCheckpoint(\"nextword1.h5\", monitor='loss', verbose=1,\n save_best_only=True, mode='auto')\n\nreduce = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=3, min_lr=0.0001, verbose = 1)\n\nlogdir='logsnextword1'\ntensorboard_Visualization = TensorBoard(log_dir=logdir)\n# -\n\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=Adam(lr=0.001))\nmodel.fit(X, y, epochs=10, batch_size=64, callbacks=[checkpoint, reduce, tensorboard_Visualization])\n\n\n\nid nonlinearity\ndef sigmoid(x):\n output = 1/(1+np.exp(-x))\n return output\n\n# convert output of sigmoid function to its derivative\ndef sigmoid_output_to_derivative(output):\n return output*(1-output)\n\n# training dataset generation\nint2binary = {}\nbinary_dim = 8\n\nlargest_number = pow(2,binary_dim)\nbinary = np.unpackbits(\n np.array([list(range(largest_number))],dtype=np.uint8).T,axis=1)\nfor i in range(largest_number):\n int2binary[i] = binary[i]\n\n# input variables\nalpha = 0.1\ninput_dim = 2\nhidden_dim = 16\noutput_dim = 1\n\n\n# initialize neural network weights\nsynapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1\nsynapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1\nsynapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1\n\nsynapse_0_update = np.zeros_like(synapse_0)\nsynapse_1_update = np.zeros_like(synapse_1)\nsynapse_h_update = np.zeros_like(synapse_h)\n\n# +\n# generate a simple addition problem (a + b = c)\na_int = np.random.randint(largest_number/2) # int version\na = int2binary[a_int] # binary encoding\n\nb_int = np.random.randint(largest_number/2) # int version\nb = int2binary[b_int] # binary encoding\n\n# true answer\nc_int = a_int + b_int\nc = int2binary[c_int]\n# -\n\n\n\n\n\n\n\n\n\n# training logic\nfor j in range(10000):\n \n # generate a simple addition problem (a + b = c)\n a_int = np.random.randint(largest_number/2) # int version\n a = int2binary[a_int] # binary encoding\n\n b_int = np.random.randint(largest_number/2) # int version\n b = int2binary[b_int] # binary encoding\n\n # true answer\n c_int = a_int + b_int\n c = int2binary[c_int]\n \n # where we'll store our best guess (binary encoded)\n d = np.zeros_like(c)\n\n overallError = 0\n \n layer_2_deltas = list()\n layer_1_values = list()\n layer_1_values.append(np.zeros(hidden_dim))\n \n # moving along the positions in the binary encoding\n for position in range(binary_dim):\n \n # generate input and output\n X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]])\n y = np.array([[c[binary_dim - position - 1]]]).T\n\n # hidden layer (input ~+ prev_hidden)\n layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h))\n\n # output layer (new binary representation)\n layer_2 = sigmoid(np.dot(layer_1,synapse_1))\n\n # did we miss?... if so, by how much?\n layer_2_error = y - layer_2\n layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2))\n overallError += np.abs(layer_2_error[0])\n \n # decode estimate so we can print it out\n d[binary_dim - position - 1] = np.round(layer_2[0][0])\n \n # store hidden layer so we can use it in the next timestep\n layer_1_values.append(copy.deepcopy(layer_1))\n \n future_layer_1_delta = np.zeros(hidden_dim)\n \n for position in range(binary_dim):\n \n X = np.array([[a[position],b[position]]])\n layer_1 = layer_1_values[-position-1]\n prev_layer_1 = layer_1_values[-position-2]\n \n # error at output layer\n layer_2_delta = layer_2_deltas[-position-1]\n # error at hidden layer\n layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1)\n\n # let's update all our weights so we can try again\n synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)\n synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)\n synapse_0_update += X.T.dot(layer_1_delta)\n \n future_layer_1_delta = layer_1_delta\n \n\n synapse_0 += synapse_0_update * alpha\n synapse_1 += synapse_1_update * alpha\n synapse_h += synapse_h_update * alpha \n\n synapse_0_update *= 0\n synapse_1_update *= 0\n synapse_h_update *= 0\n \n # print out progress\n if(j % 1000 == 0):\n print(\"Error:\" + str(overallError))\n print(\"Pred:\" + str(d))\n print(\"True:\" + str(c))\n out = 0\n for index,x in enumerate(reversed(d)):\n out += x*pow(2,index)\n print(str(a_int) + \" + \" + str(b_int) + \" = \" + str(out))\n print(\"------------\")\n\n\n\n\n\n\n\n\n\n# +\nimport copy, numpy as np\nnp.random.seed(0)\n\n# compute sigmoid nonlinearity\ndef sigmoid(x):\n output = 1/(1+np.exp(-x))\n return output\n\n# convert output of sigmoid function to its derivative\ndef sigmoid_output_to_derivative(output):\n return output*(1-output)\n\n# training dataset generation\nint2binary = {}\nbinary_dim = 8\n\nlargest_number = pow(2,binary_dim)\nbinary = np.unpackbits(\n np.array([list(range(largest_number))],dtype=np.uint8).T,axis=1)\nfor i in range(largest_number):\n int2binary[i] = binary[i]\n\n# input variables\nalpha = 0.1\ninput_dim = 2\nhidden_dim = 16\noutput_dim = 1\n\n\n# initialize neural network weights\nsynapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1\nsynapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1\nsynapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1\n\nsynapse_0_update = np.zeros_like(synapse_0)\nsynapse_1_update = np.zeros_like(synapse_1)\nsynapse_h_update = np.zeros_like(synapse_h)\n\n# training logic\nfor j in range(10000):\n \n # generate a simple addition problem (a + b = c)\n a_int = np.random.randint(largest_number/2) # int version\n a = int2binary[a_int] # binary encoding\n\n b_int = np.random.randint(largest_number/2) # int version\n b = int2binary[b_int] # binary encoding\n\n # true answer\n c_int = a_int + b_int\n c = int2binary[c_int]\n \n # where we'll store our best guess (binary encoded)\n d = np.zeros_like(c)\n\n overallError = 0\n \n layer_2_deltas = list()\n layer_1_values = list()\n layer_1_values.append(np.zeros(hidden_dim))\n \n # moving along the positions in the binary encoding\n for position in range(binary_dim):\n \n # generate input and output\n X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]])\n y = np.array([[c[binary_dim - position - 1]]]).T\n\n # hidden layer (input ~+ prev_hidden)\n layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h))\n\n # output layer (new binary representation)\n layer_2 = sigmoid(np.dot(layer_1,synapse_1))\n\n # did we miss?... if so, by how much?\n layer_2_error = y - layer_2\n layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2))\n overallError += np.abs(layer_2_error[0])\n \n # decode estimate so we can print it out\n d[binary_dim - position - 1] = np.round(layer_2[0][0])\n \n # store hidden layer so we can use it in the next timestep\n layer_1_values.append(copy.deepcopy(layer_1))\n \n future_layer_1_delta = np.zeros(hidden_dim)\n \n for position in range(binary_dim):\n \n X = np.array([[a[position],b[position]]])\n layer_1 = layer_1_values[-position-1]\n prev_layer_1 = layer_1_values[-position-2]\n \n # error at output layer\n layer_2_delta = layer_2_deltas[-position-1]\n # error at hidden layer\n layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1)\n\n # let's update all our weights so we can try again\n synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)\n synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)\n synapse_0_update += X.T.dot(layer_1_delta)\n \n future_layer_1_delta = layer_1_delta\n \n\n synapse_0 += synapse_0_update * alpha\n synapse_1 += synapse_1_update * alpha\n synapse_h += synapse_h_update * alpha \n\n synapse_0_update *= 0\n synapse_1_update *= 0\n synapse_h_update *= 0\n \n # print out progress\n if(j % 1000 == 0):\n print(\"Error:\" + str(overallError))\n print(\"Pred:\" + str(d))\n print(\"True:\" + str(c))\n out = 0\n for index,x in enumerate(reversed(d)):\n out += x*pow(2,index)\n print(str(a_int) + \" + \" + str(b_int) + \" = \" + str(out))\n print(\"------------\")\n# -\n\n\n\n\n\n\n\n\n\n\n except:\n columns.append('{}th Most Common Venue'.format(ind+1))\n\n# create a new dataframe\nneighborhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n\nfor ind in np.arange(toronto_grouped.shape[0]):\n neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n\nneighborhoods_venues_sorted\n# -\n\n# ### Cluster Neighborhoods¶\n#\n# #### Run k-means to cluster the neighborhood into 5 clusters.\n\n# +\n# set number of clusters\nkclusters = 5\n\ntoronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n\n# run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10]\n# -\n\n# #### Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.\n\n# +\ntoronto_merged = toronto_data\n\n# add clustering labels\ntoronto_merged['Cluster Labels'] = kmeans.labels_\n\n# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\ntoronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n\ntoronto_merged.head() # check the last columns!\n# -\n\n# #### Finally, let's visualize the resulting clusters\n\n# +\n# create map\nmap_clusters = folium.Map(location=[toronto_coords[0], toronto_coords[1]], zoom_start=12)\n\n# set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i+x+(i*x)**2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):\n label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n folium.CircleMarker(\n [lat, lon],\n radius=5,\n popup=label,\n color=rainbow[cluster-1],\n fill=True,\n fill_color=rainbow[cluster-1],\n fill_opacity=0.7).add_to(map_clusters)\n \nmap_clusters\n# -\n\n# ### Examine Clusters¶\n# ### Cluster 1\n\ntoronto_merged.loc[toronto_merged['Cluster Labels'] == 0, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]\n\n# ### Cluster 2\n\ntoronto_merged.loc[toronto_merged['Cluster Labels'] == 1, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]\n\n# ### Cluster 3\n\ntoronto_merged.loc[toronto_merged['Cluster Labels'] == 2, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]\n\n# ### Cluster 4\n\ntoronto_merged.loc[toronto_merged['Cluster Labels'] == 3, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]\n\n# ### Cluster 5\n\ntoronto_merged.loc[toronto_merged['Cluster Labels'] == 4, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]\n"},"script_size":{"kind":"number","value":14185,"string":"14,185"}}},{"rowIdx":918,"cells":{"path":{"kind":"string","value":"/PythonDS_Day5.ipynb"},"content_id":{"kind":"string","value":"03075464ff5997a10bb7c93da84b18092ec4ad3e"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"JuliaLo1979/60-Days-Python-Data-Science-Coding-Marathon"},"repo_url":{"kind":"string","value":"https://github.com/JuliaLo1979/60-Days-Python-Data-Science-Coding-Marathon"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":5109,"string":"5,109"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nimport numpy as np\n\n#scores for 6 students\nenglish_score = np.array([55,89,76,65,48,70])\nmath_score = np.array([60,85,60,68,np.nan,60])\nchinese_score = np.array([65,90,82,72,66,77])\n\n#English\nif np.isnan(english_score).sum() == 0:\n print('English has no nan')\n print('平均:', np.mean(english_score))\n print('最大值:', np.amax(english_score))\n print('最小值:', np.amin(english_score))\n print('標準差:', np.std(english_score))\nelse:\n print('English has nan')\n print('平均:', np.nanmean(english_score))\n print('最大值:', np.nanmax(english_score))\n print('最小值:', np.nanmin(english_score))\n print('標準差:', np.nanstd(english_score))\nprint() \n\n#math\nif np.isnan(math_score).sum() == 0:\n print('math has no nan')\n print('平均:', np.mean(math_score))\n print('最大值:', np.amax(math_score))\n print('最小值:', np.amin(math_score))\n print('標準差:', np.std(math_score))\nelse:\n print('math has nan')\n print('平均:', np.nanmean(math_score))\n print('最大值:', np.nanmax(math_score))\n print('最小值:', np.nanmin(math_score))\n print('標準差:', np.nanstd(math_score))\nprint() \n\n#chinese\nif np.isnan(chinese_score).sum() == 0:\n print('chinese has no nan')\n print('平均:', np.mean(chinese_score))\n print('最大值:', np.amax(chinese_score))\n print('最小值:', np.amin(chinese_score))\n print('標準差:', np.std(chinese_score))\nelse:\n print('chinese has nan')\n print('平均:', np.nanmean(chinese_score))\n print('最大值:', np.nanmax(chinese_score))\n print('最小值:', np.nanmin(chinese_score))\n print('標準差:', np.nanstd(chinese_score))\n\n# -\n\nmath_score[4] = 55\nmath_score\n\n#math\nif np.isnan(math_score).sum() == 0:\n print('math has no nan')\n print('平均:', np.mean(math_score))\n print('最大值:', np.amax(math_score))\n print('最小值:', np.amin(math_score))\n print('標準差:', np.std(math_score))\nelse:\n print('math has nan')\n print('平均:', np.nanmean(math_score))\n print('最大值:', np.nanmax(math_score))\n print('最小值:', np.nanmin(math_score))\n print('標準差:', np.nanstd(math_score))\nprint() \n\n# +\na = np.corrcoef(chinese_score, english_score)\nb = np.corrcoef(chinese_score, math_score)\nprint(a)\nprint(b)\n\nif a[0,1]>b[0,1]:\n print('與國文成績相關係數最高的學科是英文')\nelse:\n print('與國文成績相關係數最高的學科是數學')\n# -\n\n\n"},"script_size":{"kind":"number","value":2480,"string":"2,480"}}},{"rowIdx":919,"cells":{"path":{"kind":"string","value":"/5.4.2High Condition Number.ipynb"},"content_id":{"kind":"string","value":"fffb0546f881735fae65968f847d99db07f9a02b"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"darkraai/PyTorch-Models"},"repo_url":{"kind":"string","value":"https://github.com/darkraai/PyTorch-Models"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":101888,"string":"101,888"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# \n# \"IBM\n# \n#\n\n# \"cognitiveclass.ai\n\n#

Loss Function with a High Condition Number with and Without Momentum

\n\n#

Table of Contents

\n#

In this lab, we will generate data that will produce a Loss Function with a High Condition Number. You will create two models; one with the momentum term and one without the momentum term.

\n#\n# \n#

Estimated Time Needed: 30 min

\n#\n#
\n\n#

Preparation

\n\n# We'll need the following libraries: \n\n# +\n# Import the libraries we need for this lab\n\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits import mplot3d\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch import nn, optim\n\ntorch.manual_seed(1)\n\n\n# -\n\n# The class plot_error_surfaces is just to help you visualize the data space and the parameter space during training and has nothing to do with Pytorch. \n\n# +\n# Define the class for plot out the surface\n\nclass plot_error_surfaces(object):\n \n # Constructor\n def __init__(self, w_range, b_range, X, Y, n_samples=30, go=True):\n W = np.linspace(-w_range, w_range, n_samples)\n B = np.linspace(-b_range, b_range, n_samples)\n w, b = np.meshgrid(W, B) \n Z = np.zeros((n_samples, n_samples))\n count1 = 0\n self.y = Y.numpy()\n self.x = X.numpy()\n for w1, b1 in zip(w, b):\n count2 = 0\n for w2, b2 in zip(w1, b1):\n Z[count1, count2] = np.mean((self.y - w2 * self.x + b2) ** 2)\n count2 += 1\n count1 += 1\n self.Z = Z\n self.w = w\n self.b = b\n self.LOSS_list = {}\n \n # Setter\n def set_para_loss(self, model, name, loss):\n if (not (name in self.LOSS_list)):\n self.LOSS_list[name] = []\n w = list(model.parameters())[0].item()\n b = list(model.parameters())[1].item()\n self.LOSS_list[name].append({\"loss\": loss, \"w\": w, \"b\": b})\n \n # Plot the diagram\n def plot_ps(self, iteration=0):\n plt.contour(self.w, self.b, self.Z)\n count = 1\n if (len(self.LOSS_list) > 0):\n for key, value in self.LOSS_list.items():\n w = [v for d in value for (k, v) in d.items() if \"w\" == k]\n b = [v for d in value for (k, v) in d.items() if \"b\" == k]\n plt.scatter(w, b, cmap='viridis', marker='x', label=key)\n plt.title('Loss Surface Contour not to scale, Iteration: ' + str(iteration))\n plt.legend()\n plt.xlabel('w')\n plt.ylabel('b')\n plt.show()\n\n\n# -\n\n# \n\n#

Make Some Data

\n\n# Generate values from -2 to 2 that create a line with a slope of 0.1 and a bias of 10000. This is the line that you need to estimate. Add some noise to the data:\n\n# +\n# Define a class to create the dataset\n\nclass Data(Dataset):\n \n # Constructor\n def __init__(self):\n self.x = torch.arange(-2, 2, 0.1).view(-1, 1)\n self.f = 1 * self.x + 10000\n self.y = self.f + 0.1 * torch.randn(self.x.size())\n self.len = self.x.shape[0]\n \n # Getter\n def __getitem__(self, index): \n return self.x[index], self.y[index]\n \n # Get Length\n def __len__(self):\n return self.len\n\n\n# -\n\n# Create a dataset object: \n\n# +\n# Create a dataset object\n\ndataset = Data()\n# -\n\n# Plot the data\n\n# +\n# Plot the data\n\nplt.plot(dataset.x.numpy(), dataset.y.numpy(), 'rx', label='y')\nplt.plot(dataset.x.numpy(), dataset.f.numpy(), label='f')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.legend()\n\n\n# -\n\n# \n\n#

Create the Model and Total Loss Function (Cost)

\n\n# Create a linear regression class \n\n# +\n# Define linear regression class\n\nclass linear_regression(nn.Module):\n \n # Constructor\n def __init__(self, input_size, output_size):\n super(linear_regression, self).__init__()\n self.linear = nn.Linear(input_size, output_size)\n \n # Prediction\n def forward(self, x):\n yhat = self.linear(x)\n return yhat\n\n\n# -\n\n# We will use PyTorch's build-in function to create a criterion function; this calculates the total loss or cost \n\n# +\n# Use the build-in function to create a criterion function\n\ncriterion = nn.MSELoss()\n# -\n\n# Create a linear regression object, and an SGD optimizer object with no momentum.\n\n# +\n# Create a linear regression object and the optimizer without momentum\n\nmodel = linear_regression(1, 1)\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n# -\n\n# Create a linear regression object, and an SGD optimiser object with momentum .\n\n# +\n# Create a linear regression object and the optimizer with momentum\n\nmodel_momentum = linear_regression(1, 1)\noptimizer_momentum = optim.SGD(model_momentum.parameters(), lr=0.01, momentum=0.2)\n# -\n\n# Create a dataloader object: \n\n# +\n# Create a data loader\n\ntrainloader = DataLoader(dataset=dataset, batch_size=1, shuffle=True)\n# -\n\n# PyTorch randomly initializes your model parameters. If we use those parameters, the result will not be very insightful as convergence will be extremely fast. In order to prevent that, we will initialize the parameters such that it will take longer to converge.\n\n# +\n# Set parameters\n\nmodel.state_dict()['linear.weight'][0] = -5000\nmodel.state_dict()['linear.bias'][0] = -100000\nmodel_momentum.state_dict()['linear.weight'][0] = -5000\nmodel_momentum.state_dict()['linear.bias'][0] = -100000\n# -\n\n# Create a plotting object, not part of PyTorch, only used to help visualize \n\n# +\n# Plot the surface\n\nget_surface = plot_error_surfaces(5000, 100000, dataset.x, dataset.y, 100, go=False)\nget_surface.plot_ps()\n\n\n# -\n\n# \n\n#

Train the Model via Stochastic Gradient Descent

\n\n# Run 1 epochs of stochastic gradient descent and view parameter space. \n\n# +\n# Train the model\n\ndef train_model(epochs=1):\n for epoch in range(epochs):\n for i, (x, y) in enumerate(trainloader):\n #no momentum\n yhat = model(x)\n loss = criterion(yhat, y)\n\n #momentum\n yhat_m = model_momentum(x)\n loss_m = criterion(yhat_m, y)\n\n #apply optimization to momentum term and term without momentum \n\n #for plotting \n #get_surface.get_stuff(model, loss.tolist())\n #get_surface.get_stuff1(model_momentum, loss_m.tolist())\n\n get_surface.set_para_loss(model=model_momentum, name=\"momentum\" ,loss=loss_m.tolist())\n get_surface.set_para_loss(model=model, name=\"no momentum\" , loss=loss.tolist())\n\n optimizer.zero_grad()\n optimizer_momentum.zero_grad()\n loss.backward()\n loss_m.backward()\n optimizer.step()\n optimizer_momentum.step()\n get_surface.plot_ps(iteration=i)\ntrain_model()\n# -\n\n# The plot above shows the different parameter values for each model in different iterations of SGD. The values are overlaid over the cost or total loss surface. The contour lines somewhat miss scaled but it is evident that in the vertical direction they are much closer together implying a larger gradient in that direction. The model trained with momentum shows somewhat more displacement in the hozontal direction.\n\n# The plot below shows the log of the cost or total loss, we see that the term with momentum converges to a minimum faster and to an overall smaller value. We use the log to make the difference more evident.\n\n# +\n# Plot the loss\n\nloss = [v for d in get_surface.LOSS_list[\"no momentum\"] for (k, v) in d.items() if \"loss\" == k]\nloss_m = [v for d in get_surface.LOSS_list[\"momentum\"] for (k, v) in d.items() if \"loss\" == k]\nplt.plot(np.log(loss), 'r', label='no momentum' )\nplt.plot(np.log(loss_m), 'b', label='momentum' )\nplt.title('Cost or Total Loss' )\nplt.xlabel('Iterations ')\nplt.ylabel('Cost')\nplt.legend()\nplt.show()\n# -\n\n# \n\n# \n# \"PyTorch\n# \n\n#

About the Authors:

\n#\n# Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD. \n\n# Other contributors: Michelle Carey, Mavis Zhou\n\n#
\n\n# Copyright &copy; 2018 cognitiveclass.ai. This notebook and its source code are released under the terms of the MIT License.\n"},"script_size":{"kind":"number","value":9945,"string":"9,945"}}},{"rowIdx":920,"cells":{"path":{"kind":"string","value":"/notebooks/HW4_Chakravarty_Subhayu copy.ipynb"},"content_id":{"kind":"string","value":"171e8fde3372f1aba31a2a31278dff8a5c26f7f8"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"s7chak/academic_python"},"repo_url":{"kind":"string","value":"https://github.com/s7chak/academic_python"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":3612,"string":"3,612"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# #
FinTech Assignment #4 : Bitcoin Transaction and Mining
\n# ###
Submission by Subhayu Chakravarty
\n\n# ### Importing required libraries\n\nimport hashlib\n\n# # Mining Bitcoin Exercise\n\n# ## 1. Mining\n\n# +\n# Initial Block Header\ntransaction='Cesare sends one bitcoin to Shimon'\nprevious_transaction_hash='85738f8f9a7f1b04b5329c590ebcb9e425925c6d0984089c43a022de4f19c281'\ntimestamp='2018-01-07 21:05:34'\nbits='3'\nnonce='0'\n\nhashed_transaction=hashlib.sha256(transaction.encode('utf-8')).hexdigest()\n\n\nblock_header=hashed_transaction+' '+previous_transaction_hash+' '+timestamp+' '+bits\nprint('\\nInitial block header without nonce :')\nprint(block_header)\n\nprint('\\nBlock header with nonce=0:')\nprint(block_header+' 0')\n\nhashed_block_header=hashlib.sha256((block_header+' 0').encode('utf-8')).hexdigest()\nprint('\\nHashed block header:')\nprint(hashed_block_header)\n# -\n\n# Finding the winning nonce\ncounter=0\nwhile counter<100000:\n nonce=str(counter)\n h=block_header+' '+nonce\n hashed_header=hashlib.sha256(h.encode('utf-8')).hexdigest()\n if hashed_header[0:3]=='000':\n print(hashed_header)\n print('\\nWinning Nonce:')\n print(nonce)\n break\n counter+=1\n\n\n# ##
The End
\n"},"script_size":{"kind":"number","value":1538,"string":"1,538"}}},{"rowIdx":921,"cells":{"path":{"kind":"string","value":"/Python Assignment 4.ipynb"},"content_id":{"kind":"string","value":"af837af56fc03907ea536ced0b30f11dc6150dbe"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Deepakagarwal1999/Assignment-4"},"repo_url":{"kind":"string","value":"https://github.com/Deepakagarwal1999/Assignment-4"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":4278,"string":"4,278"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ## Hospital readmissions data analysis and recommendations for reduction\n#\n# ### Background\n# In October 2012, the US government's Center for Medicare and Medicaid Services (CMS) began reducing Medicare payments for Inpatient Prospective Payment System hospitals with excess readmissions. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions.\n#\n# ### Exercise overview\n#\n# In this exercise, you will:\n# + critique a preliminary analysis of readmissions data and recommendations (provided below) for reducing the readmissions rate\n# + construct a statistically sound analysis and make recommendations of your own \n#\n# More instructions provided below. Include your work **in this notebook and submit to your Github account**. \n#\n# ### Resources\n# + Data source: https://data.medicare.gov/Hospital-Compare/Hospital-Readmission-Reduction/9n3s-kdb3\n# + More information: http://www.cms.gov/Medicare/medicare-fee-for-service-payment/acuteinpatientPPS/readmissions-reduction-program.html\n# + Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n# ****\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport bokeh.plotting as bkp\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom scipy import stats\n# %matplotlib inline\n\n# read in readmissions data provided\nhospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')\nhospital_read_df.head(2)\n\n# ****\n# ## Preliminary analysis\n\n# deal with missing and inconvenient portions of data \nclean_hospital_read_df = hospital_read_df[(hospital_read_df['Number of Discharges'] != 'Not Available')]\nclean_hospital_read_df.loc[:, 'Number of Discharges'] = clean_hospital_read_df['Number of Discharges'].astype(int)\nclean_hospital_read_df = clean_hospital_read_df.sort('Number of Discharges')\n\n# +\n# generate a scatterplot for number of discharges vs. excess rate of readmissions\n# lists work better with matplotlib scatterplot function\nx = [a for a in clean_hospital_read_df['Number of Discharges'][81:-3]]\ny = list(clean_hospital_read_df['Excess Readmission Ratio'][81:-3])\n\nfig, ax = plt.subplots(figsize=(8,5))\nax.scatter(x, y,alpha=0.2)\n\nax.fill_between([0,350], 1.15, 2, facecolor='red', alpha = .15, interpolate=True)\nax.fill_between([800,2500], .5, .95, facecolor='green', alpha = .15, interpolate=True)\n\nax.set_xlim([0, max(x)])\nax.set_xlabel('Number of discharges', fontsize=12)\nax.set_ylabel('Excess rate of readmissions', fontsize=12)\nax.set_title('Scatterplot of number of discharges vs. excess rate of readmissions', fontsize=14)\n\nax.grid(True)\nfig.tight_layout()\n# -\n\n# ****\n#\n# ## Preliminary report\n#\n# **A. Initial observations based on the plot above**\n# + Overall, rate of readmissions is trending down with increasing number of discharges\n# + With lower number of discharges, there is a greater incidence of excess rate of readmissions (area shaded red)\n# + With higher number of discharges, there is a greater incidence of lower rates of readmissions (area shaded green) \n#\n# **B. Statistics**\n# + In hospitals/facilities with number of discharges < 100, mean excess readmission rate is 1.023 and 63% have excess readmission rate greater than 1 \n# + In hospitals/facilities with number of discharges > 1000, mean excess readmission rate is 0.978 and 44% have excess readmission rate greater than 1 \n#\n# **C. Conclusions**\n# + There is a significant correlation between hospital capacity (number of discharges) and readmission rates. \n# + Smaller hospitals/facilities may be lacking necessary resources to ensure quality care and prevent complications that lead to readmissions.\n#\n# **D. Regulatory policy recommendations**\n# + Hospitals/facilties with small capacity (< 300) should be required to demonstrate upgraded resource allocation for quality care to continue operation.\n# + Directives and incentives should be provided for consolidation of hospitals and facilities to have a smaller number of them with higher capacity and number of discharges.\n\n# ****\n#\n# ## Exercise\n#\n# Include your work on the following **in this notebook and submit to your Github account**. \n#\n# A. Do you agree with the above analysis and recommendations? Why or why not?\n# \n# B. Provide support for your arguments and your own recommendations with a statistically sound analysis:\n#\n# 1. Setup an appropriate hypothesis test.\n# 2. Compute and report the observed significance value (or p-value).\n# 3. Report statistical significance for $\\alpha$ = .01. \n# 4. Discuss statistical significance and practical significance\n#\n#\n#\n# You can compose in notebook cells using Markdown: \n# + In the control panel at the top, choose Cell > Cell Type > Markdown\n# + Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n#\n# ****\n\ndf = clean_hospital_read_df\ndf['Provider Number'].nunique()\nlen(df)\n\ndfl=df[(df['Number of Discharges'] < 100) & (df['Number of Discharges'] > 0)]\ndfh =df[df['Number of Discharges'] > 1000]\n\n\n\ncol = 'Excess Readmission Ratio'\ncol_ex = 'Expected Readmission Rate'\ncol_pd = 'Predicted Readmission Rate'\n\ndfl[col].hist(normed=1,bins=15)\ndfh[col].hist(normed=1,bins=15)\n\n\n\n# +\nln = len(dfl)\nhn = len(dfh)\n\nlmn = dfl[col].mean()\nhmn = dfh[col].mean()\n\nlstd = dfl[col].std()\nhstd = dfh[col].std()\n\nprint('Excess Readmission Ratio:\\n')\nprint('< 100 discharges:')\nprint('Number =',ln,'\\nMean Ratio =',lmn,'\\nStd dev =',lstd)\nprint('')\nprint('> 100 discharges:')\nprint('Number =',hn,'\\nMean Ratio =',hmn,'\\nStd dev =',hstd)\n# -\n\nmn_diff = lmn - hmn\nstd_diff = np.sqrt(lstd**2/ln + hstd**2/hn)\nprint(' Difference of means =',mn_diff)\nprint('Std Dev of difference =',std_diff)\nprint(' Significance to 1%:',2.35*std_diff)\n\nz = mn_diff/std_diff\nprint('z score =',z)\n\np_value = 2 * stats.norm.cdf(0, mn_diff, std_diff)\np_value\n\n\n\n\n"},"script_size":{"kind":"number","value":6304,"string":"6,304"}}},{"rowIdx":922,"cells":{"path":{"kind":"string","value":"/Udemy/05-Data-Visualization-with-Matplotlib/Untitled.ipynb"},"content_id":{"kind":"string","value":"2da3e9ee4a01a5e45ecba2cc026d34bafd1fa601"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"SabuhiTapdigli/Python_Examples"},"repo_url":{"kind":"string","value":"https://github.com/SabuhiTapdigli/Python_Examples"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":77428,"string":"77,428"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n# ## What is Regression Analysis?\n# Regression analysis is a common statistical method used in finance to determine the relationship between variables. The process helps understand the factors that are important and irrelevant and how they affect each other.\n#\n# Let’s cover the key terms:\n#\n# - **Dependent variable:** This is the target response variable we’re trying to predict or understand.\n#\n# - **Independent variable(s):** These are the independent input factors that we think might influence the dependent variable. \n#\n#\n# For instance, if we want to predict the price of homes, the home price prediction would be the dependent variable, and the independent variable or independent variables would be the independent variables. Examples of independent variables or factors influencing the home price might be square feet, the number of rooms, garage, finished basement, etc.\n#\n\n# This notebook is meant to be read in conjunction with the blogpost found at https://analyzingalpha.com/linear-regression-python\n\n# # Get Data Using the FRED API\n\n# ### Links\n# - Blog Post\n# - YouTube Video\n\n# ## Install the FRED API\n\n# !pip install fredapi\n# !pip install statsmodels\n\n# +\n## Import Packages\n\n# +\nimport fredapi as fa\nimport numpy as np\nimport pandas as pd\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport statsmodels.api as sm\n\nfrom local_settings import fred as settings\n# -\n\nfred = fa.Fred(settings['api_key'])\n\ncurcir = fred.get_series('CURRCIR') ; curcir.name = 'curcir'\ngdp = fred.get_series('GDP') ; gdp.name = 'gdp'\nsp500 = fred.get_series('sp500') ; sp500.name = 'sp500'\n#sp500 = fred.get_series('WILL5000INDFC') ; sp500.name = 'sp500'\n\ndf = pd.merge(sp500, gdp, left_index=True, right_index=True)\ndf = df.merge(curcir, left_index=True, right_index=True)\ndf = df.dropna()\n\n\ndf['tl_gdp'] = sm.OLS(df['sp500'].values, sm.add_constant(df['gdp'].values)).fit().fittedvalues\ndf.head()\n\n# +\nfig = go.Figure()\nfig.add_trace(go.Scatter(name='GDP vs. SP500', x=df['gdp'], y=df['sp500'], mode='markers'))\nfig.add_trace(go.Scatter(name='Best Fit Line', x=df['gdp'], y=df['tl_gdp'], mode='lines',\n line=dict(color='orange')))\n\nfig.add_trace(go.Scatter(name='GDP vs. SP500', x=df.index, y=df['sp500'], mode='markers',\n opacity=0, showlegend=False, hoverinfo='skip', xaxis=\"x2\"))\n\nfig.update_layout(xaxis1={'side':'bottom'},\n xaxis2={'showgrid':False,\n 'overlaying':'x',\n 'side':'top',\n 'tickangle':45\n })\nfig.update_layout(title=\"Simple Linear Regression\",\n xaxis2_range=[df.index[0], df.index[-1]])\nfig.show()\n\n# +\nimport plotly.graph_objects as go\n# Get X values\nx_vals = fig.data[0]['x']\n\n# Get Errors\nerrors = {}\nfor d in fig.data:\n errors[d['mode']]=d['y']\n\nerrors\n# Make line shape for each error\nshapes = []\nfor i, x in enumerate(x_vals):\n shapes.append(go.layout.Shape(\n type='line',\n name='Error',\n x0=x,\n y0=errors['lines'][i],\n x1=x,\n y1=errors['markers'][i],\n line=dict(\n color='black',\n width=1),\n opacity=0.5,\n layer='above')\n )\n\nfig.update_layout(shapes=shapes, title='Simple Linear Regression w/ Error')\nfig.show()\n\n\n# +\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nrng = np.random.RandomState(1)\nx = 8 * rng.rand(50)\ny = np.sin(x) + 0.1 * rng.randn(50)\n\n#Create single dimension\nx= x[:,np.newaxis]\ny= y[:,np.newaxis]\n\ninds = x.ravel().argsort() # Sort x values and get index \nx = x.ravel()[inds].reshape(-1,1)\ny = y[inds] #Sort y according to x sorted index\n\nx = [item for x in x for item in x]\ny= [item for y in y for item in y]\n\nimport statsmodels.api as sm\n\nmodel = sm.OLS(y, x).fit()\nypred = model.predict(x) \n\nfig = go.Figure()\nfig.add_trace(go.Scatter(name='Sine Example',\n x=x,\n y=y,\n mode='markers'))\n\n\nfig.add_trace(go.Scatter(name='Best Fit Line', x=x, y=ypred, mode='lines',\n line=dict(color='orange')))\n\nfig.update_layout(title=\"Linear Regression\")\n\n\n\n# +\nimport statsmodels.api as sm\n\nmodel = sm.OLS(y, x).fit()\nypred = model.predict(x) \n\nfig.show()\n# -\n\nmodel.summary()\n\n# +\nrng = np.random.RandomState(1)\nx = 8 * rng.rand(50)\ny = np.sin(x) + 0.1 * rng.randn(50)\n\n#Create single dimension\nx= x[:,np.newaxis]\ny= y[:,np.newaxis]\n\ninds = x.ravel().argsort() # Sort x values and get index \nx = x.ravel()[inds].reshape(-1,1)\ny = y[inds] #Sort y according to x sorted index\n\nfrom sklearn.preprocessing import PolynomialFeatures\npolynomial_features= PolynomialFeatures(degree=3)\nxp = polynomial_features.fit_transform(x)\nmodel = sm.OLS(y, xp).fit()\nypred = model.predict(xp) \n\nx = [item for x in x for item in x]\ny= [item for y in y for item in y]\n\nfig = go.Figure()\nfig.add_trace(go.Scatter(name='Sine Example',\n x=x,\n y=y,\n mode='markers'))\n\n\nfig.add_trace(go.Scatter(name='Best Fit Line', x=x, y=ypred, mode='lines',\n line=dict(color='orange')))\n\n\nfig.update_layout(title=\"Polynomial Regression\")\n# -\n\nmodel = sm.OLS(df['sp500'].values, sm.add_constant(df['gdp'].values)).fit()\n\nmodel.summary()\n\npd.DataFrame(index=df.index, data=model.resid).head()\n\n#df2['tl'] = sm.OLS(df['sp500'].values, sm.add_constant(df2[['gdp', 'curcir']].values)).fit().fittedvalues\ndf['tl_curcir'] = sm.OLS(df['sp500'].values, sm.add_constant(df[ 'curcir'].values)).fit().fittedvalues\ndf.head()\n\n# +\nfig = go.Figure()\n#fig.add_trace(go.Scatter(name='GDP vs. SP500', x=df2['gdp'], y=df2['sp500'], mode='markers'))\nfig.add_trace(go.Scatter(name='CURCIR vs. SP500', x=df['curcir'], y=df['sp500'], mode='markers'))\nfig.add_trace(go.Scatter(name='Best Fit Line', x=df['curcir'], y=df['tl_curcir'], mode='lines',\n line=dict(color='orange')))\n\nfig.add_trace(go.Scatter(name='CURCIR vs. SP500', x=df.index, y=df['sp500'], mode='markers',\n opacity=0, showlegend=False, hoverinfo='skip', xaxis=\"x2\"))\n\nfig.update_layout(xaxis1={'side':'bottom'},\n xaxis2={'showgrid':False,\n 'overlaying':'x',\n 'side':'top',\n 'tickangle':45\n })\nfig.update_layout(title=\"Simple Linear Regression\",\n xaxis2_range=[df.index[0], df.index[-1]])\nfig.show()\n# -\n\nmodel2 = sm.OLS(df['sp500'].values, sm.add_constant(df[ 'curcir'].values)).fit()\nmodel2.summary()\n\nmodel3 = sm.OLS(df['sp500'].values,\n sm.add_constant(df[['gdp', 'curcir']].values)).fit()\nmodel3.summary()\n\n# ## Visualizing Multiple Linear Regression\n#\n# We can perform simple linear regression and graph them separately like the below.\n#\n\ndf['tl_gdp_curcir'] = model3 = sm.OLS(df['sp500'].values,\n sm.add_constant(df[['gdp', 'curcir']].values)).fit().fittedvalues\n\n# +\nfig = make_subplots(rows=1, cols=2)\nfig.add_trace(go.Scatter(name=\"GDP vs. SP500\",\n x=df['gdp'],\n y=df['sp500'],\n mode='markers',\n ), row=1,col=1\n )\nfig.add_trace(go.Scatter(name='Best Fit',\n x=df['gdp'], y=df['tl_gdp']), row=1, col=1)\n\nfig.add_trace(go.Scatter(name=\"CURCIR vs. SP500\",\n x=df['curcir'],\n y=df['sp500'],\n mode='markers',\n ), row=1,col=2\n )\nfig.add_trace(go.Scatter(name='Best Fit',\n x=df['curcir'], y=df['tl_curcir']), row=1, col=2)\n\nfig.update_layout(title=\"Simple Linear Regression, Multiple Plots \",\n xaxis2_range=[df.index[0], df.index[-1]])\nfig.show()\n# -\n\n# But in truth, having two linear models is nice, but the linear regression line is just the best fit line for each independent simple linear regression model we covered above. \n#\n# It’s time to put on our 3d glasses.\n#\n# Let’s create a multiple linear regression model 3d graph where the y-values are the s&p500, and the x and z values are GDP and currency in circulation, respectively.\n#\n\n# +\n\nx_min, x_max = df['gdp'].min(), df['gdp'].max()\ny_min, y_max = df['sp500'].min(), df['sp500'].max()\nz_min, z_max = df['curcir'].min(), df['curcir'].max()\n\np_min, p_max = df['tl_gdp_curcir'].min(), df['tl_gdp_curcir'].max()\n\nxrange = np.arange(x_min, x_max, int((x_max-x_min) / 10))\nyrange = np.arange(y_min, y_max, int((y_max-y_min) / 10))\nzrange = np.arange(z_min, z_max, int((z_max-z_min) / 10))\n\nprange = np.arange(p_min, p_max, int((p_max-p_min) / 10))\n\nfig = go.Figure()\nfig.add_trace(go.Scatter3d(name='SP500 vs. GDP & Currency',\n x=df['gdp'],\n y=df['sp500'],\n z=df['curcir'],\n mode='markers',\n ))\nfig.add_trace(go.Scatter3d(name='Best Fit Line',\n x=xrange,\n y=prange,\n z=zrange))\n\nfig.update_layout(scene = dict(\n xaxis_title='GDP',\n yaxis_title='SP500',\n zaxis_title='Currency in Circulation'\n))\n\nfig.update_layout(title=\"Multiple Linear Regression\")\nfig.show()\n# -\n\n# The straight-line moves up and to the right, my favorite direction (trading joke). We can see as both GDP and Currency in Circulation increase, so does the S&P 500 price.\n#\n#\n# Why don’t we add some random data to see how that affects our model. Let’s add a random one-dimensional array between 1 and 1000 to our model.\n#\n\nnp.random.seed(1337) # used to replicate randomness\nrand = np.random.choice([1, 1000, 20], df.shape[0])\ndf.loc[:, 'rand'] = rand\ndf.head()\n\n\n# We know this is random and won’t help our regression model. Let’s see how it performs.\n#\n\nmodel4 = sm.OLS(df['sp500'].values,\n sm.add_constant(df[['gdp', 'curcir', 'rand']].values)).fit()\nmodel4.summary()\n\n\n# The r-squared didn't improve, which should be obvious -- we added random data. But how do we know if a feature is statistically significant? How do we know this new input feature helps our predicted value? Let’s dive deeper. \n#\n# Well, there’s more to it than this, but a good rule of thumb is that if the p-value is 0.05 or lower, the coefficient and independent variable are said to be statistically significant. \n#\n# In other words, if the p-value is small and the increase in r2 is large, add the variable to the input features; otherwise, discard.\n#\n#\n# We can see above that our p-value for x3, our random data, is 0.785, so we should remove it from our model -- even if it improves our target variable, which it didn’t.\n#\n# There’s another issue that we need to discuss. Look at the notes from the summary.\n#\n\n# ## Multicollinearity in Regression\n# Multi-what? When we perform linear regression, the independent variables should be … well… independent. We should understand that a regression coefficient represents the change in the predicted response for each 1 unit change in the independent variable, _holding all other independent variables constant_. \n#\n# There are additional problems and different types of multicollinearity, but in short, you can’t trust the p-values to identify statistically significant variables. \n#\n# So how do we know if the independent features are independent?\n#\n# We can detect multicollinearity with VIF.\n#\n#\n\n# ### Variance Inflation Factor\n#\n# Variance inflation factor or VIF detects multicollinearity in regression analysis. \n#\n# A VIF of 1 indicates two variables are not correlated, a VIF greater than 1 and less than 5 indicates a moderate correlation, and a VIF of 5 or above indicates a high correlation.\n# We can use `Statsmodels` to determine the VIF for each feature.\n#\n\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nvif = pd.DataFrame()\ndf2 = df[['gdp','curcir', 'rand']]\nvif['feature'] = df2.columns\nvif['VIF'] = [variance_inflation_factor(df2.values, i) for i in range(len(df2.columns))]\nvif\n\n# ## Cross Validation\n\n#\n# The goal of a regression model in most cases is to predict future values. We’ve used all of the data until now when building/training our linear regression model. We’re overfitting because we’re building a model using observed data and asking how well it will predict that historical data.\n#\n# If we use our linear regression model with next quarter’s GDP to predict the _future_ S&P 500 price, then we’re finally making a prediction.\n#\n# We should be breaking up the data into a training and test set, or even better yet, training sets and test sets. We’ll use different slices of history, the training sets, to make predictions about different periods in history, which are our testing sets.\n#\n# This would help us determine if the currency in circulation or GDP was better for predicting equity prices. As we saw, GDP was the winner in the first example, and currency in circulation bested GDP over a more extended period, but what about in the middle?\n#\n# It’s plain to see that this type of train/test set is more robust and often comes up with a better regression model leading to a more accurate predicted response. This is a common practice in scientific computing and machine learning. The only concern with machine learning models is that such models are prone to overfitting -- we’ll discuss this in a bit.\n#\n# I will use `sklearn` to create the training data, and test data splits. I’ll also use the linear regression model from `sklearn`, but linear regression works with both packages and can use either. We’re going to need to import a lot more libraries, and this time, instead of using `plotly`, we’ll use `matplotlib` in conjunction with `seaborn`. \n#\n# We’ll first grab the imports. \n\n# ### Get Imports\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt \nfrom sklearn.metrics import r2_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\n\n# Next, let's organize the columns and review the data.\n\ncolumns = ['gdp','curcir', 'rand', 'sp500']\ndf = df[columns]\ndf.head()\n\n# ### Scale & Normalize Data\n\n# Machine learning algorithms work better when features share a similar scale and are normally distributed. Let’s scale and standardize the variables between 0 and 1 using `sklearn.preprocessing.MinMaxScaler`. \n\nscaler = MinMaxScaler()\nscaled = scaler.fit_transform(df[columns])\ndf2 = pd.DataFrame(scaled)\ndf2.columns = columns #scaler returns nd.array\ndf2.head()\n\n# Notice that all of the numbers are now scaled between one and zero. Also, remember that overfitting thing? We just did it…\n#\n# Overfitting means that our model fits too closely to a particular set of data and may fail to predict observed values reliably.\n#\n# In the above case, we scaled and fit the data to the entire data set. We can’t train on our test data because we’ll be making predictions on data that we used to create our regression model.\n#\n# We should only ever use `MinMaxScaler.fit_transform` with training data and use `MinMaxScaler.transform` with test data. The reason is that we can’t scale and normalize our data based on test data. We should only scale and fit on training data.\n#\n\n# ### Split Data Into Train and Test Sets\n\n# There are other ways to overfit, too. We’ll discuss a few more ways shortly. For now, let’s separate our data into training and testing sets. We’ll train on 70% of the data and test on the remaining 30%. We'll also scale our data properly instead of overfitting like we did above.\n#\n# Always remember to only call `transform` and not `fit_transform` on the test data. You should never fit to testing data!\n\n# +\n# Create training data.\ntrain_size = 0.7\ndf_train, df_test = train_test_split(df,\n train_size=train_size,\n test_size=round(1-train_size,2),\n shuffle=False\n )\n# Scale the test and train data.\nscaler = MinMaxScaler()\ndf_train[columns] = scaler.fit_transform(df_train[columns])\ndf_test[columns] = scaler.transform(df_test[columns]) # fit_transform\n\n# Separate into training and testing sets\ny_train = df_train.pop('sp500')\nX_train = df_train\n\ny_test = df_test.pop('sp500')\nX_test = df_test\nprint(X_train.head())\nprint(y_train.head())\nprint(X_test.head())\nprint(y_test.head())\n# -\n\n# Now let's fix our multicollinearity issue identified by VIF. \n\n# ### Recursive Feature Elimination\n\n# Instead of manually removing our features, imagine if we had numerous and weren’t sure which ones we should eliminate? Machine learning to the rescue.\n#\n# Recursive feature elimination does just that. It’s simple to do. We furnish a hyperparameter of the number of parameters we want, and it does the hard work for us. -- A hyperparameter is a parameter for parameters.\n#\n# Let’s see it in action.\n#\n\nfrom sklearn.feature_selection import RFE\nlm = LinearRegression()\nrfe = RFE(lm, n_features_to_select=1)\nrfe = rfe.fit(X_train, y_train)\nprint(X_train.columns)\nprint(rfe.support_)\nprint(rfe.ranking_)\n\n# Notice that our `n_features_to_select` hyperparameter was set to one, causing RFE to select only GDP. We can also see the rankings are 1, 2, 3 for GDP, currency in circulation, and our random variable, respectively.\n\n# ### Create Linear Regression Model\n\n# Let’s now understand a little more what we did above, and create another linear regression model below.\n#\n# We'll create a LinearRegression object and fit the training data to it. I’ll then use that trained LinearRegression object to predict the y_values. I’ll then compare the y_pred to the actual values (y_test) and print out our r2. `sklearn` requires the data be in a 1d array. We didn't need to do this above because the RFE took care of it for us.\n#\n\n# +\nlm = LinearRegression()\n# Only use GDP as determined by RME & VIF\n# lm required 1d array\nlm.fit(X_train['gdp'].values.reshape(-1,1), y_train)\n\n# Use test data for prediction\ny_pred = lm.predict(X_test['gdp'].values.reshape(-1,1))\n\nr2 = r2_score(y_test, y_pred)\nprint(r2)\n# -\n\n# RFE selects the best features recursively and applies the LinearRegression model to it. With this in mind, we should -- and will -- get the same answer for both linear regression models.\n\ny_pred = rfe.predict(X_test)\nr2 = r2_score(y_test, y_pred)\nprint(r2)\n\n# I wanted to show you both ways of creating a `LinearRegression` model. Keep in mind that RFE can be used with all sorts of estimators such as a `DecisionTreeClassifer`.\n\n# ### Cross-Validation Using K-Folds in Python\n\n# Instead of splitting the data into one train set and one test set, we can slice the data into multiple periods and create multiple training and test sets. Let’s use four k-folds as an example. We’ll create a KFold object with four splits. The splits will segregate utilizing the test data indices. The first set will take the first 16 elements; the second will be the following 16 elements, the next 15 elements, and finally, our most recent 15 elements. Our array length is 62 and not evenly divisible by 4.\n\n# +\nkf = KFold(n_splits = 4)\nfor train_index, test_index in kf.split(X_train):\n print(\"Train: \", train_index,\"\\nTest: \", test_index, \"\\n\\n\")\n \n \n# -\n\n# Notice how we now have four groups of test and train data. We can quickly estimate our r2 for each test group.\n\nscores = cross_val_score(lm, X_train, y_train, scoring='r2', cv=kf)\nscores\n\n# ## An Overfitting Conclusion\n\n# We see that the original linear regression model, which we thought was terrific, turns out to not be that great at predicting future S&P prices. There is some predictive power, but it isn't enough for me to put my money behind it. \n#\n# The good news is that you now have everything you need to perform simple and multiple linear regression in Python to create even better predictive models -- for the markets or whatever you choose.\n#\n# I hope you enjoyed, and if you have any questions, please let me know in the comments below.\n#\n"},"script_size":{"kind":"number","value":20775,"string":"20,775"}}},{"rowIdx":923,"cells":{"path":{"kind":"string","value":"/versions/20171006_14:27_commands.ipynb"},"content_id":{"kind":"string","value":"4c3ab532142435eedcb893db717e2c85d2617968"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"afcarl/firecloud_sugar"},"repo_url":{"kind":"string","value":"https://github.com/afcarl/firecloud_sugar"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":18066,"string":"18,066"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: pytorch\n# language: python\n# name: pytorch\n# ---\n\n# + [markdown] id=\"view-in-github\" colab_type=\"text\"\n# \"Open\n\n# + id=\"c611e4cb\"\n#read dataframe\nimport pandas as pd\nlistings=pd.read_csv('listings.csv')\n\n# + id=\"9ca6cee1\"\n#read geodataframe\n# #!pip install geopandas\nimport geopandas as gd\ngeo=gd.read_file('neighbourhoods.geojson')\n\n# + id=\"40db58f2\"\n#importing Map plot Library\n# #!pip install -U plotly\nimport plotly.express as px\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 225} id=\"1b56f07d\" outputId=\"547fdb1a-8df7-4bd5-a221-3a5752931dc4\"\ngeo=geo[['neighbourhood','geometry']].set_index(\"neighbourhood\")\ngeo.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} id=\"9383cdb2\" outputId=\"73a2bce5-ee85-4b58-9cf4-ee84556688f2\"\ndf=pd.DataFrame(listings['neighbourhood_cleansed'].value_counts())\n#df['neigbourhood']=df.index\ndf.reset_index(inplace=True)\ndf.rename(columns={'index':'neighbourhood_cleansed','neighbourhood_cleansed':'no_of_listings'},inplace=True)\ndf.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 323} id=\"9514333f\" outputId=\"cf576e5c-bc74-48bf-8014-c23f6a445649\"\nimport matplotlib.pyplot as plt\nlistings['neighbourhood_cleansed'].value_counts().plot(kind = 'bar', color = ['gold'], figsize = (40, 10))\nplt.title('Histogram of Listings', fontsize = 20)\nplt.xlabel('Neighbourhood')\nplt.ylabel('Number of Listings')\nplt.show()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 322} id=\"9212a1cc\" outputId=\"4812276d-e1c6-4557-e37f-7f306ccde7c2\"\nlistings.groupby('neighbourhood_cleansed')['number_of_reviews'].sum().plot(kind = 'bar', color = ['purple'], figsize = (40, 10))\nplt.title('Histogram of Reviews', fontsize = 20)\nplt.xlabel('Neighbourhood')\nplt.ylabel('Number of reviews')\nplt.show()\n\n# + [markdown] id=\"2f9e7f79\"\n# ### Ans A Top 10 Neighbourhood according to Listings and Reviews\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 302} id=\"76705de8\" outputId=\"2e64615a-0a56-4cb5-8b79-b54ab8ba2d1d\"\nlistings['neighbourhood_cleansed'].value_counts().head(10).plot(kind='bar')\nplt.xlabel('Zip_Codes')\nplt.ylabel('No. of Listings')\nplt.show()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} id=\"979839d7\" outputId=\"07dfd6cb-6fab-4981-d2b5-44e2fb8a7923\"\nans_a=pd.DataFrame(listings['neighbourhood_cleansed'].value_counts())\nans_a.reset_index(inplace=True)\nans_a.rename(columns={'index':'neighbourhood','neighbourhood_cleansed':'Listings'},inplace=True)\nans_a['neighbourhood']=ans_a['neighbourhood'].astype(str).astype(object)\nans_a.head()\n\n# + [markdown] id=\"feb37a7c\"\n# The top Neighbourhood according to number of listing is locality with zip code **78704**\n\n# + [markdown] id=\"b6372b2f\"\n# ### A.2 Mapping The Listing Pointers with thier Neighbourhood Polygons\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} id=\"9cad9694\" outputId=\"0eb8e55b-b23f-4b52-e0dd-77f3e6580ce1\"\nans_geo=geo.copy()\nans_geo.reset_index(inplace=True)\nans_geo\npd.merge(ans_geo,ans_a,on='neighbourhood').head()\n\n# + [markdown] id=\"439932ef\"\n# #### Reviews\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 301} id=\"068446e6\" outputId=\"f41d6faa-f415-4209-a955-d8191cc673fa\"\nlistings.groupby('neighbourhood_cleansed')['number_of_reviews'].sum().sort_values(ascending=False).head(10).plot(kind='bar')\nplt.xlabel('Zip Codes')\nplt.ylabel('No. of Reviews')\nplt.show()\n\n# + [markdown] id=\"b815ae89\"\n# The Top Neibhbourhood according to number of reviews is again with zip code **78704**\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} id=\"4b085e00\" outputId=\"08bf183c-57df-44a2-e9bc-d6fd25b29ba8\"\na={}\nfor n in listings['neighbourhood_cleansed'].unique():\n a[n]=listings.loc[listings['neighbourhood_cleansed']==n]['room_type'].value_counts()\nroom_count=pd.DataFrame.from_dict(a,orient='index')\nroom_count.reset_index(inplace=True)\nroom_count.rename(columns={'index':'neighbourhood_cleansed'},inplace=True)\nroom_count.fillna(value=0,inplace=True)\nroom_count.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} id=\"5191e6aa\" outputId=\"faee0741-41ec-49c9-825c-4db2754cb966\"\nfinal_df=pd.merge(df,room_count,on='neighbourhood_cleansed')\nfinal_df.head()\n\n# + [markdown] id=\"4cedc1dc\"\n# ### Ans B\n\n# + [markdown] id=\"df8b3809\"\n# ### Map Visualization Broken Down By Single Rooms\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 542} id=\"a6a02a92\" outputId=\"2831ecff-d36f-44de-91d6-e46c2db3466c\"\nfig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='Private room',labels={'no_of_listings':'no_of_listings'},hover_data=[\"no_of_listings\"],color_continuous_scale=\"Viridis\")\nfig.update_geos(fitbounds=\"locations\", visible=False)\nfig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\nfig.show()\n\n# + [markdown] id=\"19fc7e88\"\n# ### Map Visualization Broken Down By Entire home/apt\n\n# + id=\"cc281e95\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 542} outputId=\"68fb6cf5-7d34-41b9-b4e4-9fa7dc8cbe52\"\nfig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='Entire home/apt',labels={'no_of_listings':'no_of_listings'},hover_data=[\"no_of_listings\"],color_continuous_scale='blackbody')\nfig.update_geos(fitbounds=\"locations\", visible=False)\nfig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\nfig.show()\n\n# + [markdown] id=\"62ff0b31\"\n# ### AnsC: Top 10 Hosts\n\n# + id=\"09130ebd\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 395} outputId=\"680d5bb5-ed92-498d-be4e-1c582973bee5\"\nlistings['host_name'].value_counts().head(10).plot(kind='bar')\nplt.xlabel('Hosts')\nplt.ylabel('Listings')\nplt.show()\n\n# + id=\"4e592d8e\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"afcab754-7f9c-495f-f812-a44bfca0ebb9\"\nlistings.groupby('neighbourhood_cleansed')['host_name'].value_counts()\n\n# + id=\"86e6d11f\"\nb={}\nfor n in listings['neighbourhood_cleansed'].unique():\n b[n]=listings.loc[listings['neighbourhood_cleansed']==n]['host_name'].value_counts()\nname_count=pd.DataFrame.from_dict(b,orient='index')\nname_count.fillna(0,inplace=True)\n\n# + id=\"8ea7c8d2\"\nimport numpy as np\nmake=np.argmax(name_count.values,axis=1)\n\n# + id=\"cb4c08b2\"\ntop_by_zip=name_count.columns[make]\n\n# + id=\"38088e8f\"\nlink={}\nfor val,name in zip(name_count.index,top_by_zip):\n link[val]=name\n\n# + [markdown] id=\"d41f604e\"\n# ### C.2 Top Hosts Region-Wise\n\n# + id=\"9777279e\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} outputId=\"c0f510ec-25f2-4ebe-c936-186afd7265c1\"\nto_add=pd.DataFrame.from_dict(link,orient='index')\nto_add.reset_index(inplace=True)\nto_add.rename(columns={'index':'neighbourhood_cleansed',0:'Top Host'},inplace=True)\nto_add.head()\n\n# + [markdown] id=\"c40e6242\"\n# ### Ans D Analysis and Insights\n\n# + [markdown] id=\"0d3101c3\"\n# Lets Create a Hovering Map So that whenever you hover over a particular region you can have maximum info about it.\n\n# + id=\"d5a71c19\"\nfinal_df=pd.merge(final_df,to_add,on='neighbourhood_cleansed')\n\n\n# + id=\"3c9e86e8\"\ndef remove_dollar(row):\n if row[0] == '$':\n return row[1:]\n return row\nlistings['price'] = listings['price'].apply(lambda row: float(remove_dollar(row).replace(',','')))\n\n# + id=\"dadba878\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} outputId=\"f66dd8a4-688b-4621-8336-45eab3c8c01f\"\n# Listing price and count by zip\nzip_rate = listings.groupby(['neighbourhood_cleansed'])[['price','review_scores_value']].agg('mean')\nzip_rate.reset_index(inplace=True)\nzip_rate['price']=zip_rate['price'].round(2)\nzip_rate['review_scores_value']=zip_rate['review_scores_value'].round(2)\nzip_rate.rename(columns={'index':'neighbourhood_cleansed','price':'Mean Price($)','review_scores_value':'Score'},inplace=True)\nzip_rate.head()\n\n# + id=\"fa94e1a7\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 195} outputId=\"e0dffc1b-c81d-43f3-8a3d-5364ac48bd93\"\nfinal_df=pd.merge(final_df,zip_rate,on='neighbourhood_cleansed')\nfinal_df.head()\n\n# + id=\"fc83d854\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 542} outputId=\"bc492584-1880-4a38-b016-f279034cbe8f\"\nfig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='no_of_listings',labels={'no_of_listings':'no_of_listings'},hover_data=[\"Entire home/apt\",\"Private room\",\"Hotel room\",\"Shared room\",\"Top Host\",\"Mean Price($)\",\"Score\"])\nfig.update_geos(fitbounds=\"locations\", visible=False)\nfig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\nfig.show()\n\n# + [markdown] id=\"193c1d51\"\n# - According to the Price Value and Room-Type we can Redirect User to Particular Host of suitable region.\n# - Build out a Recommendations that allows filteration Based on The Above given Properties.\n# - Increase engagement of areas whose rating are good but Listings are Less.\n# - Make A seosanlized Report of What is Favourable during Which Month.\n\n# + id=\"641535e4\"\n\nrts for export to FC\n# cohorts = fc_interface.\\\n# prepare_cohorts_for_metadata_export(paths_to_samples_info, google_bucket_id, \\\n# blacklist=[\"CCLF_AA1012-Tumor-SM-F67DF\"])\n#\n# cohorts_sample_set_metadata = \"cohort_files/fc_upload_sample_set_cohorts.txt\"\n# res = fc_interface.upload_entities_from_tsv(namespace, workspace, cohorts_sample_set_metadata)\n\n# + editable=false run_control={\"frozen\": true}\n# # Export metadata\n# (r1, r2, r3, r4, r5, r6) = fc_interface.export_batch_metadata_to_fc('TSCA21', namespace, workspace)\n\n# + editable=false run_control={\"frozen\": true}\n# ### Cohort of all tumors\n# res = fc_interface.upload_cohort_all_tumors(paths_to_samples_info, google_bucket_id, \\\n# 'Cum_Tumors_22_all', namespace, workspace, ['DW039-Tumor-SM-DB2IF'])\n# -\n\n# ---\n\n# ---\nas.layers` to build the model.\n# All fully connected layers should include bias terms. For initialization, just use the default initializer used by the `tf.keras.layers` functions.\n#\n# Architecture:\n# * Fully connected layer with input size 784 and output size 256\n# * LeakyReLU with alpha 0.01\n# * Fully connected layer with output size 256\n# * LeakyReLU with alpha 0.01\n# * Fully connected layer with output size 1 \n# \n# The output of the discriminator should thus have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image.\n#\n# Implement `discriminator()` in `cs231n/gan_tf.py`\n\n# Test to make sure the number of parameters in the discriminator is correct:\n\n# +\nfrom cs231n.gan_tf import discriminator\n\ndef test_discriminator(true_count=267009, discriminator=discriminator):\n model = discriminator()\n cur_count = count_params(model)\n if cur_count != true_count:\n print('Incorrect number of parameters in discriminator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n else:\n print('Correct number of parameters in discriminator.')\n \ntest_discriminator()\n# -\n\n# ## Generator\n# Now to build a generator. You should use the layers in `tf.keras.layers` to construct the model. All fully connected layers should include bias terms. Note that you can use the tf.nn module to access activation functions. Once again, use the default initializers for parameters.\n#\n# Architecture:\n# * Fully connected layer with inupt size tf.shape(z)[1] (the number of noise dimensions) and output size 1024\n# * `ReLU`\n# * Fully connected layer with output size 1024 \n# * `ReLU`\n# * Fully connected layer with output size 784\n# * `TanH` (To restrict every element of the output to be in the range [-1,1])\n# \n# Implement `generator()` in `cs231n/gan_tf.py`\n\n# Test to make sure the number of parameters in the generator is correct:\n\n# +\nfrom cs231n.gan_tf import generator\n\ndef test_generator(true_count=1858320, generator=generator):\n model = generator(4)\n cur_count = count_params(model)\n if cur_count != true_count:\n print('Incorrect number of parameters in generator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n else:\n print('Correct number of parameters in generator.')\n \ntest_generator()\n# -\n\n# # GAN Loss\n#\n# Compute the generator and discriminator loss. The generator loss is:\n# $$\\ell_G = -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n# and the discriminator loss is:\n# $$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n# Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.\n#\n# **HINTS**: Use [tf.ones](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/ones) and [tf.zeros](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/zeros) to generate labels for your discriminator. Use [tf.keras.losses.BinaryCrossentropy](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/losses/BinaryCrossentropy) to help compute your loss function.\n#\n# Implement `discriminator_loss, generator_loss` in `cs231n/gan_tf.py`\n\n# Test your GAN loss. Make sure both the generator and discriminator loss are correct. You should see errors less than 1e-8.\n\n# +\nfrom cs231n.gan_tf import discriminator_loss\n\ndef test_discriminator_loss(logits_real, logits_fake, d_loss_true):\n d_loss = discriminator_loss(tf.constant(logits_real),\n tf.constant(logits_fake))\n print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n\ntest_discriminator_loss(answers['logits_real'], answers['logits_fake'],\n answers['d_loss_true'])\n\n# +\nfrom cs231n.gan_tf import generator_loss\n\ndef test_generator_loss(logits_fake, g_loss_true):\n g_loss = generator_loss(tf.constant(logits_fake))\n print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n\ntest_generator_loss(answers['logits_fake'], answers['g_loss_true'])\n# -\n\n# # Optimizing our loss\n# Make an `Adam` optimizer with a 1e-3 learning rate, beta1=0.5 to mininize G_loss and D_loss separately. The trick of decreasing beta was shown to be effective in helping GANs converge in the [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) paper. In fact, with our current hyperparameters, if you set beta1 to the Tensorflow default of 0.9, there's a good chance your discriminator loss will go to zero and the generator will fail to learn entirely. In fact, this is a common failure mode in GANs; if your D(x) learns too fast (e.g. loss goes near zero), your G(z) is never able to learn. Often D(x) is trained with SGD with Momentum or RMSProp instead of Adam, but here we'll use Adam for both D(x) and G(z). \n#\n# Implement `get_solvers` in `cs231n/gan_tf.py`\n\nfrom cs231n.gan_tf import get_solvers\n\n# + [markdown] tags=[\"pdf-ignore\"]\n# # Training a GAN!\n# Well that wasn't so hard, was it? After the first epoch, you should see fuzzy outlines, clear shapes as you approach epoch 3, and decent shapes, about half of which will be sharp and clearly recognizable as we pass epoch 5. In our case, we'll simply train D(x) and G(z) with one batch each every iteration. However, papers often experiment with different schedules of training D(x) and G(z), sometimes doing one for more steps than the other, or even training each one until the loss gets \"good enough\" and then switching to training the other. \n#\n# **If you are a Colab user, it is recommeded to change colab runtime to GPU.**\n# -\n\n# #### Train your GAN! This should take about 10 minutes on a CPU, or about 2 minutes on GPU.\n\n# +\nfrom cs231n.gan_tf import run_a_gan\n\n# Make the discriminator\nD = discriminator()\n\n# Make the generator\nG = generator()\n\n# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\nD_solver, G_solver = get_solvers()\n\n# Run it!\nimages, final = run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)\n\n# +\nnumIter = 0\nfor img in images:\n print(\"Iter: {}\".format(numIter))\n show_images(img)\n plt.show()\n numIter += 20\n print()\n \n\n# -\n\n# **Please tag the cell below on Gradescope while submitting.**\n\nprint('Vanilla GAN Final images')\nshow_images(final)\nplt.show()\n\n# # Least Squares GAN\n# We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alternative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n# $$\\ell_G = \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n# and the discriminator loss:\n# $$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$\n#\n#\n# **HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`score_real` and `score_fake`).\n#\n# Implement `ls_discriminator_loss, ls_generator_loss` in `cs231n/gan_tf.py`\n\n# Test your LSGAN loss. You should see errors less than 1e-8.\n\n# +\nfrom cs231n.gan_tf import ls_discriminator_loss, ls_generator_loss\n\ndef test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n \n d_loss = ls_discriminator_loss(tf.constant(score_real), tf.constant(score_fake))\n g_loss = ls_generator_loss(tf.constant(score_fake))\n print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n\ntest_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])\n# -\n\n# Create new training steps so we instead minimize the LSGAN loss:\n\n# +\n# Make the discriminator\nD = discriminator()\n\n# Make the generator\nG = generator()\n\n# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\nD_solver, G_solver = get_solvers()\n\n# Run it!\nimages, final = run_a_gan(D, G, D_solver, G_solver, ls_discriminator_loss, ls_generator_loss)\n\n# +\nnumIter = 0\nfor img in images:\n print(\"Iter: {}\".format(numIter))\n show_images(img)\n plt.show()\n numIter += 20\n print()\n \n\n# -\n\n# **Please tag the cell below on Gradescope while submitting.**\n\nprint('LSGAN Final images')\nshow_images(final)\nplt.show()\n\n# # Deep Convolutional GANs\n# In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like \"sharp edges\" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks as our discriminators and generators.\n#\n# #### Discriminator\n# We will use a discriminator inspired by the TensorFlow MNIST classification [tutorial](https://www.tensorflow.org/get_started/mnist/pros), which is able to get above 99% accuracy on the MNIST dataset fairly quickly. *Be sure to check the dimensions of x and reshape when needed*, fully connected blocks expect [N,D] Tensors while conv2d blocks expect [N,H,W,C] Tensors. Please use `tf.keras.layers` to define the following architecture:\n#\n# Architecture:\n# * Conv2D: 32 Filters, 5x5, Stride 1, padding 0\n# * Leaky ReLU(alpha=0.01)\n# * Max Pool 2x2, Stride 2\n# * Conv2D: 64 Filters, 5x5, Stride 1, padding 0\n# * Leaky ReLU(alpha=0.01)\n# * Max Pool 2x2, Stride 2\n# * Flatten\n# * Fully Connected with output size 4 x 4 x 64\n# * Leaky ReLU(alpha=0.01)\n# * Fully Connected with output size 1\n#\n# Once again, please use biases for all convolutional and fully connected layers, and use the default parameter initializers. Note that a padding of 0 can be accomplished with the 'VALID' padding option.\n#\n# Implement `dc_discriminator` in `cs231n/gan_tf.py`\n\n# +\nfrom cs231n.gan_tf import dc_discriminator\n\n# model = dc_discriminator()\ntest_discriminator(1102721, dc_discriminator)\n# -\n\n# #### Generator\n# For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. Please use `tf.keras.layers` for your implementation. You might find the documentation for [tf.keras.layers.Conv2DTranspose](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Conv2DTranspose) useful. The architecture is as follows.\n#\n# Architecture:\n# * Fully connected with output size 1024 \n# * `ReLU`\n# * BatchNorm\n# * Fully connected with output size 7 x 7 x 128 \n# * `ReLU`\n# * BatchNorm\n# * Resize into Image Tensor of size 7, 7, 128\n# * Conv2D^T (transpose): 64 filters of 4x4, stride 2\n# * `ReLU`\n# * BatchNorm\n# * Conv2d^T (transpose): 1 filter of 4x4, stride 2\n# * `TanH`\n#\n# Once again, use biases for the fully connected and transpose convolutional layers. Please use the default initializers for your parameters. For padding, choose the 'same' option for transpose convolutions. For Batch Normalization, assume we are always in 'training' mode.\n#\n# Implement `dc_generator` in `cs231n/gan_tf.py`\n\n# +\nfrom cs231n.gan_tf import dc_generator\n\n\ntest_generator(6595521, generator=dc_generator)\n# -\n\n# We have to recreate our network since we've changed our functions.\n\n# ### Train and evaluate a DCGAN\n# This is the one part of A3 that significantly benefits from using a GPU. It takes 3 minutes on a GPU for the requested five epochs. Or about 50 minutes on a dual core laptop on CPU (feel free to use 3 epochs if you do it on CPU).\n\n# +\n# Make the discriminator\nD = dc_discriminator()\n\n# Make the generator\nG = dc_generator()\n\n# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\nD_solver, G_solver = get_solvers()\n\n# Run it!\nimages, final = run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, num_epochs=3)# origin:5\n# -\n\nnumIter = 0\nfor img in images:\n print(\"Iter: {}\".format(numIter))\n show_images(img)\n plt.show()\n numIter += 20\n print()\n \n\n# **Please tag the cell below on Gradescope while submitting.**\n\nprint('DCGAN Final images')\nshow_images(final)\nplt.show()\n\n# + [markdown] tags=[\"pdf-inline\"]\n# ## INLINE QUESTION 1\n#\n# We will look at an example to see why alternating minimization of the same objective (like in a GAN) can be tricky business.\n#\n# Consider $f(x,y)=xy$. What does $\\min_x\\max_y f(x,y)$ evaluate to? (Hint: minmax tries to minimize the maximum value achievable.)\n#\n# Now try to evaluate this function numerically for 6 steps, starting at the point $(1,1)$, \n# by using alternating gradient (first updating y, then updating x using that updated y) with step size $1$. **Here step size is the learning_rate, and steps will be learning_rate * gradient.**\n# You'll find that writing out the update step in terms of $x_t,y_t,x_{t+1},y_{t+1}$ will be useful.\n#\n# Breifly explain what $\\min_x\\max_y f(x,y)$ evaluates to and record the six pairs of explicit values for $(x_t,y_t)$ in the table below.\n#\n# ### Your answer:\n# \n# $y_0$ | $y_1$ | $y_2$ | $y_3$ | $y_4$ | $y_5$ | $y_6$ \n# ----- | ----- | ----- | ----- | ----- | ----- | ----- \n# 1 | 2 | 1 | -1 | -2 | -1 | 1\n# $x_0$ | $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | $x_6$ \n# 1 | -1 | -2 | -1 | 1 | 2 | 1\n# \n# 产生了一个循环,可能的原因是在局部最大局部最小的过程中,y从一个局部最大在x极小的影响下到了另一个局部最大,在调整后y又回到了原来的位置\n\n# + [markdown] tags=[\"pdf-inline\"]\n# ## INLINE QUESTION 2\n# Using this method, will we ever reach the optimal value? Why or why not?\n#\n# ### Your answer: \n# 我认为是不行的,因为数据集的本质分布和生成器的分布理论上是不同的,所以存在一个最优判别分类器,而当判别器训练过快会使得判别器参数更新梯度消失,无法更新。\n\n# + [markdown] tags=[\"pdf-inline\"]\n# ## INLINE QUESTION 3\n# If the generator loss decreases during training while the discriminator loss stays at a constant high value from the start, is this a good sign? Why or why not? A qualitative answer is sufficient.\n#\n# ### Your answer: \n# 我认为是个好现象,此时说明生成器在不断优化,而同时判别器能够持续为生成器优化提梯度。\n# -\n\n\n"},"script_size":{"kind":"number","value":24452,"string":"24,452"}}},{"rowIdx":924,"cells":{"path":{"kind":"string","value":"/MetabolonR_pipeline.ipynb"},"content_id":{"kind":"string","value":"82e12fb500f925ba315ca53a87100b05e27bbe04"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"hassamalhajaji/MetabolonR"},"repo_url":{"kind":"string","value":"https://github.com/hassamalhajaji/MetabolonR"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".r"},"size":{"kind":"number","value":3871422,"string":"3,871,422"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .r\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: R\n# language: R\n# name: ir\n# ---\n\n# + [markdown] toc=true\n#

Table of Contents

\n#
\n# -\n\n# # Pipeline overview \n# This pipeline is designed to summarize all the preprocessing and down stream analysis for the metabolomics data in one single notebook. You can use it as a template for your analysis. Please feel free to add more functions and distribute it with others.\n# You can contact me if you have any questions: Fadhl Alakwaa alakwaaf@umich.edu\n#\n# ![image.png](attachment:image.png)\n#\n\n# # Install and load all required packages\n\n# +\n# Load the libararies\nlibrary(glmnet)\nlibrary(ggplot2)\n#library(MetaboAnalystR) # It will take long time to install, you can comment this line \nlibrary(\"scales\")\nlibrary(pheatmap)\nlibrary(tidyverse)\nlibrary(reshape2)\nlibrary(magrittr)\nlibrary(\"ggpubr\")\nlibrary(lsmeans)\nlibrary(multcomp)\nlibrary(corrplot)\nlibrary(\"Hmisc\")\nlibrary(ComplexHeatmap)\nlibrary(circlize)\n\n#install.packages(\"glmnet\")\n#install.packages(\"ggplot2\")\n#devtools::install_github(\"xia-lab/MetaboAnalystR\", build = TRUE, build_vignettes = TRUE, build_manual =T)\n#install.packages(\"scales\")\n#install.packages(\"pheatmap\")\n#install.packages(\"tidyverse\")\n#install.packages(\"reshape\")\n#install.packages(\"magrittr\")\n#install.packages(\"ggpubr\")\n#install.packages(\"ggplot2\")\n#install.packages(\"lsmeans\")\n#install.packages(\"multcomp\")\n#install.packages(\"ggplot2\")\n#install.packages(\"corrplot\")\n#install.packages(\"Hmisc\")\n#BiocManager::install(\"ComplexHeatmap\")\n#install.packages(\"circlize\")\n# -\n\n# # Demo Example \n# As an example, we showed all steps on a metabolomics data detecected from lean and obese pataients. Some obese patients had neuropathy and others did not have it. We are interested in metaboloties that are different between two obese groups.\n# ![image.png](attachment:image.png)\n\n# # Save Your session \n# Save Your session anytime so you can easily resume\n\nsave.image(\"MetabolonR.RDat\")\n\nload(\"MetabolonR.RDat\")\n\n# # Load the metabolomics data, metabolites and samples meta information\n# You have to load the actual/relative abundances of the metabolites in (.csv) format (Samples are in rows and metabolites in columns). You need also to upload the annotation file of your metabolites; and the clinical information for your samples. \n\n#Load the data\nmetabolomics_meta= read.csv(\"metabolomics_meta_R21_SOV.csv\") \nsamples_meta= read.csv(\"samples_meta_R21_SOV.csv\",na.strings=c(\"NA\",\"NaN\", \"\")) # samples unique ids is the sample id \nmetabolomics_data= read.csv(\"metabolomics_data_R21_SOV.csv\",check.names=F,row.names=1) # metabolomics id is the comp id\ndim(metabolomics_data)\ndim(metabolomics_meta)\ndim(samples_meta)\nprint(\"********************Metabolomics data\")\nhead(metabolomics_data)\nprint(\"********************Metaboloites annotation\")\nhead(metabolomics_meta)\nprint(\"********************Samples annotation\")\nhead(samples_meta)\n\n# # Data normalization, transformation, and missing imputation\n# I used some script from MetaboanalystR package\n#\n# We will used KNN for imputation and log2 function for log normalization\n\n# ## Replace non-numeric values by NA\n\n# +\n int.mat <- metabolomics_data\n rowNms <- rownames(int.mat)\n colNms <- colnames(int.mat)\n naNms <- sum(is.na(int.mat))\n num.mat <- suppressWarnings(apply(int.mat, 2, as.numeric))\n if (sum(is.na(num.mat)) > naNms) {\n num.mat <- apply(int.mat, 2, function(x) as.numeric(gsub(\",\", \n \"\", x)))\n if (sum(is.na(num.mat)) > naNms) {\n print(\"Non-numeric values were found and replaced by NA.\")\n }\n else {\n print(\"All data values are numeric.\")\n }\n }\n \n \n# -\n\n# ## Remove metabolites with a constant or single value across samples\n\n int.mat <- num.mat\n rownames(int.mat) <- rowNms\n colnames(int.mat) <- colNms\n varCol <- suppressWarnings(apply(int.mat, 2, var, na.rm = T))\n constCol <- (varCol == 0 | is.na(varCol))\n constNum <- sum(constCol, na.rm = T)\n if (constNum > 0) {\n print(paste(constNum, \n \"features with a constant or single value across samples were found and deleted.\"))\n int.mat <- int.mat[, !constCol]\n }\n \n\n# ## Calculate the percentage of missinginess\n\n totalCount <- nrow(int.mat) * ncol(int.mat)\n naCount <- sum(is.na(int.mat))\n naPercent <- round(100 * naCount/totalCount, 1)\n print(paste(\"A total of \", naCount, \" (\", naPercent, \n \"%) missing values were detected.\", sep = \"\"))\n \n\n# ## delete metaboloite if it was missed accros more than 50% of samples\n\n good.inx <- apply(is.na(int.mat), 2, sum)/nrow(int.mat) < 0.5\n int.mat1 <- as.data.frame(int.mat[,good.inx])\n print(paste(sum(!good.inx), \"variables were removed for threshold\", \n round(100 * 0.5, 2), \"percent.\"))\n\n# ## Impute the remaining missed metaboloties using KKN\n\nnew.mat2 <- t(impute::impute.knn(t(int.mat1))$data)\n\n# ## Log transformation\n\nmin.val <- min(abs(new.mat2[new.mat2 != 0]))/2\nnorm.data <- log2((new.mat2 + sqrt(new.mat2^2 + min.val))/2)\nhead(norm.data)\n\nprint(paste(dim(metabolomics_data)[2]-dim(norm.data)[2] ,\"is the total number of removed metaboloites\"))\nprint(paste(dim(norm.data)[2] ,\"out of\",dim(metabolomics_data)[2],\"is kept\"))\n\n# ## Check data distrubution after and before data distribution\n\n# ### Metabolites\n\n# +\n par(mfrow=c(2,2)) \n plot(density(apply(new.mat2, 2, mean, na.rm = TRUE)), \n col = \"darkblue\", las = 2, lwd = 2, main = \"\", xlab = \"\", \n ylab = \"\")\n \n mtext(\"Before Normalization\", 3, 1)\n plot(density(apply(norm.data, 2, mean, na.rm = TRUE)), \n col = \"darkblue\", las = 2, lwd = 2, main = \"\", xlab = \"\", \n ylab = \"\")\n mtext(\"After Normalization\", 3, 1)\n\n rangex.pre <- range(new.mat2[, 1:20], na.rm = T)\n rangex.norm <- range(norm.data[, 1:20], na.rm = T)\n \n boxplot(new.mat2[, 1:20], names = colnames(new.mat2[, 1:20]), \n ylim = rangex.pre, las = 2, col = \"lightgreen\", horizontal = T)\n mtext(\"Before Normalization\", 3, 1)\n\n boxplot(norm.data[, 1:20], names = colnames(norm.data[, 1:20]), \n ylim = rangex.norm, las = 2, col = \"lightgreen\", horizontal = T)\n mtext(\"After Normalization\", 3, 1)\n\n \n# -\n\n# ### Samples\n\n par(mfrow=c(1,2)) \nboxplot(new.mat2[1:20,1:20], names = rownames(new.mat2[1:20,1:20]), \n ylim = rangex.pre, las = 2, col = \"lightgreen\", horizontal = T)\n mtext(\"Samples Before Normalization\", 3, 1)\n\n boxplot(norm.data[1:20,1:20 ], names = rownames(norm.data[1:20,1:20 ]), \n ylim = rangex.norm, las = 2, col = \"lightgreen\", horizontal = T)\n mtext(\"Samples After Normalization\", 3, 1)\n\nNormalized_data= norm.data\ndim(Normalized_data)\nhead(Normalized_data)\n\n# ## Which metabolites was removed?\n\n# +\nmetabolites_meta_old=read.csv(\"metabolomics_meta_R21_SOV.csv\") \nremoved_metabolites=(setdiff(metabolites_meta_old$BIOCHEMICAL,colnames(Normalized_data)))\n\nsubpathway_meta_number=metabolites_meta_old %>% group_by(SUB_PATHWAY) %>% summarise(total_number_of_metabolites=n()) \n\nremoved_metabolites_per_pathway=metabolites_meta_old %>% dplyr::select('BIOCHEMICAL','SUB_PATHWAY')%>% filter(BIOCHEMICAL %in% removed_metabolites)%>% \ngroup_by(SUB_PATHWAY) %>% summarise(number_of_removed_metabolites=n()) %>%arrange(desc(number_of_removed_metabolites))\n\nmerge(removed_metabolites_per_pathway,subpathway_meta_number, by.x='SUB_PATHWAY',by.y='SUB_PATHWAY') %>% \narrange(desc(number_of_removed_metabolites))%>%\n head\n#metabolites_meta_old[match(removed_metabolites,as.character(metabolites_meta_old$BIOCHEMICAL)),] %>% head\n# -\n\n# We need to update metabolites meta file\nmetabolomics_meta=(metabolomics_meta[!metabolomics_meta$BIOCHEMICAL %in% (removed_metabolites),] )\nrownames(metabolomics_meta)=NULL\nhead(metabolomics_meta)\ndim(metabolomics_meta)\n\n# make sure that samples names in samples meta file ans merabolomics files are matched\nmatch(rownames(Normalized_data),samples_meta$SAMPLE_NAME)\n\n# # PCA\n\n# +\ndf_pca <- prcomp(Normalized_data)\ndf_out <- as.data.frame(df_pca$x)\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GROUP_ID,shape=samples_meta$OBESITY ))+\ngeom_point()+ggtitle(\"\")+labs(color='')+\n geom_point(size=8,alpha=0.5)+ #Size and alpha just for fun\ntheme( plot.title = element_text(hjust = 0.5,size=15,face = \"bold\"),\n axis.text.x = element_text( size = 15, angle = 45, hjust = .5, vjust = 0.5, face = \"plain\"),\n axis.text.y = element_text( size = 15, angle = 0, hjust = 1, vjust = 0, face = \"plain\"), \n axis.title.x = element_text( size = 15, angle = 0, hjust = .5, vjust = 0, face = \"bold\"),\n axis.title.y = element_text( size = 15, angle = 90, hjust = .5, vjust = .5, face = \"bold\"),\n #legend.title=element_text(size=20),\n legend.title=element_blank(), # remove legend title name\n legend.text = element_text(size=15,face=\"plain\"),\n strip.text = element_text(size = 15,face=\"plain\") ,\n legend.position=\"right\",\n \n # for transparent background\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid.major = element_blank(), # get rid of major grid\n panel.grid.minor = element_blank(), # get rid of minor grid\n legend.background = element_rect(fill = \"transparent\"), # get rid of legend bg\n legend.box.background = element_rect(fill = \"transparent\"), # get rid of legend panel bg\n axis.line = element_line(colour = \"black\") # adding a black line for x and y axis\n) +xlab(paste0(\"PC 1 (\", round(df_pca$sdev[1],1),\"%)\"))+\nylab(paste0(\"PC 2 (\", round(df_pca$sdev[2],1),\"%)\"))\n\n# ggsave(\"PCA.tiff\", plot = last_plot(), device = NULL, path = NULL,\n# scale = 1, width = 10, units = c(\"in\"),\n# dpi = 300, limitsize = TRUE,bg = \"transparent\")\n\n# -\n\ndf_pca <- prcomp(Normalized_data)\ndf_out <- as.data.frame(df_pca$x)\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GROUP_ID ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$OBESITY ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$NEUROPATHY ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GENDER ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\nggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$AGE ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\nggplot(df_out,aes(x=PC1,y=PC2,color=Normalized_data_label$AGE1 ))+geom_point()+ggtitle(\"PCA ALL meta\")+labs(color='')+\ntheme(plot.title = element_text(hjust = 0.5))\n\n# # Source of variation\n# Here you want to know the source of variability in your data (is it sex, age, obesity or neuropathy). The sources of variation plot present the relative contribution of each factor such as Age, gender, race and others included in the ANOVA model towards explaining the variability of the data for a feature analyzed by the ANOVA.\n# ![image.png](attachment:image.png)\n#\n\n# ## Overall source of variation \n# It seems, as we expect, obesity is the major source of variation in metabolomics data, as you can see from below figure\n\n# +\n# We are interested to look at the effect of the below factors on changing the metabolites level\ninterested_factors=c(\"SAMPLE_NAME\",\"AGE\",\"OBESITY\",\"GENDER\",\"NEUROPATHY\") \nsamples_meta_interested=subset(samples_meta,select= interested_factors)\n#samples_meta_interested$ers_quartile=factor(samples_meta_interested$ers_quartile)\n#rownames(samples_meta_interested)=samples_meta_interested$SAMPLE_NAME\n#samples_meta_interested$SAMPLE_NAME=NULL\nstr(samples_meta_interested)\n#head(samples_meta_interested)\n\n\nF_for_all1=matrix(0,nrow=ncol(samples_meta_interested),ncol = ncol(Normalized_data))\ncobre_all<-cbind(samples_meta_interested,Normalized_data)\n#design <- model.matrix(~cobre_all$group_hmdb,data=cobre_all)\n\n\nfor (i in 1:ncol(Normalized_data)){\n\nlm.out2 = lm(cobre_all[,(i+ncol(samples_meta_interested))] ~ AGE+OBESITY+GENDER+NEUROPATHY,\n data=cobre_all, na.action=na.omit) #\naa=summary(lm.out2)\nF_for_all1[nrow(F_for_all1),i]=mean(aa$coefficients[,2])\n#do not care about sequentional \nlibrary(\"car\") \nzz=Anova(lm.out2)\nzz=na.omit(zz)\nF_for_all1[1:(nrow(F_for_all1)-1),i]=zz$`F value`\n \n }\n\nzz1=apply(F_for_all1,1,mean)\nzz1=as.data.frame(t(zz1))\ndim(zz1)\ncolnames(zz1)=c(c(interested_factors[-1],\"error\"))\nlibrary(ggplot2)\nlibrary(reshape)\nzz2=melt(zz1)\n\nggplot(data=zz2,aes(x=variable,y=value))+\n geom_bar(stat=\"identity\",fill=\"steelblue\")+\n labs(title=\"Sources of variation (Overall) \",x=\"\", y = \"Mean F Ratio\")+\n theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n#ggsave(\"add_SOV.pdf\", plot = last_plot(), #R21_SOV_obesity.pdf\n # scale = 1, units = \"in\",\n # dpi = 300, limitsize = TRUE)\n\n\n# -\n\n# ## Source of variation per super pathway\n# The number beside pathway names are the number of metaboloties involoved in that pathway\n\n# +\nrownames(F_for_all1)=c(c(interested_factors[-1],\"error\"))\ncolnames(F_for_all1)=colnames(Normalized_data)\n#head(F_for_all1)\n\nss=t(F_for_all1)\nss_ALL=data.frame(ss,pathway=metabolomics_meta$SUB_PATHWAY)\n#head(ss_ALL)\n\nlibrary(data.table)\n\nss_ALL1=data.frame(ss,pathway=metabolomics_meta$SUPER_PATHWAY)\nss_ALL1 %>% group_by(pathway) %>% mutate(N_metaboliesinP=(n())) %>%summarise_all(funs(mean)) %>% \nmutate(pathway=paste(pathway,N_metaboliesinP,sep=\" \")) %>% dplyr::select(-N_metaboliesinP,-error) %>%as.data.frame()%>% melt()%>% \narrange(pathway,desc(value)) %>%\nggplot(aes(x=pathway,y=value,fill=variable))+\n geom_bar(stat=\"identity\")+\n labs(title=\"Sources of variation (super pathway) \",x=\"\", y = \"Mean F Ratio\")+\n theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=7,angle = 45, hjust =1)) +\nscale_fill_manual(values=c( \"#00ba38\", \"#00bfc4\",\"#619cff\", \"#b79f00\"))\n\n# -\n\n# ## Source of variation per sub pathway\n\n# +\nss_ALL %>% group_by(pathway) %>% mutate(N_metaboliesinP=(n())) %>%summarise_all(funs(mean)) %>% \nmutate(pathway=paste(pathway,N_metaboliesinP,sep=\" \")) %>% dplyr::select(-N_metaboliesinP,-error) %>%as.data.frame()%>% melt()%>% \narrange(pathway,desc(value)) %>%\nggplot(aes(x=pathway,y=value,fill=variable))+\n geom_bar(stat=\"identity\")+\n labs(title=\"Sources of variation (sub pathway)\",x=\"\", y = \"Mean F Ratio\")+\n theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=7,angle = 45, hjust =1)) +\nscale_fill_manual(values=c( \"#00ba38\", \"#00bfc4\",\"#619cff\", \"#b79f00\"))\n\n#ggsave(\"add_SOV_sub_pathways.pdf\", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf\n # scale = 1, units = \"in\",width=30,\n # dpi = 300, limitsize = TRUE)\n\n# +\n# Metaboloties changed by neuropathy\nss_ALL %>% rownames_to_column('gene') %>% filter(NEUROPATHY > quantile(NEUROPATHY, 0.991))%>%\narrange(desc(NEUROPATHY))%>%column_to_rownames('gene')\n\nNEUROPATHY_ALL=ss_ALL %>% rownames_to_column('gene') %>% filter(NEUROPATHY > quantile(NEUROPATHY, 0.991))%>%\narrange(desc(NEUROPATHY))%>%column_to_rownames('gene')%>%rownames\n# -\n\n# Metaboloties changed by obesity\nss_ALL %>% rownames_to_column('gene') %>% filter(OBESITY > quantile(OBESITY, 0.991))%>%\narrange(desc(OBESITY))%>%column_to_rownames('gene')\n\n# Metaboloties changed by age\nss_ALL %>% rownames_to_column('gene') %>% filter(AGE > quantile(AGE, 0.991))%>%\narrange(desc(AGE))%>%column_to_rownames('gene')\n\n# Metaboloties changed by SEX\nss_ALL %>% rownames_to_column('gene') %>% filter(GENDER > quantile(AGE, 0.991))%>%\narrange(desc(GENDER))%>%column_to_rownames('gene')\n\n# ## Box plot\n# You can plot any metaboloties of intereset in a box plot with p value using ggpubr package\n\nNormalized_data_label=merge(Normalized_data,samples_meta[c('GROUP_ID','SAMPLE_NAME','OBESITY','AGE','GENDER','NEUROPATHY')],\n by.x=\"row.names\",by.y=\"SAMPLE_NAME\")\nrownames(Normalized_data_label)=Normalized_data_label[,1]\nNormalized_data_label=Normalized_data_label[-1]\nhead(Normalized_data_label)\n\nNormalized_data_label$AGE1=factor(Normalized_data_label$AGE)\ntable(Normalized_data_label$AGE1)\nlevels(Normalized_data_label$AGE1)\nlevels(Normalized_data_label$AGE1)=c(rep(\"20-40\",15),rep(\"41-60\",20),rep(\"61-74\",9))\nlevels(Normalized_data_label$AGE1)\n\n# +\nNormalized_data_label %>% rownames_to_column('gene') %>% \ndplyr::select(gene,NEUROPATHY_ALL,NEUROPATHY) %>% column_to_rownames('gene')%>% melt() %>%\n# add p_values\n#library(\"ggpubr\")\n ggplot(aes(x=variable, y=value,fill=NEUROPATHY)) + \n geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+\n stat_summary(fun.y=\"mean\", color=\"white\", geom=\"point\",shape=18, size=3,position=position_dodge(width=0.75))+\n theme(text = element_text(size=12),\n axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=12))+\n\nstat_compare_means( label=\"p.signif\",method = \"anova\",size = 5)+\n facet_wrap( ~ variable, scales=\"free\")+xlab(\"\")+ylab(\"\")#+\n#geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge())\n#ggsave(\"NEUROPATHY_ALL_boxplot-NEUROPATHY.pdf\", plot = last_plot(), \n # scale = 1, units = \"in\",width=10,\n # dpi = 300, limitsize = TRUE)\n\n# +\nNormalized_data_label %>% rownames_to_column('gene') %>% \ndplyr::select(gene,NEUROPATHY_ALL,NEUROPATHY,GENDER) %>% column_to_rownames('gene')%>% melt() %>%\n# add p_values\n#library(\"ggpubr\")\n ggplot(aes(x=variable, y=value,fill=NEUROPATHY)) + \n geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+\n stat_summary(fun.y=\"mean\", color=\"white\", geom=\"point\",shape=18, size=3,position=position_dodge(width=0.75))+\n theme(text = element_text(size=10),\n axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=8))+\n\nstat_compare_means( label=\"p.signif\",method = \"t.test\",size = 5)+\n facet_wrap( ~ variable+GENDER, scales=\"free\")+xlab(\"\")+ylab(\"\")#+\n#geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge())\n#ggsave(\"NEUROPATHY_ALL_boxplot-NEUROPATHY-GENDER.pdf\", plot = last_plot(), \n# scale = 1, units = \"in\",width=10,\n # dpi = 300, limitsize = TRUE)\n\n# +\nNormalized_data_label %>% rownames_to_column('gene') %>% \ndplyr::select(gene,NEUROPATHY_ALL,GROUP_ID) %>% column_to_rownames('gene')%>% melt() %>%\n# add p_values\n#library(\"ggpubr\")\n ggplot(aes(x=variable, y=value,fill=GROUP_ID)) + \n geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+\n stat_summary(fun.y=\"mean\", color=\"white\", geom=\"point\",shape=18, size=3,position=position_dodge(width=0.75))+\n theme(text = element_text(size=12),\n axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=12))+\n\nstat_compare_means( label=\"p.signif\",method = \"anova\",size = 5)+\n facet_wrap( ~ variable, scales=\"free\")+xlab(\"\")+ylab(\"\")#+\n#geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge())\n#ggsave(\"NEUROPATHY_ALL_boxplot-dotpoints.pdf\", plot = last_plot(), \n # scale = 1, units = \"in\",width=10,\n # dpi = 300, limitsize = TRUE)\n# -\n\n# # Biomarker analysis\n\n# ## Differentiated metabolites (DMs) using Limma (Statistical Summary)\n# limma did not identify any DMs between obese patients with and without neuropathy\n#\n#\n\nmatch(rownames(Normalized_data),samples_meta$SAMPLE_NAME)\n\n\n\n# +\nlibrary(limma)\ntype = as.character(samples_meta$GROUP_ID)\n\ndesign <- model.matrix(~0+factor(type))\n\ncolnames(design) <- levels(factor(type))\n\n\n\ncontrast<-makeContrasts(Obese_N-Obese_NoN,\n Obese_N-Lean_NoN,\n Obese_NoN-Lean_NoN,\n levels=design)\nfit <- lmFit(as.matrix(t(Normalized_data)), design)\nfit2 <- contrasts.fit(fit, contrast)\nfit2 <- eBayes(fit2)\n# -\n\n\n\n# +\nObese_N.vs.Obese_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 1)\nObese_N.vs.Lean_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 2)\nObese_NoN.vs.Lean_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 3)\n\nhead(Obese_N.vs.Obese_NoN)\nhead(Obese_N.vs.Lean_NoN)\nhead(Obese_NoN.vs.Lean_NoN)\n\ndim(Obese_N.vs.Obese_NoN)\ndim(Obese_N.vs.Lean_NoN)\ndim(Obese_NoN.vs.Lean_NoN)\n# -\n\nfold_change=data.frame(apply(topTable(fit2, adjust.method='fdr', number=999999999,p.value=1)[,c(1,2,3)],2,\n function(x){2^x}),check.names=F) #Not log FC\nhead(fold_change)\nclass(fold_change)\n\nlimma_dataframe_FC=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05) #,confint=T\nhead(limma_dataframe_FC) \n\n\n\n# ## DMs using T-test (Statistical Summary)\n# Here we used pairwise.t.test function to find significant metabolites between different groups. You can adjusted for your comparison testing using parameter p.adjust.method\n#\n# Bonferroni correction (\"bonferroni\")\n#\n# Holm (1979) (\"holm\")\n#\n# Hochberg (1988) (\"hochberg\")\n#\n# Hommel (1988) (\"hommel\")\n#\n# Benjamini & Yekutieli (2001) (\"BY\")\n#\n#\n#\n\nmatch(samples_meta$SAMPLE_NAME,rownames(Normalized_data))\n\n# +\ntemp=Normalized_data %>% data.frame(check.names=F)%>% rownames_to_column(\"gene\") %>% mutate(group=samples_meta$GROUP_ID) %>% \ncolumn_to_rownames(\"gene\")\ndf2 <- temp %>%\n gather(Column, Value, -group)\n#head(df2)\np_value <- df2 %>% split(.$Column) %>% map(function(x) pairwise.t.test(x$Value, x$group, paired = F,var.eq=F,pool.sd=F, \n p.adjust.method =\"none\"))%>% map_df( \"p.value\")\nxx=data.frame(p_value,check.names=F)\nrownames(xx)=c(\"Obese_N.vs.Lean_NoN\",\"Obese_NoN.vs.Lean_NoN\",\"Obese_N.vs.Obese_N\",\"Obese_NoN.vs.Obese_N\")\nxx=xx[-3,]\n#head(xx)\n \nxx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,2))%>%\narrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% dim%>% .[1]%>% \n paste(\"Number of metaboloties between obese patients with neuropathy and lean is\",.)\n\n\nObese_N.vs.Lean_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,2))%>%\narrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% rownames()\n\n\nxx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,3)) %>%arrange_at(1,desc=F) %>% \nfilter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% dim %>%.[1]%>% \n paste(\"Number of metaboloties between obese patients with neuropathy and lean is\",.)\n\n\nObese_NoN.vs.Lean_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,3))%>%\narrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% rownames()\n\n\nxx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,4)) %>%arrange_at(1,desc=F) %>% \nfilter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% dim%>%.[1]%>% \n paste(\"Number of metaboloties between obese patients with neuropathy and with out is\",.)\n\n\nObese_N.vs.Obese_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column(\"gene\") %>% select_at(c(1,4))%>%\narrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames(\"gene\") %>% rownames()\n \np_adjusted_value=xx %>%t() %>% data.frame(check.names=F)\nhead(p_adjusted_value)\n# -\n\n# ### Venn digram\n\n# +\nlibrary(limma) \n# Venn digram\nuniverse <- sort(unique(c(Obese_NoN.vs.Lean_NoN, Obese_N.vs.Lean_NoN, Obese_N.vs.Obese_NoN)))\nCounts <- matrix(0, nrow=length(universe), ncol=3)\n\nfor (i in 1:length(universe)) {\n Counts[i,1] <- universe[i] %in% Obese_NoN.vs.Lean_NoN\n Counts[i,2] <- universe[i] %in% Obese_N.vs.Lean_NoN\n Counts[i,3] <- universe[i] %in% Obese_N.vs.Obese_NoN\n}\ncolnames(Counts) <- c(\"Obese_NoN.vs.\\nLean_NoN\",\"Obese_N.vs.\\nLean_NoN\",\"Obese_N.vs.\\nObese_NoN\")\ncols<-c(\"Red\", \"Green\", \"Blue\")\n#tiff(\"venn.tiff\")\nvennDiagram(vennCounts(Counts), circle.col=cols,cex=0.9)\n#dev.off()\n# -\n\n# ## DMs using lm adjusted for sex and age\n# Here we used glht function in multcomp package, you can correct for multiple comparsion using adjusted(\"fdr\") option\n\n# +\n# Load drugs information to include them in the model\ndrugs_Evan=read.csv(\"Fadhl Project Bariatric Meds 12_2_2019_Drugs information.csv\")\ndrugs_Evan=drugs_Evan %>% dplyr::select(Study_ID,contains(\"total\")) %>% dplyr::rename('CLIENT_IDENTIFIER'='Study_ID',\n 'beta_blocker'='beta_blocker_total',\n 'statin'='total_Statin')\nhead(drugs_Evan)\ndim(drugs_Evan)\n\ndrugs_Evan1=read.csv(\"Fadhl Project IWMC Meds 12_2_2019_Drugs2.csv\",stringsAsFactors=F)\n#drugs_Evan1$Patient.ID=as.character(drugs_Evan1$Ptient.ID)\ndrugs_Evan1=drugs_Evan1 %>% dplyr::filter(Patient.ID %in% as.character(samples_meta$CLIENT_IDENTIFIER) &\n Medication.Type %in% c('Beta blockers', 'HMG Co-A redcutase inhibitors' ) )%>%\ndistinct(Patient.ID,Medication.Type, .keep_all = TRUE) %>%\n group_by(Patient.ID) %>%\n mutate(Medication.Type = paste0(Medication.Type, collapse = \",\")) %>%dplyr::select(Patient.ID,Medication.Type) %>% \n distinct(Patient.ID, .keep_all = TRUE) %>%\nmutate(beta_blocker_total=unlist(lapply( (str_split(Medication.Type,',')), function(x) {'Beta blockers' %in% x })),\n total_Statin=unlist(lapply( (str_split(Medication.Type,',')), function(x) {'HMG Co-A redcutase inhibitors' %in% x })))%>%\ndplyr::rename('CLIENT_IDENTIFIER'='Patient.ID','beta_blocker'='beta_blocker_total',\n 'statin'='total_Statin') %>% dplyr::select(-Medication.Type)\n\nhead(drugs_Evan1,10)\ndim(drugs_Evan1)\nstr(drugs_Evan1)\n#length(intersect(drugs_Evan1$Patient.ID,samples_meta$CLIENT_IDENTIFIER))\n\n# +\nsamples_meta1=samples_meta %>%left_join(.,rbind(drugs_Evan,data.frame(drugs_Evan1))) \nhead(samples_meta1)\n#tail(samples_meta1)\n\ndim(samples_meta1)\n\n# +\n# Drugs\nlibrary(dplyr)\n#samples_meta1 %>% group_by(NEUROPATHY) %>% count(statin) %>%mutate(percentage = (n / sum(n)*100))\n#samples_meta1 %>% group_by(GROUP_ID) %>% count(statin) %>%mutate(percentage = (n / sum(n)*100))\nsamples_meta1 %>% filter(!GROUP_ID=='Lean_NoN')%>%group_by(GROUP_ID,GENDER) %>% \ncount(statin) %>% #drop_na %>%\nmutate(percentage = (n / sum(n)*100))%>%\nmutate(statin= as.factor(statin)) %>% \nmutate(statin = plyr::revalue(statin, c(\"FALSE\"='NO',\"TRUE\"='YES','NA'='NA'))) %>%\n\nggplot( aes(x=GROUP_ID,y= percentage,fill =statin,label=paste0(round(percentage,1),'%') ) ) + \ngeom_bar(stat=\"identity\")+ facet_wrap( ~ GENDER, scales=\"free\")+ \ngeom_text( size=5, position = position_stack(vjust = 0.5) )+\ntheme( plot.title = element_text(hjust = 0.5,size=20,face = \"bold\"),\n axis.text.x = element_text(color = \"grey20\", size = 15, angle = 45, hjust = .5, vjust = 0.5, face = \"bold\"),\n axis.text.y = element_text(color = \"grey20\", size = 15, angle = 0, hjust = 1, vjust = 0, face = \"bold\"), \n axis.title.x = element_text(color = \"grey20\", size = 20, angle = 0, hjust = .5, vjust = 0, face = \"bold\"),\n axis.title.y = element_text(color = \"grey20\", size = 20, angle = 90, hjust = .5, vjust = .5, face = \"bold\"),\n legend.title=element_text(size=20),\n legend.text = element_text(size=20,face=\"bold\"),\n strip.text = element_text(size = 20,face=\"bold\") \n \n \n )+xlab(\"\")+ylab(\"Percentage %\")+\nguides(fill=guide_legend(title=\"statins\"))+ggtitle(\"Percentage of patients are taking statins\")\n\n# ggsave(\"statins.tiff\", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf\n# scale = 1, units = \"in\",\n# dpi = 300, limitsize = TRUE)\n\n\n\n# Drugs\nlibrary(dplyr)\n#samples_meta1 %>% group_by(NEUROPATHY) %>% count(beta_blocker) %>%mutate(percentage = (n / sum(n)*100))\n#samples_meta1 %>% group_by(GROUP_ID) %>% count(beta_blocker) %>%mutate(percentage = (n / sum(n)*100))\nsamples_meta1 %>% filter(!GROUP_ID=='Lean_NoN')%>%group_by(GROUP_ID,GENDER) %>%\ncount(beta_blocker) %>% \nmutate(percentage = (n / sum(n)*100))%>%#drop_na %>%\nmutate(statin= as.factor(beta_blocker)) %>% \nmutate(beta_blocker = plyr::revalue(statin, c(\"FALSE\"='NO',\"TRUE\"='YES','NA'='NA'))) %>%\n\nggplot( aes(x=GROUP_ID,y= percentage,fill =beta_blocker,label=paste0(round(percentage,1),\"%\") ) ) + \ngeom_bar(stat=\"identity\")+ facet_wrap( ~ GENDER, scales=\"free\")+ \ngeom_text( size=5, position = position_stack(vjust = 0.5) )+\ntheme( plot.title = element_text(hjust = 0.5,size=20,face = \"bold\"),\n axis.text.x = element_text(color = \"grey20\", size = 15, angle = 45, hjust = .5, vjust = 0.5, face = \"bold\"),\n axis.text.y = element_text(color = \"grey20\", size = 15, angle = 0, hjust = 1, vjust = 0, face = \"bold\"), \n axis.title.x = element_text(color = \"grey20\", size = 20, angle = 0, hjust = .5, vjust = 0, face = \"bold\"),\n axis.title.y = element_text(color = \"grey20\", size = 20, angle = 90, hjust = .5, vjust = .5, face = \"bold\"),\n legend.title=element_text(size=20),\n legend.text = element_text(size=20,face=\"bold\"),\n strip.text = element_text(size = 20,face=\"bold\") \n \n \n )+xlab(\"\")+ylab(\"Percentage %\")+\nguides(fill=guide_legend(title=\"beta blocker\"))+\nggtitle(\"Percentage of patients are taking \\n beta blocker\")\n\n# ggsave(\"beta_blocker.tiff\", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf\n# scale = 1, units = \"in\",\n# dpi = 300, limitsize = TRUE)\n\n# +\n#library(lsmeans)\n#library(multcomp)\noptions(scipen=999)\ninterested_factors=c(\"SAMPLE_NAME\",\"AGE\",\"GENDER\",\"GROUP_ID\",\"beta_blocker\",\"statin\") \nsamples_meta_interested=subset(samples_meta1,select= interested_factors)\nF_for_all=matrix(0,ncol=length(levels(Normalized_data_label$GROUP_ID)),nrow = ncol(Normalized_data))\ncobre_all<-cbind(samples_meta_interested,Normalized_data)\n\nfor (i in 1:ncol(Normalized_data)){\nlm.out2 = lm(cobre_all[,(i+ncol(samples_meta_interested))] ~ GROUP_ID+AGE+GENDER+beta_blocker+statin,\n data=cobre_all, na.action=na.omit) #\nl2 <- glht(lm.out2, linfct = mcp(GROUP_ID = \"Tukey\"))\nsummary(l2,test=adjusted(\"fdr\"))\n#F_for_all[i,1]=as.numeric((unlist(summary(l2)[10]))[12])\n#F_for_all[i,2]=as.numeric((unlist(summary(l2)[10]))[13])\n#F_for_all[i,3]=as.numeric((unlist(summary(l2)[10]))[14])\nF_for_all[i,1]=as.numeric((unlist(summary(l2,test=adjusted(\"bonferroni\"))[10]))[12])\nF_for_all[i,2]=as.numeric((unlist(summary(l2,test=adjusted(\"bonferroni\"))[10]))[13])\nF_for_all[i,3]=as.numeric((unlist(summary(l2,test=adjusted(\"bonferroni\"))[10]))[14])\n}\n\n#head(F_for_all)\nrownames(F_for_all)=colnames(Normalized_data)\ncolnames(F_for_all)=c('Obese_N - Lean_NoN','Obese_NoN - Lean_NoN','Obese_NoN - Obese_N ')\n#head(F_for_all)\n\nF_for_all %>% data.frame %>% filter (.[3] < 0.05) %>% dim %>%.[1]%>% \n paste(\"Number of metaboloties between obese patients with neuropathy and without is\",.)\n\n\nF_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[4] < 0.05) %>% column_to_rownames('gene')\n# -\n\nObese_N.vs.Lean_NoN=\nF_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[2] < 0.05) %>% column_to_rownames('gene')%>% rownames \nObese_NoN.vs.Lean_NoN=\nF_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[3] < 0.05) %>% column_to_rownames('gene')%>% rownames \nObese_N.vs.Obese_NoN=\nF_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[4] < 0.05) %>% column_to_rownames('gene')%>% rownames \n\n# ### Venn digram\n\n# +\nlibrary(limma) \n# Venn digram\nuniverse <- sort(unique(c(Obese_NoN.vs.Lean_NoN, Obese_N.vs.Lean_NoN, Obese_N.vs.Obese_NoN)))\nCounts <- matrix(0, nrow=length(universe), ncol=3)\n\nfor (i in 1:length(universe)) {\n Counts[i,1] <- universe[i] %in% Obese_NoN.vs.Lean_NoN\n Counts[i,2] <- universe[i] %in% Obese_N.vs.Lean_NoN\n Counts[i,3] <- universe[i] %in% Obese_N.vs.Obese_NoN\n}\ncolnames(Counts) <- c(\"Obese_NoN vs \\nLean_NoN\",\"Obese_N vs \\nLean_NoN\",\"Obese_N vs\\n Obese_NoN\")\ncols<-c(\"Red\", \"Green\", \"Blue\")\n#tiff(\"venn.tiff\")\n#pdf(\"venn_adjusted.pdf\",width=10,height=10)\nvennDiagram(vennCounts(Counts), circle.col=cols,cex=1)\n#dev.off()\n\n# +\nlength(universe[which(Counts[,3]==1 & Counts[,1]==0 & Counts[,2]==0)])\nObese_N.vs.Obese_NoN_M=universe[which(Counts[,3]==1 & Counts[,1]==0 & Counts[,2]==0)]\nObese_N.vs.Obese_NoN_M\n\nObese_N.vs.Obese_NoN_M_FC=fold_change[Obese_N.vs.Obese_NoN_M,'Obese_N...Obese_NoN',drop=F]\nObese_N.vs.Obese_NoN_M_FC_pathway= merge(Obese_N.vs.Obese_NoN_M_FC,metabolomics_meta[c(\"SUB_PATHWAY\",\"BIOCHEMICAL\",\"SUPER_PATHWAY\")],\n by.x=\"row.names\",by.y=\"BIOCHEMICAL\") %>% \nmutate(logFC=log2(Obese_N...Obese_NoN),pathway=paste0(SUPER_PATHWAY,\" \",\"(\",SUB_PATHWAY,\")\")) \nObese_N.vs.Obese_NoN_M_FC_pathway$Row.names=as.character(Obese_N.vs.Obese_NoN_M_FC_pathway$Row.names)\n\n#pdf(\"lm_adjusted_6_FC_SUPER_PATHWAY.pdf\",width=12)\n\nggplot(Obese_N.vs.Obese_NoN_M_FC_pathway ,aes(x=reorder(stringr::str_wrap(Row.names,40),logFC),y=logFC,fill=SUPER_PATHWAY))+\n geom_bar(stat=\"identity\",width=0.8,position=position_dodge(width=0.1))+\n labs(title=\" \",x=\"\", y = \"Log2(Fold Change)\")+\n theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=20,angle = 45, hjust =1)) +\ntheme(legend.position=\"right\")+\nggtitle(\"Obese_N vs Obese_NoN \")+\n theme( plot.title = element_text(hjust = 0.5,size=20,face = \"bold\"),\n axis.text.x = element_text(color='black', size = 20, angle = 60,face='plain'),\n axis.text.y = element_text(color='black', size = 20, angle = 0), \n axis.title.x = element_text( size = 20, angle = 0,face='bold'),\n axis.title.y = element_text( size = 20, angle = 90),\n legend.title=element_text(size=20),\n legend.text = element_text(size=20),\n# strip.text = element_text(size = 12,face=\"bold\") \n \n )+guides(fill=guide_legend(title=\"Pathway\"))+coord_flip()\n#dev.off()\n# ggsave(\"lm_adjusted_6_FC_SUPER_PATHWAY.tiff\", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf\n# scale = 1.2, units = \"in\",width=11,height=10,\n# dpi = 300, limitsize = TRUE)\n# -\n\n# ## DMs using elastic net\n# Here we used glmnet package to identify DMs between obese patients with and without neuropathy.\n# Elastic net method selects metabolites that have non-zero coefficients at different values of lambda, guided by two penalty parameters alpha and lambda. Alpha sets the degree of mixing between lasso (when alpha=1) and the ridge regression (when alpha=0). Lambda controls the shrunk rate of coefficients regardless of the value of alpha. When lambda equals zero, no shrinkage is performed and the algorithm selects all the features. As lambda increases, the coefficients are shrunk more strongly and the algorithm retrieves all features with non-zero coefficients. \n#\n#\n\ntemp=Normalized_data_label %>% rownames_to_column(\"gene\") %>% filter(!GROUP_ID=='Lean_NoN') %>% \ndplyr::select(colnames(Normalized_data),gene,GROUP_ID) %>% column_to_rownames(\"gene\")\nx <- data.matrix(temp[,-ncol(temp)]) # metabolites\ny <- factor(temp[,ncol(temp)],levels = c(\"Obese_N\", \"Obese_NoN\")) # lables\n#y <- temp$GROUP_ID\n#write.csv(temp,\"Normalized_data_label_neuropathy.csv\")\ntable(y)\n\n# ### Find the best alpha\n\n# +\n# Select alpha of the elastic net\n#http://ellisp.github.io/blog/2016/08/13/fitbit-lasso\n#library(magrittr)\nset.seed(123)\nalphas <- seq(from = 0, to = 1, length.out = 10)\nres <- matrix(0, nrow = length(alphas), ncol = 6) # five columns for results - five repeats of each CV run\nfor(i in 1:length(alphas)){\n for(j in 2:6){\n cvmod <- cv.glmnet(x, y, alpha = alphas[i],family='binomial',nfolds=10, \n standardize=TRUE, type.measure='class')\n res[i, c(1, j)] <- c(alphas[i], sqrt(min(cvmod$cvm)))\n }\n}\nres <- data.frame(res)\nres$average_rmse <- apply(res[ , 2:6], 1, mean)\nres <- res[order(res$average_rmse), ]\nnames(res)[1] <- \"alpha\"\n\nres %>%\n dplyr::select(-average_rmse) %>%\n gather(trial, rmse, -alpha) %>%\n ggplot(aes(x = alpha, y = rmse)) +\n geom_point() +\n geom_smooth(se = FALSE) +\n labs(y = \"Root mean square error\") +\n ggtitle(\"Cross validation best RMSE for differing values of alpha\")\n\n# best alpha varies according to the random seed set earlier but with seed 123 it is 0.22\nbestalpha <- res[1, 1]\nbestalpha\n# -\n\n# ### Find the best lamda \n\n# +\n# select Lamda\n#https://stats.stackexchange.com/questions/97777/variablity-in-cv-glmnet-results\n\nlambdas = NULL\nmetabolites_list=list()\n#metabolites_list\nfor (j in 1:10)\n{\nfor (i in 1:100)\n{\n #fit <- cv.glmnet(x, y, family='binomial',nfolds=10, \n # standardize=TRUE, type.measure='class')\n \n #cv.glmnet does NOT search for values for alpha. A specific value should be supplied, else alpha=1 is assumed by default.\n \n fit <- cv.glmnet(x, y, family='binomial',nfolds=10, \n standardize=TRUE, type.measure='class',alpha=bestalpha)\n \n errors = data.frame(fit$lambda,fit$cvm)\n lambdas <- rbind(lambdas,errors)\n}\n# take mean cvm for each lambda\nlambdas <- aggregate(lambdas[, 2], list(lambdas$fit.lambda), mean)\n\n# select the best one\nbestindex = which(lambdas[2]==min(lambdas[2]))\nbestlambda = lambdas[bestindex,1]\n#bestlambda\nlambdas = NULL\ncv.lasso<- glmnet(x, y,lambda=bestlambda,family='binomial',standardize=TRUE)\n#coef(cv.lasso, s=bestlambda)\nzz=as.matrix(coef(cv.lasso, s=bestlambda)[,1])\n\nzz1=as.matrix(zz[order(abs(zz[,1]),decreasing = T),])\n\nzz2=data.frame(zz1[zz1!=0 & rownames(zz1)!=\"(Intercept)\",1])\ncolnames(zz2)=\"coffiecents\"\n\nmetabolites_list[[j]]=rownames(zz2)\n}\n# -\n\nmetabolites_list_consensus=Reduce(intersect,metabolites_list) \nmetabolites_list_consensus\nprint(paste(\"Elastic net identify\",length(metabolites_list_consensus),\n \"metaboloites belong to lipid, amino acids and xenobiotic\"))\n\nmetabolomics_meta_LASSO=metabolomics_meta[match(metabolites_list_consensus,metabolomics_meta$BIOCHEMICAL),\n c('BIOCHEMICAL','SUPER_PATHWAY','SUB_PATHWAY','KEGG','HMDB_ID','PUBCHEM','CAS')]\npalette=c('#83d532','#f47f2a','#00a1e3','#e72621','#fbb416','#771965','#003a63')\nhead(metabolomics_meta_LASSO)\n#write.csv(metabolomics_meta_LASSO,\"metabolomics_meta_LASSO.csv\")\n\n# +\nmycols=c('#83d532','#f47f2a','#00a1e3','#e72621','#fbb416','#771965','#003a63',\"#868686FF\", \"#0073C2FF\")\n\nmetabolomics_meta_LASSO %>%\n group_by(SUPER_PATHWAY) %>%\n summarise(volume = n()) %>%\n mutate(share=volume/sum(volume)) %>%\n ungroup() %>% \n arrange(desc(volume)) %>%\n mutate(SUPER_PATHWAY=factor(SUPER_PATHWAY, levels = as.character(SUPER_PATHWAY))) %>% \n ggplot(aes(x=2, y= share, fill=SUPER_PATHWAY)) +\n geom_bar( stat = \"identity\", color = \"white\") +\ncoord_polar(\"y\", start = 0)+\n geom_text(aes(label = paste0(round(share*100,0),\"%\")), position = position_stack(vjust = 0.5))+\n #coord_polar(theta = \"y\") + \n xlim(0.5, 2.5)+\nscale_fill_manual(values = mycols) +\n theme_void()+guides(fill=guide_legend(title=\"Pathway\"))\n#guides(fill = guide_legend(reverse = TRUE))\n ggsave(\"pie chart.pdf\", plot = last_plot(), \n scale = 1, units = \"in\",width=10,\n dpi = 300, limitsize = TRUE)\n# -\n\n# # Metabolites Pathways analysis \n# We used the 62 metabolites identified by elastic net and the ConsensusPathDB, the online tool to identify the significant enriched pathways \n# http://cpdb.molgen.mpg.de/\n#\n#\n\n# +\nlibrary(ggrepel)\n#library(animation)\nlibrary(stringr)\noverlapping=read.csv(file=\"lasso_62_pathways.csv\",check.names = T)\noverlapping_filtered <- overlapping\n#cbind(overlapping_filtered,overlapping_filtered$Avergae.Overlap..metabolites..percentage/100*5)\n\n\nzz=str_wrap(overlapping_filtered$pathway,width = 50)\n\n#pdf(\"Ryan_pathway_analysis.pdf\",width=15,height=8)\n\np6 <- ggplot() +\n \n geom_point( data=overlapping_filtered, mapping=aes(x = overlapping_filtered$size,\n y =-log(overlapping_filtered$q.value),\n color=overlapping_filtered$source,\n size=overlapping_filtered$size)) +\n scale_size(range = c(10, 30),guide = 'none')+\n \n labs(x = \"Size of metabolomics pathway\", y = \"-Log(q-value)\",color=\"Pathway source\",size=\"# of overlaped genes\") +\n ggtitle(\"Metabolites pathways analysis\")+\n #scale_fill_continuous(low = \"orange\", high = \"orange4\")+\n #geom_label(aes(label=overlapping_filtered$pathway_name),color = 'white', size = 3.5)\n #geom_text(data=overlapping_filtered,aes(label=overlapping_filtered$pathway_name),size=3)+\n geom_label_repel(aes(x = overlapping_filtered$size,\n y =-log(overlapping_filtered$q.value), color=overlapping_filtered$source,\n label =str_wrap(overlapping_filtered$pathway,width=20)) ,\n min.segment.length = unit(2, 'lines'),\n #nudge_x = ifelse(overlapping_filtered$num_overlapping_metabolites == 11, 2, 0),\n #nudge_y = ifelse(overlapping_filtered$num_overlapping_metabolites == 5, 0.1, 0) ,\n #nudge_y = ifelse(overlapping_filtered$Q.joint== 0.00023, 0.2, 0),\n size = 3.5,force=1, arrow = arrow(length = unit(0.02, \"npc\")),segment.color = 'red',\n box.padding = unit(0.35, \"lines\"),\n point.padding = unit(0.5, \"lines\"),show_guide = F) +\n \n #scale_x_continuous(limits = c(4, 23))+\n #scale_y_continuous(limits = c(1.5, 10))+\n #scale_x_continuous(breaks=c(4:30))+\n #theme(legend.position = \"right\")+\n theme(plot.title = element_text(hjust = 0.5),axis.text=element_text(size=14,face=\"bold\"),\n axis.title=element_text(size=14,face=\"bold\"))\n#geom_text( show.legend = F )\n\nprint(p6)\n#ggsave(file=\"bench_query_sort.pdf\", width=10, dpi=300)\n#dev.off()\n# -\n\n# # Machine learning \n# We will use the 62 metabolites identified by elastic net to build a classification model to predict the neuropathy stataus of obese patients. I used the script from my liliko r package\n#\n# https://cran.r-project.org/src/contrib/Archive/lilikoi/lilikoi_0.1.0.tar.gz\n#\n# https://academic.oup.com/gigascience/article/7/12/giy136/5237705\n#\n#\n\nlasso_metabolites_data= Normalized_data_label %>% rownames_to_column('gene')%>% filter(!GROUP_ID=='Lean_NoN') %>% \ndroplevels() %>% dplyr::select(label=GROUP_ID,gene,metabolites_list_consensus ) %>%column_to_rownames('gene') \nhead(lasso_metabolites_data)\n\ntable(lasso_metabolites_data$label)\n\nresult=machine_learning(lasso_metabolites_data[,!colnames(lasso_metabolites_data) %in% 'label'],\n metabolites_list_consensus,lasso_metabolites_data$label);\n\n# +\nx1=varImp(result$models[[1]], scale = TRUE)$importance\nx2=varImp(result$models[[2]], scale = TRUE)$importance[1]\nx3=varImp(result$models[[3]], scale = TRUE)$importance[1]\nx4=varImp(result$models[[4]], scale = TRUE)$importance\nx5=varImp(result$models[[5]], scale = TRUE)$importance\nx6=varImp(result$models[[6]], scale = TRUE)$importance[1]\nx7=varImp(result$models[[7]], scale = TRUE)$importance\nx1=x1[ order(rownames(x1)) , ,drop=F]\nx2=x2[ order(rownames(x2)) , ,drop=F]\nx3=x3[ order(rownames(x3)) , ,drop=F]\nx4=x4[ order(rownames(x4)) , ,drop=F]\nx5=x5[ order(rownames(x5)) , ,drop=F]\nx6=x6[ order(rownames(x6)) , ,drop=F]\nx7=x7[ order(rownames(x7)) , ,drop=F]\npathways_heatmap=data.frame(x1,x2,x3,x4,x5,x6,x7)\ncolnames(pathways_heatmap)=c('RPART', 'LDA', 'SVM', 'RF', 'GBM', 'PAM', 'LOG')\n# check this line witnh the code you have developed for the Group lasso\nrownames(pathways_heatmap)=colnames(Normalized_data)[match(rownames(pathways_heatmap),make.names(colnames(Normalized_data)))]\n#pathways_heatmap\nrange01 <- function(x){(x-min(x))/(max(x)-min(x))}\npathways_heatmap_scaled=range01(pathways_heatmap)\n#pathways_heatmap_scaled\n\nlibrary(pheatmap)\n\n\npheatmap(as.matrix(pathways_heatmap_scaled),fontsize=8,\n breaks= seq(0, 1, by=0.1), \n color= colorRampPalette(c(\"white\", \"red\"))(length(seq(0, 1, by=0.1))),\n #filename = \"pathways_heatmap_ER1.pdf\"\n )\n\n\n# +\n# Out of the 62 metabolites, we selected 12, which have the largest aggregative impact on modles performance\npathways_heatmap_scaled %>% rownames_to_column(\"d\")%>%mutate(add=PAM+LDA+SVM+RF+LOG+GBM+RPART)%>% \narrange(-add) %>% mutate(quintile = ntile(add, 5)) %>% head\n\npathways_heatmap_scaled_lasso=pathways_heatmap_scaled %>% rownames_to_column(\"d\")%>%mutate(add=PAM+LDA+SVM+RF+LOG+GBM+RPART)%>% \narrange(-add) %>% mutate(quintile = ntile(add, 5)) %>% filter(quintile==5) %>% .[[1]]\npathways_heatmap_scaled_lasso\nlength(pathways_heatmap_scaled_lasso)\n# -\n\npheatmap(as.matrix(pathways_heatmap_scaled[rownames(pathways_heatmap_scaled)%in% pathways_heatmap_scaled_lasso,]),fontsize=8,\n breaks= seq(0, 1, by=0.1), \n color= colorRampPalette(c(\"white\", \"red\"))(length(seq(0, 1, by=0.1))),\n #filename = \"pathways_heatmap_ER1_lasso_selected.pdf\",\n fontsize_row=12,fontsize_col=15 )\n\n# # Cytoscape correlation analysis\n# We will generate the correlation between most important metabolites (12) that were identified by elastic net and lipids metabolites to feed them to cytoscape. The first file is the edgje file which has the Spearman correlation values between metabolites and the second one is the nodes annotation file.\n#\n#\n\n# +\nonly_NP=Normalized_data_label %>% rownames_to_column('epi_study_id')%>%\nfilter(GROUP_ID=='Obese_N') %>% droplevels %>%\ndplyr::select(-GROUP_ID,-OBESITY,-AGE,-GENDER,-NEUROPATHY) %>%\ncolumn_to_rownames('epi_study_id') \n\nonly_DB= Normalized_data_label %>% rownames_to_column('epi_study_id')%>%\nfilter(GROUP_ID=='Obese_NoN')%>%droplevels %>% \ndplyr::select(-GROUP_ID,-OBESITY,-AGE,-GENDER,-NEUROPATHY) %>%\ncolumn_to_rownames('epi_study_id')\n\n# -\n\n\n\n# +\nmetabolomics_meta[match(colnames(only_NP) , metabolomics_meta$BIOCHEMICAL),] %>% \nfilter(SUPER_PATHWAY =='Lipid') %>% filter(!BIOCHEMICAL %in% metabolites_list_consensus)%>%\ndplyr::select(BIOCHEMICAL) %>% .[[1]] %>% as.character %>%length\n\nlipid_metabolites=metabolomics_meta[match(colnames(only_NP), metabolomics_meta$BIOCHEMICAL),] %>% \nfilter(SUPER_PATHWAY =='Lipid') %>% filter(!BIOCHEMICAL %in% metabolites_list_consensus)%>%\ndplyr::select(BIOCHEMICAL) %>% .[[1]] %>% as.character\n\n# +\nonly_NP_lasso=only_NP %>%\nrownames_to_column('gene')%>% \ndplyr::select(pathways_heatmap_scaled_lasso,gene)%>%\ncolumn_to_rownames('gene')\ndim(only_NP_lasso)\n\nonly_NP_lipid=only_NP %>%\nrownames_to_column('gene')%>% \ndplyr::select(lipid_metabolites,gene)%>%\ncolumn_to_rownames('gene')\ndim(only_NP_lipid)\n\n# +\nbac=as.matrix(t(only_NP_lasso))\nfug=as.matrix(t(only_NP_lipid))\n\nP_K_K=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[3]][1,2]\n })\n}))\nrownames(P_K_K)=rownames(bac)\ncolnames(P_K_K)=rownames(fug)\n\n# r -value\nR_K_K=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[1]][1,2]\n })\n}))\nrownames(R_K_K)=rownames(bac)\ncolnames(R_K_K)=rownames(fug)\n# -\n\n# filtered only significant and strong correlation (p<0.05, -0.35 < r < 0.35 )\nindex= data.frame(which(P_K_K >= 0.05,arr.ind = TRUE))\nindex1= data.frame((which((R_K_K <= 0.35 & R_K_K >= -0.35 ),arr.ind = TRUE)))\nrownames(index) <- c()\nrownames(index1) <- c()\n\n# select only signficant correlation(p<0.05) and strong correlation (-0.35% filter(!cor==0 & !p>0.05)\nhead(my_cor_matrix_Cyto)\ndim(my_cor_matrix_Cyto)\n\n# Create edge file\ncolnames(my_cor_matrix_Cyto)=c('Source','Target','r','p')\nwrite.csv(my_cor_matrix_Cyto,\"my_cor_matrix_Cyto.csv\")\n\n#Creat nodes file\nnodes=unique(c(my_cor_matrix_Cyto$Source,my_cor_matrix_Cyto$Target))\nnodes_C=metabolomics_meta[match(nodes,metabolomics_meta$BIOCHEMICAL),c('BIOCHEMICAL',\n 'KEGG', 'HMDB_ID', 'SUPER_PATHWAY','SUB_PATHWAY')] %>% dplyr::rename('id'='BIOCHEMICAL')\nhead(nodes_C)\ndim(nodes_C)\nwrite.csv(nodes_C,\"nodes_Cytoscape.csv\")\n\n# # Complex Heatmap\n# I used the below code to generate the below beautiful figure. I tried to make the code simple but from the name of the package, it should be complex. \n#\n#\n#\n# ![image.png](attachment:image.png)\n#\n#\n\n# ## Neuropathy\n\n# +\nbac=as.matrix(t(only_NP_lasso))\nfug=as.matrix(t(only_NP_lipid))\n\nP_K_K=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[3]][1,2]\n })\n}))\nrownames(P_K_K)=rownames(bac)\ncolnames(P_K_K)=rownames(fug)\n\n# r -value\nR_K_K=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[1]][1,2]\n })\n}))\nrownames(R_K_K)=rownames(bac)\ncolnames(R_K_K)=rownames(fug)\n\nindex= data.frame(which(P_K_K >= 0.05,arr.ind = TRUE))\nindex1= data.frame((which((R_K_K <= 0.35 & R_K_K >= -0.35 ),arr.ind = TRUE)))\nrownames(index) <- c()\nrownames(index1) <- c()\n#head(index)\n\n\nfor(i in 1:nrow(index)){\n R_K_K[index[i,]$row, index[i,]$col] <- 0\n \n }\nfor(i in 1:nrow(index1)){\n R_K_K[index1[i,]$row, index1[i,]$col] <- 0\n \n }\n\n# remove zeros sum columns\nr=R_K_K\nZeroColumn=colnames(r)[which(colSums(r)==0)]\n#ZeroColumn\nr=r[,!colnames(r) %in% ZeroColumn]\nZeroRows=rownames(r)[which(rowSums(r)==0)]\nr=r[!rownames(r) %in% ZeroRows,]\n\nrow_ann=rownames(r) %>% data.frame() %>% dplyr::rename('lasso'='.') %>%inner_join(.,fold_change%>% \nrownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>%\ndplyr::rename('FC'='Obese_NoN...Lean_NoN') %>% inner_join(.,limma_dataframe_FC %>% \nrownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>%\ndplyr::rename('logFC'='Obese_NoN...Lean_NoN')%>% mutate(sign=sign(logFC)) %>% \ninner_join(.,metabolomics_meta %>% dplyr::select(lasso=BIOCHEMICAL,SUB_PATHWAY),\n by='lasso') %>% column_to_rownames('lasso')\nhead(row_ann)\n\n\ncol_ann=colnames(r) %>% data.frame() %>% dplyr::rename('lasso'='.') %>%inner_join(.,fold_change%>% \nrownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>%\ndplyr::rename('FC'='Obese_NoN...Lean_NoN') %>% inner_join(.,limma_dataframe_FC %>% \nrownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>%\ndplyr::rename('logFC'='Obese_NoN...Lean_NoN')%>% mutate(sign=sign(logFC))%>% \ninner_join(.,metabolomics_meta %>% dplyr::select(lasso=BIOCHEMICAL,SUB_PATHWAY),\n by='lasso') %>% column_to_rownames('lasso')\n\n\n# -\n\n\n\n# ## Non neuropathy\n\n# +\nonly_DB_lasso=only_DB %>%\nrownames_to_column('gene')%>% \ndplyr::select(rownames(r),gene)%>%\ncolumn_to_rownames('gene')\ndim(only_DB_lasso)\n\nonly_DB_lipid=only_DB %>%\nrownames_to_column('gene')%>% \ndplyr::select(colnames(r),gene)%>%\ncolumn_to_rownames('gene')\ndim(only_DB_lipid)\n\nbac=as.matrix(t(only_DB_lasso))\nfug=as.matrix(t(only_DB_lipid))\n\nP_K_K_DB=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[3]][1,2]\n })\n}))\nrownames(P_K_K_DB)=rownames(bac)\ncolnames(P_K_K_DB)=rownames(fug)\n\n# r -value\nR_K_K_DB=t(sapply(1:nrow(bac), function(x) {\n sapply(1:nrow(fug), function(y) {\n rcorr(bac[x,],fug[y,],type=c(\"spearman\"))[[1]][1,2]\n })\n}))\nrownames(R_K_K_DB)=rownames(bac)\ncolnames(R_K_K_DB)=rownames(fug)\n\nR_K_K_DB_KEEP= R_K_K_DB\nP_K_K_DB_KEEP= P_K_K_DB\n\nindex= data.frame(which(P_K_K_DB >= 0.05,arr.ind = TRUE))\nindex1= data.frame((which((R_K_K_DB <= 0.35 & R_K_K_DB >= -0.35 ),arr.ind = TRUE)))\nrownames(index) <- c()\nrownames(index1) <- c()\ndim(index)\n\n#https://stackoverflow.com/questions/47037504/r-converting-a-data-frame-of-row-column-indices-to-a-matrix\n# select only signficant correlation(p<0.05) and strong correlation (-0.5% rownames_to_column('gene') %>% arrange(SUB_PATHWAY) %>% \ncolumn_to_rownames('gene')\ncol_order =rownames(col_ann)\nr_NP <- r[, col_order]\nr_DB <- r_DB[, col_order]\nmatch(colnames(r_NP),colnames(r_DB))\n\n###### color for columns\nn=length(unique(col_ann$SUB_PATHWAY))\nlibrary(randomcoloR)\npaletteCol <- distinctColorPalette(n)\nnames(paletteCol)=unique(col_ann$SUB_PATHWAY)\nhead(paletteCol)\n######colors for rows\nn=length(unique(row_ann$SUB_PATHWAY))\n#library(randomcoloR)\npaletteRow <- distinctColorPalette(n)\nnames(paletteRow)=unique(row_ann$SUB_PATHWAY)\nhaed(paletteRow)\n\n# +\n################ANNOTATION OF THE ROWS AND COLUMNS\nha_row = HeatmapAnnotation(Sub_pathway=row_ann[,4],which='row',col= list(Sub_pathway=paletteRow),\n FC = anno_barplot(row_ann[,1],gp = gpar(fill = ifelse(row_ann[,3]==1,c('red'),c('green') ),\n col = ifelse(row_ann[,3]==1,c('red'),c('blue') ) ),which = \"row\"))\nha_row\ndraw(ha_row)\n\n\nha_col_buttom = HeatmapAnnotation(FC = anno_barplot(col_ann[,1],gp = gpar(fill = ifelse(col_ann[,3]==1,c('red'),c('blue') ),\n col = ifelse(col_ann[,3]==1,c('red'),c('blue') )))\n ) \n\nha_col_top = HeatmapAnnotation(Sub_pathway=col_ann[,4],col= list(Sub_pathway=paletteCol),show_legend = T,border=2)\n\n\n\n# +\n####################################Draw two heatmaps\n\nmat=r_NP\n\nht1=Heatmap(as.matrix((mat)), name = \"NP\",\n col = colorRamp2(c(-1, 0, 1), c(\"green\", \"white\", \"red\")),\n cluster_rows = F,\n cluster_columns = F,\n border='black',\n show_column_names = F,\n column_title = \"NP\",\n #cluster_columns = dend,\n row_names_side = \"right\",\n column_names_side = \"bottom\",\n row_names_gp = gpar(fontsize = 15),\n column_names_gp = gpar(fontsize = 10,rot=90),\n #km = 3,\n #gap = unit(3, \"mm\")\n #bottom_annotation = ha_col_buttom,\n top_annotation = ha_col_top,\n #left_annotation = ha_row,\n show_heatmap_legend = T,\n width = unit(30, \"inch\"),\n height = unit(2, \"inch\"),\n column_dend_side='top',\n #cluster_column_slices = TRUE\n column_dend_height = unit(10, \"inch\"),\n row_dend_width = unit(10, \"inch\"),\n #row_hclust_side = \"right\"\n # use_raster = TRUE, raster_device = \"tiff\"\n #heatmap_legend_param(list(title_position = \"topcenter\")),\n # heatmap_legend_param = list(color_bar = \"continuous\",at=c(-1,0,1))\n \n)\n\n\n\nmat=r_DB\nht2=Heatmap(as.matrix((mat)), name = \"DB\",\n col = colorRamp2(c(-1, 0, 1), c(\"green\", \"white\", \"red\")),\n cluster_rows = F,\n border='black',\n cluster_columns = F,\n column_title = \"DB\",\n #cluster_columns = dend,\n row_names_side = \"right\",\n column_names_side = \"bottom\",\n row_names_gp = gpar(fontsize = 15),\n column_names_gp = gpar(fontsize = 8,rot=90),\n column_names_rot = 45,\n #km = 3,\n #gap = unit(3, \"mm\")\n bottom_annotation = ha_col_buttom,\n #top_annotation = ha_col_top,\n #left_annotation = ha_row,\n show_heatmap_legend = T,\n width = unit(30, \"inch\"),\n height = unit(2, \"inch\"),\n column_dend_side='top',\n #cluster_column_slices = TRUE\n column_dend_height = unit(10, \"inch\"),\n row_dend_width = unit(10, \"inch\"),\n #row_hclust_side = \"right\"\n # use_raster = TRUE, raster_device = \"tiff\"\n #heatmap_legend_param(list(title_position = \"topcenter\")),\n \n)\n\n\nht_list=ht1 + ht2\n\n\n\n#pdf(\"heatmap_no_clustering_NP_DB1.pdf\", width = 80, height = 20)\n\n\ndraw(ht_list, annotation_legend_side = \"left\",\n legend_title_gp = gpar(fontsize = 10, fontface = \"plain\"),heatmap_legend_side = \"top\",\n gap = unit(10, \"cm\") )\n\n#dev.off()\n\n\n# +\n########### only Acyl Carnitine\n\n\n####arrange columns r based in subpathways\ncol_ann=col_ann %>% rownames_to_column('gene') %>% arrange(SUB_PATHWAY) %>%\nfilter(str_detect(SUB_PATHWAY,'Acyl Carnitine'))%>%\n column_to_rownames('gene')\ncol_order =rownames(col_ann)\nr_NP <- r[, col_order]\nr_DB <- r_DB[, col_order]\nmatch(colnames(r_NP),colnames(r_DB))\n\n\n###### color for columns\nn=length(unique(col_ann$SUB_PATHWAY))\nlibrary(randomcoloR)\npaletteCol <- distinctColorPalette(n)\nnames(paletteCol)=unique(col_ann$SUB_PATHWAY)\n#paletteCol\n######colors for rows\nn=length(unique(row_ann$SUB_PATHWAY))\n#library(randomcoloR)\npaletteRow <- distinctColorPalette(n)\nnames(paletteRow)=unique(row_ann$SUB_PATHWAY)\n#paletteRow\n#mat=r1\n#dim(mat) # 4 x 98\n\n\n################ANNOTATION OF THE ROWS AND COLUMNS\nha_row = HeatmapAnnotation(which='row',\n FC = anno_barplot(row_ann[,1],gp = gpar(fontsize=20,fill = ifelse(row_ann[,3]==1,c('red'),c('blue') ),\n col = ifelse(row_ann[,3]==1,c('red'),c('blue') ) ))) \n#ha_row\n#draw(ha_row)\n\n\nha_col_buttom = HeatmapAnnotation(FC = anno_barplot(height = unit(1, \"inch\"),col_ann[,1],gp = gpar(fontsize=20,fill = ifelse(col_ann[,3]==1,c('red'),c('blue') ),\n col = ifelse(col_ann[,3]==1,c('red'),c('blue') )))\n) \n\nha_col_top = HeatmapAnnotation(Sub_pathway=col_ann[,4],col= list(Sub_pathway=paletteCol),show_legend = T,border=2)\n\n\n\n\nmat=r_NP\n\nht1=Heatmap(as.matrix((mat)), name = \"NP\",\n col = colorRamp2(c(-1, 0, 1), c(\"green\", \"white\", \"red\")),\n cluster_rows = F,\n cluster_columns = F,\n border='black',\n show_column_names = F,\n column_title = \"NP\",\n #cluster_columns = dend,\n row_names_side = \"right\",\n column_names_side = \"bottom\",\n row_names_gp = gpar(fontsize = 20),\n column_names_gp = gpar(fontsize = 20,rot=90),\n #km = 3,\n #gap = unit(3, \"mm\")\n #bottom_annotation = ha_col_buttom,\n top_annotation = ha_col_top,\n #right_annotation = ha_row,\n show_heatmap_legend = T,\n width = unit(20, \"inch\"),\n height = unit(3, \"inch\"),\n column_dend_side='top',\n #cluster_column_slices = TRUE\n column_dend_height = unit(20, \"inch\"),\n row_dend_width = unit(20, \"inch\"),\n #row_hclust_side = \"right\"\n # use_raster = TRUE, raster_device = \"tiff\"\n #heatmap_legend_param(list(title_position = \"topcenter\")),\n # heatmap_legend_param = list(color_bar = \"continuous\",at=c(-1,0,1))\n \n)\n\n\n\nmat=r_DB\nht2=Heatmap(as.matrix((mat)), name = \"DB\",\n col = colorRamp2(c(-1, 0, 1), c(\"green\", \"white\", \"red\")),\n cluster_rows = F,\n border='black',\n cluster_columns = F,\n column_title = \"DB\",\n #cluster_columns = dend,\n row_names_side = \"right\",\n column_names_side = \"bottom\",\n row_names_gp = gpar(fontsize = 20),\n column_names_gp = gpar(fontsize = 20,rot=90),\n column_names_rot = 45,\n #km = 3,\n #gap = unit(3, \"mm\")\n bottom_annotation = ha_col_buttom,\n #top_annotation = ha_col_top,\n #right_annotation = ha_row,\n show_heatmap_legend = T,\n width = unit(20, \"inch\"),\n height = unit(5, \"inch\"),\n column_dend_side='top',\n #cluster_column_slices = TRUE\n column_dend_height = unit(20, \"inch\"),\n row_dend_width = unit(20, \"inch\"),\n #row_hclust_side = \"right\"\n # use_raster = TRUE, raster_device = \"tiff\"\n #heatmap_legend_param(list(title_position = \"topcenter\")),\n \n)\n\n\nht_list=ht1 + ht2\n\n\n\n#pdf(\"heatmap_no_clustering_NP_DB1_Acyl Carnitine1.pdf\", width = 60, height = 20)\n\n\ndraw(ht_list, annotation_legend_side = \"left\",\n legend_title_gp = gpar(fontsize = 40, fontface = \"plain\"),heatmap_legend_side = \"top\",\n gap = unit(10, \"cm\") )\n\n#dev.off()\n\n\n\n# -\n\n# # Demographic table\n# I used qwraps2 to generate the summary of the samples such as the mean, SD, and its corresponding p value using t-test, Fisher, and ANOVA\n#\n#\n\n#https://cran.rstudio.com/web/packages/qwraps2/vignettes/summary-statistics.html\nlibrary(magrittr)\n#install.packages(\"qwraps2\")\nlibrary(qwraps2)\n\n\ntable1=read.csv(\"Fadhl Project with Metabolic Variables 12_3_2019.csv\",stringsAsFactors=F)\ntable1 =table1 %>% filter(Sample_ID %in% samples_meta$CLIENT_IDENTIFIER) %>%\ndplyr::select(Sample_ID,matches(\"Sex|Age|BMI|BP|weight|Waist|Cholesterol|Triglycerides|LDL\"))%>% \ndplyr::rename('CLIENT_IDENTIFIER'='Sample_ID')\ndim(table1)\n#head(table1)\nsamples_meta2= samples_meta %>% left_join(.,table1,by='CLIENT_IDENTIFIER')\nhead(samples_meta2)\n\n\n\n# +\nour_summary2 <-\n list(\"AGE\" =\n list(\n \"AGE mean (sd)\" = ~ qwraps2::mean_sd(.data$AGE)),\n \"BMI\" =\n list(\n \"BMI mean (sd)\" = ~ qwraps2::mean_sd(.data$BMI.x)),\n \"systav_10c\" =\n list(\n \"Blood pressure: Systolic mean (sd)\" = ~ qwraps2::mean_sd(.data$SBP..mmHg.)),\n \"diasav_10c\" =\n list(\n \"Blood pressure: Diastolic mean (sd)\" = ~ qwraps2::mean_sd(.data$DBP..mmHg.)),\n \"Sex\" =\n list(\n \"Sex: Female\" = ~ qwraps2::n_perc(.data$Sex == 'Female'),\n \"Sex: Male\" = ~ qwraps2::n_perc(.data$Sex == \"Male\")),\n \"Weigh\" =\n list(\n \"Weigh mean (sd)\" = ~ qwraps2::mean_sd(.data$Weight..Kg.)),\n \n \"Waist\" =\n list(\n \"Waist mean (sd)\" = ~ qwraps2::mean_sd(.data$Waist.Circumference..cm.,na_rm = T)),\n \n \"Cholesterol\" =\n list(\n \"Cholesterol mean (sd)\" = ~ qwraps2::mean_sd(.data$Cholesterol,na_rm = T)),\n \n \"Tgly\" =\n list(\n \"Tgly mean (sd)\" = ~ qwraps2::mean_sd(.data$Triglycerides,na_rm = T)),\n \n \"LDL_Chol\" =\n list(\n \"LDL_Chol mean (sd)\" = ~ qwraps2::mean_sd(.data$LDL,na_rm = T))\n \n \n )\n\nwhole <- summary_table(samples_meta2, our_summary2)\n#whole\n\nby_cyl <- summary_table(dplyr::group_by(samples_meta2, GROUP_ID), our_summary2)\n\n#both <- cbind(whole, by_cyl)\nboth=by_cyl\nboth\n\n# -\n\n# difference in means\nmpvals <-\n list(lm(AGE ~ GROUP_ID, data = samples_meta2),\n lm(BMI.x ~ GROUP_ID, data = samples_meta2),\n lm(SBP..mmHg. ~ GROUP_ID, data = samples_meta2),\n lm(DBP..mmHg. ~ GROUP_ID, data = samples_meta2),\n lm(Weight..Kg. ~ GROUP_ID, data = samples_meta2),\n lm(Waist.Circumference..cm. ~ GROUP_ID, data = samples_meta2),\n lm(Cholesterol ~ GROUP_ID, data = samples_meta2),\n lm(Triglycerides ~ GROUP_ID, data = samples_meta2),\n #lm(Crea ~ GROUP_ID, data = samples_meta2),\n #lm(HbA1ci ~ GROUP_ID, data = samples_meta2),\n # lm(HDL_Chol ~ GROUP_ID, data = samples_meta2),\n #lm(U_Alb_m ~ GROUP_ID, data = samples_meta2),\n lm(LDL ~ GROUP_ID, data = samples_meta2)) %>%\n lapply(aov) %>%\n lapply(summary) %>%\n lapply(function(x) x[[1]][[\"Pr(>F)\"]][1]) %>%\n lapply(frmtp) %>%\n do.call(c, .)\n mpvals\n\n\n\nfpvalSex <- frmtp(fisher.test(table(samples_meta2$GROUP_ID, samples_meta2$Sex))$p.value)\nfpvalSex\n\nboth <- cbind(both, \"P-value\" = \"\")\nboth[grepl(\"mean \\\\(sd\\\\)\", rownames(both)), \"P-value\"] <- mpvals\nboth[grepl(\"Sex\", rownames(both)), \"P-value\"] <- fpvalSex\nboth\n\nwrite.csv(both,\"Table1.csv\",fileEncoding = \"UTF-8\")\n\n# # My functions\n\n# +\n#' A machine learning Function\n#'\n#' This function for classification using 7 different machine learning algorithms\n#' and it plot the ROC curves and the AUC, SEN, and specificty\n#' @param PDSmatrix from PDSfun function and selected_Pathways_Weka from featuresSelection function\n#' @keywords classifcation\n#' @export\n#' @examples machine_learning(PDSmatrix,selected_Pathways_Weka)\n#' machine_learning(PDSmatrix,selected_Pathways_Weka)\n#'\n#'\n#'\n\nmachine_learning<-function(PDSmatrix,selected_Pathways_Weka,Label){\nrequire(caret)\nrequire(pROC)\nrequire(ggplot2)\nrequire(gbm)\nprostate_df=data.frame(((PDSmatrix[,selected_Pathways_Weka])),Label=Label, check.names=T)\ncolnames(prostate_df)[which(names(prostate_df) == \"Label\")]='subtype'\n\n\nperformance_training=matrix( rep( 0, len=21), nrow = 3) #AUC SENS SPECF\nperformance_testing=matrix( rep( 0, len=56), nrow = 8) # ROC SENS SPEC\n\nperformance=matrix(rep( 0, len=56), nrow = 8) # ROC SENS SPEC\n\nperformance_training_list <- list()\nperformance_testing_list <- list()\n\n# var.cart= list()\n# var.lda= list()\n# var.svm= list()\n# var.rf= list()\n# var.gbm= list()\n# var.pam= list()\n# var.log= list()\n\n model=list()\n\n ###############Shuffle stat first\n #rand <- sample(nrow(prostate_df))\n #prostate_df=prostate_df[rand, ]\n \n ###############Randomly Split the data in to training and testing \n set.seed(2000)\n trainIndex <- createDataPartition(prostate_df$subtype, p = 1,list = FALSE,times = 1)\n irisTrain <- prostate_df[ trainIndex,]\n irisTest <- prostate_df[ trainIndex,]\n #irisTrain$subtype=as.factor(paste0(\"X\",irisTrain$subtype))\n #irisTest$subtype=as.factor(paste0(\"X\",irisTest$subtype))\n ################################Training and tunning parameters \n # prepare training scheme\n #control <- trainControl(method=\"cv\", number=10,classProbs = TRUE,summaryFunction = twoClassSummary\n # ,sampling='smote')\n control <- trainControl(method=\"LOOCV\", number=10,classProbs = TRUE,summaryFunction = twoClassSummary\n ,sampling='smote')\n \n #1- RPART ALGORITHM\n set.seed(7) #This ensures that the same resampling sets are used,which will come in handy when we compare the resampling profiles between models.\n \n #assign(paste0(\"fit.cart\",k),train(subtype~., data=irisTrain, method=\"rpart\", trControl=control,metric=\"ROC\"))\n \n # supress the warning messgae\n #options(warn=-1)\n #options(warn=0)\n #?suppressWarnings()\n \n garbage <- capture.output(fit.cart <- train(subtype~., data=irisTrain, \n method = 'rpart', trControl=control,metric=\"ROC\"))\n #fit.cart <- train(subtype~., data=irisTrain, method = 'rpart', trControl=control,metric=\"ROC\") #loclda \n model[[1]]=fit.cart\n performance_training[1,1]=max(fit.cart$results$ROC)#AUC\n performance_training[2,1]=fit.cart$results$Sens[which.max(fit.cart$results$ROC)]# sen\n performance_training[3,1]=fit.cart$results$Spec[which.max(fit.cart$results$ROC)]# spec\n \n #Model Testing \n cartClasses <- predict( fit.cart, newdata = irisTest,type=\"prob\")\n cartClasses1 <- predict( fit.cart, newdata = irisTest) \n cartConfusion=confusionMatrix(data = cartClasses1, irisTest$subtype)\n cart.ROC <- roc(predictor=as.numeric(unlist(cartClasses[1])),response=irisTest$subtype,levels=rev(levels(irisTest$subtype)))\n \n \n \n performance_testing[1,1]=as.numeric(cart.ROC$auc)#AUC\n performance_testing[2,1]=cartConfusion$byClass[1]#SENS\n performance_testing[3,1]=cartConfusion$byClass[2]#SPEC\n performance_testing[4,1]=cartConfusion$overall[1]#accuracy\n performance_testing[5,1]=cartConfusion$byClass[5]#precision\n performance_testing[6,1]=cartConfusion$byClass[6]#recall = sens\n performance_testing[7,1]=cartConfusion$byClass[7]#F1\n performance_testing[8,1]=cartConfusion$byClass[11]#BALANCED ACCURACY \n \n \n \n #2-LDA ALGORITHM \n set.seed(7) \n #assign(paste0(\"fit.lda\",k),train(subtype~., data=irisTrain, method=\"pls\", trControl=control,metric=\"ROC\"))\n garbage <- suppressWarnings(capture.output(fit.lda <- train(subtype~., data=irisTrain, method = 'lda', \n trControl=control,metric=\"ROC\",trace=F))) #loclda) \n #fit.lda <- train(subtype~., data=irisTrain, method = 'lda', trControl=control,metric=\"ROC\") #loclda \n model[[2]]=fit.lda\n performance_training[1,2]=max(fit.lda$results$ROC)#AUC\n performance_training[2,2]=fit.lda$results$Sens[which.max(fit.lda$results$ROC)]# sen\n performance_training[3,2]=fit.lda$results$Spec[which.max(fit.lda$results$ROC)]# spec\n \n #Model Testing\n ldaClasses <- predict( fit.lda, newdata = irisTest,type=\"prob\")\n ldaClasses1 <- predict( fit.lda, newdata = irisTest)\n ldaConfusion=confusionMatrix(data = ldaClasses1, irisTest$subtype)\n \n \n \n lda.ROC <- roc(predictor=as.numeric(unlist(ldaClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n \n \n performance_testing[1,2]=as.numeric(lda.ROC$auc)#AUC\n performance_testing[2,2]=ldaConfusion$byClass[1]#SENS\n performance_testing[3,2]=ldaConfusion$byClass[2]#SPEC\n performance_testing[4,2]=ldaConfusion$overall[1]#accuracy\n performance_testing[5,2]=ldaConfusion$byClass[5]#precision\n performance_testing[6,2]=ldaConfusion$byClass[6]#recall = sens\n performance_testing[7,2]=ldaConfusion$byClass[7]#F1\n performance_testing[8,2]=ldaConfusion$byClass[11]#BALANCED ACCURACY\n \n #3- SVM ALGORITHM\n set.seed(7)\n garbage <- capture.output(fit.svm <- train(subtype~., data=irisTrain, method=\"svmRadial\", \n trControl=control,metric=\"ROC\"))\n #fit.svm <- train(subtype~., data=irisTrain, method=\"svmRadial\", trControl=control,metric=\"ROC\")\n #assign(paste0(\"fit.svm\",k),train(subtype~., data=irisTrain, method=\"svmRadical\", trControl=control,metric=\"ROC\"))\n model[[3]]=fit.svm\n performance_training[1,3]=max(fit.svm$results$ROC) #AUC\n performance_training[2,3]=fit.svm$results$Sens[which.max(fit.svm$results$ROC)]# sen\n performance_training[3,3]=fit.svm$results$Spec[which.max(fit.svm$results$ROC)]# spec\n \n #Model Testing\n svmClasses <- predict( fit.svm, newdata = irisTest,type=\"prob\")\n svmClasses1 <- predict( fit.svm, newdata = irisTest)\n svmConfusion=confusionMatrix(data = svmClasses1, irisTest$subtype)\n \n \n \n svm.ROC <- roc(predictor=as.numeric(unlist(svmClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n\n \n \n \n performance_testing[1,3]=as.numeric(svm.ROC$auc)#AUC\n performance_testing[2,3]=svmConfusion$byClass[1]#SENS\n performance_testing[3,3]=svmConfusion$byClass[2]#SPEC\n performance_testing[4,3]=svmConfusion$overall[1]#accuracy\n performance_testing[5,3]=svmConfusion$byClass[5]#precision\n performance_testing[6,3]=svmConfusion$byClass[6]#recall = sens\n performance_testing[7,3]=svmConfusion$byClass[7]#F1\n performance_testing[8,3]=svmConfusion$byClass[11]#BALANCED ACCURACY\n \n #4-RF ALGORITHM\n set.seed(7)\n garbage <- capture.output(fit.rf <- train(subtype~., data=irisTrain, method=\"rf\", trControl=control,metric=\"ROC\"))\n #fit.rf <- train(subtype~., data=irisTrain, method=\"rf\", trControl=control,metric=\"ROC\")\n model[[4]]=fit.rf\n performance_training[1,4]=max(fit.rf$results$ROC) #AUC\n performance_training[2,4]=fit.rf$results$Sens[which.max(fit.rf$results$ROC)]# sen\n performance_training[3,4]=fit.rf$results$Spec[which.max(fit.rf$results$ROC)]# spec\n \n #Model Testing\n rfClasses <- predict( fit.rf, newdata = irisTest,type=\"prob\")\n rfClasses1 <- predict( fit.rf, newdata = irisTest)\n rfConfusion=confusionMatrix(data = rfClasses1, irisTest$subtype)\n \n \n rf.ROC <- roc(predictor=as.numeric(unlist(rfClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n \n \n \n \n performance_testing[1,4]=as.numeric(rf.ROC$auc)#AUC\n performance_testing[2,4]=rfConfusion$byClass[1]#SENS\n performance_testing[3,4]=rfConfusion$byClass[2]#SPEC\n performance_testing[4,4]=rfConfusion$overall[1]#accuracy\n performance_testing[5,4]=rfConfusion$byClass[5]#precision\n performance_testing[6,4]=rfConfusion$byClass[6]#recall = sens\n performance_testing[7,4]=rfConfusion$byClass[7]#F1\n performance_testing[8,4]=rfConfusion$byClass[11]#BALANCED ACCURACY\n \n #5- GBM ALGORITHM \n set.seed(7)\n garbage <- suppressWarnings(capture.output(fit.gbm <- train(subtype~., data=irisTrain, \n method=\"gbm\", trControl=control,metric=\"ROC\")))\n # fit.gbm <- train(subtype~., data=irisTrain, method=\"gbm\", trControl=control,metric=\"ROC\")\n model[[5]]=fit.gbm\n performance_training[1,5]=max(fit.gbm$results$ROC) #AUC\n performance_training[2,5]=fit.gbm$results$Sens[which.max(fit.gbm$results$ROC)]# sen\n performance_training[3,5]=fit.gbm$results$Spec[which.max(fit.gbm$results$ROC)]# spec\n \n #Model Testing\n gbmClasses <- predict( fit.gbm, newdata = irisTest,type=\"prob\")\n gbmClasses1 <- predict( fit.gbm, newdata = irisTest)\n gbmConfusion=confusionMatrix(data = gbmClasses1, irisTest$subtype)\n \n \n gbm.ROC <- roc(predictor=as.numeric(unlist(gbmClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n \n \n \n \n performance_testing[1,5]=as.numeric(gbm.ROC$auc)#AUC\n performance_testing[2,5]=gbmConfusion$byClass[1]#SENS\n performance_testing[3,5]=gbmConfusion$byClass[2]#SPEC\n performance_testing[4,5]=gbmConfusion$overall[1]#accuracy\n performance_testing[5,5]=gbmConfusion$byClass[5]#precision\n performance_testing[6,5]=gbmConfusion$byClass[6]#recall = sens\n performance_testing[7,5]=gbmConfusion$byClass[7]#F1\n performance_testing[8,5]=gbmConfusion$byClass[11]#BALANCED ACCURACY\n \n #6- PAM ALGORITHM \n set.seed(7)\n garbage <- capture.output(fit.pam <- train(subtype~., data=irisTrain, method=\"pam\", \n trControl=control,metric=\"ROC\"))#plr) #loclda)\n #fit.pam <- train(subtype~., data=irisTrain, method=\"pam\", trControl=control,metric=\"ROC\")#plr\n model[[6]]=fit.pam\n performance_training[1,6]=max(fit.pam$results$ROC) #AUC\n performance_training[2,6]=fit.pam$results$Sens[which.max(fit.pam$results$ROC)]# sen\n performance_training[3,6]=fit.pam$results$Spec[which.max(fit.pam$results$ROC)]# spec\n \n #Model Testing\n pamClasses <- predict( fit.pam, newdata = irisTest,type=\"prob\")\n pamClasses1 <- predict( fit.pam, newdata = irisTest)\n pamConfusion=confusionMatrix(data = pamClasses1, irisTest$subtype)\n \n \n pam.ROC <- roc(predictor=as.numeric(unlist(pamClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n \n \n \n \n performance_testing[1,6]=as.numeric(pam.ROC$auc)#AUC\n performance_testing[2,6]=pamConfusion$byClass[1]#SENS\n performance_testing[3,6]=pamConfusion$byClass[2]#SPEC\n performance_testing[4,6]=pamConfusion$overall[1]#accuracy\n performance_testing[5,6]=pamConfusion$byClass[5]#precision\n performance_testing[6,6]=pamConfusion$byClass[6]#recall = sens\n performance_testing[7,6]=pamConfusion$byClass[7]#F1\n performance_testing[8,6]=pamConfusion$byClass[11]#BALANCED ACCURACY\n \n\n #7- logistic regression\n \n set.seed(7)\n garbage <- suppressWarnings(capture.output(fit.log <- train(subtype~., data=irisTrain, \n method=\"glmnet\", trControl=control,metric=\"ROC\")))\n #fit.log <- train(subtype~., data=irisTrain, method=\"glm\", trControl=control,metric=\"ROC\")#\n model[[7]]=fit.log\n performance_training[1,7]=max(fit.log$results$ROC) #AUC\n performance_training[2,7]=fit.log$results$Sens[which.max(fit.log$results$ROC)]# sen\n performance_training[3,7]=fit.log$results$Spec[which.max(fit.log$results$ROC)]# spec\n \n #Model Testing\n logClasses <- predict( fit.log, newdata = irisTest,type=\"prob\")\n logClasses1 <- predict( fit.log, newdata = irisTest)\n logConfusion=confusionMatrix(data = logClasses1, irisTest$subtype)\n log.ROC <- roc(predictor=as.numeric(unlist(logClasses[1])),response=irisTest$subtype,\n levels=rev(levels(irisTest$subtype)))\n \n performance_testing[1,7]=as.numeric(log.ROC$auc)#AUC\n performance_testing[2,7]=logConfusion$byClass[1]#SENS\n performance_testing[3,7]=logConfusion$byClass[2]#SPEC\n performance_testing[4,7]=logConfusion$overall[1]#accuracy\n performance_testing[5,7]=logConfusion$byClass[5]#precision\n performance_testing[6,7]=logConfusion$byClass[6]#recall = sens\n performance_testing[7,7]=logConfusion$byClass[7]#F1\n performance_testing[8,7]=logConfusion$byClass[11]#BALANCED ACCURACY\n \n# performance_testing_list[[k]]<<- performance_testing\n# performance_training_list[[k]]<<- performance_training\n \n performance_testing_list[[1]] <- performance_testing\n performance_training_list[[1]] <- performance_training\n \n #performance_training=matrix( rep( 0, len=21), nrow = 3) #AUC SENS SPECF\n #performance_testing=matrix( rep( 0, len=56), nrow = 8) # ROC SENS SPEC\n \n\n #####plot the variable importance\n #par(mfrow=c(7,1))\n# plot(plot(varImp(fit.cart, scale = FALSE,top=20),main=\"RPART\"))\n# plot(plot(varImp(fit.lda, scale = FALSE,top=20),main=\"LDA\"))\n# plot(plot(varImp(fit.svm, scale = FALSE,top=20),main=\"SVM\"))\n# plot(plot(varImp(fit.rf, scale = FALSE,top=20),main=\"RF\"))\n# plot(plot(varImp(fit.gbm, scale = FALSE,top=20),main=\"GBM\"))\n# plot(plot(varImp(fit.pam, scale = FALSE,top=20),main=\"PAM\"))\n# plot(plot(varImp(fit.log, scale = FALSE,top=20),main=\"LOG\"))\n \n \n \n #############plot ROC\n smooth_method=\"density\" #\"binormal\" #\"density\" \n#plot(cart.ROC, col=\"red\" )\n#pdf(\"ROC_ER1.pdf\",width=10,height=10)\nplot(smooth(cart.ROC,method=smooth_method),col=\"red\",cex.lab=1.5)\n#plot(cart.ROC,col=\"red\",print.auc=T)\npar(new=TRUE)\n#plot( lda.ROC, col=\"green\" )\n#plot.roc(lda.ROC,col=\"green\",print.auc=T)\n#plot.roc(smooth(lda.ROC,method=\"binormal\"),col=\"green\",print.auc=T)\n#plot.roc(smooth(lda.ROC,method=\"density\"),col=\"green\",print.auc=T)\n#plot.roc(smooth(lda.ROC,method=\"fitdistr\"),col=\"green\",print.auc=T)\n#plot.roc(smooth(lda.ROC,method=\"logcondens\"),col=\"green\",print.auc=T) \nplot(smooth(lda.ROC,method=smooth_method),col=\"green\",cex.lab=1.5)\n#plot(lda.ROC,col=\"green\",print.auc=T)\npar(new=TRUE)\n#plot(svm.ROC, col=\"black\" )\nplot(smooth(svm.ROC,method=smooth_method),col=\"black\",cex.lab=1.5)\n#plot(svm.ROC,col=\"black\",print.auc=T)\npar(new=TRUE)\n#plot(rf.ROC, col=\"orange\" )\nplot(smooth(rf.ROC,method=smooth_method),col=\"orange\",cex.lab=1.5)\n#plot(rf.ROC,col=\"orange\",print.auc=T)\npar(new=TRUE)\n#plot(gbm.ROC, col=\"blue\" )\nplot(smooth(gbm.ROC,method=smooth_method),col=\"blue\",cex.lab=1.5)\n#plot(gbm.ROC,col=\"blue\",print.auc=T)\npar(new=TRUE)\n#plot( pam.ROC, col=\"hotpink\" )\nplot(smooth(pam.ROC,method=smooth_method),col=\"hotpink\",cex.lab=1.5)\n#plot(pam.ROC,col=\"hotpink\",print.auc=T)\npar(new=TRUE)\n#plot(log.ROC, col=\"lightgoldenrod2\", main=\"Testing ROC\" )\nplot(smooth(log.ROC,method=smooth_method),col=\"lightgoldenrod2\",main=\"Testing ROC\",cex.lab=1.5)\n#plot(log.ROC,col=\"lightgoldenrod2\",main=\"Testing ROC\",print.auc=T)\n \nlegend(0.2, 0.4, legend=c('RPART','LDA','SVM','RF','GBM','PAM','LOG'), \n col=c(\"red\", \"green\",\"black\",\"orange\",\"blue\",\"hotpink\",\"lightgoldenrod2\"), lty=1:2, cex=1) \n #dev.off()\n######################performance plotting\n#require(ggplot)\nrequire(reshape2)\nlist_test=performance_testing_list\nlist_train=performance_training_list\n\nAUC_train=lapply(list_train, function(x) x[1,])\nAUC_test=lapply(list_test, function(x) x[1,])\n \nSENS_train=lapply(list_train, function(x) x[2,])\nSENS_test=lapply(list_test, function(x) x[2,]) \n \nSPEC_train=lapply(list_train, function(x) x[3,])\nSPEC_test=lapply(list_test, function(x) x[3,])\n \nF1_test=lapply(list_test, function(x) x[7,])\nBalanced_accuracy_test=lapply(list_test, function(x) x[8,])\n\n \noutput1 <- do.call(rbind,lapply(AUC_train,matrix,ncol=7,byrow=TRUE))\noutput2 <- do.call(rbind,lapply(AUC_test,matrix,ncol=7,byrow=TRUE))\n \noutput3 <- do.call(rbind,lapply(SENS_train,matrix,ncol=7,byrow=TRUE))\noutput4 <- do.call(rbind,lapply(SENS_test,matrix,ncol=7,byrow=TRUE))\n \noutput5 <- do.call(rbind,lapply(SPEC_train,matrix,ncol=7,byrow=TRUE))\noutput6 <- do.call(rbind,lapply(SPEC_test,matrix,ncol=7,byrow=TRUE))\n\noutput7 <- do.call(rbind,lapply(F1_test,matrix,ncol=7,byrow=TRUE))\noutput8 <- do.call(rbind,lapply(Balanced_accuracy_test,matrix,ncol=7,byrow=TRUE))\n \nAUC_train_mean=apply(output1,2,mean)\nAUC_test_mean=apply(output2,2,mean)\nAUC=data.frame(AUC=t(cbind(t(AUC_train_mean),t(AUC_test_mean))))\n\n \nSENS_train_mean=apply(output3,2,mean)\nSENS_test_mean=apply(output4,2,mean)\nSENS=data.frame(SENS=t(cbind(t(SENS_train_mean),t(SENS_test_mean))))\n \nSPEC_train_mean=apply(output5,2,mean)\nSPEC_test_mean=apply(output6,2,mean)\nSPEC=data.frame(SPEC=t(cbind(t(SPEC_train_mean),t(SPEC_test_mean))))\n\nF1_test_mean=apply(output7,2,mean)\nF1=data.frame(F1=t(t(F1_test_mean)))\n \nBalanced_accuracy_test_mean=apply(output8,2,mean)\nBalanced_accuracy=data.frame(Balanced_accuracy=t(t(Balanced_accuracy_test_mean)))\n\n\ntrainingORtesting=t(cbind(t(rep(\"training\",7)),t(rep(\"testing\",7))))\ntesting_only=t(t(rep(\"testing\",7)))\n \nperformance_data=data.frame(AUC=AUC,SENS=SENS,SPEC=SPEC,trainingORtesting, \n Algorithm=(rep(t(c('RPART','LDA','SVM','RF','GBM','PAM','LOG')),2)) )\n \nperformance_data_test=data.frame(AUC=data.frame(AUC=t((t(AUC_test_mean)))),\n SENS=data.frame(SENS=t((t(SENS_test_mean)))),\n SPEC=data.frame(SPEC=t((t(SPEC_test_mean)))),\n F1=F1,\n Balanced_accuracy=Balanced_accuracy\n ,testing_only,Algorithm=(rep(t(c('RPART','LDA','SVM','RF','GBM','PAM','LOG')),1)) )\n\n#print(performance_data_test) \n textLabels = geom_text(\n aes(x=Algorithm, label=round(value,2),fill=variable),\n position = position_dodge(width = 1),\n vjust = -0.5, size = 2 )\n#performance_data \nmelted_performance_data=suppressMessages(melt(performance_data) )\nmelted_performance_data_test=suppressMessages(melt(performance_data_test) )\n#melted_performance_data \n \n \n \n \n \n#pdf(\"pdf1_ER1.pdf\",width=10,height=10)\n \np1=ggplot(data=melted_performance_data[trainingORtesting=='training',], aes(x=Algorithm, y=value,fill=variable)) + \ngeom_bar(stat=\"identity\",position=position_dodge()) +xlab(\"\")+ylab(\"\")+ggtitle(\"Training\")+theme(plot.title = element_text(hjust = 0.5)\n ,axis.text=element_text(size=15,face=\"bold\"),axis.title=element_text(size=14,face=\"bold\"))+labs(fill=\"\")+\n textLabels\nprint(p1)\n \n #dev.off()\n \n #pdf(\"pdf2_ER1.pdf\",width=10,height=10)\np2=ggplot(data=melted_performance_data[trainingORtesting=='testing',], aes(x=Algorithm, y=value,fill=variable)) + \ngeom_bar(stat=\"identity\",position=position_dodge()) +xlab(\"\")+ylab(\"\")+ggtitle(\"Testing\")+theme(plot.title = element_text(hjust = 0.5)\n ,axis.text=element_text(size=15,face=\"bold\"),axis.title=element_text(size=14,face=\"bold\"))+labs(fill=\"\")+\n textLabels\n print(p2)\n # dev.off()\n \n # pdf(\"pdf3_ER1.pdf\",width=10,height=10)\np3=ggplot(data=melted_performance_data_test, aes(x=Algorithm, y=value,fill=variable)) + \ngeom_bar(stat=\"identity\",position=position_dodge()) +xlab(\"\")+ylab(\"\")+ggtitle(\"Testing\")+theme(plot.title = element_text(hjust = 0.5)\n ,axis.text=element_text(size=10,face=\"bold\"),axis.title=element_text(size=14,face=\"bold\"))+labs(fill=\"\")+\n textLabels\n print(p3)\n # dev.off()\n \n #Which algorithm performs better based on the its AUC on testing\n \n res=list()\n res$melted_performance_data= melted_performance_data \n res$models=model\n res$performance=performance_testing\n res$train_inx= trainIndex\n #res$melted_performance_data_test= melted_performance_data_test\n #print the performance metrics for the best algorithms\n best_model=res$models[which.max(res$performance[1,])] # the best model has the high AUC\n method=(unlist(best_model)[[1]])\n \n if (method=='glmnet'){method='log'}\n if (method=='svmRadial'){method='svm'}\n \n#pdf(\"best_model_performance_ER1.pdf\",width=10,height=10)\n dd=filter(melted_performance_data_test,Algorithm==toupper(method))\n p4=ggplot(data=dd, aes(x=Algorithm, y=value,fill=variable)) + \n geom_bar(stat=\"identity\",position=position_dodge()) +xlab(\"\")+ylab(\"\")+ggtitle(\"Testing\")+theme(plot.title = element_text(hjust = 0.5)\n ,axis.text=element_text(size=15,face=\"bold\"),axis.title=element_text(size=14,face=\"bold\"))+labs(fill=\"\")\n print(p4)\n \n#dev.off()\n \n \n \n \n return(res)\n }\n \n \n# -\n\nflat_cor_mat <- function(cor_r, cor_p){\n #This function provides a simple formatting of a correlation matrix\n #into a table with 4 columns containing :\n # Column 1 : row names (variable 1 for the correlation test)\n # Column 2 : column names (variable 2 for the correlation test)\n # Column 3 : the correlation coefficients\n # Column 4 : the p-values of the correlations\n library(tidyr)\n library(tibble)\n cor_r <- rownames_to_column(as.data.frame(cor_r), var = \"row\")\n cor_r <- gather(cor_r, column, cor, -1)\n cor_p <- rownames_to_column(as.data.frame(cor_p), var = \"row\")\n cor_p <- gather(cor_p, column, p, -1)\n cor_p_matrix <- left_join(cor_r, cor_p, by = c(\"row\", \"column\"))\n cor_p_matrix\n}\n\n\n\n# # print session information\n# print session information, so anybody can easily reporuduce ypur results using the same verion of the packags you used\n\nsessionInfo(package = NULL)\n"},"script_size":{"kind":"number","value":95076,"string":"95,076"}}},{"rowIdx":925,"cells":{"path":{"kind":"string","value":"/Python-Addicts/Rearrange_Pos_and_Negatives.ipynb"},"content_id":{"kind":"string","value":"a19de2bdb60364c8f2673f2cfdeea9679d4248fc"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"glryz/Pythoncular"},"repo_url":{"kind":"string","value":"https://github.com/glryz/Pythoncular"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2021-09-04T19:39:55","string":"2021-09-04T19:39:55"},"gha_updated_at":{"kind":"timestamp","value":"2021-09-04T17:51:43","string":"2021-09-04T17:51:43"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":3684,"string":"3,684"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ## Functions and Parameters\n#\n\ndef profileDet(username,followers=1):\n print(\"username: \"+username)\n print(\"followers are \"+str(followers))\n \n\n\nprofileDet(\"Raj\",1000)\n\nprofileDet(\"Arushi\")\n\n\nclass Shark:\n def swim(self):\n print(\"The Shark is swimming\")\n \n def be_awesome(self):\n print(\"shark is being awesome\")\n def shark_dead(self):\n print(\"shark is dead\")\n\n\n\ndoby = Shark()\ndoby.swim()\ndoby.be_awesome()\ndoby.shark_dead()\n\n\nclass Vehicle:\n def __init__(self):\n print(\"vehicle created,constructor is called\")\n def __del__(self):\n print(\"vehicle deleted, destructor is called\")\n \n\n\n# +\ncar = Vehicle()\ndel car\n\n \n# -\n\n\n"},"script_size":{"kind":"number","value":973,"string":"973"}}},{"rowIdx":926,"cells":{"path":{"kind":"string","value":"/main.ipynb"},"content_id":{"kind":"string","value":"e6cb3b3b38789d1c86da893c47add25f8b2b88bc"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"shivamiitgoa/EDA-and-Clurstering-on-Iris-Dataset"},"repo_url":{"kind":"string","value":"https://github.com/shivamiitgoa/EDA-and-Clurstering-on-Iris-Dataset"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1619409,"string":"1,619,409"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + [markdown] _uuid=\"606ced8841f97a693b77254486a15ab5241bf0d3\"\n# # Project 2: Exploratory Data Analysis and Unsupervised Learning\n\n# + _uuid=\"474d4d75dc784dc027a803680ad81fd3cb21dc06\"\n# Ignoring warning\nimport warnings\nwarnings.simplefilter('ignore')\n# Importing useful libraries\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.cluster import AgglomerativeClustering\nfrom mpl_toolkits.mplot3d import Axes3D\n# %matplotlib inline\n# Fixing random state for reproducibility\nnp.random.seed(160010010)\n\n# + [markdown] _uuid=\"3142e3b123c3a46da9ff45581c428c342c701700\"\n# ## Preparing iris data\n\n# + _uuid=\"8b8c0ddb1e6e044616a5be463ddb80183ca7b628\"\niris = datasets.load_iris()\niris_data = pd.DataFrame(iris.data)\niris_data['target'] = pd.Series(iris.target)\niris_data.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species']\ntarget_classes = ['setosa','versicolor','virginica']\nprint(\"The number of observations are :\", iris_data.shape[0])\nprint(\"Name of columns are :\", iris_data.columns.values)\nprint(\"Here are some rows from our final dataframe:\")\nprint(iris_data.head())\n\n# + [markdown] _uuid=\"c5d178251b56bc9c10679353db231c76cc8a425a\"\n# ## Question 1\n\n# + [markdown] _uuid=\"eaa652c736ffb16b291e8ca8c7100576540254b4\"\n# ### 1.1 Perform a visual exploration of the Iris dataset using scatterplots\n\n# + _uuid=\"347a27688282850512c6a360860119eed26e111a\"\n# this formatter will label the colorbar with the correct target names\nformatter = plt.FuncFormatter(lambda i , *args: target_classes[i])\nplt.scatter(x=iris_data.sepal_length,y=iris_data.sepal_width,c=iris_data.species)\nplt.colorbar(ticks=[0,1,2],format=formatter)\nplt.xlabel(\"sepal length (cm)\")\nplt.ylabel(\"sepal width (cm)\")\n\n# + _uuid=\"d21c753ea154ea33d514f19c35c5d5070a9a4888\"\nplt.scatter(x=iris_data.petal_length,y=iris_data.petal_width,c=iris_data.species)\nplt.colorbar(ticks=[0,1,2],format=formatter)\nplt.xlabel(\"petal length (cm)\")\nplt.ylabel(\"petal width (cm)\")\n\n# + [markdown] _uuid=\"7ec00a1c61a6eaad16e2060649233e3907e03a40\"\n# ### 1.2 Use pairplot() for the whole dataset to look at all of our features simultaneously\n\n# + _uuid=\"f35fe20360bec256b22eed1f025438ccf3c6a44c\"\n# Giving each species name in our dataframe\niris_data_with_labels = iris_data.copy(deep=True)\niris_data_with_labels.species = pd.Series([target_classes[x] for x in iris_data.species])\nprint(\"New dataframe :\")\nprint(iris_data_with_labels.head())\n\n# + _uuid=\"ca1a9e56430d6ebe62a38dbbc752b96ff41be922\"\nsns.pairplot(iris_data_with_labels, hue=\"species\") # making matrix plot between each variables and coloring points based on its\n# category\n\n# + [markdown] _uuid=\"8d77acceea35f6f846f9d0e2e3982f78813b2995\"\n# ### Correlation matrix\n\n# + _uuid=\"634e50f724395a0550fbe05036ca6bfec7aba4d0\"\ncorr = iris_data.corr()\nsns.heatmap(corr,annot=True)\n\n# + [markdown] _uuid=\"c43203162005605bf23408d35db48e5a43152d8f\"\n# ### 1.3 Explain what insights you can get from the plots\n# We can observe from our pairplot that if we petal length and petal width of setosa is very different from that of other two species. Petal length and petal width are also very correlated. In the scatter plot of petal length with petal width, we can see that setosa is seperated from other two species and there is a indistinguishable boundary between versicolor and virginica. So if our goal has been to seperate setosa from the two other species, then petal length and petal width will be considered the ideal features.\n#\n# Sepal length and sepal width of all three species are not very seperate. By observing the pairplot, we can see that in almost all scatter plots, setosa is seperate from the other two species. In some scatter plots, versicolor and virginica and seperated by indisguishable boundary and in other plots, they are intermingled.\n\n# + [markdown] _uuid=\"7b48b3ef12df59f8179435d0d7a97bd70e202e28\"\n# ### 1.4 What conclusions could be drawn regarding the correlations among the numerical features in our dataset.\n# We can observe from our correlation matrix that, there is a high correlation between \"sepal length and petal length\", \"sepal length and petal width\", and \"petal length and petal width\". So we can say that sepal lenth, petal length, and petal width are highly correlated among themselves but they are not correlated significantly with the sepal width.\n\n# + [markdown] _uuid=\"42acb35c776c793fc9e41834ebed92f7c0caebae\"\n# ## Question 2\n\n# + [markdown] _uuid=\"c537a23f2cdb2aef41efcea395f1d9b7ddf6960e\"\n# ### 2.1 Visualize the features of Iris images using histograms ,boxplots\n\n# + _uuid=\"1e20735547dfa80625809583c22807b7cbb43a25\"\n# Creating histogram\niris_data_with_labels.hist(bins=10)\n\n# + [markdown] _uuid=\"03e7da0829ccb37e1add35ecda35ed63041e7455\"\n# ### Creating boxplots\n\n# + _uuid=\"a84f114296bb690871d7dba246a945b40be723f0\"\nsns.boxplot(x='species', y='sepal_length', data=iris_data_with_labels, order=[\"virginica\", \"versicolor\", \"setosa\"])\n\n# + _uuid=\"46dbbe2b70e877250365e9d3357f0e13eaf51b4c\"\nsns.boxplot(x='species', y='sepal_width', data=iris_data_with_labels, order=[\"virginica\", \"versicolor\", \"setosa\"])\n\n# + _uuid=\"2a24b597f8b1825711615317373dacb341accbe1\"\nsns.boxplot(x='species', y='petal_length', data=iris_data_with_labels, order=[\"virginica\", \"versicolor\", \"setosa\"])\n\n# + _uuid=\"b8e4bbb6a4aaf62c710a320e54451ec9f17b2e81\"\nsns.boxplot(x='species', y='petal_width', data=iris_data_with_labels, order=[\"virginica\", \"versicolor\", \"setosa\"])\n\n# + [markdown] _uuid=\"97cc8eadffa75cc2d3bd8a3f6f7dee31d385ea05\"\n# ### 2.2 State your inferences about the iris dataset\n# From histograms and boxplots, we can see that petal width and petal length of different species are quite separate. sepal length is also little seperate. But sepal width is not separate for different species. And we can see a trend in petal width, petal length and sepal length of flowers of different species, as we are moving from viginica to setosa, these lengths are decreasing.\n\n# + [markdown] _uuid=\"8ba5f57048a6786d2f92de2aa80beaa7278b08b3\"\n# ## Question 3: Visualizing the dataset using 3D-plots\n\n# + [markdown] _uuid=\"e12cdfa2f4a43c4750f75480d4e814549d8b8a09\"\n# ### 3.1 Analyse the Iris dataset by plotting a 3D view using any three features\n\n# + _uuid=\"3494b08cf8b9ce2aa379bb7e7bd51c98bf21729a\"\n# Since petal width, petal length and sepal length are correlated with the species of flower, we are going\n# to plot these variables on the 3 axes\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data.petal_width, iris_data.petal_length, iris_data.sepal_length, c=iris_data.species)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nplt.show()\n\n# + [markdown] _uuid=\"c62cb435b9d93eaddf08ddfc34763bb2341a8e4d\"\n# ### 3.2 Explain your observations\n# We can see that all the three species are clustered. The cluster of setosa is separate from the other two species. And there is an indistinguishable separation between versicolor and virginica. \n#\n# I have chosen sepal length, petal width and petal length as our three axes because the clusters formed by taking these three features are more saperate than taking any other set of three features.\n\n# + [markdown] _uuid=\"8bf5cf9d414f27b9fc7b2976b254d80b8aa662d0\"\n# ## Question 4: Implement k-means clustering algorithm and test using the Iris dataset\n\n# + _uuid=\"3e720b14f21b47ecd9b723464ede4774ae61ace6\"\nimport random\nimport numpy as np\n\ndef EuclidianDistance(x,y):\n # This function will return Euclidian distance between x and y,\n # where x and y are n-dimensional vector\n xi = np.array(list(x))\n yi = np.array(list(y))\n return np.sqrt(np.sum(np.square(xi - yi)))\n\ndef Calculate_Mean_Square_Error(assignment_of_nodes,current_centers,dataframe):\n # This funciton will calculate the mean square error or J \n # When provided with centers, assignment of nodes and dataframe\n result = 0\n length_of_dataframe = dataframe.shape[0]\n for x in range(length_of_dataframe):\n result += EuclidianDistance(dataframe.loc[dataframe.index[x], :],\n current_centers.loc[current_centers.index[int(assignment_of_nodes[x])], :]) ** 2\n result = result / length_of_dataframe\n return result\n\ndef KmeansCluster(dataframe, number_of_clusters, maximum_number_of_iteration = 100000):\n # This function will assign a group to every data point, and then it will return\n # the assignment, all the assignments that was calculated in each iteration, and \n # a list of value of J in each iteration.\n length_of_dataframe = dataframe.shape[0]\n width_of_dataframe = dataframe.shape[1]\n # choose k random points and make them centers\n random_indices = random.sample(list(range(length_of_dataframe)), number_of_clusters)\n current_centers = dataframe.loc[random_indices, :]\n # assign label to each of the observation points\n all_assignments = []\n mean_square_list = []\n assignment_of_nodes = np.zeros(length_of_dataframe)\n previous_assignment_of_nodes = np.copy(assignment_of_nodes)\n # iterate till maximum number of times or when the assignment of nodes is not changing\n for ix in range(maximum_number_of_iteration):\n # assign group to every data point \n for i in range(length_of_dataframe):\n current_assignment = 0\n for j in range(number_of_clusters):\n current_distance = EuclidianDistance(dataframe.loc[dataframe.index[i], :],\n current_centers.loc[current_centers.index[current_assignment], :])\n new_distance = EuclidianDistance(dataframe.loc[dataframe.index[i], :],\n current_centers.loc[current_centers.index[j], :])\n if new_distance < current_distance:\n current_assignment = j\n assignment_of_nodes[i] = current_assignment\n this_assignment = list(assignment_of_nodes)\n this_assignment = [int(x) for x in this_assignment]\n all_assignments.append(this_assignment)\n mean_square_list.append(Calculate_Mean_Square_Error(assignment_of_nodes,\n current_centers,dataframe))\n if np.sum(previous_assignment_of_nodes == assignment_of_nodes) == dataframe.shape[0]:\n break\n previous_assignment_of_nodes = np.copy(assignment_of_nodes)\n # calculating the center again\n for i in range(number_of_clusters):\n current_centers.loc[current_centers.index[i]] = dataframe.loc[assignment_of_nodes == i, :].mean(0)\n # post-processing results\n assignment_of_nodes = list(assignment_of_nodes)\n assignment_of_nodes = [int(x) for x in assignment_of_nodes]\n return (assignment_of_nodes, all_assignments, mean_square_list)\n\n\n# + _uuid=\"a32b9ebaa7bfb54028757aa597f6d8ab0bc9aed9\"\nclusters, all_assignment, mean_square_list = KmeansCluster(iris_data[list(iris_data.columns[:-1])],3)\n\n# + _uuid=\"97849acb387c0aa6f1cf2203a133c4d861987f21\"\n# Ploting final cluster assignment\nnewframe = iris_data[list(iris_data.columns[:-1])]\nlabel_classes = ['class-0','class-1','class-2']\nnewframe[\"clusters\"] = pd.Series([label_classes[i] for i in clusters])\nsns.pairplot(newframe, hue='clusters')\n\n# + [markdown] _uuid=\"360579080b9fc319503173bba651f477d99dbe9a\"\n# ### 4.1 Perform change of color code for clusters at each iterations\n\n# + _uuid=\"08ec415447757e71e8aac979beefdebd5c29dbe3\"\n# %matplotlib notebook\nimport time\n\n#initialise the graph and settings\nfig = plt.figure()\nax = fig.add_subplot(111)\nplt.ion()\n\nfig.show()\nfig.canvas.draw()\nax = fig.gca(projection='3d')\nnumber_of_iteration_completed = 0\nfor current_cluster in all_assignment:\n ax.clear() # - Clear\n current_frame = iris_data[list(iris_data.columns[:-1])]\n current_frame[\"clusters\"] = pd.Series(current_cluster)\n ax.scatter(current_frame.petal_width, current_frame.petal_length, \n current_frame.sepal_length, c=current_frame.clusters)\n ax.set_xlabel('petal width (cm)')\n ax.set_ylabel('petal length (cm)')\n ax.set_zlabel('sepal length (cm)')\n number_of_iteration_completed += 1\n title = \"After \" + str(number_of_iteration_completed) + \" iterations\"\n ax.set_title(title)\n fig.canvas.draw()\n time.sleep(2)\n# Plotting final assignment\nfinal_frame = iris_data[list(iris_data.columns[:-1])]\nfinal_frame[\"clusters\"] = pd.Series(clusters)\n# %matplotlib inline\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(final_frame.petal_width, final_frame.petal_length, final_frame.sepal_length, c=final_frame.clusters)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title('Final Assignment')\n\n# + [markdown] _uuid=\"0fb0f6d744fbb6ddcb219c7d3b76c867d8c0a58d\"\n# ### 4.2 Compute the sum of squared error (i.e. function J from class notes) for each iteration\n\n# + _uuid=\"ed18ced7d54b363b4f3e5d033e7209e0ab5dc8da\"\nnumber_of_iterations = len(mean_square_list)\nfor i in range(number_of_iterations):\n print(\"Value of J after \" + str(i+1) + \" iterations is \" + str(mean_square_list[i]))\n\n# + [markdown] _uuid=\"9b5c5e886c4790c272f5f9c6d57230c05fa1aa46\"\n# ### 4.3 Visualize the sum of squared error and check for convergence of the k-means algorithm using line plot (error vs. iteration: iteration numbers on x- axis and error values on y-axis)\n\n# + _uuid=\"064949eaf7c321ade4ab863521b278ff5060bcb5\"\n# %matplotlib inline\nplt.plot(list(range(1,len(mean_square_list) + 1)), mean_square_list, 'k')\nplt.xlabel('Number of iterations')\nplt.ylabel('Value of J(error)')\nplt.title('How value of J is changing with the number of iterations.')\n\n# + [markdown] _uuid=\"1c3566e98716459d2aead5313a3ebcce2a4d6750\"\n# ### 4.4 Suggest different ways to choose the number of iterations to get quality clusters\n# * We will stop looping when J is not changing much, like if difference between J of i'th iteration and (i+1)'th iteration is less than 0.001\n# * We will stop looping if centers are not changing.\n# * We will stop looping when assignment of nodes to clusters are not changing.\n# * We can also set a maximum number of iterations combinded with above two methods.\n\n# + [markdown] _uuid=\"f2b248e31db7d96d4daccd32f1b5290acf6f3433\"\n# ## Question 5: Compare the results of both k-means and agglomerative clustering algorithms\n\n# + [markdown] _uuid=\"7a4298faeaaeda8c1ca4db124eed348b28f77448\"\n# ### First we will run both algorithms on out dataset and store the results for further questions\n\n# + _uuid=\"3b16a233086ef62860870f390d47679d65470df2\"\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import AgglomerativeClustering\n\niris_data_without_labels = iris_data[list(iris_data.columns[:-1])]\n\n# Run kmeans algorithm and print the result\nkmeans_model = KMeans(n_clusters=3)\nkmeans_result = kmeans_model.fit(iris_data_without_labels)\nprint(\"Labels assigned to our data by kmeans : \", kmeans_result.labels_)\n\n# Run agglomerative clustering algorithm and print the result\nagglomerative_clustering_model = AgglomerativeClustering(n_clusters=3)\nagglomerative_clustering_result = agglomerative_clustering_model.fit(iris_data_without_labels)\nprint(\"Labels assigned to our data by agglomerative clustering : \", agglomerative_clustering_result.labels_)\n\n# Printing True labels\nprint(\"True labels : \",np.array(iris_data.species))\n\n# + [markdown] _uuid=\"9116115c7445a27d8e22f763efceac05df042f17\"\n# ### 5.1 Compare the performance of k-means and agglomerative clustering methods on the iris dataset. \n\n# + [markdown] _uuid=\"e0fab8b40178544ecaab554516a5d64c87ca4059\"\n# #### Comparing the results of both algorithms with true labels\n\n# + _uuid=\"36ce791301c0e15ecbd75b26f2d4598ca4664760\"\nprint(\"Result of kmeans : \")\nkmeans_unique_class , kmeans_unique_class_counts = np.unique(kmeans_result.labels_, return_counts=True)\nfor x , y in zip(kmeans_unique_class, kmeans_unique_class_counts):\n print(\"The number of observations assigned class\",x,\"is\",y)\n\nprint(\"Result of agglomerative clustering : \")\nagglomerative_clustering_unique_class , agglomerative_clustering_unique_class_counts = np.unique(agglomerative_clustering_result.labels_, return_counts=True)\nfor x , y in zip(agglomerative_clustering_unique_class, agglomerative_clustering_unique_class_counts):\n print(\"The number of observations assigned class\",x,\"is\",y)\n\nprint(\"True labels :\")\ntarget_classes = ['setosa','versicolor','virginica']\ntrue_unique_class , true_unique_class_counts = np.unique(np.array(iris_data.species), return_counts=True)\nfor x , y in zip(true_unique_class, true_unique_class_counts):\n print(\"The number of observations assigned class\",target_classes[x],\"is\",y)\n\n# + [markdown] _uuid=\"07651a6b790c0041b1ca888e3d7da1311b02f3fe\"\n# #### Analysis:\n# We have observed the number to points assigned to each classes by different algorithms and comparing them with true classes. We can see that these numbers are similar in both algorithms but they differ significantly with the actual number of data points in each classes. Though the number of points assigned to each classes are similar, but kmeans are giving comparatively better results than the another one.\n\n# + [markdown] _uuid=\"00490edb70915ca6a0f6344fe463a12e64feac46\"\n# ### 5.2 Compare the two algorithms with respect to the cluster formation; for example, plot the results of the two algorithms using 3-D scatter plots, and explain. \n\n# + _uuid=\"37744aeddf347114ab79c131b090dd14aa96f9e1\"\n# we are taking petal width, petal length and sepal length as our 3 axes\n\n# Creating plot for kmeans\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=pd.Series(kmeans_result.labels_))\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"kmeans\")\nplt.show()\n\n# Creating plot for agglomerative clustering\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=pd.Series(agglomerative_clustering_result.labels_))\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"agglomerative clustering\")\nplt.show()\n\n# Creating plot with true labels\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=iris_data.species)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"true labels\")\nplt.show()\n\n# + [markdown] _uuid=\"59fa83551dcab45c2ed2df307f537b0f0c057e8e\"\n# #### Analysis:\n# We can make following observations from the above 3D plots : \n# * All points belonging to Setosa, are grouped in a single class by both algorithms.\n# * Some points beloging to versicolor, are assigned to the other class by both algorithms.\n# * The results produced by both of the algorithms are very similar.\n\n# + [markdown] _uuid=\"33b140f32036b2d3f86d5b4241fa6ac7c2a48f8c\"\n# ### 5.3 Study the effect of initial configuration for the two algorithms.\n\n# + [markdown] _uuid=\"696b7533494d58c19f576ebe58855edaa5d32585\"\n# #### 5.3.1 Effect of initial configuration for k-means\n\n# + _uuid=\"a9bf601948b7cee082020df4b4c63f9896e5fbda\"\n# Case 1 - All initial points are same\ncase_1_init = np.array([(iris_data_without_labels.loc[0,:]) for x in range(3)])\ncase_1_kmeans_model = KMeans(n_clusters=3,init=case_1_init,n_init=1)\ncase_1_kmeans_model_result = case_1_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_1_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"All initial points are same\")\nplt.show()\n\n# Case 2 - All initial points are in same class\ncase_2_init = np.array([(iris_data_without_labels.loc[x,:]) for x in range(3)])\ncase_2_kmeans_model = KMeans(n_clusters=3,init=case_2_init,n_init=1)\ncase_2_kmeans_model_result = case_2_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_2_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"All initial points are in same class\")\nplt.show()\n\n# Case 3 - With init = 'random'\ncase_3_kmeans_model = KMeans(n_clusters=3,init='random',n_init=1)\ncase_3_kmeans_model_result = case_3_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_3_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"With init = 'random'\")\nplt.show()\n\n# Case 4 - With init = 'k-means++'\ncase_4_kmeans_model = KMeans(n_clusters=3,init='k-means++',n_init=1)\ncase_4_kmeans_model_result = case_4_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_4_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"With init = 'k-means++'\")\nplt.show()\n\n# + [markdown] _uuid=\"558fa59768ee20c393ca90e314a9e6f5b942dc22\"\n# Since there is no difference between the plots with the different initialization, we are going to limit maximun iteration to 5\n\n# + _uuid=\"b97ebd467991392a0f42185ad1f22114331db8df\"\n# Case 1 - All initial points are same\ncase_1_init = np.array([(iris_data_without_labels.loc[0,:]) for x in range(3)])\ncase_1_kmeans_model = KMeans(n_clusters=3,init=case_1_init,n_init=1, max_iter=5)\ncase_1_kmeans_model_result = case_1_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_1_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"All initial points are same\")\nplt.show()\n\n# Case 2 - All initial points are in same class\ncase_2_init = np.array([(iris_data_without_labels.loc[x,:]) for x in range(3)])\ncase_2_kmeans_model = KMeans(n_clusters=3,init=case_2_init,n_init=1, max_iter=5)\ncase_2_kmeans_model_result = case_2_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_2_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"All initial points are in same class\")\nplt.show()\n\n# Case 3 - With init = 'random'\ncase_3_kmeans_model = KMeans(n_clusters=3,init='random',n_init=1, max_iter=5)\ncase_3_kmeans_model_result = case_3_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_3_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"With init = 'random'\")\nplt.show()\n\n# Case 4 - With init = 'k-means++'\ncase_4_kmeans_model = KMeans(n_clusters=3,init='k-means++',n_init=1, max_iter=5)\ncase_4_kmeans_model_result = case_4_kmeans_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_4_kmeans_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"With init = 'k-means++'\")\nplt.show()\n\n# + [markdown] _uuid=\"fc325a1599306aea8fb279caa941d26dd08e5e63\"\n# #### Analysis\n# By limiting the maximum iteration to 5, we can see that if we choose centriods badly then it will take more iterations to make better clusters.\n\n# + [markdown] _uuid=\"7ad949f2294b48b4988493c7ac8cb13090a5f19f\"\n# #### 5.3.2 Study of the effect of initial configurations on agglomerative clustering algorithm\n\n# + _uuid=\"2790e2155e8b9e5fe329fdcf80ab19266454b08f\"\n# Case 1 - Choosing 'ward' linkage\ncase_1_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='ward')\ncase_1_agglomerative_clustering_model_result = case_1_agglomerative_clustering_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_1_agglomerative_clustering_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"Case 1 - Choosing 'ward' linkage\")\nplt.show()\n\n# Case 2 - Choosing 'complete' linkage\ncase_2_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='complete')\ncase_2_agglomerative_clustering_model_result = case_2_agglomerative_clustering_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_2_agglomerative_clustering_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"Case 2 - Choosing 'complete' linkage\")\nplt.show()\n\n# Case 3 - Choosing 'average' linkage\ncase_3_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='average')\ncase_3_agglomerative_clustering_model_result = case_3_agglomerative_clustering_model.fit(iris_data_without_labels)\n# Creating plot\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.scatter(iris_data_without_labels.petal_width, \n iris_data_without_labels.petal_length, \n iris_data_without_labels.sepal_length, \n c=case_3_agglomerative_clustering_model_result.labels_)\nax.set_xlabel('petal width (cm)')\nax.set_ylabel('petal length (cm)')\nax.set_zlabel('sepal length (cm)')\nax.set_title(\"Case 3 - Choosing 'average' linkage\")\nplt.show()\n\n# + [markdown] _uuid=\"4fb05978daf62c3ed4490544ce54c8249fc1741e\"\n# Here we can see there are difference between how running agglomerative clustering with different linkage assigned classes to some points of versicolor and virginica. So to explore further, we will also look on each cases at the number of data points present in each classes.\n\n# + _uuid=\"d7db1536230cb708355d716d53d5d337358bdccc\"\n# Case 1 - Choosing 'ward' linkage\ncase_1_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='ward')\ncase_1_agglomerative_clustering_model_result = case_1_agglomerative_clustering_model.fit(iris_data_without_labels)\nprint(\"Case 1 - Choosing 'ward' linkage\")\ncase_1_agglomerative_clustering_unique_class , case_1_agglomerative_clustering_unique_class_counts = np.unique(case_1_agglomerative_clustering_model_result.labels_, return_counts=True)\nfor x , y in zip(case_1_agglomerative_clustering_unique_class , case_1_agglomerative_clustering_unique_class_counts):\n print(\"The number of observations assigned class\",x,\"is\",y)\n \n\n# Case 2 - Choosing 'complete' linkage\ncase_2_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='complete')\ncase_2_agglomerative_clustering_model_result = case_2_agglomerative_clustering_model.fit(iris_data_without_labels)\nprint(\"Case 2 - Choosing 'complete' linkage\")\ncase_2_agglomerative_clustering_unique_class , case_2_agglomerative_clustering_unique_class_counts = np.unique(case_2_agglomerative_clustering_model_result.labels_, return_counts=True)\nfor x , y in zip(case_2_agglomerative_clustering_unique_class , case_2_agglomerative_clustering_unique_class_counts):\n print(\"The number of observations assigned class\",x,\"is\",y)\n\n# Case 3 - Choosing 'average' linkage\ncase_3_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='average')\ncase_3_agglomerative_clustering_model_result = case_3_agglomerative_clustering_model.fit(iris_data_without_labels)\nprint(\"Case 3 - Choosing 'average' linkage\")\ncase_3_agglomerative_clustering_unique_class , case_3_agglomerative_clustering_unique_class_counts = np.unique(case_3_agglomerative_clustering_model_result.labels_, return_counts=True)\nfor x , y in zip(case_3_agglomerative_clustering_unique_class , case_3_agglomerative_clustering_unique_class_counts):\n print(\"The number of observations assigned class\",x,\"is\",y)\n\n# + [markdown] _uuid=\"3902c2082adb5275cfb7b2abec9e8735dc03b203\"\n# From above results, we can see that it is the 'complete' linkage that is giving us bad results comparitive to other two linkages. And we can see that here the difference is results is significant. So one method to select which linkage to use is apply all three linkages and analyse which linkage is giving us best results.\n\n# + [markdown] _uuid=\"cea1500b15f5d0e3c7f2be1ce151e2315e19a422\"\n# ## Question 6: Selecting k. Come up with an empirical strategy.\n\n# + [markdown] _uuid=\"ffcc3378e9b5cb33d6ed4453656509e5498a9997\"\n# ### 6.1 How do you choose k for the k-means algorithm? \n# * Elbow method : Run k-mean algorithm for with different values of k and then we will choose k till which error value is decreasing sharply.\n# * Run agglomerative clustering algorithm on taking a smaller set of orginal data : Agglomerative clustering algorithms is more expensive in terms of time than k-means algorithm. But it gives us an idea about the number of clusters present in dataset.\n# * While choosing k we will make sure that k << m (where m = number of data points).\n# * Visualizion of data points are used to get an idea about number of clusters.\n# * Use other information about the dataset if it's available. For example suppose you have a data of weights of people and you want to make one cluster for men and one for women. Here you should use k = 2\n# ### 6.2 How do you choose k for the agglomerative clustering algorithm? \n# * Dendrogram fromed from the dataset is used to have an idea about choosing a nice k.\n# * Run agglomerative clustering algorithm for with different values of k and then we will choose k till which error value is decreasing sharply.\n# * While choosing k we will make sure that k << m (where m = number of data points).\n# * Visualizion of data points are used to get an idea about number of clusters.\n# * Use other information about the dataset if it's available. For example suppose you have a data of weights of people and you want to make one cluster for men and one for women. Here you should use k = 2\n# #### There are some methods which are common to all clustering algorithms, so some methods are written for both algorithms.\n\n# + _uuid=\"ad6c5140689af5c8ee8fd89dc034645683d91eff\"\nerror_list_for_different_k = []\nfor number_of_clusters in range(1,11):\n # Run kmeans algorithm and save the error to list\n kmeans_model = KMeans(n_clusters=number_of_clusters)\n kmeans_result = kmeans_model.fit(iris_data_without_labels)\n error_list_for_different_k.append(kmeans_result.inertia_)\nplt.plot(list(range(1,11)), error_list_for_different_k)\nplt.show()\n\n# + [markdown] _uuid=\"8af27f40318c60671c704e92849476e2d97746ba\"\n# #### Elbow method in action\n# From above plot, we can see that the error value is decreasing sharply till k = 3. And we also have context of problem that we want to cluster 3 different species of iris, so we have chosen k = 3\n"},"script_size":{"kind":"number","value":33129,"string":"33,129"}}},{"rowIdx":927,"cells":{"path":{"kind":"string","value":"/week6/.ipynb_checkpoints/data-checkpoint.ipynb"},"content_id":{"kind":"string","value":"e16d72e2ff305e6ba842c6072e7299e03ba37a18"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"zingjanet/code1161base"},"repo_url":{"kind":"string","value":"https://github.com/zingjanet/code1161base"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2017-06-08T22:55:18","string":"2017-06-08T22:55:18"},"gha_updated_at":{"kind":"timestamp","value":"2017-05-05T02:20:19","string":"2017-05-05T02:20:19"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":381422,"string":"381,422"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 2\n# language: python\n# name: python2\n# ---\n\n# + deletable=true editable=true\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport os\n# -\n\n# %matplotlib inline\nplt.rcParams['figure.figsize'] = (20, 10)\n\nsaved_style_state = matplotlib.rcParams.copy()\n\nos.path.isfile(\"Outcomes_Scores.csv\")\nfilepath = \"Outcomes_Scores.csv\"\nbasix_data = pd.read_csv(filepath)\nprint \"loading from file\"\nprint \"done\"\n\nbasix_data.head()\n\nbasix_data.columns\n\nrow_one = basix_data.iloc[1]\nrow_one\n\nbasix_data[\"ENERGY SCORE\"]\n\nbasix_data[\"ENERGY SCORE\"].plot()\n\nbasix_data[\"ENERGY SCORE\"][basix_data[\"ENERGY SCORE\"] < 120].hist()\n\nbasix_data[\"ENERGY SCORE\"][basix_data[\"ENERGY SCORE\"] < 40]\n\nbasix_data['DATA SET'].value_counts().plot(kind=\"bar\")\n\nbasix_data['DATA SET'][basix_data[\"ENERGY SCORE\"] < 40].value_counts().plot(kind=\"bar\")\n\nbasix_data.LGA.value_counts().plot(kind=\"bar\")\n\nbasix_data['LGA'][basix_data[\"ENERGY SCORE\"] < 40].value_counts().plot(kind=\"bar\")\n\nfailed_energy_data = basix_data[\"ENERGY SCORE\"][basix_data[\"ENERGY SCORE\"] < 40]\n\nplt.hist(failed_energy_data, bins=10, facecolor='blue', alpha=0.2)\nplt.hist(failed_energy_data, bins=50, facecolor='green', alpha=1)\nplt.show()\n\n\n"},"script_size":{"kind":"number","value":1436,"string":"1,436"}}},{"rowIdx":928,"cells":{"path":{"kind":"string","value":"/[hw7_2]transfer_learning.ipynb"},"content_id":{"kind":"string","value":"22f5e9c12aaf35f88ddb9a7eb8fa475b73b9dee3"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"dotapetro/MLStuff"},"repo_url":{"kind":"string","value":"https://github.com/dotapetro/MLStuff"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1050168,"string":"1,050,168"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Fitting tranists using MCMC\n#\n# This guide shows how to fit transits using a fairly uncommon technique. Typically this fitting process is done using the enitre light curve (unfolded). In this notebook, I perform folding and binning before fitting the transit. If you are having trouble fitting a transit using the traditional method (no binning and folding), this program can probably help you to obtain good initial estimates on some unknown parameters. \n#\n#\n# Advantages to folding & binning before fitting are:\n# * Less data points (>1000 times faster solving, ~1 min vs >24 hours)\n# * Less free variables to solve for, so better accuracy in results\n#\n# Disadvantages:\n# * No error estimation on t0 or period since its required to be known beforehand during folding\n# * requires knowing an accurate period and t0 beforehand.\n#\n#\n#\n# The parameters we solve for in this notebook are:\n# * radius ratio\n# * limb darkening coefficents (u1, u2)\n# * inclination angle\n# * semi-major axis (not direectly solved, is calculated from the other solved params)\n#\n#\n# Params that need to be known to use this program:\n# * stellar mass\n# * stellar radius\n# * orbital period\n# * t0\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom os.path import join as opj\nimport exoplanet as xo\nimport pymc3 as pm\nfrom copy import deepcopy\nfrom astropy.constants import R_sun, M_sun, R_earth, M_earth\ncdir = os.getcwd()\nos.chdir('/media/rd1/kwillis/class_rv_lc')\nfrom kepler_utils import phase_fold_time, global_view\nos.chdir(cdir)\nimport corner\nimport pymc3_ext as pmx\n\nfrom IPython.core.display import display, HTML\ndisplay(HTML(\"\"))\n\n\n# +\n# %matplotlib notebook\n\n# #%matplotlib inline\n\n# +\ndef bjd2bkjd(bjd):\n \n return bjd - 2454833.0\n\n\ndef bkjd2bjd(bkjd):\n \n return bkjd + 2454833.0\n\n\ndef mask_transits(t0_bkjd, period_day, duration_day, time_bkjd, flux, \n lc=None, return_index=True):\n \n # http://www.raa-journal.org/docs/Supp/ms4253tab1.txt\n \n if lc != None:\n \n time_bkjd = np.array(lc.astropy_time.value)\n\n flux = lc.flux\n \n t0f_bkjd = np.copy(t0_bkjd)\n \n tots_bkjd = get_transit_times(t0f_bkjd, period_day, duration_day, time_bkjd, flux)\n \n # For each tranist find all datapoints within the tranist duration window\n \n ntot_idx = np.ones(len(flux), dtype=bool)\n \n for tot in tots_bkjd:\n \n tot_idx = (time_bkjd > tot - duration_day / 2) & (time_bkjd < tot + duration_day / 2)\n \n ntot_idx = ntot_idx & ~tot_idx\n \n if return_index:\n return ~ntot_idx # Returns where transits occur\n else:\n return time_bkjd[ntot_idx], flux[ntot_idx]\n \n \ndef get_transit_times(t0_bkjd, period_day, duration_day, time_bkjd, flux):\n \n t0f_bkjd = np.copy(t0_bkjd)\n \n while t0f_bkjd > time_bkjd[0]:\n \n t0f_bkjd -= period_day\n \n #t0f_bkjd += period_day\n \n while t0f_bkjd < time_bkjd[0]:\n \n t0f_bkjd += period_day\n \n #t0f_bkjd -= period_day\n \n return np.arange(t0f_bkjd, time_bkjd.max(), step=period_day)\n\n\n# Folding and binning functions\n\ndef signal(time_array, flux, period_id, t0=0.0, num_bins=2000):\n \n # fold and bin transit\n \n time_array = phase_fold_time(time_array, period_id, t0)\n sorted_i = np.argsort(time_array)\n time_array = time_array[sorted_i]\n flux = flux[sorted_i]\n\n global_view0 = global_view(time_array, flux, period_id, num_bins=num_bins)\n \n t = np.linspace(np.min(time_array), np.max(time_array), num_bins)\n\n return t, global_view0\n\n\ndef signal_no_bin(time_array, flux, period_id, t0):\n\n time_array = phase_fold_time(time_array, period_id, t0)\n sorted_i = np.argsort(time_array)\n time_array = time_array[sorted_i]\n flux = flux[sorted_i]\n\n return time_array, flux\n\n\ndef create_mcmc_model(t, f, ferr, pm, ig, enable_lc_log=False, optimize_q=True):\n \n import pymc3 as pm\n\n with pm.Model() as model:\n\n # The baseline flux\n #mean = pm.Normal(\"mean\", mu=ig['cont'], sd=ig['cont_sd'])\n \n t0 = ig['t0']\n \n period = ig['P']\n \n # quadratic limb darkening paramters\n u1 = pm.Uniform('u1', \n lower=ig['u1_lb'],\n upper=ig['u1_ub'],\n shape=1, \n testval=np.array([ig['u1']]))\n \n u2 = pm.Uniform('u2', \n lower=ig['u2_lb'],\n upper=ig['u2_ub'],\n shape=1, \n testval=np.array([ig['u2']]))\n \n u = pm.math.concatenate([u1, u2])\n\n \n # radius ratio rplanet/rstar\n rr = pm.Uniform(\"r_ratio\", lower=ig['r_ratio_lb'], upper=ig['r_ratio_ub'], shape=1, testval=np.array([ig['r_ratio_sv']]))\n \n \n # orbit plane inclinatation\n incl = pm.Uniform(\"incl\", lower=ig['incl_lb'], \n upper=ig['incl_ub'], \n shape=1, \n testval=np.array([ig['incl_sv']]))\n \n \n # Star 1 radius\n R1 = ig['r_star']\n \n \n # planet radius\n R2 = pm.Deterministic(\"R2\", rr * R1)\n \n # Set up a Keplerian orbit for the planets\n orbit = xo.orbits.KeplerianOrbit(period=period, t0=t0, incl=incl, r_star=R1, m_star=ig['m_star'])\n \n pm.Deterministic(\"a\", orbit.a)\n\n # Compute the model light curve using starry\n light_curves = xo.LimbDarkLightCurve(u).get_light_curve(orbit=orbit, r=R2, t=t)\n \n light_curve = pm.math.sum(light_curves, axis=-1) + ig['cont']\n\n # Here we track the value of the model light curve for plotting purposes\n if enable_lc_log:\n pm.Deterministic(\"light_curves\", light_curves)\n\n # In this line, we simulate the dataset that we will fit\n sim = xo.eval_in_model(light_curve)\n\n # The likelihood function assuming known Gaussian uncertainty\n pm.Normal(\"obs\", mu=light_curve, sd=ferr, observed=f)\n\n \n ############################################################################\n # Optimize\n \n map_soln = model.test_point\n \n if optimize_q:\n\n map_soln = pmx.optimize(map_soln, [incl])\n map_soln = pmx.optimize(map_soln, [u1, u2])\n map_soln = pmx.optimize(map_soln, [incl])\n map_soln = pmx.optimize(map_soln)\n\n \n return model, pm, map_soln, sim\n\n\ndef plot_pre_mcmc(t, f, ferr, map_soln):\n \n # for plotting after optimization\n \n per = ig[\"P\"]\n t0 = ig[\"t0\"]\n\n plt.figure(figsize=(7, 5))\n plt.plot(t, f-1, \".k\", ms=4, label=\"data\")\n\n #if len(t0s) == 1:\n for i, l in enumerate(\"a\"):\n plt.plot(t, map_soln[\"light_curves\"][:, i], lw=1, label=\"planet {0}\".format(l))\n plt.xlim(t.min(), t.max())\n plt.ylabel(\"relative flux\")\n plt.xlabel(\"time [days]\")\n plt.legend(fontsize=10)\n plt.title(\"map model\")\n\n\n# -\n\n# # Load saved normalized light curve data\n#\n# You probably wont save data in the same way that I have here, so you will need to edit this cell to load your data properly.\n#\n# Things you should save when you save your data:\n# * time\n# * normalized flux\n# * flux error\n# * t0\n# * orbital period\n# * transit duration\n\n# +\n########### User Params #############\n\ntarget_name = '11904151'\n\n#######################################\n\nlc_data_dir = opj('/media/rd1/kwillis/light_curve_routines/data/norm_lcs', target_name + '_LC_data.npz')\n\nlc_lf = np.load(lc_data_dir)\n\n\ndata = {'lc_flux': lc_lf['flux_norm'], \n 'lc_flux_err': lc_lf['flux_err_norm'],\n 'lc_time': lc_lf['time_norm'],\n }\n\nlen(data['lc_time']), lc_lf['t0_pri_day'], lc_lf['p_day'], lc_lf['d_day']\n# -\n\n# # Fold and bin the light curve using the best t0 and period you found elsewhere\n\n# +\n########### User Params #############\n\nbincnt = 3000 # How many bins in your fold. Note that the output will be smaller than this, since we will crop the fold using the param below\n\nfold_edge_crop_pct = 42 # Percent of datapoints to crop out at the left and right edge. Example: 30% crop with a bincnt of 10 --> [YYYNNNNYYY] --> [NNNN] so, final output LC would hav 4 datapoints\n\n#######################################\n\n\n# Fold then bin\nt_fold, f_fold = signal(np.array(lc_lf['time_norm']), np.array(lc_lf['flux_norm']), lc_lf['p_day'][0], t0=lc_lf['t0_pri_day'][0], num_bins=bincnt)\n\n# Fold error\nt_fold, fe_fold = signal(np.array(lc_lf['time_norm']), np.array(lc_lf['flux_err_norm']), lc_lf['p_day'][0], t0=lc_lf['t0_pri_day'][0], num_bins=bincnt)\n\nl_idx = int(np.ceil(len(t_fold) * fold_edge_crop_pct / 100))\n\ndata = {'lc_flux': f_fold[l_idx:-l_idx], \n 'lc_flux_err': fe_fold[l_idx:-l_idx] / 100,\n 'lc_time': t_fold[l_idx:-l_idx],\n }\n\n\n\nplt.figure(figsize=(13,7))\nplt.plot(data['lc_time'], data['lc_flux'], '.k')\n\n\nlen(data['lc_time']), len(data['lc_flux']), len(data['lc_flux_err'])\n# -\n\n# # If required, do some unit conversions and calulations to derive some fitting parameters\n#\n# Here I needed to convert some radius values and calculate a radius ratio estimate\n#\n# R_sun and R_earth are constants loaded from astropy, in unit meters\n\n# +\nR1_est = 1.48e9 / R_sun.value / 2\nR2_est = 1.47 * (R_earth / R_sun).value\nr_ratio_est = R2_est / R1_est\n\nR1_est, R2_est, r_ratio_est, 1/r_ratio_est\n# -\n\n# # State your intial guesses and solving bounds\n#\n# lb & ub are lower and upper bound. Make sure that your starting value is between these bounds!\n\n# +\n########### User Params #############\n \nig = {'cont': 1.0, # Continuum level\n 't0': 0.0, # t0 [day]\n 'P': 0.837491225, # period [day]\n 'u2': 0.0, # Limb darkening param - edge curvature \n 'u2_lb': -1.0,\n 'u2_ub': 2.0, \n 'u1': 0.0, # Limb darkening param - edge curvature \n 'u1_lb': -1.0,\n 'u1_ub': 2.0,\n 'incl_sv': 89.0 / 180 * np.pi, # orbit inclination [rad]\n 'incl_lb': 80.0 / 180 * np.pi, \n 'incl_ub': 90.0 / 180 * np.pi, \n 'r_ratio_sv': 0.01267, # radius ratio (r_planet / r_star) - depth of transit\n 'r_ratio_lb': 0.000, \n 'r_ratio_ub': 1.000, \n 'r_star': R1_est, # radius of primary star [R_sun]\n 'm_star': 0.92, # mass of primary star [M_sun]\n }\n# -\n\n# # Create orbit model and optimize your parameters\n\n# +\nmodel, pm, map_soln, f_sim = create_mcmc_model(data['lc_time'], data['lc_flux'], data['lc_flux_err'], pm, ig, enable_lc_log=1, optimize_q=1)\n\nplot_pre_mcmc(data['lc_time'], data['lc_flux'], data['lc_flux_err'], map_soln)\n# -\n\n# # Run MCMC on your orbit model\n#\n# Params you can change:\n# * tune: how many samples per chain to get MCMC algo in tune with your data. I set a very high value (3000), but could probably get same results with 300.\n# * draws: how many samples per chain to try. Basically how many fits the algo will attempt. THe more you have, the better. However, too many past a certain point does you no good. Some difficult problems may require 30k draws, while easy ones can be done in <1000. Typically, these fits will need at least 3000 to look good in the corner plot and get good error estimates.\n# * chains: should be at least as big as the number of variables you are solving for. Think of chaings like the number of time syou will try to solve the system with N \"draws\". When all chains, which solve in parallel, are converging in on the same answer, the MCMC algo is happy.\n\n# +\nnp.random.seed(42)\n\nwith model:\n \n trace = pm.sample(tune=200,\n draws=3000,\n start=map_soln,\n chains=6,\n init=\"adapt_full\",\n step=xo.get_dense_nuts_step(target_accept=0.9))\n# -\n\n# # Show a summary of the solved parameters\n\n# +\nmcmc_summary = pm.summary(trace, kind='stats')\n\nmcmc_summary\n\n# +\nflat_samples = np.copy(pm.trace_to_dataframe(trace, varnames=['r_ratio', 'incl', 'u1', 'u2']))\n\nfig = corner.corner(flat_samples, \n labels=mcmc_summary.index.to_numpy(),\n quantiles=[0.16, 0.5, 0.84], \n show_titles=True, \n #fig=fig,\n #title_kwargs={\"fontsize\": fontsize}, \n #label_kwargs={\"fontsize\": fontsize}\n );\n# -\n\n# # Make final plot showing the fit from the optimizer (LSE) and MCMC (MLE)\n\n# +\ntrace_lcs = np.copy(pm.trace_to_dataframe(trace, varnames=['light_curves']))\n\n\nplt.figure(figsize=(13,7))\n\nax = plt.subplot(2, 1, 1)\nplt.plot(data['lc_time'], data['lc_flux'] - 1, '.k')\n\n# plot 2000 random light curves from the MCMC trace. Gives you a good visual of the error in the fit.\nsample_cnt = 2000\nif sample_cnt > trace_lcs.shape[0]:\n sample_cnt = trace_lcs.shape[0] - 10\nfor trace_lc in trace_lcs[np.random.choice(np.arange(trace_lcs.shape[0]), 2000)]:\n \n plt.plot(data['lc_time'], trace_lc - 1 + np.median(data['lc_flux'] - trace_lc), '-r', alpha=0.01, lw=6)\n \n# plot the average light curve fit from MCMC\nmle_lc = np.nanmedian(trace_lcs, axis=0)\nplt.plot(data['lc_time'], mle_lc - 1 + np.median(data['lc_flux'] - mle_lc), '-r', label='MLE Solution', lw=1, alpha=1.0)\nplt.plot(data['lc_time'], np.ravel(map_soln['light_curves']) - 1 + np.median(data['lc_flux'] - np.ravel(map_soln['light_curves'])), '--', c=[0, 1, 0], label='LSE Solution', lw=3, alpha=0.7)\nplt.legend()\nplt.ylim(np.min(data['lc_flux'] - 1), np.max(data['lc_flux'] - 1))\n\nplt.subplot(2, 1, 2, sharex=ax, sharey=ax)\ndf = (data['lc_flux'] - mle_lc) - 1\nplt.title('Residual RMS = ' + str(np.around(np.std(df), 6)))\nplt.plot(data['lc_time'], df, '.k');\n# -\n\n\n"},"script_size":{"kind":"number","value":14377,"string":"14,377"}}},{"rowIdx":929,"cells":{"path":{"kind":"string","value":"/빅데이터를 지탱하는 기술/1-3장 실습/웹 서버의 액세스 로그 예.ipynb"},"content_id":{"kind":"string","value":"06e7e50818aca741a3c4f346a0ea61d7227a4130"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"junhong625/-"},"repo_url":{"kind":"string","value":"https://github.com/junhong625/-"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":2292669,"string":"2,292,669"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n# ## 파일 다운 링크\n# ftp://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html\n#\n# 1. 파일 다운 후 다운된 파일을 열기\n#\n# 2. 빨간 동그라미 쳐진 링크 클릭\n# ![%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-04-19%20%E1%84%8B%E1%85%A9%E1%84%92%E1%85%AE%209.56.04.png](attachment:%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-04-19%20%E1%84%8B%E1%85%A9%E1%84%92%E1%85%AE%209.56.04.png)\n#\n\n# +\n## 'rb'와 str()을 활용한 UnicodeDecodeError 해결코드\n\nimport re\nimport pandas as pd\n\npattern = re.compile('^\\S+ \\S+ \\S+ \\[(.*)\\] \"(.*)\" (\\S+) (\\S+)$')\n\ndef parse_access_log(path):\n for line in open(path,'rb'):\n for m in pattern.finditer(str(line)):\n yield m.groups()\n\ncolumns = ['time', 'request', 'status', 'bytes']\npd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns)\n\n# +\n## cp850으로 encoding하여 해결한 UnicodeDecodeError 해결코드\n\nimport re\nimport pandas as pd\n\npattern = re.compile('^\\S+ \\S+ \\S+ \\[(.*)\\] \"(.*)\" (\\S+) (\\S+)$')\n\ndef parse_access_log(path):\n for line in open(path, encoding='cp850'):\n for m in pattern.finditer(line):\n yield m.groups()\n\ncolumns = ['time', 'request', 'status', 'bytes']\npd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns)\n# -\n\ndf = pd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns)\n\ndf.time = pd.to_datetime(df.time, format='%d/%b/%Y:%X', exact=False)\n\ndf.head(2)\n\ndf.to_csv('access_log.csv', index=False)\n\n# !head -3 access_log.csv\n\n\n"},"script_size":{"kind":"number","value":1790,"string":"1,790"}}},{"rowIdx":930,"cells":{"path":{"kind":"string","value":"/4_FE_RentListingInqueries.ipynb"},"content_id":{"kind":"string","value":"5e5538e802b17a97b0ad9a822a5171a2ee4bca1d"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"SnailDove/snaildove.github.io.jupyter-notebook"},"repo_url":{"kind":"string","value":"https://github.com/SnailDove/snaildove.github.io.jupyter-notebook"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1868379,"string":"1,868,379"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport pandas as pd\n\n\nfile_one = \"../db/northamerica_station_information.csv\"\n\nfile_two = \"../db/city_gasday_fcst.csv\"\n\nfile_one_df = pd.read_csv(file_one)\n\nfile_one_df.head()\n\nfile_one_df.rename(columns = {\"identifier\": \"Station\"}, inplace = True)\n\nfile_one_df.head()\n\nfile_two_df = pd.read_csv(file_two, encoding=\"ISO-8859-1\")\n\nfile_two_df.head()\n\nmerge_table = pd.merge(file_one_df, file_two_df, on=\"Station\", how=\"right\")\nmerge_table.head()\n\ndel merge_table[\"HDD\"]\n\ndel merge_table[\"CDD\"]\n\nmerge_table.head()\n\nnew_df = merge_table.loc[merge_table[\"country\"] == \"US\",:]\n\nnew_df.head()\n\nnew_df1 = new_df.rename(columns = {\"Station\":\"station_id\",\"name\":\"station_name\",\"Production Date\":\"production_date\",\"Date\":\"forecast_date\",\n \"Fcst Mn\":\"fcst_mn\",\"Fcst Mx\":\"fcst_mx\",\"Fcst Avg\":\"fcst_avg\",\"Norm Mn\":\"norm_mn\",\"Norm Max\":\"norm_mx\"\n })\n\nnew_df1.reset_index().rename(columns = {new_df1.index.name: \"id\"})\n\n# +\n# new_df1['id'] = range(1, len(new_df1) + 1)\n# -\n\nnew_df1['id'] = new_df1.index\n\nnew_df1.set_index('id')\n\nnew_df1.loc()\n\nnew_df1.head()\n\nnew_df1 = new_df1.reset_index()\n\nnew_df1.head()\n\ndel new_df1[\"index\"]\n\ndel new_df1[\"id\"]\n\nnew_df1.head()\n\nday_df = new_df1.loc[new_df1[\"forecast_date\"] == \"12/6/2018\",:]\n\nday_df\n\nday_df[\"station_id\"].nunique()\n\nday_df = day_df.reset_index()\n\nday_df.head()\n\ndel day_df[\"index\"]\n\nday_df.head()\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String, Float \nfrom sqlalchemy.ext.automap import automap_base\nimport pymysql\npymysql.install_as_MySQLdb()\n\n# +\nBase = declarative_base()\n\nclass Data(Base):\n __tablename__ = 'data'\n id = Column(Integer, primary_key=True)\n station_id = Column(String(255))\n lat = Column(Integer)\n lon = Column(Integer)\n station_name = Column(String(255))\n state = Column(String(255))\n country = Column(String(255))\n production_date = Column(Integer)\n forecast_date = Column(Integer)\n #fcst_mn = Column(Integer)\n #fcst_mx = Column(Integer)\n fcst_avg = Column(Integer)\n norm_mn = Column(Integer)\n norm_mx = Column(Integer)\n\n\n# -\n\nengine = create_engine(\"sqlite:///data.sqlite\")\nconn = engine.connect()\n\nBase.metadata.create_all(engine)\n\nfrom sqlalchemy.orm import Session\nsession = Session(bind=engine)\n\nfor i in range(len(day_df)):\n data = Data(station_id=day_df[\"station_id\"][i],\n lat=day_df[\"lat\"][i],\n lon=day_df[\"lon\"][i],\n station_name=day_df[\"station_name\"][i],\n state=day_df[\"state\"][i],\n country=day_df[\"country\"][i],\n production_date=day_df[\"production_date\"][i],\n forecast_date=day_df[\"forecast_date\"][i],\n #fcst_mn=new_df1[\"fcst_mn\"][i],\n #fcst_mx=new_df1[\"fcst_mx\"][i],\n fcst_avg=day_df[\"fcst_avg\"][i],\n norm_mn=day_df[\"norm_mn\"][i],\n norm_mx=day_df[\"norm_mx\"][i])\n session.add(data)\n session.commit()\n\nqaz = engine.execute(\"SELECT * FROM data\")\n\nfor record in qaz:\n print(record)\n\ntype(day_df[\"fcst_mn\"][1])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# +\n# from sqlalchemy import create_engine\n# import pymysql\n# pymysql.install_as_MySQLdb()\n\n# +\n# engine = create_engine(\"mysql://root:@localhost/Project2\")\n\n# +\n# conn = engine.connect()\n\n# +\n# new_df.to_sql(name='forecast', con=engine, if_exists='append', index=False)\n\n# +\n# data = pd.read_sql(\"SELECT * FROM forecast\", conn)\n\n# +\n# data.head()\n\n# +\n# engine.table_names()\n\n# +\nfrom sqlalchemy import create_engine\n\n# Imports the methods needed to abstract classes into tables\nfrom sqlalchemy.ext.declarative import declarative_base\n\n# Allow us to declare column types\nfrom sqlalchemy import Column, Integer, String, Float \n\n# PyMySQL \nimport pymysql\npymysql.install_as_MySQLdb()\n\n# +\nBase = declarative_base()\n\nclass Forecast(Base):\n __tablename__ = 'forecast'\n station_id = Column(String, primary_key=True)\n \nr(range(train_df.shape[0]), train_df.price.values, color = color[6])\nplt.xlabel('the number of train data', fontsize=12)\nplt.ylabel('price', fontsize=12)\nplt.show()\n\n# 由散点图看出,在这个特征上,有一些离群点,移除掉它们,重新画图。\n\n# +\nulimit = np.percentile(train_df.price.values, 99.5)\n#print(ulimit)\ntrain_df['price'].loc[train_df['price'] > ulimit] = ulimit\n\nplt.figure(figsize=(8,6))\nsns.distplot(train_df.price.values, bins=50, kde=True)\nplt.xlabel('price', fontsize=12)\nplt.show()\n# -\n\n# 这个分布向右倾斜的厉害,我们可以使用 `numpy.log` 函数使其变的近似正态分布。\n\n# +\ntrain_df['price'].loc[train_df['price'] > ulimit] = ulimit\n\nplt.figure(figsize=(8,6))\nsns.distplot(np.log(train_df.price.values), bins=50, kde=True)\nplt.xlabel('price', fontsize=12)\nplt.show()\n# -\n\norder = ['low', 'medium', 'high']\nsns.stripplot(train_df.interest_level, train_df.price.values, jitter=True, order=order)\nplt.title(\"Price VS Interest Level\")\nplt.show()\n\n# low interest的price看起来分布比较均匀,中度(medium)感兴趣的价格分布更窄,high interest level的 price分布最窄,基本分布在 1500~ 8000 之间\n#\n# **violinplot** 提供不同类别条件下特征更多的分部信息\n# 核密度估计(KDE)\n# 三个4分位数(quartile):1/4, 1/2, 3/4\n# 1.5倍四分数间距(nterquartile range, IQR)\n# IQR :第三四分位数和第一分位数的区别(即Q1~Q3的差距),表示变量的分散情况,播放差更稳健的统计量\n\norder = ['low', 'medium', 'high']\nsns.violinplot(x=\"interest_level\", y = 'price', data = train_df, order = order)\nplt.xlabel(\"interest level\", fontsize = 12)\nplt.ylabel('price', fontsize = 12)\nplt.show()\n\n# #### Longitude & Latitude\n# 经度和维度是虽是数值型变量,但其物理含义是房屋的地理位置。\n\n# +\nllimit = np.percentile(train_df.latitude.values, 1)\nulimit = np.percentile(train_df.latitude.values, 99)\ntrain_df['latitude'].loc[train_df['latitude'] < llimit] = llimit\ntrain_df['latitude'].loc[train_df['latitude'] > ulimit] = ulimit\n\nplt.figure(figsize=(8,6))\nsns.distplot(train_df.latitude.values, bins=50, kde=True)\nplt.xlabel('latitude', fontsize=12)\nplt.show()\n# -\n\n# 大部分地方纬度都在40.60~40.90之间\n\n# +\nllimit = np.percentile(train_df.longitude.values, 1)\nulimit = np.percentile(train_df.longitude.values, 99)\ntrain_df['longitude'].loc[train_df['longitude'] < llimit] = llimit\ntrain_df['longitude'].loc[train_df['longitude'] > ulimit] = ulimit\n\nplt.figure(figsize=(8, 6))\nsns.distplot(train_df.longitude.values, bins=50, kde=True)\nplt.xlabel('longitude', fontsize=12)\nplt.show()\n# -\n\n# 地方经度都分布在-73.850~74.025之间,因此这个数据是跟纽约城相关的\n\n# +\nfrom mpl_toolkits.basemap import Basemap\nfrom matplotlib import cm\n\nwest, south, east, north = -74.025, 40.60, -73.850, 40.86\n\nfig = plt.figure(figsize=(18,15))\nax = fig.add_subplot(111)\nm = Basemap(projection='merc', llcrnrlat=south, urcrnrlat=north,\n llcrnrlon=west, urcrnrlon=east, lat_ts=south, resolution='i')\nx, y = m(train_df['longitude'].values, train_df['latitude'].values)\nm.hexbin(x, y, gridsize=400,\n bins='log', cmap=cm.YlOrRd_r);\n# -\n\nsns.lmplot(x = \"longitude\" , y = \"latitude\" , fit_reg = False , hue = 'interest_level',\n hue_order = ['low', 'medium', 'high'] , size = 9, scatter_kws = {'alpha':0.4,'s':30},\n data = train_df[(train_df.longitude > train_df.longitude.quantile(0.005))\n &(train_df.longitude < train_df.longitude.quantile(0.995))\n &(train_df.latitude > train_df.latitude.quantile(0.005)) \n &(train_df.latitude < train_df.latitude.quantile(0.995))]\n )\nplt.xlabel('Longitude')\nplt.ylabel('Latitude')\n\n# 上述显示去掉了经度和纬度偏大或偏小的数据点。可以看出higt interet的房屋在一小段很集中。\n#\n# 还有一种作图,我就不列出来了,需要安装工具包:\n#\n# ```python\n# import gpxpy as gpx import gpxpy.gpx\n#\n# gpx = gpxpy.gpx.GPX()\n#\n# for index, row in train.iterrows():\n#\n# #print (row['latitude'], row['longitude'])\n#\n# if row['interest_level'] == 'high': #opting for all nominals results in poor performance of Google Earth gps_waypoint = gpxpy.gpx.GPXWaypoint(row['latitude'],row['longitude'],elevation=10) gpx.waypoints.append(gps_waypoint)\n#\n# filename = \"GoogleEarth.gpx\" FILE = open(filename,\"w\") FILE.writelines(gpx.to_xml()) FILE.close()\n# ```\n\n# ### 类别型特征\n\n# #### display_address\n\n# +\ncnt_srs = train_df.groupby('display_address')['display_address'].count()\n\nfor i in [2, 10, 50, 100, 500]:\n print('Display_address that appear less than {} times: {}%'.format(i, round((cnt_srs < i).mean() * 100, 2)))\n\nplt.figure(figsize=(12, 6))\nplt.hist(cnt_srs.values, bins=100, log=True, alpha=0.9)\nplt.xlabel('Number of times display_address appeared', fontsize=12)\nplt.ylabel('log(Count)', fontsize=12)\nplt.show()\n# -\n\n# 大部分display_address出现次数都少于100次,没有display_address出现次数超过500次的\n#\n# 让我们看看前10个display_address:\n\n# +\ntop10_addr = train_df.display_address.value_counts().nlargest(10).index.tolist()\n\nfig = plt.figure(figsize=(8, 6))\nax = sns.countplot(x=\"display_address\", hue=\"interest_level\", data=train_df[train_df.display_address.isin(top10_addr)])\n\nplt.xlabel('Display_address')\nplt.ylabel('Number of advert occurences')\nplt.tick_params(\n axis='x', #变化应用于x轴\n which='both', # major ticket和minor tickets都会受到影响\n bottom='on', # 打开沿着底端边缘的tickets\n top='off', # 关闭沿着顶端边缘的tickets\n labelbottom='on') # 打开底端的label\n\nplt.xticks(rotation='vertical')\n\n### Adding percentitles over bars \nheight = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches]\nncol= int(len(height) / 3)\ntotal = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)]\nfor i, p in enumerate(ax.patches):\n ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), \n ha = \"center\")\n# -\n\n# #### Created\n\ntrain_df[\"created\"] = pd.to_datetime(train_df[\"created\"])\ntrain_df[\"date_created\"] = train_df[\"created\"].dt.date\ntrain_df[\"year_created\"] = train_df[\"created\"].dt.year\ntrain_df[\"month_created\"] = train_df[\"created\"].dt.month\ntrain_df['hour_created'] = train_df['created'].dt.hour\ntrain_df['weekday_created'] = train_df['created'].dt.weekday\ntrain_df['quarter_created'] = train_df['created'].dt.quarter\ntrain_df['weekend_created'] = ((train_df['weekday_created'] == 5) & (train_df['weekday_created'] == 6))\n\n# +\ncnt_srs = train_df['date_created'].value_counts().sort_index()\n\nplt.figure(figsize=(20,4))\nax = sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\nax.xaxis_date()\nplt.xticks(rotation='vertical')\nplt.show()\n\n# +\ncnt_srs = train_df['date_created'].value_counts()\n\nplt.figure(figsize=(12,4))\nax = plt.subplot(111)\nax.bar(cnt_srs.index, cnt_srs.values, alpha=0.8)\nax.xaxis_date()\nplt.xticks(rotation='vertical')\nplt.show()\n# -\n\n# **注意**:让我们看看测试集是否与训练集在同一个时间段\n\n# +\ntest_df[\"created\"] = pd.to_datetime(test_df[\"created\"])\ntest_df[\"date_created\"] = test_df[\"created\"].dt.date\ncnt_srs = test_df['date_created'].value_counts()\n\nplt.figure(figsize=(12,4))\nax = plt.subplot(111)\nax.bar(cnt_srs.index, cnt_srs.values, alpha=0.8)\nax.xaxis_date()\nplt.xticks(rotation='vertical')\nplt.show()\n# -\n\n# 更细致地来看看是数据在以小时为单位的范围分布情况\n\n# +\ntrain_df[\"hour_created\"] = train_df[\"created\"].dt.hour\ncnt_srs = train_df['hour_created'].value_counts()\n\nplt.figure(figsize=(12,6))\nsns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[2])\nplt.xticks(rotation='vertical')\nplt.show()\n# -\n\n# 数据主要分布在一天中的头几个小时,或许是这时候网络流量比较少,数据更新正在进行。\n\nhourDF = train_df.groupby(['hour_created', 'interest_level'])['hour_created'].count().unstack()\nhourDF[['low', 'medium', 'high']].plot(kind='bar', stacked=True)\n\norder = ['low', 'medium', 'high']\nsns.countplot(x=\"hour_created\", hue=\"interest_level\", data = train_df)\n\nhourDF = train_df.groupby(['month_created', 'interest_level'])['month_created'].count().unstack()\nhourDF[['low', 'medium', 'high']].plot(kind='bar', stacked=True)\n\n# #### building_id\n\n# +\ntop10_building_id = train_df.building_id.value_counts().nlargest(10).index.tolist()\n\nfig = plt.figure(figsize=(8, 6))\nax = sns.countplot(x=\"building_id\", hue=\"interest_level\", data=train_df[train_df.building_id.isin(top10_building_id)])\n\nplt.xlabel('building_id')\nplt.ylabel('Number of advert occurences')\nplt.tick_params(\n axis='x', #变化应用于x轴\n which='both', # major ticket和minor tickets都会受到影响\n bottom='on', # 打开沿着底端边缘的tickets\n top='off', # 关闭沿着顶端边缘的tickets\n labelbottom='on') # 打开底端的label\n\nplt.xticks(rotation='vertical')\n\n### Adding percentitles over bars \nheight = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches]\nncol= int(len(height) / 3)\ntotal = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)]\nfor i, p in enumerate(ax.patches):\n ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), \n ha = \"center\")\n# -\n\n# #### manager_id\n\n# +\ntop10_managers = train_df.manager_id.value_counts().nlargest(10).index.tolist()\n\nfig = plt.figure(figsize=(8, 6))\nax = sns.countplot(x=\"manager_id\", hue=\"interest_level\", data=train_df[train_df.manager_id.isin(top10_managers)])\n\nplt.xlabel('manager_id')\nplt.ylabel('Number of advert occurences')\nplt.tick_params(\n axis='x', #变化应用于x轴\n which='both', # major ticket和minor tickets都会受到影响\n bottom='on', # 打开沿着底端边缘的tickets\n top='off', # 关闭沿着顶端边缘的tickets\n labelbottom='on') # 打开底端的label\n\nplt.xticks(rotation='vertical')\n\n### Adding percentitles over bars \nheight = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches]\nncol= int(len(height) / 3)\ntotal = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)]\nfor i, p in enumerate(ax.patches):\n ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), \n ha = \"center\")\n# -\n\n# #### listing_id\n\nsns.distplot(train_df.listing_id.values, bins = 50, kde = True)\nplt.xlabel('listing_id')\nplt.show()\n\norder = ['low', 'medium', 'high']\nsns.stripplot(train_df.interest_level, train_df.listing_id, jitter=True, order=order)\nplt.title(\"Listing_ID VS Interest Level\")\nplt.show()\n\norder = ['low', 'medium', 'high']\nsns.violinplot(x=\"interest_level\", y = 'listing_id', data = train_df, order = order)\nplt.xlabel(\"Interest Level\", fontsize = 12)\nplt.ylabel('Listing_ID', fontsize = 12)\nplt.show()\n\n# #### Number of Photos\n#\n# 图片数据非常大,我们首先来看一下数量特征\n\n# +\ntrain_df[\"num_photos\"] = train_df[\"photos\"].apply(len)\ncnt_srs = train_df['num_photos'].value_counts()\n\nplt.figure(figsize=(12,6))\nsns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\nplt.xlabel('Number of Photos', fontsize=12)\nplt.ylabel('Number of Occurrences', fontsize=12)\nplt.show()\n# -\n\nulimit = np.percentile(train_df['num_photos'], 99)\ntrain_df['num_photos'].loc[train_df['num_photos'] > ulimit] = ulimit\nplt.figure(figsize=(12,6))\nsns.violinplot(x=\"num_photos\", y=\"interest_level\", data=train_df, order =['low','medium','high'])\nplt.xlabel('Number of Photos', fontsize=12)\nplt.ylabel('Interest Level', fontsize=12)\nplt.show()\n\n# #### Number of features\n# 看看特征的数量和它的分布\n\n# +\ntrain_df[\"num_features\"] = train_df[\"features\"].apply(len)\ncnt_srs = train_df['num_features'].value_counts()\n\nplt.figure(figsize=(12,6))\nsns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\nplt.ylabel('Number of Occurrences', fontsize=12)\nplt.xlabel('Number of features', fontsize=12)\nplt.show()\n# -\n\nulimit = np.percentile(train_df['num_features'], 99)\ntrain_df['num_features'].loc[train_df['num_features'] > ulimit] = ulimit\nplt.figure(figsize=(12,10))\nsns.violinplot(y=\"num_features\", x=\"interest_level\", data=train_df, order =['low','medium','high'])\nplt.xlabel('Interest Level', fontsize=12)\nplt.ylabel('Number of features', fontsize=12)\nplt.show()\n\n# #### description words counts \n\n# +\ntrain_df['num_description_words'] = train_df['description'].apply(lambda x: len(x.strip().split(\" \")))\ntrain_df['len_description'] = train_df['description'].apply(len)\n\n#print(train_df['len_description'].head(10))\n#print(train_df['num_description_words'].head(10))\n#print(train_df['description'].iloc[0])\n#print(len(train_df['description'].iloc[0]))\n\n# +\nfig = plt.figure()\norder = ['low', 'medium', 'high']\n\nsns.stripplot(train_df['interest_level'], train_df['len_description'], jitter = True, order = order)\nplt.title('Length of description VS Interest_level')\nplt.show()\n# -\n\nplt.figure()\nsns.violinplot(x=\"len_description\", y=\"interest_level\", data = train_df, order = order)\nplt.xlabel('Length of description')\nplt.ylabel('Interest Level')\nplt.show()\n\nplt.figure(figsize=(400,10))\nax = sns.countplot(train_df.len_description)\nplt.xticks(rotation='vertical')\nplt.xlabel('Length of description')\nplt.ylabel('Number of occurrences')\nplt.show()\n\nplt.figure(figsize=(8,5))\norder = ['low', 'medium', 'high']\nsns.stripplot(train_df['interest_level'], train_df['num_description_words'], jitter = True, order = order)\nplt.title('Num description words VS Interest_level')\n#plt.xticks(rotation='vertical')\nplt.xlabel('Number of words of description')\nplt.ylabel('Number of occurrences')\nplt.show()\n\nplt.figure()\nsns.violinplot(x=\"num_description_words\", y=\"interest_level\", data = train_df, order = order)\nplt.xlabel('Number of words of description')\nplt.ylabel('Interest Level')\nplt.show()\n\n# +\nplt.figure(figsize=(50,10))\nulimit = np.percentile(train_df.num_description_words.values, 99)\nllimit = np.percentile(train_df.num_description_words.values, 1)\ntrain_df.num_description_words.loc[train_df.num_description_words > ulimit] = ulimit\ntrain_df.num_description_words.loc[train_df.num_description_words < llimit] = llimit\n\nax = sns.countplot(train_df.num_description_words)\nplt.xticks(rotation='vertical')\nplt.xlabel('Number of words of description')\nplt.ylabel('Number of occurrences')\nplt.show()\n# -\n\n# ### 词云(display_address, street_address, features)\n\n# +\nfrom wordcloud import WordCloud\n\ntext = ''\ntext_da = ''\ntext_street = ''\n\n#i = 0;\nfor ind, row in train_df.iterrows():\n #if(0 == i % 1000):\n # print(i)\n \n for feature in row['features']:\n text = \" \".join([text, \"_\".join(feature.strip().split(\" \"))])\n text_da = \" \".join([text_da, \"_\".join(row['display_address'].strip().split(\" \"))])\n text_street = \" \".join([text_street, \"_\".join(row['street_address'].strip().split(\" \"))])\n i = i + 1;\n \ntext = text.strip()\ntext_da = text_da.strip()\ntext_street = text_street.strip()\n# -\n\nplt.figure(figsize=(12, 6))\nwordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40)\nwordcloud.generate(text)\nwordcloud.recolor(random_state=0)\nplt.imshow(wordcloud)\nplt.title('WordCloud for fatures', fontsize=30)\nplt.axis('off')\nplt.show()\n\n# 允许养猫和允许养狗,其实可以合并成允许养宠物!\n\nplt.figure()\nwordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40)\nwordcloud.generate(text_da)\nwordcloud.recolor(random_state=0)\nplt.imshow(wordcloud)\nplt.title(\"WordCloud for Display Address\", fontsize=30)\nplt.axis(\"off\")\nplt.show()\n\n# 都是纽约比较繁华的街道!\n\n# wordcloud for street address\nplt.figure()\nwordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40)\nwordcloud.generate(text_street)\nwordcloud.recolor(random_state=0)\nplt.imshow(wordcloud)\nplt.title(\"Wordcloud for street address\", fontsize=30)\nplt.axis(\"off\")\nplt.show()\n\n# ### 特征之间的相关性\n\n# +\ncontFeaturelist = []\ncontFeaturelist.append('bathrooms')\ncontFeaturelist.append('bedrooms')\ncontFeaturelist.append('price')\n\nprint(contFeaturelist)\n\n# +\ncorrelationMatrix = train_df[contFeaturelist].corr().abs()\nplt.subplots()\nsns.heatmap(correlationMatrix, annot=True)\n\n#Mask unimportant features\nsns.heatmap(correlationMatrix, mask=correlationMatrix < 1, cbar = False)\nplt.show()\n# -\n\n# ## 案例分析\n#\n# ### Rent Listing Inqueries 数据集的特征工程\n#\n# #### 导入相应的包\n#\n# import必要的工具包,用于文件的读取和特征编码\n\n# +\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom scipy import sparse\n\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom sklearn.cluster import KMeans\nfrom nltk.metrics import distance as distance\n\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom MeanEncoder import MeanEncoder\n# -\n\n# #### 读取数据\n\ndpath = './'\ntrain = pd.read_json(dpath + 'train.json')\ntest = pd.read_json(dpath + 'test.json')\ntrain.head().T\n\n# #### 标签interest_level\n#\n# **从类别型的标签interest_level编码为数字**\n#\n# 从前面的分析和常识来看,listing_id对确定interest_level没有用,去掉特征编码对训练集和测试集都要做,所以干脆将二者连起来一起处理\n\n# +\ny_map = {'low' : 2 , 'medium' : 1 , 'high' : 0}\ntrain['interest_level'] = train['interest_level'].apply(lambda x: y_map[x])\n \n#y_train = train.interest_level.values\ny_train = train.interest_level\ntrain = train.drop(['listing_id' , 'interest_level'] , axis = 1)\n \nlisting_id = test.listing_id.values\ntest = test.drop('listing_id' , axis = 1)\n \nntrain = train.shape[0]\n# concat函数是在pandas底下的方法,可以将数据根据不同的轴作简单的融合\ntrain_test = pd.concat((train , test) , axis = 0).reset_index(drop = True)\n# -\n\nprint(y_train.head())\n\n# #### price,bathrooms,bedrooms\n#\n# 数值型特征,+ / - / * / / ,特征的单调变换对XGBoost不必要\n\n# remove some noise\nulimit = np.percentile(train_test.price.values , 99.99)\nprint(ulimit)\n\ntrain_test['price'].loc[train_test['price'] > ulimit] = ulimit\n\n# remove some noise\nulimit = np.percentile(train_test.bathrooms.values , 99.99)\nprint(ulimit)\noutliers = train_test[\"bathrooms\"].loc[train_test[\"bathrooms\"] > ulimit]\nprint(outliers)\n\n#pandas.loc 选取指定列进行操作\n#df.loc[行标签,列标签]df.loc['a':'b']#选取ab两行数据df.loc[:,'one']#选取one列的数据\n#下面的代码是分别将bathrooms列中,值为112,10,20的值分别置换成1.5,1,2\ntrain_test.loc[train_test[\"bathrooms\"] == 112 , \"bathrooms\"] = 1.5\ntrain_test.loc[train_test[\"bathrooms\"] == 10 , \"bathrooms\"] = 1\ntrain_test.loc[train_test[\"bathrooms\"] == 20 , \"bathrooms\"] = 2\n\nulimit = np.percentile(train_test.bathrooms.values , 99.99)\nprint(ulimit)\noutliers = train_test[\"bathrooms\"].loc[train_test[\"bathrooms\"] > ulimit]\nprint(outliers)\n\n# **构造新特征**\n# 1. price_bathrooms:单位bathroom的价格\n# 2. price_bedrooms:单位bedroom的价格\n\ntrain_test['price_bathrooms'] = (train_test[\"price\"]) / (train_test[\"bathrooms\"] + 1.0)\ntrain_test['price_bedrooms'] = (train_test[\"price\"] / (train_test[\"bedrooms\"]) + 1.0)\n\n# **构造新特征**\n# 1. room_diff: bathroom房间数 - bedrooms房间数\n# 2. room_num: bathroom房间数 - bedroom房间数\n\ntrain_test[\"room_diff\"] = train_test[\"bathrooms\"] - train_test[\"bedrooms\"]\ntrain_test[\"room_num\"] = train_test[\"bedrooms\"] + train_test[\"bathrooms\"]\n\nplt.figure(figsize=(8,4))\nsns.countplot(train_test.bathrooms);\nplt.ylabel('Number of Occurrences', fontsize=12)\nplt.xlabel('bathrooms', fontsize=12)\nplt.show()\n\n# #### 创建日期\n\n# +\ntrain_test['Date'] = pd.to_datetime(train_test['created'])\ntrain_test['Year'] = train_test['Date'].dt.year\ntrain_test['Month'] = train_test['Date'].dt.month\ntrain_test['Day'] = train_test['Date'].dt.day\ntrain_test['Wday'] = train_test['Date'].dt.dayofweek\ntrain_test['Yday'] = train_test['Date'].dt.dayofyear\ntrain_test['hour'] = train_test['Date'].dt.hour\n \ntrain_test = train_test.drop(['Date', 'created'], axis=1)\n# -\n\n# #### description\n\n# count of words present in description colum\ntrain_test[\"num_description_words\"] = train_test[\"description\"].apply(lambda x: len(x.split(\" \")))\ntrain_test = train_test.drop(['description'] , axis = 1)\n\n\n# #### manager_id\n#\n# 将manager分为几个等级 top 1%, 2%, 5, 10, 15, 20, 25, 30, 50\n\n# +\ndef getTopXLimit(X, counted_feature_data):\n '''\n X: the top number\n counted_feature_data: counted single feature data\n functionality: return the top Xth limit of relevant feature\n '''\n top_limit = np.percentile(counted_feature_data.values, 100 - X)\n return top_limit\n\ndef getTopX(X, counted_feature_data):\n '''\n X: top number\n feature_data : counted feature data\n '''\n return counted_feature_data[counted_feature_data.values >= getTopXLimit(X, counted_feature_data)]\n\ndef storeTopX(X : int, source : dict, feature_name : str, to_data : dict, to_feature : str, isReturn: bool = False):\n '''\n X: top number\n source: source of data\n feature_name: name of feature\n to_data: ,\n to_feature: name of stored feature\n return: to_data['top_X_' + to_feature]\n '''\n feature_counts = source[feature_name].value_counts();\n to_data[('top_{}_' + to_feature).format(X)] = source[feature_name].apply(lambda x: 1 if x in feature_counts.index.values[feature_counts.values >= getTopXLimit(X, feature_counts)] else 0)\n if isReturn:\n return to_data[('top_{}_' + to_feature).format(X)]\n else:\n return None\n\n\n# +\nprint(getTopX(1, train_test['manager_id'].value_counts()))\n\ntops = [1, 2, 5, 10, 15, 20, 30, 50]\nfor i in tops:\n storeTopX(i,train_test, 'manager_id', train_test, 'manager_id', False)\n print(\"Stored top {} of manager id relevant to the number of rental inqueries\".format(i))\n \nprint(train_test['top_1_manager_id'].head())\n# -\n\n# #### building_id\n#\n# 类似manager_id处理\n\n# +\nprint(getTopX(1, train_test['building_id'].value_counts()))\n\ntops = [1, 2, 5, 10, 15, 20, 30, 50]\nfor i in tops:\n storeTopX(i,train_test, 'building_id', train_test, 'building_id', False)\n print(\"Stored top {} of building id relevant to the number of rental inqueries\".format(i))\n \nprint(train_test.head(10))\n# -\n\n# #### photos\n\n# +\ntrain_test['photos_count'] = train_test['photos'].apply(lambda x: len(x))\ntrain_test.drop(['photos'] , axis = 1 , inplace = True)\n\nprint(train_test['photos_count'].head(10))\n# -\n\n# #### latitude,longtitude\n#\n# 聚类降维编码(#用训练数据训练,对训练数据和测试数据都做变换)到中心的距离(论坛上讨论到曼哈顿中心的距离更好)\n\n# +\n# Clustering\nntrain = train.shape[0]\ntrain_location = train_test.loc[:ntrain - 1, ['latitude', 'longitude']]\ntest_location = train_test.loc[ntrain:, ['latitude', 'longitude']]\n \nkmeans_cluster = KMeans(n_clusters=20)\nres = kmeans_cluster.fit(train_location)\nres = kmeans_cluster.predict( pd.concat((train_location, test_location), axis=0).reset_index(drop=True))\n \ntrain_test['cenroid'] = res\n \n# L1 distance\ncenter = [ train_location['latitude'].mean(), train_location['longitude'].mean()]\ntrain_test['distance'] = abs(train_test['latitude'] - center[0]) + abs(train_test['longitude'] - center[1])\n# -\n\nprint(train_test['distance'].head())\nprint(train_test['cenroid'].head())\n\n# #### display_address\n\ntrain_test['display_address'] = train_test['display_address'].apply(lambda x: x.lower().strip())\nprint(train_test['display_address'].head())\n\n# #### street_address\n\ntrain_test['street_address'] = train_test['street_address'].apply(lambda x: x.lower().strip())\nprint(train_test['street_address'].head())\n\n# #### 类别型特征\n#\n# LableEncode\n\n# +\n#categoricals = [x for x in train_test.columns if train_test[x].dtype == 'object']\ncategoricals = ['building_id', 'manager_id', 'display_address', 'street_address']\nprint(train_test.loc[:5, categoricals])\n\nfor feat in categoricals:\n lbl = LabelEncoder()\n lbl.fit(list(train_test[feat].values))\n train_test[feat] = lbl.transform(list(train_test[feat].values))\n# -\n\ntrain_test.loc[:5, categoricals]\n\n# 定义**高基数类别型特征编码函数** (manager_id, building_id, display_address,street_address ) 对这些特征进行**均值编码**(该特征值在每个类别的概率,即原来的一维特征变成了C-1维特征,C为标签类别数目)\n\nfrom MeanEncoder import MeanEncoder\n\n# +\nme = MeanEncoder(categoricals)\n \n#trian\n#import pdb\n#pdb.set_trace()\ntrain_new = train_test.iloc[:ntrain, :]\ntrain_new_cat = me.fit_transform(train_new, y_train)\n#test\ntest_new = train_test.iloc[ntrain:, :]\ntest_new_cat = me.transform(test_new)\n# -\n\nprint(train_new_cat.head(1).T)\n\n# #### features\n# 描述特征文字长度 特征中单词的词频,相当于以数据集features中出现的词语为字典的one-hot编码(虽然是词频,但在这个任务中每个单词)\n\ntrain_test['features']\n\n# +\ntrain_test['features_count'] = train_test['features'].apply(lambda x: len(x))\ntrain_test['features2'] = train_test['features']\ntrain_test['features2'] = train_test['features2'].apply(lambda x: ' '.join(x))\n \nc_vect = CountVectorizer(stop_words='english', max_features=300, ngram_range=(1, 1))\nc_vect_sparse = c_vect.fit_transform(train_test['features2'])\nc_vect_sparse_cols = c_vect.get_feature_names()\n \ntrain_test.drop(['features', 'features2'], axis=1, inplace=True)\n \n#hstack作为特征处理的最后一部,先将其他所有特征都转换成数值型特征才能处理\ntrain_test_sparse = sparse.hstack([train_test, c_vect_sparse]).tocsr()\n# -\n\ntrain_test['features_count']\n\ntrain_test_sparse\n\nc_vect_sparse_cols\n\n# #### 特征处理结果存为文件\n\n# +\n#存为csv格式方便用excel查看\ntrain_test_new = pd.DataFrame(train_test_sparse.toarray())\nX_train = train_test_new.iloc[:ntrain, :]\nX_test = train_test_new.iloc[ntrain:, :]\n \ntrain_new = pd.concat((X_train, y_train), axis=1).reset_index(drop=True)\ntrain_new.to_csv(dpath + 'RentListingInquries_FE_train.csv', index=False)\nX_test.to_csv(dpath + 'RentListingInquries_FE_test.csv', index=False)\n\n# +\nfrom scipy.io import mmwrite\n \nX_train_sparse = train_test_sparse[:ntrain, :]\nX_test_sparse = train_test_sparse[ntrain:, :]\n \ntrain_sparse = sparse.hstack([X_train_sparse, sparse.csr_matrix(y_train).T]).tocsr()\n \nmmwrite(dpath + 'RentListingInquries_FE_train.txt',train_sparse)\nmmwrite(dpath + 'RentListingInquries_FE_test.txt',X_test_sparse)\n \n#存为libsvm稀疏格式,直接调用XGBoost的话用稀疏格式更高效\n#from sklearn.datasets import dump_svmlight_file\n#dump_svmlight_file(, y_train, dpath + 'RentListingInquries_FE_train.txt',X_train_sparse) \n#dump_svmlight_file(X_test_sparse, dpath + 'RentListingInquries_FE_test.txt')\n\n# +\ntrain_test_new = pd.DataFrame(train_test_sparse.toarray())\nX_train = train_test_new.iloc[:ntrain, :]\nX_test = train_test_new.iloc[ntrain:, :]\n \ntrain_new = pd.concat((X_train, y_train), axis=1)\n"},"script_size":{"kind":"number","value":29261,"string":"29,261"}}},{"rowIdx":931,"cells":{"path":{"kind":"string","value":"/MMP12_experiments.ipynb"},"content_id":{"kind":"string","value":"481ed991b4789c9f8a0ecad8e9abfed06cef7498"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"rnaimehaom/scaffold-constrained-generation"},"repo_url":{"kind":"string","value":"https://github.com/rnaimehaom/scaffold-constrained-generation"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":359445,"string":"359,445"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# IMPORT THE LIBRARIES NEEDED\n\n# !pip install python_speech_features\n\n# +\nfrom python_speech_features import mfcc\nimport scipy.io.wavfile as wav\nimport numpy as np\n\nfrom tempfile import TemporaryFile\nimport os\nimport pickle\nimport random \nimport operator\n# -\n\nimport math\nimport numpy as np\n\n\n# FUNCTION TO PERFORM ACTUAL DISTANCE CALCULATION BETWEEN FEATURE VECTORS\n\ndef distance(instance1 , instance2 , k ):\n distance =0 \n mm1 = instance1[0] \n cm1 = instance1[1]\n mm2 = instance2[0]\n cm2 = instance2[1]\n distance = np.trace(np.dot(np.linalg.inv(cm2), cm1)) \n distance+=(np.dot(np.dot((mm2-mm1).transpose() , np.linalg.inv(cm2)) , mm2-mm1 )) \n distance+= np.log(np.linalg.det(cm2)) - np.log(np.linalg.det(cm1))\n distance-= k\n return distance\n\n\n# DEFINING A FUNCTION THAT WILL EVALUATE THE MODEL\n\ndef getAccuracy(testSet, predictions):\n correct = 0 \n for x in range (len(testSet)):\n if testSet[x][-1]==predictions[x]:\n correct+=1\n return 1.0*correct/len(testSet)\n\n\n# DEFINING A FUNCTION TO GET NEIGHBOURS\n\ndef getNeighbors(trainingSet, instance, k):\n distances = []\n for x in range (len(trainingSet)):\n dist = distance(trainingSet[x], instance, k )+ distance(instance, trainingSet[x], k)\n distances.append((trainingSet[x][2], dist))\n distances.sort(key=operator.itemgetter(1))\n neighbors = []\n for x in range(k):\n neighbors.append(distances[x][0])\n return neighbors\n\n\n# CLASSIFYING / IDENTIFYING THE CLASS OF THE NEIGHBOURS\n\ndef nearestClass(neighbors):\n classVote = {}\n\n for x in range(len(neighbors)):\n response = neighbors[x]\n if response in classVote:\n classVote[response]+=1 \n else:\n classVote[response]=1\n\n sorter = sorted(classVote.items(), key = operator.itemgetter(1), reverse=True)\n return sorter[0][0]\n\n\n\n# DIRECTORY THAT HOLDS THE DATA SET\n\ndirectory = 'C:\\\\Users\\\\Roshini\\\\Desktop\\\\GROUP PROJECT\\\\Data\\\\genres\\\\'\nf= open(\"my.dat\" ,'wb')\ni=0\n\n\n# +\nfor folder in os.listdir(directory):\n i+=1\n if i==11 :\n break \n for file in os.listdir(directory+'/'+folder): \n (rate,sig) = wav.read(directory+folder+'/'+file)\n mfcc_feat = mfcc(sig,rate ,winlen=0.020, appendEnergy = False)\n covariance = np.cov(np.matrix.transpose(mfcc_feat))\n mean_matrix = mfcc_feat.mean(0)\n feature = (mean_matrix , covariance , i)\n pickle.dump(feature , f)\n\nf.close()\n# -\n\n# SPLIT THE DATASET INTO TRAINING AND TESTING SETS RESPECTIVELY\n\n# +\ndataset = []\ndef loadDataset(filename , split , trSet , teSet):\n with open(\"my.dat\" , 'rb') as f:\n while True:\n try:\n dataset.append(pickle.load(f))\n except EOFError:\n f.close()\n break \n\n for x in range(len(dataset)):\n if random.random() 0.999:\n good += 1\n elif has_substruct[i]>0.999:\n substructs += 1\n elif activities[i]>0.999:\n actives += 1\n else:\n failures += 1\n\n return good/total, substructs/total, actives/total, failures/total\n\n\n\n# -\n\ngood_custom, substructs_custom, actives_custom, failures_custom = return_results([\"data/results/Scaffold_constrained_RNN_\" + str(i) + \"/memory\" for i in range(9)]) \n\ngood_bench, substructs_bench, actives_bench, failures_bench = return_results([\"data/results/RNN_\" + str(i) + \"/memory\" for i in range(9)]) \n\n# +\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"pgf\")\nmatplotlib.rcParams.update({\n \"pgf.texsystem\": \"pdflatex\",\n 'font.family': 'serif',\n 'text.usetex': True,\n 'pgf.rcfonts': False,\n})\n\nlabels = ['Active, right scaffold', 'Right scaffold, not active', 'Active, without scaffold', 'Not active, without scaffold']\n\nfont = {'family' : 'normal',\n 'size' : 20}\n\nmatplotlib.rc('font', **font)\nx = np.arange(len(labels)) # the label locations\nwidth = 0.35 \nfig, ax = plt.subplots(figsize=(25,25))\nfig.set_size_inches(w=17, h=7)\n\nrects1 = ax.bar(x - width/2, [good_custom, substructs_custom, actives_custom, failures_custom], width, label='Scaffold-constrained generator')\nrects2 = ax.bar(x + width/2, [good_bench, substructs_bench, actives_bench, failures_bench], width, label='SMILES based RNN')\n#ax.set_xlabel('Criteria met')\n# ax.set_ylabel('% of molecules', font)\nplt.ylabel('Percentage of molecules', fontsize=23)\nax.set_xticks(x)\nax.set_xticklabels(labels)\nax.legend(prop={'size': 20})\nplt.savefig('MMP_12.pgf')\n# -\n\n# # Performance of the classification model on the test set \n\nclf = joblib.load(\"data/MMP12/final_activity_model.pkl\")\n\nwith open('data/MMP12/test_set.smi') as f:\n content = f.readlines()\nsmiles = [x.strip() for x in content] \ntest_fps = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(s), 4) for s in smiles]\n\ndf = pd.read_csv('data/MMP12/mmp12.csv')\nsmiles = df[\"Smiles\"]\nfull_datatest_fps = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(s), 4) for s in smiles]\npIC50 = df[\"pIC50_MMP12\"]\n\ny_test = []\nfor fp in test_fps:\n for i, query_fp in enumerate(full_datatest_fps):\n if DataStructs.TanimotoSimilarity(query_fp, fp)==1:\n try:\n y_test.append(float(pIC50[i]))\n except:\n # Then it's \"inactive\"\n y_test.append(4)\ny_test = np.array(y_test)\n\n# +\ny_pred = clf.predict(np.array(test_fps))\nprint('Mean squared error: %.2f'\n % mean_squared_error(y_test, y_pred))\n# The coefficient of determination: 1 is perfect prediction\nprint('Coefficient of determination: %.2f'\n % r2_score(y_test, y_pred))\n\n# Plot outputs\nplt.scatter(y_test, y_test, color='black')\nplt.scatter(y_test, y_pred, color='blue', linewidth=3)\n# -\n\n\n"},"script_size":{"kind":"number","value":7359,"string":"7,359"}}},{"rowIdx":932,"cells":{"path":{"kind":"string","value":"/Task 2/.ipynb_checkpoints/task 2-checkpoint.ipynb"},"content_id":{"kind":"string","value":"7cae2a2a43fbfc7a9899ba17e95821ce11e122ec"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"krunalkumar-degamdiya/The-Spark-Foundation-Internship"},"repo_url":{"kind":"string","value":"https://github.com/krunalkumar-degamdiya/The-Spark-Foundation-Internship"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":140575,"string":"140,575"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nimport os\nimport geopandas\n\nimport json\nimport topojson\n\nfrom IPython.display import SVG, display\nfrom shapely import geometry\n# %matplotlib inline\n# -\n\n# ### natural earth low resolution\n\ndata = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\ndata.plot()\ndata.head()\n\n# apply topolgy where vertices are snapped to a grid before applying topology (quantization)\ntj_data = topojson.join(data)\n\ntj_data\n\n# write computed topology to file\ntj_path = '../tests/files_topojson/naturalearth_lowres.topojson'\nwith open(tj_path, 'w') as fp:\n json.dump(tj_data, fp)\n\n# compare file sizes of the geojson and topjson file\ngj_path = '../tests/files_geojson/naturalearth_lowres.geojson'\ntj_kb = os.path.getsize(tj_path)/1000\ngj_kb = os.path.getsize(gj_path)/1000\nprint('topojson naturalearth_loweres: {}kb\\ngeojson naturalearth_loweres: {}kb'.format(tj_kb, gj_kb))\n\n# read the saved topojson file into geopandas and see that it works!\ndata_tj = geopandas.read_file(tj_path)\ndata_tj.plot()\ndata_tj.head()\n\n# +\n# for gdf_row in data_tj.iterrows():\n# print(gdf_row[1]['name'])\n# g1_svg = gdf_row[1].geometry._repr_svg_() \n# display(SVG(g1_svg))\n# -\n\n# %%prun -l 10\n# present timing of applying the whole topology\ntj_data = topojson.topology(data, snap_vertices=True, gridsize_to_snap=1e6)\n\n# %%prun -l 10\n# present timing split out in the different subtasks\nex = topojson.extract(data)\njo = topojson.join(ex, quant_factor=1e4)\ncu = topojson.cut(jo)\nde = topojson.dedup(cu)\nha = topojson.hashmap(de)\n\n\n"},"script_size":{"kind":"number","value":1793,"string":"1,793"}}},{"rowIdx":933,"cells":{"path":{"kind":"string","value":"/week4_approx/practice_approx_qlearning.ipynb"},"content_id":{"kind":"string","value":"414fb9f5cd499e63208e4a5037d6cd3ccfc077c8"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"ArezooAalipanah/RL_Examples"},"repo_url":{"kind":"string","value":"https://github.com/ArezooAalipanah/RL_Examples"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":34433,"string":"34,433"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# The following additional libraries are needed to run this\n# notebook. Note that running on Colab is experimental, please report a Github\n# issue if you have any problem.\n\n# !pip install d2l==1.0.0\n# !pip install -U mxnet-cu112==1.9.1\n\n\n# + [markdown] origin_pos=1\n# # Attention Scoring Functions\n# :label:`sec_attention-scoring-functions`\n#\n#\n# In :numref:`sec_attention-pooling`,\n# we used a number of different distance-based kernels, including a Gaussian kernel to model\n# interactions between queries and keys. As it turns out, distance functions are slightly more expensive to compute than dot products. As such, \n# with the softmax operation to ensure nonnegative attention weights,\n# much of the work has gone into *attention scoring functions* $a$ in :eqref:`eq_softmax_attention` and :numref:`fig_attention_output` that are simpler to compute. \n#\n# ![Computing the output of attention pooling as a weighted average of values, where weights are computed with the attention scoring function $\\mathit{a}$ and the softmax operation.](../img/attention-output.svg)\n# :label:`fig_attention_output`\n#\n\n# + origin_pos=2 tab=[\"mxnet\"]\nimport math\nfrom mxnet import np, npx\nfrom mxnet.gluon import nn\nfrom d2l import mxnet as d2l\n\nnpx.set_np()\n\n\n# + [markdown] origin_pos=6\n# ## [**Dot Product Attention**]\n#\n#\n# Let's review the attention function (without exponentiation) from the Gaussian kernel for a moment:\n#\n# $$\n# a(\\mathbf{q}, \\mathbf{k}_i) = -\\frac{1}{2} \\|\\mathbf{q} - \\mathbf{k}_i\\|^2 = \\mathbf{q}^\\top \\mathbf{k}_i -\\frac{1}{2} \\|\\mathbf{k}_i\\|^2 -\\frac{1}{2} \\|\\mathbf{q}\\|^2.\n# $$\n#\n# First, note that the final term depends on $\\mathbf{q}$ only. As such it is identical for all $(\\mathbf{q}, \\mathbf{k}_i)$ pairs. Normalizing the attention weights to $1$, as is done in :eqref:`eq_softmax_attention`, ensures that this term disappears entirely. Second, note that both batch and layer normalization (to be discussed later) lead to activations that have well-bounded, and often constant, norms $\\|\\mathbf{k}_i\\|$. This is the case, for instance, whenever the keys $\\mathbf{k}_i$ were generated by a layer norm. As such, we can drop it from the definition of $a$ without any major change in the outcome. \n#\n# Last, we need to keep the order of magnitude of the arguments in the exponential function under control. Assume that all the elements of the query $\\mathbf{q} \\in \\mathbb{R}^d$ and the key $\\mathbf{k}_i \\in \\mathbb{R}^d$ are independent and identically drawn random variables with zero mean and unit variance. The dot product between both vectors has zero mean and a variance of $d$. To ensure that the variance of the dot product still remains $1$ regardless of vector length, we use the *scaled dot product attention* scoring function. That is, we rescale the dot product by $1/\\sqrt{d}$. We thus arrive at the first commonly used attention function that is used, e.g., in Transformers :cite:`Vaswani.Shazeer.Parmar.ea.2017`:\n#\n# $$ a(\\mathbf{q}, \\mathbf{k}_i) = \\mathbf{q}^\\top \\mathbf{k}_i / \\sqrt{d}.$$\n# :eqlabel:`eq_dot_product_attention`\n#\n# Note that attention weights $\\alpha$ still need normalizing. We can simplify this further via :eqref:`eq_softmax_attention` by using the softmax operation:\n#\n# $$\\alpha(\\mathbf{q}, \\mathbf{k}_i) = \\mathrm{softmax}(a(\\mathbf{q}, \\mathbf{k}_i)) = \\frac{\\exp(\\mathbf{q}^\\top \\mathbf{k}_i / \\sqrt{d})}{\\sum_{j=1} \\exp(\\mathbf{q}^\\top \\mathbf{k}_j / \\sqrt{d})}.$$\n# :eqlabel:`eq_attn-scoring-alpha`\n#\n# As it turns out, all popular attention mechanisms use the softmax, hence we will limit ourselves to that in the remainder of this chapter.\n#\n# ## Convenience Functions\n#\n# We need a few functions to make the attention mechanism efficient to deploy. This includes tools for dealing with strings of variable lengths (common for natural language processing) and tools for efficient evaluation on minibatches (batch matrix multiplication). \n#\n#\n# ### [**Masked Softmax Operation**]\n#\n# One of the most popular applications of the attention mechanism is to sequence models. Hence we need to be able to deal with sequences of different lengths. In some cases, such sequences may end up in the same minibatch, necessitating padding with dummy tokens for shorter sequences (see :numref:`sec_machine_translation` for an example). These special tokens do not carry meaning. For instance, assume that we have the following three sentences:\n#\n# ```\n# Dive into Deep Learning \n# Learn to code \n# Hello world \n# ```\n#\n# Since we do not want blanks in our attention model we simply need to limit $\\sum_{i=1}^n \\alpha(\\mathbf{q}, \\mathbf{k}_i) \\mathbf{v}_i$ to $\\sum_{i=1}^l \\alpha(\\mathbf{q}, \\mathbf{k}_i) \\mathbf{v}_i$ for however long, $l \\leq n$, the actual sentence is. Since it is such a common problem, it has a name: the *masked softmax operation*. \n#\n# Let's implement it. Actually, the implementation cheats ever so slightly by setting the values of $\\mathbf{v}_i$, for $i > l$, to zero. Moreover, it sets the attention weights to a large negative number, such as $-10^{6}$, in order to make their contribution to gradients and values vanish in practice. This is done since linear algebra kernels and operators are heavily optimized for GPUs and it is faster to be slightly wasteful in computation rather than to have code with conditional (if then else) statements.\n#\n\n# + origin_pos=7 tab=[\"mxnet\"]\ndef masked_softmax(X, valid_lens): #@save\n \"\"\"Perform softmax operation by masking elements on the last axis.\"\"\"\n # X: 3D tensor, valid_lens: 1D or 2D tensor\n if valid_lens is None:\n return npx.softmax(X)\n else:\n shape = X.shape\n if valid_lens.ndim == 1:\n valid_lens = valid_lens.repeat(shape[1])\n else:\n valid_lens = valid_lens.reshape(-1)\n # On the last axis, replace masked elements with a very large negative\n # value, whose exponentiation outputs 0\n X = npx.sequence_mask(X.reshape(-1, shape[-1]), valid_lens, True,\n value=-1e6, axis=1)\n return npx.softmax(X).reshape(shape)\n\n\n# + [markdown] origin_pos=11\n# To [**illustrate how this function works**],\n# consider a minibatch of two examples of size $2 \\times 4$,\n# where their valid lengths are $2$ and $3$, respectively. \n# As a result of the masked softmax operation,\n# values beyond the valid lengths for each pair of vectors are all masked as zero.\n#\n\n# + origin_pos=12 tab=[\"mxnet\"]\nmasked_softmax(np.random.uniform(size=(2, 2, 4)), np.array([2, 3]))\n\n# + [markdown] origin_pos=16\n# If we need more fine-grained control to specify the valid length for each of the two vectors of every example, we simply use a two-dimensional tensor of valid lengths. This yields:\n#\n\n# + origin_pos=17 tab=[\"mxnet\"]\nmasked_softmax(np.random.uniform(size=(2, 2, 4)),\n np.array([[1, 3], [2, 4]]))\n\n# + [markdown] origin_pos=21\n# ### Batch Matrix Multiplication\n# :label:`subsec_batch_dot`\n#\n# Another commonly used operation is to multiply batches of matrices by one another. This comes in handy when we have minibatches of queries, keys, and values. More specifically, assume that \n#\n# $$\\mathbf{Q} = [\\mathbf{Q}_1, \\mathbf{Q}_2, \\ldots, \\mathbf{Q}_n] \\in \\mathbb{R}^{n \\times a \\times b}, \\\\\n# \\mathbf{K} = [\\mathbf{K}_1, \\mathbf{K}_2, \\ldots, \\mathbf{K}_n] \\in \\mathbb{R}^{n \\times b \\times c}.\n# $$\n#\n# Then the batch matrix multiplication (BMM) computes the elementwise product\n#\n# $$\\textrm{BMM}(\\mathbf{Q}, \\mathbf{K}) = [\\mathbf{Q}_1 \\mathbf{K}_1, \\mathbf{Q}_2 \\mathbf{K}_2, \\ldots, \\mathbf{Q}_n \\mathbf{K}_n] \\in \\mathbb{R}^{n \\times a \\times c}.$$\n# :eqlabel:`eq_batch-matrix-mul`\n#\n# Let's see this in action in a deep learning framework.\n#\n\n# + origin_pos=22 tab=[\"mxnet\"]\nQ = np.ones((2, 3, 4))\nK = np.ones((2, 4, 6))\nd2l.check_shape(npx.batch_dot(Q, K), (2, 3, 6))\n\n\n# + [markdown] origin_pos=26\n# ## [**Scaled Dot Product Attention**]\n#\n# Let's return to the dot product attention introduced in :eqref:`eq_dot_product_attention`. \n# In general, it requires that both the query and the key\n# have the same vector length, say $d$, even though this can be addressed easily by replacing \n# $\\mathbf{q}^\\top \\mathbf{k}$ with $\\mathbf{q}^\\top \\mathbf{M} \\mathbf{k}$ where $\\mathbf{M}$ is a matrix suitably chosen for translating between both spaces. For now assume that the dimensions match. \n#\n# In practice, we often think of minibatches for efficiency,\n# such as computing attention for $n$ queries and $m$ key-value pairs,\n# where queries and keys are of length $d$\n# and values are of length $v$. The scaled dot product attention \n# of queries $\\mathbf Q\\in\\mathbb R^{n\\times d}$,\n# keys $\\mathbf K\\in\\mathbb R^{m\\times d}$,\n# and values $\\mathbf V\\in\\mathbb R^{m\\times v}$\n# thus can be written as \n#\n# $$ \\mathrm{softmax}\\left(\\frac{\\mathbf Q \\mathbf K^\\top }{\\sqrt{d}}\\right) \\mathbf V \\in \\mathbb{R}^{n\\times v}.$$\n# :eqlabel:`eq_softmax_QK_V`\n#\n# Note that when applying this to a minibatch, we need the batch matrix multiplication introduced in :eqref:`eq_batch-matrix-mul`. In the following implementation of the scaled dot product attention,\n# we use dropout for model regularization.\n#\n\n# + origin_pos=27 tab=[\"mxnet\"]\nclass DotProductAttention(nn.Block): #@save\n \"\"\"Scaled dot product attention.\"\"\"\n def __init__(self, dropout):\n super().__init__()\n self.dropout = nn.Dropout(dropout)\n\n # Shape of queries: (batch_size, no. of queries, d)\n # Shape of keys: (batch_size, no. of key-value pairs, d)\n # Shape of values: (batch_size, no. of key-value pairs, value dimension)\n # Shape of valid_lens: (batch_size,) or (batch_size, no. of queries)\n def forward(self, queries, keys, values, valid_lens=None):\n d = queries.shape[-1]\n # Set transpose_b=True to swap the last two dimensions of keys\n scores = npx.batch_dot(queries, keys, transpose_b=True) / math.sqrt(d)\n self.attention_weights = masked_softmax(scores, valid_lens)\n return npx.batch_dot(self.dropout(self.attention_weights), values)\n\n\n# + [markdown] origin_pos=31\n# To [**illustrate how the `DotProductAttention` class works**],\n# we use the same keys, values, and valid lengths from the earlier toy example for additive attention. For the purpose of our example we assume that we have a minibatch size of $2$, a total of $10$ keys and values, and that the dimensionality of the values is $4$. Lastly, we assume that the valid length per observation is $2$ and $6$ respectively. Given that, we expect the output to be a $2 \\times 1 \\times 4$ tensor, i.e., one row per example of the minibatch.\n#\n\n# + origin_pos=32 tab=[\"mxnet\"]\nqueries = np.random.normal(0, 1, (2, 1, 2))\nkeys = np.random.normal(0, 1, (2, 10, 2))\nvalues = np.random.normal(0, 1, (2, 10, 4))\nvalid_lens = np.array([2, 6])\n\nattention = DotProductAttention(dropout=0.5)\nattention.initialize()\nd2l.check_shape(attention(queries, keys, values, valid_lens), (2, 1, 4))\n\n# + [markdown] origin_pos=36\n# Let's check whether the attention weights actually vanish for anything beyond the second and sixth column respectively (because of setting the valid length to $2$ and $6$).\n#\n\n# + origin_pos=37 tab=[\"mxnet\"]\nd2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n xlabel='Keys', ylabel='Queries')\n\n\n# + [markdown] origin_pos=39\n# ## [**Additive Attention**]\n# :label:`subsec_additive-attention`\n#\n# When queries $\\mathbf{q}$ and keys $\\mathbf{k}$ are vectors of different dimension,\n# we can either use a matrix to address the mismatch via $\\mathbf{q}^\\top \\mathbf{M} \\mathbf{k}$, or we can use additive attention \n# as the scoring function. Another benefit is that, as its name indicates, the attention is additive. This can lead to some minor computational savings. \n# Given a query $\\mathbf{q} \\in \\mathbb{R}^q$\n# and a key $\\mathbf{k} \\in \\mathbb{R}^k$,\n# the *additive attention* scoring function :cite:`Bahdanau.Cho.Bengio.2014` is given by \n#\n# $$a(\\mathbf q, \\mathbf k) = \\mathbf w_v^\\top \\textrm{tanh}(\\mathbf W_q\\mathbf q + \\mathbf W_k \\mathbf k) \\in \\mathbb{R},$$\n# :eqlabel:`eq_additive-attn`\n#\n# where $\\mathbf W_q\\in\\mathbb R^{h\\times q}$, $\\mathbf W_k\\in\\mathbb R^{h\\times k}$, \n# and $\\mathbf w_v\\in\\mathbb R^{h}$ are the learnable parameters. This term is then fed into a softmax to ensure both nonnegativity and normalization. \n# An equivalent interpretation of :eqref:`eq_additive-attn` is that the query and key are concatenated\n# and fed into an MLP with a single hidden layer. \n# Using $\\tanh$ as the activation function and disabling bias terms, \n# we implement additive attention as follows:\n#\n\n# + origin_pos=40 tab=[\"mxnet\"]\nclass AdditiveAttention(nn.Block): #@save\n \"\"\"Additive attention.\"\"\"\n def __init__(self, num_hiddens, dropout, **kwargs):\n super(AdditiveAttention, self).__init__(**kwargs)\n # Use flatten=False to only transform the last axis so that the\n # shapes for the other axes are kept the same\n self.W_k = nn.Dense(num_hiddens, use_bias=False, flatten=False)\n self.W_q = nn.Dense(num_hiddens, use_bias=False, flatten=False)\n self.w_v = nn.Dense(1, use_bias=False, flatten=False)\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, queries, keys, values, valid_lens):\n queries, keys = self.W_q(queries), self.W_k(keys)\n # After dimension expansion, shape of queries: (batch_size, no. of\n # queries, 1, num_hiddens) and shape of keys: (batch_size, 1,\n # no. of key-value pairs, num_hiddens). Sum them up with\n # broadcasting\n features = np.expand_dims(queries, axis=2) + np.expand_dims(\n keys, axis=1)\n features = np.tanh(features)\n # There is only one output of self.w_v, so we remove the last\n # one-dimensional entry from the shape. Shape of scores:\n # (batch_size, no. of queries, no. of key-value pairs)\n scores = np.squeeze(self.w_v(features), axis=-1)\n self.attention_weights = masked_softmax(scores, valid_lens)\n # Shape of values: (batch_size, no. of key-value pairs, value\n # dimension)\n return npx.batch_dot(self.dropout(self.attention_weights), values)\n\n\n# + [markdown] origin_pos=44\n# Let's [**see how `AdditiveAttention` works**]. In our toy example we pick queries, keys and values of size \n# $(2, 1, 20)$, $(2, 10, 2)$ and $(2, 10, 4)$, respectively. This is identical to our choice for `DotProductAttention`, except that now the queries are $20$-dimensional. Likewise, we pick $(2, 6)$ as the valid lengths for the sequences in the minibatch.\n#\n\n# + origin_pos=45 tab=[\"mxnet\"]\nqueries = np.random.normal(0, 1, (2, 1, 20))\n\nattention = AdditiveAttention(num_hiddens=8, dropout=0.1)\nattention.initialize()\nd2l.check_shape(attention(queries, keys, values, valid_lens), (2, 1, 4))\n\n# + [markdown] origin_pos=49\n# When reviewing the attention function we see a behavior that is qualitatively quite similar to that of `DotProductAttention`. That is, only terms within the chosen valid length $(2, 6)$ are nonzero.\n#\n\n# + origin_pos=50 tab=[\"mxnet\"]\nd2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n xlabel='Keys', ylabel='Queries')\n\n# + [markdown] origin_pos=52\n# ## Summary\n#\n# In this section we introduced the two key attention scoring functions: dot product and additive attention. They are effective tools for aggregating across sequences of variable length. In particular, the dot product attention is the mainstay of modern Transformer architectures. When queries and keys are vectors of different lengths,\n# we can use the additive attention scoring function instead. Optimizing these layers is one of the key areas of advance in recent years. For instance, [NVIDIA's Transformer Library](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html) and Megatron :cite:`shoeybi2019megatron` crucially rely on efficient variants of the attention mechanism. We will dive into this in quite a bit more detail as we review Transformers in later sections. \n#\n# ## Exercises\n#\n# 1. Implement distance-based attention by modifying the `DotProductAttention` code. Note that you only need the squared norms of the keys $\\|\\mathbf{k}_i\\|^2$ for an efficient implementation. \n# 1. Modify the dot product attention to allow for queries and keys of different dimensionalities by employing a matrix to adjust dimensions. \n# 1. How does the computational cost scale with the dimensionality of the keys, queries, values, and their number? What about the memory bandwidth requirements?\n#\n\n# + [markdown] origin_pos=53 tab=[\"mxnet\"]\n# [Discussions](https://discuss.d2l.ai/t/346)\n#\n"},"script_size":{"kind":"number","value":16930,"string":"16,930"}}},{"rowIdx":934,"cells":{"path":{"kind":"string","value":"/enelpi/json process/.ipynb_checkpoints/is_valid_json-checkpoint.ipynb"},"content_id":{"kind":"string","value":"b4fa3a9f240379fcf1af8a9938e841381d8f7078"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"aucan/Turkish-Reading-Comprehension-Question-Answering-Dataset"},"repo_url":{"kind":"string","value":"https://github.com/aucan/Turkish-Reading-Comprehension-Question-Answering-Dataset"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":139298,"string":"139,298"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport json\n\nwith open('../../data/final_dataset/final_dev_dataset_v2.json') as f:\n data1 = json.load(f),\n\ndata1\n\ntext = \"İstanbul'un Fethinin hemen ardından II. Mehmed şehrin onarımına başladı. Amacı Doğu Roma’yı yıkmak değil onu Osmanlı yapısı içinde diriltmekti.Kuracağı imparatorluk bir İslâm devleti olmakla birlikte Doğu Roma gibi kozmopolit bir yapıya sahip olacaktı.Fatih, Rum Ortodoks Patrikhanesi, Ermeni Patrikhanesi ve Yahudi hahambaşı bulunmasına izin verdi. 6 Ocak 1454’te Yorgo Skolaris'i yeni Ortodoks patriği olarak atadı.Ayasofya camiye çevrildiğinden Patrikliğe resmî makam yeri olarak Havariyun Kilisesi verildi. Şehirdeki Yahudilerin hahambaşı olarak Moşe Kapsali atadı.1461 yılında ise Bursa Psikoposu Hovakim İstanbul Ermeni Patriği olarak atandı.II. Mehmed Theodosius Forumu’nun olduğu yerde ilk sarayının inşasını başlattı. Daha sonraki yıllarda ise Sarayburnu’nda Topkapı Sarayı’nı inşa ettirdi.\"\n\nlen(text)\n\nwith open('data/1792-1922/test_data.json') as f:\n data2 = json.load(f),\n\ndata2\n\n\n"},"script_size":{"kind":"number","value":1288,"string":"1,288"}}},{"rowIdx":935,"cells":{"path":{"kind":"string","value":"/functions.ipynb"},"content_id":{"kind":"string","value":"a6518d4715b614c85fbf1bb23756da0a10d926d1"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Rachel-Veenstra/AGRON935-Class-Notes"},"repo_url":{"kind":"string","value":"https://github.com/Rachel-Veenstra/AGRON935-Class-Notes"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6401,"string":"6,401"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # FUN functions\n\n# ##### Step 1 - create function\n# ##### Step 2 - call/use function\n\n## Defining a function\ndef squarednum(x):\n a = x**2\n print(a)\n\n\nsquarednum(2)\n\n## calling the function\nc = squarednum(2)\nprint(c)\n\n\n# +\n## WORKSPACE\n## example - leaving and coming back with the answer \n# -\n\ndef numsquared(x):\n a = x**2\n return a ## stops process and stores/shows value\n\n\nnumsquared(2)\n\nfor i in range(0,10):\n print(numsquared(i))\n\n# +\n## FEB 21 - WRITE A FUNCTION THAT CALCULATES AND RETURNS THE AREA AND VOLUME OF A CONE\n# Inputs - radius and height of cone\n# Volume = pi*r**2*h/3\n# Area = base area\n\nimport math\n\ndef cone(R,H):\n \"\"\"Calculates the area and colume of a cone \n \n Inputs: \n radius in cm \n height in cm\n Outputs: \n cone volume in cm^3 \n cone base area in cm^2\n \n Author: RV\n Date: 21-Feb-2019\n \"\"\"\n area = (math.pi)*(R**2) ## area in cm^2 \n vol = (area)*H/3 ## volume in cm^3\n print(\"Your cone's base area is \" + str(area) +\", and it's volume is \" + str(vol) + \".\")\n return area, vol\n\n\n# -\n\ncone(2,5)\n\ncone(H=5,R=2) ## can change order of variables if called as original names\n\nvalues = cone(2,5)\nprint(type(values))\nvalues[0], values[1]\n\n# +\n## Function to compute the sum of all the integers between 1 - 100\nimport math\n\ndef fun100():\n x = sum(range(1,101))\n return x\n\n\n# -\n\nfun100()\n\n\ndef funsum(a,b):\n y = sum(range(a,(b+1)))\n return y\n\n\nfunsum(0,100)\n"},"script_size":{"kind":"number","value":1757,"string":"1,757"}}},{"rowIdx":936,"cells":{"path":{"kind":"string","value":"/notebooks/20220107_jaccard_sim_of_nbhd_with_dda_boxplot.ipynb"},"content_id":{"kind":"string","value":"72824857b3b592318772fb486bbf3198743aa6cd"},"detected_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"dib-lab/2020-ibd"},"repo_url":{"kind":"string","value":"https://github.com/dib-lab/2020-ibd"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2022-06-27T15:33:38","string":"2022-06-27T15:33:38"},"gha_updated_at":{"kind":"timestamp","value":"2022-05-30T00:26:51","string":"2022-05-30T00:26:51"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".r"},"size":{"kind":"number","value":68230,"string":"68,230"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .r\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: R\n# language: R\n# name: ir\n# ---\n\n# This notebook was another forray trying to dig into the similarity of neighborhoods across disease states.\n#\n# The goal was to determine if there are disease specific sequences; i.e. determine whether sequences that are more abundant in CD are only present in CD. \n#\n# This notebook didn't really end up addressing that question...but I kept it around anyway :)\n\nsetwd(\"..\")\n\nlibrary(readr)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(tidyr)\n\n# ### read in metadata\n\nmetadata <- read_tsv(\"inputs/working_metadata.tsv\", show_col_types = F) %>%\n select(library_name, study_accession, diagnosis) %>%\n distinct() %>%\n mutate(diagnosis = factor(diagnosis, levels = c(\"nonIBD\", \"CD\", \"UC\")))\n\nacc_to_species <- read_csv(\"outputs/genbank/gather_vita_vars_gtdb_shared_assemblies.x.genbank.lineages.csv\",\n col_names = c(\"accession\", \"kingdom\", \"phylum\", \"class\",\n \"order\", \"family\", \"genus\", \"species\"), show_col_types = F) %>%\n select(accession, species) %>%\n mutate(accession = gsub(\"_genomic.fna.gz\", \"\", accession))\n\n# ### sourmash compare\n\nacc_string <- \"GCF_008121495.1\"\n\ncomp <- read_csv(paste0(\"outputs/sgc_genome_queries_vs_pangenome_corncob_sequences_comp/\",\n acc_string,\n \"_CD_decreased_contig_comp.csv\"), show_col_types = F)\ncolnames(comp) <- gsub(paste0(\"_\", acc_string), \"\", colnames(comp))\ncolnames(comp) <- gsub(\"outputs/sgc_pangenome_catlases_corncob_sequences/\", \"\", colnames(comp))\ncolnames(comp) <- gsub(\"_contigs.fa\", \"\", colnames(comp))\n\ncomp$library_name <- colnames(comp)\ncomp_dec <- comp %>%\n pivot_longer(cols = -library_name, names_to = \"comp_lib_name\", values_to = \"jaccard\") %>%\n distinct() %>%\n filter(comp_lib_name == paste0(acc_string, \"_\", \"CD_decreased\")) %>%\n left_join(metadata, by = \"library_name\") %>%\n filter(!is.na(diagnosis)) %>%\n mutate(abundance = \"decreased\")\n\ncomp <- read_csv(paste0(\"outputs/sgc_genome_queries_vs_pangenome_corncob_sequences_comp/\",\n acc_string,\n \"_CD_increased_contig_comp.csv\"), show_col_types = F)\n\ncolnames(comp) <- gsub(paste0(\"_\", acc_string), \"\", colnames(comp))\ncolnames(comp) <- gsub(\"outputs/sgc_pangenome_catlases_corncob_sequences/\", \"\", colnames(comp))\ncolnames(comp) <- gsub(\"_contigs.fa\", \"\", colnames(comp))\n\ncomp$library_name <- colnames(comp)\ncomp_inc <- comp %>%\n pivot_longer(cols = -library_name, names_to = \"comp_lib_name\", values_to = \"jaccard\") %>%\n distinct() %>%\n filter(comp_lib_name == paste0(acc_string, \"_\", \"CD_increased\")) %>%\n left_join(metadata, by = \"library_name\") %>%\n filter(!is.na(diagnosis))%>%\n mutate(abundance = \"increased\")\n\nall_comp <- bind_rows(comp_inc, comp_dec) %>%\n mutate(abundance = factor(abundance, levels = c(\"increased\", \"decreased\")))\n\nggplot(all_comp, aes(x = diagnosis, y = jaccard, fill = diagnosis)) +\n geom_boxplot(alpha = .6) + \n facet_wrap(~abundance) +\n scale_fill_manual(values = c(\"black\", \"orange\", \"steelblue\"), name = \"Diagnosis\") + \n theme_classic() +\n labs(y = \"Jaccard similarity\")\n\n\n"},"script_size":{"kind":"number","value":3338,"string":"3,338"}}},{"rowIdx":937,"cells":{"path":{"kind":"string","value":"/project3/04.FanZhu_source/CODE&DATA/TransitionPath/Sapporo-Hakkodate/Split/Sapporo-Hakodate-Split.ipynb"},"content_id":{"kind":"string","value":"1f156b4264319fcfa3ae799d590f3e61da0ef730"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"yuany-pku/2017_CSIC5011"},"repo_url":{"kind":"string","value":"https://github.com/yuany-pku/2017_CSIC5011"},"star_events_count":{"kind":"number","value":5,"string":"5"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":75486,"string":"75,486"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [conda root]\n# language: python\n# name: conda-root-py\n# ---\n\nimport pandas as pd\nimport numpy as np\nfrom discreteMarkovChain import markovChain\n\n# ## Reading Data: Score and Adjancy Matrix\n\nadj_mat_data = pd.read_csv('JRPASS_split.csv',header=0,index_col=0,nrows=19)\nadj_mat = adj_mat_data.values\nadj_mat_data\n\nscore_data = pd.read_csv('Score_split.csv',header=0,index_col=0)\nscore_data\n\n# Here we use split strategy to solve passing by the airport problem. Score of Splitting node is a hyper-parameter, which could be tuned according to, for example, the stationary distribution. Here we set both of the splitting node score to be 500.\n\nscore = np.max(score_data.values[[0,2,4]],axis=0)\nN = score.shape[0]\npd.DataFrame(score.reshape((1,-1)), columns=city, index=['score'])\n\ncity = adj_mat_data.columns\nprint(city)\n\n# ## Transition Matrix\n\ntran_mat = np.matmul(adj_mat, np.diag(score))\ndeg = np.sum(tran_mat,axis=1)\ntran_mat = np.matmul(np.diag(1/deg), tran_mat)\n\ndf = pd.DataFrame(tran_mat, index=city, columns=city)\ndf.to_csv('Tran_mat_split.csv')\ndf\n\n# ## Stationary Distribution\n\n# Solve a linear equation $\\pi * P = \\pi$. Here we use package ``discreteMarkovChain``\n\nmc = markovChain(P=tran_mat)\n\nmc.computePi('linear')\npd.DataFrame(mc.pi.reshape((1,-1)), columns=city, index=['Prob'])\n\n# ## Committor function\n\n# Solve a (discrete) Dirichlet boundary problem for committor function, here we should let the boundary be the first col and last col.\n\na1 = np.zeros(N)\na1[0] = 1\na2 = np.zeros(N)\na2[-1] = 1\na = np.vstack((tran_mat[1:-1,0], (tran_mat[1:-1,1:-1]-np.identity(N-2)).T, tran_mat[1:-1,-1])).T\na = np.vstack((a1,a,a2))\n\nb = np.zeros(N)\nb[0] = 0\nb[-1] = 1\n\nq = np.linalg.solve(a,b)\npd.DataFrame(q.reshape(1,-1), columns=city, index=['commitor'])\n\n# ## Effective Flux \n\n# Solve the effective flux on each edge (x,y)\n# Firstly, calculate reactive current, which describes the ``reactive flow passing by x and y``.\n# Then, effective flux is basically the max(J(xy)-J(yx),0)\n\nJ_dir = np.zeros((N,N))\nfor x in range(N):\n for y in range(N):\n J_dir[x,y] = mc.pi[x] * (1-q[x]) * tran_mat[x,y] * q[y]\n\nJ_eff = np.maximum(J_dir - J_dir.T, 0)\nprint(J_eff[0])\nnp.max(J_eff)\n\n# ## Transition Current of a Node\n\n# Compute the transition flux through each node x$\\in$V.\n\nflux = np.zeros(N)\nflux[0] = np.sum(J_eff[0])\nflux[-1] = np.sum(J_eff[:,-1])\nfor x in np.arange(1,N-1):\n flux[x] = np.sum(J_eff[x,])\n #print(J_eff[x,].sum() - J_eff[:,x].sum())\nprint(flux)\n\npd.DataFrame( np.reshape( flux / np.sum(flux[1:-1]), (1,-1)), index=['Current'], columns=city)\n\n# ## Graph Force Layout\n\nnodes = []\nfor index, name in enumerate(city):\n if index == 0 or index == N-1:\n nodes.append({\n 'id':name,\n 'group':'red',\n 'radius':np.mean(flux[1:-1])/2+np.max(flux[1:-1])/2\n })\n else:\n nodes.append({\n 'id':name,\n 'group':'blue',\n 'radius':flux[index]\n })\n\nnodes\n\nmean_0 = np.mean(J_eff[0][J_eff[0]!=0])\nmean_n = np.mean(J_eff[:,-1][J_eff[:,-1]!=0])\nmean = np.percentile(J_eff[1:-1,1:-1][J_eff[1:-1,1:-1]!=0], q=65)\nJ_eff_copy = J_eff.copy()\nJ_eff_copy[0] *= mean/mean_0\nJ_eff_copy[:,-1] *= mean/mean_n\n\ncut_thresh = np.percentile(J_eff[1:-1,1:-1][J_eff[1:-1,1:-1]!=0],q=75) # stress 1/5\n\n\ndef f(index1, index2, dis, cut_thresh=cut_thresh):\n if index1 == 0 or index1 == N-1 or index2 == 0 or index2==N-1:\n return 'red'\n elif dis > cut_thresh:\n return 'green'\n else:\n return 'gray'\n\n\nlinks = []\nfor index1, a in enumerate(adj_mat):\n for index2, b in enumerate(a):\n if index1>index2: \n if J_dir[index1,index2] > J_dir[index2,index1]:\n links.append({\n 'source':city[index1],\n 'target':city[index2],\n 'value':J_eff_copy[index1,index2],\n 'group': f(index1, index2, J_eff[index1, index2])\n })\n elif J_dir[index2,index1] > J_dir[index1,index2]:\n links.append({\n 'source':city[index2],\n 'target':city[index1],\n 'value':J_eff_copy[index2,index1],\n 'group': f(index1, index2, J_eff[index2, index1])\n })\n\ngraph = {'nodes':nodes,\n 'links':links\n }\n\nimport json\nwith open('Sapporo-Hakodate_TP_Sp.json', 'w') as outfile:\n json.dump(graph, outfile)\n"},"script_size":{"kind":"number","value":4643,"string":"4,643"}}},{"rowIdx":938,"cells":{"path":{"kind":"string","value":"/Conditioner.ipynb"},"content_id":{"kind":"string","value":"444ea30e9539c83f987b265455886d47ed732895"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"alxkorn/PhygeText"},"repo_url":{"kind":"string","value":"https://github.com/alxkorn/PhygeText"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":49136,"string":"49,136"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Homework 5: Machine Reading\n\n# ## 1. Random QA\n\n# 1) What is the probability $p_{k,n}$ that our random QA system will output an answer with $k$ tokens when given a context paragraph with $n$ token as input?\n\n# $$p_{k,n} = $$\n\n# 2) For a fixed $n$ of $100$, produce a plot of $p_{k,n}$ and $\\hat{p}_{k,n}$ vs $k$. Design a monte-carlo experiment to estimate the values for $\\hat{p}_{k,n}$.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef p_k_n(k, n):\n #implement answer from part 1 here\n p_k_n = 0.5\n return p_k_n\n\n\ndef monte_carlo_p_k_n(k, n, T = int(1e5)):\n #T is the number of times you run experiment\n p_k_n_hat = 1\n return p_k_n_hat\n\n\nk_array = np.arange(1, 100, 5)\np_k_n_hat = [monte_carlo_p_k_n(k, n=100, T = int(1e5)) for k in k_array]\np_k_n = [p_k_n(k, n=100) for k in k_array]\n\nplt.plot(k_array, p_k_n_hat, label = r'$\\hat{p_{k,n}}$')\nplt.plot(k_array, p_k_n, label = r'$p_{k, n}$')\nplt.legend()\n\n\n# 3) Calculate the expected length of the answer given by your random QA system. i,e write an expression for $L_n = E[K]$ (It's fine to leave it as a summation).\n\n# $$ L_n = $$\n\n# 4) Plot $\\hat{L_n}$ estimated through monte-carlo simulations and $L_n$ for $n = 5, 10, 25, 50, 100, 250$ and $500$.\n\n# +\ndef L_n(n):\n #implement answer from above\n l_n = 5\n return l_n\n\ndef monte_carlo_L_n(n, T = int(1e5)):\n #T is the number of times you run experiment\n l_n = 1\n return l_n\n\n\n# -\n\nn_array = [5, 10, 25, 50, 100, 250, 500]\nl_n_hat = [monte_carlo_L_n(n, T = int(1e5)) for n in n_array]\nl_n = [L_n( n) for n in n_array]\n\nplt.plot(n_array, l_n_hat, '-o', label = r'$\\hat{L_n}$')\nplt.plot(n_array, l_n, '-*', label = r'$L_n$')\nplt.legend()\n\n\n# 5) Calculate the probability, $p_n$ that Random QA system outputs the correct answer to your question. \n\n# $$p_n = $$\n\n# 6) In SQuAD 2.0 data set, the answer for your question can either lie within the context paragraph or there could be no answer within the given paragraph. Let $\\alpha$ represent the fraction of questions for which the answer \\textbf{does not} lie within the paragraph.\n\ndef estimate_alpha(list_of_answers):\n return alpha\n\n\n# +\n## load dataset and call function to find alpha\n\n# +\n## implement random QA model\n## load dataset \n## find F1 and EM on Dev Set\n# -\n\n# ##### F1 Score on Dev Set:\n# ##### EM on Dev Set: \n\n# ## RNN Based Model\n\n# ### 2.1.1 Diagram of Baseline\n\nfrom IPython.display import Image\n\nImage('https://img.mukewang.com/5af3eb2400015bd813980728.png')\n\n\n# ### 2.1.2 Performance of Baseline\n\n# +\n### code to load your model and evaluate on the dev set\n\n### model = \n\ndef evaluate_on_dev_set(model, devloader):\n return f1_score, em_score\n\n\n# -\n\n# ##### F1 Score on Dev Set:\n# ##### EM on Dev Set: \n\n# ## Improving the Baseline\n\n# ### 2.2.1 Diagram of your architecture\n\nImage('https://img.mukewang.com/5af3eb2400015bd813980728.png')\n\n# ### 2.2.2 Performance of your Architecture\n\n# +\n## load your saved model\n\n# evaluate_on_dev_set(your_model, dev_set)\n# -\n\n# ##### F1 Score on Dev Set:\n# ##### EM on Dev Set: \n\n# ## 3 Fine Tuning Bert\n\n# +\n## load your saved model\n\n# evaluate_on_dev_set(your_bert_model, dev_set)\n# -\n\n# ##### F1 Score on Dev Set:\n# ##### EM on Dev Set: \n\n# ## 4 Analysis\n\n# Example 1\n#\n# Example 2\n#\n# Example 3\n\n\n"},"script_size":{"kind":"number","value":3535,"string":"3,535"}}},{"rowIdx":939,"cells":{"path":{"kind":"string","value":"/DAV Assignment.ipynb"},"content_id":{"kind":"string","value":"249de23203c234542a80e5430a25e2227c6dfa51"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"swaraj70/FIFA-19-DATA-ANALYTICS-AND-VISUALIZATION"},"repo_url":{"kind":"string","value":"https://github.com/swaraj70/FIFA-19-DATA-ANALYTICS-AND-VISUALIZATION"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":150459,"string":"150,459"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Reference data accuracy assessment by Radiant Earth\n#\n# Radiant Earth is conducting an accuracy assessment of DE Africa cropmask reference data using the airbus high-res satellite archive. This notebook produces a confusion matrix between DE AFrica's labels and Radiant Earth's labels. \n#\n# Inputs will be:\n#\n# 1. `` : The results from collecting training data in the CEO tool\n#\n# Output will be:\n# 1. A `confusion error matrix` containing Overall, Producer's, and User's accuracy, along with the F1 score.\n#\n# ***\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sn\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score\n\n# ## Analysis Parameters\n\nfolder = 'data/training_validation/collect_earth/central/'\ngjson = 'data/training_validation/collect_earth/central/Central_region_RE_sample_validated.geojson'\n\n# ### Load the dataset\n\n#ground truth shapefile\ndf = gpd.read_file(gjson)\ndf.head()\n\n# ### Clean up dataframe\n#\n\n#rename columns\ndf = df.rename(columns={'Class':'Prediction',\n 'Validation_Class':'Actual'})\ndf.head()\n\n# ***\n\n# ### Reclassify prediction & actual columns\n#\n# 1 = crop, \n# 0 = non-crop\n\n# +\ndf['Prediction'] = np.where(df['Prediction']=='non-crop', 0, df['Prediction'])\ndf['Prediction'] = np.where(df['Prediction']=='crop', 1, df['Prediction'])\n\ndf['Actual'] = np.where(df['Actual']=='non-crop', 0, df['Actual'])\ndf['Actual'] = np.where(df['Actual']=='crop', 1, df['Actual'])\n\ndf.head()\n# -\n\n# ### Generate a confusion matrix with all classes\n\n# +\nconfusion_matrix = pd.crosstab(df['Actual'],\n df['Prediction'],\n rownames=['Actual'],\n colnames=['Prediction'],\n margins=True)\n\nconfusion_matrix\n# -\n\n# ### Reclassify into a binary assessment\n\n# +\ncounts = df.groupby('Actual').count()\n\nprint(\"Total number of samples: \" + str(len(df)))\nprint(\"Number of 'mixed' samples: \"+ str(counts[counts.index=='mixed']['Prediction'].values[0]))\n# print(\"Number of 'N/A' samples: \"+ str(counts[counts.index=='N/A']['Prediction'].values[0]))\n\nprint(\"Dropping 'mixed' and 'N/A' samples\")\n\ndf = df.drop(df[df['Actual']=='mixed'].index)\ndf = df.drop(df[df['Actual']=='N/A'].index)\n# -\n\n# ---\n#\n# ### Recreate confusion matrix\n\n# +\nconfusion_matrix = pd.crosstab(df['Actual'],\n df['Prediction'],\n rownames=['Actual'],\n colnames=['Prediction'],\n margins=True)\n\nconfusion_matrix\n# -\n\n# ### Calculate User's and Producer's Accuracy\n\n# `Producer's Accuracy`\n\nconfusion_matrix[\"Producer's\"] = [confusion_matrix.loc[0, 0] / confusion_matrix.loc[0, 'All'] * 100,\n confusion_matrix.loc[1, 1] / confusion_matrix.loc[1, 'All'] * 100,\n np.nan]\n\n# `User's Accuracy`\n\n# +\nusers_accuracy = pd.Series([confusion_matrix[0][0] / confusion_matrix[0]['All'] * 100,\n confusion_matrix[1][1] / confusion_matrix[1]['All'] * 100]\n ).rename(\"User's\")\n\nconfusion_matrix = confusion_matrix.append(users_accuracy)\n# -\n\n# `Overall Accuracy`\n\nconfusion_matrix.loc[\"User's\",\"Producer's\"] = (confusion_matrix.loc[0, 0] + \n confusion_matrix.loc[1, 1]) / confusion_matrix.loc['All', 'All'] * 100\n\n# `F1 Score`\n#\n# The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall), and is calculated as:\n#\n# $$\n# \\begin{aligned}\n# \\text{Fscore} = 2 \\times \\frac{\\text{UA} \\times \\text{PA}}{\\text{UA} + \\text{PA}}.\n# \\end{aligned}\n# $$\n#\n# Where UA = Users Accuracy, and PA = Producer's Accuracy\n\n# +\nfscore = pd.Series([(2*(confusion_matrix.loc[\"User's\", 0]*confusion_matrix.loc[0, \"Producer's\"]) / (confusion_matrix.loc[\"User's\", 0]+confusion_matrix.loc[0, \"Producer's\"])) / 100,\n f1_score(df['Actual'].astype(np.int8), df['Prediction'].astype(np.int8), average='binary')]\n ).rename(\"F-score\")\n\nconfusion_matrix = confusion_matrix.append(fscore)\n# -\n\n# ### Tidy Confusion Matrix\n#\n# * Limit decimal places,\n# * Add readable class names\n# * Remove non-sensical values \n\n# round numbers\nconfusion_matrix = confusion_matrix.round(decimals=2)\n\n# rename booleans to class names\nconfusion_matrix = confusion_matrix.rename(columns={0:'Non-crop', 1:'Crop', 'All':'Total'},\n index={0:'Non-crop', 1:'Crop', 'All':'Total'})\n\n#remove the nonsensical values in the table\nconfusion_matrix.loc[\"User's\", 'Total'] = '--'\nconfusion_matrix.loc['Total', \"Producer's\"] = '--'\nconfusion_matrix.loc[\"F-score\", 'Total'] = '--'\nconfusion_matrix.loc[\"F-score\", \"Producer's\"] = '--'\n\nconfusion_matrix\n\n# ### Export csv\n\n# +\n# confusion_matrix.to_csv(folder+ 'radiant_earth_reference_data_accuracy_results.csv')\n# -\n\n\npend_dict_others)\n#others_df['total_payments'] = others_df['value_reported']\ndf_gemsjade = pd.concat([df_gemsjade, others_df])\ndf_gemsjade\n# -\n\ndf_gemsjade['name_of_revenue_stream'] = df_gemsjade['name_of_revenue_stream'].replace({'Other significant payments (&gt; 50,000 USD)': 'Other significant payments (> 50,000 USD)'})\n\ncompany_totals = df_gemsjade.pivot_table(index=['Company_name_cl'], aggfunc='sum')['value_reported']\ncompany_totals = company_totals.to_frame()\ncompany_totals.rename(columns={'value_reported': 'total_payments'}, inplace=True)\ncompany_totals.reset_index(level=0, inplace=True)\ncompany_totals.sort_values(by=['total_payments'], ascending = False, inplace=True)\ncompany_totals\n\ndf_gemsjade = pd.merge(df_gemsjade, company_totals, on='Company_name_cl')\n\n# ## Remove negative payments for Sankey\n\ndf_gemsjade = df_gemsjade[df_gemsjade[\"value_reported\"] > 0]\ndf_gemsjade = df_gemsjade.sort_values(by=['total_payments'], ascending=False)\ndf_gemsjade.drop(['Unnamed: 0'], axis=1)\ndf_gemsjade\n\ndf_gemsjade_summary = df_gemsjade[df_gemsjade['Company_name_cl'] != 'Companies not in EITI Reconciliation']\ndf_gemsjade_summary = df_gemsjade_summary.groupby(['name_of_revenue_stream','paid_to','target_type','type']).sum().reset_index()\ndf_gemsjade_summary['Company_name_cl'] = 'Companies in EITI Reconciliation'\ndf_gemsjade_summary = df_gemsjade[df_gemsjade['Company_name_cl'] == 'Companies not in EITI Reconciliation'] \\\n .append(df_gemsjade_summary)\ndf_gemsjade_summary\n\n# ## Prepare Source-Target-Value dataframe\n\n# +\n\nlinks_companies = pd.DataFrame(columns=['source','target','value','type'])\n\n# +\nto_append = df_gemsjade.groupby(['name_of_revenue_stream','paid_to'],as_index=False)['type','value_reported','total_payments'].sum()\n\n#to_append[\"target\"] = \"Myanmar Gems Enterprise\"\nto_append.rename(columns = {'name_of_revenue_stream':'source', 'value_reported' : 'value', 'paid_to': 'target'}, inplace = True)\n\nto_append = to_append.sort_values(by=['value'], ascending = False)\nto_append['target_type'] = 'entity'\n\nlinks_companies = pd.concat([links_companies,to_append])\n\nprint(to_append['value'].sum())\nlinks_companies\n\n# +\n## Page 239 of 2015-16 Report. Appendix 8: SOEs reconciliation sheets\nappend_dict_transfers = [{'source': 'Myanmar Gems Enterprise', 'type': 'entity',\n 'target': 'Corporate Income Tax (Inter-Government)', 'value': 53788313000 },\n \n {'source': 'Myanmar Gems Enterprise', 'type': 'entity', \n 'target': 'Commercial Tax (Inter-Government)', 'value': 15000000 },\n \n {'source': 'Myanmar Gems Enterprise', 'type': 'entity',\n 'target': 'Production Royalties (Inter-Government)', 'value': 17249087176 },\n \n {'source': 'Myanmar Gems Enterprise', 'type': 'entity', \n 'target': 'State Contribution (Inter-Government)', 'value': 46833942000 },\n \n \n {'source': 'Corporate Income Tax (Inter-Government)', 'target_type': 'entity',\n 'target': 'Internal Revenue Department', 'value': 53788313000 },\n \n {'source': 'Commercial Tax (Inter-Government)', 'target_type': 'entity', \n 'target': 'Internal Revenue Department', 'value': 15000000 },\n \n {'source': 'Production Royalties (Inter-Government)', 'target_type': 'entity',\n 'target': 'Department of Mines', 'value': 17249087176 },\n \n {'source': 'State Contribution (Inter-Government)', 'target_type': 'entity', \n 'target': 'Ministry of Planning and Finance', 'value': 46833942000 },\n \n \n {'source': 'Myanmar Gems Enterprise', 'type': 'entity',\n 'target': 'Other Accounts', 'value': 107705106000 },\n \n \n {'source': 'Other Accounts', 'target_type': 'entity',\n 'target': 'Ministry of Planning and Finance', 'value': 107705106000 },\n \n {'source': 'Internal Revenue Department', 'type': 'entity', 'target_type': 'entity',\n 'target': 'Ministry of Planning and Finance', 'value': 393194500968 }]\n\n\n\nappend_dict_transfers_df = pd.DataFrame(append_dict_transfers)\n\nlinks_summary = pd.concat([links_companies, append_dict_transfers_df])\nlinks_govt = append_dict_transfers_df\n#links = pd.concat([links, append_dict_transfers_df])\n\n# +\n\nto_append = df_gemsjade.groupby(['name_of_revenue_stream','Company_name_cl','type'],as_index=False) \\\n ['value_reported','total_payments'] \\\n .agg({'value_reported':sum,'total_payments':'first'})\nto_append.rename(columns = {'Company_name_cl':'source','name_of_revenue_stream':'target', 'value_reported' : 'value'}, inplace = True)\nto_append = to_append.sort_values(by=['total_payments'], ascending = False)\nlinks_companies = pd.concat([links_companies,to_append])\n\nprint(to_append['value'].sum())\n#links\nto_append\n\n# +\n\nto_append = df_gemsjade_summary.groupby(['name_of_revenue_stream','Company_name_cl','type'],as_index=False) \\\n ['value_reported','total_payments'] \\\n .agg({'value_reported':sum,'total_payments':'first'})\nto_append.rename(columns = {'Company_name_cl':'source','name_of_revenue_stream':'target', 'value_reported' : 'value'}, inplace = True)\nto_append = to_append.sort_values(by=['total_payments'], ascending = False)\nlinks_summary = pd.concat([links_summary,to_append])\nlinks_summary\n\n\n# +\ndef prep_nodes_links(links):\n unique_source = links['source'].unique()\n unique_targets = links['target'].unique()\n\n unique_source = pd.merge(pd.DataFrame(unique_source), links, left_on=0, right_on='source', how='left')\n unique_source = unique_source.filter([0,'type'])\n unique_targets = pd.merge(pd.DataFrame(unique_targets), links, left_on=0, right_on='target', how='left')\n unique_targets = unique_targets.filter([0,'target_type'])\n unique_targets.rename(columns = {'target_type':'type'}, inplace = True)\n\n unique_list = pd.concat([unique_source[0], unique_targets[0]]).unique()\n\n unique_list = pd.merge(pd.DataFrame(unique_list), \\\n pd.concat([unique_source, unique_targets]), left_on=0, right_on=0, how='left')\n\n unique_list.drop_duplicates(subset=0, keep='first', inplace=True)\n\n replace_dict = {k: v for v, k in enumerate(unique_list[0])}\n unique_list\n return [unique_list,replace_dict]\n\n\n#unique_list = pd.concat([links['source'], links['target']]).unique()\n#replace_dict = {k: v for v, k in enumerate(unique_list)}\n\n# -\n\n[unique_list_summary,replace_dict_summary] = prep_nodes_links(links_summary)\n[unique_list_companies,replace_dict_companies] = prep_nodes_links(links_companies)\n[unique_list_govt,replace_dict_govt] = prep_nodes_links(links_govt)\n\n\ndef write_nodes_links(filename,unique_list,replace_dict,links):\n links_replaced = links.replace({\"source\": replace_dict,\"target\": replace_dict})\n nodes = pd.DataFrame(unique_list)\n nodes.rename(columns = {0:'name'}, inplace = True)\n nodes_json= pd.DataFrame(nodes).to_json(orient='records')\n links_json= pd.DataFrame(links_replaced).to_json(orient='records')\n \n data = { 'links' : json.loads(links_json), 'nodes' : json.loads(nodes_json) }\n data_json = json.dumps(data)\n data_json = data_json.replace(\"\\\\\",\"\")\n #print(data_json)\n #with open('sankey_data.json', 'w') as outfile:\n # json.dump(data_json, outfile)\n\n text_file = open(filename + \".json\", \"w\")\n text_file.write(data_json)\n text_file.close()\n\n\nwrite_nodes_links(\"sankey_data_2015-16_summary\",unique_list_summary,replace_dict_summary,links_summary)\nwrite_nodes_links(\"sankey_data_2015-16_companies\",unique_list_companies,replace_dict_companies,links_companies)\nwrite_nodes_links(\"sankey_data_2015-16_govt\",unique_list_govt,replace_dict_govt,links_govt)\n\n\n\n\n"},"script_size":{"kind":"number","value":13520,"string":"13,520"}}},{"rowIdx":940,"cells":{"path":{"kind":"string","value":"/.ipynb_checkpoints/test_ML_sandbox-checkpoint.ipynb"},"content_id":{"kind":"string","value":"085801b67c1a2212742474a5b5136f2895f3a7b0"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"sunnyCUD/one_camera_knee_angle"},"repo_url":{"kind":"string","value":"https://github.com/sunnyCUD/one_camera_knee_angle"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":437626,"string":"437,626"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\njobs = []\nwith open(\"n_jobs.txt\") as f:\n s = f.readline()\n while True:\n s = f.readline()\n if s is None or s is \"\":\n break\n w, l = int(s.split(\" \")[0]), int(s.split(\" \")[1])\n jobs.append((w, l))\n\n# ### N_JOBS\n# Your task in this problem is to run the greedy algorithm that\n# schedules jobs in decreasing order of the difference (weight - length).\n# Recall from lecture that this algorithm is not always optimal. \n# *IMPORTANT*: if two jobs have equal difference (weight - length), \n# you should schedule the job with higher weight first. \n# Beware: if you break ties in a different way, you are likely to get the wrong answer. You should report the sum of weighted completion times of the resulting schedule --- a positive integer --- in the box below.\n#\n#\n\nnon_opt_jobs = []\nfor j in jobs:\n non_opt_jobs.append((j[0], j[1], j[0] - j[1]))\nnon_opt_jobs.sort(key = lambda x: (x[2], x[0]), reverse=True)\ncomp_time = 0\nlength = 0\nfor j in non_opt_jobs:\n length += j[1]\n comp_time += j[0] * length\nprint(f\" the weighted completion time is {comp_time}\")\n\n# ### N_JOBS_OPTIMAL\n#\n# Your task now is to run the greedy algorithm that schedules jobs (optimally) in decreasing order of the ratio (weight/length). \n# In this algorithm, it does not matter how you break ties. \n# You should report the sum of weighted completion times of the resulting schedule --- a positive integer --- in the box below.\n\nopt_jobs = []\nfor j in jobs:\n opt_jobs.append((j[0], j[1], j[0] / j[1]))\nopt_jobs.sort(key = lambda x: (x[2], x[0]), reverse=True)\ncomp_time = 0\nlength = 0\nfor j in opt_jobs:\n length += j[1]\n comp_time += j[0] * length\nprint(f\" the weighted completion time is {comp_time}\")\n\n# ### PRIM's MST\n#\n# Your task is to run Prim's minimum spanning tree algorithm on this graph. You should report the overall cost of a minimum spanning tree --- an integer, which may or may not be negative --- in the box below.\n#\n# IMPLEMENTATION NOTES: This graph is small enough that the straightforward O(mn) time implementation of Prim's algorithm should work fine.\n#\n# OPTIONAL: For those of you seeking an additional challenge, try implementing a heap-based version. The simpler approach, which should already give you a healthy speed-up, is to maintain relevant edges in a heap (with keys = edge costs). The superior approach stores the unprocessed vertices in the heap, as described in lecture. Note this requires a heap that supports deletions, and you'll probably need to maintain some kind of mapping between vertices and their positions in the heap.\n\nfrom sortedcontainers import SortedDict\nimport random\nimport sys\ngraph = None\nwith open(\"prim_edges.txt\") as f:\n graph = {int(x): [] for x in range(1, int(f.readline().split(\" \")[0]) + 1)}\n while True:\n s = f.readline()\n if s is None or s is \"\":\n break\n e1, e2, l = int(s.split(\" \")[0]), int(s.split(\" \")[1]), int(s.split(\" \")[2])\n graph[e1].append((e2,l))\n graph[e2].append((e1,l))\n\n\nclass PrimDict(dict):\n def __setitem__(self, key, value):\n if self.get(key, sys.maxsize) > value:\n dict.__setitem__(self, key, value)\n\n\nvisited = set()\ndistances = PrimDict()\ncost = 0\nnext_node = random.randint(0, len(graph.keys()) - 1)\nvisited.add(next_node)\nwhile visited != graph.keys():\n for node in graph[next_node]:\n if node[0] not in visited:\n distances[node[0]] = node[1]\n while visited != graph.keys():\n smallest_item = min(distances.items(), key = lambda x: x[1])\n next_node = smallest_item[0]\n if next_node not in visited:\n visited.add(next_node)\n cost += smallest_item[1]\n del distances[next_node]\n break\n\nprint(f\" overall cost is {cost}\")\n\n\n"},"script_size":{"kind":"number","value":4061,"string":"4,061"}}},{"rowIdx":941,"cells":{"path":{"kind":"string","value":"/Lists_in Python_Day1.ipynb"},"content_id":{"kind":"string","value":"7a719cc1c38cb9c1c2101ab1dda7731916c89860"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Sahzadah/PythonFiles"},"repo_url":{"kind":"string","value":"https://github.com/Sahzadah/PythonFiles"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":8581,"string":"8,581"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + _cell_guid=\"3caaab12-4abb-4aed-b2b5-638146f15c6d\" _uuid=\"b574f83f-bdff-46f8-b5a8-aa119b074ffc\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 4.845353, \"end_time\": \"2021-09-10T05:28:45.734303\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:40.888950\", \"status\": \"completed\"}\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport shutil\nfrom time import time\nimport torch\nfrom copy import copy\nfrom glob import glob\nfrom PIL import Image, ImageFile\nfrom torch import nn\nfrom torch import optim\nfrom torch.autograd import Variable\nfrom torch.utils.data import random_split, DataLoader\nfrom torchvision import datasets, transforms, models\nfrom tqdm import tqdm\nimport recgn_utils # utility script \n\n# + _cell_guid=\"710ff97d-5e7e-42fb-bbe1-2cf8adfaccad\" _uuid=\"29035e2f-897e-4da2-9aa4-6e3f69252a97\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.07208, \"end_time\": \"2021-09-10T05:28:45.822675\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:45.750595\", \"status\": \"completed\"}\n# Verifica se CUDA está disponível \ngpu_on = torch.cuda.is_available()\n\nif not gpu_on:\n print('Use a CPU. CUDA não está disponível...')\nelse:\n print('Use a GPU. CUDA está disponível...')\n\n# + _cell_guid=\"d0664a9c-63d9-43f7-ba13-b630c3c505b5\" _uuid=\"49c4a59d-990f-4e91-9817-e4ace4eb5006\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.024969, \"end_time\": \"2021-09-10T05:28:45.863978\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:45.839009\", \"status\": \"completed\"}\n# Configure alguns parametros:\n\n# Path raiz do dataset \nrcgn_dir = '../input/dog-breed-recognition-v3/dogs/recognition'\n\n# Numero de classes a serem selecionadas para enroll (de 1 a 20)\nnum_classes_enroll = 5\n\n# Numero medio de imagens selecionadas para enroll, em cada classe \nnum_img_enroll = 11\n\n# Total de imagens utilizadas no enroll\ntotal_img_enroll = num_classes_enroll * num_img_enroll\n\n# Tamanho do dataset\nbatch_size = num_classes_enroll #TBC\n\n# Numero de workers\nnum_workers = 0\n\n# Numero de epocas\nnum_epochs = 25\n\n# Metadados e Modelo treinado com fine-tune, na Parte-1 \ncheckpoint_path = \"../input/modelp1v9ep15nllloss/model_epoch_15_acc_84.4318_loss_0.5199.pth\"\n\n# + jupyter={\"outputs_hidden\": true} papermill={\"duration\": 0.625949, \"end_time\": \"2021-09-10T05:28:46.506204\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:45.880255\", \"status\": \"completed\"}\n# Paths contendo imagens originais para treino e teste\ndata_dir = rcgn_dir + '/enroll'\ntest_dir = rcgn_dir + '/test'\n\n# Plota quantidade ordenada de imagens por classe, para verficar se estao desbalanceadas\n_, _ = recgn_utils.check_class(data_dir)\n\n# + papermill={\"duration\": 0.027632, \"end_time\": \"2021-09-10T05:28:46.551671\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:46.524039\", \"status\": \"completed\"}\n# Lista nomes de todas as classes\nfull_class_names = [item.split('/')[-2] for item in sorted(glob(data_dir + \"/*/\"))]\nprint(f'> Lista das classes disponiveis para enroll = {full_class_names}\\n')\n\n# Seleciona subset de classes para enroll, e.g. classes de 1 a 5\npartial_class_names = full_class_names[0:num_classes_enroll] \nprint(f'> Lista das classes selecionadas para enroll = {partial_class_names}')\n\n\n# + papermill={\"duration\": 0.026681, \"end_time\": \"2021-09-10T05:28:46.596563\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:46.569882\", \"status\": \"completed\"}\n# Seleciona as classes para treinamento/validacao no enroll\n# Copia arquivos apenas das classes selecionadas\ndef sel_class(class_names, from_path, to_path):\n print(f'Copiando {len(class_names)} classes de {from_path}/enroll/ para {to_path}/enroll/')\n print(f'Copiando {len(class_names)} classes de {from_path}/test/ para {to_path}/test/')\n for name in class_names:\n old_path_train = (from_path + '/enroll/' + name)\n old_path_test = (from_path + '/test/' + name)\n new_path_train = (to_path + '/enroll/' + name)\n new_path_test = (to_path + '/test/' + name)\n shutil.copytree(old_path_train, new_path_train)\n shutil.copytree(old_path_test, new_path_test)\n\n\n# + papermill={\"duration\": 5.614787, \"end_time\": \"2021-09-10T05:28:52.228377\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:46.613590\", \"status\": \"completed\"}\n# Define diretorios para transferencia de imagens\nnew_rcgn_dir = '/kaggle/working/rcgn_sample'\n\n# Esvazia diretorio de destino (i.e. apaga e recria)\n# !rm -rf {new_rcgn_dir}\n# !mkdir {new_rcgn_dir}\n\n# Realiza transferencia das classes selecionadas, para novo diretorio de desino\nfrom_path = rcgn_dir\nto_path = new_rcgn_dir\nsel_class(partial_class_names, from_path, to_path)\n\n# Lista novo diretorio de imagens\n# !ls {new_rcgn_dir}\n\n# + papermill={\"duration\": 0.028428, \"end_time\": \"2021-09-10T05:28:52.275021\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.246593\", \"status\": \"completed\"}\n# NOVOS paths contendo imagens para treino (enroll) e teste\ndata_dir = new_rcgn_dir + '/enroll'\ntest_dir = new_rcgn_dir + '/test'\n\n# Lista nomes das classes selecionadas\npartial_class_names = [item.split('/')[-2] for item in sorted(glob(data_dir + \"/*/\"))]\nprint(f'> Lista das classes selecionadas para enroll = {partial_class_names}\\n')\n\n# Calcula numero de classes\nnum_classes_enroll = len(partial_class_names)\nprint(f'> Numero de classes selecionadas = {num_classes_enroll}')\n\n# + _cell_guid=\"da8c9060-33e8-433f-a803-876bcfeed4a1\" _uuid=\"060147b4-9e26-4107-8748-0e6ef7279774\" papermill={\"duration\": 0.033633, \"end_time\": \"2021-09-10T05:28:52.327822\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.294189\", \"status\": \"completed\"}\n# Cria datasets de imagens de treino e teste \nds_train = datasets.ImageFolder(data_dir)\nds_test = datasets.ImageFolder(test_dir)\n\n# Calcula total de imagens\ntotal_img = len(ds_train)\nprint(f'> Numero de total de imagens disponiveis para enroll = {total_img}')\n\n# + _cell_guid=\"97f1429b-0543-4f32-8361-16d38b3fed3c\" _uuid=\"18bd37ea-3178-4289-8576-f45c2139beba\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.044642, \"end_time\": \"2021-09-10T05:28:52.391229\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.346587\", \"status\": \"completed\"}\n# Define tamanho dos datasets de acordo com parametros iniciais \n# Isto eh: total_img_enroll = num_classes_enroll * num_img_enroll\n\ntrain_size = math.floor(num_img_enroll * 0.9) * num_classes_enroll\nvalid_size = math.ceil(num_img_enroll * 0.1) * num_classes_enroll\nrest_size = total_img - train_size - valid_size\n\ntrain_set, val_set, _ = random_split(ds_train, [train_size, valid_size, rest_size],\n torch.Generator().manual_seed(2147483647))\n\nprint(f'Numero final de imagens de treinamento: {len(train_set)}')\nprint(f'Numero final de imagens de validacao: {len(val_set)}')\nprint(f'Numero final de imagens para enroll = {train_size + valid_size}')\n\n# + _cell_guid=\"d7a9da13-07cd-484e-91d3-696ff90b4ae2\" _uuid=\"b20b67be-8f7a-477f-b0f0-7dbd7346e9ac\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.035458, \"end_time\": \"2021-09-10T05:28:52.445803\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.410345\", \"status\": \"completed\"}\n# Define valores mean e std para normalizar as imagens\n# TODO: Valores baseados no ImageNet. Idealmente calcular o mean e std do dataset original\nimg_mean = np.array((0.485, 0.456, 0.406))\nimg_std = np.array((0.229, 0.224, 0.225))\n\n# Define e aplica transformações nos datasets de treinamento, validação e teste\ntrain_set.dataset = copy(ds_train)\ntrain_set.dataset.transform = transforms.Compose([\n transforms.RandomHorizontalFlip(),\n transforms.RandomRotation(10),\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize(img_mean, \n img_std)])\nval_set.dataset.transform = transforms.Compose([transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(img_mean, \n img_std)])\n\ntest_transforms = transforms.Compose([transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(img_mean, \n img_std)])\n\ntest_set = datasets.ImageFolder(test_dir, transform=test_transforms)\nprint(f'Numero final de imagens de testes:', len(test_set))\n\n# Cria conjunto de loaders\ntrain_loader = DataLoader(train_set, batch_size=batch_size, num_workers=num_workers, shuffle=True)\nvalid_loader = DataLoader(val_set, batch_size=batch_size, num_workers=num_workers, shuffle=True)\ntest_loader = DataLoader(test_set, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n\nloaders = {'train': train_loader, 'valid': valid_loader, 'test': test_loader}\n\n# + _cell_guid=\"ab120cb5-2ead-4afb-aab9-8d11d386cfe7\" _uuid=\"5e83ad2c-ffd9-40dc-a691-eea546ccb6b4\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.421651, \"end_time\": \"2021-09-10T05:28:52.886345\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.464694\", \"status\": \"completed\"}\n# Exibe algumas imagens do loader com correspondentes labels\n\nmeanm = np.mean(img_mean)\nstdm = np.mean(img_std) \nrecgn_utils.sample_img_show(train_loader, partial_class_names, meanm, stdm)\n\n\n# + papermill={\"duration\": 0.035669, \"end_time\": \"2021-09-10T05:28:52.948538\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.912869\", \"status\": \"completed\"}\n# Funcao que carrega modelo treinado com fine-tune, na Parte-1 \n\ndef load_model(checkpoint_path):\n chpt_dict = torch.load(checkpoint_path, map_location=torch.device('cpu'))\n chpt_out_features = 100\n # Recria model class\n model = models.resnet152(pretrained=True)\n classifier = nn.Sequential(nn.Linear(model.fc.in_features, 512),\n nn.ReLU(),\n nn.Linear(512, 256),\n nn.ReLU(),\n nn.Dropout(0.5),\n nn.Linear(256, chpt_out_features),\n nn.LogSoftmax(dim=1))\n model.fc = classifier\n model.load_state_dict(chpt_dict['model_state_dict'])\n return model\n\n\n# + papermill={\"duration\": 12.225649, \"end_time\": \"2021-09-10T05:29:05.200572\", \"exception\": false, \"start_time\": \"2021-09-10T05:28:52.974923\", \"status\": \"completed\"}\n# Carrega modelo treinado com fine-tune, na Parte-1 \nmodel = load_model(checkpoint_path)\nprint(model.fc)\n\n# + papermill={\"duration\": 5.440076, \"end_time\": \"2021-09-10T05:29:10.669569\", \"exception\": false, \"start_time\": \"2021-09-10T05:29:05.229493\", \"status\": \"completed\"}\n# Cria novo classificador\n\n# Congela camadas para treinamento (feature extraction)\nfor param in model.parameters():\n param.requires_grad = False\n \nclassifier = nn.Sequential(nn.Linear(2048, 512), # model.fc.in_features = 2048\n nn.ReLU(),\n nn.Linear(512, 256),\n nn.ReLU(),\n nn.Dropout(0.5),\n nn.Linear(256, num_classes_enroll),\n nn.LogSoftmax(dim=1))\nmodel.fc = classifier\n\n# Define loss function (categorical cross-entropy)\n# https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html\ncriterion = nn.NLLLoss()\n\n# Define otimizador de treinamento e diferentes taxas de aprendizado ao longo da rede\n# https://pytorch.org/docs/stable/generated/torch.optim.SGD.html\noptimizer = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n\nif gpu_on:\n model.cuda()\n\n# + _cell_guid=\"29a0fcd4-84ac-4465-8ff9-7be71e5554b4\" _uuid=\"fec29ae4-8e12-48db-8bc5-9e381ff0e064\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 55.511081, \"end_time\": \"2021-09-10T05:30:06.210717\", \"exception\": false, \"start_time\": \"2021-09-10T05:29:10.699636\", \"status\": \"completed\"}\n# Treina modelo \nstart = time()\n\n# Considera 3 condicoes de parada:\n# 1) Valid loss medio crescente\n# 2) Numero de epocas = num_epochs\n# 3) Tempo maximo = total_img_enroll - 1\n\nmax_time = total_img_enroll - 1\nmodel = recgn_utils.train_model(model, criterion, optimizer, loaders, num_epochs, \n gpu_on, max_time)\n\nend = time()\nprint(f'Tempo total (aprox.) = {end - start} segundos') \nprint(f'Tempo medio por imagem (aprox.) = {(end-start)/total_img} segundos') \n\n# + _cell_guid=\"f0c41353-9281-40af-9122-2e21a2208f39\" _uuid=\"2e15d460-ff39-4313-98fd-751e52e4e675\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 31.371689, \"end_time\": \"2021-09-10T05:30:39.346183\", \"exception\": false, \"start_time\": \"2021-09-10T05:30:07.974494\", \"status\": \"completed\"}\n# Testa modelo treinado com loader de testes\n\nprob_pass, prob_fail = recgn_utils.test_model(model, criterion, test_loader, gpu_on)\n\n# + _cell_guid=\"31e7dd36-19ca-4d78-bb6b-d55ac455476e\" _uuid=\"2d4ff006-af72-40f5-b718-52fa3847e342\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.568605, \"end_time\": \"2021-09-10T05:30:40.045645\", \"exception\": false, \"start_time\": \"2021-09-10T05:30:39.477040\", \"status\": \"completed\"}\n# Plota distribuição de probabilidades nos cassos de pass e fail do teste \n\nplt.hist(prob_fail, bins = np.arange(0,1.05,0.05)) \nplt.hist(prob_pass, bins = np.arange(0,1.05,0.05), alpha = 0.7) \nlabels= [\"Fail\",\"Pass\"]\nplt.legend(labels)\nplt.xlabel('Probability')\nplt.ylabel('Frequency')\nplt.title('Max outputs')\n\n# + _cell_guid=\"7bc667bf-0ef1-4960-be06-f023e470d9cc\" _uuid=\"213f6709-ea53-4e23-b902-47eb8b41cef7\" jupyter={\"outputs_hidden\": false} papermill={\"duration\": 0.565477, \"end_time\": \"2021-09-10T05:30:40.807650\", \"exception\": false, \"start_time\": \"2021-09-10T05:30:40.242173\", \"status\": \"completed\"}\n# Seleciona uma foto aleatoria e testa o modelo\n\nenroll_dir = './rcgn_sample/test/*/*'\nenroll_data = np.array(glob(enroll_dir))\nimg_path = np.random.choice(enroll_data, 1)[0]\nrecgn_utils.imshow(img_path)\nprint(f'Foto selecionada aleatoriamente em:{img_path}')\npred_breed, pred_prob = recgn_utils.predict_breed_dog(model, partial_class_names, img_mean, img_std, img_path, gpu_on)\nprint(f'Probabilidade de {pred_prob*100:.2f}% de ser um {pred_breed}')\n"},"script_size":{"kind":"number","value":14640,"string":"14,640"}}},{"rowIdx":942,"cells":{"path":{"kind":"string","value":"/Programming for Geographical Information Analysis Advanced Skills - Assessment Two.ipynb"},"content_id":{"kind":"string","value":"72f1cf8d8f7db582496f86aa005604fed1c78700"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"annabelelizabethwhipp/Programming-for-Geographical-Information-Analysis-Advanced-Skills-Assessment-2"},"repo_url":{"kind":"string","value":"https://github.com/annabelelizabethwhipp/Programming-for-Geographical-Information-Analysis-Advanced-Skills-Assessment-2"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2019-04-01T12:29:48","string":"2019-04-01T12:29:48"},"gha_updated_at":{"kind":"timestamp","value":"2019-04-01T12:28:07","string":"2019-04-01T12:28:07"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":615057,"string":"615,057"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Time Series Analysis\n\n# This project aims to analyse time series from a Wi-Fi sensor located in the town of Otley, West Yorkshire. For the purpose of this project data from one sensor has been utilised. The project utilises a number of techniques, including: \n#\n# - Visualising time series data\n#\n#\n# - Calculating the moving average based on a 30 day period\n#\n#\n# - First order differencing\n#\n#\n# - Anomaly detection\n#\n#\n# - ARIMA modelling\n#\n#\n# - Stepwise modelling\n#\n#\n# The purpose of the project is to utilise techniques which provide an insight into the temporal fluctuations within the dataset. \n#\n#\n# The Wi-Fi sensor data represents footfall and is for the year 2016, covering 1st Janauary - 31st December. The data was originally at an hourly level but was aggregated to a daily level.\n#\n# Please note that the data must be in date order before proceeding \n\n# - Importing the packages\n\n# +\n# Import packages\nimport sklearn\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport scipy.stats as scs\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport statsmodels.tsa.api as smt\nimport statsmodels.formula.api as smf \n\n# %matplotlib inline\nsns.set()\n\nfrom pandas import Series\nfrom itertools import product \nfrom tqdm import tqdm_notebook\nfrom scipy.optimize import minimize \nfrom sklearn.metrics import r2_score\nfrom pmdarima.arima import auto_arima\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom sklearn.metrics import mean_absolute_error\nfrom dateutil.relativedelta import relativedelta \nfrom sklearn.model_selection import TimeSeriesSplit\nfrom statsmodels.tsa.arima_model import ARIMAResults\n# -\n\n# # Import the dataset with .read_csv() and check the first 5 rows with .head()\n#\n# - We can see that there are 2 columns of data: date and count\n# \n#\n# - Date is the timestamp from the day, month and year the data was collected and count is the footfall recorded by all the cameras combined on each day\n\n# +\n# reading in the data which is saved as a CSV file and giving it the name 'df'\ndf = pd.read_csv('FootfallData.csv')\n\n# printing the first 5 rows of the dataframe \ndf.head()\n# -\n\n# ## Using the .info() method to check the data types, number of rows, etc\n\n# retrieving information about the dataframe\ndf.info()\n\n# # Wrangling the data\n\n# - The columns of the dataframe are renamed so that they have no whitespaces\n#\n# - To do this a list of what the columns are called is reassigned to df.columns\n\n# +\n# the names of the column headings are specified as 'date' and 'count'\ndf.columns = ['date', 'count']\n\n\"\"\"\nthe first 5 rows are printed in order to check the headings \nare displayed correctly\n\"\"\"\ndf.head()\n# -\n\n# - The date column is turned into a DateTime data type and is made the index of the dataframe\n\n# +\n# the 'date' column of the dataframe is converted into a datetime data type\ndf.date = pd.to_datetime(df.date)\n\n# the date is set as the index of the dataframe \ndf.set_index('date', inplace=True)\n# -\n\n# printing the first 5 values of the dataframe \ndf.head()\n\n# # Exploratory Data Analysis (EDA)\n\n# - Plotting the data as a time series\n#\n# - Arguments can be specified such as figsize, linewidth and fontsize\n#\n# - A label can be applied to the x-axis and the font size of the label can be specified \n\n# +\n\"\"\"\nplotting the data frame, specifying the size of the figure,\nline width and font size\n\"\"\"\ndf.plot(figsize=(20,10), linewidth=3, fontsize=20)\n\n\"\"\"\"\nadding a label to the x-axis of the plot with the name 'Time' and specifying \nthat it uses font size 20\n\"\"\"\nplt.xlabel('Time', fontsize=20);\n# -\n\n# - Above is a plot of the data in which we can see the fluctuations in footfall across the period of the year. Generally, monthly fluctuations can be identified as at the end of one month/the beginning of another there is a drop in footfall. The exception is for the month of February in which there appears to be two significant peaks, one towards towards the second week fo the month, and another which occurs near the end of the month. From this plot there is one extreme value which can be identified, which takes places on the 29th June. This coincides with a popular annual cycle race in Otley town centres and attracts a large number of visitors. It is challenging to establish any pattern which occurs across the course of the year from this plot. \n\n# # Trends and Seasonality in Time Series Data\n#\n# - Identifying trends in time series data\n#\n#\n# - There are several ways to identify trends in time series data\n#\n#\n# - One way is to take the rolling average \n#\n#\n# - This means that for each time point you take the average of the points either side of it \n#\n#\n# - The number of points is specified by a window size which needs to be selected\n#\n\n# +\n\"\"\"\nspecifying the column of the dataframe from which we \nwant to calculate the rolling average\n\"\"\"\n\ncount = df[['count']]\n\n\"\"\"\nhere we calulate the rolling mean with a window length of 30\nthis means that a monthly rolling mean is created \nthe size of the figure is then specified\n\"\"\"\nrolling_periods = [30]\nfor p in rolling_periods:\n count.rolling(window=p).mean().plot(figsize=(20,10), linewidth=3, fontsize=20)\n plt.show()\n\n# -\n\n# Here we can see that there is a distinct trend in the data with increases occuring from April onwards until a peak is reached in mid-October. Figures start to fall once again after the first week in December. There is a significant increase which occurs during the final week of February and remains at over 800 counts until the last week of March in which there is a rapid decrease. Rolling averages can be extremely useful as they smooth out trends which appear in the plot of the time series data. General trends over the period of 2016 can be identified and the findings can be utilised by both the private and the public sector.\n\n# # Seasonal Patterns in Time Series Data\n\n# Seasonal components of time series data can be analysed by removing the trend from a time series so that seasonsality can be investigated more easily.\n#\n#\n# One way to remove the trend is called differencing, where you look at the difference between successive date points. This is called first order differencing. This method is demonstrated below:\n\n# #### First order differencing\n\n# The diff() and plot() methods are utilised to compute and plot the first order difference of the counts\n\n\"\"\"\nhere we are calculating the difference between two counts for different time points \nthen plotting the values of those differences\na value of 0 would mean that there was no difference between a count\nand the count for the previous day\n\"\"\"\ncount.diff().plot(figsize=(20,10), linewidth=3, fontsize=20)\nplt.xlabel('Year', fontsize=20);\n\n# - This method measures the difference between counts at each time point, for example, the difference between the count on the 1st of January and the 2nd of January. Negative values occur when there is a decrease between time points. Positive values occur when there is an increase in counts in between data points.\n#\n#\n# - First order differencing is useful for turning the time series into a stationary time series \n#\n#\n# - Stationary time series are useful because many time series forecasting methods are based on the assumption that the time series is approximately stationary\n\n# - First order differencing is useful for turning the time series into a stationary time series \n#\n# - Stationary time series are useful because many time series forecasting methods are based on the assumption that the time series is approximately stationary\n#\n\n# Below the first difference ordering values are printed:\n\n# +\n\"\"\"\nprinting the values of count.diff \n(the difference between the data for two time points)\n\"\"\"\nx = count.diff()\n\n# making a dataframe called 'stationary' with the data 'x'\nstationary = pd.DataFrame(data = x)\n\n# showing the dataframe 'stationary'\nstationary\n\n\n# -\n\n# First order differencing is a useful tool for a number of reasons. Firstly, it makes the data stationary which can be useful for a range of time series analysis techniques. Additionally, we are able to see the changes between days which can aid the detection of trends, especially if we want to investigate specific events.\n\n# # Anomaly detection \n\n# - Anomaly detection detects data points within a dataset that do not fit well with the rest of the data\n#\n#\n# - Below a simple anomaly detection system is created using the moving average\n\n# creating a function\ndef plotMovingAverage(series, window, plot_intervals=True, \n scale=1.96, plot_anomalies=True):\n\n \"\"\"\n series - dataframe with timeseries\n window - rolling window size \n plot_intervals - show confidence intervals\n plot_anomalies - show anomalies \n\n \"\"\"\n # specifying the moving average also referred to as the rolling mean\n rolling_mean = series.rolling(window=window).mean()\n # plotting the figure\n plt.figure(figsize=(15,5))\n # plotting the figure title \n plt.title(\"Moving average\\n window size = {}\".format(window))\n # plotting the rolling mean\n plt.plot(rolling_mean, \"g\", label=\"Rolling mean trend\")\n\n # Plot confidence intervals for smoothed values (the moving average)\n if plot_intervals:\n mae = mean_absolute_error(series[window:], rolling_mean[window:])\n deviation = np.std(series[window:] - rolling_mean[window:])\n lower_bond = rolling_mean - (mae + scale * deviation)\n upper_bond = rolling_mean + (mae + scale * deviation)\n plt.plot(upper_bond, \"r--\", label=\"Upper Bond / Lower Bond\")\n plt.plot(lower_bond, \"r--\")\n \n # Having the intervals, find abnormal values\n if plot_anomalies:\n anomalies = pd.DataFrame(index=series.index, columns=series.columns)\n anomalies[seriesupper_bond] = series[series>upper_bond]\n plt.plot(anomalies, \"ro\", markersize=10)\n \n # plotting the labels, legend and the grid markings \n plt.plot(series[window:], label=\"Counts\")\n plt.legend(loc=\"upper left\")\n plt.grid(True)\n\n\n# this dectects if we have a 50% change in footfall values\ncount.iloc[-50] = count.iloc[-50] * 0.5 \n\n\"\"\"\nplotting the moving average specifying a window size of 30\na window size of 30 was chosen to reflect the monthly patterns which\noccur within the dataset\nthe number '30' represents the number of days within the month\n\"\"\" \nplotMovingAverage(count, 30)\n\n\n# - 6 anomalies were identified\n\n# - The model did not just capture changes between months due to seasonality, therefore it is likely that there may be underlying reasosns for these anomalies.\n\n# - The 29th of June is highlighted as a significant peak, this coincides with the annual cycling race which takes place in Otley town centre.\n\n# - There are some dates with very low counts and some of 0, which suggests issues with the Wi-Fi sensors on these dates\n\n# # ARIMA modelling\n\n# ARIMA models are a form of statistical models commonly utilised for analyzing and forecasting time series data\n\n# ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average\n\n# - AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations.\n#\n#\n# - I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.\n#\n#\n# - MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.\n#\n# There are 3 integers used as parameters within ARIMA models: p, d and q. These paramaters account for seasonality, trend and noise within datasets. \n#\n# - p: auto-regressive element\n#\n# - d: integrated part of the model\n#\n# - q: moving average element\n\n# +\n# wrapper around run time error of ARIMA class \ndef __getnewargs__(self):\n\treturn ((self.endog),(self.k_lags, self.k_diff, self.k_ma))\nARIMA.__getnewargs__ = __getnewargs__\n \n# load data\nseries = Series.from_csv('FootfallData.csv', header=0)\n\n# prepare data\nX = series.values\nX = X.astype('float32')\n\n\"\"\"\nfit model\nthe three values following order represent P, D and Q \nwhich are the model parameters\nthe model parameters can be tweaked to change the results\n\"\"\"\nmodel = ARIMA(X, order=(2,1,3))\nmodel_fit = model.fit()\n\n# save the model\nmodel_fit.save('model.pkl')\n\n# load the model\nloaded = ARIMAResults.load('model.pkl')\n# -\n\n# - Below the results of the ARIMA model are printed\n#\n# - It summarises coefficient values, z score and p- values\n\n# printing a stastical summary of the fit of the ARIMA model\nprint(model_fit.summary())\n\n# The model summary provides a lot of information regarding the ARIMA model. The table in the middle is the coefficients table where the values listes under the heading coef are the weights of each term. \n#\n# The coefficient column highlights the weight (importance) of each feature and how each value impacts upon the time series. The coefficient value for the moving average was nearly-1, thus significant.\n#\n# The P> column shows the P values. The P values tell us the significance of each feature weight. The MA (moving average)and AR (autoregression) have a P value which is less than 0.05 therefore they should be kept in the model\n#\n\n# ### Plotting the residual errors\n\n# The residual errors can be plotted to ensure that there aren't any patterns\n\n# +\n# plot residual errors and the kernel density estimation of the residuals\nresiduals = pd.DataFrame(model_fit.resid)\n\n# plot the residuals\nresiduals.plot()\n\n# plot the kernel density estimates\nresiduals.plot(kind='kde')\n\n# show the plots\nplt.show()\n\n# print summary statistics for the residuals\nprint(residuals.describe())\n# -\n\n# - The mean of the residuals is close to 0 but as it is not 0 there is still room for improvement in the model\n#\n# - The results are distributed normally \n\n# ## The same process is now repeated utilising different P, D and Q paramters \n\n# +\n# load data\nseries = Series.from_csv('FootfallData.csv', header=0)\n\n# prepare data\nX = series.values\nX = X.astype('float32')\n\n\"\"\"\nfit model\nthe three values following order represent P, D and Q \nwhich are the model parameters\nthe model parameters can be tweaked to change the results\n\"\"\"\nmodel = ARIMA(X, order=(2,1,4))\nmodel_fit = model.fit()\n\n# save the model\nmodel_fit.save('model.pkl')\n\n# load the model\nloaded = ARIMAResults.load('model.pkl')\n# -\n\n# printing a stastical summary of the fit of the ARIMA model\nprint(model_fit.summary())\n\n# +\n# plot residual errors and the kernel density estimation of the residuals\nresiduals = pd.DataFrame(model_fit.resid)\n\n# plot the residuals\nresiduals.plot()\n\n# plot the kernel density estimates\nresiduals.plot(kind='kde')\n\n# show the plots\nplt.show()\n\n# print summary statistics for the residuals\nprint(residuals.describe())\n# -\n\n# The new parameters produce a model with a lower AIC and more of the variables have a significant P-value\n# The mean of the residuals is slightly higher \n\n# # Creating a stepwise model\n\n# Stepwise models are a method of fitting regression model\n#\n# The choice of the predictive variables is carried out by an automatic procedure\n#\n\n# here we are setting up the parameters of the stepwise model \nstepwise_model = auto_arima(df, start_p=1, start_q=1,\n # m=7 relates to weekly fluctuations\n max_p=7, max_q=4, m=7,\n start_P=0, seasonal=True,\n trace=True,\n # don't show warnings\n suppress_warnings=True, \n stepwise=True) # only uses stepwise models\n\"\"\"\nprint the AIC values of the stepwise model\nthe lower the value of the AIC, the better the model\nthe AIC of the model with the lowest AIC is printed after the fit name\nthe AIC takes into account the goodness of fit and the simplicity of the model\n\"\"\" \nprint(stepwise_model.aic())\n\n# +\n# specifying the data which will be included in the train set\ntrain = df.loc['2016-01-01':'2016-10-31']\n\n# specifying the data which will be included in the test set\ntest = df.loc['2016-11-01':'2016-12-31']\n# -\n\n# fit the stepwise model using the train dataframe\nstepwise_model.fit(train)\n\n# print the length of the test dataset\nlen(test)\n\n# name a variable called future forecast and assign it to the 61 predicted values\nfuture_forecast = stepwise_model.predict(n_periods=61)\n\n# print the dataframe future forecast\nprint(future_forecast)\n\n# +\n\"\"\"\nname a variable called future_forecast and create a dataframe \nwhich has a column called prediction\n\"\"\"\nfuture_forecast = pd.DataFrame(future_forecast,index \n = test.index,columns=['Prediction'])\n\n# link the test data frame with the future_forecast dataframe\noutput_data = pd.concat([test,future_forecast],axis=1)\n\n# print the dataframe 'output_data'\nprint(output_data)\n\n# +\n# plot the figure\nplt.figure()\n\n# plot the output_data\noutput_data.plot()\n\n# show the plot\nplt.show()\n# -\n\n# Here we can see that for the month of November the timestep model is able to predict the counts relatively successfully. The temporal spacing of the flucutations in the counts are also predicted for December, however the model clearly does not capture realistic counts. Given the dataset which has been read in, we would not expect figures for December to be accuractely predicted. The impact of Christmas is significant on the counts of footfall, with the number of counts significantly less than in other months.\n"},"script_size":{"kind":"number","value":17890,"string":"17,890"}}},{"rowIdx":943,"cells":{"path":{"kind":"string","value":"/5.12 plot_historical_data.ipynb"},"content_id":{"kind":"string","value":"3f4e750d28e5737e1febc316fc80aff6fc3f3730"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"bjkim2004/crawling2"},"repo_url":{"kind":"string","value":"https://github.com/bjkim2004/crawling2"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":34163,"string":"34,163"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nclass Node:\n def __init__(self, key):\n self.key = key\n self.left = None\n self.right = None\n def insert(self, newKey):\n newNode = Node(newKey)\n if(self.key /dev/null\n\n# + id=\"xepI4mP2dY8h\" colab_type=\"code\" colab={}\n# !wget -q www-us.apache.org/dist/spark/spark-2.4.5/spark-2.4.5-bin-hadoop2.7.tgz\n\n# + id=\"yfgFEFUFddyh\" colab_type=\"code\" outputId=\"0e05f5ec-7045-49fa-abc2-43c7381ad003\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000}\n# !tar -xvf spark-2.4.5-bin-hadoop2.7.tgz\n\n# + id=\"vd7Z3YWdd0ka\" colab_type=\"code\" colab={}\n# !pip install -q findspark\n\n# + id=\"1StB7X4Zdj4D\" colab_type=\"code\" colab={}\nimport os\nos.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\nos.environ[\"SPARK_HOME\"] = \"/content/spark-2.4.5-bin-hadoop2.7\"\n\n# + id=\"uBEcPr4Mdq26\" colab_type=\"code\" colab={}\nimport findspark\nfindspark.init()\nfrom pyspark.sql import SparkSession\n\nspark = SparkSession.builder.master(\"local[*]\").getOrCreate()\n\n# + [markdown] id=\"I4L9P9nheCsW\" colab_type=\"text\"\n# # Загрузка данных из CSV\n\n# + id=\"mVFvolGYwg-2\" colab_type=\"code\" outputId=\"5aaaf385-1883-4771-fbc1-816078d2edf2\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 34}\ndata = spark.read.csv('iris2.csv', inferSchema=True, header=True)\ndata\n\n# + id=\"JeOUHOs-whIW\" colab_type=\"code\" outputId=\"bcf54c0c-443b-4e98-f0a1-f3dc9b887910\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 102}\ndata.take(5)\n\n# + id=\"zjQyCdpMefBQ\" colab_type=\"code\" outputId=\"9e263570-d8c2-4608-fd1a-9f73f4bb04a8\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 34}\ndata.count()\n\n# + [markdown] id=\"roRdH8LIesna\" colab_type=\"text\"\n# ## Для каждого типа цветка определите максимальное, минимальное и среднее значение 4-х параметров\n\n# + id=\"lCm0S6akewQn\" colab_type=\"code\" outputId=\"56e611ea-283c-47e5-9680-f132d181baca\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 102}\ndata.dtypes\n\n# + id=\"MGmZPpd-wzUT\" colab_type=\"code\" outputId=\"8abc0963-0fa2-427d-a9ad-6b91c731c573\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 153}\nvariety_types = data.dropDuplicates(['variety'])\nvariety_types.show()\n\n# + id=\"gyr7WBhv2kQD\" colab_type=\"code\" outputId=\"39d338ec-5261-4640-c417-a3e68d196aed\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 187}\ndata_Virginica = data.where(data['variety'] == 'Virginica')\ndata_Virginica.describe().show()\n\n# + id=\"c5rs6sa13ZkZ\" colab_type=\"code\" outputId=\"1c659dfc-98bd-4ea9-d7cb-d16955468d79\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 187}\ndata_Setosa = data.where(data['variety'] == 'Setosa')\ndata_Setosa.describe().show()\n\n# + id=\"4kbdc42C3tke\" colab_type=\"code\" outputId=\"27c02d89-a567-4efb-843d-098060204fc3\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 187}\ndata_Versicolor = data.where(data['variety'] == 'Versicolor')\ndata_Versicolor.describe().show()\n\n# + [markdown] id=\"SLKrPsdw4Wgm\" colab_type=\"text\"\n# ## Визуализируйте точечный график (plt.scatter) по каждой паре параметров\n\n# + id=\"cpbj5Vrc4YMW\" colab_type=\"code\" colab={}\nimport pandas as pd\nimport matplotlib.pyplot as plt\n# %matplotlib inline\n\n# + id=\"D3z-GofPw5PQ\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 748} outputId=\"b1b69a0d-580d-4354-c9fe-b694a1409e8c\"\nimport seaborn as sns\n\ndataset = data.toPandas()\ng = sns.pairplot(dataset)\n\n# + [markdown] id=\"ksQgtwVhx5gH\" colab_type=\"text\"\n# ## Попробуйте отделить какой-нибудь тип цветка от всех остальных (сформулируйте правило на основе диаграммы - ЕСЛИ ЗНАЧЕНИЕ ПАРАМЕТРА X БОЛЬШЕ/МЕНЬШЕ Y, ТО ЦВЕТОК СКОРЕЕ ВСЕГО ОТНОСИТСЯ/НЕ ОТНОСИТСЯ К ТИПУ Z)\n#\n\n# + id=\"pygGxAQIx97H\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 748} outputId=\"980f53fd-66fd-4b5c-c451-b9f9510530f4\"\nfig = sns.pairplot(data=dataset, hue='variety')\nplt.show()\n\n# + [markdown] id=\"bzrH55k-zN2N\" colab_type=\"text\"\n# ## Сделайте отдельную колонку для своего предсказания\n\n# + id=\"1Bw5XaXpzQHi\" colab_type=\"code\" colab={}\nfrom pyspark.sql.functions import udf\nfrom pyspark.sql.types import *\n\n# + id=\"9dLDCSHd1TeB\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 459} outputId=\"9b0407e7-5318-4bf8-84ce-5a4d026bc14e\"\ndatatest = data\ndatatest.show()\n\n\n# + id=\"clYPR1qw1r58\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 459} outputId=\"c9e2c4ab-abb3-4d11-a1f8-1dbb7da74363\"\ndef valueToCategory(value1, value2, value3, value4):\n if (5.8 >= value1 >= 4.3) and (4.4 >= value2 >= 2.3) and (1.9 >= value3 >= 1.0) and (0.6 >= value4 >= 0.1): return 'Setosa'\n elif (7.0 >= value1 >= 4.9) and (3.4 >= value2 >= 2.0) and (5.1 >= value3 >= 3.0) and (1.8 >= value4 >= 1.0): return 'Versicolor'\n elif (7.9 >= value1 >= 4.9) and (3.8 >= value2 >= 2.2) and (6.9 >= value3 >= 4.5) and (2.5 >= value4 >= 1.4): return 'Virginica'\n else: return 'n/a'\n\nudfValueToCategory = udf(valueToCategory, StringType())\ndf_with = datatest.withColumn(\"category\", udfValueToCategory(\"sepal_length\", \"sepal_width\", \"petal_length\",\"petal_width\"))\ndf_with.show()\n\n\n# + [markdown] id=\"D7hiVs47-QTS\" colab_type=\"text\"\n# ## Оцените качество (сколько раз Вы угадали с ответом и сколько раз не угадали)\n\n# + id=\"dI9WOeu6-cWg\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 459} outputId=\"5f34e288-61b7-473a-f573-58c446a14117\"\ndef quality(value1, value2):\n if value1 == value2 : return 1\n elif value1 != value2: return 0\n else: return 'n/a'\n\nudfQuality = udf(quality, IntegerType())\ndatatest_with = df_with.withColumn(\"quality\", udfQuality(\"variety\", \"category\"))\ndatatest_with.show()\n\n# + id=\"IJ2VCkHr_n_t\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 207} outputId=\"cef018cd-9e33-4f7b-8918-f74e5a071ea7\"\ndatatest_with.describe().show()\n\n# + id=\"dw0i9WsLARKt\" colab_type=\"code\" colab={}\n# точность моей модели составляет 95%\n"},"script_size":{"kind":"number","value":6076,"string":"6,076"}}},{"rowIdx":945,"cells":{"path":{"kind":"string","value":"/assignment2-wells.ipynb"},"content_id":{"kind":"string","value":"c37a052c3e9c9472c63e69c7b5189caaf702a287"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"run-cmw/data-mining-practice"},"repo_url":{"kind":"string","value":"https://github.com/run-cmw/data-mining-practice"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":396004,"string":"396,004"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Exploring and Preprocessing Data with Pandas and Scikit-Learn\n\n# ## 1 Iris Dataset\n\n# Load Iris dataset\nimport pandas as pd\nurl = \"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n# Name is a class, not a feature\ndf = pd.read_csv(url, header=None, names=[\"Sepal Length\", \"Sepal Width\", \"Petal Length\", \"Petal Width\", \"Name\"])\n\n# ## 1.1 Summary Statistics\n#\n# Print the first 5 elements of your DataFrame using the command head(). How many features\n# are there and what are their types (e.g., numeric, nominal)?\n\ndf.head()\n\n# - There are 4 features ('Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width')\n# - All 4 of the features are numeric (cm measurements)\n\n# Compute and display summary statistics for each numeric feature available in the dataset.\n# These must include the minimum value, maximum value, mean, standard deviation, count, and\n# 25:50:75% percentiles.\n\ndf.describe()\n\n# ## 1.2 Data Visualization\n#\n# Histograms: To illustrate the feature distributions, create a histogram for each numeric feature in the dataset. You may plot each histogram individually or combine them all into a single\n# plot. When generating histograms for this assignment, use the default number of bins.\n\n# +\nsepal_length = df['Sepal Length']\nsepal_length_hist = sepal_length.hist(color='r', alpha=0.6)\n\nsepal_width = df['Sepal Width']\nsepal__width_hist = sepal_width.hist(color='orange', alpha=0.5)\n\npetal_length = df['Petal Length']\npetal__length_hist = petal_length.hist(color='yellow', alpha=0.6)\n\npetal_width = df['Petal Width']\npetal_width_hist = petal_width.hist(color='g', alpha=0.4)\n# -\n\nsepal_length = df['Sepal Length']\nsepal_length_hist = sepal_length.hist(color='r', alpha=0.5)\n\nsepal_width = df['Sepal Width']\nsepal__width_hist = sepal_width.hist(color='orange', alpha=0.5)\n\npetal_length = df['Petal Length']\npetal__length_hist = petal_length.hist(color='yellow', alpha=0.5)\n\npetal_width = df['Petal Width']\npetal_width_hist = petal_width.hist(color='g', alpha=0.5)\n\n# Box Plots: To further assess the data, create a box plot for each numeric feature in the\n# dataset. All of the box plots will be combined into a single plot. \n\nbox = df.boxplot(grid=False, return_type='axes')\n\n# ## 2 Ames, Iowa Housing Data\n\n# Load Ames Housing dataset\nimport pandas as pd\npd.set_option(\"display.max_columns\", 100)\nurl = \"https://raw.githubusercontent.com/cs6220/cs6220.spring2019/master/data/AmesHousing.txt\"\ndf = pd.read_csv(url, sep=\"\\t\")\n\n# ## 2.1 Imputation\n#\n# Identify and impute the features with missing values:\n#\n# - How many features have missing values?\n\nimport numpy as np\ndf\n\n# Get the non-null count for each of the 82 columns, and convert the values to a list\nnon_nulls_list = df.count().values.tolist()\n# Since there are 2930 rows, having 2930 non_nulls mean there are no nulls.\n# So filter list based on non-null values less than 2930.\nfiltered_list = [i for i in non_nulls_list if i < 2930]\n# The length of the filtered list is the number of features with null/missing values.\nlen(filtered_list)\n\n# - Fill each missing nominal feature value with the string “Missing”.\n\n# First select only nominal features, then use fillna() (otherwise fillna() will alter numeric features)\nnominal_cols = df.select_dtypes(exclude=np.number)\nnominal_cols.fillna('Missing')\n\n# - Interpolate each missing numeric feature value using linear interpolation.\n\n# First select only numeric features\nnumeric_cols = df.select_dtypes(np.number)\nnumeric_cols\n\n# Drop non-feature numeric cols\nnumeric_cols = numeric_cols.drop(['Order', 'PID', 'SalePrice'], axis=1)\nnumeric_cols\n\n# Then interpolate\nnumeric_cols = numeric_cols.interpolate(method='linear')\nnumeric_cols\n\n# ## 2.2. Standardization\n#\n# - Standardize the imputed feature data so that the values of each numeric feature are standard normally distributed (i.e., each feature is Gaussian with zero mean and unit variance).\n\n# +\nfrom sklearn import preprocessing\n\nnumeric_cols_scaled = preprocessing.scale(numeric_cols)\nnumeric_cols_scaled\n# -\n\n# - Visualize the results using box plots. How do the plots differ from box plots made before feature standardization? Which feature has the outlier furthest from the mean before and after standardization?\n\n# +\n# Scaled Numeric Plot\nimport matplotlib.pyplot as plt\n\nfig1, ax1 = plt.subplots(figsize=(20, 20))\nx1 = numeric_cols_scaled\n\nax1.set_title('Scaled Numeric Plot')\nax1.boxplot(x1, patch_artist=True);\n\n# Kate, is there a way to not show all of the text output before the graph?\n# Answer (from Kate's README feedback): To suppress text output with plots, put a semicolon after the command that\n# draws the plot. In your case for 2.2, you would just have: ax1.boxplot(x1, patch_artist=True); \n# -\n\n# - Restating: How do the plots differ from box plots made before feature standardization? Which feature has the outlier furthest from the mean before and after standardization?\n#\n# There were no box plots made before feature standardization, so I did that below. These scaled plots differ in that the medians are so similar. I didn't even need to drop features to have a decent visualization, whereas with the boxplots for unstandardized features are not easy to read - even after dropping the features with larger values.\n#\n# Misc Val (label 34 above) has the outlier furtherst from the mean after standardization. Before standardization, it appears that Lot Area has the outlier furthest from the mean.\n\n# Non-scaled Numeric Plot\nnumeric_cols.boxplot(grid=False, figsize=(35, 30))\n\n# Identify features with large values\ngiants = numeric_cols.columns[numeric_cols.min() > 50].values\ngiants\n\n# Remove identified features for a clearer plot\nnumeric_cols_no_giants = numeric_cols.drop(giants, axis=1)\nnumeric_cols_no_giants.boxplot(grid=False, figsize=(35, 20))\n\n# ## 2.3 Feature Selection\n#\n# - To get an idea of their relative importance, estimate the mutual information between the numeric features and the class column, ‘SalePrice’.\n\n# +\nfrom sklearn.feature_selection import mutual_info_regression, SelectKBest\n\nX = numeric_cols_scaled\ny = df['SalePrice']\n\nmi = mutual_info_regression(X, y)\n# mi /= np.max(mi) # needed?\nmi\n\n# -\n\n# - What are the top 5 numeric features ranked by mutual information? Note that features with a higher estimated mutual information are considered more informative.\n\n# Get the sorted values' index positions for the mutial info regression above\nmi_sorted = mi.argsort()\nmi_sorted\n\n# Get the top 5 index positions\ntop_5 = mi_sorted[::-1][:5]\ntop_5\n\n# +\n# Get the feature list names\nfeature_list = list(numeric_cols.columns)\ntop_count = 5\ncounter = 0\n\n# Print the top 5 (according to index position) feature list names\nwhile counter < top_count:\n print(feature_list[top5[counter]])\n counter += 1\n# -\n\n# - How do you expect the values for the top-ranked feature to affect the sales price (i.e., would you expect the sales price to increase when its values go up or down)? Why?\n\n# I expect increasing values for the top-ranked feature (Pool Area) to cause the sales price to also increase. Since pools often increase a home's value, it stands to reason that a larger pool will increase the sales price even more - especially when it is the top-ranked feature.\n\n# +\n# Kate's feedback: K best features not right, although mutual info results look good. \n# Check out the mutual info function SelectKBest()\n# Tried: SelectKBest(mi, k=5).fit(X, y)\n# Result -> TypeError: The score function should be a callable, ... () was passed.\n# Kate suggestion: Do you get the same error if you put your code in this form? \n\nselector = SelectKBest(mi, k=5)\nselector.fit(X, y)\n"},"script_size":{"kind":"number","value":7904,"string":"7,904"}}},{"rowIdx":946,"cells":{"path":{"kind":"string","value":"/main22_but_bad_prediction_acc.ipynb"},"content_id":{"kind":"string","value":"b45b36f283a86ec9ed08e926cd04ec1cd5f877a5"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"kgoldra/capstone_Xplisit"},"repo_url":{"kind":"string","value":"https://github.com/kgoldra/capstone_Xplisit"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2021-05-06T10:48:46","string":"2021-05-06T10:48:46"},"gha_updated_at":{"kind":"timestamp","value":"2021-05-06T03:03:56","string":"2021-05-06T03:03:56"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":9014427,"string":"9,014,427"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"view-in-github\" colab_type=\"text\"\n# \"Open\n\n# + id=\"-tE649_CgBoO\"\nimport os\nimport zipfile\nimport random\nimport tensorflow as tf\nimport shutil\nimport keras_preprocessing\nfrom keras_preprocessing import image\nfrom keras_preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.optimizers import RMSprop\nfrom shutil import copyfile\nfrom os import getcwd\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport cv2\nimport numpy as np\nfrom sklearn.utils import class_weight\nfrom google.colab import files\nfrom keras.models import load_model\n\n\n\n# + id=\"LzB_ebqpCHu4\"\ntry:\n shutil.rmtree('/content/Data')\n hutil.rmtree('/content/nail diseases')\nexcept:\n pass\n\n# + id=\"abqQQjekYrbj\"\npath_nails = f\"/content/nail diseases.zip\"\n\nlocal_zip = path_nails\nzip_ref = zipfile.ZipFile(local_zip, 'r')\nzip_ref.extractall('/content/')\nzip_ref.close()\n\n# + [markdown] id=\"dVlSGWoQwziY\"\n#\n\n# + id=\"dcRZQER1PFLM\"\nORIGINDIR = \"/content/nail diseases\"\nDATADIR = \"/content/Data\"\nTRAINORTEST = [\"training\", \"testing\"]\nCATEGORIES = [\"aloperia areata\", \n \"beau's lines\", \n \"bluish nail\", \n \"clubbing\", \n \"darier's disease\", \n \"eczema\", \n \"koilonychia\", \n \"leukonychia\", \n \"lindsay's nails\", \n \"muehrck-e's lines\", \n \"normal\", \n \"onycholycis\", \n \"pale nail\", \n \"red lunula\", \n \"splinter hemmorrage\", \n \"terry's nail\", \n \"white nail\", \n \"yellow nails\"]\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"HLaMuqujPNky\" outputId=\"a4f0aea1-945f-4825-dd35-8631f1d7aee2\"\ncounter = 0\n\nfor trainortest in TRAINORTEST:\n path = os.path.join(DATADIR, trainortest)\n for category in CATEGORIES:\n counter += 1\n originpath = os.path.join(ORIGINDIR, category)\n path2 = os.path.join(path, category)\n if(counter) <= 18:\n print(category, \"Datasets Has: \", len(os.listdir(originpath)) ,\"Data\")\n os.makedirs(path2)\n pass\n\n\n# + id=\"iAMe8DqK2RaH\"\ndef split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n files = []\n for filename in os.listdir(SOURCE):\n file = SOURCE + filename\n if os.path.getsize(file) > 0:\n files.append(filename)\n else:\n print(filename + \" is zero length, so ignoring.\")\n \n training_sets = int(len(files) * SPLIT_SIZE)\n testing_sets = int(len(files) - training_sets)\n randomed = random.sample(files,len(files))\n training_files = randomed[0:training_sets]\n testing_files = randomed[:testing_sets]\n \n for filename in training_files:\n source = SOURCE + filename\n destination = TRAINING + filename\n copyfile(source, destination)\n \n for filename in testing_files:\n source = SOURCE + filename\n destination = TESTING + filename\n copyfile(source, destination)\n\n\n# + id=\"bzM_lhjz2Z7-\"\nnormal_DIR = \"/content/nail diseases/normal/\"\nTRAINING_normal_DIR = \"/content/Data/training/normal/\"\nTESTING_normal_DIR = \"/content/Data/testing/normal/\"\n\naloperia_DIR = \"/content/nail diseases/aloperia areata/\"\nTRAINING_aloperia_DIR = \"/content/Data/training/aloperia areata/\"\nTESTING_aloperia_DIR = \"/content/Data/testing/aloperia areata/\"\n\nbeau_DIR = \"/content/nail diseases/beau's lines/\"\nTRAINING_beau_DIR = \"/content/Data/training/beau's lines/\"\nTESTING_beau_DIR = \"/content/Data/testing/beau's lines/\"\n\nbluish_DIR = \"/content/nail diseases/bluish nail/\"\nTRAINING_bluish_DIR = \"/content/Data/training/bluish nail/\"\nTESTING_bluish_DIR = \"/content/Data/testing/bluish nail/\"\n\nclubbing_DIR = \"/content/nail diseases/clubbing/\"\nTRAINING_clubbing_DIR = \"/content/Data/training/clubbing/\"\nTESTING_clubbing_DIR = \"/content/Data/testing/clubbing/\"\n\nDarier_DIR = \"/content/nail diseases/darier's disease/\"\nTRAINING_Darier_DIR = \"/content/Data/training/darier's disease/\"\nTESTING_Darier_DIR = \"/content/Data/testing/darier's disease/\"\n\neczema_DIR = \"/content/nail diseases/eczema/\"\nTRAINING_eczema_DIR = \"/content/Data/training/eczema/\"\nTESTING_eczema_DIR = \"/content/Data/testing/eczema/\"\n\nkoilonychia_DIR = \"/content/nail diseases/koilonychia/\"\nTRAINING_koilonychia_DIR = \"/content/Data/training/koilonychia/\"\nTESTING_koilonychia_DIR = \"/content/Data/testing/koilonychia/\"\n\nleukonychia_DIR = \"/content/nail diseases/leukonychia/\"\nTRAINING_leukonychia_DIR = \"/content/Data/training/leukonychia/\"\nTESTING_leukonychia_DIR = \"/content/Data/testing/leukonychia/\"\n\nlindsay_DIR = \"/content/nail diseases/lindsay's nails/\"\nTRAINING_lindsay_DIR = \"/content/Data/training/lindsay's nails/\"\nTESTING_lindsay_DIR = \"/content/Data/testing/lindsay's nails/\"\n\nMuehrck_DIR = \"/content/nail diseases/muehrck-e's lines/\"\nTRAINING_Muehrck_DIR = \"/content/Data/training/muehrck-e's lines/\"\nTESTING_Muehrck_DIR = \"/content/Data/testing/muehrck-e's lines/\"\n\nonycholycis_DIR = \"/content/nail diseases/onycholycis/\"\nTRAINING_onycholycis_DIR = \"/content/Data/training/onycholycis/\"\nTESTING_onycholycis_DIR = \"/content/Data/testing/onycholycis/\"\n\npale_nail_DIR = \"/content/nail diseases/pale nail/\"\nTRAINING_pale_nail_DIR = \"/content/Data/training/pale nail/\"\nTESTING_pale_nail_DIR = \"/content/Data/testing/pale nail/\"\n\nred_lunula_DIR = \"/content/nail diseases/red lunula/\"\nTRAINING_red_lunula_DIR = \"/content/Data/training/red lunula/\"\nTESTING_red_lunula_DIR = \"/content/Data/testing/red lunula/\"\n\nsplinter_hemmorrage_DIR = \"/content/nail diseases/splinter hemmorrage/\"\nTRAINING_splinter_hemmorrage_DIR = \"/content/Data/training/splinter hemmorrage/\"\nTESTING_splinter_hemmorrage_DIR = \"/content/Data/testing/splinter hemmorrage/\"\n\nterry_DIR = \"/content/nail diseases/terry's nail/\"\nTRAINING_terry_DIR = \"/content/Data/training/terry's nail/\"\nTESTING_terry_DIR = \"/content/Data/testing/terry's nail/\"\n\nwhite_DIR = \"/content/nail diseases/white nail/\"\nTRAINING_white_DIR = \"/content/Data/training/white nail/\"\nTESTING_white_DIR = \"/content/Data/testing/white nail/\"\n\nyellow_DIR = \"/content/nail diseases/yellow nails/\"\nTRAINING_yellow_DIR = \"/content/Data/training/yellow nails/\"\nTESTING_yellow_DIR = \"/content/Data/testing/yellow nails/\"\n\n\nsplit_size = .70\n\n\n\n\nsplit_data(Darier_DIR, TRAINING_Darier_DIR, TESTING_Darier_DIR, split_size)\nsplit_data(Muehrck_DIR, TRAINING_Muehrck_DIR, TESTING_Muehrck_DIR, split_size)\nsplit_data(aloperia_DIR, TRAINING_aloperia_DIR, TESTING_aloperia_DIR, split_size)\nsplit_data(beau_DIR, TRAINING_beau_DIR, TESTING_beau_DIR, split_size)\nsplit_data(bluish_DIR, TRAINING_bluish_DIR, TESTING_bluish_DIR, split_size)\nsplit_data(clubbing_DIR, TRAINING_clubbing_DIR, TESTING_clubbing_DIR, split_size)\nsplit_data(eczema_DIR, TRAINING_eczema_DIR, TESTING_eczema_DIR, split_size)\nsplit_data(koilonychia_DIR, TRAINING_koilonychia_DIR, TESTING_koilonychia_DIR, split_size)\nsplit_data(leukonychia_DIR, TRAINING_leukonychia_DIR, TESTING_leukonychia_DIR, split_size)\nsplit_data(lindsay_DIR, TRAINING_lindsay_DIR, TESTING_lindsay_DIR, split_size)\nsplit_data(onycholycis_DIR, TRAINING_onycholycis_DIR, TESTING_onycholycis_DIR, split_size)\nsplit_data(pale_nail_DIR, TRAINING_pale_nail_DIR, TESTING_pale_nail_DIR, split_size)\nsplit_data(red_lunula_DIR, TRAINING_red_lunula_DIR, TESTING_red_lunula_DIR, split_size)\nsplit_data(splinter_hemmorrage_DIR, TRAINING_splinter_hemmorrage_DIR, TESTING_splinter_hemmorrage_DIR, split_size)\nsplit_data(terry_DIR, TRAINING_terry_DIR, TESTING_terry_DIR, split_size)\nsplit_data(white_DIR, TRAINING_white_DIR, TESTING_white_DIR, split_size)\nsplit_data(yellow_DIR, TRAINING_yellow_DIR, TESTING_yellow_DIR, split_size)\nsplit_data(normal_DIR, TRAINING_normal_DIR, TESTING_normal_DIR, split_size)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"R4g2NKzq7-F9\" outputId=\"07fd1b99-8790-4791-d710-876f8b70fadf\"\nprint(len(os.listdir(\"/content/Data/testing/normal\")))\nprint(len(os.listdir(\"/content/Data/training/normal\")))\n\n# + id=\"Yn07ozaavH30\"\nIMGSIZE = 150\n\n# + id=\"D3WZthQUt4H6\"\ntraining_data = []\n\ndef create_training_data():\n path = os.path.join(DATADIR, \"training\")\n for category in CATEGORIES:\n path2 = os.path.join(path, category)\n class_label = CATEGORIES.index(category)\n for img in os.listdir(path2):\n img_array = cv2.imread(os.path.join(path2,img))\n new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE))\n training_data.append([new_array, class_label])\n\ncreate_training_data()\n\n# + id=\"92jlht5vt7H2\"\ntesting_data = []\n\ndef create_testing_data():\n path = os.path.join(DATADIR, \"testing\")\n for category in CATEGORIES:\n path2 = os.path.join(path, category)\n class_label = CATEGORIES.index(category)\n for img in os.listdir(path2):\n img_array = cv2.imread(os.path.join(path2,img))\n new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE))\n testing_data.append([new_array, class_label])\n\ncreate_testing_data()\n\n# + id=\"K2lNZgnJdrsX\"\nX = []\nx_label = []\ny = []\ny_label = []\n\nfor features, label in training_data:\n X.append(features)\n x_label.append(label)\n\n \nfor features, label in testing_data:\n y.append(features)\n y_label.append(label)\n\n\n# + id=\"I2_f4KYn-0pu\"\nweight = class_weight.compute_class_weight('balanced',\n np.unique(np.ravel(y)),\n np.ravel(y))\n\n# + id=\"mB0PApMnEBoB\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"28bd4d89-20a5-4b55-b641-d07ad1e09a24\"\nweights = {i : weight[i] for i in range(18)}\n\nprint (weights)\n\n# + id=\"g0mjP69v_BEi\" colab={\"base_uri\": \"https://localhost:8080/\"} outputId=\"76efdec8-9ac0-4861-92c3-b70d00f35785\"\nX = np.array(X).reshape(-1, IMGSIZE,IMGSIZE, 3).astype('float')\ny = np.array(y).reshape(-1, IMGSIZE,IMGSIZE, 3).astype('float')\nx_label = np.array(x_label).astype('float')\ny_label = np.array(y_label).astype('float')\n\nprint(X.shape)\nprint(y.shape)\nprint(x_label.shape)\nprint(y_label.shape)\n\n# + id=\"MfuUqwnBWA5g\"\ntraining_datagen = ImageDataGenerator(\n rescale = 1.0/255.,\n\t rotation_range=40,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n fill_mode='nearest')\n\n\nvalidation_datagen = ImageDataGenerator(\n rescale = 1./255.\n)\n\n# + id=\"wYq8BSEKV9IJ\"\nmodel = tf.keras.models.Sequential([\n # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n # This is the first convolution\n tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape= (150,150,3)),\n tf.keras.layers.MaxPooling2D(2, 2),\n # The second convolution\n tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n # The third convolution\n tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n # The fourth convolution\n tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n # Flatten the results to feed into a DNN\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dropout(0.5),\n # 512 neuron hidden layer\n tf.keras.layers.Dense(256, activation='relu'),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dense(12, activation='softmax')\n])\n\n# + id=\"x8plq71fzgCA\"\ntrain_generator = training_datagen.flow(\n X,\n x_label,\n batch_size = 32\n)\n\nvalidation_generator = validation_datagen.flow(\n y,\n y_label,\n batch_size = 32\n)\n\n\n# + id=\"ZRRWx2w6ZVzd\"\nclass myCallback(tf.keras.callbacks.Callback):\n def on_epoch_end(self, epoch, logs={}):\n if(logs.get('accuracy')>=95):\n print(\"\\nReached 95% Accuracy so cancelling training!\")\n self.model.stop_training = True\n\n\n# + id=\"VmI3558OG7u8\"\nfrom keras.callbacks import Callback,ModelCheckpoint\nfrom keras.models import Sequential,load_model\nfrom keras.layers import Dense, Dropout\nfrom keras.wrappers.scikit_learn import KerasClassifier\nimport keras.backend as K\n\n\n# + id=\"9CyZAFvCG-Li\"\ndef get_f1(y_true, y_pred): #taken from old keras source code\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n precision = true_positives / (predicted_positives + K.epsilon())\n recall = true_positives / (possible_positives + K.epsilon())\n f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())\n return f1_val\n\n\n# + id=\"r4HmNly5g4Um\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} outputId=\"21d49edd-276f-4254-b0dc-8ea3e078fea0\"\ncallbacks=myCallback()\n\nmodel.summary()\n\nmodel.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'rmsprop', metrics=['accuracy',get_f1])\n\n\nhistory = model.fit(\n train_generator,\n steps_per_epoch = 500/32,\n validation_data = validation_generator,\n validation_steps = 226/32,\n epochs = 500, callbacks = [callbacks], class_weight=weights\n)\n\nmodel.save('model.h5')\nfiles.download('model.h5')\n\n\n\n\n# + [markdown] id=\"h7dUWqKrHxUq\"\n#\n\n# + id=\"bEhqLe6_Kyrq\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 573} outputId=\"f358b5ac-7a0c-462d-9942-3b6c7a79b4bf\"\ndef plot_train_history(history1):\n # Summarize history for accuracy\n plt.plot(history.history['accuracy'])\n plt.plot(history.history['val_accuracy'])\n plt.title('Model accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n plt.show()\n\n # Summarize history for loss\n plt.plot(history.history['loss'])\n plt.plot(history.history['val_loss'])\n plt.title('Model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n plt.show()\n\nplot_train_history(history)\n\n# + id=\"_FUzGPWZivmd\"\nmodel.save_weights(\"model.h5\")\n\n\n# + id=\"gRDq9yYnh0ju\"\ndef preparation(filepath):\n img_array = cv2.imread(filepath)\n new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE))\n return new_array.reshape(-1, IMGSIZE, IMGSIZE, 3)\n\n\n# + id=\"hFjbaUs4pVo2\" colab={\"resources\": {\"http://localhost:8080/nbextensions/google.colab/files.js\": {\"data\": \"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\", \"ok\": true, \"headers\": [[\"content-type\", \"application/javascript\"]], \"status\": 200, \"status_text\": \"\"}}, \"base_uri\": \"https://localhost:8080/\", \"height\": 1000} outputId=\"bef6dfa1-9fd1-4bf2-b187-b96e7babe817\"\nfrom google.colab import files\nfrom keras.preprocessing import image\n\nuploaded = files.upload()\n\n# + id=\"B5rPUXW--LRZ\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} outputId=\"8da0b043-397c-4350-ce81-d7eabc9b2bdf\"\nfor fn in uploaded.keys():\n predictions = model.predict([preparation(fn)])\n img = image.load_img(fn, target_size=(150, 150))\n img = np.array(img)\n img = img/255.\n xy = img\n xy = np.expand_dims(img, axis=0)\n \n imgplot = plt.imshow(img)\n plt.show()\n\n print(fn)\n print(predictions)\n print(xy.shape)\n \n\n\n# + id=\"7qbn9JOx5vKW\"\nclass_names = CATEGORIES\ntest_label = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n\n\n# + id=\"8gB9s-Q35UYY\"\ndef plot_image(i, predictions_array, true_label, img):\n predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n plt.grid(False)\n plt.xticks([])\n plt.yticks([])\n \n plt.imshow(img, cmap=plt.cm.binary)\n\n predicted_label = np.argmax(predictions_array)\n color = 'blue'\n\n \n plt.xlabel(\"{} {:2.0f}%\".format(class_names[predicted_label],\n 100*np.max(predictions_array),\n class_names[true_label]),\n color=color)\n\n\n# + id=\"C8qZQ0j-y0Uk\"\ndef plot_value_array(i, predictions_array, true_label):\n predictions_array, true_label = predictions_array[i], true_label[i]\n plt.grid(False)\n plt.xticks([])\n plt.yticks([])\n thisplot = plt.bar(range(18), predictions_array, color=\"#777777\")\n plt.ylim([0, 1]) \n predicted_label = np.argmax(predictions_array)\n \n thisplot[predicted_label].set_color('red')\n\n\n# + id=\"VkF6pENvyC2G\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 203} outputId=\"8e71733c-57c9-4795-d28f-faf271d82107\"\ni = 0\nplt.figure(figsize=(6,3))\nplt.subplot(1,2,1)\nplot_image(i, predictions, test_label, xy)\nplt.subplot(1,2,2)\nplot_value_array(i, predictions, test_label)\nplt.show()\n\n# + id=\"HkQgFFGl4ywl\"\nimport tensorflow as tf\n\n# Load the model.\nnew_model= tf.keras.models.load_model(filepath=\"nailss.h5\")\n\n# Convert the model.\nconverter = tf.lite.TFLiteConverter.from_keras_model(new_model)\ntflite_model = converter.convert()\n\n# Save the TF Lite model.\nwith tf.io.gfile.GFile('nailss.tflite', 'wb') as f:\n f.write(tflite_model)\n\n# + [markdown] id=\"oe3uXaKqNSf0\"\n# # **Don't Run**\n\n# + id=\"_7XsVGZZNUIH\"\nfrom keras import backend as K\n\ndef recall_m(y_true, y_pred):\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n recall = (true_positives / (possible_positives + K.epsilon()))\n return recall\n\ndef precision_m(y_true, y_pred):\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n precision = (true_positives / (predicted_positives + K.epsilon()))\n return precision\n\ndef f1_m(y_true, y_pred):\n precision = precision_m(y_true, y_pred)\n recall = recall_m(y_true, y_pred)\n return 2*((precision*recall)/(precision+recall+K.epsilon()))\n"},"script_size":{"kind":"number","value":24712,"string":"24,712"}}},{"rowIdx":947,"cells":{"path":{"kind":"string","value":"/Blap14_1_ha.ipynb"},"content_id":{"kind":"string","value":"c41f5a5016911094609b7be9ccc14dfe9de909d6"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"P-R-McWhirter/BLAP_spectra_analysis"},"repo_url":{"kind":"string","value":"https://github.com/P-R-McWhirter/BLAP_spectra_analysis"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":12343,"string":"12,343"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\n# Import what we need for the script.\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n# %matplotlib inline\nfrom EqW import *\nfrom tqdm import tqdm_notebook as tqdm\n\n\n# +\n# Create a function which generates a gaussian.\n\ndef gaussian(x, mu, sig, pwr):\n return pwr * (np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))))\n\n\n# +\n# Define a continuum fit function.\n\ndef region_around_line(w, flux, cont, pf = 0):\n '''cut out and normalize flux around a line\n\n Parameters\n ----------\n w : 1 dim np.ndarray\n array of wanvelenghts\n flux : np.ndarray of shape (N, len(w))\n array of flux values for different spectra in the series\n cont : list of lists\n wavelengths for continuum normalization [[low1,up1],[low2, up2]]\n that described two areas on both sides of the line\n '''\n #index is true in the region where we fit the polynomial\n indcont = ((w > cont[0][0]) & (w < cont[0][1])) |((w > cont[1][0]) & (w < cont[1][1]))\n #index of the region we want to return\n indrange = (w > cont[0][0]) & (w < cont[1][1])\n fluxmean = np.mean(flux[:,np.where(indcont)])\n # make a flux array of shape\n # (nuber of spectra, number of pointsin indrange)\n f = np.zeros((flux.shape[0], indrange.sum()))\n for i in range(flux.shape[0]):\n # fit polynom of second order to the continuum region\n linecoeff = np.polyfit(w[indcont], flux[i, indcont], pf)\n # devide the flux by the polynom and put the result in our\n # new flux array\n f[i,:] = flux[i,indrange]/np.polyval(linecoeff, w[indrange])\n if fluxmean < 0:\n f = -f\n return w[indrange], f\n\n\n# +\n# Define error functions for the optimisation of the gaussian fit. Penalise fits far from the Ha line using regularisation (check if this is appropriate).\n\ndef error(data, flux, wavelength):\n mu, sig, pwr = data\n if sig < 1.8 or sig > 25:\n return np.inf\n fit = gaussian(wavelength, mu, sig, pwr)\n return np.sum(np.power(flux - fit, 2.)) + 0.01 * np.power(mu - 6563, 2.)\n\ndef error2(data, flux, wavelength):\n mu, sig, pwr = data\n if sig < 1.8 or sig > 25:\n return np.inf\n fit = gaussian(wavelength, mu, sig, pwr)\n return np.power(flux - fit, 2.)\n\n\n# +\n# Import the scipy.optimize.minimize function\n\nfrom scipy.optimize import minimize\n\n# +\n# Import SpectRes package to rebin the gaussian into the spectrum wavelength bins whilst conserving flux.\n\nfrom spectres import spectres\n\n\n# +\n# Define a new error function using SpectRes for the optimisation.\n\ndef reerr(data, w, f, gauw):\n mu, sig, pwr = data\n if sig < 1.8 or sig > 25:\n return np.inf\n res_fluxes = spectres(w, gauw, gaussian(gauw, mu, sig, pwr))\n return np.sum(np.power(f - res_fluxes, 2.)) + 0.1 * np.power(mu - 6563, 2.)\n\n\n# -\n\ndef halinefit(file, rang, quiet = False, cfit = 0):\n \n flux = np.load(file)\n wavelength = np.load('wavelength.npy')\n \n wha, fha = region_around_line(wavelength, np.reshape(flux, (1, np.size(flux))), rang, pf = cfit)\n fha = np.reshape(fha, np.size(wha))\n \n if not quiet:\n plt.plot(wavelength, flux)\n plt.xlim((rang[0][0]-10,rang[1][1]+10))\n plt.ylim((-0.4e-17,0.2e-17))\n plt.show()\n \n x0 = np.array((6563, 10, -5))\n gauw = np.linspace(rang[0][0]-10, rang[1][1]+10, 1000)\n res = minimize(reerr, x0, args=(wha, fha, gauw), method='Nelder-Mead', tol=1e-6)\n \n if not quiet:\n plt.plot(wha, fha)\n plt.plot(gauw, gaussian(gauw, res.x[0], res.x[1], res.x[2]))\n \n res_spec = spectres(wha, gauw, gaussian(gauw, res.x[0], res.x[1], res.x[2]))\n \n if not quiet:\n plt.show()\n \n cont = fha - res_spec\n \n if not quiet:\n plt.plot(wha, cont)\n plt.show()\n \n ew = (np.sum(gaussian(gauw, res.x[0], res.x[1], res.x[2]))/res.x[2])*(gauw[1]-gauw[0])\n \n snr = np.abs(res.x[2]) / np.std(cont)\n \n if not quiet:\n print(res.x)\n \n print(np.std(cont))\n \n print(snr)\n \n quans = np.quantile(cont, [0.05, 0.95])\n \n return ew, snr, quans[0], quans[1], res.x[0]\n \n\n\n# +\nnp.random.seed(10)\n\news = np.zeros(1000)\nsnrs = np.zeros(1000)\nconts_low = np.zeros(1000)\nconts_high = np.zeros(1000)\nwls = np.zeros(1000)\n\nfor i in tqdm(range(1000)):\n a1 = 0\n a2 = 0\n while np.abs(a2 - a1) < 20:\n a1 = np.random.uniform(low = 6000, high = 6550)\n a2 = np.random.uniform(low = 6000, high = 6550)\n if a2 < a1:\n h = a1\n a1 = a2\n a2 = h\n \n b1 = 0\n b2 = 0\n while np.abs(b2 - b1) < 20:\n b1 = np.random.uniform(low = 6600, high = 7200)\n b2 = np.random.uniform(low = 6600, high = 7200)\n if b2 < b1:\n h = b1\n b1 = b2\n b2 = h\n \n rang = [[a1, a2],[b1, b2]]\n ews[i], snrs[i], conts_low[i], conts_high[i], wls[i] = halinefit('blap14_group1_mean_subtracted.npy', rang, quiet = True)\n# -\n\nquans_ews = np.quantile(ews, [0.05, 0.5, 0.95])\nquans_ews\n\nnp.std(ews[(ews > quans_ews[0]) & (ews < quans_ews[2])])\n\nquans_snrs = np.quantile(snrs, [0.05, 0.5, 0.95])\nquans_snrs\n\nnp.std(snrs[(snrs > quans_snrs[0]) & (snrs < quans_snrs[2])])\n\nquans_conts_low = np.quantile(conts_low, [0.05, 0.5, 0.95])\nquans_conts_low\n\nnp.std(conts_low[(conts_low > quans_conts_low[0]) & (conts_low < quans_conts_low[2])])\n\nquans_conts_high = np.quantile(conts_high, [0.05, 0.5, 0.95])\nquans_conts_high\n\nnp.std(conts_high[(conts_high > quans_conts_high[0]) & (conts_high < quans_conts_high[2])])\n\nquans_wls = np.quantile(wls, [0.05, 0.5, 0.95])\nquans_wls\n\nnp.std(wls[(wls > quans_wls[0]) & (wls < quans_wls[2])])\n"},"script_size":{"kind":"number","value":5925,"string":"5,925"}}},{"rowIdx":948,"cells":{"path":{"kind":"string","value":"/Foundations of ballot polling.ipynb"},"content_id":{"kind":"string","value":"c98f7580ebc561661f24bdd61ff1b3579d00edb1"},"detected_licenses":{"kind":"list like","value":["BSD-2-Clause"],"string":"[\n \"BSD-2-Clause\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"umbernhard/rlamath"},"repo_url":{"kind":"string","value":"https://github.com/umbernhard/rlamath"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":42065,"string":"42,065"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.pylab import rcParams\nrcParams['figure.figsize'] =15,9\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.seasonal import seasonal_decompose\nfrom statsmodels.tsa.arima_model import ARIMA\nimport statsmodels.api as sm\nfrom sklearn.metrics import mean_squared_error\n\ndf = pd.read_csv('Queen_1year_Raw.csv')\n\ndf.tail()\n\ndf.head(20)\n\ndf.shape\n\ndf.info()\n\ndf['start_time'] = pd.to_datetime(df.ArrivalTime).astype('datetime64[s]')\ndf['end_time'] = pd.to_datetime(df.DepartureTime).astype('datetime64[s]')\n\ndf['start_from_fc']= df.start_time.dt.floor('5min')\ndf['end_from_fc'] = df.end_time.dt.ceil('5min')\n\ndf.head()\n\ndf.dtypes\n\nn = (df.end_from_fc - df.start_from_fc)\n# n,(n.dt.total_seconds())/60\ndf['diff'] = (n.dt.total_seconds())/60\n\ndf.head()\n\ndf['slots'] = df['diff']/5\ndf['slots']=df['slots'].astype('int64')\ndf['bins'] = df.slots.map( lambda x: np.arange(1,x+1,1) if x > 1.0 else np.arange(1,x+1))\n\ndf['start'] = df.start_time.dt.date\ndf['start'] = pd.to_datetime(df.start,format='%Y\\%m\\%d')\n\ndf.tail()\n\n\ndef explode(df, lst_cols, fill_value=''):\n if lst_cols and not isinstance(lst_cols, list):\n lst_cols = [lst_cols]\n idx_cols = df.columns.difference(lst_cols)\n lens = df[lst_cols[0]].str.len()\n if (lens > 0).all():\n return pd.DataFrame({\n col:np.repeat(df[col].values, lens)\n for col in idx_cols\n }).assign(**{col:np.concatenate(df[col].values) for col in lst_cols}) \\\n .loc[:, df.columns]\n else:\n return pd.DataFrame({\n col:np.repeat(df[col].values, lens)\n for col in idx_cols\n }).assign(**{col:np.concatenate(df[col].values) for col in lst_cols}) \\\n .append(df.loc[lens==0, idx_cols]).fillna(fill_value) \\\n .loc[:, df.columns]\n\n\nnew_df=explode(df, ['bins'], fill_value='')\n\nnew_df.loc[new_df['bins']==\"\"]\n\nnew_df['bins'] = (new_df.start_from_fc + pd.to_timedelta(5*(new_df['bins']), unit='m'))\nnew_df['bins1'] = new_df.bins - pd.to_timedelta(5, unit='m')\n\nnew_df.groupby(['bins1','bins']).count()[['start_from_fc']]\n\ndf_5min = new_df.groupby(['bins1','bins']).count()[['start_from_fc']].add_suffix('_Count').reset_index()\n\ndf_5min.rename(columns={'bins1':'start_time','bins':'end_time'\n ,'start_from_fc_Count':'no_of_cars'}\n ,inplace=True)\n\ndf_5min.tail(50)\n\ndf_all_sensors_1stweek = df_5min.groupby([df_5min.start_time.dt.dayofweek])\\\n.sum()[['no_of_cars']].add_suffix('_Count').reset_index()\n\ndf_all_sensors_1stweek.set_index('start_time').plot()\n\ndf_all_sensors_1stweek\n\n\n\ndf_5min = pd.read_csv('queen.csv')\n\ndf_5min.columns\n\ndf_5min['start_time'] = pd.to_datetime(df_5min.start_time).astype('datetime64[s]')\n\ndf_all_sensors_1stweek = df_5min.groupby([df_5min.start_time.dt.dayofweek]).sum()[['no_of_cars']].add_suffix('_Count').reset_index()\n\ndf_all_sensors_1stweek.set_index('start_time').plot()\n\ndf_all_sensors_1stweek\n\ndf_5min.dtypes\n\ndf_5min.loc[(df_5min['start_time']>='2017-08-03') & \n (df_5min['start_time']<='2017-08-10')].groupby([df_5min.start_time.dt.date]).count()[['no_of_cars']]\n\ndf_5min.drop(columns={'Unnamed: 0'},inplace=True)\n\ndf_5min = df_5min.loc[(df_5min['start_time']<='2017-08-27') & (df_5min['start_time']>='2017-08-03')]\n\ndf_tst_5min= df_5min[['start_time','no_of_cars']]\ndf_tst_5min = df_tst_5min.set_index('start_time')\n\ndf_tst_5min.head()\n\ndf_tst_5min.plot(figsize=(15,9))\n\nindexedDataset_logScale = np.log(df_tst_5min)\nindexedDataset_logScale.plot(figsize=(15,9))\n\n\ndef test_stationarity(timeseries):\n \n movingAverage = timeseries.rolling(window=50).mean()\n movingSTD = timeseries.rolling(window=50).std()\n \n orig = plt.plot(timeseries, color='blue', label='Original')\n mean = plt.plot(movingAverage, color='red', label='Rolling Mean')\n std = plt.plot(movingSTD, color='black', label='Rolling Std')\n plt.legend(loc='best')\n plt.title('Rolling Mean & Standard Deviation')\n plt.show(block=False)\n \n print('Results of Dickey Fuller Test:')\n dftest = adfuller(timeseries['no_of_cars'], autolag='AIC')\n dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n for key,value in dftest[4].items():\n dfoutput['Critical Value (%s)'%key] = value\n print(dfoutput)\n\n\ntest_stationarity(indexedDataset_logScale)\n\nfrom statsmodels.graphics.tsaplots import plot_acf\nfig=plt.figure(figsize=(13,8))\nax1=fig.add_subplot(211)\nplot_acf(indexedDataset_logScale,lags=50,ax=ax1)\n\nfrom statsmodels.graphics.tsaplots import plot_pacf\nfig=plt.figure(figsize=(13,8))\nax1=fig.add_subplot(211)\nplot_pacf(indexedDataset_logScale,lags=50,ax=ax1)\n\n# +\nmodel = ARIMA(indexedDataset_logScale, order=(8,0,0))\nresults_AR = model.fit(disp=-1)\nplt.plot(indexedDataset_logScale, color='green',label='Actual Values')\nplt.plot(results_AR.fittedvalues, color='red', label='Predicted Values')\nplt.legend(loc='best')\nplt.title('RSS: %.4f'%sum((results_AR.fittedvalues - indexedDataset_logScale['no_of_cars'])**2))\nprint('Plotting AR model')\n\nprint(\"Lag: \", results_AR.aic, results_AR.bic, results_AR.hqic)\n# -\n\narma_model_2 = sm.tsa.ARMA(indexedDataset_logScale, (2, 0)).fit(disp=False)\narma_model_3 = sm.tsa.ARMA(indexedDataset_logScale, (3, 0)).fit(disp=False)\narma_model_5 = sm.tsa.ARMA(indexedDataset_logScale, (5, 0)).fit(disp=False)\narma_model_7 = sm.tsa.ARMA(indexedDataset_logScale, (7, 0)).fit(disp=False)\narma_model_10 = sm.tsa.ARMA(indexedDataset_logScale, (10, 0)).fit(disp=False)\n\nprint(\"Lag 2: \", arma_model_2.aic, arma_model_2.bic, arma_model_2.hqic)\nprint(\"Lag 3: \", arma_model_3.aic, arma_model_3.bic, arma_model_3.hqic)\nprint(\"Lag 5: \", arma_model_5.aic, arma_model_5.bic, arma_model_5.hqic)\nprint(\"Lag 7: \", arma_model_7.aic, arma_model_7.bic, arma_model_7.hqic)\nprint(\"Lag 10: \", arma_model_10.aic, arma_model_10.bic, arma_model_10.hqic)\n\nfrom pyramid.arima import auto_arima\n\n# !pip install pyramid-arima\n\nstepwise_model = auto_arima(df_tst_5min, start_p=1, start_q=1,\n max_p=3, max_q=3, m=12,\n start_P=0, seasonal=True,\n d=1, D=1, trace=True,\n error_action='ignore', \n suppress_warnings=True, \n stepwise=True)\nprint(stepwise_model.aic())\n\nX = indexedDataset_logScale.values\nX1= df_tst_5min.values\nsize = int(len(X) * 0.66)\ntrain, test = X[0:size], X[size:len(X)]\nsize1 = int(len(X1) * 0.66)\ntrain1, test1 = X1[0:size1], X1[size1:len(X1)]\nprint(train.shape)\nprint(test.shape)\n\nhistory = [x for x in train1]\npredictions = list()\nfor t in range(len(test1)):\n\tmodel = ARIMA(history, order=(4,1,0))\n\tmodel_fit = model.fit(disp=0)\n\toutput = model_fit.forecast()\n\tyhat = output[0]\n\tpredictions.append(yhat)\n\tobs = test1[t]\n\thistory.append(obs)\n# \tprint('predicted=%f, expected=%f' % (yhat, obs))\nerror = (mean_squared_error(test1, predictions))\nprint('Test MSE: %.3f' % error)\n# plot\nplt.plot(test1, color='green',label='Actual Values')\nplt.plot(predictions, color='red', label='Predicted Values',alpha=0.6)\nplt.legend(loc='best')\nplt.title('Actual vs Predicted Values')\nplt.show(block=False)\n\nn = sm.tsa.SARIMAX(test1, order=(10,1,1), seasonal_order=(10,1,1,150)).fit(disp=False)\nprint(n.aic)\nj = n.predict(0,2465)\nplt.plot(test1)\nplt.plot(j)\nplt.plot(figsize=(25,12))\n\ndf_tst_5min.head()\n\n\n"},"script_size":{"kind":"number","value":7712,"string":"7,712"}}},{"rowIdx":949,"cells":{"path":{"kind":"string","value":"/binder/atom_rules.ipynb"},"content_id":{"kind":"string","value":"a7bfa6cfac7549c99357ff2649e7bc93b67d062f"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"LoLab-VU/pyvipr"},"repo_url":{"kind":"string","value":"https://github.com/LoLab-VU/pyvipr"},"star_events_count":{"kind":"number","value":38,"string":"38"},"fork_events_count":{"kind":"number","value":4,"string":"4"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":3709,"string":"3,709"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ShuZhe_022 % [d]2021-01-22; [xmxp](); [n]爷爷的金子; [l]绘本; [t]Grandpa's Gold; [by]Kerry Saadien-Raad; Elsabé Milandri; Mathilde de Blois; [p/s]bookdash; [ebook](https://bookdash.org/books/grandpas-gold-by-kerry-saadien-raad-elsabe-milandri-and-mathilde-de-blois/);\n#\n# ---\n\n# ![01](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_01.jpg) \n#\n# ---\n#\n# ### 爷爷的金子 \n#\n# Kerry Saadien-Raad | Elsabé Milandri | Mathilde de Blois \n#\n# ---\n#\n# 动物宝宝们在谈论他们的爷爷. \n#\n# ![05](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_05.jpg) \n#\n# ---\n#\n# 长颈鹿宝宝说(giraffe), 我的爷爷可以建造插入天空的高塔, 他曾经为国王建造了一栋摩天大楼(skyscraper). \n#\n# ![06](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_06.jpg) \n#\n# ---\n#\n# 猎豹宝宝(Cheetah)说, 我的爷爷可以钓到大海里任意的鱼, 他曾经钓了一只鲸鱼(whale), 并把它放到了自己的浴缸(bath). \n#\n# ![07](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_07.jpg) \n#\n# ---\n#\n# 山羊宝宝(Mountain Goat)说, 我的爷爷可以爬到天上的云朵里去, 他只用了四个小时就爬上了世界上最高的山(the tallest mountain in the world). \n#\n# ![08](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_08.jpg) \n#\n# ---\n#\n# 大象宝宝(Elephant)说, 我的爷爷可以烹饪盛宴(cook a feast), 他曾经一个人(all by himself)就为总统的(president's)生日派对(birthday party)供应了所有的伙食. \n#\n# ![09](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_09.jpg) \n#\n# ---\n#\n# 猴子宝宝(Monkey)说, 好吧, 我的爷爷既不会造房子(build), 也不会爬山(climb), 也不会做饭(cook), 但是他有金子! 他把金子藏在了自己的嘴里, 每天晚上的时候还会把金子泡在(soak)玻璃杯中的水里. \n#\n# ![10](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_10.jpg) \n#\n# ---\n#\n# 动物宝宝们都不相信猴子宝宝所说的话, 现场陷入了一阵混乱. 猴子宝宝说, 好吧, 如果你们不相信我的话(if you dont belive me), 就跟着我去看看好啦(come and see). \n#\n# ![11](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_11.jpg) \n#\n# ---\n#\n# 于是, 动物宝宝们一起相约去看猴子宝宝的爷爷了. \n#\n# ![12](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_12.jpg) \n#\n# ---\n#\n# 猴子说, 你们看到了吗(You see)? 他还能把它们拿出来呢! \n#\n# ![13](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_13.jpg) \n#\n# ---\n#\n# 动物宝宝们大喊着, 这不可能! \n#\n# ![14](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_14.jpg) \n#\n# ---\n#\n# 猴子爷爷\"啊~\"了一句, 便把假牙拿了出来. 还说, 我这里还有足够的... \n#\n# ![15](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_15.jpg) \n#\n# ---\n#\n# 给每个人的金子... \n#\n# ![16](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_16.jpg) \n#\n# ---\n#\n# 哈~ 原来只是一副假牙(denture)啊! \n#\n# ![18](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_18.jpg) \nange(len(targets)), targets]\n return -out.sum()/len(out)\n\n\n# + id=\"XxuHQJUbX_qr\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739011808, \"user_tz\": -330, \"elapsed\": 25, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef log_softmax_crossentropy_with_logits(logits,target):\n\n out = np.zeros_like(logits)\n out[np.arange(len(logits)),target] = 1\n \n softmax = np.exp(logits) / np.exp(logits).sum(axis=-1,keepdims=True)\n \n return (- out + softmax) / logits.shape[0]\n\n\n# + id=\"WMo5G_2tYC_J\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739011809, \"user_tz\": -330, \"elapsed\": 25, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef forward(context_idxs, theta):\n m = embeddings[context_idxs].reshape(1, -1)\n n = linear(m, theta)\n o = log_softmax(n)\n \n return m, n, o\n\n\n# + id=\"6zHc5qNFYGxe\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739011810, \"user_tz\": -330, \"elapsed\": 25, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef backward(preds, theta, target_idxs):\n m, n, o = preds\n \n dlog = log_softmax_crossentropy_with_logits(n, target_idxs)\n dw = m.T.dot(dlog)\n \n return dw\n\n\n# + id=\"lydbVt9iYJyk\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739011813, \"user_tz\": -330, \"elapsed\": 27, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef optimize(theta, grad, lr=0.03):\n theta -= grad * lr\n return theta\n\n\n\n# + id=\"s1CGhuV1YNyu\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012499, \"user_tz\": -330, \"elapsed\": 39, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ntheta = np.random.uniform(-1, 1, (2 * context_size * embed_dim, vocab_size))\n\n# + id=\"ht7FhmzJYRjC\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012501, \"user_tz\": -330, \"elapsed\": 37, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\nepoch_losses = {}\n\nfor epoch in range(80):\n\n losses = []\n\n for context, target in data:\n context_idxs = np.array([word_to_ix[w] for w in context])\n preds = forward(context_idxs, theta)\n\n target_idxs = np.array([word_to_ix[target]])\n loss = NLLLoss(preds[-1], target_idxs)\n\n losses.append(loss)\n\n grad = backward(preds, theta, target_idxs)\n theta = optimize(theta, grad, lr=0.03)\n \n \n epoch_losses[epoch] = losses\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 328} id=\"-_TpJjTkYVht\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012505, \"user_tz\": -330, \"elapsed\": 38, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}} outputId=\"25a3db32-0380-4dd2-bbf6-e179c411e230\"\nix = np.arange(0,80)\n\nfig = plt.figure()\nfig.suptitle('Epoch/Losses', fontsize=20)\nplt.plot(ix,[epoch_losses[i][0] for i in ix])\nplt.xlabel('Epochs', fontsize=12)\nplt.ylabel('Losses', fontsize=12)\n\n\n# + id=\"EzU29aPPYaL5\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012508, \"user_tz\": -330, \"elapsed\": 33, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef predict(words):\n context_idxs = np.array([word_to_ix[w] for w in words])\n preds = forward(context_idxs, theta)\n word = ix_to_word[np.argmax(preds[-1])]\n \n return word\n\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 35} id=\"DObq7FJCYekO\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012510, \"user_tz\": -330, \"elapsed\": 32, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}} outputId=\"77acb0ca-bb0c-44f1-e06f-ed4e342f5c85\"\npredict(['we', 'are', 'to', 'study'])\n\n\n# + id=\"fmgdZ_LzYh2m\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012512, \"user_tz\": -330, \"elapsed\": 29, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}}\ndef accuracy():\n wrong = 0\n\n for context, target in data:\n if(predict(context) != target):\n wrong += 1\n \n return (1 - (wrong / len(data)))\n\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"-diBISHTYlea\" executionInfo={\"status\": \"ok\", \"timestamp\": 1623739012513, \"user_tz\": -330, \"elapsed\": 29, \"user\": {\"displayName\": \"20WH1DB008 VALUGUBELLY ANUPAMA\", \"photoUrl\": \"\", \"userId\": \"02251695447687599737\"}} outputId=\"b6f7f82c-4317-4858-c237-69f1b2722f3b\"\naccuracy()\n"},"script_size":{"kind":"number","value":7736,"string":"7,736"}}},{"rowIdx":950,"cells":{"path":{"kind":"string","value":"/conv1d_demo.ipynb"},"content_id":{"kind":"string","value":"43e29a446a52ada3b5463c01410680020e13066e"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"guanguanboy/TestPytorch"},"repo_url":{"kind":"string","value":"https://github.com/guanguanboy/TestPytorch"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":4079,"string":"4,079"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # 深入分析一维卷积nn.Conv1d\n#\n# - Applies a 1D convolution over an input signal composed of several input planes\n#\n# - 因为一维卷积是在最后维度上扫的\n#\n# Shape:\n# - Input: :math:`(N, C_{in}, L_{in})`\n# - Output: :math:`(N, C_{out}, L_{out})` where\n#\n# .. math::\n# L_{out} = \\left\\lfloor\\frac{L_{in} + 2 \\times \\text{padding} - \\text{dilation}\n# \\times (\\text{kernel\\_size} - 1) - 1}{\\text{stride}} + 1\\right\\rfloor\n# \n# Lout = (Lin + 2*padding - kernel_size)/stride + 1\n# \n# - https://www.jianshu.com/p/45a26d278473\n# - https://blog.csdn.net/sunny_xsc1994/article/details/82969867\n#\n\n# +\nimport torch\nimport torch.nn as nn\n\nm = nn.Conv1d(16, 33, 3, stride=2)\ninput = torch.randn(20, 16, 50)\noutput = m(input)\nprint(output.shape) #torch.Size([20, 33, 24])\n# -\n\nconv1 = nn.Conv1d(in_channels=256, out_channels=100, kernel_size=2)\ninput = torch.randn(32, 35, 256)\ninput = input.permute(0, 2, 1)\noutput = conv1(input)\nprint(output.shape) #torch.Size([32, 100, 34])\n\n# - https://zhuanlan.zhihu.com/p/95058866\n\nm = nn.Conv1d(3,2,2)\ninput = torch.randn(4,3,5)\nprint(input)\noutput = m(input)\nprint(output)\n \n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if nx < 0 or nx >= n or ny < 0 or ny >= n:\n continue\n if graph[nx][ny] == 0:\n continue\n \n if not visited[nx][ny]:\n dfs(graph, visited, (nx, ny))\n\n \n \nn = int(input())\ngraph = [] \nvisited =[[False] * n for _ in range(n)]\n\nfor _ in range(n):\n a = input() \n graph.append(list(map(int, a)))\n\nresult = []\nfor i in range(n):\n for j in range(n):\n res = 0\n if not visited[i][j] and graph[i][j] != 0: \n dfs(graph, visited, (i, j))\n result.append(res)\n \nresult.sort() \nprint(len(result))\nfor i in range(len(result)):\n print(result[i])\n# -\n\n# ## 문제 1012. 유기농 배추 (O)\n\n# +\n## import sys\n# input = sys.stdin.readline\n\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1 , 1]\n\ndef dfs(graph, x, y):\n graph[x][y] = 0\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if nx < 0 or nx >= m or ny < 0 or ny >= n:\n continue\n if graph[nx][ny] == 0:\n continue\n dfs(graph, nx, ny)\n \n\nfor i in range(int(input())):\n m, n, k = map(int, input().split())\n graph = [[0] * n for _ in range(m)]\n result = 0\n for _ in range(k):\n x, y = map(int, input().split())\n graph[x][y] = 1\n# print(graph)\n for i in range(m):\n for j in range(n):\n if graph[i][j] == 1:\n dfs(graph, i, j)\n result += 1\n print(result)\n\n\n# -\n\n# ## 문제 2606. 바이러스 (O)\n\n# +\ndef dfs(graph, start, result, visited):\n result.append(start)\n visited[start] = True\n for i in graph[start]:\n if not visited[i]:\n dfs(graph, i, result, visited)\n\nn = int(input())\nk = int(input())\nstart = 1\nvisited = [False] * (n+1)\ngraph = [[] for _ in range(n+1)]\n\nresult = []\nfor _ in range(k):\n a, b = map(int, input().split())\n graph[a].append(b)\n graph[b].append(a)\n\n\ndfs(graph, start, result, visited)\n\nprint(len(result)-1)\n# -\n\n# ## 문제 1987. 알파벳 (x, 시간초과)\n\n# +\n# 예시 답안, alpha리스트를 통해 중복검사\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\nr, c = map(int, input().split())\ngraph = [list(map(lambda x: ord(x) - 65, input().rstrip())) for _ in range(r)]\nalpha = [0] * 26 \n\ndef dfs(start, cnt):\n global result\n result = max(result, cnt)\n\n x, y = start\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n \n if 0 <= nx < r and 0 <= ny < c and alpha[graph[nx][ny]] == 0:\n alpha[graph[nx][ny]] = 1\n dfs((nx, ny), cnt + 1)\n alpha[graph[nx][ny]] = 0\n\n\nresult = 1\nalpha[graph[0][0]] = 1\ndfs((0,0), 1)\nprint(result)\n\n\n# -\n\n# ## 문제 11724. 연결 요소의 개수(O)\n\n# +\ndef dfs(graph, node):\n visited[node] = True\n \n for i in graph[node]:\n if not visited[i]:\n dfs(graph, i)\n\nn, m = map(int, input().split())\ngraph = [[] for _ in range(n+1)]\nvisited = [False] * (n+1)\nfor _ in range(m):\n a, b = map(int, input().split())\n graph[a].append(b)\n graph[b].append(a)\n \nresult = 0\nfor i in range(1, n+1):\n if not visited[i]:\n dfs(graph, i)\n result += 1\n \nprint(result)\n"},"script_size":{"kind":"number","value":4568,"string":"4,568"}}},{"rowIdx":951,"cells":{"path":{"kind":"string","value":"/diagno/DrDiagno.ipynb"},"content_id":{"kind":"string","value":"ac62994df4670823aea643b03f3df60124ad41a1"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"GanapathyPT/Dr.Diagno_Backend"},"repo_url":{"kind":"string","value":"https://github.com/GanapathyPT/Dr.Diagno_Backend"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":245249,"string":"245,249"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom catboost import CatBoostClassifier\nimport joblib\nimport pickle\n\ndf = pd.read_csv(\"dataset.csv\")\npd.set_option(\"display.max_columns\", None)\ndf\n\ndf.isnull().sum()\n\ncols = [i for i in df.iloc[:,1:].columns]\ntmp = pd.melt(df.reset_index() ,id_vars = ['index'], value_vars = cols )\ntmp['found'] = 1\ntmp.head(40)\n\nnewdf = pd.pivot_table(tmp, \n values = 'found',\n index = 'index',\n columns = 'value')\nnewdf.insert(0,'label',df['Disease'])\nnewdf = newdf.fillna(0)\nnewdf\n\nX = newdf.drop('label',axis=1)\ny = newdf['label']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0, stratify=y)\n\nrfc = CatBoostClassifier(iterations=2000, eval_metric = \"AUC\")\nrfc.fit(X_train, y_train)\n\ny_pred = rfc.predict(X_test)\nprint(\"Confustion Matrix:\", confusion_matrix(y_test,y_pred))\nprint(\"Accuracy:\", accuracy_score(y_test,y_pred)*100)\nprint(classification_report(y_test,y_pred))\n\npd.DataFrame(y_pred, y_test)\n\nlst = []\nsym_dict = {}\nfor i in newdf.columns:\n sym_dict[i]=0\n lst.append(i)\nprint(sym_dict)\n\nnewdf.columns\n\ny_pred = rfc.predict_proba(X_test)\n\ny_pred\n\nnewdf[\"label\"].unique()\n\na = np.zeros((1, 131))\na\n\n# +\n\ny_pred = rfc.predict_proba(X_tes)\ny_pred1 = rfc.predict(X_tes)\n# -\n\ny_pred\n\ny_pred1\n\njoblib.dump(rfc, \"rf.pkl\")\n\nX_tes = [[1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.]]\nmodel = pickle.load(open(\"D:\\AI Proj\\My Own Projects\\RainPrediction\\models\\cat.pkl\", \"rb\"))\nmodel.predict(X_tes)\n\n\ndu.com/index.php?title=webapi/guide/webservice-geocoding\n address = area\n url = 'http://api.map.baidu.com/geocoder/v2/?address=' + address + '&output=json&ak=' + ak\n json_data = requests.get(url = url).json() \n coor_loc = json_data['result']['location']\n return coor_loc\n except:\n return \"nocoor\" # 不换ak多半都是 'nocoor'\n \nprint(df.loc[0,'area']) \narea2coor(df.loc[0,'area'])\n# -\n\n# ## 经纬度\n# 上面先测试下,能拿到经纬度后,创建经纬度列\n\n# %%time\ndf['coor_loc'] = df.area.apply(area2coor)\ndf.coor_loc\n\n# 确定`'coor_loc'`列是字典形式后,就可以直接从字典中拿到经度和维度了。\n\ncoor1 = df.coor_loc.values.tolist()\ntype(coor1),coor1\n\n# ## 拆分经度纬度\n# 选出非'nocoor'的数据,再分别拿到经度和纬度,然后就可以导出数据,以便后面在BDP里操作。\n\ndf_coor = df[df['coor_loc'] != 'nocoor']\ndf_coor['lng'] = df_coor['coor_loc'].apply(lambda x: x['lng']) # 经度\ndf_coor['lat'] = df_coor['coor_loc'].apply(lambda x: x['lat']) # 纬度\ndf_coor[['lng','lat']]\n\n# ## 保存数据\n\ndf_coor.to_csv('Sina_Finance_Comments_All_20180811_toBDP.csv', encoding='utf-8', line_terminator='\\r\\n')\n\n# ## 动态热力图\n# 古柳以前也用过 BDP,所以这回拿到数据后,就想着间隔近一年的时间后重新绘制动态热力图,虽则早已生疏了,但以前机智的写过一篇“使用手册”:[(送福利)BDP绘制微博转发动态热力图](https://zhuanlan.zhihu.com/p/29557747),于是按照文中步骤很快就重新捡回并制作出来了。\n#\n# 具体步骤就不截图演示了,更详细的步骤请参考上面给出的文章,内含爬取的微博转发数据集,可供把玩(用Gephi一则热门微博的14层转发网络图谱:[《Gephi绘制微博转发图谱:以“@老婆孩子在天堂”为例》](https://zhuanlan.zhihu.com/p/29557827))\n# \n# \n#\n# 此处仅记录大致操作步骤如下:\n# - 网上搜索:[BDP个人版](https://me.bdp.cn/home.html),注册账号以便使用;\n# - 点击“数据源”,点击“立即添加”,点击“CSV上传”,按照跳出的页面,上传本地对应的CSV文件,“逗号”分割,确定后,等待上传成功后,就能看到数据,此处将相应的时间列,设定为日期,否则后面动态展示时可能会出错。点击下一步,改不改文件名,目录,随意,之后下一步,完成数据上传;\n# - 点击菜单栏右上角“新建图表”,选择“经纬度地图”后确定;\n# - 经度选择上传的CSV数据里的“lng”列,纬度选择“lat”列,坐标系选择为百度地图;\n# - 将工作表中文件拖曳到图层里,就能在地图上加载出数据,非常简单地拿到了地图;\n#\n# 更改设置参数,以便录制 GIF 时展示效果更佳:\n# - 热力半径:8像素\n# - 时间粒度:按时\n# - 时间间隔:2小时 / 1小时\n# - 自定义速度:FPS:8 / 12 \n#\n# 可根据数据量、数据展示的效果、以及自身的要求自行修改。最后就拿到了文章评论的动态热力图,还是蛮酷的。\n#\n# \n\n\n"},"script_size":{"kind":"number","value":4652,"string":"4,652"}}},{"rowIdx":952,"cells":{"path":{"kind":"string","value":"/opt_jafroc.ipynb"},"content_id":{"kind":"string","value":"1089ce4abd6f8feddd8b1a20fca2e8db1b627b38"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"pyo-lee/Anlaysis_Lunit"},"repo_url":{"kind":"string","value":"https://github.com/pyo-lee/Anlaysis_Lunit"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":26914,"string":"26,914"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %load_ext autoreload\n# %autoreload 2\n\nimport sys\nr_path_data = \"../src/utils/descriptive_engine/\"\nsys.path.append(r_path_data)\nfrom descriptives import *\n\nimport time\n\n\n\n# +\nimport os\nimport sys\nr_path_data = \"../src/models/kmeans/\"\nsys.path.append(r_path_data)\nfrom kmeans import *\nfrom create_interactive_chart import *\n\n\nr_path_data = \"../src/utils/load_data/\"\nsys.path.append(r_path_data)\nfrom load_dataframes import *\n# -\n\nr_path_data = \"../src/models/sequence_analysis/data/clustering_results/cluster_results_Jul_Aug_10000_sample.csv\"\ndf = pd.read_csv(r_path_data)\n\nr_path = \"../src/utils/geolocation/\"\nsys.path.append(r_path)\nfrom preprocessing import *\n\nstr_to_list(df)\n\ndf_medoid = df[df['medoids']==1]\ndf_medoid\n\ndf_medoid=df_medoid.reset_index(drop=True)\ndf_medoid\n\nr_path = \"../src/utils/read_shapefiles/\"\nsys.path.append(r_path)\nfrom read_files_2 import *\n\ndf = read_shapefiles_in(path_to_shapefile='../src/utils/read_shapefiles/', file_name='shape_files_path.json', version='municipalities',only_tusc=False, apply_crs=True)\n\ndict_loc = dict(zip(df['PRO_COM'].values, df['COMUNE'].values))\n\nloc = list(df_medoid['locations_list'])[1]\n\nloc.pop()\n\nloc.pop(0)\n\nloc\n\n[dict_loc[int(i)] for i in loc]\n\n\n\n\n\n\n\n# +\nr_path = \"../src/utils/read_shapefiles/\"\nsys.path.append(r_path)\nfrom read_files_2 import *\n\ndef get_com_names(path_to_shapefile, file_name, version, only_tusc=False, apply_crs=True):\n \"\"\"\n Returns a dictionary with keys as PRO_COM and values as COMUNE names\n \"\"\"\n df = read_shapefiles_in(path_to_shapefile, file_name, version, only_tusc=False, apply_crs=True)\n dict_loc = dict(zip(df['PRO_COM'].values, df['COMUNE'].values))\n return dict_loc\n\n\n# -\n\ndict_loc = get_com_names(path_to_shapefile='../src/utils/read_shapefiles/', file_name='shape_files_path.json', version='municipalities',only_tusc=False, apply_crs=True)\n\n\ndef pro_com_to_comune(row):\n \"\"\"\n Returns comune name based on pro_com\n \"\"\"\n loc = list(row['locations_list']) \n # loc.pop()\n # loc.pop(0) ### removes coutry code\n return [dict_loc[int(i)] for i in loc]\n\n\n# +\nr_path = \"../src/utils/geolocation/\"\nsys.path.append(r_path)\nfrom preprocessing import *\n\nr_path_data = \"../results/sequence_analysis/Netherlands_summer/cluster_results_Netherlands_summer_0d_to_30d_WDaligned_FALSE_win_8_wCtryTRUE_N_30000_CONSTANT_LCS_NClus_4.csv\"\n\n\ndef get_medoid_comunes(r_path_data, dict_loc):\n \"\"\"\n Returns a DataFrame which includes a column that contains comune names of medoid trajectories\n \"\"\"\n df = pd.read_csv(r_path_data)\n str_to_list(df)\n df_medoid = df[df['medoids']==1]\n df_medoid = df_medoid.reset_index(drop=True)\n df_medoid['comune']=df_medoid.apply(pro_com_to_comune,1)\n return df_medoid\n\n\n# -\n\ndf_medoid = get_medoid_comunes(r_path_data, dict_loc)\n\ndf_medoid['comune']=df_medoid.apply(pro_com_to_comune,1)\ndf_medoid\n\ndf_medoid\n\n\ndef trip_list(df_row):\n # trip = list(map(int,df_row['locations_list'].split(', ')))\n trip = df_row['locations_list']\n times = list(map(int,df_row['times'].split(', ')))\n #times = df_row['times'].split(', ').tolist()\n times.append(int((pd.to_datetime(df_row['en_time']) - pd.to_datetime(df_row['st_time'])).seconds/60 - sum(times)))\n df_trip = pd.DataFrame(data={'pro_com': trip,'times':times}) \n return times\n\n\ndef trip_list(df_row):\n #trip = list(map(int,df_row['locations'].split(', ')))\n trip = df_row['locations_list']\n times = list(map(int,df_row['times'].split(', ')))\n times.append(int((pd.to_datetime(df_row['en_time']) - pd.to_datetime(df_row['st_time'])).seconds/60 - sum(times)))\n df_trip = pd.DataFrame(data={'pro_com': trip,'times':times}) \n return df_trip\n\n\ndf = trip_list(df_medoid.loc[0,])#.to_dict('list')\n\ndict(zip(df['pro_com'].values, df['times'].values))\n\ntrip_list(df_medoid.loc[0,]).to_dict\n\ndf_medoid.apply()\n\ndf_medoid\n\nlen(df_medoid)\n\n\ndef get_N_of_clusters_in_traj(df_medoid):\n N = len(df_medoid)\n return N\n\n\ndef join_medoid_customer_features(traj_result, username, season, country):\n \"\"\"\n Returns a dataframe with trajectory clustering results and customer features joined for the medoids \n Params:\n traj_result: dataframe with trajectory clustering result: customer_nr,column called cluster\n username: username to access aws\n season: season for clustering used \n country: country used for clustering (note: there is NO option for all)\n \"\"\"\n user_features=get_k_means_data(username,season, country).set_index(\"customer_nr\")\n features_with_trajectory=user_features[['hrs_in_italy']].join(traj_result.set_index('customer_nr'), how = \"inner\")\n return features_with_trajectory\n\n\nresult = join_medoid_customer_features(df_medoid, 'ywang99587','summer','Netherlands')\n\n\ndef get_time_spent_in_italy(df_medoid):\n \"\"\"\n Helper funcion, which calculates how many days customers spend in Italy\n result: clustering result with customer features\n \"\"\"\n df_medoid['days_in_italy'] = np.round(pd.DataFrame(result[['hrs_in_italy']])/24)\n return df_medoid\n\n\nget_time_spent_in_italy(result)\n\n\ndef start_and_end_trip(row):\n comunes = row['comune']\n start = comunes[0]\n end = comunes[-1]\n return (start,end)\n\n\ndef add_start_end(df_medoid):\n df_medoid['st_end_comune'] = df_medoid.apply(start_and_end_trip,1)\n return df_medoid\n\n\nadd_start_end(df_medoid)\n\n\ndef num_locs_visited_total(df_medoid):\n for i in range(len(df_medoid)):\n comunes = df_medoid.loc[i,'comune']\n df_medoid.loc[i,'num_comunes_visited'] = len(set(comunes))\n return df_medoid\n\n\nnum_locs_visited_total(df_medoid)\n\ncomunes = df_medoid.loc[1,'comune']\n\ncomunes\n\nset(comunes)\n\nstart_and_end_trip(df_medoid.loc[1,])[1]\n\n\n\n\n\n\n\n# # K-means\n\n# +\nimport json\n\nwith open('../pipeline/config_location_k_means.json') as f:\n params = json.load(f)\n \nusername = \"ywang99587\"\nseason = \"pre-summer\"\ncountry = \"all\"\nnc = 4\n#names = params[\"names\"]\ncolors = params[\"colors\"]\nmapbox_access_token = \"pk.eyJ1IjoidmFzYXJoZWx5aW8iLCJhIjoiY2prYjV2djh0M2R3NDNxbWw3dTFqdGZvbyJ9.stZ2MjMsogAYJ9fMb-lrsg\"\n# -\n\nprint(season,country)\nresult1=get_cluster_results(username,season, country, features, nc)\nnames=calculate_cluster_size(result1, 'label').index\n\ntype(names[0])\n\ndf_reg_tus=read_tusc(\"../src/utils/read_shapefiles/\")\n\ndf_reg_tus.crs\n\nnames[int(i)])\n\nclusters=calculate_cluster_size(result1, 'label')\nclusters\n\nclus = result1['label'].value_counts().index\n\nclus\n\nseq = pd.Series([0,1,2,3], index=clus)\n\nseq.index.get_loc(int(1.0))\n\ntype(clus[0])\n\nx=result1[result1['label']==clus[0]][['avg_lat', 'avg_lon', 'label']]\nx.head()\n\nx.index\n\nnames[int(clus[0])]\n\ncluster_names=calculate_cluster_size(result1, 'label').index\ncluster_names\n\nlist(zip(range(0,len(clusters)), cluster_names[:len(clusters)]))\n\nclusters.ratio.iloc[2]\n\n# +\n#get_cluster_country_distr(result1, 'label')\n# -\n\n# result1 = result1.sample(10000, replace=False)\nnames = ['City Hoppers','Coast lovers','Explorers','Countrysiders']\n\nf=plot_kmeans(result1, names, colors, country, season, mapbox_access_token)\n\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, iplot, plot\n\n#from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\ninit_notebook_mode(connected=True)\niplot(f)\n\nget_kmeans_description(result1, season, country, \"label\", nc, 5, names)\n\nseason='pre-summer'\ncountry='all'\nnc=4\nn=5\nresult=get_cluster_results('ovasarhelyi',season, country, features, nc=4)\nnames=calculate_cluster_size(result, 'label').index\nf=plot_kmeans(result, names, colors, country, season, mapbox_access_token)#iplot(f)\nget_kmeans_description(result_samp, season, country, \"label\", nc, 5, names)\n\nget_kmeans_description(result_samp, season, country, \"label\", nc, 5, names)\n\n# # Trajectories\n\npath_to_result='../src/models/sequence_analysis/data/clustering_results/'\nd=pd.read_csv(path_to_result+'cluster_results_hungary_winter.csv')\n\ntraj_result=d\ncountry = 'hungary'\nseason = 'winter'\nvar = 'cluster'\n\nnames=calculate_cluster_size(traj_result, 'cluster').index\n\nget_trajectory_description(traj_result, username, season, country, var, names,print_it=True)\n4)))\n\n#interest_list = ['u2/t2']\nprint(interest_list)\n\nwith open('jafroc_respiratory(opt_resp).txt', 'w') as csvfile:\n for interest_dir in interest_list:\n print(interest_dir)\n gt_masks = []\n human_masks = []\n for index, file_name in enumerate(mapping_cases):\n if file_name.split('-')[0] == 'B':\n hospital_name = 'brmh'\n elif file_name.split('-')[0] == 'K':\n hospital_name = 'kyuh'\n elif file_name.split('-')[0] == 'G':\n hospital_name = 'gugh'\n else:\n raise ValueError('invalid hospital name')\n\n json_root_path = 'D:/lunit/data/review_result_20200705/{}-A1/{}/respiratory'.format(hospital_name.upper(), hospital_name)\n json_file = os.path.join(json_root_path, (file_name+'.dcm.json'))\n\n# heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name)\n\n with open(json_file, \"r\") as f:\n data = json.load(f)\n\n# handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR')\n# pixel_array = handler.pixels\n# pixel_array = (pixel_array * 255).astype(np.uint8)\n height, width = data['height'], data['width']\n pixel_array = np.zeros((height,width))\n\n# mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax']\n# mca_list = ['Nodule / Mass']\n mca_list = ['Consolidation']\n# mca_list = ['Pneumothorax']\n gt_masks.append(get_gt_final_mask(data))\n\n human_root_path = 'D:/lunit/data/cxr_opt_respiratory'\n human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json'\n human_json_full = os.path.join(human_root_path, interest_dir, 'with_AI', human_json_name)\n with open(human_json_full, \"r\") as f:\n human_data = json.load(f)\n\n human_masks.append(get_human_output(pixel_array, human_data))\n\n new_shape = (512, 512)\n human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks]\n\n gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks]\n\n\n resized_human_outputs = []\n for index, human_output in enumerate(human_outputs):\n resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape))\n\n jafroc_value = metric.jafroc(resized_human_outputs, gt_masks)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value), file=csvfile)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value))\n\n jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123)\n print(jaf_ci, file=csvfile)\n print(jaf_ci)\n \ncsvfile.close()\n\n# +\nmapping_df = pd.read_csv('./data/brmh_2_mapping_table_healthcheck.csv')\nmapping_cases = mapping_df['case_no'].tolist()\n\ninterest_list = []\nfor i in range(9):\n interest_list.append('u{}_u{}'.format(str(i+2),str(i+11)))\n\n#interest_list = ['u2/t2']\nprint(interest_list)\n\nwith open('jafroc_respiratory(opt_health).txt', 'w') as csvfile:\n for interest_dir in interest_list:\n print(interest_dir)\n gt_masks = []\n human_masks = []\n for index, file_name in enumerate(mapping_cases):\n if file_name.split('-')[0] == 'B':\n hospital_name = 'brmh'\n elif file_name.split('-')[0] == 'K':\n hospital_name = 'kyuh'\n elif file_name.split('-')[0] == 'G':\n hospital_name = 'gugh'\n else:\n raise ValueError('invalid hospital name')\n\n json_root_path = 'D:/lunit/data/review_result_20200705/{}-A2/{}/healthcheck'.format(hospital_name.upper(), hospital_name)\n json_file = os.path.join(json_root_path, (file_name+'.dcm.json'))\n\n# heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name)\n\n with open(json_file, \"r\") as f:\n data = json.load(f)\n\n# handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR')\n# pixel_array = handler.pixels\n# pixel_array = (pixel_array * 255).astype(np.uint8)\n height, width = data['height'], data['width']\n pixel_array = np.zeros((height,width))\n\n# mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax']\n# mca_list = ['Nodule / Mass']\n mca_list = ['Consolidation']\n# mca_list = ['Pneumothorax']\n gt_masks.append(get_gt_final_mask(data))\n\n human_root_path = 'D:/lunit/data/cxr_opt_healthcheck'\n human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json'\n human_json_full = os.path.join(human_root_path, interest_dir, 'without_AI', human_json_name)\n with open(human_json_full, \"r\") as f:\n human_data = json.load(f)\n\n human_masks.append(get_human_output(pixel_array, human_data))\n\n new_shape = (512, 512)\n human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks]\n\n gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks]\n\n\n resized_human_outputs = []\n for index, human_output in enumerate(human_outputs):\n resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape))\n\n jafroc_value = metric.jafroc(resized_human_outputs, gt_masks)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value), file=csvfile)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value))\n\n jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123)\n print(jaf_ci, file=csvfile)\n print(jaf_ci)\n \ncsvfile.close()\n\n# +\nmapping_df = pd.read_csv('./data/brmh_2_mapping_table_healthcheck.csv')\nmapping_cases = mapping_df['case_no'].tolist()\n\ninterest_list = []\nfor i in range(9):\n interest_list.append('u{}_u{}'.format(str(i+2),str(i+11)))\n\n#interest_list = ['u2/t2']\nprint(interest_list)\n\nwith open('jafroc_respiratory(opt_health).txt', 'w') as csvfile:\n for interest_dir in interest_list:\n print(interest_dir)\n gt_masks = []\n human_masks = []\n for index, file_name in enumerate(mapping_cases):\n if file_name.split('-')[0] == 'B':\n hospital_name = 'brmh'\n elif file_name.split('-')[0] == 'K':\n hospital_name = 'kyuh'\n elif file_name.split('-')[0] == 'G':\n hospital_name = 'gugh'\n else:\n raise ValueError('invalid hospital name')\n\n json_root_path = 'D:/lunit/data/review_result_20200705/{}-A2/{}/healthcheck'.format(hospital_name.upper(), hospital_name)\n json_file = os.path.join(json_root_path, (file_name+'.dcm.json'))\n\n# heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name)\n\n with open(json_file, \"r\") as f:\n data = json.load(f)\n\n# handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR')\n# pixel_array = handler.pixels\n# pixel_array = (pixel_array * 255).astype(np.uint8)\n height, width = data['height'], data['width']\n pixel_array = np.zeros((height,width))\n\n# mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax']\n# mca_list = ['Nodule / Mass']\n mca_list = ['Consolidation']\n# mca_list = ['Pneumothorax']\n gt_masks.append(get_gt_final_mask(data))\n\n human_root_path = 'D:/lunit/data/cxr_opt_healthcheck'\n human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json'\n human_json_full = os.path.join(human_root_path, interest_dir, 'with_AI', human_json_name)\n with open(human_json_full, \"r\") as f:\n human_data = json.load(f)\n\n human_masks.append(get_human_output(pixel_array, human_data))\n\n new_shape = (512, 512)\n human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks]\n\n gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks]\n\n\n resized_human_outputs = []\n for index, human_output in enumerate(human_outputs):\n resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape))\n\n jafroc_value = metric.jafroc(resized_human_outputs, gt_masks)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value), file=csvfile)\n print(\"jafroc:\\t{:.3f}\".format(jafroc_value))\n\n jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123)\n print(jaf_ci, file=csvfile)\n print(jaf_ci)\n \ncsvfile.close()\n# -\n\n\n\n\n"},"script_size":{"kind":"number","value":17562,"string":"17,562"}}},{"rowIdx":953,"cells":{"path":{"kind":"string","value":"/HW12014.ipynb"},"content_id":{"kind":"string","value":"793435a979b9def4bce8762ae7cc7d637f95ad21"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"erikrichardlarson/HarvardCS109-HW"},"repo_url":{"kind":"string","value":"https://github.com/erikrichardlarson/HarvardCS109-HW"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":683006,"string":"683,006"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\n# +\ncelsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype = float)\nfarenheit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype = float)\n\nfor i,c in enumerate(celsius_q):\n print('{} градусов цельсия = {} градусов Фаренгейта'.format(c, farenheit_a[i]))\n# -\n\nl0 = tf.keras.layers.Dense(units=1, input_shape=[1])\n\nmodel = tf.keras.Sequential([l0])\n\nmodel.compile(loss = 'mean_squared_error', optimizer = tf.keras.optimizers.Adam(0.1))\n\nhistory = model.fit(celsius_q, farenheit_a, epochs = 500, verbose = False)\n\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.plot(history.history['loss']);\n\nprint(model.predict([100.0]))\n\nprint('это значения переменных слоя:{}'.format(l0.get_weights()) )\n\nl0= tf.keras.layers.Dense(units=4, input_shape=[1])\nl1= tf.keras.layers.Dense(units=4)\nl2= tf.keras.layers.Dense(units=1)\n\nmodel = tf.keras.Sequential([l0, l1, l2])\n\nmodel.compile(loss = 'mean_squared_error', optimizer = tf.keras.optimizers.Adam(0.1))\nmodel.fit(celsius_q, farenheit_a, epochs = 500, verbose = False)\n\nprint('значения внутренних переменных слоя1 {}'.format(l0.get_weights()))\nprint('значения внутренних переменных слоя2 {}'.format(l1.get_weights()))\nprint('значения внутренних переменных слоя3 {}'.format(l2.get_weights()))\n\n\n"},"script_size":{"kind":"number","value":1662,"string":"1,662"}}},{"rowIdx":954,"cells":{"path":{"kind":"string","value":"/30_교육과정_실습파일/courses/machine_learning/deepdive/08_image/.ipynb_checkpoints/mnist_models_review_01-checkpoint.ipynb"},"content_id":{"kind":"string","value":"b7f73b3a729216b92197a2281e6499dfe1aeb434"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"younseun/google-asl-study"},"repo_url":{"kind":"string","value":"https://github.com/younseun/google-asl-study"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":19498,"string":"19,498"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nimport warnings\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import RandomForestRegressor\n# %matplotlib inline\n\n# # Load dataset\n\n# import dataset\ntrain = pd.read_csv('train.csv')\ntest = pd.read_csv('test.csv')\nsubmit = pd.read_csv('gender_submission.csv')\n\n# # Data Description\n# Variable\tDefinition\tKey\n# survival\tSurvival\t0 = No, 1 = Yes\n# pclass\tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd\n# sex\tSex\t\n# Age\tAge in years\t\n# sibsp\t# of siblings / spouses aboard the Titanic\t\n# parch\t# of parents / children aboard the Titanic\t\n# ticket\tTicket number\t\n# fare\tPassenger fare\t\n# cabin\tCabin number\t\n# embarked\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton\n\n# # Find the missing values\n#\n# Age and Cabin are missing values in train and test set\n\ntrain.info()\n\ntrain.isnull().mean()\n\ntest.info()\n\ntest.isnull().mean()\n\n# # Statistics of dataset\n\ntrain.describe()\n\ntest.describe()\n\n# # Combining train and test set \n#\n# Before preprocessing, we conbine train and test set.This step will let us more convenience.\n# Beacause we preprocess the missing values just once. \n#\n# After combining train and test set, we have to reset our index or index order will be mess.\n\ndata = train.append(test)\n\ndata.reset_index(inplace=True, drop=True)\n\n# # Starting Analysis data\n#\n# Using the seaborn to visualize data's relation between Survived.\n#\n# Pclass vs Survived\n#\n# Sex vs Survived\n#\n# Embarked vs Survived\n#\n# Age vs Survived\n#\n# Fare vs Survived\n#\n# parch vs Survived\n#\n# SibSp vs Survived\n#\n# Family_Size vs Survived\n\nsns.countplot(data['Survived'])\n\n# Pclass vs Survived\nsns.countplot(data['Pclass'], hue=data['Survived'])\n\n# Sex vs Survived\nsns.countplot(data['Sex'], hue=data['Survived'])\n\n# Embarked vs Survived\nsns.countplot(data['Embarked'], hue=data['Survived'])\n\n# age vs survived\ng = sns.FacetGrid(data, col='Survived')\ng.map(sns.distplot, 'Age')\n\n# Fare vs survived\ng = sns.FacetGrid(data, col='Survived')\ng.map(sns.distplot, 'Fare')\n\n# parch(parents / children aboard the Titanic) vs survived\ng = sns.FacetGrid(data, col='Survived')\ng.map(sns.distplot, 'Parch')\n\n# SibSp(of siblings / spouses aboard) vs survived\ng = sns.FacetGrid(data, col='Survived')\ng.map(sns.distplot, 'SibSp')\n\n# create new columns data 'Family_Size' which combine with 'Parch' and 'SibSp'\ndata['Family_Size'] = data['Parch'] + data['SibSp']\n\n# Family_Size vs survived\ng = sns.FacetGrid(data, col='Survived')\ng.map(sns.distplot, 'Family_Size')\n\n# # Preprocess\n\n# get the title(Ms..., Miss..., etc...)\ndata['Title1'] = data['Name'].str.split(\", \", expand=True)[1]\ndata['Name'].str.split(\", \", expand=True).head(3)\n\ndata['Title1'].head(3)\n\ndata['Title1'] = data['Title1'].str.split(\".\", expand=True)[0]\ndata['Title1'].head(3)\n\ndata['Title1'].unique()\n\npd.crosstab(data['Title1'], data['Sex']).T.style.background_gradient(cmap='summer_r')\n\npd.crosstab(data['Title1'], data['Survived']).T.style.background_gradient(cmap='summer_r')\n\ndata.groupby(['Title1'])['Age'].mean()\n\n# replace few title to common\ndata['Title2'] = data['Title1'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','the Countess','Jonkheer','Col','Rev','Capt','Sir','Don','Dona'],\n ['Miss','Mrs','Miss','Mr','Mr','Mrs','Mrs','Mr','Mr','Mr','Mr','Mr','Mr','Mrs'])\n\ndata['Title2'].unique()\n\npd.crosstab(data['Title2'], data['Sex']).T.style.background_gradient(cmap='summer_r')\n\npd.crosstab(data['Title2'], data['Survived']).T.style.background_gradient(cmap='summer_r')\n\ndata['Ticket_info'] = data['Ticket'].apply(lambda x : x.replace(\".\",\"\").replace(\"/\",\"\").strip().split(' ')[0] if not x.isdigit() else 'X')\n\ndata['Ticket_info'].unique()\n\ndata['Embarked'] = data['Embarked'].fillna('S')\n\ndata['Fare'] = data['Fare'].fillna(data['Fare'].mean())\n\ndata['Cabin'].head(5)\n\ndata['Cabin'] = data['Cabin'].apply(lambda x : str(x)[0] if not pd.isnull(x) else 'NoCabin')\n\ndata['Cabin'].unique()\n\nsns.countplot(data['Cabin'], hue=data['Survived'])\n\n# convert categorical values to int for fitting classifier later.\ndata['Sex'] = data['Sex'].astype('category').cat.codes\ndata['Embarked'] = data['Embarked'].astype('category').cat.codes\ndata['Pclass'] = data['Pclass'].astype('category').cat.codes\ndata['Title1'] = data['Title1'].astype('category').cat.codes\ndata['Title2'] = data['Title2'].astype('category').cat.codes\ndata['Cabin'] = data['Cabin'].astype('category').cat.codes\ndata['Ticket_info'] = data['Ticket_info'].astype('category').cat.codes\n\n# +\ndataAgeNull = data[data[\"Age\"].isnull()]\ndataAgeNotNull = data[data[\"Age\"].notnull()]\nremove_outlier = dataAgeNotNull[(np.abs(dataAgeNotNull[\"Fare\"]-dataAgeNotNull[\"Fare\"].mean())>(4*dataAgeNotNull[\"Fare\"].std()))|\n (np.abs(dataAgeNotNull[\"Family_Size\"]-dataAgeNotNull[\"Family_Size\"].mean())>(4*dataAgeNotNull[\"Family_Size\"].std())) \n ]\nrfModel_age = RandomForestRegressor(n_estimators=2000,random_state=42)\nageColumns = ['Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title1', 'Title2','Cabin','Ticket_info']\nrfModel_age.fit(remove_outlier[ageColumns], remove_outlier[\"Age\"])\n\nageNullValues = rfModel_age.predict(X= dataAgeNull[ageColumns])\ndataAgeNull.loc[:,\"Age\"] = ageNullValues\ndata = dataAgeNull.append(dataAgeNotNull)\ndata.reset_index(inplace=True, drop=True)\n# -\n\ndataTrain = data[pd.notnull(data['Survived'])].sort_values(by=[\"PassengerId\"])\ndataTest = data[~pd.notnull(data['Survived'])].sort_values(by=[\"PassengerId\"])\n\ndataTrain.columns\n\ndataTrain = dataTrain[['Survived', 'Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']]\ndataTest = dataTest[['Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']]\n\ndataTrain.head(3)\n\n# using SVM to predict\nfrom sklearn.svm import SVC\nclassifier = SVC(kernel='rbf')\nclassifier.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0])\n\nrf_res = classifier.predict(dataTest)\nsubmit['Survived'] = rf_res\nsubmit['Survived'] = submit['Survived'].astype(int)\nsubmit.to_csv('submit.csv', index= False)\n\n# +\n# using RandomForest to predict\nrf = RandomForestClassifier(criterion='gini', \n n_estimators=1000,\n min_samples_split=12,\n min_samples_leaf=1,\n oob_score=True,\n random_state=1,\n n_jobs=-1) \n\nrf.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0])\nprint(\"%.4f\" % rf.oob_score_)\n# -\n\n\n\n\n tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file\n \n EVAL_INTERVAL = 60\n \n mnist = input_data.read_data_sets(\"mnist/data\", one_hot = True, reshape = False)\n \n train_input_fn = tf.estimator.inputs.numpy_input_fn(\n x = {\"image\": mnist.train.images},\n y = mnist.train.labels,\n batch_size = 100,\n num_epochs = None,\n shuffle = True,\n queue_capacity = 5000\n )\n\n eval_input_fn = tf.estimator.inputs.numpy_input_fn(\n x = {\"image\": mnist.test.images},\n y = mnist.test.labels,\n batch_size = 100,\n num_epochs = 1,\n shuffle = False,\n queue_capacity = 5000\n )\n\n estimator = tf.estimator.Estimator(\n model_fn = image_classifier,\n model_dir = output_dir,\n params = hparams)\n\n train_spec = tf.estimator.TrainSpec(\n input_fn = train_input_fn,\n max_steps = hparams[\"train_steps\"])\n\n exporter = tf.estimator.LatestExporter(name = \"exporter\", serving_input_receiver_fn = serving_input_fn)\n\n eval_spec = tf.estimator.EvalSpec(\n input_fn = eval_input_fn,\n steps = None,\n exporters = exporter)\n\n tf.estimator.train_and_evaluate(estimator = estimator, train_spec = train_spec, eval_spec = eval_spec)\n\n\noutput_dir=\"/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/08_image/mnist_trained_review\"\nhparams={'train_batch_size': 100, 'model':'dnn', 'output_dir': '/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/08_image/mnist_trained', 'job_dir': 'junk', 'ksize2': 5, 'nfil1': 10, 'dprob': 0.25, 'train_steps': 100, 'ksize1': 5, 'batch_norm': False, 'nfil2': 20, 'learning_rate': 0.01}\ntrain_and_evaluate(output_dir, hparams)\n\n\n# + language=\"bash\"\n# python3 task_review_01.py \\\n# --output_dir=${PWD}/mnist_trained \\\n# --train_steps=100 \\\n# --learning_rate=0.01\n"},"script_size":{"kind":"number","value":8748,"string":"8,748"}}},{"rowIdx":955,"cells":{"path":{"kind":"string","value":"/apicalls_scraping.ipynb"},"content_id":{"kind":"string","value":"73b52941cc24faea3be027d4646b913a8dc0bb1d"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"JohnTheTripper/MovieProjectFIDS"},"repo_url":{"kind":"string","value":"https://github.com/JohnTheTripper/MovieProjectFIDS"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2020-03-06T16:00:24","string":"2020-03-06T16:00:24"},"gha_updated_at":{"kind":"timestamp","value":"2020-03-06T15:54:16","string":"2020-03-06T15:54:16"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":124518,"string":"124,518"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [conda env:lendingclub]\n# language: python\n# name: conda-env-lendingclub-py\n# ---\n\n# %load_ext autoreload\n# %autoreload 2\n\n# +\nimport os\nimport pickle\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n# testing\nfrom pandas.testing import assert_frame_equal\nfrom tqdm import tqdm\n\nimport j_utils.munging as mg\nfrom lendingclub.lc_utils import gen_datasets\nfrom lendingclub import config\n\npd.options.display.max_columns = 999\npd.options.display.max_rows = 60\npd.options.display.max_seq_items = None\n# -\n\n# # Make the train script\n\n# input should be model type (should try to accept list)\n# bundled with the parameters for each type of model\n#\n# should return/create the saved model and anything necessary for data processing\n# for the model\n\n# +\n# %%writefile ../../lendingclub/modeling/08_train.py\nimport os\nimport sys\nimport argparse\nimport pickle\nimport joblib\nimport numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\nfrom catboost import CatBoostRegressor, CatBoostClassifier\nfrom lendingclub import config, utils\nimport j_utils.munging as mg\n\ndef prepare_data(model_n, data, proc=None, ds_type='train'):\n '''\n returns the processed data for a model, which could be different between\n model types e.g. can handle categoricals or not. additionally returns\n a tuple of anything necessary to process valid/test data in the same manner\n ds_type must be 'train', 'valid', or 'test'\n '''\n assert ds_type in ['train', 'valid', 'test'], print('ds_type invalid')\n if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']:\n return data, None\n# elif model_n == 'logistic_regr':\n else:\n if ds_type == 'train':\n temp = mg.train_proc(data)\n procced = temp[0]\n return procced, temp[1:]\n elif ds_type in ['test', 'valid']:\n assert proc, print('must pass data processing artifacts')\n temp = mg.val_test_proc(data, *proc)\n return temp\n\n \ndef train_model(model_n, X_train, y_train, X_valid=None, y_valid=None):\n '''\n Fit model and return model\n '''\n if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']:\n return 42\n elif model_n == 'logistic_regr':\n lr_model = LogisticRegression(class_weight='balanced')\n lr_model.fit(X_train, y_train)\n return lr_model\n elif model_n == 'catboost_regr':\n # basic params for regressor\n params = {\n 'iterations': 100000,\n 'one_hot_max_size': 10,\n # 'learning_rate': 0.01,\n # 'has_time': True,\n 'depth': 7,\n 'l2_leaf_reg': .5,\n 'random_strength': 5,\n 'loss_function': 'RMSE',\n 'eval_metric': 'RMSE',#'Recall',\n 'random_seed': 42,\n 'use_best_model': True,\n 'task_type': 'GPU',\n # 'boosting_type': 'Ordered',\n # 'loss_function': 'Log',\n 'custom_metric': ['MAE', 'RMSE', 'MAPE', 'Quantile'],\n 'od_type': 'Iter',\n 'od_wait': 300,\n }\n obj_cols = X_train.select_dtypes(['object', 'datetime']).columns\n categorical_features_indices = [X_train.columns.get_loc(col) for col in obj_cols]\n catboost_regr = CatBoostRegressor(**params)\n catboost_regr.fit(X_train, y_train, cat_features=categorical_features_indices,\n eval_set=(X_valid, y_valid,), logging_level='Verbose', plot=True) #\n return catboost_regr\n elif model_n == 'catboost_clf':\n # basic params\n params = {\n 'iterations': 100000,\n 'one_hot_max_size': 10,\n 'learning_rate': 0.01,\n 'depth': 7,\n 'l2_leaf_reg': .5,\n 'random_strength': 5,\n # 'has_time': True,\n 'eval_metric': 'Logloss',#'Recall',\n 'random_seed': 42,\n 'logging_level': 'Silent',\n 'use_best_model': True,\n 'task_type': 'GPU',\n # 'boosting_type': 'Ordered',\n # 'loss_function': 'Log',\n 'custom_metric': ['F1', 'Precision', 'Recall', 'Accuracy', 'AUC'],\n 'od_type': 'Iter',\n 'od_wait': 300,\n }\n # get categorical feature indices for catboost\n obj_cols = X_train.select_dtypes(['object', 'datetime']).columns\n categorical_features_indices = [X_train.columns.get_loc(col) for col in obj_cols]\n catboost_clf = CatBoostClassifier(**params)\n catboost_clf.fit(X_train, y_train, cat_features=categorical_features_indices,\n eval_set=(X_valid, y_valid,), logging_level='Verbose', plot=True) #\n return catboost_clf\n \ndef export_models(m, model_n):\n if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']:\n with open(os.path.join(config.modeling_dir, '{0}_model.pkl'.format(model_n)), 'wb') as file:\n pickle.dump(m, file)\n elif model_n == 'logistic_regr':\n joblib.dump(m,os.path.join(config.modeling_dir, '{0}_model.pkl'.format(model_n)))\n elif model_n in ['catboost_clf', 'catboost_regr']:\n m.save_model(os.path.join(config.modeling_dir, '{0}_model.cb'.format(model_n)))\n \ndef export_data_processing(proc_arti, model_n):\n if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']:\n with open(os.path.join(config.modeling_dir, '{0}_model_proc_arti.pkl'.format(model_n)), 'wb') as file:\n pickle.dump(proc_arti, file)\n elif model_n in ['logistic_regr', 'catboost_clf', 'catboost_regr']:\n joblib.dump(proc_arti, os.path.join(config.modeling_dir, '{0}_model_proc_arti.pkl'.format(model_n)))\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model', '-m', help='specify model(s) to train')\n\nif not len(sys.argv) > 1:\n models = ['logistic_regr'] # , 'A', 'B', 'C', 'D', 'E', 'F', 'G'\n\nargs = parser.parse_args()\nif args.model:\n models = args.model.split()\n# models = ['logistic_regr']\n\n\nif not os.path.isdir(config.modeling_dir):\n os.makedirs(config.modeling_dir)\n \n\ntr_val_base_data, tr_val_eval_data, _ = utils.load_dataset(ds_type='train')\n# ensure ordering is correct for time series split\ntr_val_base_data, tr_val_eval_data = mg.sort_train_eval(tr_val_base_data, tr_val_eval_data, 'id', 'issue_d')\n\n\nfor model_n in models:\n print('training {0}'.format(model_n))\n # do 3 steps of TS cross validation, with valid size at 5% (20 splits)\n tscv = mg.time_series_data_split(tr_val_eval_data, 'issue_d', 20, 1)\n for tr_idx, val_idx in tscv:\n # split out validation from train_data\n if model_n in ['logistic_regr', 'catboost_clf']:\n y_train = tr_val_eval_data.loc[tr_idx, 'target_loose']\n y_valid = tr_val_eval_data.loc[val_idx, 'target_loose']\n else:\n y_train = tr_val_eval_data.loc[tr_idx, '0.07']\n y_valid = tr_val_eval_data.loc[val_idx, '0.07']\n X_train = tr_val_base_data.loc[tr_idx]\n X_valid = tr_val_base_data.loc[val_idx]\n \n X_train, proc_arti = prepare_data(model_n, X_train, ds_type='train')\n X_valid = prepare_data(model_n, X_valid, proc = proc_arti, ds_type='valid')\n m = train_model(model_n, X_train, y_train, X_valid, y_valid)\n\n #save stuff\n export_models(m, model_n)\n export_data_processing(proc_arti, model_n)\n# -\n\nm = train_model(model_n, X_train, y_train)\n\nexport_models(m, model_n)\nexport_data_processing(proc_arti, model_n)\n\n# # copied code to incorporate into train script\n\nimport pandas as pd\nimport os\nfrom lendingclub import config\nimport pickle\ndpath = config.data_dir\n\n# from lendingclub.lc_utils import gen_datasets\nfrom j_utils import munging as mg\nfrom sklearn.model_selection import train_test_split\n\n# ls {dpath}\n\n# +\nbase_loan_info = pd.read_feather(os.path.join(dpath, 'base_loan_info.fth'))\neval_loan_info = pd.read_feather(os.path.join(dpath, 'eval_loan_info.fth'))\nwith open(os.path.join(dpath, 'train_test_ids.pkl'), 'rb') as f:\n train_test_ids = pickle.load(f)\n \nuse_ids = train_test_ids['train']\n\nprint(base_loan_info.shape, eval_loan_info.shape, len(use_ids))\n\ntv_base_loan_info = base_loan_info.query('id in @use_ids')\ntv_eval_loan_info = eval_loan_info.query('id in @use_ids')\n# -\n\nprint\n\n\n\ntt_base_loan_info = pd.read_feather(os.path.join(dpath, 'train_testable_base_loan_info.fth'))\ntt_eval_loan_info = pd.read_feather(os.path.join(dpath, 'train_testable_eval_loan_info.fth'))\nprint(tt_base_loan_info.shape, tt_eval_loan_info.shape)\n\nX_train, X_valid, y_train, y_valid = train_test_split(train_loan_info, train_eval_loan_info['target_strict'], test_size=.05)\n\n# fastai style processing\nX_train, all_train_colnames, max_dict, min_dict, new_null_colnames, fill_dict, cats_dict, norm_dict = mg.train_proc(X_train)\nX_valid = mg.val_test_proc(X_valid, all_train_colnames, max_dict, min_dict, fill_dict, cats_dict, norm_dict)\n"},"script_size":{"kind":"number","value":9128,"string":"9,128"}}},{"rowIdx":956,"cells":{"path":{"kind":"string","value":"/learn/pandas/Exercise_ Summary Functions and Maps.ipynb"},"content_id":{"kind":"string","value":"68682be7481ab585782647bd8e62e4f85815611b"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"sidorkinandrew/kaggle"},"repo_url":{"kind":"string","value":"https://github.com/sidorkinandrew/kaggle"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":11996,"string":"11,996"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ## 第4章朴素贝叶斯法-习题\n#\n# ### 习题4.1\n# &emsp;&emsp;用极大似然估计法推出朴素贝叶斯法中的概率估计公式(4.8)及公式 (4.9)。\n\n# **解答:** \n# **第1步:**证明公式(4.8):$\\displaystyle P(Y=c_k) = \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k)}{N}$ \n# 由于朴素贝叶斯法假设$Y$是定义在输出空间$\\mathcal{Y}$上的随机变量,因此可以定义$P(Y=c_k)$概率为$p$。 \n# 令$\\displaystyle m=\\sum_{i=1}^NI(y_i=c_k)$,得出似然函数:$$L(p)=f_D(y_1,y_2,\\cdots,y_n|\\theta)=\\binom{N}{m}p^m(1-p)^{(N-m)}$$使用微分求极值,两边同时对$p$求微分:$$\\begin{aligned}\n# 0 &= \\binom{N}{m}\\left[mp^{(m-1)}(1-p)^{(N-m)}-(N-m)p^m(1-p)^{(N-m-1)}\\right] \\\\\n# & = \\binom{N}{m}\\left[p^{(m-1)}(1-p)^{(N-m-1)}(m-Np)\\right]\n# \\end{aligned}$$可求解得到$\\displaystyle p=0,p=1,p=\\frac{m}{N}$ \n# 显然$\\displaystyle P(Y=c_k)=p=\\frac{m}{N}=\\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k)}{N}$,公式(4.8)得证。\n#\n# ----\n#\n# **第2步:**证明公式(4.9):$\\displaystyle P(X^{(j)}=a_{jl}|Y=c_k) = \\frac{\\displaystyle \\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)}{\\displaystyle \\sum_{i=1}^N I(y_i=c_k)}$ \n# 令$P(X^{(j)}=a_{jl}|Y=c_k)=p$,令$\\displaystyle m=\\sum_{i=1}^N I(y_i=c_k), q=\\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)$,得出似然函数:$$L(p)=\\binom{m}{q}p^q(i-p)^{m-q}$$使用微分求极值,两边同时对$p$求微分:$$\\begin{aligned}\n# 0 &= \\binom{m}{q}\\left[qp^{(q-1)}(1-p)^{(m-q)}-(m-q)p^q(1-p)^{(m-q-1)}\\right] \\\\\n# & = \\binom{m}{q}\\left[p^{(q-1)}(1-p)^{(m-q-1)}(q-mp)\\right]\n# \\end{aligned}$$可求解得到$\\displaystyle p=0,p=1,p=\\frac{q}{m}$ \n# 显然$\\displaystyle P(X^{(j)}=a_{jl}|Y=c_k)=p=\\frac{q}{m}=\\frac{\\displaystyle \\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)}{\\displaystyle \\sum_{i=1}^N I(y_i=c_k)}$,公式(4.9)得证。\n\n# ### 习题4.2\n# &emsp;&emsp;用贝叶斯估计法推出朴素贝叶斯法中的慨率估计公式(4.10)及公式(4.11)\n\n# **解答:** \n# **第1步:**证明公式(4.11):$\\displaystyle P(Y=c_k) = \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + \\lambda}{N+K \\lambda}$ \n# 加入先验概率,在没有任何信息的情况下,可以假设先验概率为均匀概率(即每个事件的概率是相同的)。 \n# 可得$\\displaystyle p=\\frac{1}{K} \\Leftrightarrow pK-1=0\\quad(1)$ \n# 根据习题4.1得出先验概率的极大似然估计是$\\displaystyle pN - \\sum_{i=1}^N I(y_i=c_k) = 0\\quad(2)$ \n# 存在参数$\\lambda$使得$(1) \\cdot \\lambda + (2) = 0$ \n# 所以有$$\\lambda(pK-1) + pN - \\sum_{i=1}^N I(y_i=c_k) = 0$$可得$\\displaystyle P(Y=c_k) = \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + \\lambda}{N+K \\lambda}$,公式(4.11)得证。 \n#\n# ----\n#\n# **第2步:**证明公式(4.10):$\\displaystyle P_{\\lambda}(X^{(j)}=a_{jl} | Y = c_k) = \\frac{\\displaystyle \\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k) + \\lambda}{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + S_j \\lambda}$ \n# 根据第1步,可同理得到$$\n# P(Y=c_k, x^{(j)}=a_{j l})=\\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\\lambda}{N+K S_j \\lambda}$$ \n# $$\\begin{aligned} \n# P(x^{(j)}=a_{jl} | Y=c_k)\n# &= \\frac{P(Y=c_k, x^{(j)}=a_{j l})}{P(y_i=c_k)} \\\\\n# &= \\frac{\\displaystyle \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\\lambda}{N+K S_j \\lambda}}{\\displaystyle \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + \\lambda}{N+K \\lambda}} \\\\\n# &= (\\lambda可以任意取值,于是取\\lambda = S_j \\lambda) \\\\\n# &= \\frac{\\displaystyle \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\\lambda}{N+K S_j \\lambda}}{\\displaystyle \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + \\lambda}{N+K S_j \\lambda}} \\\\ \n# &= \\frac{\\displaystyle \\sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\\lambda}{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + \\lambda} (其中\\lambda = S_j \\lambda)\\\\\n# &= \\frac{\\displaystyle \\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k) + \\lambda}{\\displaystyle \\sum_{i=1}^N I(y_i=c_k) + S_j \\lambda}\n# \\end{aligned} $$ \n# 公式(4.11)得证。\n\n\n"},"script_size":{"kind":"number","value":3591,"string":"3,591"}}},{"rowIdx":957,"cells":{"path":{"kind":"string","value":"/Murales-0.9.ipynb"},"content_id":{"kind":"string","value":"ffd973a78cf2ae0cad00c59196a1412fe9678da9"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"ygingras/mp-84-atelier"},"repo_url":{"kind":"string","value":"https://github.com/ygingras/mp-84-atelier"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_event_created_at":{"kind":"timestamp","value":"2021-03-13T17:00:05","string":"2021-03-13T17:00:05"},"gha_updated_at":{"kind":"timestamp","value":"2021-03-13T03:11:16","string":"2021-03-13T03:11:16"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":5876,"string":"5,876"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 2\n# language: python\n# name: python2\n# ---\n\n# # ASTRONOMY WITH THE 21-CM LINE; SOME MICROWAVE ELECTRONICS\n\n# # 4.2. Software—Some Useful Python Procedures for Time Conversion\n\n\n print_function\n\nimport os, sys, h5py\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sb\nimport tensorflow as tf\nimport scipy\n\nimport sys\nsys.path.append('../../..')\nimport mutagenesisfunctions as mf\nfrom deepomics import neuralnetwork as nn\nfrom deepomics import utils, fit, visualize, saliency\n\nfrom Bio import AlignIO\nimport time as time\nimport pandas as pd\n\n#---------------------------------------------------------------------------------------------------------------------------------\n'''DEFINE LOOP'''\ntrials = ['med']#['small', 'med', 'large']\nexp = 'toyhp' #for both the data folder and the params folder\nexp_data = 'data_%s'%(exp)\n\nfor t in trials:\n\n #---------------------------------------------------------------------------------------------------------------------------------\n\n '''OPEN DATA'''\n\n starttime = time.time()\n\n #Open data from h5py\n filename = '%s_50k_%s.hdf5'%(exp, t)\n data_path = os.path.join('../..', exp_data, filename)\n with h5py.File(data_path, 'r') as dataset:\n X_data = np.array(dataset['X_data'])\n Y_data = np.array(dataset['Y_data'])\n\n numdata, seqlen, dims = X_data.shape\n X_data = np.expand_dims(X_data, axis=2)\n\n # get validation and test set from training set\n test_frac = 0.3\n valid_frac = 0.1\n N = numdata\n split_1 = int(N*(1-valid_frac-test_frac))\n split_2 = int(N*(1-test_frac))\n shuffle = np.random.permutation(N)\n\n #set up dictionaries\n train = {'inputs': X_data[shuffle[:split_1]], \n 'targets': Y_data[shuffle[:split_1]]}\n valid = {'inputs': X_data[shuffle[split_1:split_2]], \n 'targets': Y_data[shuffle[split_1:split_2]]}\n test = {'inputs': X_data[shuffle[split_2:]], \n 'targets': Y_data[shuffle[split_2:]]}\n\n print ('Data extraction and dict construction completed in: ' + mf.sectotime(time.time() - starttime))\n\n\n #---------------------------------------------------------------------------------------------------------------------------------\n\n\n '''SAVE PATHS AND PARAMETERS'''\n params_results = '../../results'\n\n modelarch = 'resbind'\n trial = t\n modelsavename = '%s_%s'%(modelarch, trial)\n\n\n\n '''BUILD NEURAL NETWORK'''\n\n def cnn_model(input_shape, output_shape):\n\n # create model\n layer1 = {'layer': 'input', #41\n 'input_shape': input_shape\n }\n layer2 = {'layer': 'conv1d',\n 'num_filters': 96,\n 'filter_size': input_shape[1]-29,\n 'norm': 'batch',\n 'activation': 'relu',\n 'dropout': 0.3,\n 'padding': 'VALID',\n }\n layer3 = {'layer': 'conv1d_residual',\n 'filter_size': 5,\n 'function': 'relu',\n 'dropout_block': 0.1,\n 'dropout': 0.3,\n 'mean_pool': 10,\n }\n\n layer4 = {'layer': 'dense', # input, conv1d, dense, conv1d_residual, dense_residual, conv1d_transpose,\n # concat, embedding, variational_normal, variational_softmax, + more\n 'num_units': 196,\n 'norm': 'batch', # if removed, automatically adds bias instead\n 'activation': 'relu', # or leaky_relu, prelu, sigmoid, tanh, etc\n 'dropout': 0.5, # if removed, default is no dropout\n }\n\n\n layer5 = {'layer': 'dense',\n 'num_units': output_shape[1],\n 'activation': 'sigmoid'\n }\n\n model_layers = [layer1, layer2, layer3, layer4, layer5]\n\n # optimization parameters\n optimization = {\"objective\": \"binary\",\n \"optimizer\": \"adam\",\n \"learning_rate\": 0.0003,\n \"l2\": 1e-5,\n #\"label_smoothing\": 0.05,\n #\"l1\": 1e-6,\n }\n return model_layers, optimization\n\n tf.reset_default_graph()\n\n # get shapes of inputs and targets\n input_shape = list(train['inputs'].shape)\n input_shape[0] = None\n output_shape = train['targets'].shape\n\n # load model parameters\n model_layers, optimization = cnn_model(input_shape, output_shape)\n\n # build neural network class\n nnmodel = nn.NeuralNet(seed=247)\n nnmodel.build_layers(model_layers, optimization)\n\n # compile neural trainer\n save_path = os.path.join(params_results, exp)\n param_path = os.path.join(save_path, modelsavename)\n nntrainer = nn.NeuralTrainer(nnmodel, save='best', file_path=param_path)\n\n #---------------------------------------------------------------------------------------------------------------------------------\n\n sess = utils.initialize_session()\n '''TEST'''\n if TEST:\n\n # set best parameters\n nntrainer.set_best_parameters(sess)\n\n # test model\n loss, mean_vals, std_vals = nntrainer.test_model(sess, test, name='test')\n if WRITE:\n metricsline = '%s,%s,%s,%s,%s,%s,%s'%(exp, modelarch, trial, loss, mean_vals[0], mean_vals[1], mean_vals[2])\n fd = open('test_metrics.csv', 'a')\n fd.write(metricsline+'\\n')\n fd.close()\n '''SORT ACTIVATIONS'''\n nntrainer.set_best_parameters(sess)\n predictionsoutput = nntrainer.get_activations(sess, test, layer='output')\n plot_index = np.argsort(predictionsoutput[:,0])[::-1]\n\n #---------------------------------------------------------------------------------------------------------------------------------\n '''FIRST ORDER MUTAGENESIS'''\n if FOM:\n num_plots = range(1)\n for ii in num_plots: \n\n X = np.expand_dims(test['inputs'][plot_index[ii]], axis=0)\n\n mf.fom_heatmap(X, layer='dense_1_bias', alphabet='rna', nntrainer=nntrainer, sess=sess, figsize=(15,1.5))\n\n\n plt.close()\n\n #---------------------------------------------------------------------------------------------------------------------------------\n\n\n\n# -\n\n\n"},"script_size":{"kind":"number","value":6409,"string":"6,409"}}},{"rowIdx":958,"cells":{"path":{"kind":"string","value":"/notebookexample/bike-sharing-in-the-bay-area.ipynb"},"content_id":{"kind":"string","value":"5c1fc73f7b3b1ac2221a7389006dd6e49e089f85"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"leestott/Dockercodespace"},"repo_url":{"kind":"string","value":"https://github.com/leestott/Dockercodespace"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":605209,"string":"605,209"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n#\n# ___\n# # Linear Regression - USA Housing\n#\n# By Himani Desai\n#\n\n#\n# ## Check out the data\n#\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# %matplotlib inline\n\nUSAhousing = pd.read_csv('USA_Housing.csv')\n\nUSAhousing.head()\n\nUSAhousing.info()\n\nUSAhousing.describe()\n\nUSAhousing.columns\n\n# # EDA\n#\n\nsns.pairplot(USAhousing)\n\nsns.distplot(USAhousing['Price'])\n\nsns.heatmap(USAhousing.corr())\n\n# ## Training a Linear Regression Model\n#\n#\n# ### X and y arrays\n\nX = USAhousing[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n 'Avg. Area Number of Bedrooms', 'Area Population']]\ny = USAhousing['Price']\n\n# ## Train Test Split\n#\n#\n\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101)\n\n# ## Creating and Training the Model\n\nfrom sklearn.linear_model import LinearRegression\n\nlm = LinearRegression()\n\nlm.fit(X_train,y_train)\n\n# ## Model Evaluation\n#\n#\n\n# print the intercept\nprint(lm.intercept_)\n\ncoeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])\ncoeff_df\n\n# ## Predictions from our Model\n#\n#\n\npredictions = lm.predict(X_test)\n\nplt.scatter(y_test,predictions)\n\n# **Residual Histogram**\n\nsns.distplot((y_test-predictions),bins=50);\n\n# ## Regression Evaluation Metrics\n#\n#\n#\n\nfrom sklearn import metrics\n\nprint('MAE:', metrics.mean_absolute_error(y_test, predictions))\nprint('MSE:', metrics.mean_squared_error(y_test, predictions))\nprint('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))\nch.\n\ncommute.hist('Duration', bins=60, unit='Second')\n\n# ### Exploring the Data with `group` and `pivot` ###\n#\n# We can use `group` to identify the most highly used Start Station:\n\nstarts = commute.group('Start Station').sort('count', descending=True)\nstarts\n\n# The largest number of trips started at the Caltrain Station on Townsend and 4th in San Francisco. People take the train into the city, and then use a shared bike to get to their next destination.\n\n# The `group` method can also be used to classify the rentals by both Start Station and End Station.\n\ncommute.group(['Start Station', 'End Station'])\n\n# Fifty-four trips both started and ended at the station on 2nd at Folsom. A much large number (437) were between 2nd at Folsom and 2nd at Townsend. \n#\n# The `pivot` method does the same classification but displays its results in a contingency table that shows all possible combinations of Start and End Stations, even though some of them didn't correspond to any trips. Remember that the first argument of a `pivot` statement specifies the column labels of the pivot table; the second argument labels the rows.\n#\n# There is a train station as well as a Bay Area Rapid Transit (BART) station near Beale at Market, explaining the high number of trips that start and end there.\n\ncommute.pivot('Start Station', 'End Station')\n\n# We can also use `pivot` to find the shortest time of the rides between Start and End Stations. Here `pivot` has been given `Duration` as the optional `values` argument, and `min` as the function which to perform on the values in each cell.\n\ncommute.pivot('Start Station', 'End Station', 'Duration', min)\n\n# Someone had a very quick trip (271 seconds, or about 4.5 minutes) from 2nd at Folsom to Beale at Market, about five blocks away. There are no bike trips between the 2nd Avenue stations and Adobe on Almaden, because the latter is in a different city.\n\n# ### Drawing Maps ###\n# The table `stations` contains geographical information about each bike station, including latitude, longitude, and a \"landmark\" which is the name of the city where the station is located.\n\nstations = Table.read_table(path_data + 'station.csv')\nstations\n\n# We can draw a map of where the stations are located, using `Marker.map_table`. The function operates on a table, whose columns are (in order) latitude, longitude, and an optional identifier for each point.\n\nMarker.map_table(stations.select('lat', 'long', 'name'))\n\n# The map is created using [OpenStreetMap](http://www.openstreetmap.org/#map=5/51.500/-0.100), which is an open online mapping system that you can use just as you would use Google Maps or any other online map. Zoom in to San Francisco to see how the stations are distributed. Click on a marker to see which station it is.\n\n# You can also represent points on a map by colored circles. Here is such a map of the San Francisco bike stations.\n\nsf = stations.where('landmark', are.equal_to('San Francisco'))\nsf_map_data = sf.select('lat', 'long', 'name')\nCircle.map_table(sf_map_data, color='green', radius=200)\n\n# ### More Informative Maps: An Application of `join` ###\n# The bike stations are located in five different cities in the Bay Area. To distinguish the points by using a different color for each city, let's start by using group to identify all the cities and assign each one a color.\n\ncities = stations.group('landmark').relabeled('landmark', 'city')\ncities\n\ncolors = cities.with_column('color', make_array('blue', 'red', 'green', 'orange', 'purple'))\ncolors\n\n# Now we can join `stations` and `colors` by `landmark`, and then select the columns we need to draw a map.\n\njoined = stations.join('landmark', colors, 'city')\ncolored = joined.select('lat', 'long', 'name', 'color')\nMarker.map_table(colored)\n\n# Now the markers have five different colors for the five different cities.\n\n# To see where most of the bike rentals originate, let's identify the start stations:\n\nstarts = commute.group('Start Station').sort('count', descending=True)\nstarts\n\n# We can include the geographical data needed to map these stations, by first joining `starts` with `stations`:\n\nstation_starts = stations.join('name', starts, 'Start Station')\nstation_starts\n\n# Now we extract just the data needed for drawing our map, adding a color and an area to each station. The area is 1000 times the count of the number of rentals starting at each station, where the constant 1000 was chosen so that the circles would appear at an appropriate scale on the map.\n\nstarts_map_data = station_starts.select('lat', 'long', 'name').with_columns(\n 'color', 'blue',\n 'area', station_starts.column('count') * 1000\n)\nstarts_map_data.show(3)\nCircle.map_table(starts_map_data)\n\n# That huge blob in San Francisco shows that the eastern section of the city is the unrivaled capital of bike rentals in the Bay Area.\ntelechargement-enseignants-maj-2018)\n#\n#\n# **Question 3** :\n# Tester le programme **Ouvrir** suivant, et expliquer son fonctionnement dans la cellule texte qui se trouve en dessous du programme **Ouvrir**. On précisera bien le rôle du slice **line[0:-1]**.\n\n# Programme Ouvrir\nfile = open(\"sujet1.txt\", \"r\")\nlines = file.readlines()\nfile.close()\ntexte=\"\"\nfor line in lines:\n texte=texte+line[0:-1]\nprint(texte)\n\n# Donner la réponse à la question 3, dans la cellule texte ci-après.\n\n# + active=\"\"\n#\n# -\n\n# ### c) Utilisation du programme de Boyer-Moore\n# **Question 4** :\n# en utilisant les programmes **Boyer-Moore 2** et **Ouvrir**, écrire en dessous le programme de **Boyer-Moore 3** qui vous permettra de dire quels sont les personnes parmi les sujets 1 à 4 qui seront successibles de contracter la maladie de Huntington dans les années à venir.\n\n# Boyer-Moore 3\n...\n\n# Donner la réponse à la question 4, dans la cellule texte ci-après.\n\n# + active=\"\"\n#\n# -\n\n# ## 3) Aspect historique, et mise en perspective\n# ### a) Qui sont Boyer et Moore ?\n# Robert Stephen Boyer et J Strother Moore (La lettre J est son prénom et n'est pas une abréviation) ont inventé l'algorithme de recherche de chaînes de Boyer–Moore, un algorithme de recherche de chaînes particulièrement efficace, en 1977.\n#\n#\n# * Robert Stephen Boyer : professeur retraité d'informatique, de mathématiques et de philosophie à l'Université du Texas à Austin \n#\n# ![image2](image2.jpg)\n#\n# * J Strother Moore : professeur en informatique à l'université du Texas à Austin.\n#\n# ![image3](image3.jpg)\n#\n# ### b) 85 algorithmes de recherche textuelle\n# Voici un lien qui Recense plus de 85 algorithmes différents de recherche textuelle, les plus célèbres datant des années 1970, mais plus de la moitié ont moins de 10 ans : \n#\n# [Lien vers le site](http://monge.univ-mlv.fr/~lecroq/string/)\n"},"script_size":{"kind":"number","value":8601,"string":"8,601"}}},{"rowIdx":959,"cells":{"path":{"kind":"string","value":"/Modulo01/Desafio03/Desafio03.ipynb"},"content_id":{"kind":"string","value":"1aa4756edf94fe8dd2d0764145463654663ed639"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"thiagoiori/Bootcamp_DS"},"repo_url":{"kind":"string","value":"https://github.com/thiagoiori/Bootcamp_DS"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1662975,"string":"1,662,975"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"view-in-github\" colab_type=\"text\"\n# \"Open\n\n# + id=\"L6sYFqbTNwb2\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport numpy as np\n\npd.options.display.float_format = \"{:.2f}\".format\n\n# + id=\"0HN2AC9rK4SW\"\ndados = pd.read_csv(\"https://raw.githubusercontent.com/thiagoiori/Bootcamp_DS/main/Modulo01/Desafio02/A201526189_28_143_208.csv\", \n sep=\";\", \n encoding=\"ISO-8859-1\", \n skiprows=3, \n skipfooter=12, \n engine=\"python\", \n decimal=\",\")\n\n# + [markdown] id=\"WW5d6vm1s1wD\"\n# Fatiando o dataframe (slice)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"huU-0hsNtfzL\" outputId=\"8a162045-1cb6-4ff7-e755-9317d80fe8ec\"\ncolunas_usaveis = dados.mean().index.tolist()\n\ncolunas_usaveis.insert(0, \"Unidade da Federação\")\ncolunas_usaveis\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"wj51fyPmuANf\" outputId=\"e93b49fb-0ff1-4d07-e553-a8a595bf2894\"\ndados[colunas_usaveis]\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"qhNjfamfulEA\" outputId=\"a6fd3b74-37a9-4e2c-d3d5-233ed9ddc9b0\"\nusaveis = dados[colunas_usaveis]\nusaveis = usaveis.set_index(\"Unidade da Federação\")\nusaveis\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"newS4EBExkTy\" outputId=\"edf92829-a51e-4d97-86c6-d212cd953d3e\"\nusaveis.plot(figsize=(12, 9))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 489} id=\"8wwKreqYylKc\" outputId=\"14d3ffae-403b-4e1d-b548-db6bf7334421\"\nusaveis.T.plot(figsize=(12, 6))\n\n# + id=\"HzWitdiQy8gs\"\nusaveis = usaveis.drop(\"Total\", axis=1)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 489} id=\"zJnUQ6P5zPaU\" outputId=\"b488985f-545c-439f-cf9b-69a6e50e3903\"\nusaveis.T.plot(figsize=(12, 6))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 403} id=\"Qu7mQZFYD-26\" outputId=\"25aa0076-0c68-4946-f130-abdd9585b1df\"\nusaveis[\"Total\"] = usaveis.sum(axis=1)\nordenado_por_gasto = usaveis.sort_values(\"Total\", ascending=False)\nordenado_por_gasto = ordenado_por_gasto.drop(\"Total\", axis=1)\nordenado_por_gasto.head(5).T.plot(figsize=(12, 6))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 403} id=\"5vc_ccKMIMnV\" outputId=\"6e2ba96a-b42a-4b6f-f874-d711c0ecba2c\"\ncolunas_interessadas = ordenado_por_gasto.columns[6:]\nordenado_por_gasto = ordenado_por_gasto[colunas_interessadas]\nordenado_por_gasto.head().T.plot(figsize=(12,6))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"tlUfmKRyQiZm\" outputId=\"4020dd7c-ff1f-4692-d493-c8401c482c31\"\nusaveis.index\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 356} id=\"aw6Efss1QkNV\" outputId=\"6fbb3199-9e98-4946-87e9-4a3ce8bfbf00\"\nusaveis[\"Total\"] = usaveis.sum(axis=1)\nusaveis.head()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 436} id=\"VqKZwQUMzyb9\" outputId=\"a1e13b82-d92e-4aef-ca16-aad19c958501\"\nusaveis.sample(n=7)\n\n# + id=\"MqWgsx6lAa-4\"\n\n\n# + [markdown] id=\"HVYWYwwrPXlA\"\n# # Desafios\n#\n# Desafio 01: Escolher uma palete de cores mais adequada do matplotlib.\n#\n# Desafio 02: Adicionar uma coluna mostrando a região (Norte, Nodeste, Sul, Suldeste e Centro-Oeste) de cada estado.\n#\n# Desafio 03: Formatar o gráfico de custos por mês dos 5 estados, deixando ele agradável (Bonitão, segundo o Gui)\n#\n# Desafio 04: Adicione o seu estado aos 5 estados plotados anteriormente\n#\n# Desafio 05: Buscar os casos de dengue no Brasil (época de maior número de casos e regiões mais atingidas) e se os picos de alguns estados em fevereiro e verão de modo geral, pode ser reflexos dos casos de dengue\n#\n# Desafio 06: Plotar o gráfico dos custos apenas dos estados da região sudeste e verificar se os picos de 2013/Fev teve comportamento similar em todos os demais estados da região\n#\n# Desafio 07: Adicionar seu estado escolhido novamente, deixe o gráfico informativo e tire conclusões sobre seus estados comparando com os demais. Tire suas conclusões e compartilhe com a gente.\n\n# + [markdown] id=\"Ou4jeTsrseZB\"\n# ## Desafio 01\n# Escolher uma palete de cores mais adequada do matplotlib.\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 0} id=\"HIvXNZUHsW49\" outputId=\"9cef2641-04be-4958-f51f-32f2d0ef6649\"\nax = ordenado_por_gasto.T.plot(figsize=(12,6), color=\"blue\")\nax.legend(loc=\"right\", bbox_to_anchor=(.8, 0.2, 0.5, 0.5))\nleg = ax.get_legend()\n\ncolors = ['#FF6633', '#FFB399', '#FF33FF', '#FFFF99', '#00B3E6', \n '#E6B333', '#3366E6', '#999966', '#99FF99', '#B34D4D',\n '#80B300', '#809900', '#E6B3B3', '#6680B3', '#66991A', \n '#FF99E6', '#CCFF1A', '#FF1A66', '#E6331A', '#33FFCC',\n '#FF6633', '#B366CC', '#4D8000', '#B33300', '#CC80CC', \n\t\t '#66664D']\n\nfor idx, color in enumerate(colors):\n ax.get_lines()[idx].set_color(color)\n leg.legendHandles[idx].set_color(color)\n\n\n# + [markdown] id=\"I6y5s4GtJga4\"\n# ## Desafio 02\n# Adicionar uma coluna mostrando a região (Norte, Nodeste, Sul, Suldeste e Centro-Oeste) de cada estado\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 408} id=\"s9WUf9GjJmY6\" outputId=\"ac71aed3-7875-4c1b-f245-f88b0d552293\"\ncondition = [(ordenado_por_gasto.index.str[:1] == \"1\"),\n (ordenado_por_gasto.index.str[:1] == \"2\"),\n (ordenado_por_gasto.index.str[:1] == \"3\"),\n (ordenado_por_gasto.index.str[:1] == \"4\"),\n (ordenado_por_gasto.index.str[:1] == \"5\")]\n\nvalues = [\"Norte\", \"Nordeste\", \"Sudeste\", \"Sul\", \"Centro-Oeste\"]\n\nordenado_por_gasto[\"Região\"] = np.select(condition, values)\nordenado_por_gasto.head()\n\n\n# + id=\"VYuNyI93Lqn5\"\n\n\n# + [markdown] id=\"ZBAtcimjqZg6\"\n# ## Desafio 03\n# Formatar o gráfico de custos por mês dos 5 estados, deixando ele agradável (Bonitão, segundo o Gui)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 390} id=\"nmzwafPAqiFY\" outputId=\"af53529e-62a8-4b75-8ec6-27424053b182\"\nordenado_por_gasto = ordenado_por_gasto[colunas_interessadas]\nax = ordenado_por_gasto.head().T.plot(figsize=(12,6))\nax.legend(loc=\"right\", bbox_to_anchor=(.75, .2, 0.5, 0.5))\nax.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:,.2f}\"))\nplt.title(\"Top 5 maiores gastos\")\nplt.grid(axis=\"y\")\nplt.ylabel(\"Gasto em R$\")\nplt.show()\n\n# + [markdown] id=\"CgXk9yy30sbA\"\n# ## Desafio 04\n# Adicione o seu estado aos 5 estados plotados anteriormente\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 390} id=\"1dKbZgxB0w8L\" outputId=\"8864fec0-107f-4a5b-a67e-1c189b25e45e\"\nordenado_por_gasto = ordenado_por_gasto[colunas_interessadas]\nmaiores_6_gastos = ordenado_por_gasto.head()\nmaiores_6_gastos = maiores_6_gastos.append(ordenado_por_gasto.loc[\"23 Ceará\"])\nax = maiores_6_gastos.T.plot(figsize=(12,6))\nax.legend(loc=\"right\", bbox_to_anchor=(.75, .2, 0.5, 0.5))\nax.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:,.2f}\"))\nplt.title(\"Top 6 maiores gastos\")\nplt.grid(axis=\"y\")\nplt.ylabel(\"Gasto em R$\")\nplt.show()\n\n# + [markdown] id=\"Q1fbidcy7Sd3\"\n# ## Desafio 05\n# Buscar os casos de dengue no Brasil (época de maior número de casos e regiões mais atingidas) e se os picos de alguns estados em fevereiro e verão de modo geral, pode ser reflexos dos casos de dengue\n\n# + id=\"sR8Bk4_52v6k\"\n# dados = pd.read_csv(\"https://raw.githubusercontent.com/thiagoiori/Bootcamp_DS/main/Modulo01/Desafio02/A201526189_28_143_208.csv\", \n# sep=\";\", \n# encoding=\"ISO-8859-1\", \n# skiprows=3, \n# skipfooter=12, \n# engine=\"python\", \n# decimal=\",\")\n\n# + [markdown] id=\"R_dg73RYe6XN\"\n# ## Desafio 06\n# Plotar o gráfico dos custos apenas dos estados da região sudeste e verificar se os picos de 2013/Fev teve comportamento similar em todos os demais estados da região\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 390} id=\"9QFu6zJee_8E\" outputId=\"e4bbcd78-e995-4f25-d8d9-d1520be5f9e3\"\ncondition = [(ordenado_por_gasto.index.str[:1] == \"1\"),\n (ordenado_por_gasto.index.str[:1] == \"2\"),\n (ordenado_por_gasto.index.str[:1] == \"3\"),\n (ordenado_por_gasto.index.str[:1] == \"4\"),\n (ordenado_por_gasto.index.str[:1] == \"5\")]\n\nvalues = [\"Norte\", \"Nordeste\", \"Sudeste\", \"Sul\", \"Centro-Oeste\"]\n\nordenado_por_gasto[\"Região\"] = np.select(condition, values)\nordenado_por_gasto_SE = ordenado_por_gasto.loc[ordenado_por_gasto[\"Região\"] == \"Sudeste\"]\n\nordenado_por_gasto_SE = ordenado_por_gasto_SE[colunas_interessadas]\nax = ordenado_por_gasto_SE.T.plot(figsize=(12,6))\nax.legend(loc=\"right\", bbox_to_anchor=(.75, .2, 0.5, 0.5))\nax.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:,.2f}\"))\nplt.title(\"Gastos na saúde da Região SE\")\nplt.grid(axis=\"y\")\nplt.ylabel(\"Gasto em R$\")\nplt.show()\n\n# + [markdown] id=\"Sfjuh2kJdDid\"\n# ## Desafio 07\n# Adicionar seu estado escolhido novamente, deixe o gráfico informativo e tire conclusões sobre seus estados comparando com os demais. Tire suas conclusões e compartilhe com a gente.\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 390} id=\"poGIbNDHdNyD\" outputId=\"0a8b7aeb-d54b-4ab0-873f-f27ec6c423d9\"\ncondition = [(ordenado_por_gasto.index.str[:1] == \"1\"),\n (ordenado_por_gasto.index.str[:1] == \"2\"),\n (ordenado_por_gasto.index.str[:1] == \"3\"),\n (ordenado_por_gasto.index.str[:1] == \"4\"),\n (ordenado_por_gasto.index.str[:1] == \"5\")]\n\nvalues = [\"Norte\", \"Nordeste\", \"Sudeste\", \"Sul\", \"Centro-Oeste\"]\n\nordenado_por_gasto[\"Região\"] = np.select(condition, values)\nordenado_por_gasto_CO = ordenado_por_gasto.loc[ordenado_por_gasto[\"Região\"] == \"Sul\"]\n\nordenado_por_gasto_CO = ordenado_por_gasto_CO[colunas_interessadas]\nordenado_por_gasto_CO = ordenado_por_gasto_CO.append(ordenado_por_gasto.loc[\"35 São Paulo\"][colunas_interessadas])\nordenado_por_gasto_CO\nax = ordenado_por_gasto_CO.T.plot(figsize=(12,6))\nax.legend(loc=\"right\", bbox_to_anchor=(.75, .2, 0.5, 0.5))\nax.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:,.2f}\"))\nplt.title(\"Gastos na saúde da Região Sul x SP\")\nplt.grid(axis=\"y\")\nplt.ylabel(\"Gasto em R$\")\nplt.show()\n\n# + id=\"SxLgh1KUeWzD\"\n\n"},"script_size":{"kind":"number","value":10568,"string":"10,568"}}},{"rowIdx":960,"cells":{"path":{"kind":"string","value":"/assignment2/assignment2:1.ipynb"},"content_id":{"kind":"string","value":"c8de5156e6880991bc375b0f6bb845c41d04d470"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"JavaStudentAlex/ML_DM_assignments"},"repo_url":{"kind":"string","value":"https://github.com/JavaStudentAlex/ML_DM_assignments"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":38422,"string":"38,422"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\nimport csv\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\n#read dataset from file\ndataset = []\n\nwith open(\"assignment02.csv\") as csvfile:\n reader = csv.reader(csvfile)\n for word in csvfile:\n color,radius,weight,class_name = word.strip().split(\",\")\n if color == \"Color\":\n continue\n new_fruit = (color, float(radius), float(weight), class_name)\n dataset.append(new_fruit)\ndataset = np.array(dataset,dtype=\"object\")\nprint(\"Data is read\")\ndataset\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\nfruit_markers = {\"Apple\":\"+\",\n \"Pear\":\"x\",\n \"Lemon\":\"o\"}\n\ndef plot_scatter_radius_weight(data, class_name):\n raws_to_plot = np.where(data[:,3] == class_name)\n matrix_to_plot = data[raws_to_plot]\n plt.scatter(matrix_to_plot[:,1],matrix_to_plot[:,2], c=\"black\", marker=fruit_markers[class_name])\n\nfor fruit_name in fruit_markers.keys():\n plot_scatter_radius_weight(dataset,fruit_name)\nplt.grid()\nplt.xlabel(\"Radius [cm]\")\nplt.ylabel(\"Weight [grams]\")\nprint(\"Scatter plot is built\")\nplt.show()\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\nfruit_bar_markers = {\"Apple\":\"/\",\n \"Pear\" : \"o\",\n \"Lemon\": \"\\\\\",}\n\ncolors = (\"Green\", \"Yellow\", \"Red\")\nlength_between_colors = 5\n\ndef plot_bar_fruit_color_frequency(data, fruit):\n color_graph_pos = []\n bar_length_measurements = [] \n all_fruit = data[np.where(data[:, 3] == fruit)]\n for color_index in range(len(colors)):\n color_fruit = all_fruit[np.where(all_fruit[:,0] == colors[color_index])]\n frout_color_bar_position = list(fruit_bar_markers.keys()).index(fruit) if len(color_fruit) != 0 else 0\n current_color_graph_pos = frout_color_bar_position + color_index*length_between_colors\n color_graph_pos.append(current_color_graph_pos)\n bar_length_measurements.append(len(color_fruit))\n plt.bar(color_graph_pos, bar_length_measurements, color=\"grey\",width=1, hatch=fruit_bar_markers[fruit], label=fruit)\n return color_graph_pos\n\npositions = []\nfor fruit in fruit_bar_markers.keys():\n current_bar_pos = plot_bar_fruit_color_frequency(dataset, fruit)\n positions.append(current_bar_pos)\nticks = np.median(positions, axis=0)\naxes = plt.gca()\naxes.set_xticks(ticks)\naxes.set_xticklabels(colors)\naxes.legend()\nplt.ylabel(\"Frequency\")\nprint(\"Bar plot is built\")\nplt.show()\n\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\ndef float_not_equal(num1, num2):\n float_scale = pow(10, -11)\n return not abs(num1-num2) < float_scale\n\ndef rows_equal(row_a, row_b): \n if row_a[0] != row_b[0] or row_a[3] != row_b[3]:\n return False\n if float_not_equal(row_a[1], row_b[1]) or float_not_equal(row_a[2], row_b[2]):\n return False\n return True\n\nduplicates = []\nfor i in range(len(dataset)):\n for j in range(i+1, len(dataset[i])):\n if rows_equal(dataset[i], dataset[j]):\n duplicates.append(j)\ndataset = np.delete(dataset, duplicates, axis=0)\nprint(\"Duplicates are removed\")\ndataset\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\nzero_indecies = np.where(dataset == 0.0)\nrows, cols = zero_indecies\nfor i in range(len(rows)):\n x = rows[i]\n y = cols[i]\n array_for_mean = dataset[:,y]\n val = np.mean(np.delete(array_for_mean, x))\n dataset[x,y] = val\nprint(\"Zero values replaced by mean\")\ndataset\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\ncolors_numeration = {\"Green\" : 0,\n \"Red\" : 1,\n \"Yellow\" : 2}\nfor k,v in colors_numeration.items():\n dataset[dataset == k] = v\nprint(\"String values instead of class value are replaced for numbers\")\ndataset\n\n# + pycharm={\"name\": \"#%%\\n\", \"is_executing\": false}\nmin_val = np.min(dataset[:,:3], axis=0)\nmax_val = np.max(dataset[:,:3], axis=0)\ndataset[:, :3] = (dataset[:, :3] - min_val)/(max_val - min_val)\nprint(\"Matrix normalized through the min max criteria\")\ndataset\n"},"script_size":{"kind":"number","value":4324,"string":"4,324"}}},{"rowIdx":961,"cells":{"path":{"kind":"string","value":"/_build/jupyter_execute/FFG/0.ipynb"},"content_id":{"kind":"string","value":"2861b1a50a41526aacb46e045804fd5209000cad"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"StraightDraw/Geometry"},"repo_url":{"kind":"string","value":"https://github.com/StraightDraw/Geometry"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":4932,"string":"4,932"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\nimport torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nprint(f'torch version: {torch.__version__}')\nprint(f'torchvision version: {torchvision.__version__}')\n\nimport numpy as np\nimport os\nimport time\nimport wandb\nimport IPython.display\nfrom IPython.display import display\nfrom PIL import Image\n\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.style.use('ggplot')\n# %matplotlib inline\n\nwandb.login()\n\ndata_root = os.path.expanduser('~/ml_datasets/')\npath_to_dataset = os.path.join(data_root, 'cifar10')\n\n# ## Define the parameters\n\nconfig_dict = {\n 'batch_size': 32,\n 'num_epochs': 20,\n 'learning_rate': 0.003,\n 'base_lr': 0.001,\n 'max_lr': 0.05\n}\n\n# ## Define the data\n\n# +\ntransform = torchvision.transforms.Compose(\n [torchvision.transforms.ToTensor()]\n)\n\nbatch_size = config_dict['batch_size']\n\n# data for training\ntrain_dataset = torchvision.datasets.CIFAR10(\n path_to_dataset,\n transform=transform,\n download=True, \n train=True,\n)\n\ntrain_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=batch_size,\n shuffle=True\n)\n\n# data for testing\ntest_dataset = torchvision.datasets.CIFAR10(\n path_to_dataset,\n transform=transform,\n download=True, \n train=False, \n)\n\ntest_dataloader = torch.utils.data.DataLoader(\n test_dataset,\n batch_size=batch_size,\n shuffle=False\n)\n\n# -\n\nclasses = ('plane', 'car', 'bird', 'cat', 'deer', \n 'dog', 'frog', 'horse', 'ship', 'truck')\n\nimages, labels = next(iter(train_dataloader))\n\ntype(images), images.shape, type(labels), labels.shape\n\n\n# ## Define a convolutional neural network\n\nclass LeNet5(nn.Module):\n \n def __init__(self):\n super(LeNet5, self).__init__()\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5) # output shape: (N, 6, 28, 28)\n self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # output shape: (N, C=6, 14, 14)\n self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5) # output shape: (N, 16, 10, 10)\n # self.pool will also be applied after self.conv2, the second pooling makes the output shape: (N, 16, 5, 5)\n self.fc1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n \n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = torch.flatten(x, start_dim=1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n \n\n\nlenet5 = LeNet5()\n\nlogits = lenet5(images)\nprint(f'input shape: {images.shape}, output shape: {logits.shape}')\n\n\n# ## Define objects to keep track of metrics during training\n\n# +\nclass Mean(object):\n \"\"\"\n Vanilla version of tf.keras.metrics.Mean\n \"\"\"\n\n def __init__(self):\n self.value = 0.\n self.n = 0\n\n def __call__(self, value):\n self.value += value\n self.n += 1\n\n def reset_state(self):\n self.value = 0.\n self.n = 0\n\n def result(self):\n return self.value / float(self.n)\n\n\nclass SparseCategoricalAccuracy(object):\n \"\"\"\n Vanilla version of tf.keras.metrics.SparseCategoricalAccuracy\n \"\"\"\n\n def __init__(self):\n self.hits = 0\n self.total = 0\n\n def __call__(self, logits, targets):\n self.hits += (torch.argmax(logits, dim=1) == targets).sum().item()\n self.total += logits.size(0)\n\n def reset_state(self):\n self.hits = 0\n self.total = 0\n\n def result(self):\n return float(self.hits) / float(self.total)\n\n\n\n# -\n\n# ## Define an training loop\n\ndef make_model(config_dict):\n model = LeNet5()\n optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\n loss_func = torch.nn.CrossEntropyLoss()\n scheduler = torch.optim.lr_scheduler.CyclicLR(\n optimizer, \n base_lr=config_dict['base_lr'], \n max_lr=config_dict['max_lr'],\n mode='triangular2'\n )\n \n return model, optimizer, scheduler, loss_func\n\n\n\ndef run_one_epoch(dataloader, model, optimizer, scheduler, loss_func, training=True):\n loss_metric = Mean()\n acc_metric = SparseCategoricalAccuracy()\n for images, labels in dataloader:\n if training:\n optimizer.zero_grad()\n logits = model(images)\n batch_loss = loss_func(logits, labels)\n batch_loss.backward()\n optimizer.step()\n scheduler.step()\n else:\n with torch.no_grad():\n logits = model(images)\n batch_loss = loss_func(logits, labels)\n \n loss_metric(batch_loss)\n acc_metric(logits, labels)\n \n return loss_metric.result(), acc_metric.result()\n \n\n\ndef train_model(train_dataloader, test_dataloader, model, optimizer, scheduler, loss_func, config_dict):\n \n wandb.watch(model, loss_func, log=\"all\", log_freq=1)\n \n for epoch_idx in range(config_dict['num_epochs']):\n model.train()\n train_loss, train_acc = run_one_epoch(train_dataloader, model, optimizer, scheduler, loss_func, True)\n \n model.eval()\n test_loss, test_acc = run_one_epoch(test_dataloader, model, None, None, loss_func, False)\n \n wandb.log(\n {'epoch': epoch_idx,\n 'train_loss': train_loss,\n 'train_acc': train_acc,\n 'test_loss': test_loss,\n 'test_acc': test_acc,\n 'lr': scheduler.get_last_lr()[0]\n },\n step=epoch_idx\n )\n \n if (epoch_idx+1) % 5 == 0:\n print(f'finished epoch {epoch_idx}, train_loss: {train_loss:.3f}, train_acc: {train_acc:.3f}, test_loss: {test_loss:.3f}, test_acc: {test_acc:.3f}, last_lr: {scheduler.get_last_lr()[0]:.5f}')\n \n\n\ndef run(train_dataloader, test_dataloader, config_dict, path_to_model=None):\n \n with wandb.init(project='CIFAR10-LeNet5-PyTorch', config=config_dict):\n model, optimizer, scheduler, loss_func = make_model(config_dict)\n train_model(train_dataloader, test_dataloader, model, optimizer, scheduler, loss_func, config_dict)\n \n if path_to_model is not None:\n torch.save(model.state_dict(), path_to_model)\n loaded_model = LeNet5()\n loaded_mode.load_state_dict(torch.load(path_to_model))\n\n\n\nrun(train_dataloader, test_dataloader, config_dict)\n\nIPython.display.Image(filename='LeNet5_CIFAR10_WandB_dashboard.png') #, width=100, height=100)\n\n\nce)\n x_target = x[:size[1]]\n x = self.convs[i]((x, x_target), edge_index)\n if i != self.num_layers - 1:\n x = self.bns[i](x)\n x = F.relu(x)\n x = F.dropout(x, p=self.dropout, training=self.training)\n \n # Append the node embeddings to xs\n xs.append(x.cpu())\n \n # Concat all embeddings into one tensor\n x_all = torch.cat(xs, dim=0)\n\n return x_all\n\n\n# + [markdown] id=\"7cfm7K3wRqqY\"\n# ## Training and Testing\n#\n# Now lets implement the training and testing functions.\n#\n# In both training and testing, we need to sample batch from the dataloader.\n#\n# Each batch in the `NeighborSampler` dataloader holds three elements:\n# * `batch_size`: The batch size specified in the dataloader.\n# * `n_id`: All nodes (in index format) used in the adjacency matrices.\n# * `adjs`: The three-element tuples.\n\n# + id=\"-JN0-_QCRn8N\"\ndef train(model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"batch\"):\n model.train()\n\n total_loss = 0\n if mode == \"batch\":\n for batch_size, n_id, adjs in train_loader:\n # Move all adj sparse tensors to GPU\n adjs = [adj.to(device) for adj in adjs]\n optimizer.zero_grad()\n\n # Index on the node features\n out = model(data.x[n_id], adjs)\n train_label = data.y[n_id[:batch_size]].squeeze(-1)\n loss = loss_fn(out, train_label)\n loss.backward()\n optimizer.step()\n total_loss += loss.item()\n else:\n optimizer.zero_grad()\n out = model(data.x, data.adj_t, mode=mode)[train_idx]\n train_label = data.y.squeeze(1)[train_idx]\n loss = loss_fn(out, train_label)\n loss.backward()\n optimizer.step()\n total_loss = loss.item()\n\n return total_loss\n\n@torch.no_grad()\ndef test(model, data, all_loader, split_idx, evaluator, mode=\"batch\"):\n model.eval()\n\n if mode == \"batch\":\n out = model.inference(data.x, all_loader)\n else:\n out = model(data.x, data.adj_t, mode=\"all\")\n\n y_true = data.y.cpu()\n y_pred = out.argmax(dim=-1, keepdim=True)\n\n train_acc = evaluator.eval({\n 'y_true': y_true[split_idx['train']],\n 'y_pred': y_pred[split_idx['train']],\n })['acc']\n valid_acc = evaluator.eval({\n 'y_true': y_true[split_idx['valid']],\n 'y_pred': y_pred[split_idx['valid']],\n })['acc']\n test_acc = evaluator.eval({\n 'y_true': y_true[split_idx['test']],\n 'y_pred': y_pred[split_idx['test']],\n })['acc']\n\n return train_acc, valid_acc, test_acc\n\n\n# + [markdown] id=\"AiehZ8OiR2q9\"\n# ## Mini-batch Training\n\n# + id=\"zFaI2eCARy0v\"\nargs = {\n 'device': device,\n 'num_layers': 2,\n 'hidden_dim': 128,\n 'dropout': 0.5,\n 'lr': 0.01,\n 'epochs': 100,\n}\n\nbatch_model = SAGE(data.num_features, args['hidden_dim'],\n dataset.num_classes, args['num_layers'],\n args['dropout']).to(device)\nbatch_model.reset_parameters()\n\noptimizer = torch.optim.Adam(batch_model.parameters(), lr=args['lr'])\nloss_fn = F.nll_loss\n\nbest_batch_model = None\nbest_valid_acc = 0\n\nbatch_results = []\n\nfor epoch in range(1, 1 + args[\"epochs\"]):\n loss = train(batch_model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"batch\")\n result = test(batch_model, data, all_loader, split_idx, evaluator, mode=\"batch\")\n batch_results.append(result)\n train_acc, valid_acc, test_acc = result\n if valid_acc > best_valid_acc:\n best_valid_acc = valid_acc\n best_batch_model = copy.deepcopy(batch_model)\n print(f'Epoch: {epoch:02d}, '\n f'Loss: {loss:.4f}, '\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * valid_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\nbest_result = test(best_batch_model, data, all_loader, split_idx, evaluator, mode=\"batch\")\ntrain_acc, valid_acc, test_acc = best_result\nprint(f'Best model: '\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * valid_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"-OyqW-1pSMLW\"\n# ## Full-batch Training\n\n# + id=\"mU5eAviTSFMO\"\n# Use the same parameters for a full-batch training\nargs = {\n 'device': device,\n 'num_layers': 2,\n 'hidden_dim': 128,\n 'dropout': 0.5,\n 'lr': 0.01,\n 'epochs': 100,\n}\n\nall_model = SAGE(data.num_features, args['hidden_dim'],\n dataset.num_classes, args['num_layers'],\n args['dropout']).to(device)\nall_model.reset_parameters()\n\noptimizer = torch.optim.Adam(all_model.parameters(), lr=args['lr'])\nloss_fn = F.nll_loss\n\nbest_all_model = None\nbest_valid_acc = 0\n\nall_results = []\n\nfor epoch in range(1, 1 + args[\"epochs\"]):\n loss = train(all_model, data, train_loader, train_idx, optimizer, loss_fn, mode=\"all\")\n result = test(all_model, data, all_loader, split_idx, evaluator, mode=\"all\")\n all_results.append(result)\n train_acc, valid_acc, test_acc = result\n if valid_acc > best_valid_acc:\n best_valid_acc = valid_acc\n best_all_model = copy.deepcopy(all_model)\n print(f'Epoch: {epoch:02d}, '\n f'Loss: {loss:.4f}, '\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * valid_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\nbest_result = test(best_all_model, data, all_loader, split_idx, evaluator, mode=\"all\")\ntrain_acc, valid_acc, test_acc = best_result\nprint(f'Best model: '\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * valid_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"NrECcOQQSZo1\"\n# ## Visualization\n\n# + id=\"sh_qvSG1SV63\"\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nbatch_results = np.array(batch_results)\nall_results = np.array(all_results)\n\nx = np.arange(1, 101)\n\nplt.figure(figsize=(9, 7))\n\nplt.plot(x, batch_results[:, 1], label=\"Batch Validation\")\nplt.plot(x, batch_results[:, 2], label=\"Batch Test\")\nplt.plot(x, all_results[:, 1], label=\"All Validation\")\nplt.plot(x, all_results[:, 2], label=\"All Test\")\nplt.title('Model Accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend()\nplt.show()\n\n# + [markdown] id=\"WFb2OAvOSn_O\"\n# # 2 Neighbor Sampling with Different Ratios\n#\n# Now we will implement a simplified version of Neighbor Sampling by using DeepSNAP and NetworkX, and train models with different neighborhood sampling ratios.\n#\n# To make the experiments faster, we will use the Cora graph here.\n\n# + [markdown] id=\"P9U0F7bnSz9u\"\n# ## Setup\n\n# + id=\"PUF4on-fSxcq\"\nimport copy\nimport torch\nimport random\nimport numpy as np\nimport networkx as nx\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch_geometric.nn import SAGEConv\nfrom torch.utils.data import DataLoader\nfrom torch_geometric.datasets import Planetoid\nfrom torch.nn import Sequential, Linear, ReLU\nfrom deepsnap.dataset import GraphDataset\nfrom deepsnap.graph import Graph\n\npyg_dataset = Planetoid('./tmp', \"Cora\")\n\n\n# + [markdown] id=\"qw6k-KdFTEYw\"\n# ## GNN Model\n\n# + id=\"PvUlNi2TS09i\"\nclass GNN(torch.nn.Module):\n def __init__(self, input_dim, hidden_dim, output_dim, args):\n super(GNN, self).__init__()\n self.dropout = args['dropout']\n self.num_layers = args['num_layers']\n\n self.convs = nn.ModuleList()\n self.bns = nn.ModuleList()\n\n self.convs.append(SAGEConv(input_dim, hidden_dim))\n self.bns.append(nn.BatchNorm1d(hidden_dim))\n\n for l in range(self.num_layers - 2):\n self.convs.append(SAGEConv(hidden_dim, hidden_dim))\n self.bns.append(nn.BatchNorm1d(hidden_dim))\n self.convs.append(SAGEConv(hidden_dim, hidden_dim))\n\n self.post_mp = nn.Linear(hidden_dim, output_dim)\n\n def forward(self, data, mode=\"batch\"):\n if mode == \"batch\":\n edge_indices, x = data\n for i in range(len(self.convs) - 1):\n edge_index = edge_indices[i]\n x = self.convs[i](x, edge_index)\n x = self.bns[i](x)\n x = F.relu(x)\n x = F.dropout(x, p=self.dropout, training=self.training)\n x = self.convs[-1](x, edge_indices[len(self.convs) - 1])\n else:\n x, edge_index = data.node_feature, data.edge_index\n for i in range(len(self.convs) - 1):\n x = self.convs[i](x, edge_index)\n x = self.bns[i](x)\n x = F.relu(x)\n x = F.dropout(x, p=self.dropout, training=self.training)\n x = self.convs[-1](x, edge_index)\n x = self.post_mp(x)\n x = F.log_softmax(x, dim=1)\n return x\n\n\n# + [markdown] id=\"Ulp1A3evcJ-I\"\n# ## Neighbor Sampling\n#\n# Here we implement functions that will sample neighbors by using DeepSNAP and NetworkX.\n#\n# Notice that node classification task on Cora is a semi-supervised classification task, here we keep all the labeled training nodes (140 nodes) by setting the last ratio to 1.\n\n# + id=\"LI4qHkE4cQOh\"\ndef sample_neighbors(nodes, G, ratio, all_nodes):\n # This fuction takes a set of nodes, a NetworkX graph G and neighbor sampling ratio.\n # It will return sampled neighbors (unioned with input nodes) and edges between \n neighbors = set()\n edges = []\n for node in nodes:\n neighbors_list = list(nx.neighbors(G, node))\n\n # We only sample the (ratio * number of neighbors) neighbors\n num = int(len(neighbors_list) * ratio)\n if num > 0:\n # Random shuffle the neighbors\n random.shuffle(neighbors_list)\n neighbors_list = neighbors_list[:num]\n for neighbor in neighbors_list:\n # Add neighbors\n neighbors.add(neighbor)\n edges.append((neighbor, node))\n return neighbors, neighbors.union(all_nodes), edges\n\ndef nodes_to_tensor(nodes):\n # This function transform a set of nodes to node index tensor\n node_label_index = torch.tensor(list(nodes), dtype=torch.long)\n return node_label_index\n\ndef edges_to_tensor(edges):\n # This function transform a set of edges to edge index tensor\n edge_index = torch.tensor(list(edges), dtype=torch.long)\n edge_index = torch.cat([edge_index, torch.flip(edge_index, [1])], dim=0)\n edge_index = edge_index.permute(1, 0)\n return edge_index\n\ndef relable(nodes, labeled_nodes, edges_list):\n # Relable the nodes, labeled_nodes and edges_list\n relabled_edges_list = []\n sorted_nodes = sorted(nodes)\n node_mapping = {node : i for i, node in enumerate(sorted_nodes)}\n for orig_edges in edges_list:\n relabeled_edges = []\n for edge in orig_edges:\n relabeled_edges.append((node_mapping[edge[0]], node_mapping[edge[1]]))\n relabled_edges_list.append(relabeled_edges)\n relabeled_labeled_nodes = [node_mapping[node] for node in labeled_nodes]\n relabeled_nodes = [node_mapping[node] for node in nodes]\n return relabled_edges_list, relabeled_nodes, relabeled_labeled_nodes\n\ndef neighbor_sampling(graph, K=2, ratios=(0.1, 0.1, 0.1)):\n # This function takes a DeepSNAP graph, K the number of GNN layers, and neighbor \n # sampling ratios for each layer. This function returns relabeled node feature, \n # edge indices and node_label_index\n\n assert K + 1 == len(ratios)\n\n labeled_nodes = graph.node_label_index.tolist()\n random.shuffle(labeled_nodes)\n num = int(len(labeled_nodes) * ratios[-1])\n if num > 0:\n labeled_nodes = labeled_nodes[:num]\n nodes_list = [set(labeled_nodes)]\n edges_list = []\n all_nodes = labeled_nodes\n for k in range(K):\n # Get nodes and edges from the previous layer\n nodes, all_nodes, edges = \\\n sample_neighbors(nodes_list[-1], graph.G, ratios[len(ratios) - k - 2], all_nodes)\n nodes_list.append(nodes)\n edges_list.append(edges)\n \n # Reverse the lists\n nodes_list.reverse()\n edges_list.reverse()\n\n relabled_edges_list, relabeled_all_nodes, relabeled_labeled_nodes = \\\n relable(all_nodes, labeled_nodes, edges_list)\n\n node_index = nodes_to_tensor(relabeled_all_nodes)\n # All node features that will be used\n node_feature = graph.node_feature[node_index]\n edge_indices = [edges_to_tensor(edges) for edges in relabled_edges_list]\n node_label_index = nodes_to_tensor(relabeled_labeled_nodes)\n log = \"Sampled {} nodes, {} edges, {} labeled nodes\"\n print(log.format(node_feature.shape[0], edge_indices[0].shape[1] // 2, node_label_index.shape[0]))\n return node_feature, edge_indices, node_label_index\n\n\n# + [markdown] id=\"ooy6Hcf7TIhI\"\n# ## Training and Testing\n\n# + id=\"iSmZhpzPTGPY\"\ndef train(train_graphs, val_graphs, args, model, optimizer, mode=\"batch\"):\n best_val = 0\n best_model = None\n accs = []\n graph_train = train_graphs[0]\n graph_train.to(args['device'])\n for epoch in range(1, 1 + args['epochs']):\n model.train()\n optimizer.zero_grad()\n if mode == \"batch\":\n node_feature, edge_indices, node_label_index = neighbor_sampling(graph_train, args['num_layers'], args['ratios'])\n node_feature = node_feature.to(args['device'])\n node_label_index = node_label_index.to(args['device'])\n for i in range(len(edge_indices)):\n edge_indices[i] = edge_indices[i].to(args['device'])\n pred = model([edge_indices, node_feature])\n pred = pred[node_label_index]\n label = graph_train.node_label[node_label_index]\n elif mode == \"community\":\n graph = random.choice(train_graphs)\n graph = graph.to(args['device'])\n pred = model(graph, mode=\"all\")\n pred = pred[graph.node_label_index]\n label = graph.node_label[graph.node_label_index]\n else:\n pred = model(graph_train, mode=\"all\")\n label = graph_train.node_label\n pred = pred[graph_train.node_label_index]\n loss = F.nll_loss(pred, label)\n loss.backward()\n optimizer.step()\n\n train_acc, val_acc, test_acc = test(val_graphs, model)\n accs.append((train_acc, val_acc, test_acc))\n if val_acc > best_val:\n best_val = val_acc\n best_model = copy.deepcopy(model)\n print(f'Epoch: {epoch:02d}, '\n f'Loss: {loss:.4f}, '\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n return best_model, accs\n\ndef test(graphs, model):\n model.eval()\n accs = []\n for graph in graphs:\n graph = graph.to(args['device'])\n pred = model(graph, mode=\"all\")\n label = graph.node_label\n pred = pred[graph.node_label_index].max(1)[1]\n acc = pred.eq(label).sum().item()\n acc /= len(label)\n accs.append(acc)\n return accs\n\n\n# + id=\"HV7i0v0ETKzf\"\nargs = {\n 'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n 'dropout': 0.5,\n 'num_layers': 2,\n 'hidden_size': 64,\n 'lr': 0.005,\n 'epochs': 50,\n 'ratios': (0.8, 0.8, 1),\n}\n\n# + [markdown] id=\"rLpRYKbnTQnj\"\n# ## Full-Batch Training\n\n# + id=\"pMGGjbJBTOo1\"\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\n\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\ngraphs = [graph_train, graph_val, graph_test]\nall_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode=\"all\")\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"JWkGiwB6Thr4\"\n# ## Sampling with Ratios 0.8\n\n# + id=\"yWusJ9u3Tfhv\"\nargs['ratios'] = (0.8, 0.8, 1)\n\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\n\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\ngraphs = [graph_train, graph_val, graph_test]\nbatch_best_model, batch_accs = train(graphs, graphs, args, model, optimizer)\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"w_FjkNHDT4c6\"\n# ## Sampling with Ratios 0.3\n\n# + id=\"booJ6DASTjO4\"\n# Change the ratio to 0.3\nargs['ratios'] = (0.3, 0.3, 1)\n\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\n\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\ngraphs = [graph_train, graph_val, graph_test]\nbatch_best_model, batch_accs_1 = train(graphs, graphs, args, model, optimizer)\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"EePAvNlGUM2K\"\n# ## Visualization\n\n# + id=\"7etNAkXAT55d\"\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nbatch_results = np.array(batch_accs)\nbatch_results_1 = np.array(batch_accs_1)\nall_results = np.array(all_accs)\n\nx = np.arange(1, 51)\n\nplt.figure(figsize=(9, 7))\n\nplt.plot(x, batch_results[:, 0], label=\"Batch 0.8 Train\")\nplt.plot(x, batch_results[:, 1], label=\"Batch 0.8 Validation\")\nplt.plot(x, batch_results[:, 2], label=\"Batch 0.8 Test\")\nplt.plot(x, batch_results_1[:, 0], label=\"Batch 0.3 Train\")\nplt.plot(x, batch_results_1[:, 1], label=\"Batch 0.3 Validation\")\nplt.plot(x, batch_results_1[:, 2], label=\"Batch 0.3 Test\")\nplt.plot(x, all_results[:, 0], label=\"All Train\")\nplt.plot(x, all_results[:, 1], label=\"All Validation\")\nplt.plot(x, all_results[:, 2], label=\"All Test\")\nplt.title('Model Accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend()\nplt.show()\n\n# + [markdown] id=\"bkA7-0groq7q\"\n# Here all accuracies are evaluated on the full-batch mode.\n\n# + [markdown] id=\"Iee0U8KGURc8\"\n# # 3 Cluster Sampling\n#\n# Instead of the neighbor sampling, we can use another approach, subgraph (cluster) sampling, to scale up GNN. This approach is proposed in Cluster-GCN ([Chiang et al. (2019)](https://arxiv.org/abs/1905.07953)).\n#\n# In this section, we will implement vanilla Cluster-GCN and experiment with 3 different community partition algorithms.\n#\n# Notice that this section requires you have run the `Setup`, `GNN Model` and `Training and Testing` cells of the last section.\n\n# + [markdown] id=\"_BXjP79gUYir\"\n# ## Setup\n\n# + id=\"UGQ_VKp8UOEm\"\nimport copy\nimport torch\nimport random\nimport numpy as np\nimport networkx as nx\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport community as community_louvain\n\nfrom torch_geometric.nn import SAGEConv\nfrom torch.utils.data import DataLoader\nfrom torch_geometric.datasets import Planetoid\nfrom torch.nn import Sequential, Linear, ReLU\nfrom deepsnap.dataset import GraphDataset\nfrom deepsnap.graph import Graph\n\npyg_dataset = Planetoid('./tmp', \"Cora\")\n\n# + id=\"bzMatyCSUaB6\"\nargs = {\n 'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n 'dropout': 0.5,\n 'num_layers': 2,\n 'hidden_size': 64,\n 'lr': 0.005,\n 'epochs': 150,\n}\n\n\n# + [markdown] id=\"ekV-sokSUeLc\"\n# ## Partition the Graph into Clusters\n#\n# Here we use following three community detection / partition algorithms to partition the graph into different clusters:\n# * [Kernighan–Lin algorithm (bisection)](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection.html)\n# * [Clauset-Newman-Moore greedy modularity maximization](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html#networkx.algorithms.community.modularity_max.greedy_modularity_communities)\n# * [Louvain algorithm](https://python-louvain.readthedocs.io/en/latest/api.html)\n#\n#\n# To make the training more stable, we discard the cluster that has less than 10 nodes.\n#\n# Let's first define these algorithms as DeepSNAP transformation on a graph.\n\n# + id=\"N8XeT005UcKh\"\ndef preprocess(G, node_label_index, method=\"louvain\"):\n graphs = []\n labeled_nodes = set(node_label_index.tolist())\n if method == \"louvain\":\n community_mapping = community_louvain.best_partition(G, resolution=10)\n communities = {}\n for node in community_mapping:\n comm = community_mapping[node]\n if comm in communities:\n communities[comm].add(node)\n else:\n communities[comm] = set([node])\n communities = communities.values()\n elif method == \"bisection\":\n communities = nx.algorithms.community.kernighan_lin_bisection(G)\n elif method == \"greedy\":\n communities = nx.algorithms.community.greedy_modularity_communities(G)\n\n for community in communities:\n nodes = set(community)\n subgraph = G.subgraph(nodes)\n # Make sure each subgraph has more than 10 nodes\n if subgraph.number_of_nodes() > 10:\n node_mapping = {node : i for i, node in enumerate(subgraph.nodes())}\n subgraph = nx.relabel_nodes(subgraph, node_mapping)\n # Get the id of the training set labeled node in the new graph\n train_label_index = []\n for node in labeled_nodes:\n if node in node_mapping:\n # Append relabeled labeled node index\n train_label_index.append(node_mapping[node])\n\n # Make sure the subgraph contains at least one training set labeled node\n if len(train_label_index) > 0:\n dg = Graph(subgraph)\n # Update node_label_index\n dg.node_label_index = torch.tensor(train_label_index, dtype=torch.long)\n graphs.append(dg)\n return graphs\n\n\n# + [markdown] id=\"7CYEamCAU-TJ\"\n# ## Louvain Preprocess\n\n# + id=\"-TrC6ybWU7eO\"\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\ngraphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"louvain\")\nprint(\"Partition the graph in to {} communities\".format(len(graphs)))\navg_num_nodes = 0\navg_num_edges = 0\nfor graph in graphs:\n avg_num_nodes += graph.num_nodes\n avg_num_edges += graph.num_edges\navg_num_nodes = int(avg_num_nodes / len(graphs))\navg_num_edges = int(avg_num_edges / len(graphs))\nprint(\"Each community has {} nodes in average\".format(avg_num_nodes))\nprint(\"Each community has {} edges in average\".format(avg_num_edges))\n\n# + [markdown] id=\"O03uXIuGVIgJ\"\n# ## Louvain Training\n\n# + id=\"iSbGf5ADVFQq\"\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\nlouvain_best_model, louvain_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], louvain_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"6CvTf0ANVO9U\"\n# ## Bisection Preprocess\n\n# + id=\"HkV0zlhgVJ7u\"\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\ngraphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"bisection\")\nprint(\"Partition the graph in to {} communities\".format(len(graphs)))\navg_num_nodes = 0\navg_num_edges = 0\nfor graph in graphs:\n avg_num_nodes += graph.num_nodes\n avg_num_edges += graph.num_edges\navg_num_nodes = int(avg_num_nodes / len(graphs))\navg_num_edges = int(avg_num_edges / len(graphs))\nprint(\"Each community has {} nodes in average\".format(avg_num_nodes))\nprint(\"Each community has {} edges in average\".format(avg_num_edges))\n\n# + [markdown] id=\"IqMCvP8wVVms\"\n# ## Bisection Training\n\n# + id=\"k1wgFg1bVRGY\"\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\nbisection_best_model, bisection_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], bisection_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"5PROPwoOVcJy\"\n# ## Greedy Preprocess\n\n# + id=\"h3DVamWqVT92\"\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\ngraphs = preprocess(graph_train.G, graph_train.node_label_index, method=\"greedy\")\nprint(\"Partition the graph in to {} communities\".format(len(graphs)))\navg_num_nodes = 0\navg_num_edges = 0\nfor graph in graphs:\n avg_num_nodes += graph.num_nodes\n avg_num_edges += graph.num_edges\navg_num_nodes = int(avg_num_nodes / len(graphs))\navg_num_edges = int(avg_num_edges / len(graphs))\nprint(\"Each community has {} nodes in average\".format(avg_num_nodes))\nprint(\"Each community has {} edges in average\".format(avg_num_edges))\n\n# + [markdown] id=\"93pR_-kxVgma\"\n# ## Greedy Training\n\n# + id=\"lQgQY-jPVd_U\"\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\ngreedy_best_model, greedy_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode=\"community\")\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], greedy_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"D5edKKT6Vk1C\"\n# ## Full-Batch Training\n\n# + id=\"N5tIXxC8ViFD\"\ngraphs_train, graphs_val, graphs_test = \\\n GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True)\n\ngraph_train = graphs_train[0]\ngraph_val = graphs_val[0]\ngraph_test = graphs_test[0]\n\nmodel = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device'])\noptimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\ngraphs = [graph_train, graph_val, graph_test]\nall_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode=\"all\")\ntrain_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model)\nprint('Best model:',\n f'Train: {100 * train_acc:.2f}%, '\n f'Valid: {100 * val_acc:.2f}% '\n f'Test: {100 * test_acc:.2f}%')\n\n# + [markdown] id=\"6RpuETv7Vpx0\"\n# ## Visualization\n\n# + id=\"PMK33kY5VmF5\"\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nlouvain_results = np.array(louvain_accs)\nbisection_results = np.array(bisection_accs)\ngreedy_results = np.array(greedy_accs)\nall_results = np.array(all_accs)\n\nx = np.arange(1, 151)\n\nplt.figure(figsize=(9, 7))\n\nplt.plot(x, louvain_results[:, 1], label=\"Louvain Validation\")\nplt.plot(x, louvain_results[:, 2], label=\"Louvain Test\")\nplt.plot(x, bisection_results[:, 1], label=\"Bisection Validation\")\nplt.plot(x, bisection_results[:, 2], label=\"Bisection Test\")\nplt.plot(x, greedy_results[:, 1], label=\"Greedy Validation\")\nplt.plot(x, greedy_results[:, 2], label=\"Greedy Test\")\nplt.plot(x, all_results[:, 1], label=\"All Validation\")\nplt.plot(x, all_results[:, 2], label=\"All Test\")\nplt.title('Model Accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend()\nplt.show()\n"},"script_size":{"kind":"number","value":35533,"string":"35,533"}}},{"rowIdx":962,"cells":{"path":{"kind":"string","value":"/IBM_Data_Engineering/Connecting to Db2 database.ipynb"},"content_id":{"kind":"string","value":"694ec3bc15458043a02aae683a691420dc582efa"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"amoldsdev/IBM-Data-Engineering-"},"repo_url":{"kind":"string","value":"https://github.com/amoldsdev/IBM-Data-Engineering-"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":11579,"string":"11,579"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"bJdXH59FyqUo\"\n# Connectin to Db2 database through Python\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"Mbv-C7E6yt7Y\" executionInfo={\"status\": \"ok\", \"timestamp\": 1620054875549, \"user_tz\": 180, \"elapsed\": 29198, \"user\": {\"displayName\": \"Luciano Muratore\", \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64\", \"userId\": \"00398105948454054445\"}} outputId=\"0bbfc4dc-3700-4306-a6d8-238c6beebd68\"\npip install ibm_db\n\n# + id=\"B3lloVWQzEiB\" executionInfo={\"status\": \"ok\", \"timestamp\": 1620054927750, \"user_tz\": 180, \"elapsed\": 986, \"user\": {\"displayName\": \"Luciano Muratore\", \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64\", \"userId\": \"00398105948454054445\"}}\nimport ibm_db\n\n# + id=\"eoCqmp7PzGxd\" executionInfo={\"status\": \"ok\", \"timestamp\": 1620055016942, \"user_tz\": 180, \"elapsed\": 932, \"user\": {\"displayName\": \"Luciano Muratore\", \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64\", \"userId\": \"00398105948454054445\"}}\n#Placeholder values\n\ndsn_hostname = \"\" \ndsn_uid = \"\" \ndsn_pwd = \"\" \n\ndsn_driver = \"{IBM DB2 ODBC DRIVER}\"\ndsn_database = \"BLUDB\" \ndsn_port = \"50000\" \ndsn_protocol = \"TCPIP\" \n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"KQ78vo9CzMFG\" executionInfo={\"status\": \"ok\", \"timestamp\": 1620055019380, \"user_tz\": 180, \"elapsed\": 940, \"user\": {\"displayName\": \"Luciano Muratore\", \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64\", \"userId\": \"00398105948454054445\"}} outputId=\"8c813ef8-3260-412f-c834-c3ca47b4becb\"\n#Create the dsn connection string\n\ndsn = (\n \"DRIVER={0};\"\n \"DATABASE={1};\"\n \"HOSTNAME={2};\"\n \"PORT={3};\"\n \"PROTOCOL={4};\"\n \"UID={5};\"\n \"PWD={6};\").format(dsn_driver, dsn_database, dsn_hostname, dsn_port, dsn_protocol, dsn_uid, dsn_pwd)\n\n#print the connection string to check correct values are specified\nprint(dsn)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"Kep6cHeAzeSP\" executionInfo={\"status\": \"ok\", \"timestamp\": 1620055033802, \"user_tz\": 180, \"elapsed\": 1498, \"user\": {\"displayName\": \"Luciano Muratore\", \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64\", \"userId\": \"00398105948454054445\"}} outputId=\"704f476d-f83e-467d-f84f-0ca2bcd213c8\"\n#Create the database connection\n\ntry:\n conn=ibm_db.connect(dsn,\"\",\"\")\n print(\"Connected to database: \", dsn_database,\"as user: \",dsn_uid, \"on host: \",dsn_hostname)\nexcept:\n print(\"Unable to connect: \", ibm_db.conn_errormsg()) \n\n# + id=\"lAtZwQKFzhAC\"\n#Retrieve Metadata for the Database Server\nserver = ibm_db.server_info(conn)\n\nprint (\"DBMS_NAME: \", server.DBMS_NAME)\nprint (\"DBMS_VER: \", server.DBMS_VER)\nprint (\"DB_NAME: \", server.DB_NAME)\n\n# + id=\"hNppDQ1tzkMK\"\n#Retrieve Metadata for the Database Client / Driver\nclient = ibm_db.client_info(conn)\n\nprint (\"DRIVER_NAME: \", client.DRIVER_NAME) \nprint (\"DRIVER_VER: \", client.DRIVER_VER)\nprint (\"DATA_SOURCE_NAME: \", client.DATA_SOURCE_NAME)\nprint (\"DRIVER_ODBC_VER: \", client.DRIVER_ODBC_VER)\nprint (\"ODBC_VER: \", client.ODBC_VER)\nprint (\"ODBC_SQL_CONFORMANCE: \", client.ODBC_SQL_CONFORMANCE)\nprint (\"APPL_CODEPAGE: \", client.APPL_CODEPAGE)\nprint (\"CONN_CODEPAGE: \", client.CONN_CODEPAGE)\n\n# + id=\"1RkPaQHhzpFt\"\n#close connection\n\nibm_db.close(conn)\n"},"script_size":{"kind":"number","value":3734,"string":"3,734"}}},{"rowIdx":963,"cells":{"path":{"kind":"string","value":"/evento_sjc.ipynb"},"content_id":{"kind":"string","value":"a35551ad71d6c783dddcc62815de2a8c344606ff"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"jeference/analise_vendaval_sjc"},"repo_url":{"kind":"string","value":"https://github.com/jeference/analise_vendaval_sjc"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":2426965,"string":"2,426,965"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"y2pvpjUuwKFg\"\n# # Análises do Evento de vendaval no município de São José dos Campos em 09 de Março de 2021\n\n# + [markdown] id=\"rzvokE7KwdLZ\"\n# Eventos extremos podem ocorrer em diferentes grandezas, como chuvas, ventos e nível de rios. Tais extremos podem deflagar desastres naturais/socioambientais - processos complexos que envolvem construções sociais e gatilhos naturais. O evento meteorológico extremo que ocorreu no dia 09 de março de 2021 em São José dos Campos, interior de São Paulo, pode ser classificado como um Vendaval. Mesmo não havendo registros de inundações ou deslizamentos/movimentos de massa nessa data na cidade, o vendaval gerou diversos impactos aos cidadãoes joseenses, especialmente pelas quedas de árvores e galhos e pela interrupção do fornecimento de energia elétrica - essa é a justificativa para o trabalho aqui apresentado.\n\n# + [markdown] id=\"XoSseXAcx59O\"\n# ## Análises Meteorológicas\n\n# + [markdown] id=\"szjrgJjwyRJt\"\n# Fala do Meteorologista e Pesquisador Giovanni Dollif sobre o processo meteorológico: https://g1.globo.com/sp/vale-do-paraiba-regiao/noticia/2021/03/10/meteorologista-aponta-que-sao-jose-dos-campos-foi-atingida-por-tornado.ghtml\n#\n# Registra-se, ainda, que a taxa de precipitação chegou a 7 mm em 10 min, com acumulado da ordem de 50 mm para todo o evento em alguns pontos da cidade (referência pelo pluviômetro do Cemaden situado no Parque Tecnológico de São José dos Campos). O total esperado para o mês de março na cidade é de aproximadamente 150 mm. A título de comparação, no evento de 06 de março de 2015, a taxa de precipitação chegou a 15 mm em 10 minutos, acumulando 59 mm em um intervalo de 1 hora (Santos et al., 2015).\n\n# + [markdown] id=\"_mPbV4gx_TPU\"\n# ### Nível de chuva\n\n# + [markdown] id=\"lcpIkB4MRDea\"\n# #### Importando as bibliotecas\n#\n\n# + id=\"RVok9M7asYYg\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import timedelta\n\n# + [markdown] id=\"YiRWb0wsROkT\"\n# #### Lendo os dados de nível de chuva [mm] no dia do evento\n\n# + id=\"aSiNWOMGPeBo\"\ndf = pd.read_csv(\"data.csv\", sep=';', index_col=False) # Lendo o CSV\ndf.columns=df.columns.str.replace('\\t','') # Ta separado por ; mas tem uns tabs aleatórios, aqui vai limpar os tabs do nome das colunas\ndf['valorMedida']=df['valorMedida'].str.replace('\\t','.').astype(float) # tirando os tabs dos valores e transformando em inteiro\ndf['latitude']=df['latitude'].str.replace('\\t','.') # mesma coisa pra latitude\ndf['longitude']=df['longitude'].str.replace('\\t','.') # e longitude\nconverting = timedelta(hours = 3) # convertendo de utc -3h pra hora local\ndf[\"datahora\"] = pd.to_datetime(df[\"datahora\"]) - converting # transformando a coluna de data e hora no tipo datetime\n\n# + [markdown] id=\"f2fPKycT-PCO\"\n# #### Dividindo os dados só para o horário do ocorrido\n\n# + id=\"ilT2Q7g0-PiH\"\nstart_time = pd.to_datetime(\"2021-03-09 17:00:00\") # definindo o começo dos dados\nfinish_time = pd.to_datetime(\"2021-03-10 21:00:00\") # definindo o fim dos dados\ndf = df[df['datahora'] > start_time ]\ndf = df[df['datahora'] < finish_time ]\nestacoes = set(df['nomeEstacao'])\n\n# + [markdown] id=\"HivbkPEO-UfU\"\n# #### Plot para cada estação meteorológica \n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} id=\"W1hLUpNG-T66\" outputId=\"cb7474a1-64a7-479d-b909-5a566ebe7468\"\nfor estacao in estacoes: # pra percorrer todas as estações únicas\n cod = set(df[df['nomeEstacao'] == estacao][\"codEstacao\"]) # guardando o código\n lat = set(df[df['nomeEstacao'] == estacao][\"latitude\"]) # a latitude\n long = set(df[df['nomeEstacao'] == estacao][\"longitude\"]) # a longitude\n title = f\"Estação: {estacao}, Codigo: {list(cod)[0]} \\nlat/long: {list(lat)[0]},{list(long)[0]}\" # pra colocar no título de cada uma\n df[df[\"nomeEstacao\"] == estacao].plot(x = 'datahora', y = \"valorMedida\", title = title, grid = True) # fazendo o gráfico com um slicing pra cada estação\n\n\n# + [markdown] id=\"BtLhRYWE-Y6Q\"\n#\n#\n#\n# #### Plot para todas as estações meteorológicas juntas\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 310} id=\"Wev4CeVZ-YZo\" outputId=\"15391473-90bf-4d15-b1be-15fce1ec732b\"\n# agora gráfico com todas as estações\nplt.figure() # Criando a figura\nplt.title(\"All stations\") # Titulo\nplt.grid()\nfor i in estacoes:\n plt.plot(df[df['nomeEstacao'] == i][\"datahora\"], df[df['nomeEstacao'] == i][\"valorMedida\"], label = i) # Fazendo o plot\nplt.xticks(rotation=45) # Rodando os ticks pra não ficar atropelando\nplt.legend(bbox_to_anchor=(1.05, 1)) # mudando a legenda pra fora do gráfico\nplt.show() # mostrando o gráfico\n\n# + [markdown] id=\"XU1cg_Al9LOX\"\n# ### Formação de nuvens \n#\n\n# + [markdown] id=\"wRrBbc20-NEO\"\n# A animação de imagens de satélite mostra a rápida formação de nuvens carregadas (convecção profunda), na região e horário onde foram registradas as descargas elétricas na atmosfera, o vento no aeroporto, a chuva e os estragos na cidade. A forma da convecção nas imagens não mostra um formato claro, mas há semelhança com o padrão de uma linha de instabilidade (https://www.sciencedirect.com/topics/earth-and-planetary-sciences/squall-line, https://www.nssl.noaa.gov/education/svrwx101/thunderstorms/types/ ) . São necessárias análises mais detalhadas, possivelmente de dados de Radar Meteorológico (preferencialmente de Banda X), para uma classificação mais confiável.\n#\n\n# + [markdown] id=\"L923aSop-3bQ\"\n# ### Ocorrências de raios\n\n# + [markdown] id=\"L2sHBcJ6_3nn\"\n# A figura (abaixo) de acumulados de descargas elétricas na atmosfera revelam uma significativa concentração de registros dentro do município de São José dos Campos entre 18h e 20h.\n#\n\n# + [markdown] id=\"KAR3BRV3AGm4\"\n# ![raios_vp_20210309.gif](data:image/gif;base64,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)\n\n# + [markdown] id=\"htjcTlRaAiYN\"\n# ## Análise da mobilidade urbana nas regiões mais afetadas pelo vendaval\n\n# + [markdown] id=\"4hzOh4BEAn78\"\n# O setor de transportes e mobilidade está entre os mais afetados por eventos meteorológicos intensos. No evento de 09 de março de 2021 em São José dos Campos/SP não foi diferente.\n#\n# Interessado nas características temporais, espaciais e espaço-temporais da mobilidade urbana em São José dos Campos/SP? Aqui está um artigo científico novinho em folha sobre o tema:\n# https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248126\n#\n#\n\n# + [markdown] id=\"uavKI8MlCpyM\"\n# ### Origem\n\n# + [markdown] id=\"3-qWypwkBW9U\"\n# A Figura abaixo mostra um mapa com o número de pessoas que fizeram viagem com início entre 18h e 20h com origem nas Zonas de Tráfego (ZTs) 14 (Jd. Aquarius e Colinas) e 32 (Jd. Alvorada e Jd. das Indústrias). Nota-se que há um grande número de pessoas que se deslocam dentro da própria zona. Desconsiderando esses deslocamentos intrazonas, as ZTs 28, 34, 29 e 30 são as que recebem maior fluxo de pessoas com origem nas ZTs 14 e 32.\n#\n\n# + [markdown] id=\"FejdGpFqBeMS\"\n# ![mapaOrigens.jpeg](data:image/jpeg;base64,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)\n\n# + [markdown] id=\"4IQ-hV45Ctxr\"\n# ### Destino\n\n# + [markdown] id=\"3k7u9OHkB6UV\"\n# A Figura abaixo, por sua vez, traz um mapa com o número de pessoas que fizeram viagem com início entre 18h e 20h com destino às Zonas de Tráfego (ZTs) 14 (Jd. Aquarius e Colinas) e 32 (Jd. Alvorada e Jd. das Indústrias). Nota-se que há um grande número de pessoas que se deslocam dentro das próprias zonas analisadas. Desconsiderando esses deslocamentos intrazonas, as ZTs 1 e 6 são as que possuem maior fluxo de pessoas com destino às ZTs 14 e 32, seguidas pelas ZTs 31, 7, 25 e 13 respectivamente. Os dados de mobilidade urbana analisados referem-se à Pesquisa Origem-Destino realizada em 2011.\n#\n\n# + [markdown] id=\"A-KlK96HCOwb\"\n# 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)\n\n# + [markdown] id=\"Sur3xY4uxfZ4\"\n# ## Análise dos impactos do evento sob a perspectiva de redes complexas\n\n# + [markdown] id=\"mjIEE5Li0aUM\"\n# A partir de dados do sistema viário de São José dos Campos/SP, obtidos através da plataforma OpenStreetMaps, foi construída uma rede de arruamento através da aplicação gis4graph (https://github.com/aurelienne/gis4graph).\n# O grafo (objeto computacional que representa a rede), conta com 1184 ruas e foi construído com base no sistema viário da região Oeste do município, devido ao alto impacto gerado na região em consequência do vendaval. A imagem abaixo mostra o sistema viário do município\n#\n\n# + [markdown] id=\"B7lLQnEWu0NK\"\n# ![WhatsApp Image 2021-03-11 at 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KCet7pR+St+Oi6200SuFSMZy91JLmcrebv17e89N9rttJngHgBv26/2dfhVrPj3U/g98JrjTodR8Q+NfGOkJrt3/b+qT3F7Pcyy28kUTWkZWEoEQtKzJEoyHPlhRq+zowhiJNxhFK+l11beyerb6epo6UKtef1dcvPJtJu66JL7klfTXV2Oz8J+KtE+Mn7dfgzxxay/a9Bj+A9v4s8M2t9APMtJtU1PbJdIDnypTbxRxOQc4YrnGa7Y4WEKFbERldqUIabNPnd7NJ7xVttN1245Yl1KlOg46WlK/ZrljZ29X9zMGP4F+GfjF8Xf2t/2eNR1KTTfCniV/BXiCS10+NUWz1GaF5biZVAA3zPYQSOerEkk5JrycNiJzlOko2VOovPmTUJyT8ndx8tbHp4ihClGFVfFUg/k7zimvSyet7ta6aHc/BjHiX9uf8AaO1jXVtr278J6b4Q8O6JJJCplsNPnsJLyeFHxkJLcSl2Hcouegx3qiqdD2ibvOTv/wBuqNrdt3dbN62ucLq+0qqDjbkite929/NW37adzi/gt8DfDnj2++I/w6a9lsNB+F/x/i8X+Hra2jHkWjxQ2t41lHHwscPmXEw2pgKZCQPXkw03UoU5vTklUil5JyS/O/4HTXUadepTt8cYNvzaTb872t6Mt/H/AOE+hfs5fCPwL8QvD+qC2tPhj8V4/GOo3vk7Jf7L1bVZor63G3PyrDqKoT3S3BPpV4Wm6VXD0oWsl7NN20jJNdf71vm7mVdKrTrzabbftLJNtyjZ9O0U/u8ziP2kPB2r/tFeP/2hPE3h++1KEfBbwPZaJ4ZntAJA/iKOePXLh7dgSUmQ2tjbuAN3zkcY5zhUVGEsbG11JWk7aRh8V0/Nuz233NZw9tKOCmm04u61TvUVo2a30Se/Y9FtPGln+0t+0t8Br/T4rmDRfC/w/m+Kt1bkLJA91qkaWdhC5IyssQa8kB45UV1KHJiKtSN+WK5Yu+vv3upLazgl1vr2vflcnOhThNq8neSt/Js0/Kb/AA77fXNZmoUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfNXx+/Yf+C/j74NeLPB3ws+CPwq8OeLNW09rfSdV/wCEatLP7JOWUh/OggaWPgHlATzWGIhOpBKm7O8X8lJNr5q6NaMowk3NXVn97TS+56m18TP2abqNvBHi39m6+8OfDrxT8N01GPRrAaOF0K/tb0Brmxu7e2MbLFJMkcnmJlkfc4VmORr71Oo6lO2q5Wn1V7rXdOL1VtNWmncxjCM4RhVb0ad10drPR6O60d9drNWLHwT+CfxH0r4iax8fPj54p8Paz8Q9b0S18ORWvhuymttI0XTYZWla3tzM7TTmWZvNeSXBz8qhVGDreMYOMerTfyVrL73117Im0pTUpWVk0rebvdv0S0SVvM9c8X+FdE8deE9a8EeJbU3OkeIdOudK1CEOUMltPG0cqhlwVyjsMjkVjUh7SLje1+vbzV7q69DWEuSSla9j5YsP2VP2ifEvhrQ/2fvjD8W/Bmv/AAW8MT6Y4a10KaPxB4ktLGVZYNO1AvIbeGFWjhDSwgyyLAuSpdyemNZ+2jiHpKLT02bS3ad766tO6vtbS2dSEHCVKGkWrb6pdk1bW2l1Z9T6G+Nvwm8PfHX4T+J/hL4od4tP8S2DWjTxrue3lBDwzqDwWjlSOQA8EoAeK5qkHONk7PR/c7/8P5G1Goqc+Zq61T9GrP8ADyfoeL6P+z9+0X8QvGngu7/ac+JXgbXfDHww1qLXdDt/Dmiz213r+owQNFbahqTSyFLeSJneUQ2w8tnkbPyqoHRGoozlUtq00ktlffvey0XVb36PGUbw5F3Tv106fPr3PqCsij5j+If7KHiDxr+2D4R+OUeqaCvgTTo7DU9e0aZH+2Xuu6bFqEem3ifIUPlDUByXUjyV4bggw16FWc07XWmt7trlldbJcq0avrfRbt1n7WnCPVP0slqrNb3k3dPp16D/ANsP9lHX/wBpLXfhzfaFr2n6fp+ian9i8W2t1LJE2p+Hprm0uLmCJo0fMu6xi2K21TubLLjmsPN0a/tLLla10vdxkpRXTS6d9dE9mTVhCrS5XfmT0fa8ZRfzs9On4H0zUjCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPH/AIh/sq/ALxppXia4/wCFE/DWXxJr1vev/at34Ysmne+nVz9okm8kyFzI28vy2cnk1hiYTq0JwpO0mmk+ztozWhOFOrGVRXSauvK5yHhn9lvxJ4G/Z5+Hvw8+Fni/TPhz428Hf2Tqmo32hWROla/qlvaJBdrqMCeS97BcbTudysuVifOU2nZtxkpw1aT0ezuv61tddDCME4uE3a73W61uv+G2fXQtfDb4J/GnxH8UdA+OH7UPirwZfeIfB+n32neGtC8G2NzDpenPdkLcXrTXTmeeeSFUi2kLGig4BZi1aRcYJtbtWfZa3svmlrp2sKSlJq+yd/XS3+en47n0Iyh1KHOGGDgkH8x0rNq6saJ2dz5O1L9nr9r2bwvf/AS3/aG8O33wx1W0u7K48U6zpt3e+N0s55XLWhleb7LMxhcw/amVXCncsYZVILe1XLXSta2n2u909Fp6367sSvTfNSeu+vR+XdX119NkjpviB+zT4u8M6z4K+I37LGteGvDnivwF4UbwPa6d4ntbi70vVtCBiMNpcyxv9oiMLxCVJULMWyHyrGm23UlU/mVrdtb3XbqvT0Ki0qKpO7ad0/VWd1brZPp6a6dV+z78EPEHw01Dxr8RfiP4m0/xF8Q/iTqFtqPiG+02xa0soI7e3WG1sbaNmZjDAm9Vkc+Y+4s/NaOSVONOPS717v8ASySS1/Gyhq83PySt00/V9Tn/AInfBj42aJ8VNU+N/wCzF4o8Eadr/inTLHSPE2ieL9Nnk03UhaO5t74TWjLOlzFFLJEFO5HQqDtKA1jCPI5W+00/na2nla2nz3NZzdSMVJv3U0u1m72t6tu/n069h+zx8En+B3g3UtL1bxTJ4o8TeJtcvfE3iXXHthbDUNSumG+RIAzLCiokUaopwAmeMmtZOKioQWi+9vdt9Lvy6W3erzk3Kcqj6vbol0S62Xm2/M6L4xeAIvit8J/GXwzmlihHinQr7SVllUlYXnhZEkIHPyswbj0rnrwc6bUUm91fa61XR9bdGaUZKE05bdbduv4HDfslfBTxR8D/AIUTaL8RNU03VvHHiPXdT8S+KtS06SR7a91G7nY+YnmIjACFYExsUZQ4FdNeUKqUUtLdrXu23pd9W15q2hzUYSprV/8ADJWXRdr+rZyP7Fv7KWvfsyxePP8AhJ/EkGsza5q0VnoTw3cswtfDlmHGnW8gkRNky/aLjeF3L8y4ZqlTcqUVOznZczta7UYwXltFPZatmjilUbhpHWy7XlKT131cu/RWPpepGFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAhIAJJwB1NGwHKy/Fj4Ww+Crn4kyfEjwuPCVo7Rz67/a9udPidZfKZWuA/lhhL+7xuzv+XrxSclGKk9n+Pp3Gk5ScVut/L17EPwy+MXws+M2jSeIPhX490XxRYwOIp5NOulla3c5wsqfejYgEgOASORxzVuLWrJUk3bqdjUjCgAoAKACgAoAKACgAoAKAPn/APYK+KXjv40/sneBviZ8S9d/tnxJrP8Aaf229+yw2/m+VqV1DH+7hRI1xHEi/KozjJySSQD6AoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8K/bj+L3jL4Efsr+PPil8PZ7WDxDpNvZw2M9zAJkt3ub2C2MoQ/Kzos7OgYMm9V3K65UgHb/s/eKNd8b/AAF+G3jTxRffbdZ1/wAIaPqmo3PlJH591PZRSSvsQBF3OzHCgAZwABxQB31ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8/8A7fL68n7IPxKOgG9Dtp0CX5swTKNLN1CNQPHO37Gbnd/s7qF7Pnh7a/JzRvbe11/XoXBSfNyWvyyte38r76X7db7a2PGPiTD8DfjD8U/gF+yp8EE8H6r8OtO1G68f+J9P8MG3nsYLOxhzZpcJDmJobi5uMOrglyVJ5YE1C88T7SeqhFu2yvLROPnG7dl0eqsZTjGOH5IaObWvktZJrzSSu9U13PS/D914Uvv+ChWtv8PzHLLb/DJofHM2mvutf7SGpQLpsd55fyfbVgS8C+Z+9EPA+QAVlS526jXwe72+L3r+b03eyej1NKqilT/nu79+W0bfifTlWSfB/jL4q/tP/GT9vfxz+zD8JvjxD8K9B8D+FLXVhPH4Usdba+mZbKR2cXIDIx/tEL8r7Qtuvy5ZmoA9G/4Z4/bs/wCkiv8A5iLR/wD45QAf8M8ft2f9JFf/ADEWj/8AxygA/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAD/hnj9uz/pIr/5iLR//AI5QBleJfgF/wUSstMa48J/t9afq18rZ+zXvwz0myRkwc4kUS/NnAAKgc8sMc9mBp4SrV5cbUlCHeMVN39HKGnndvyZzYueIp0+bDQU5dnJx/FRlr8vmfJWrftE/t4aRey6XdftXSJe2kkkF5E3gPRcRTI7KyqyoQ4wB83HJI5ABP7Lh/BlYmmq0Md7srOL9k9U0ndpzVvTt9y/Mq3ia6E3TlhPeV01z7NNq3wu/r/w7+ifgj4E/4KFfFnwc/ivX/wBtS+8Is12YrW1u/hRpMhuYPKjdbhGby8o28gEAg7CQT2/MuKciw3D2OWDw2JVfS7cUlyyu04u0paq13rfXY+6yDNq+c4V4mvQdLWyTbd1ZNSV1HR3006bmt8Lf2Jv2sfgt4E0z4Z/DT9vj+xvDejed9isv+FWabceV5szzSfvJp3kbMkrt8zHGcDAAA+bPcOr/AOGeP27P+kiv/mItH/8AjlAB/wAM8ft2f9JFf/MRaP8A/HKAD/hnj9uz/pIr/wCYi0f/AOOUAH/DPH7dn/SRX/zEWj//ABygCOf9n/8Ablto/Nn/AOCjIRenPwj0fk+gHmcn2FACW/7Pv7eE0fmSf8FDjECflVvhHo+7b2JHmcH2/wD1UAS/8M8ft2f9JFf/ADEWj/8AxygA/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAIZ/gF+3PAyxn/goozyP91E+EOjlj7/63ge54oAIPgB+3bONw/4KGzRqRkGT4Q6Mv4Y83P6UATf8M8ft2f8ASRX/AMxFo/8A8coAP+GeP27P+kiv/mItH/8AjlAB/wAM8ft2f9JFf/MRaP8A/HKAD/hnj9uz/pIr/wCYi0f/AOOUAH/DPH7dn/SRX/zEWj//ABygA/4Z4/bs/wCkiv8A5iLR/wD45QAf8M8ft2f9JFf/ADEWj/8AxygA/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAD/hnj9uz/pIr/5iLR//AI5QAf8ADPH7dn/SRX/zEWj/APxygA/4Z4/bs/6SK/8AmItH/wDjlAB/wzx+3Z/0kV/8xFo//wAcoAq3XwF/bss5FM//AAUQItypzMPhJo52t2BXzOh7H1wO4oAtf8M8ft1/9JFf/MRaP/8AHKAIrn9n79uu3i8w/wDBRMtllQAfCPRurMFH/LX1NAHnfxRt/wBqz4TXh0jxB/wUX1G91Y6e+qJpum/BnRprh7ZHwzgGZVACiWQ5YAJBKT0AP1OQ8H5lxFT9vhuWNPmUOebtFSauls3q+WKstZSiurt4Gb8SYLJp+yr3lPl5uWKu7J6votFd77Rb9b3gD4ff8FBfGceoazqP7b82jeHXMMnh3UpPhhokz63bybyJhbhxJBlRGwV/mIkHTBrkzvKMNlHs6MMQqlbVVIKLXs5K3u812p63TcdE4+aOjK8xr5lz1JUeSlo4Sck+eLvry7x0s7PXXyO2tv2ff28J4/Mk/wCChrw5Pyq/wj0bdjHUgS8Hrx/+qvBPXJf+GeP27P8ApIr/AOYi0f8A+OUAH/DPH7dn/SRX/wAxFo//AMcoAP8Ahnj9uz/pIr/5iLR//jlAB/wzx+3Z/wBJFf8AzEWj/wDxygCOX9nr9vFVBh/4KIq53DcD8JdHXjuR+85Ptx9aAIj8AP29m4j/AOCgrhiAcyfCbRFUexKyk/pQBOv7PH7d2wF/+CiYDY5A+EejkZ+vmUAMt/2f/wBue5iEsf8AwUXBB6gfCPR8qe4P7zgj0oAl/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAD/hnj9uz/pIr/5iLR//AI5QAf8ADPH7dn/SRX/zEWj/APxygA/4Z4/bs/6SK/8AmItH/wDjlAB/wzx+3Z/0kV/8xFo//wAcoAiuv2fv27La1muf+Hie7yo2fb/wqPRxnAzj/WUAS/8ADPH7dn/SRX/zEWj/APxygA/4Z4/bs/6SK/8AmItH/wDjlAER/Z+/brFytsf+CifLxl1P/Co9G7EAj/Wf7Q/WgCX/AIZ4/bs/6SK/+Yi0f/45QAf8M8ft2f8ASRX/AMxFo/8A8coArN8Av27ze/Y4/wDgoep2oJHZvhNow+UkgbR5hz0Pp29aALP/AAzx+3Z/0kV/8xFo/wD8coAP+GeP27P+kiv/AJiLR/8A45QAf8M8ft2f9JFf/MRaP/8AHKAIJPgF+3PFcpbzf8FF1TzVzGT8JNH+Zs8r/rOvI+uT6UAT/wDDPH7dn/SRX/zEWj//ABygA/4Z4/bs/wCkiv8A5iLR/wD45QAf8M8ft2f9JFf/ADEWj/8AxygA/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAD/hnj9uz/pIr/5iLR//AI5QBynxS/Ym/ax+NPgTU/hn8S/2+P7Z8N6z5P22y/4VZptv5vlTJNH+8hnSRcSRI3ysM4wcgkEA1vCn7KH7Z/gjwto/gvwv/wAFB/sWjaBp9vpenW3/AAqfSpPItYI1jiTe8xdtqKoyxJOMkk80Aav/AAzx+3Z/0kV/8xFo/wD8coAP+GeP27P+kiv/AJiLR/8A45QBXufgF+3bbOob/goc7o4IUp8ItHJ39lx5nGfUnHHOKAIj8Af2/Y8NL/wUERkxlvK+FOjsw+gLjP5/nQBah/Z+/bnnQSQ/8FGA6nuPhHo//wAcoA+Xf2nfiT/wUb+BXxd8FfBT4f8A7Q/iH4jeIfGsMktk8Xw00TT7WR1IzFDLiYO8ahpJ94jWGNonYlXyoB6x8HdP/a++K1lq2nRf8FM/DL+LPC95/Z3iTRtC+HOlamumXRBZIxKyQPKCnBcRBBIk8YLNC+ADtz8EP29hI4b/AIKBNFEPuyy/CTSVUjPU4Y7fxoAsf8KG/b1jCzT/APBQlWt2UN5tv8J9HlwD0ON4JHuM0AXV/Z6/bqdQ6f8ABRcMrDII+EejEEf9/aAF/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAD/hnj9uz/pIr/5iLR//AI5QAf8ADPH7dn/SRX/zEWj/APxygA/4Z4/bs/6SK/8AmItH/wDjlAB/wzx+3Z/0kV/8xFo//wAcoAP+GeP27P8ApIr/AOYi0f8A+OUAH/DPH7dn/SRX/wAxFo//AMcoAP8Ahnj9uz/pIr/5iLR//jlAB/wzx+3Z/wBJFf8AzEWj/wDxygA/4Z4/bs/6SK/+Yi0f/wCOUAH/AAzx+3Z/0kV/8xFo/wD8coAP+GeP27P+kiv/AJiLR/8A45QAf8M8ft2f9JFf/MRaP/8AHKAD/hnj9uz/AKSK/wDmItH/APjlAB/wzx+3Z/0kV/8AMRaP/wDHKAD/AIZ4/bs/6SK/+Yi0f/45QAf8M8ft2f8ASRX/AMxFo/8A8coAP+GeP27P+kiv/mItH/8AjlAB/wAM8ft2f9JFf/MRaP8A/HKAD/hnj9uz/pIr/wCYi0f/AOOUAH/DPH7dn/SRX/zEWj//ABygA/4Z4/bs/wCkiv8A5iLR/wD45QAf8M8ft2f9JFf/ADEWj/8AxygA/wCGeP27P+kiv/mItH/+OUAH/DPH7dn/AEkV/wDMRaP/APHKAOQ+L/w3/bs+FPwp8Y/E7/hv/wDtT/hE9Cvta+w/8Kq0eD7T9ngeXyvM3Ns3bMbtrYznB6UAYP7OOj/tjfHH4IeEfirbfHWCJdfsTKwuruaOUvHI8TsypCVGWjY8HHPQdAAekf8ACj/2zf8AovWnf+B9z/8AGKAD/hR/7Zv/AEXrTv8AwPuf/jFAB/wo/wDbN/6L1p3/AIH3P/xigA/4Uf8Atm/9F607/wAD7n/4xQAf8KP/AGzf+i9ad/4H3P8A8YoAP+FH/tm/9F607/wPuf8A4xQAf8KP/bN/6L1p3/gfc/8AxigA/wCFH/tm/wDRetO/8D7n/wCMUAH/AAo/9s3/AKL1p3/gfc//ABigA/4Uf+2b/wBF607/AMD7n/4xQAf8KP8A2zf+i9ad/wCB9z/8YoAP+FH/ALZv/RetO/8AA+5/+MUAH/Cj/wBs3/ovWnf+B9z/APGKAD/hR/7Zv/RetO/8D7n/AOMUAH/Cj/2zf+i9ad/4H3P/AMYoAP8AhR/7Zv8A0XrTv/A+5/8AjFAB/wAKP/bN/wCi9ad/4H3P/wAYoAP+FH/tm/8ARetO/wDA+5/+MUAH/Cj/ANs3/ovWnf8Agfc//GKAD/hR/wC2b/0XrTv/AAPuf/jFAB/wo/8AbN/6L1p3/gfc/wDxigA/4Uf+2b/0XrTv/A+5/wDjFAB/wo/9s3/ovWnf+B9z/wDGKAD/AIUf+2b/ANF607/wPuf/AIxQAf8ACj/2zf8AovWnf+B9z/8AGKAD/hR/7Zv/AEXrTv8AwPuf/jFAB/wo/wDbN/6L1p3/AIH3P/xigA/4Uf8Atm/9F607/wAD7n/4xQAf8KP/AGzf+i9ad/4H3P8A8YoAP+FH/tm/9F607/wPuf8A4xQAf8KP/bN/6L1p3/gfc/8AxigA/wCFH/tm/wDRetO/8D7n/wCMUAH/AAo/9s3/AKL1p3/gfc//ABigA/4Uf+2b/wBF607/AMD7n/4xQAf8KP8A2zf+i9ad/wCB9z/8YoAP+FH/ALZv/RetO/8AA+5/+MUAH/Cj/wBs3/ovWnf+B9z/APGKAD/hR/7Zv/RetO/8D7n/AOMUAH/Cj/2zf+i9ad/4H3P/AMYoAP8AhR/7Zv8A0XrTv/A+5/8AjFAB/wAKP/bN/wCi9ad/4H3P/wAYoAP+FH/tm/8ARetO/wDA+5/+MUAH/Cj/ANs3/ovWnf8Agfc//GKAMM6p+0n8E/i38P8ARPHXxOt9fs/F2qRWElurvPH5LTwxyZ8yNCrfvQVZTnI54JBAPsugDifjT49vfhh8L/EHjvT9K0jUrjSbdZEtdX1uDR7OXdIqES3k/wC6hGGJy3BIA6kVFR2j9y+92/rubUKftJ210Teivsm/01fRa9Dyv9jbVfA3iTw3rniLwf8AB34QeApLuWBbuL4feItP1gXBAcj7VLZW8UaMpLbV3SfefBHOd50kqSvfW/TTZde+uvbTV3OZSvJnrfwx+EPwx+DHh8+FvhX4H0jwzpjSebLDYW4QzyYx5kr/AH5XwANzknAAzgCpcm0ovZFWV2+rOvqRnwD8G/8AlMh8dv8Asn9n/wCiNCoA+/qACgAoAKAPnzWf2uD4GuZV+KfwV8a+FrP+0biygvmhjuLeVUGYz5gKqXfbJ8qF1AUFXcElf0rDeHX9qxX9kY+jWlyxk43cZK++mrsrrVqLu7OMXa/w9fjT+z5P+0sJUpR5mlKyadttdFd66JtaXTa25nxl+3n8Mrj4e6xdeCJNZt/E7iWz022u9PXcjmP5Lsnc0Xlqx+6WLkjlNvNerlnhJm0MzpQx6g6CtKbjJ6q+sNlLma6pcttpX0ODHeIuXywNSWD5lW1UU49baS3cbLte9/s2Pln9nT4KXn7QHxEm0vVdSvLfS7OJtQ1e/QF5WBcARq7BlEsjMSC/ZZGw23B/XeNOKIcH5Yq1GCdST5YR2W27SafLFLW3VxWl7r844YyGfEmOdOpJqCXNKXXfZPVczffom9bWP1F0uyk03TbTT5dQur97aBIWuroqZpyqgb5CiqpY4ycKBknAFfyRXqqvVlVUVFNt2V7K/RXbdl0u2/M/oqjTdKnGDk5WSV3u/N2SV31skWqyNAoAKAIbm7htVBlbLNnYijLuR2UdTQBIrEoHdShIyQSMr7HHFAFO0QXs51KRMoBttgey93x2LfyA9TQBeoAKACgCheakqMtvZsrytKsJYjKRk9cngZH93OeRQBYtbRLYMdxklkOZJWA3OffHp0A7CgCegAoAKACgAoAKACgAoAghuHeZ4JoRG6/MmGyHT16dR3HbI55oAnoAKACgAoAKAIrq2ivLeS2mGUkGD7e9AFG0TUdMRILhkubWNQivHG3mKMcZUZyO3HPQ+tAHC/F/x54u8P6Zpa/DbwjD4m1vVb+TTLG1uLtbWJLr7JPOJXMmA4jWByU3JnIAYECvouGsrwOZ4io8zrulRpRU5NRcm1zwhZWu02572lbqmjxc8x+LwNGCwNJVKs5csU2kr8spXd97cu11fuY3wU/Zp8JfA+/uPFFpq893qk9ncWlwyQiC0WB5xMFSMl5flCIoMk0hIU884HrcVcdYziiisLUgo004yV3eV1Hlu2uWOt27RhFa/f5/D/CeGyGo68JNzaaelo2cr6LWWlktZSZ63ZQyP/pt1kzPnYrD/VITkL9cYz7/AEFfDn1RboAKACgAoAKACgAoAKAKlxYJLKt1bsILhT/rAgO4Yxtb1HT8uMUAPs5ppUdLlVE0TlH2fdJwCCM+xFAFigAoAKACgAoAq6pJ5Wn3DFN/yFcYyOeMn2Gcn2zQBZVldQ6MGVhkEHII9aAFoApah+7mtJ0OJBL5Yz02EZfOf9lSfqBQBcVldQ6MGVhkEHII9aAFoAznh/tC7SdFWNLWXHmhf3jleqg9lzkH1wR7kA0aACgAoAgvIDc2ssKnDsvyHJGGHKnI9Dg0AOt51uI94BVgdrqeqsOoP+fegCWgAoAKACgAoAKACgAoAKAGxyJKiyxsGRhlSOhHrQA6gCvLYWU7mWS1jMh/5aBcP+DDkUAeYftMfB3xF8bPgX4m+FPgvx5e+D7/AFmyeCK8ttuycEHNtcEqziCXO2QxkOQTyylkcA/KnwH+2f4P/Yd+K1r8HPhD8NbuHwd4c182nxE1jU7aP/hI/FU8KyW8x2sdlpDBI7vb2ysCTEpeRDNOGAP2b8N+ItF8X+HdL8WeGtQjv9I1qyg1Gwu487Li2mQSRyLkA4ZWUjIzzQBLJa3EFwbmwMeJP9bC5Kox67gQDg9c8c59qAKCXqQyLNpzSPuP7+yCMxjOecYHyNnPBwDz9aANW3vYLljGpZZVGWjdSrgfQ9R7jigB7zBZo4QMtJuJ5xhQOT78lR+NAElABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P/AKW3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QB59+0B4r+H3gb4L+MPGPxT8P2Wu+F9G0uS8v9LvLWO5jvtmDHB5coKMzyeWq7hgMVJxjNZVp8kb2u20kvNtJd+rWttNzahGc52p32d7dkm5dul9Oux4H4E8C/tweHtLku/h18Pv2Y/hVp2p7b1PDltpN/LPblgNsN3NaGKGWVFJVpIl2ll+Xg5reV4+63e33eqMFaXvJHo/wU+NXxN1H4l618APj/AOGvDWlePtI0SDxLaXvhm7lm0vWtKlneEzwpMPOt2ilURPHISSTuUlSDWkaTnRdZfZaT+abVvXlf3eeik5QmotXUk2nZ20tdPpfVNa6p+R7tWJR8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgDmPHvgXwJ44061X4g6Ta6hp2jXI1REvJWW2jkjRh5ki7gjqFZ8hwVwTkV62UZtmOVVZf2bNxnUXJ7qXM02tE7Nptpaxs7nn5jl+CzCnH69FSjB82r0TSerV7NWvvdH5m/G7XtM+KHxhurH4Z+EdOtdNhmGi6LZ6JZqgvEjdgsoWNRuaRmLDjIUovO3J/qzhbCVchySNTNa8pTa9pUlUk3ytpXjdt2UUrPXV3fWx/Pmf4inm+ayhl9JKCfJBQXxJN62SV3J67bWXQ/RT4Q/A7wr8FLa8svB2qa01nfpCZ7S7uxLAbhECNcIu35JHxl9pAPAwAqBf5k4j4rxnFEo1MdCHNG9pRjaXK3dRbvrGP2bq61d7uTf7tknD+GyGMoYWUuWVrpu6ulbmSto31to+1krei18we6FABQAhOBk9qAMyC+tgw1C6cJJcIBBCPmfy85BCjJJPU49AO2aAJ2t5751a6zFbjBEIblz/ALft/sgkepPSgCC70hknbUNNmkhmJ3SRowCy/geAfc8Z/OgCaGW6a2S5s7hbxCCQHUKz+2RgA9sEdu1ACJqU1wWS206bepwwmIjA9zyTg84IBoAkNiLglr9vODdIs/u19sfxfU/hjpQBMba3MP2cwp5QwAgGAMdMDtQBLQAUAFABQAUAFABQAUAFAEU9tBcFTKnzIcqwJVl9cEcigCCRb21HmxzNcoo+eN1G/A/ulQMn2PX1FAFmKWKeNZoXDo4yrA8GgB9ABQAUAFABQB4x8cLez8I+H7j4pDxbNoPiHwbpN2ulytNE9tfvPhxBJFMrAtK8CqfL2vzw/Ax9nwdOpjsSsjlQVWjXnBz0lzRSuudSi1blUm/evHy1d/meJYwwlB5qqrp1KUZcu1m3Z8rUk/iaS0tLzNf4e/tCfCT4xmXR/BviWd9Sj07+0rm0msp4ZbWMFA25ivlsVd0UhHYE9CRzXLn3BmccN0/bY+klT5uVSUotN6tWSfNZpNq6XnZ6G+UcTZbnc/ZYOd58vM4tNNLRO7tbRtLRvyuj0u2eWS2iknTZKyKXXHRiOR+dfLHvktABQAUAFABQAjbipCEBscEjIBoArJdvHJFb3sYjkl4V0JKM3PGexwM4P4ZxQA61laWS5ywZUm2IR6BVyPwbcKALFAFKdja38dwwYxXCrA5AztYN8n4Hcw+uKALtABQAUAFABQAUAZ0cv2C5OnokjowVoFx93O7cAT/CuB9MgegoAmS6vBlZtMkyO8ciMp+mSD+lAFC9NzqzGOyVV8qMrIkrY+Zm+6cZHBjwfUMcdc0AaVjC9vAYWQKqyPsGc/JuJH04OMegFADrq4+zxhgAzswRFJxuY9BnmgAs4Db2yRPt38s5UnBYnLEZ56k0ATUAFABQAUAVVVYL9juIF0oOM8b16/iVx+CUAWqACgAoAKACgAoAKACgCtqDYtJIlG55gYkX1Zhj8h1PsCaALCqqKERQqqMAAYAFAC0AFABQB8Af8FFv+CdWqftH+JdJ+LvwgvNF0vxYfJ03xFHqLC2tbu0XiO+eZVLeZAoCMCrl4ggXBhVJADmf2AP2kfhz4D8YJ+yf8J28U+L/AIe+G7DVtY134hayUgs7S7Ta7SxRbillpbCGZlMjs7S3IJCgOzgH6S0AFAENzaW94gS4iDbTlT0ZT6gjkH6UAUrA3D6lcC6YNJbRJEHA4bJJ3Y7EjbkeooA06ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/wTh/5Mm+Fn/YNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAK1rqOn3095bWV/b3E2nzC2u44pVdreUxpKI5ADlGMcsb7Tg7XU9GBIBZoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gDz79oG/wDhRp3wW8YTfHKdYfAc2mSWmuyGCaUrbTEREhYVaXdudcMg3KcMMYyMqyhKFp7XX33VvudrG2HjUlO1LezfbRJuXbpf12Pl/wCCNr8VfjPoN9efszf8FJrzXfCGj+Tptsmu/DW1vtSsAqDas9zP9nknkIBO+SPLDBOT8x6JwqOCnPrfVfL8dUc8XBe7HofQXwV/Z/uvhrr+r/Ebx98StY+I/wAQ/ENja6bqPiLU7W3tEitICzJbWVrAoS0gLu0jRqW3SEuxJo5+WDpx0Td3ru0rL7tberFyXnzy3Wi06P8A4ZX9D1+oLPgH4N/8pkPjt/2T+z/9EaFQB9/UAFABQBT1jWdH8PadNrGv6rZ6bYW4BmurydYYYwSFG53IUZJAGT1IFb4bDV8ZVVDDQc5vZRTbfXRLV6GVevSw1N1a0lGK3baSXzZ8OftbftR2njTwwnw28KWd9ZC9MkXiWw1aw8q6sZre5jaKNWDFd26Ji2Cw2lACDuA/f/DvgKeWYt5rjJKXLZ0pQleMlKLUm1a9rSSV7O991Zn49xpxdHH4dZfhk4811UjKNpRcZJpb2vda7q1utzqP+Cfnww8OS+HNT+K2o6cJ9aTU5NO0+aVQRawrCu94vRn850Y+iYGMtnyfGHPsVHFU8npStScFKSX2m5Oyl5R5U0u7u9lb0vDbKaDoTzKpG9TmcYt9Ekrteb5mn5L1Psevw8/VAoAKACgAoAoCG3s9SjKW8USTRsisi7cvnJBx6gcfQ0AX6AGSypBE80rbUjUsxxnAAyaAK1jDIs09w0JgSbBERfOCCcsQOATkcD0yeTQA51K6lDIMhXhkVj2JBUqPrjfj8aALVABQAUAFABQAUAVbAyuk0ssjMWnkAB/hVWKgD/vnP40AWqACgAoAKACgAoAoSi40xJbiLE1vlpXjZgpjySzFTjnrnB9OtAF4EEZB4oAWgAoAKACgDNkt7W8uL/T9Tt454ruMReTMgaOaDbhlKnhhlnyD2I7Yq6dSdKaqU21JaprRprqmTOEakXCauno09meHfEz4Ly+Hbz4ifHpviBr0ur2uh3Fzo8Md1LClmkCi4NtJiQrPA0kOBEVVNssm4OxV0/Rcl4ljmFLA8NvDQVKVRRqOyblzPk5l7t4TUZfHdyvGNnFXi/i80yOWDqYrO/by9pGDcFdpR5fe5XraUW18NkrN3TdmvedMne60+3uJTl3jUsfU45Nfm59sWqACgCvftcpZTvaY85UJT5c8/T1oAZHY2ssYeSSS5WRc5kkLKwPOdv3fyFADDZPaETWMkrbRgwvKzK4743HhvQ9PzzQBK2oWwiSVGaTzAWRUUlmx147enOMHg0AV7i5mu7cxJYTR+aQm+dE2pk4yVzk47cdcUAXYII7aJYIVwijj+pPvQBJQBDeW/wBqtpIA2xmHyt/dYcqfwODQAWk/2m1iuMAGRAxAOcHHIz7GgCagAoAKAKSaxp0sjxQzmUx8sY42cDjPUAigBTqltkIEuC7HCqbd1LHBOASAM4B70AUW0m8muzqzyKtyHUxRlyAiA8qWGeoLZwDz365ANGC6kM32a6hWGVlLoFfcrAdcHA5GRxjuPfABC5Sz1GW4aCUpcRIC8cZf5lJ6hcnoR27UASSXVw422dm7MR9+X92in3z8x/AfjQA06XDMGe6kaaZukhwDEf8ApmP4eefX1JoAV5L2zO6QfaYBjLKP3q9idoGG9eMcZ4NADv7U0wjP9oW3TP8ArV/xoAnhmiuI1mgkWRG6MpyDQA+gAoAzZp572WCO1CrE0iyByTl0VgSQOy9BnnOemOaANKgAoAKACgAoAKACgAoAiubdLlArEqyncjr95G9RQAyyuftVusjFd4+WQLn5WHUYPI+h5oAsUAFABQAUAfmL+3f+zDZ+BZbW6vfido3wt/ZhE02r634e0CIxajqevzTGV4IbUfLfXE+yIQGR/JtIoHIjjjg/egHrf7Bn/BQDwb+0L4p1b4INpN7oknh+xgHhCfWdVN3qWuadbwrFK13IQA96CnnvsLbllfg+S8rgH2/QAUAU7s/ZbiO+BARtsM2f7pPyt+BP5MfQUAXKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AfBn7I//BUL9nP4Gfs5+CfhR4y0XxxNrPh60mgu3sNMt5Ldme4lkGxmuFJG1x1Uc5oA/Sv4bfEHw78V/AOgfEnwk902j+JLCLUbI3UDQy+VIuQGRuhHTjIPUEggkA6WgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA/NTxr49+IP/BQ79oT4g/sa3QPgTwZ8PNflurvXNJu7lru8sLKSSyubOdMiCZ7qWaKWLeqiBYZMrcGgDl/+CbGmatN+2r8ZdU+G+jXl74Q0wvpuv+IfFety3uszSGWUI/7to45J7y5t3uXaSGTykieNZAXzKAfqnQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AeP/td/FHxT8Ff2cPG/xR8FG0Gt6BZxT2Zu4fNi3NcRRncmRkbXPek370V3lFffJJ/mdWEoxrVHGXSM384wlJfijkPjd40+NPi/48eGv2bvgx48tvh7LP4XufGut+KJtDi1SZrWK7itY7O0in/c72eQmRnBKqUIx91yKlOcn9mPL1WrlzWVt0rRbvbfQw5LUY1P5m16WSd/ndL7yX4IePPi34T+OXiD9mX41+O7Lx7qVt4Zt/GmheJrbRo9NmnsJLuW2mt7yGI+SsscqoEMYAdMk/MCB0RpOdCVa1uVxXXXmUmn/wCSu+vYwbcKkY7qSb6aW5Va17tO6d7b3V9iP43aL+39f+Prif8AZ/8AF3wa07wb9nhW2g8RQXxv/N2/vTIY4pEIL52lSvy7QRkFmwND5c/Y6tfi1Zf8FSvi/bfHTUfDt/43T4fx/wBqXHh5ZRp75/sUw+UJVV+ITEGyo+YNjIwaAP0xoAKAOU+KXjODwB4B1vxQ2q6TY3VnZTvYHVJdkE10sbNFEfmUsWZcbVO49ua9jIMslnGZUcIoSlGUlzcivJRulJ7NKyd7tWXU83N8cstwVTE80YtJ8vNs5WbS3V7votex4D8SPj94F+In7J7eOtc8LPrLXV1BpV3o/wBqntII9VUB8StFIrvAuBKqhjuHlg7WyU/R8k4PzDJeMv7Pw9b2fKnOM7RlJ03popRaU38LdtPeautJfE5pxJg8z4a+uVqfPdqLheUUprXVppuK+JK+uidnqvin4f8Aww8c/FLVP7P8I6Jd3yxTW0F3dLDJJDZLNII0eYorMqDknAJCo5xhTj94zjPcvyCj7XG1FG6k4q6Tlyq7UbtJvpuldpXu0fkmW5TjM3qcmFg5WaTdm1G7sm7Ju3yeib6H6j/DD4SeE/hfpVrBoGlxWV6dPtbTUGtZZVgupYkCmYwlthkJBzIV3kHkmv5Hz7iLGZ9WlLEzco80pRuk5RUnfl5rX5V/LflXRH9F5TkuGyinFUI8suWKlZuzaW9r2u+9rvqzuK8A9gKACgAoAKAI54Y7iJoZV3K36ehHofegCrDfJbg2+pXUKTRnAZmCeYvZsZ/A+4NAFp0hu4ChIeKVeqnqD3BFAEME0kUwsrp9zlS0cn/PRR1z6MMjPr1HcAANRRzbebEhaSB1lUDOTtPIGO5XI/GgCxHIksayxsGRwGUjuD0oAdQAUAFABQBUlmuJ5JLa0whjKq8rfwkjJ2jHJwR145HXBFAE1vbx2sCW8IwqDAz1PqT7mgBWuIEnW2aZBK4JVCeSB7UASUAFABQAUAFABQBn710qQh1C2crZVgOIWIAwR0Ck856Ak560AXJri3t0Ek88caE4DOwAJ+poAgGraWRkajbcHH+tX/GgCsmrm8vPsVjhMKX82SMsGHHQAjg5ByT+HINAFnyNRkGJb5Ixngww4PtyxYfp+PqAVdQtb2KOOe1luLq6jkBiDFQg4O7cBgYxkZ68jBoA5n4i+D9O+JPw08R+AfEUsqRXdo0XnJHJNLFKh8yKQRx7WlKOiOEB/eBQMfMRXs8PZrWyTNKGPoWcoSWjaSaekld3UbxbXM/hvfoeZnOX0s0wFXCVdFJbpN2a1TstXZpO3XYn+Eni+Hxl4PivheWs9zazPZ3a26yIIbiPiSMxy/vYiGziOQB1BUN6mc8yyWU42VDlai7SjezvF6p3j7su3NH3W07FZVjo5jhY1rpy2la6tJaNWeq72eqvqdpXkHohQAUAUtNmTy/su754nkUALgBFcgDjjgbeOvSgC7QBUtLRIbu7uAjKZXAGTxjAPHpli2fegBLovHdQO8ri3chCoAwJM5Uk4yAenXrt9TQBcoAKACgClLCbKc3kHEcjDz4+3JA8wehHf1H0oAu0AFAFK4V724+ypIVgj5nx1cnkID245PsQO5oAdcWIYie1YwzxptQg4UgZwrDoV5+o7YoAa2/UbKKeICOZGEihifldThlJH/AlP1NAE9tdRXIZVO2SPAkjP3kPof8AHoe1AEWqKhtQzRB2WSMpzgq24AEH15/Hp3oAt0AFABQAUAQXlqLuHy9+x1YPG+ASjjocH/OM0AVUju7iBb+wmSGS5iV3SRCyltowevHoTzxj0oAka61BowsWmMspGMvInlqfXIOSPw/KgBfsMtwoGo3AnUNu8tU2xn0BByTj649uKAF3kaqY3U4a33RnjHDfP7909uKALdABQAUAFABQAUAFAEVx9p8vFp5XmE9ZM4A+g60AQSXF5aBprmOOSHIJ8oHdGOckg/eA45GD14oAbeJZAC8+0LBI4GyVG+/3AwPv/Tn2oAk067a8thLIoWRWZHXupBxyOccYOPegC1QAUAFAHL/E74b+Evi/8P8AXvhn460xL/QvEVm9ldxMqllB5WRCwIWWNwsiPjKuisOQKAPxl0D9gT43fs9/HHXfG/iT4nWnw48C/Ce8i1VPihc2LPBcLmNrYWtkSXuppDKsbwDdGGEsReQlFlAP1y/Zz/aL+HP7UPw4j+J3wzk1Aad9tn065tdQgWK6tLmLBMcqozpko8cg2sw2yLnByAAeoUANdElRo5FDK4KsD0IPagCr511aDbPC9xGDgSx4LY/2l/8Aic5x0HSgCWK9tZnESTp5hGfLJw44zyp5H5UAT0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHKeMvir8OPh7rHhzw/428aaVo2p+Lr4aboVnd3ASbUbksieXCnVzuliUkcAyICRuGQD5P8A2n/29/C9re6h8C/gz4R8fePPEOoXN34a1u78ESG21LQbhWuop0tg0MsrXiLZ3UiYjVNkYkWUDDAA9J/Yw/Yy8F/sp+HdV1K1u9S1jxh4tkFzrGq6rsN1FFuZ47MeWzoPL3nzHVj5smXJ2iNIwDwz4m/sc/tH/Ar43/EL9oT9iCTRAnjPRGgfwxNqEcLx6pczp588cN1C1tJHH811GrTwbJWKgPCptpwD3f8AYj/bK8K/tf8Aw8uNStLKbTfF/hmKyg8UaebZo7dLiaNiJ7Ziz7oJHinCKzmRfLIcY2u4B9HUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AcN8cLD4W6l8I/Fdn8bIbeXwK2myNriTGUBrZcMdvk/vS+QuwR/vC+0JliBWdTksufa6++6t+NjSk6kZXp72f3Wd/la9/I8R+AfwF/Z9svibZeM/hfd/FHwzrHgGzktD4R13VtTFvbWl/Gyqxtr8uzQy/Z1dTHJs8y2UNh4Sq7Qm+WUu/uva6atK2nqvLXQwkoy5U1a3vLdXvdX/Nd9NT2P4b/A3wl8NPF/jLx9Y6p4g1vxF44vjdahqOuak13LBbrJI8FhbAgLDawmaQRxqMgEBmbauIpr2dNUlt17tpWu+7sNrmnzyd2lZeS3su13q/M9EplHwD8G/wDlMh8dv+yf2f8A6I0KgD7+oAKAPEvi5dfA/wCITa1HqEGheK/GHw0sb7ULPR7u7cKs4gWUo8QZUnjJjiV8h1U5VsHIr73h2nxBkyouk50MNi5QjKaituZxupWbi7OTjs3urqzPkc6nk+Z+0VRRq18NGUlBt72Ts1dKS0Se6Wzs7n56eJfHkWr+A9B8GadBNapb3t9rOsKoEUFzqNw4RXjiQiNUS3ihVQEXaWmAGGr+l8DlDw2Y1sdVak3GFOHWUacVdpyfvNynKTbbd0o31R+G4vMVXwVLCU1azlOfROcnZNJaJKKSWis3K2jP0P8A2RPA+q+BvgnpFj4j8IW+hazcPPNcjywt3cI0ztE9yNissgR9oRixVVXJUkov8zeI2a0c1z6rUwtd1aSslr7qailJQ1aabV7pJNt6NWk/3TgrL6uX5RThXpKE3dv+Zq7actFZ2drO9lbbZe018IfWBQAUAFABQAUAFADGiidlZ41Zl5UkZI+lAFZrKWBnk02SOHePmjdCyFvUAEbT6kdaAAM99apPGgjmjfIVjna6kqy59/mXOOhzigBRfFGKXNncRkHgrGZFbnqCucfiB1oAksVZLSLzI9jsu5kAwFY8kD2BNAE9ABQAUAFAFSR3tLl5TCWglAyY0LOrgYyQBkjAAyOfw5AAo1TTzJDF9qQPcKHiB43A9PzoAhNxBJqURtJ0kcho51RtwCAEgnHQhuP+BH8ADQoAKACgCnqF2bDyrqRyLcMUm+XOARw3HPBAH/AqAGvqbsR9ltGmDpui+bb5nTpnooBGWOOoAzmgBY21lQfNispCegWR02/oc/pQA/dqjjb5NrFn+PzGkx/wHaufzoArQaYLKaCUXEkp3kFXPyLkH7i9F5/QkUAaDRROyu8asy/dJGSPpQBlN5i6jcuhWNxKfKdidrHykLI3sQAQR/dPpggGnbTC5t4rhQQJUVwD2yM0AS0AY947XcqXFmxjcZit5Vx+9cg9iCCgGSSRnrj3APMv2f8AwzceE9M8VtZTT3o1nxVrEyGaSSQ24gvJbMCSSWR5JS32bzCxbcS7Dtk/XcY4+WOxOHjKKThQop2SSfNBVLqMUlFLn5UktkfOcNYNYShWcW2pVaj1bbVpOG7bb+C7be7PZF3BQHILY5IGAT9K+RPoxaACgCFLWJLl7pQd7jB547c49eBz7CgCagAoAztZuorZLYTSKiPcJuJ7Bfmz+aj86AJU1jSpBldRtx2+aQD+dADNOuba7mubiKeORi+1Qr7iqLx0zwCQxHqCKAL9ACMqupVlBUjBBHBFAFN9+mR+Z5kktuD84c7mjU/xA9SB3Bycd+MEAkur2K3tftCur7wBEAc+Yx+6B65oAdZ25todrkNI5LyMP4nPU/TsPYCgCegCldafExkurZNl1tyrIxXcw6bgDhh9e1ACkWWpfNFLiaE8MvyyRn0IIyM46EYPpQAp06GTBupJbggcGRuB7gLgA++M+9AFPdfS2cdtJA0d/BGs0RdtwkZeGyQcc5IPPRx74ANOB0lgjkSQyK6BgxGCwI60ASUAFADJY1mieJiQHUqSDg4PpQBDpzu9mgkVQ8ZaJtvQlGKkj0BxnFAFmgAoAr5YX21mXaYsoO5IPzfhyv50AWKACgAoAKACgAoAKACgBGVXUqygqRggjgigCtZWC2gy0plkCCMOQBhF6AY/X1P4AABfIIo2vo1AlhXeSOC6jOVPrxnGeh5oAsqyuoZWBUjIIPBFAC0AFABQB4N+2F+zB4C/aW+HKweK/CeqeINX8JLc6toNhpurpps19dCLP2E3EqvHHHcMkcbOy/JwwK4zQB8SfBr48fGT9mC6i+NP7Tmp2/wp+GVzFfaH4W+DGm+H2S+u0gkL+ZaWh8tbYLLcO73txJvnIKtlZIHAB+oXhrxHonjDw5pXi7w1qEd/pGt2UGo6fdRghZ7aaMSRSAEAgMjKeQDzQBpUAFAENzaW12nl3MCSDnG4cj6HsfcUAR/2bb9PMuv/AAKl/wDiqAEj0nTIxgWMLHn5nXexz6k5JoAatjPbMfsF0I42JPlSR71Xp93BBUe2SOeMUAOMWpkYN7bgHutucj6Zcj9KAG/2Vbks7S3JlYfNILh1J98AgfhjHtQBJazOzzW0rFpIGA3EYLqRkNjp6jjjKnp0ABZoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/wTh/5Mm+Fn/YNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+ev2if2tI/hXdXngb4P8Agpfix8TtOtxqOo+DNK1Hy76x03aubp1SKUuwaa1xbKPOdJ/MVfLR3AB8hfBj9gf4iftbfE7xJ+0X+3BY+K9E0/UNSM/h3wjPqx+0LZNcSTfZJiwMttaRh/KSJPKlOXf938pkAPvHwF+zH8DPhh8TfEvxj8DeAodN8Y+L2uX1jVPt11M9ybicTzYSWRo498qhiI1XkDtxQB6jQAh6UAfmD/wRt+MD6lrfxP8AhN42a6ufHFzct4mmv7+0kfULiJZRHdR3N3JIXfZc3PmLEyKVkubl9zmRggB+n9ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QBzPxN0TwP4j+HniPRviW1pH4VuNNuP7Ymu7j7PFb2qoWkmM2R5Plhd4lDKUKhgQVBEyV1e9mmmmt007przTVyoScXp6fJ6NejWj8j5d+Hf7XH7IXhG28TeMPCXxd8YfE3xjrH9n2htp9OuZdf1xY/NjsrTTbNoLeOaNWNwcwIEDyyPK+6QsdFKVSNqcb6tuy1u1duXZcsb62SS0V98/ci7ykklpq9N7JJ9W5O3VtvVnu3wO/aJ8F/HiHWbXRdD8UeGPEPhuSGPW/DPirSm07VtNEwZrd5oSWGyVFLoyswK9cHICt7vMn/wPX+v1KekuV7/1s+p6lSA/O3wLceILT/grT+0bdeE7GC91uH4YRyabbTnEU10LTQzEjncuFL7QfmXg9R1rry+GGqYulDGScaTlFTa3Ubrma0eqV2tH6M58ZKvDD1JYZJ1FF8qezlbRPVaN+a9T6K/ZO/aP8S/HWbxHZ+Mh4bsr3TFt5bOz02OaKaSJi4llZZZH3ID5S5U/KW+bG5M/oXiFwTheE40J4H2koz5lKUuVpNW5UnGMbNrmeu6Wmzt8bwbxRiOIXWji+RSjayjdO2t27t3Wy0267o7/AMIfGe38S/GHxj8Hr/Q20zUfDEMF3auZzL9vtnVS8uFTbFt82D5S5J8zoNrAfN5lwzLA5Jhc7p1OeFZyi9Lckk3Zau8r8stbJK3mj28FnscVmmIyqcOWVNJrW/Mna72srXjpdt38mfMH/BQG40Xw94z8PXPheb+zPEesaXeR69JZM0Mt5YuYo4VnK4EikRzpg5yqAHgKB+teD8K+MwNeOLXPRpzg6alZqM1zSk432avF6dXdatn534kypYbF0pYZ8tWcZc9tHKLso81t1pJeis9Ejg/2GfBS+Jfjda+IJr+wjh8M2014bWd0M1y8kbxJ5cZO4hC+8uAQpVAcF1NfQ+K2aPA5BLDRjJus1G6vaKTUnd2trblSbV7u10meN4fYBYrN413JWppuztdtppWW+l7t9LLuj9JK/ls/fAoAKACgAoAKACgAoAKAIL2Zre0mmjALohKA/wATfwj3ycDHvQAWNsLO0itgc7F+Yn+JupP4nJoAnoAKACgAoAKACgAoApPpNmwYxqY3aTzd4wx3c/3gRj5m4xjk0APtZWSRrGZg0kSKysABvQ5AOB0Ixg9unrgAFqgAoAKAKTQrcak/2mEMkMSmHeARkk7iPfhR7fjyASQ2EUFwZ43fo4VCRtXcQWxxnkgHr9KALNABQBWvhIEjnjI/cvvKliA/ykBeM9yOx6UATQs7xI8sfluygsmc7TjkZ70ARz2NpcnM8CvkqTnuRnH8yPcHFAGaljcWzyXH2a2jkjDLD9mtQN7MMKSckgDuDge+OaALFvYXslssGqXm9QgRkiyA/bLOfmJPHTHfrQA3UHnsPOnsbEXdwtowsrbeI1eRAT5Yc8Jv+UZ9FJPStKMYTqRjUlyxbV3a9l1dutuxFWUowcoK7S0W132v0ueL/BHx34I8GaNonwS1Rh4Y8aeGLK3tLzS9TaCMXUsqwO5hmidoX82a6XYN/mlicr97P3nFuTZjmeIq8RYf99hqspOM48ztFOSSlGSU1yxhq7ciVrStY+R4dzLBYCjTyat+6rU0k4ysrt8rumm4vmctFfmfVHvEM0dxGJYmyp9sfUEdj7V+fn2I+gAoAKAEyPWgCm+p2sm2Gzu4JZpDtQK24D1Jx6DnHGcdR1oAktrJIHNxK5muGUK8rDkgdgOij2H45PNAFmgCKe1tbnb9ptopdudu9A2PpmgB6IsahEUKqjAAGAB6UAOoAKAMbTbe7O65t1gjQDy4XlUuzxAnbjBG1cY7c9T1oAu/bLmKRhc6fIIweJIj5gI91HzfkDQBJDf2lw4iinHmEE7CCrDHXKnkfjQBYoArOjf2jFIqHBgkVmA77kwCf++sfjQBZoAr3dr9p8t0lMUsLFo3Cg4JBHIPUc8igB1pbi0tYrVWLCJAmT3wOtAE1ABQAUAU9MDCCUlsg3E5Ax0HmNx/P86ALlABQBURkl1OXoTbwqoPPBckkf8Ajq0AW6ACgAoAKACgAoAKACgAoAZLLHBGZZW2qvU/yHufagCqIrq+UvcPNaxnhYo2AYjPVmHIPTgH6k5oAZpEkqRPYXP+utW2dAAyfwsAOAMcfhQBoUAFABQAUAflx/wV+/ZCudTt1/au8B2UkstlDDY+MbVHB/cKRHb3yKeflysUgBPy+UwUBZXIBq/8ErvHvxH+DfgCx8EfH2+sfDHgrx1fwv8ADOPX9UMGo6heXDDzIrGzYF2s5C8UnmExoJZ0KCU3JZAD9MqACgAoAKACgAoArS3b+abe0hE0i/fy21E9Nx5OT6AH8KAG/YpJnaS7u5WyfkSJ2jVBxx8pyx9yfoBQA1tH01o1i+yqApyCpKtnvlgcn8TQBdoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAGySRxIZJXVFHVmOAKAPzJ1D9qj9vn9p741ePfAf7H8mgab4U8Iata6TPq2oaRDYXWnxyzSKs91DqLSTbgYZ1dUgLhYuIUchaAPoH9mH9kn4k+HvibrXx+/a4v8A4f8Ajf4nS2lppmj6xoulrGbaCEyF7mQ+RAj3TK8MAlEPmJDbIgkKu60AfW9ABQAUAFAH51+OvjnbeAv+CtuheGdLsdAa01Lw8+jeIJdJ0IHVZXurKO4jiuplQSXLobKwdNpk2ROUT5mZAAdL8EP+CrPhT4/ftG+Hvgl4H+DOuQ6P4ieaODW9Q1OGO7jMVpJOzPZIrptBiYEi4J2/NjPyEA+76ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/9LLOgD6foA85/aM+F+o/Gr4F+N/hXo+sDS7/xLo09jaXTSMkaTEZQSFQW8pmAVwASUZhg9Kxr03UilG1009dvdadvna3lub4eqqM+ZrdSX/gSav8AK9znvBXw71L4mP8AC74x/GX4fHwT8Qfh8mqQ2+j2OpW95aw/aYjayZkRSGjeJElRVYFNyqxJDA9M3FzdSF9V19b2b627+vc5qacaSpSS0a1XkmtOyd9vJa6GD8J/AvxV8S/tLeMf2hvid4Pi8F21v4ei8BeHtIi1eO+fUbOG9lun1KcxgKm9nVYkJ3qvm7lXILLDV5/VJU6kbObjK38totWb2b953atslruOtCLrJw1UVJXtq7tbdUrRWjvrqfQtSM/Gz9pvVb/Tv+Cj/wAa7OzuGji1Tw/pNrdKDxJELPSJQp9t8UbfVRX6p4P4enW4glOau4U5NeTvGN/uk18z4DxIrTpZPGEXpKcU/NWk/wA0mZSTSxrIkcrosq7JArEB1yDg+oyAfqB6V/TbipNNrbb8j8KUmrpPc+xPiJ8QX034Q/BX9ofRfFlpJ4z0OGbTRHqUQuLjVvl8m581iVkZEKSAnBI+05DhmDt+IZNk6r51m3DNei1hajUrxfLGnrzQstYpyurL+5blaTS/U8zzJ0sry7PaVVe3gnH3ldz+zK70bSs//Ar3u7v5l8X+LPGfxq+Icuu6qv2/XvEF3FbwW1tHtUMxEcMES54UfKoyST1Ykkk/q+W5fgeF8sWHo+7RpRbbb101lKT7vVvp0SSsj8+xuMxefY51qvvVKjSSX3KKX3Jde7b1P0P/AGav2atE+Cfh2O812w0zUPGdxJI13qcRMywpllSO2Z0Ro0MZBfgFmZskqEC/zNxxxxiOKMU4YeUo4VJWg9LvRtzSbTfN8OtkkrJNyb/c+FeFaOQ0FOtFSru95LW26Si2k0rb6Xbbu2rW9vr4E+vCgAoAKACgAoAKACgAoAjngiuYmgnQOjjDA0AVbO8KzPp93JieNjsLkAyoclSOmSAMHA6igC9QAUAFABQAUAFABQBELhPtJtirBtm8EjhhnBx9OM/UUAQ3NtO1xHeWsoEkalNjj5GUnJGQMg8Dnnp0oAq3Gr3AeKK1sn81gCUlUqWPdFzjOOSW5AA75FAFtIr6U7rmdYQD8qQc5GP4mYc/gB+NADX0yOYj7Xc3FwBnCuwUD3wgGT9aAHw2MME5nV5mYqVHmTM4AOM4yT6CgCzQAUAFABQAUAFABQA194Q+WFLdgxwKAKk17NBGZbjTZMRZdnV0ZVA6kZIY8Z7UAeZeI9JtPir8WrfwzrPhqwGkeBoLLXkv7iOGa5vp7iVjAlu2GMNur2cnm/MjyNGi48vd5n2WBxMshyV4zD1m6mJc6fKnJRjGKXM5K6UptVFyaOMU3K/Pbl+axdCObZosNWprkoKM7tJyk5N8qjvyxTg+bVOTSVuW/N6fYfN9pnAAWWdiAP8AZAT+ak/jXxp9KW6ACgCrqOoQaZbG5nyRnaqgcs2OB+lAGfDjW7pZJrRIVtiVmikbLuMZUMuPu55GfQ8UAJY2v9mXcdukIEhLAtuO2WMnOVz8ocYBK+mcUAbdABQAUAFABQBS1e6Fpp8sgcK7Dy0JOPmPGR64649qAJ7Q232eNbSRHiRQiFW3DA46/hQBNQAUAFABQAUAV76aS3tZJ42jUpgkyAlQMjJwPbNAEMsGqtG0Iu4iJCMyKpjaNcjO3rnjOOmPfsAPGmwkbJpZp4s5EUzb1H1zy3/AiccY6UARwzQ2EsllNcosSIrxeYwG1TuG33xtznrg+2aALqOsih0YMrDIIOQR60AVH/0G5R0yILl9rrjhZD0b2yeD7kd85ALtAHxNq/7TX7VXxU1bxTdfs7+AtJvPCPh7Whp1tqkVuZ57wbvLUhpXVHD5WZgkYMSOm9sfM3xdTOc0x06jy+mnTjKye7fTq+u+i0Vrvq/6awnhtwHwvh8JT4wxc44utS55U2+WMNOZ6Ri2nGzgnKdqklLljf3Y/X3hfU7/AFPT4L3VdPTT7y9t4bmezW5Sf7LMY1E0BkT5XMbgqWU4OeK+wpTdSmpSVm1qt7PqrrsfzjmGHp4TFVKFGfPCMmoys480b+7Lleq5lZ2eqvqbdaHGFABQAUAFABQAUAef6h8f/gxpc3iK3vfiPoyy+E/LGsxpN5jWm+VYhkKCWxK6I23OxmCttPFcE80wcHNSqL3N/LW356Ps9z63D8B8S4qOGnSwc7Ym/s21ZStFy0va14pyje3NFNxuixd/HD4P6fpml6vqfxL8OWFrrVst5p7XuoxWzXEDEgSKkhVtuQRnGMireY4SMYzlVilJXV2ldfM548G8Q1a1XD0cDVnKlLlnyQlPlkujcU1f5k3hb4nfDfx3rL2Phbx94b1m6ghMqWdhq9vczBQcPKY4nYhfmQZPTPbPN0cbhsRLko1IyfZNP8jlzHhnO8norEZjg6tKDduadOcFd9LySV9Hp5HZV0niFHUJFMkf2aNZryEh0QEAhSQGyewIJ69wPSgAkk1dF84Q2zhTzCrMWIx2c4Gfbb+NAFm3uYLuLzoH3LkjpggjqCDyDQBLQAntQBHc21te20tneW8c8E6NFLFKgZJEYYKsDwQQSCDQB+Ium/DCx+Ov7TWseIf21Phx+0R4ItPif4wTTPBeqtatBb2E93cym00yeS+tWyqoUSPySAgib5NmWQA/biytvsVnBZ/aJp/IiWLzZ33SSbQBuZu7HGSfWgCegAoAKAILm8trQL58oVmOFQcsxzjhRyevagCHddXzFQk1rb4+8cCST6d1GPXB57YoAswQQ20QhgjCIvYfzPqfegCSgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPIv2vv+TUvjF/2Iuuf+kMtAHE/8E4f+TJvhZ/2Dbn/0tuKAPpOgAoAKACgAoAKACgAoAKACgAoAKACgCC9vbPTbOfUdRu4bW0tYmmnnmkCRxRqCWdmPCqACSTwAKAPkj4s/8FIvhnofgK28W/s9+Hrn4z3tz4lg8KtYaRO1q9vfTwvLbo8bxtcyecscoiaKB45GhlTzFZcEAxfiB/wUe8R6D4l8aeEfAn7JHxK8Qan4V0yDXoI9St5NHlvNIMognv8A7LPCboRJO8aqEikLKJXfyViagDwjwd4+b/grn8ULzwf4z+FMvh34aeD9EhmfUrLWi2qaNqk77t8MhUQS/aBA0RWW2l2xwMyGJnbIB+g3wS/Z5+Fn7P2ivpXw40D7NcXdlptjqWp3EnmXmpLY2q21u874C5EafdRUTc7sFBY5APSaACgAoAKACgDkrz4R/CjUPGsXxJv/AIY+E7nxdblGi1+bRbZ9SjKLsUrclPNGF+UYbgcdKAPln9tj9mnwd4V+Huk/Gv4EfDLwRoHj/wCHF1pY0LUbnWY9A0zRbGLUjdSFYHaOwcNNNLuWXyywuJWDlwkbgH1N8JvGw+I/wz8MeOGudFmuNY0yC4vP7F1OLUbGK82AXEMNzCzRzLHMJI9ysQSh5oA62gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv8AkuXwK/7GBP8A0ss6APp+gDyb9rLxF448Jfs0/EvxN8N2dPEWm+G724s5o2IktwsZ8yeMjnzI498i/wC0i9a58U2qel9XFO3ROSUnutlfW+m504SMZVbS7O3rZ8qt1vKyseA+Ev2bPAGneA9P+Of7AnxNnufF1ki3Nxcz+JbjU9O8ZOqBprPVY5ZSkc0m4/MoieGSTOEwNvXXVTDO0I+7/Ktmr29191bR3aeqejuuShy1or2knzfzPe/95dn1VrrdbWKfwL+Nfhz47/t+xeL9EsbzSr6z+CM2l67omoRmO90XVIfECi4srhCAQ6EjsMqytgZxXdGEY4Ks4O8XKlZ/KtuujXVfmrN7ubeHhCatJSndXv0hqn1T6Pr5NNL7jrzjI/Iv42eM9O8Hf8FJP2gY9R8LWmuDX/BFposIuWUCymlsNIZLpQUbLxmPK42nJBDDHP6H4Y5ZVzLPoulWdP2S9o7faUZRTg9VpK+u+mlmfGcd4+ngcokqlNT9o+RX+y3GTUlo9VbTbXqYGl+DPGGuaTe69onhTWdQ0zTVZry9tbGWWC2VV3MZJFUqgC/MckYHPSv6fxGZ4LCVoYfEVoRqTtyxckpSu7Kybu7vRW6n4PRwGKxFKVajSlKEd2otpW1d2lZaa6mPXccp9c/8E+9F0DUdf8STa94H/tGaP7HcaVq8+kieCxnhMhkRbhlPkyt5sTAAgkRk54GfxfxixWJoYbDxw2I5E+ZTgp2clK1m4p+9Fcsk73tfzZ+neGtChVr1nWo8zXK4ycbqLje6Ure7J3TXe3kj7yr+dz9nCgAoAKACgAoAKACgAoAKAGu6RoZJHVVUZLMcACgCg8cmrhWErxWYYMu3h5SDkNnqoyBjHJ9uKAJ4HmglNrdSF9xzDIQAXGOVOABuHP1H0NAFqgAoAKACgAoAKAKuoxK9sZC8iPD+8jeMZYMB2B65zjHfNADGuNRctFbQW7NGoV3d2VfMIzgAAkgZH54oAdbWHkTm5luZZ5inl7n2jAzk4AA7/wAhQBboAKACgAoAKACgAoAKACgAoAKACgDwDx/bt4O+Pfhj4papqOo2vhmXb4ceK1vFSFtRkaRbdrqKR1UwqLu6cOu5lbPAB5/QMhmsy4dxeT0IxddN1VzRbfs0ouahJJvmbhBNOya6t7fH5vB4HOsPmVWTVJpU3Zq3O3JR502vdSnJpq7T6d/dNK2/2ZabBgCFB+OBmvz8+wLdABQBhl2kks55nSG2vSh8pWLYfIdOvAzjnA6n3yACzPp0sALWQP3i67CBIjE5OCeGU9Srfn0wANF1dzQiPVNLkiVmRBIki8OWwG65XnGMZoAls21eO5MF5GssXadcKAAOOM5Oe/Awc9RigDQoAKACgAoAo2ii8uW1J0OwDy7bd/c7uBn+I/oB60ASzWMTlpYMW87c+cijcT7/AN4exoAYJNRtlIlhW7AHDRYRs+6scfiD36UAJHqLzJGIbK481wCyvGyKnrlmGDj2yaALtABQAUAV7uzivFRZGZTG4dGXGVYd+QR+lAEGn6is0ESzs/mn5GJiZQG9GOMBunGepwM0AX6AGlEZgzIpI6EjmgCBtOtWZmHmpuJYiOZ0GT1OFIFADF0q0DI8hnlaNg6+ZO7YI5HBOO1AHgXx+8dWHxE8Xf8ADKHhzxzrPhDxNrdmt6+qWunNPFNEFZzZbkkWSPeil3k4QIpUlt7LXz2aYmOLq/2VTm4Tkr3Sv/27o7q+7e1tOp+w8B5HV4fy/wD1+xmFhicNSk4qEpqLTul7Wzi4y5W+WMficmpJLlTPdLPw5p1vob6FeRrqEVzCYr+S5giDX7MgSSSdY0VGeQcthQDk4AGBXuRoxUPZvW+97a927WWvXQ/LK2Y1qmKWKpvkcXeCi5Wgk7xjBycpJR+zeTfdt6nzR8DfHXgj4HWnjz4Z3/hzxL4W8O/DrVVkOu+Ib03C3wu7hreJhGsSNFHKYw6eVG8eA0jMDy/zmV4qhlirYWcZQhTfxSd73dlpZWTtdWTW7b7/ALRx1kOa8b1Muz3D1qWIxONg/wB1Rjy8ns4Kck5OUlKUFLllzyjNPljGLStGt4m8Tft4+N7nU7/wFZ+CvDfhu1e4udI1G2ngu/7WsyVe2KSOZlffGVKybIkO5i2zAAmrVz3FSk8PyRgruL0fMulm77rZ2S72NsBgfCnIaNKGcOvXxElGNWDUoexnZqpzRSptcsrqUeapJWXLzK7faeD/ANo3xToOq+C/h18c/Auo6f408VSXcLHR9Mlksbd45AIl3h5BLujIkdomdYw6FwoJK9mHzarSnSw2Og1UnfZOys9Ndb6atpu3W3T5zN/D/L8dh8fnPC2KjPB4ZQf7ycVUkpRvJ8touNpe7CM4xc2ny8zSUvZ9G8XaN4i02LW9AuY9T0ufeILyxkS5jlZGKsAYy3RlIOehBBwRXt06sK0eem013WqPzDGYHFZdWeGxlOVOorXjJOMldJq6aT1TTXdNMultWm/eRLb26nokql2+pwQB9OfrVnKAk1SISebHayBfmEm8xDbjnIw2COe+OlA0m3ZHi3xr/as8N/CLV4fBthpT+LPGty8SQ+GtIZ5JyZRlPMkCERkjBCBXkYOhCBW3V4uZZ3QwEvYxTnVdrRW+vfe3pq9tLan6ZwX4XZnxbQeZV5xw2BjdyrVLW93flTa5kno5NxgrSXNzLlObf4a/Hr9o/wCFiaP8ZvEVj4Fnn1yG+ktvD0krSfYEiZHsZ4i+1WMgSZWaWbBYZVdgWuWWCx+a4XkxslTbknaN/ht8LV7b63u/TQ92lxPwjwBnrxXDNKWMjGlKHNWUUvaOSaqxly81lFum4qFO6WkmpNnourfAL4SQaX4livvCcV4njJo5fEJkmkjOp3MTmaOaTy2VUbzdzHyggLSHjHFen/ZWEcakZQv7S3N5tO99PPXSx8OuP+IYVsLXpYhxeFUlRSSapxlHlcVzJtrlSiudyaSWt9TkNc/Zc+BPie40Wy8SeAIpzpNvHpFpjU72PbaxyuQi+XKqlQzyfeG/pye2VbIsvxHL7SnflVlrLZdNH5noZb4q8XZR7Z4PF8vtZupP3KTvOSScveg7XUVorLTbc4jwf8DX+DXx68K6p8IfhBpuoaDdwtBrGqahqAkvNLMhdXNv5k+VKxgEkRlnUyJkbuPNp5S8tx1OeDopwfxSb1V+1327LXVH2eN8QI8a8K4vDcS5lOGJi70qcIWhUsk0p8tPW8r25ppRajKztr9bzzJbQSXEn3Y1LH6CvqD8KI7OBoYd0mfNlPmS5OfmPbPoOAPYCgCxQBUls5RMZ7K4WFnOZFZN6PxgHGRg+4P1oAR7O6n4udQcLx8tuvl5+pyW9OhFADW0XTvKMcdusb9RKv8ArAc5zv65zQBPYzPcWVvcSY3SxI7Y6ZIBoA/G7/gr/wDDr9o2z+J9n8TvHeuxav8ADKSRNM8KmyPkW+lTOjyNbSW5kZvtLCFne45EgVBlAiwxgH0B/wAEpv20PHfxlh174QfGvx1YatrGiwWsnhu61C5jTVNQhCSCeJskNdmNYVkMmGk+dzIzZBAB+hw1KyfiC4Sdv7sJ3n8QOg9zxQA03Oou22HTQgxndPMF59MLuoAUwahKzLLeRxxk8eTHh8em5iR+Q/KgCS2srW1yYYgGb7znl2+rHk0AT0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHkX7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/wCltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAfnx/wVf8SeONI0/wZ4e/4aGsvh14B8T+bp3iDT4YrqTUtUga5t4bqSKOCLE8UNvc75IHuIhIu5cOSBQB9FfsWfCT4A/Dj4F+FtQ+BC6TrNjqmlp53i2LS1tr7XSJHZ5LhmUTYErShYpCTEAEH3aAPfaAMXwv4J8GeCILy18F+EdF0CHUbuS/vI9LsIrVbm6fG+aQRqA8jYGXOScDJoA2qAKmravpWg6dcaxrmp2mnWFohkuLq7mWGGFB1Z3YhVHuTUTqQpRc5uyXV6I6MLhMRjq0cNhYOdSTsoxTcm+ySu2/Q5XWPjZ8HNAaJNa+KvhKzedY3iSXWbdXdJG2q4XfkoT/ABfdABJIAJHLUzHB0vjqxX/byPdwnBnEePTeGwFaSV02qc7JpXava1/LduySu0dfZ3lpqFpDf2F1Dc2tzGssM0Lh45EYZVlYcMCCCCODXZGSmlKLumfPVqNTD1JUqsXGUW001ZprdNPVNdUTUzMKACgD87B8W/iN+3j+1F8Qv2fdF8PeFNS+AHhALpXiM6zZtJJPJHchDd2VzbSbku5JIrj7M3mCLyY2kZC/yEA+8/AHgLwj8LvBmj/D3wFodvo+gaDarZ2NnADtjjXqSTku7ElmdiWdmZmJYkkA6CgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/8ASyzoA+n6AE68GgDK8NeEvCvgvTn0jwd4Z0nQrCSeS6e10yyjtYWmkbdJIUjAUux5ZsZJ5NO7so9FovJdkKyu5dXq/N92V4PAXge18Zz/ABGtfCGjQ+Kruy/s641qKyjS+ntdyN5MkwG90BjjIViQNoxiiMnGLhHZtNru1dJ+qTZTbe5vUhH5fah8GNG+On/BVv46eENY1a90423gmzv7O4tth23K2WjRoZFYHfGPNJKgqTgYYV9NwpxPX4Ux/wBdoQU7rlknf4XKLdmno/d0bTS7M8LiHIqXEOE+q1ZONnzJq29mldPda6pWfmj0T4Z+OfHH7L3xQi+B3xl8R2h8CPFNLdrJaS3doYp7d3VrYtEJDG03yOoUpv8AOyM7mr9ozzKsv48yl8QZHSf1u6UfejGV4ySfPaXLzKPvRbfNbl1tZH5jlWYYzhHMVk+a1F9Xs29HKNpRb93S9nLRq1r82m7PVvFX/BPn4Yav4gttS8NeJNX0DTDKWvdNUC6DR4UBYJZDuiPDkmTzslhgADB+Py/xizbDYaVLF0o1alvdl8OuusorSXSyjybbu9z6TGeGuXV68amHqSpwvrH4tNPhb1XXfm36WPdfBHwf+G3w5uUvfBXhOz0q5WwTTnmg3BpoVbcPN5xI+eTI2XPQnHFfnuacSZrnUXTx9ZzjzOVnsm1bTsrfZVo+R9lgMky/K5c+EpKL5eW66pa6935vXzOyrwz1QoAKACgAoAKACgAoAKACgDPTzNTkinKbLSNi6BuTMcfKxHZe49eDxQBoUAVr/wCxvF5F3cLFv5Ri4VgR3U+ooAq2usW4Itru5TzVO3zRxHIfUHt264GeBmgDQmlWGJ5mDEIpYhRknA7UAR/aGiszdXcfllEMjoDu2gDOM9zQBCt/JE6JqFuLcSLlXEm5A391jgYPp2PrQBdoAiuIppUHkXBhdWDA7QwPsR6fQg0AZrSz3UyW8eoQzy8kiEDZFj+NgSSSCRgZxnntkAGlb26WsKwRliFycsckknJJPqSSaAJaACgAoAr38slvaSXEWMwjzCD0ZRyR+WfxoAsdaAImuYUuFtXbbI67lyCA3qAehPt1oAloAKACgAoAKACgCvdvIDFBE+xpnKFguSo2kkj8sZ5GSODQB4T4k8Rav8VvE+qfAW80a58MaUq3suo3JedtSv8ATELwGS2WWzaDEs0sJ3rK5EbN91s7P0PA4HD8M4GnxFTmq1T3FFWXJGo7TtNxqqpeMVLRwiua28d/jcXiq2e4qeSzj7KHvcz155QV43jem4WlJx1Um+W+z29rs7opB5NvBvmeaZhGzbdq+a2Sx7D8CfyNfnh9kSyw315iG5EcMJOX8qZizj+7naMDpnH070AO/su2Un7M0tsG+8sLlVPHp0H1GD70AVLyzhgubRFjQxT/AOjTeY5O5dpKjnv1565+tAD7RL5BMyXqoiysuyeNnKYJAIYsDyNp5z14oAmaXfts9TiCGVgEaNjtcjkYPBVuM4/InsASRlrGIm9vkaNcBXkAUgf7TZwT07CgCZLi3kOI542IOOGB5/yRQA5HSRQ6MGU8gg5BoAdQBW1Hf/Z915ZIbyX2kHBB2mgCwAAAAAAOABQAtAFa6vVtpre3CF5Ll9qqDjAHLN+AoAWa9iicxqrzOv31iXcUHXJ9OO3U9gaAHQXcVwzogkVkAJDxspwc4PI9j+VAE1ABQAUAVdTUmwmdThogJlz03Idwz7ZAoAnhkWaJJlBCuoYZ64IoAfQAUAFAHgHg7xlrnxW/aL1LVvh78XLK58EeDrb+ztf0FdPdZpL/ADcxR7ZmiCyQ7lMgkSVuY9uwBgx8HD4ieOzGU8PWTpwVpRtrfVLW2q63T6bH61m+UYXhbg6lh83y+UcbiZc9KrzrlVP93KV4qd4zs1BxlBaSvzXTR7/XvH5KYPjbwH4P+I+gyeGfHHh6z1nTJHWUwXKZCuucOpGGRhkjcpBwSM4JrDE4Wji6fsq8VKPmerk2eZjw9i1jsrrSpVEmrx7PdNbNeTTV0n0NTTdM07RtMtdG0qyhtLGxgS1treFAscMKKFRFUcBQoAA9BWsIRpxUIKyWiOHE4mtjK08TiJOU5tyk3q227tt9W3qyvp1hbGGR2BYmWRVOSDGFLRrtIwVIXIz15PNNpPczhOUHeP8AXXVdfRnhH7KkWl+AdT8dfBPRPh94i0XSPCuqPJBrOp3L3cerSnCySeasSQQt5QtXEKnO2QkjIY18/kijhZVcFCnKMYP4m783d3sktLO3n6n694oyr57QwHE2KxdKrVxFNJ04RUHSS1iuVzlOa5vaRc2rXja9nE3fiR+1X4D8K+GrjVPh9C3xG1WPV7TQotN0GYSiS8uIpJo181FcMCkMn+rWQ7wFIB3Fd8XndChTcsP+9ldRtHu02tdeie19dDyuHfC7Nc0xsaGbv6lSdOdVzqq1oQlGEnytxa96UfjcFy+8m9L0/EHwq+L/AMXNa8eeDPi14k06H4Ya0sKaLHohNtqkDRSxSoSWR1ZGUyJKJS2XjBRI1NRVwOLx86tHFyXsZW5eXSSs0+z31TvfVaJI6cBxRw7wphsvzLh+jJ5nSv7V1fepS5oyi1o4tOL5ZU+RRtGTU5Tkj1Lwn8NvBHgmLTx4e8O2kNzpmlx6Lb38iebeixjIKW5uHzK0YKqQhbaMDAGBXqUMHQwyXs4q6XLfrbtfe3kfC5rxFmmdSqPGVm41Juq4J2h7R3vNQVoqTu7ySu7u71Zuy6faTStO0RWVhgyIxVvzHftn046V0niEUltdTQXFpKwYZDwSt69VDD/ZYfQjHXmgDEW1u5ra1urRvkmljRA2fM+Usd5bkAlixPBwMUAfP/7Rs/gDSvi98HZPiJc+KxcW3iCW40iDSPIlhlvPPs2Vrh5GXagcx5VE3FXc5HGfnM6lho4rCvEOV+Z8vLa17x3u9vTzP2fwxpZ1WyHPaeURo8rpRVV1XPmUHCtdU1GLvJq+smkmlo76fT2otvjS0UBnndRtIzlAw3k+2M/mPWvoz8YLEssUETzTSLHHGpZ3Y4VVHJJJ6Ck2krsqMZTkoxV2ziNO+Ofwg1fxBqPhjS/iLod3f6Rp8uqX/k3QeC1tYiBJJJOP3ShMjcC2V6kAVxQzLCVKkqUaibirvXRJbtvbT1Pp8RwTxFhMJSx1fB1IwqTVOF42lKcvhjGHxvmtpaNn0ZyXiT9sf9mzwvdXFle/FLT7ue3UMV02Ga9R8hThJYUaNj846NwQwPKnHJW4gy2i3GVVNrtd/ilb8T6DLvCHjTM4Rq08BKMZfzuMGt94zaktv5ez2auxv2lLbX/G2jeAPhz4F1vWr3X9A/4SC11O5ha206K2ktpJLZ5ZMM6o8qLExKDazYG9htL/ALWVWvHD4eDblHmTekbNNq+71em33vQn/iHs8BlVfOM3xUKcKNb2MqcWpVXONSMaiim4xbjFuaSk7pXfLH3jhNG0z9u3xC2g+Lda1jwv4Zks767XU9FljE4ltJXjKuqQM6yCNNwRDKkgMZzI3mYXhpQzyq4VajjGzd476O3Z62Wyun5u+n1WPxHhbgI4rL8LTrV+eEPZ1E+W04qV03OKcXOVuaXJKnZq1OPJeWr8NP23/hT4j1mbwT450/Ufh3rlnO1qtrrw2RfKWCq8xA8pwqgssoQAsFDOea0wnEuFr1HRrp05J7S/z6Pve3qzi4h8E89yvCRzHKZxxtCUU+alq9lqo688bvRwcm0rtRNX9uf4a+Bvij+yn8Q7TxzYSXVp4f0K88T2M1vs8+2u7G3knjkhZ1YKSFaNj3jlkXI3Zr6I/HGmnZn4l/sn618JfCn7XXgbxN4h8eax4Z8D6Nrs2onVbqIR3CQwCaS2hnEPmArPsghmCjbtmlX7o3UCP6IdE1vRfEmkWev+HNXstU0vUIUuLO9sbhJ7e4hYZV45EJV1I5BBIIoAvUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHkX7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/AOltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAcd8YPhxp3xb+GXiP4e6jb6S7azp09vZzappMOp29neFD9nujbTAxymKXZIFbqUHI60AeFf8E7/ivqfxP+C12niT49aT8VNd0bU5ILu+0vQrnToLCJlBhtVa4t7drjCLv3+Sm3zDH8wQMwB9TUAFABQB+ff7evxK+Nkfj27+CsDRyeEPE9rYPp1jaad5k964kRtpkKl2l+0x8LGcbTGMZJz8DxPjMaq7wS/hzSsktXr373XTpY/rjwL4c4YllMOJZ3WLoSqKcpTtGCs1flTso+zlvNX5uZ7JDfhZ/wTb8S6haSaj8V/FNlpRuLNWtbHTt1zNDLJE+ROSEVWicxnCNIrFWGcYJWC4QqTXNipJaaJa7rrttptdMfE/0isFQqKjkFCVS0nzSnaKajJW5F7zamlJXkoyimna+i+8fC3hbw/wCCvD9j4V8K6TbabpWnR+Vb21vGERBkknA6szEsx6szEnJJNfdUKFPDU1SpK0Vsj+VczzPF5zjKmPx9R1Ks3dybu30XySsktkkktEatanAFADZADG4Zyg2nLA4x75oA+Gv+CUtn8ELn4fePvFnwb8MePLeTUvEX2TWda8V6nbXzajLCHaGOCSFYiQkU6zP5lujB7xl3zBAVAPuegAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/AJLl8Cv+xgT/ANLLOgD6foAKACgAoAKAPgH4N/8AKZD47f8AZP7P/wBEaFQB9IftUfAS6+NfhOxufDb2sfifw9M1xp/2r/VXEb7fNgfOVG7YjAspGU2nAdmH33AHF0OF8ZOGKu6FVWlbdNX5ZLrpdp2adnfVpI+Q4w4cln2GjLD29tTd432ae8X01smrp6q2ibZ7Fo1vqVpo9jaazqK6hfw20Ud1drCIhcTBQHkCDhNzAnaOBnFfEYmdKpXnOhHlg22o3vZX0V+tlpfqfVUI1IUoxqy5pJK7ta7tq7dLvoXKwNQoAKACgAoAKACgAoAKAK091Is621tEsspUu259qovuQCcnsMdj6UAQnT7mZM3Gp3Ku64dYioQEjkLlc49O/wCNAF1ESNFjjUKqAKoHQAdqAHUARfZwbr7UzEkR+Wo7Lzkn8fl/75FAEjKGBVgCCMEHvQBkRSzMlzpNskjLG3kRyFT+7UqMlmPBxu4A5IAz60AbFAFDWJvLtxGrSl5GH7uJN7uo+9gEEdD3GO3egB2n293ZiWG4njkgU5gOMMq9we2B2x+gwAARTvNqwa2s3CWp+WWcZy47iPsemCenPGaAJLZrOxnex+0BCdpjSR+cYxhc8nkGgC9QAUAFABQAhAIwRkGgDBlt5tNuoke6MFnPIY2dZcDGGKg5GVOAFyD0XtxQBsi2VoPInkaYZyGbAYenIxyOx60AUHv7vSykd9btLAXZRcK4ZtvJBK4B4Ucn2NAEv9rJDHNLdxqqRMqBomMgYkZA+6OxHtyOaAJrK++3KJY7dkix95nUnPphSffOcEfyALVABQBROpPtiuvsxFm4yZSfmUEEhtoB+XpySMZ9qAKPii21WXSr+fQtS+xXslhNBbXflLL9klZTsnEbkI+1tpKsQCFHI5z04OdGniKc8RHmgpJyjdq6vqrrVXWl1quhjiY1J0Zxoy5ZtOztezto7PR2fR7ng/wp+IvgbRrmTxT8UNY8Xp8RodHtLS4tvEWnmC8mS42ySw6bZQxI80P2iNxkRsy+XyQnJ/SuIMlzDExWEyiFJ4JzlJOlK8U4XSlWqSlJRlyNP4knfbm0PhsnzTB0JPE5jOp9aUIpqpG0nzWbVOCSco8yf2W1be2p7bp13o+q29l4n0u9eTSdbhjvra4gaSPzo5IwyArw3zA7xxnJII5r8zxWGq4OvPDV1acG4tdmnZrTTRn3VCvTxNKNek7xkk0+6aun9xrxCKRVMGq3MayHCK+3J47b1LHoetYGpPFaSxyiV9QuZAONjbNp/JRQBNLFFOhimjSRG6qwBB/A0AUprG4llljYQNbzypK+7JI27fl24wQQg79z6UASWka3Wl26XHzExIWIPRgByCOhB9KAKU7XFle2kl+UlRGdFuGAACsM5bsrAqBnoQT0oAtXNg08nnG3sJwBgLLByR6b8nH5UAQOkls0jIsNjLt8wETZhk55DKQMdBkgZ569RQA+DXYJCouLW4twQDvkTCfUHrt/2sAcjOMigCe6Md1cQWZIaNgZ2xyHVSMD6ZYH8PegC5QAUAU9TaVIY2hdUfzkUMRnG47cgeo3ZoAHCadCkNnAGknkwoZj8zkElmbkngHnk8UASwQzJNJPPKrM4VQFUgBQSR1J5+Y8+woAnoAKACgCG7V3tJkjjEjNGwVCcBjjpmgAtHSS2jZJTKNoG8jBJHByOxyOlAE1ABQBmeJdcTw14f1HxA+l6lqQ0+2kuPsWm2rXF3clRkRwxry7seAOBk8kDJGdap7GnKpZuy2Su35Jdzty3BPMsXTwiqRp88kuaclGEbv4pSe0Vu932Teh4T+xH4I8XeHPhzr/AIr+Ifh19L8S+M/Ed5q9yLq2eG9MR2qqziUCQfvRcOqsT8su4H5zXhcOYatRw86uIjac5Nu6s/nfXe7XrfqfqnjRnWX5jnOHwGT1vaYbC0YU48slKnfVtw5Xy/DyRk0lrHlt7qPomvoT8eCgCvczTK8dvbKhllycvnaqjGW469RxxnPWgB9tB9mhEW8ucszMRjLMSSfzJoA8M+O37M+vfHDxdaXdx8ZPEOheEXsobTVfDli8hivWjleQyDMvlKxBjGWibBjB57eFmeT1MyrJutKNOyTir663vvb8HsfqvA/iRheCsvnThltOti1KUqdaXLeF4qPLpDnaVm9Kkb8zWh3vwj+CXw5+COgnQvAOhramZY/tl9MfMu710GA80mOerEKoVFLNtVcmu/AZdh8tp+zw8bd31fq/6XZHynFnGec8aYv61m1XmtflitIQTe0Y9Oiu7yaS5pOx3ddx8qFABQAUAcy7Gw1O5igNzLunExW3QOyAq2Tg5AO5gMHsPpQBwfxj+DvxI+Kdrp0Xhf4zat8PjaPJJcPpsRkmu9wQAO8UkJXbsPAJB3c52rjy8zwNfHRjGjWdO29uv3NbH3XBHFWVcLVa1XM8shjedJRU2koWveycJp8110uraPVnn/hb9lH44eH/ABfomvaj+1z4t1iw0vULa6uLKcXSi9hjlV3gfddOCrAMvzBhhjwRkV5tDJMdSqwnLFykotO2uqvs/e6n2+aeKHC+YZfiMLQ4eo0qlSEoqa9m3BuLSkv3Kd4t30s7rfqXrz9jV/GkXiyx+LPxi8U+IrLX9VtdRsY4XEcmnJAbspArzmYFMXjDaqoBtyOuBcuH/rCqRxdaUlJprytzaa3097sjlo+LyyaWDrZBl1KjOjTnCbauqjn7K82oKm+b90tXKV767XPS9P8A2d/hHZWsyT+FIb28vPDy+GL/AFGZil1fWIhSEiVotg3skaguoVuBgjAA9OGV4WKd43bjyN9WrW1tbotz4jEceZ9WnHlrcsIVnXhBJOEKnM5pxUuZ2UpNqLbWrvdtlLwz+yr+zr4Shmg0r4Q+Hp1nKljqdudSYYzja10ZCvU52kZ4z0FZUcky6gmoUY/P3v8A0q525l4o8Y5rKMq+Y1E1/I/ZL5qmoJ/O9um7PTtP0+w0mwttL0qyt7Oys4Ut7a2t41jihiQBVREUAKoAAAAwAMV6cIRpxUYqyWyPh69eriqsq9eTlOTblJtttt3bberberb1bLFUZHCfFr4I/Df42aIdF8feH4rp40dbS/i/d3lkzDG6GUcjBw21tyMVXcrAYrhx2W4bMYcleN+z6r0f9Luj6rhXjTOuDMV9Zyms4ptc0HrCaXSUfwurSSb5ZK9zyPXf2TPGE1pb+ANB+MeowfDKXw9feFr3w20IjkayubOWOSYzDcss7zurFjGmxWcIQBsbz4ZViMPVgsPWapxVuX5b31TfNZ7LS9uz+txXH2TZzgcVLN8tjPG1pqp7W7tfnXuKKcZQgqXNFWqSbkouV370fyi/bl/Zr+NH7N6fD/4b+KdS0zVfBki3s3hSLSmeXyr2VbUahG+9Fk3tIICP4GXDKFcyqPQwdKphKDliqnNLWUm9l5LtFW/N6XPkuI8dg+IM0hRyHB+ypJRp0oRV6k9XaU7X5qs3LW17LlgnLlu/vL/gjXqPj+0+BHifwT4x8HeKNN07TdcGp6HqWqW88dnd290hjlt7MyKFIintJXkCEgPc8gMTnqpVadaKnSkmn1TujwMdgMVlleWFxtKVOpG14zi4yV0mrppNXTTWmzTP0CrQ5AoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/wTh/5Mm+Fn/YNuf8A0tuKAPpOgAoAKACgAoAKACgAoAKACgAoAKACgDyH9qjxh8dfBPwd1LVf2cvh8vi3xvLLHBawSGMx2cJDNLdNG8kZmKohVI1JYySRna6hlIB5l/wTi/Zb8UfstfAebQ/iCLZPFvifVH1nU7WBlkGnr5aRRWhlR2SUqsZcuuBumZRuCB2APqugAoAKACgAoAKACgDD8deLbLwD4J8Q+O9SsL++tPDmlXerXFrYRCW6njt4WlaOFCVDSMEIVSwBYgZHWgD8wvHv7UXxl/4KZRat+z/8Bvgzq2i+DpXE91r91rUdr5bQ3UTW8t6/kughUFZHs4C07OI2SUpE6ygH6Q/Bf4OeBvgR8O9I+HHgHRbGxstNtbeK4nt7KG3l1K5jgjhe8ufKVRJcSCJC8hGWIHoKAO4oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv+xgT/0ss6APp+gAoAKACgAoA+Afg3/ymQ+O3/ZP7P8A9EaFQB902sd9a3d2kSwyB5DKVeUqcN0YYB44Iwe6kjg4ABcW/t8us7fZ2j5ZZSBx2Oc4I96AGx6jFNKkccMxSRiqTbcIxAJ45yRgdcYPrQBboAKACgAoAKACgBGZUBZmAA6kmgCm9xNeSeTYSKI1YiWcYOD/AHVHc8jk8D3PAALEFtDbIUgjCgncx6lj6knkn3NAEtABQAUAFABQBSlUW2ox3HKx3CGOQ8bTJkbCfcjcM/QelAF2gCpqT+Tbi5C5aCRHGMZIyAwGepKkgD1NACfZ7m7A+2MiwnkwKvJHYM2cH3AGO2SOoBcoAKACgAoAa7FUZghYgEhRjJ9hnigBlvOlzCsyBgDkEMMFSDgg+4IIoAeXVSoZgCxwoJ6nGcD8AfyoAy76S4v5JdNS3iQKVZTPnEu3DYAxgjOAeeAelAD7M3McP+grmOMmNradsNER2DjOe2Ac8EYIFAEm/bOk1/YQo74jjkUhyM/wngEde2R1zigCqjJblrG5RIILaVp8kEB137kCAdcErn3wMHNAFlIpJr5LyKCWFCAZCz7fM4ODsHJIzj5sfQ4GADQoAKAKMGnTWzIkOoSLbIflh2KcD03EZx+vv3oAZd2VvDCkECMqyzIhjDkJt3ZYbc4AI3cDrmgDRoA+e/Dc1t8EvF/iHwz42vtc07RfF2uQ2Xg0y6glxpVom1Whgt4lfzrY+bK4YeUsKrHGN+AC36NmdGfFuXUcTl8YSq0KblXtFxqyd9ZTlbln7sU177m25Pl3S+KwNSPDuNqUMY5Rp1ZpUryTglbSMVfmjq2n7qikkr9X7bo9tavbSwTWSB5CJXEg3FlYkruJHJByOe6571+cn2pqxxpFGsUa7UQBVHoBQA6gAoAqC1uIZnktbiNI5W3tG8WQD3KkEYz1Oc80AMe8eMGLU7LZG4KmRT5kWMfxHAI/EY96AJrS1ht9zW0jeVJhljBBRfdfTP5UASSW9vM6STQRu0ZyjMoJU+3pQBny290ury3dvLGziBVCSDjaT045HIJzg55GOKAIra+txNBeOEiR0Fr5YP8Ax7uCTtIIGMgc8D7o7c0AaUN9Z3DlILmN29FYHPTkeo56igCegDNju4r7VREhUraBzjPPmZ25wOmBuHPXPAwM0AW7yA3EDIhKyAExsDgq2OCP89MjoaAKlnqMqWsU1+hWORFdZxgrhuQGwBtIHU42+46UAaKsrqHRgysMgg5BHrQAtABQAUAULlG04fa7Yt5QcvcRliRtY5ZlHYjk4Hv7UAXUdJEEkbBlYZBByCPWgB1AHlv7Tl/BpfwO8S31z491bwXHF9j3a5pUMkt1aZvIR8ixyRsd+fLOHHyuScjIPl5zJQwM5Oo4be8t1quzW+25914a0JYnijC0oYSGKb5/3VRpQl+7m9XKM17vxK8Xqls9V1PwquEu/hf4Puoteu9cSbQNPkXVLtGSe+Bt0IuJFZmYO/32BZiCxyT1rqwL5sLTalze6tXu9Fq/Xc8Liim6WeY2nKkqTVWovZxacYWm/ci0kmo7JpJWWiWx1NdR4QUAVL9VD204OJUmVU77g3DDH+7k+2M9AaALdABQAUAFABQBUuLi6M5tbKKNnCB3eRiFQE4HA5JOG9OnWgBklleXOI7q/HlfxJBGYy/I4LFicfTHWgC1DBDbxLDBGsaL0VRgCgCSgAoAKACgAoAKACgAoA+Qf22fHNz48n8P/s6/DnUYtW1jXNQDatYWb72HlnMMErg7UG8NK6tyohVm2r977nhzhHCYmCzjia0MDG7UZPldeai5RhBNx5lZOVk/elFRXNHnty0uM8fkk62H4WUnmLUUqsY88cNTlJQqVZtRlySfNGkpNe7GpJ6T9nf5S/bX/wCCbuu+B/h83xn8GeLdU8Ran4ftYVurK3jWE25EsYRraJEZ5BveQnLhl+QgEA7fnc3x+X5jVqungoUoybUVSUKcIx6c9NQftJPaU+eHS0Wlynr4OtjnGGMxeZ154qlGDUqsp1pzmpXlyVnOMqEY356cOSpqpXmpPmfzrYftl/td/s8eCvhbovhH4gPpWjXPhWXUbO2mtbe9j1CGbWdQZpphOJCH3o8WR5bBIlAAyWbzJYTD4SnTjh5ppq7Sjy8ju7x7S25uZWXvWtdNvPGZpis3ryxON55VdpTnU9o6ltpJtXilG0OWTk7wcrqMlGP6RfsKf8FHvD37Vl+fht428P2/hf4h29rJdpHbTA6fqsUYTe1t5jeako3OxgO8iOMuJGAcJmcx9WfEz4jeFPhH8P8AX/iX431BbLRPDljJf3chZQzKo+WOMMQGldisaJkFndVHJFAH4ufF7/grB+1Z8UvF9oPhXeQ+AtMhvFOnaVpNpHfXVy5IEa3Es0bee2SRsRERsgFGIBoA+9v2Lv29Yfi1rfh/4A/GGdE+K8uhNqFzc2tiLazuZoyzfZ+XO65a0WO6ZokEJDyhQnlgNz4XExxdJVoJqL2urXXf0e6v0PYz3Ja3D2Oll2JnGVSFuZQlzKMmruDa0coP3Z8raUk1d2PtSug8cKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKAPiL9ovQPh38VP+CiPwO+HnjLU/G0WqeFNG/wCEy0ODSLWB9O+0xXcs/mXc0khaGMnTFjYJCxkMkS+ZHgGgD7doAKACgAoAKACgAoAKACgD80fAGu+MP+CYnjMfDDxfpiTfAuysJdZ1HxalhMlz4i1nULq3gi8kOzxLdW0USqbKKRS1rbXd2Q7FI1AP0h0XWNM8RaPYeINFvEu9O1O2ivLS4TO2aGRA6OM84KkH8aALtABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwB8Hgx/4LHfHcI21j8PrMA4zg+RoXNAH3fp0EkjJfTTSNJseIrIF3Kdw3LlQAQGU4OO9AF5kRirMikocqSOhxjj8CaAGT28dwoWXf8p3Aq5Ug4x1BB7mgCuYtRtiTbzJcoST5cx2sB6BwD39R+NADl1K2ztnEluwBJEyFQMdfm+6fwJoAtAhgGUggjII70AMeeCI7ZJkQ4zhmAoArvq+nRrva5BTBPmKpZOO24DGfbOaAIJNTvZiU07SpnIIBkmHlJg9xnk/lQA61hF3J5t+We4gYHymwEjODhgoJzn1JPIOMYwADRoAKACgAoAKACgCvd3JtfJdsCNpQkjEfdBBwf++to/GgB2bS/gKgxXELcHBDKcUAUITqSPLb2Vwl1HDtXfc5B3c7l3KOcYHbqx54xQBaFnNLcLNeTxyrFzHGsW0K3945JyQKALdABQAUAFABQAUAUYY9NmnY28m2U/O8ccrIcnuyAjn6jNADLnTLh41EN9K2x9ypKRx1xh9u4EZ6nPoQQTQAWjxX8H2LVI4nuYid8bhd3s+BnsRyO5NAENxpt1Z3D3+n3bgMB5scjBgQO+W6/Qkf7woAbJI9ysbagkJjJ+Uy6exA5wed52/U4oAljtLqaOKU+V5kO6W2lSZpA2RjDFgTtOe3bpigBLrVLu1iMl1BFbFPnCtLuEygHcoOBhuhHr+eADRnuIbaMyzOFXoPUn0A7n2oASCdpSwe3lhK4OHA5B9CCR2/zxQA92KoWCFiBkKMZPtzxQBnXd9DcxNaxxXIuQVZY/JOQ6sCMnG3GQMnOMd6ALtnObm0guWUKZY1cgdsjNAHl/ihpNQ+Pvhq3n0uN20HQ7u/024iijncyXDeRLHMWfNsuEiZGWMtJslCyKqSpJ9ZhZrDcNYiUJO9WpGEottL3FzxcbK095KSckoXi3FylCUfnsRF188oxlHSnCUk9H8T5Wnd+705Wo3lZpSSUk/TdPgntrKKC5l82VFwzepr5M+hLNABQAUAFABQAUAFAFW6shNLHdQSeTcR8CQDIZc8qw7j+R5FAFJbRrOe2vLxYwyvMrugBA8x8rkkZAySMjpn0JoA0p7eG5QJPGGAIZexUjoQRyD7igCndS6hpttujC3gUHmQ7GXGTliBtIx/u9O5NACaZP5WnNqF88aGVjNIysSOQABjHBGAuBnp3NAC3eoo9vNFFb3hZomCsLV+uDgcjr+lAF9EWNQiKFVRgADAA9KAMrWfF/hPw5f6ZpXiDxPpOmXutTfZ9Ntry8jhlvZcqNkKMQZGy6DCgnLr6isqmIpUZRhUkk5bJtK/p3PQweU4/MKVWvhKE6kKSvOUYykoLV3k0moqybu7bPszXrU88KACgAoAoKv9m3JHzfZbhxtGciKQk5+isSPxPvQBfoA87/aDub2y+DviS807w54W164ghhlXT/FDQrpcoWeMkzmZ0jAUAuu51G5V5zXn5rKUcHNxjGT00lbl3W92l577n2HANOlW4jwtOtWq0YtyXPQ5vaq8JW5OSMpav3XaL91vQX9n3xPc+MPg34V8QXbeFhLc2K/uPDIZdPtYwSIreNGJaNo4/LR4z9x1dRwBRlVZ4jB06j5dV9nZdku1lo10YuPcthlHEeLwlP2toz3r2dST+1OTWklOV5Rl9qLT3Z6HXoHyAUAVr9mSFHQ4InhH4GRQf0JoAs0AFABQAhIAyTgCgCj/AGp9o3DTbZ7raQpfISPOcH5j1x1+UHtQBPY2xtbZI3wZSA0rA53uR8xyff8ATFAFigAoAKACgAoAKACgAoAKAPmz9rX42+IPD0mlfBX4Xy3J8beLWijSW0m8qW0ikkCRhXOFV5CHG7cNiqWJXKtX6DwhlWAwuGqcS52k8NRdowdn7WpbSCTaT1to/dber5Yyt81m08fm2Mp8P5O7Vqi5p1PetRpL4qknFNpJXb5U5dIpzlBPrfgj+zH8O/grZ2GtSWlvqXiu1tnhn1ycFSodiWESE7Yxhim8DeV4ZiOK+U4hzvGcTY94/HzcmnL2cW7qnGTvyxSUVdK0XPlUpqK5m9EvqstlSyLK3k2VRVKhP2bqcqtKtOnFR56km5SacuacaXO6VOUpezirtv2KGeG4TzIJkkTpuRgR+leSZHFfFj4HfCL46aEPDnxb+H2jeJ7NFkSA3sH7+137d5t51xLAzbVy0bqTgc0AfkF8Y/gp8Yf2Avibf6F+z3qU0L6zo8FifGN9qtpb3c8V/NOVhghdo1i2LZsjk+dtdUn3QtJbrH5+JccPWjiKs5cvwqKTtd31dr300V9F6s+uyWFfNstq5Nl+Gpurd1Z1Zygp+ziopQg6jSilJuUuR887pfDFp/qXqD33ij9kCS7+InhrSvitrkXgYXWsaVZR217b69rNraB5YYBHFJCzteQkJsiYK+0qhwFrvaurHycJKMlJq6XR3s/J2s/uaZ+Vf7Kllp1hrvxMt/En7JGkWHiS00HVoNJ1rxFC8MWl+JZngXTLNzfTQWNg32iKcIyolxvIjjZQGU8OBnaMqdSuqkovV6K3k0ttnvrufV8VYZVK9HF4TLJYKhVivZxbqTVTrzRnU+K6lH4fdtZ21u+2/wCCRuifDCT48X+i+Kvg/qy/Ezw3YSappGvQ6i62djZiOS0unlgadQzSfaokUqkwbzNyrEELmsFUVdOvTq88JbbaeWiT+T1XXW5lxNhKmVTp5Xi8B9VxFJNVHebdTZKVpSlG2jalTtCV24+7ypfsTXafLhQBHcXFvZ28t3dzxwQQI0kssjBURAMlmJ4AAGSTQB+T/wC3H+3t+118FfjzFZeAfG/gqHwKdtzoqaNBa6pBqtujgut5NIGljm52ukTRbVZSpJIkYA+g/wBgj/gpDp/7UepTfDL4oabpXh34ifvrnT4tOSSOx1W2Rd7LCJZHdZ0UMzRljlFLqcBlQA+4aACgAoAKACgAoAKACgAoAKACgAoAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP8AwTh/5Mm+Fn/YNuf/AEtuKAPpOgAoAKACgAoAKACgAoAKACgAoAKACgD80f8AgpbpDa7+15+zn4RbUJdO07xte2fh3WZdKuktb+6sm1qzfypZIyJjEkmyWIPlFlVnjAcOaAP0uoAKACgAoAKACgAoAKACgD87v+Cs3jH4w/D7TvDGsaZ4j8Ka18L9USKPX/AevJZxrqF5aXkU0MynzY766RmkgDxWzARi2DSZSV8AH2p8B/ip4P8AjZ8HvCnxQ8BW5ttD13T0kt7UwmI2bRkxS2xXAH7qSN48qNp2ZUlSCQDvaACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/9LLOgD6foAKACgAoAKAPz8+EkU03/AAWN+OqwXTQMPANmSyqrZHkaFxyKAPvy0t3tkZZLh5mdi5ZgB+QAwOn5kmgCegAoAKACgCp/ZdipzFAYc8HyHaLP12kZ/GgATSdLRQi6dbYAxzECfzPWgC3QAUAUdQJtZYdRUELGSk5C5/dEE5OOeDg+2T2zQBbjlimQSQyK6HoynINAD6ACgAoAKACgBGVXUo6hlYYIIyCPSgCne6TZXwcywgSMPvj1A4JHRse/uO9AEmmlW0+2dIo4w8SvsjXaoJGTgfU0AWaACgAoAKACgAoAKAKl9Fc7orqzRXkhLZRm271I5XOOuQp9OKAJLe8huDsUlJANzRONrqPcf16UAVtRimUxzCVCguYTtZCWBLKvynPHBPbufWgBmumSG0mnViY3heGRc8DcCFYfQnB9j7CgDSAAAAHA4oAWgCO4hS5gkt5CQsqFDjrgjFAGV9h/swLelNwtpGGAeDE2MsFxhCO+0AEA+vABs0AFABQBSsJjGz2U6yLKskjKWU7WUsSNrdDwwGM54oA82nkmsPjbrEfiXS/Ml1vRIR4ZvLeNCsNtaMv2iN33eZHM1zeIThRGyCH5iysB9VWhCvw5TeGnZU6j9rFt3c6ifI0rcrioQa35lJz05Wm/ApylSzqarx+OC9m1ayjD409bpuU105WuXW6Z6qoYKA5BbHJAwCfpXyp74tABQBXnvYoX8lUkml4PlxrkgepPRfxI6UAVrZruGRJ72SQCdnQo23ah3fu+h4yOOpySO/UA0aACgAoAKAGSxRTxtFNGrowwVYZBoAr6XLJLp8DS7t6rsfccksp2kn3yKALdAFKWFZZYrGFfKhgKTvtUAHDEqo/Fcn6Y70ASanqNlo+nXer6lcCC0sYJLm4lIJEcaKWZiBzwATxUznGnFzlstTbDYeri60MPRV5zail3bdkvmz5S8PfGb48ftS+DNY1H4ESW/gm40jxT9mtr/UY4zb3GlG1LbJCVnJuRJsZgkYVVlQBmwxPy9HMcdnVCc8Dam1OybtZxtttLW9m9LarV6n7rmXB3CnhnmtChxUpYuNTD80oU3JSjW9oldWlS/duPMo3k5Nxk3FXild+IfwC+I/iSXxR8Qviv4u1DX9F0Vl8Q6F4J0K+dS1xaJJLHD9taETLnzrmL90gkYPGdymNFWsXlmJr8+Ixc3KMfejCL6q7tzWv1a0V3prokseHeOMkytYXKOH8PGjXrfuauKrRT92o4xcvZe0cHblhO05OEWprlanOUvY/gF8atE+Pfw6tvHej2b2MvnyWWoWLuZDaXSYLR79qhwUeNwwHRxnBBA9fK8xhmmHVeCt0a7P8ArU/POO+DcVwLnM8qxMudWUoTtbmg7pO13Z3Ti03una6s36NXonxoUAFAFLUizta28eWZ50dlA6opyST2AOD9cDvQBdoAqatpWna9pV7oesWcd3YajbyWl1byDKTQyKVdGHoVJB+tRUhGrBwmrp6P0Z0YTFVsDXhisNJxqQalFrdSTumvNNXPjX40/Bub9ke2m+OX7PvjO88N2CXNrDqnhq7jub6wvF5VVLAOwBJIzMww0zbJoiUU/H5jl7yJPHZfNxWl4u7T/P8AHvpJaI/o7g7jCHivOPC3F+GjXqNTdOtFwp1IPd2Xup2Wtqad1Bc9KaUpL6k8GfEnS/GPgzR/Gtha3/2HVbWK5SSaxkt22uo+bY2cfN0wzAjBVmBBP1mFrxxVGNaO0lfa34f15Nn8/Z7lVXI8yr5bXtzUpOLtJTWj/mVk/PRNO6cYtNLsa3PKM+0iW+UXtxLI53ZERJVYmU/dIHUgjqe44xQBoUAFAFKW7uZp2ttOjjbyziWaQ/Ih4+UAcscfQDjmgBDpgnbff3MtwMcxHCxdc/dHXHuTQBdVVRQiKFVRgADAA9KAFoAKACgAoAKACgDD8P8AjnwV4tmubfwr4w0TWZbNilxHp+oQ3LQsDghxGxKkEjrWFLE0a7apTUrb2af5Hp4/JMzyqMZ4/DVKSls5wlFP05kr/I3K3PMCgAoA+VfBNrBf/wDBQTx9JfW0d0dM8ORTWTzqJGtZGg05S0RbJQlZplyuOJHHRiD97jnXlwVhI8qdNV5tt7qXK1FLXqnUvp03V7PyKDw1HO60o1HGtOhTjyrSM6ftZubnZWfLOFGyb87PlvH6ke0tpZVmlgR3Q5VmGdp9s9Pwr4I9cr6jBHDHJqcKhLiBC+8fxqOSreoIGOenUUAXqAPgL/gqt+zJ8LfF/gS7/aa8Xaxrtlq3g3SbfSI4NPQvHexvegxpKdjiLBuJwr4UFpFDtgKKF8cb/DzLm78t1zW/vWva+l7X0OvBvCRlN4xSa5J8vK0vf5X7O90/c5+VTt73Ldx1ST/O7Tf2/fi7ovwH1j9mfQfD/hvR/h/q7XECW+npcrfafp9zKJLmyt7mWaTKSlp9zTpM/wDpMgB2BERK9tdzCTpuo3FNRvtfW3a9t7dbfLoU/F/7Nfj3S/2QdG/aK8K+Pb7XPh9qviE2l9oKLNnS3j8yKC7uERmiU+c91DucIVM8WwuLk7ePBRqtSqV6ajNuztrzJbO9k/RPVL7j6LiWrgYzo4TKcZUr4aEbxVROPs5T1nBR5nG97c0o2UpXtdJSf13/AMEev2pPHN1rk/7MGvaVquu6EltPqmj6ksyFNCRBukhkVsMYZGb5drMUkIAQq7vH1LkpvkVle7t+Lf3u79Tw6n1rFxeIqc01FRi5O7skuWCb6JRjyxXZWWiP1fqzlCgCpq2ladr2lXuh6xZx3dhqNvJaXVvIMpNDIpV0YehUkH60Afh3qPhz9kf4H/GDXfh74O/Zn+Lvxx17ws+raB4gstf1WCzsY3U/Zjd2sVhayzFWzK0byMhi3ROuZNrRgHpHxq/Yi+Jf7KHi3R9J/Zu8Yax4dttd0/U5da+J2veJbXRbK2txMTFpnnJsa3dYvJLMWJuZCXjSNYJBHzzw/PXjWcn7qel9Lvq+79bpdEmezh84WGyutlsKFO9WUW6jjeoox15IttqMW7NuKjKW0pSjZL77/YX+Oep/Gb4OtYeL/HPhnxV4y8FXr6Dr2o6DdPPBdOgzDcbmjRWMidXj3xOyOyNglE6Dxj6MoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/AME4f+TJvhZ/2Dbn/wBLbigD6ToAKACgAoAKACgAoAKACgAoAKAPPvj/APGbw9+z78HfFPxe8SiOW28PWDzwWrStH9uu2+S2tQ6o5QyzNHHv2ME3FmG1SaAPjTwh/wAFkvhp4n0fw0o+B3ji48TaxrR0m+0TRjHfm3Dq/wBlNtKRH9qlnk8tFh2xtxOcny0EwB1n7O37BPjeP4w6j+0V+2drvhbx78RIb61vtEvNF8yKGKeGGCNLidFgt0eSJbWEQqI9ikyu292QxgH3BQAUAFABQAUAFABQAUAFAH5i/ADwN8Hv29f2w/jd8UvFNxYfED4faR/Yr6ZbXmjT6awulEsdm0JMzT+QkFvcCWJ2jiuJJxI9uuyNUAP01tra2sraKzs7eOCCBFiiiiQKkaKMBVA4AAAAAoAloAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv8AsYE/9LLOgD6foAKACgAoAKAPgH4N/wDKZD47f9k/s/8A0RoVAH39QAUAFABQAUAFABQAUAFABQBUnhnilN1YojO3+tjdyqvxwehw3AGe469sAD7e+huJGgw8cyDLRSLhgM4z6Ee4JFAFigAoAKACgAoAjniE0MkJYjzFK5HbIxQBTi1AW5itLq0khbKxBkj/AHW7oNpHY9vTvQBoUAFABQAUAI24KSgBbHAJwCfrQBUtbu4kuZLe7tvJbaHj5B3Do2COuDj0PzDigC5QAUAZd/p8+/dZxhjIWKkvtMEhH31PXB7r3/PIAhmn1e1eyRFjPlhLh5I2ARyvRVOMkcHOeMjrQA+6uQ1q1vfRvaMy4WVjlAw6HcDxzgjdjNADo9UL/MEhnRcBzazeay56ErgHH0yfagC5BcQ3KF4WyAdpBBBU+hB5B+tACymRY2MSB3A+VScAn60AZFvqFxqsTWCyoJtxWeSFTtiUcYBbqWwcY7HPbkArWN4NOsIGsrxbvKx77ZmBZGbA4I5UZI4IP4UAaKa5A1st9JbTx2zD/XMFIHbkAluvHSgCR7q9nZTY2xCIwLtODHuHcKCM59zgcd+aAKdxePPZvJ9luEktpWKytJGAjgng5fpg4PqD9KAPDPHd/wCKdE/ad8EeKdJtbKKx8T6RJ4d1C2vpm8yaCEy3UzxxY/5ZqFw/Qs4UhQ2T+i5PPBY3g/G4XEqXNQmqsWkrJyUacU315ne6tsrpu1l8XmUcVheJMLXoONqsXTabd2o3m2l5K2vd2trc+hNNBGnWoJyRAmT/AMBFfnR9oWGYKCzEAAZJPagDNvNVwnl2xjjMoKxzzSBEJx1UHJbkjtg+tAF21t4LaIJbgbT8xbOS5P8AET3J9aAHzQxXETwTIGSQFWB7igCs73dmA8kkc0ClVYsNrqOAWJ6H1PAoAtqyuoZWBUjIIPBFAC0AFAEdzK0FvLOsZkaNGcIOrEDOKAIdOjMdohaRZGlLSlk+7lyW49uaALVAFaBka8uQrliuwNk/dOM4+mCD9SfwAPJf2mvGmj2fgW/+FVv4u8O6P4q8fabd6bpCa7JLFayxlQlxumQFYn8qRxEXIDSlAN3Irx84xEI0HhVOMZ1E0ua9vPXo7PS+7sfo3hvk+Iq5pTz+eHqVMLg5wnUdJRc0024Wi3eS5op1OVNxhzPTRm3+zn4Dl+GnwR8IeD7u1W3vLXTkmvYgwbZdTEzTKWDMGw8jDIJHHGBgDoyrCvBYKnQlo0tfV6v8WeRx9n1PibiXGZpRfNCc3yu1rwilCDs0mrxinZq6663Z393cT2qtOsEbwxoXkYyEMAOTgbSDx7ivQPkD558NfsueL/hN4y8X+MPhB8TpLHTtdh1C5svCr2SRWMF9JC4tyzfOpjjlYYCxIQqqCWClX+do5LWwNapWwlWyldqNtE2tO+ifktO/X9lzLxNy7irLcFl3EWBU6lF04yrqTdSVOMlz2XuvmnBO7c5JybaUW046p+K37QfgfTfA9h45+EMPiTWvE+r3On6pLoDSw2eiwi5iit5pHAnDLIjtLuYxYGFIBVjWv17MMNGlGvR5pTbT5bpR1STfxb79OxwLhbhHOq2Pq5XmLoUcPThOmqvLKdZuEpTjFfumnFpQslO795Npo6LQP2ofg5ra+LJLjxG2jR+DNXTRdTk1SPyEE0k0kMTowJDI7RSEHIKqpZwgBropZ1g6ntLy5eR8rvpq20vk7f52PIx/hnxHgng4wo+1eKpurBU3zPljGM5JrRqUVJJ7pt2i5M9D8OeLPC3jGwOqeEfEula5ZK5jNzpt5HcxBx1XfGSM+2a9CjXpYiPNRkpLumn+R8fmOVY/KKvsMwoTpTtflnGUXbvaSTsWXja2upL0xmVXQKxH3owOwHcck8c/XgDU4A1LWNJ0fT31fV9UtLGxiCl7q5nWKJQxAXLsQBkkAc8kionONOPNN2Xdm+GwtfG1VQw0HOb2UU23ZXdktdtfQ+bPG37c/hJfGcfww+Dvhyfxl4lutQGlxXbyi30tJDuUyiUBnlSNwCxCKhQMwkwAT87iOJKTrrC4OPPNu19o+t9W0uulra3P2fKfBTHwyuWfcSVlhcNCHtHG3NVaVmo8r5YxlJaRvJyUmouF20s7wH8PfiZ48+Ilt8Xvjffx2Wrm1n0KXwxZiI6VcWR3LEJNju0sX764cpKWYu68qqbDpg8vxWIr/XMe7Ss4uCtytdOrutW7PW76JWOLiTi7I8oyp8NcJw56PNCrHET5vbRqaOVrxjyy9ynFSglHlTVpSlzL6DH2NoGGqX1zIEyjRxyqqDBPRCQe3AwOnQ9T9Cfjx1UUsc8azQuHRxlWB4IoArSRXsVxLLZxwMsyqT5kjLhxkZwAc8bfTpQBEurJbM1vqksKXChTiIlg4bpgdc8dPp60AK/2nU2EYWW2tAcuxykk3P3QOqr6ngmgC7DDDbxrDBGsaL0VRgCgB9ABQAUAFABQAUAfBnxw+M3xv8a/Fe9/Ze8Q3fhjwxp2u6paW6XVgTPOtlO4MUdw3mkBirxF0AjLFQAfLfLe/wCG2PrLNswqVIQdbDUJ1aKabSkmkpSs7PlUleLs3e8bWUl9d4lcIZDg+EcmzjBzryoYrEwo4htxhe0ZOUKd4XSnKElGfvqKTU03eB1vh7/gm/4ZtUH/AAlXxY1vUnEu4mw0+CyUx4Hy4fzjuzn5s4wRxxk9eE8Q+NaNSM6+Zc6TTa9jRSa7O0L69WmnbZp6nz2b5R4d4unOnlvD0KLlFpSeJxVSUZO/vK9SMdNGlKLV1rdOw34tfsUaV4F8Hv48+CPibV7HxR4aZdQjOpajBHDLGjbpGMzCJYGRfnEhbZiMhh829ezOfFDiLEwnUqRpVaVlejOC9m7NS5rtqSkraNzcVvy3s1wcC8B8JvG08vx6rUpTlJRxFGc/bxcouChyJShUpybtKCpKpLm+PlvCXp/7Mf7SGn/F+C98FaxqNteeK/DkZW6vrMf6Jq0SP5Zu4PlUgE7CylEH7xSowSq8Wd5Zl9XCwz7IaingqsnFLXmpTWrpzT1uteV3d4q92nGUuR4fOchzCfD3EtCVHGU4xlra1SEvhqRa0ae0kvhleLtKMox8Y+Mv/BQKW4vL7wz8ELJVjtn8l/EV5Gr+YwZgTbQnKleFIeTOQxHljhj63hLkOW8cV8VXxUZujQcUukZylzXXMtbRSTaTT95PRb7+M2VZx4XYPL4V5wWIxanJwXvTpRjycrknpeblJbOK5JJNu/L4dpv7WPx80nXY/EcvxHvrtopzPJbXe1rSUE/MjRABQhyRhdu3+EqQCP3XM+CuE8Nl8/rdGFGlFazb5XHs3OT321k3fre7v+B5TxBxPmWYU6GXyqV6037tOMXNze9lCKu9L6RV0trWPrP9lH4d+IRqeo/tIfE/XbqPxJ42tZII7C6gW2EduZlPmEMdzBlhg8sYQIn97cpX+f8AiziTB5mqWWZGuTA0b2VrOpPZ1H1at8F0nrJyvzRUf3LLcgxWRRqLOrVMfNpykp86pQtdUI2XLFqUpOraU480YRjy8k3U+lV1HT3GUvrcj2lX/Gviz0ClqN6Z4B5ABtPOjWecthShYAhT3HPJ6dfwANagCG7Wzkt5IL8QtBMpjdJsFHUjBUg8EEZ4oA+aPFH/AATZ/Yn8Y6je69N8E9PsL6/BYTaTqF3ZwQvjAeK1ilFsuDg7fKKEj5lIJB2w9b6vWhVcVLladpap2d7NaXT2euxnWp+2pyp8zjdNXW6v1XmuhStfDfxF/Y+8KtpsEMXxD+EcK+TPpotVhvdEtmZ9+xPmjlgCEblJClugiXczfp1KGQ8eyjh6NOOCxlrRtb2VTRWVklyybvaybt1m7JfCTlm/CMXWqzeKw19b/wASGuru78ytvdrX+VXb/L79rzRfhP4Z/aUi+Hv7NmvN4N8HeJ9NhmvL5Wa0tJrm/Fx5iCeWRQtp5U6WzozRQxbZkdQY5Gb8rzzJsXk2Jf1nDv6xBWUW7aN2bi37uuvvK/MlZNo/X+Ec/wAPnGGWUTx6oYCvLmqTjT53eMW4KailUklK1qblywk+flTTZ+xPgTVPC37O/wAAfCHhTXfiFZeK38O+EiNKmtTbW1zr9np9n5m6zgabbIRbop4kIxhmfBLVMpRhFyk7JHiUaNXE1Y0aMXKcmkkldtvRJJatt6JLc+B/j5/wV98W638ONN1v9nPw9aeHrm5vNR0zWZddtkvLuxJY/wBnzWwWTyt7QpJJIJY5EVyqKZAjs3UsHiXh3jFD90pKPNePxNNpcvNz6pNp8vK7NJ3TRDnhVFQ9uvbO79nyz5uVW9/m5PZ2u0nFVOdNpuHK7lb9nH/gsj441r4maT4W/aG8K+FbXwxrNzHZPrOjxy2j6U8hCrPMJpnSSANjfgoyKWcF9ojbnJPBf26P2uvjHN+1p8SbX4VfHL4jaB4a0vVE0i30+w8R3lnbRT2dvFbXJjiilCBWuIZnBAG7duIBY0AfoP8Atp+GrbR/+CfOi658ePC//Cc658PbLw5falp+palNCl/qzLFp873E9rIJSQb2eTMco3Oq5ZlLBgDxr/gkZ8XPBPinx/8AEHwj4D+B3h7wDbz6PaalfS6bq2p3r3bwTtHErfbbiUIFFzKfk253c5wMAH6d0AfKf7R3/BSf9nr9mX4iyfC3xjYeLdb161tori+j0Gxt5ksjKoeOOVp54vnaNkfChhtdckZxQB7ZY/H/AOCup/E2L4N6f8TNBufGVxYf2lBpUV0GknhzID5bD5JJFEMrNErGRUXeyhcMYhUhUXNBpryOjE4TEYKfssTTlCVr2kmnZ7Oz6M9AqznOQ8a/GH4SfDW4itPiN8UvCHhWedFkii1vW7axeRGLBWUTOpIJRwCP7jehoA3fDniXw54x0S18S+EfEGm65pF+pe11DTbuO5tp1BKkpLGSrDIIyCeQRQBpUAFABQAUAFABQAUAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFABQB8/wD7XP7H/hz9r7TPCPh/xd4+8R6Do/hvVZNQu7LS3Ty9RR4WjAYOCqTIcbJSr7Ueddh83cgB8ufs1674V/ZB/aZ1T9kf4yXbaraaXcNrfw68YeKriOG30nSZ7J2mhs3uR+6LOvkv5DCOR0nztKbHAP0joAKACgAoAKACgAoAKAMjxb4w8KeAvD154t8ceJdM0DRNPCtdajqV2ltbQhnCJukchQWdlUDPLMAMkgUAfmR4y/aB/bK/b3+LPiP4OfsuarP8MfAum6PY6y1/q9tLpN/Na3ENrPA1xdwfaJImmYvJALcx+bbs2/cN2AD9E/gf8LNJ+Dfwv8P+BNP03Q7e8sdOtE1e50jTIrCHUtRS3jinvGiiVRvkMYJJGcbR2FAHd0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/ACXL4Ff9jAn/AKWWdAH0/QAUAFABQAUAfAPwb/5TIfHb/sn9n/6I0KgD7+oAKACgAoAKACgAoAKACgAoAKAKeo+ZAn9oxbS1tG5ZW6MnBYA9j8oweaALakMAwPBGRQAtABQAUAFABQBXvoEuLZ0dGYqN67WwwYcgg9jmgBbGV5rG3mkbc0kSMx9SQCaAJ6ACgBglQytCG+dFDEY6AkgfyNAD6AKl5+4ngviwCR5ikz2VyvP4EL+GaAJ4biC5XfbzRyqDjKMGGfwoALieO2geeUgKgyckD8MnigCpHdTaid9hKYoEyPMZARI/oB3Uc5PHOMHrQBLa2s0M00806OZtuVRCqgjIzgseSMD8BQBaoAytVP2mT7HbusVwFGJy+3Zk5CAjnJK9PTn0FAEkunWK2z3F9ExKoWl/fu/AGepwTjqOOD0oAyll8m0beLqaa4MuITdMFhUbhgt3OEOM5zg9KAL2mSr/AMS3DsBJZtHt7Fk2c/8AoVAB/Z80jpaJdwFbEJ5bGIlxzkK2GHQKp9+DigBTHa3V82nXIUImZPLZNnnu3Vh6hRx9eewNAF6xkdklhkzut5WjyTnK8Feep+Ur15zmgDI121mW6W5SKZ4WZXba4Cq4BBZu44VeQRjnkZzQB4l8Y9Yj+Hev+FfilYX2mwyzXK+FbqDU5zFaPZXcgaSQMM+Q6eTuMqo+VUhjJtVR9twvRqZxgcZk6puVoSrR5V73tKcWoxdk3KMubl5dbN3ik27/ADWczpZfjsJmNWpyRc40pt6pQnJOUknKMVKKV73imlaTslb5R8J2/jn4tfCqXxre+M7xLvwz42N/B4h8TXq3cfhqGK3jZGnkaPetq7FfPlCskTWtuzQrC1xPB/PKrTrYh5bipyp4mM24+0+y1o4NtXUr7pxteKVrux/cHEfE2ScC5th1Sw0J5fisJao8NaMaim3y1IxUuSUVG/I1Pm5asn7SVle1qf7ZH7Qenxah8PYPix4O8X3mpPE2meJPDcFpqaS7nVDbxm3AQBsOMPA0u7jADq6+hi82zjL5vCV5JzdmpJJ/JaLz3i3f7y+HvD/w54ww0OIcroTWHhzRqUpVJRSaTfNJuUndJxfu1Yw5Xdu6cX9efs1/Gtvi14Kj8TX+keJxJYW1tBfapqunxQW9/cYZHmtGjwjKrpKHVACpdeOa+pybMv7RoKTjK6Su2rKT6tW03WvY/BvErgtcGZtKjCrScZyk406c3KVON04xmpe8rxkuVtvms3fQ9qOl2JGwwfuyc+WGIj65+5nb19q9c/OixDBDbp5cEKRpnO1FAH5CgB9ADI4o4UEcMaog6KowB+FAD6AIZruGBtj+YzYDEJEzkA9M7QfQ/lQBn3+oTy7IYI5be3dws1zIpj2qey55BPTcQADj1FAGpHGkUaxRqFRAFUDsB0FADZpobeMyzyKiDqWOKAIrFWKSXDoyNcP5m1uqjAUfQ4UEj1JoA+X/ABFNL8Z/2m/D0Np8JPCPiLwRZacbyz8bbjePMsQlRjDPFKIcR3UxTyJFc7kaUdUK/Mzf9oZnBqjGVJK6nve19mnbSTtyu/ftb9ww8VwhwNiYPMa1DHVJ8ksL8HLz8rvOEo8/vUo83tYuK1VN/aUvqmvpj8PKl+PMa2tmP7ueXa4/vAKzY+h2jPtkUAW6ACgDmvGHw28A+P8ATrzSvGXhHS9Vt9QWNbjz7dfMfy9/lnzBh1ZPMk2sCCu9sEZNc2IwdDFRcK0E09/ltrvpd27HtZRxHm2Q1oV8txE6coXtZuy5rc3u7NS5Y8yaadle9keOW/7CnwN0fxtonjrwguv+HrrQr22vYbS01JpbaV4pVfD+cHlwwG07ZF4NeOuGcDTrRr0bxcWnZPTR+d3+J+jVPG/ijF5ZXyvMfZ1o1oyi5SglJKUWtORxhpe6vB6lyfQf2sPBOmfEDW9K8VaP42u57yOfwdok0UcUcMct+XuEnd/KbCQNhB9oIABA6IDbpZrho1pwmqjv7kfWWt722W3vfoc0MdwDnNfLsLXoTwsFFrE1U223GklBwUede9UV5P2Sbum95HP6p8IPi58ZNV1TSfihr95oXgfxHplmk3hS3jhL6ddwukrFZ4/MQqZUdwwdiwlVGC7FCqeX4rMXL63O1KaXuaXTVnur9Vfd3Ts9kaYfi7IuDqdB8PYdVMdQqVGsQ3PknTmpRScJcjuoSUbOMVGUXKLfM7978Kvg34J+Eej3Vj8PPDlrpMl1sN25u2lvLmZQEVnkkLFVBY/uwUALNgDcc+lgcuw2XQcMPG19+rfzf5bbnxfFHGWdcZV4YjOa7qOCairKMYpu7tGKSu9Lt3k0km3ZW7mPT5LvUmtJblpLWQs0m+Q+bIIiFw2O24tjp39BXafMCTaHqunzrNpqxTokm5FbAIABAz06ZPfknOKANPw0JBpEIfbtBYJg54zzk/XP4Y69aALd1dGN1tbfa1zICVU9FH95vb+Z4oAWzsorNCFJeRzmSRvvOfUmgCxQAUAFABQAUAFABQAUAfFH7Rek6v8AtFftJ6J8Lfhjd6XYXPhW0E+seJ4IQbrTysu5185SGfyi0QSJGGJppNxXaWT2Z/XclyaWKwfLCtjr0oVNXONCm39Yatorz9nBJ+9fnaUdJH1vDFbKJ4hvP1UrYbL5KvLDNpUqmJrQSwacZK7/AHSr1ZzScHBUk+e7py+168Y+SCgD488U/wDBOjw/rPjK61fQfiTLofhu4u/tCaHFoqSeRG5DSxpN5oHJ3BGaMlF2KdwXnDJKuacN1prLMTy4eo7zoyhGdOSum04yvHW1uZJTUdFI/Us94+yjizLqMOIMs9rmFGNoYuFadKtCSUlCSlCPP7vNzODm6cp3m4pvTvNB/YQ/Z+0a4aW80zWdYjaMp5N7qTIgYkHzP9HEbbuCOuPmPGcEfpEfEvOsPR9hgI0qC5nJ+zpxV27ttp8yu27t2u313PwTFcI4LMsQsTmdSriJKKinUqSk1GKSik7qVopWir2S0SPXvAfwu+H3wx08aZ4E8KWGkREEPJEheeUFi37yZyZJME8bmOBgDAAFfK5vn2ZZ7V9rmNaU32eiWltIq0V52Svu9Wexl2UYHKafs8FSUF5bv1bu382zqa8g9EKAGuiSI0cihlYEEEZBHpQBWWweMbIdQuo0HRco2PxZSfzNACx6dbI4mlDTyg5Ekx3EHOeOy/gBQBXuLWzsZVuhGIoZP3coT5VBP3XOOnPH/AsnpQBW8R6FqOreH9T0fTNaezkvrKe1jllj84Qs6MocchiQSDy3anGTg1KLs0JpSVnsfzdfGn4V/HX4NX9l4E+NvhLXtCeyaYaamoJvt5F2xCT7LcKWjmjUCIHy3ZVJxwSc9uJzPGYzD08LiKjlCnfkTd+W6imlfVL3VZbLold35aOBw2GrTr0YKMp25mtL2u030vq9d31eiPprwN+zr+2P+1d8AvCnxJ0NNR8Ra7oXjfUI7TUfEOrMuoT2N3aWTtcmW9IWayjkgwqo7kvPcYRstgj9RjhIzabrqadpRjKnyJJq6le8ubo04tbm9DE5jhMb7TDVPZw5XaUJShUUndO0otNLle6aldn6K/Bf9gL9nn4JeDtSuvGvh2y8Q3+vxWd/4kttWujcaEL23j3sYbSRVhEMchuHR5kaSNJZBuCMy1ljcT9dxNTE8ihztu0VaKu72Su7Lsr6EYWh9Vowo8zlypK8tW7dW9Lvuz8zvHVxpvhD45eJ/it+z1+zj8L/AIn/AA7nvLO20x4tC1HVtKga9EJtYp7QzKba6ZyE8pkUeYzxqp+7Xp4ik6GV0ajo07VHK1RSk53g9YyXO4xaTi/gTcXF3d2cNGoquPqwVWd4JXg0lG0lpJPlTknZr4naSastD1X4Lfs8/Bz9oD9oXwvqmo/s4fG74S6nN4gj1e90/W7abXvDd/BaobmaK4vbxIbqGS4aJ1ZnMylpMDG4AeGeqfoj+3f8LfFXxn/ZN+IHw78FRQS6zf21ndW8czMBILW+t7qRRtViXMcDhQByxUcZzUVJ+zg5tXsr6bnTg8M8ZiKeGjJRc5KN5O0Vd2u30S6voj4s/wCCOnwf1fwd418a+N7/AMV+FLuO80o6SdMstVWbUbeWO4icvLb4BSPsGPfiufBYyGOpKtTTSfdW+foevxNw3ieFcwlluLqQnOK19nLmSd2nFuytJNNNWuj9T66z58/ml+KOoeOLr4v694O+Pfiy4udUsPFEun+I9ZW3TULqB4J3gnaElozKiAOUi3ohCIBsGMXGfLCUEl7zi79fd5rWfRPmd7fFaN78qtc6k5whTb92Lk0tFrLkvd7u3IuW7ajeXLbnlf0zx3qE/wCyR+1xquv6t4HsPFGreGde/tfw6121xb6VJbBi1hcQQ72leNUETRkzuBsUEybdzenndZ4qaq1KnPWk4SlNNcvK4fw+VJWcW4q6aUeXlUbavmyJ4bB06tKpQ/d8soQV3GUZqon7W+qkpRU1yte8p8/MpJJfqj+1B+114X8NfskeHfF7fEa6+HniX4u+F0vfDV9Dpcl5LaySWKXTjZG+YiQ6W4lDN5UlzFJyqMR4tWUoU5SgrtJ2W132v0uejgKNDEYqlRxNT2dOUoqU7OXLFtJy5Vq+Va2WrtZH5MeH/wBiX9rb4v3x8SeEfh1qfjC31xpL+PxENQh+yaizO5km+13MiK7F0k3Fm3FvXcM+xj8qqYCpOHtKc1G2sJxknfT3dU3qtbLRavRpvx8NjoYmMWoTjzX0lFprS+u6Wj6vfTdNL6R/ZY+Mv7SvwF+LWi/scfBTwTpOpW2keL2m8UNqto2ntqrvCEuczTpG8EXlx+bCxjM7CGH5XXMD+fWjCKVampKD5Yvmaa57SdlJRjZSUXJRd5K0veklp7kYLF0vY4enepSjKc5Rur0+aC5pQcp2cJT9m5xcYzTpv2cJOXN+ydZnAFABQAUAFABQAUAeRftff8mpfGL/ALEXXP8A0hloA4n/AIJw/wDJk3ws/wCwbc/+ltxQB9J0AFABQAUAFABQAUAFABQAUAFABQB+ZXx58IeJv20f+CmVn+z94o1HSrbwF8FbC08Qz2M1sZDqNtKunz3kR24Znna4t4DlwkcURdQX3LIAfprQAUAFABQAUAFABQBXv7+x0qxudU1S9gs7Kzhe4uLi4kEcUMSAs7u7EBVABJJOAATQB+ZvxZ+K2t/8FOfihovwG+COmWOofBfQtas9S8X65c6dLbajbRwyOGuIpp0KQC4ieSO2RF+0M0U5lEcJYUAfpD4N8IeH/h/4T0fwN4Tsns9E0Cyh07TrZ7iWcwW0SBI4/MlZnYKoAG5icAUAfN37Sn/BRn4G/s2eKJvBerW2q+I9c0q+tLPXbDTPKim0xbmFbiKTZcPGbkNAWbMG9EYIkzwtLHuAPqeKRJo0mjOUdQynGMg9KAH0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QAUAFABQAUAfAPwb/5TIfHb/sn9n/6I0KgD7+oAKACgAoAKACgAoAKACgAoAKAIbm6htgBJlnfOyNRlnx2A/yB3oAqafO9pHHY30ZhK4SFmIIdf4VyONwHGO+MjNAGjQAUAFABQAUAFAGNeOLOVtNgdkjuV35GT5I3YbH1zhQOjex4ANS2uIbmPfDJu2ko2VKkMOoIPINAEAv5ZDug0+aaLJAkV48MB3GWHGaAIpgb2YCK0njcLh5GkeID0Hyn58ZJ9OvPNAF23iaGBInlMjKMFj1P+ffJ9zQBVB/tGdWQn7NbyHPbzZF/9lU/mR7cgE8tpHJKJ0zHMOPMXqR6H1H1/DB5oAjGnQO5kume5YnIEpyq/Rfuj64z15oAsRRRQIIoY0jReiqAAPwFADbib7PC03kyS7f4Y13MfoKAC2uI7qBJ4s7ZFVsHqMgEA++CKAOdkaW9vY7yGQNbC9R3UA4U7kjXnuSAGx2zz1oA6C9TzbOePAO+Jlwc45B9KAIXsrfNuUt1AaUvJhfvExsCT+dAEccMNveRWjybUDGa1Qcc7SHX3A3ZH19BQBYmikVri4DOQ0AVVQZYFdxyPc7h+VAGZp1tFOHtLvR0V0wksu9WfdtB3E5yM54IJ/CgCeTTr61uftGnTB967WWZ/wAsnBLAZPXkeuOKADU9Purq4W4e7RIIBlEWAuxJ65GevAwRzyenWgBkuh2mtaBf6Dr2nxtZ6nDNa3MCsymWGRSjhmVidzAnkNnnrmoqU41YOnPZqz+Z0YPF1sBiKeLw7tOElKL0dnF3Ts7p2a6qx8GWPhDxJ+yD41ms9R0bxDd6d4h1KCG08T+F7uN3ntllPl6ddxXFrJDNOwRMLtWSPzbjyJV82QHTF5Rl/Gk40s4qqGLajepWqctOfLGNOLVS141nCMXKE+aNSSclJNKL+xzWEcZgljckpc2FoKdsPQo3r0nUnKvP90qlpYWNSpUUKkOWVGLhCdNx/eLd8T/GTRfjDqV+fBOkWficeDdDh8Z6PqOu2aW17Z3tnPDdXNh8g2SCdUt48r5QAWc75AqkefS8Ocq4ZxWEzCrOpVhPERoKNNQlGbqUoTpTjUnKNk1UUl+7ltaTpux8biOKeIMlpVsvws/q/taMp1FKU4ySp1q1CpTq04LeMqU4tOS0ldKSbNX9n/V/EHx6/ag1D462lpqth4Y8L6LDpvlee217maEF7Ug8TIkkk0vyhclIJCAzgHnx+VYjB8V4qhU5lHDt07tOPN2dnf3ZL31Z2s4u7vd/suJzXLsu8I8uwcXTniMe1XaTU/Zq+tpJK042VKV9b+1h9lpfa6OkiLJGwZWAKkHgivXPxMdQAUAFAFP7etwRHpxWZiSDIOUjx3J7nkcDr7DmgCa1tY7VCiM7FjudnYsWbAGT+XbigCVlV1KOoZWGCCMgigCldaRYz28kSWkCOyFUfyxlDzgj05OeKAI9PdridpL+N47tM7YWPyonqnZuvLdeSOBxQB4B+158Y/FvgfUvBHwv8H6V4Z1OT4iz3WkX1traTSQmGQwwIriF1dUc3D5Iyfk46EH53PcfWw86WFoqL9q2mpXtrZdGnZ3/AAP2Xwp4Sy7N8Pj8/wAyqVaawMY1IypcqlePPUbXPFxcoqCsnZe9r0Z6l8FPgz4P+B/g3/hFPB9pcRx3Ny9/dyXMxlllncAct0wqqiAKAMLk5Ysx9TL8vo5bS9jQ2vfXufB8XcYZnxtj1mOaNOSioJRVkoq70Wr1bbbbbu7KySS7+u4+WIri2gu4xFOm5QwYYJBBHQgjkH6UAV4pLiykitrqXzopCVjmbht3ZW7E4zz7dM9QC7QAUAFABQAUAMkhhlKmWJHKNuUsoO0+o9DQBH5VlZBp9kMAxhnOFAGfX6n9aAK19qNs2m3UttdKxWJwHjOQG28cjjPSgCGC8cWMMOlQC4dUXzGBBVCRk5yRub2z9SO4BY002ZEjQu7TuQ03m8S5xxuHb2A49KAL1ABQAUAFABQAUAFABQAUAfIV9D8Qv2XPj74v8b2HgPVPEvw+8eO2p31xpwEslk6M888kuE+QxGS6ZVYqjpIv7zcpC/S4Srgs9oUsuxc1QxFJNU6ktKNSHM58lRpfu5wcpuM2uWadptzaa9LG0a/1F47BS9vBuPtKCu69OpyRoxnQjKX7+lVjToxqU03UoyjzU6caKal9SeEfGPhfx5oVv4l8H65a6rptyoKT275wSAdjqfmRwCMowDKeCAa8PG4PEZdiJ4TFQcKkHZp7p2v+Kaa7pprRnk0KsMTRhiaLvCavFrZpNxbT62aafZprdM2a5jQKACgAoAKACgAoAKACgBGVXUo6hlYYIPQigCpZGW3drCeQuUG6FyOWj6YPqV6E+4PegCW7tvtUOxZDHIpDRyAZKMOh/wDrdwSKAI1ub0R4fTpGlAwSroEJ9QS2cfhn2oA8C/buuZtF/ZG+Jet3mia3r5XSYobqw0S+eylNpJcwpcYlWORlhWIu82VO6FJFJRSWVSvZ8u5dJwU4uqm43V0nZtdUnZ2fZ2duzPwv+FHxtvfgzr3ibxF8Pl17Q9Q1vQ5tE0+8stdmhl0zz3iE1w3kiP7SwiWYRoxRUleOXJaJa3xE6dSrKVGPLBvRXu0uib0u+7sr9lsZxXLCKbu0ld95W1aVtE3stWlo5Ser/TD/AIJk6n8UPF/xd8TanrH7Z6/F3wvoGhtCdLOpa61xHdXE6GCd4dTtYgyhIbgFo3cozIDgMKzUJOLmlorK/S7vZfOzt6PsJzipKDer6em/5r7z7o+PfwU8K/tE/CfXfg5411DVbLRfEH2X7TPpcscd0nkXMVwmxpI5EGXhUHKH5ScYOCJKPxR/Z/8Ai94n/ZT+IPxS8d/s86Ba61pvgjTJrLWrzxdFdwNfWUmq2VrAy2cTJ9nuPPdW2ysSEMwOGG054TC432M69dqUU0rpNJX5uVO7erUW7dLPV2ueznuMyGpXpUcmpVKfu3ftKkZuTSjzNKMIJKMm1fXmXK+WDbifqh+xL+3T4G/bA8N3dqbODw3470VA+qeHmuvNMkGQBeWzEKZISxAYY3RMVV8ho3k0PGPzc/4KGan+y/4q/awn8bHV/H2qafr0thLrWs6E2n3Ol31tDbW1ux0ibdtmaNYZI5C7bRcI6cBDnjrTrurGGHcbL4rt3Se1rfPc+jyzC5TDAVsRnEa6lJNUHCMfZynFPmU3Kzsm4X5LtJu+tj2P9vv9ibxb4/8Ag58HPi/8CfDniXX4/CXgPSvDF/otwYJdTj0qCESWc/kQArLMplmScRO4y0RjXYsj12Hzh876F+zb+0Fe33wH8O+JfhX498b/AA48Q6LaXUEGnK19bWtnql159xJbM8jW+nTeW8LFme3B8tWlKgua9SpSwGCr0aiksTFxUnFqpTUZuLXJJxkpTUJWldOMZbd2Z4fG4qdGpThT9jJ3g5P2VVyjzX54KVNqk5RtFaSnB3kpaqK/af4LfAn4Xfs9eE5/A3wi8MroWh3F/JqT2ouJZyZ5ERGYySs0jfLGg+ZiQAADgADyzQ/Gf9uzxn+zD4//AGv/ABbrerp8T7e6tfEK6J4q+yxaeYyunrDZSNYq7ZyVtpCPNOCxU/KCQOn/AGb6nP4vb80eXbk5b+/f7XNb4Ula+7LwvI8X/tV1R5J/DrP2nK/ZrWyUOa3O9Zct+VXP2v8AhZ4S07wB8MfCHgTSHv2sPDmg6fpFq1+qrdGG3t0iQzBQFEm1BuAAGc4FcxB1FABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P/AKW3FAH0nQAUAFABQAUAFABQAUAFABQAUAeNfte/HiH9m39n/wATfFU6fdX11ZC3srO3tblLeZp7iZIQ6SyQTxo0au0o8yJ0Jj2lTnFAHmn/AATs+C+t/D34V6h8UvEnxH1zxjqHxbltfEqXmu2MCagLExYs2uJ1lnmlke3aNyj3DxxFiiKrea8oB9YUAFABQAUAFABQAUAfN/8AwUZ8QeIvDP7FHxU1LwvfJaXk2mW+nyyMsRDWl1eQW93H+8BXL280yDHzAsNmH2mgDkv2EdV+F3wM/YB8J/EfxFHpPg7RF0241zxBftfw3nnztO8ZuJJYFzJLIEiRIMNNGfLtiGkjwQDxzWv2xfi7+3P8RPE/7P8A+xtrF74N0vTEgvY/HTIIxc2UYkS6MwkUTWqyyS2wt/JVp8wkuFSWQ24Bl/td/wDBL8a78HLP4ofDpL7U/jPouk6YfEsFlKslv4rvY4o4769xOVZbpyHuGcHMzK+6N5pi9AH23+zR8bNE+Nfwy07Uk8b+EvEHijSoI7LxQnhzU1vLe31BC0chBCqQkjxSMjBTGy8xvKm2RgD1mgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gAoAKACgAoA+Afg3/ymQ+O3/ZP7P/0RoVAH39QAUAFABQAUAFABQAUAFABQBSvbzG61tHJuSVX5Yy/l7iBubHAwCTz1xQBLbWMNsxlDSSSkbTJK5ZsZzgZ4A+mOgoAmkjjmQxyxq6NwVYZB/CgCmFutOU+WrXNso4Xd+8jHoCfvD6kEAd6ALcMqTxJPEcpIodTjqCMigB9ABQAUAVJ9TsIVkzeW++MH5DKoOR29qAM7TtTmkied4wJAvnTbgd7pjICJ/d7A568kZJoA1US7MolkmVU6eUq5GPXd1zn8McY70AR6dhIXtcEfZpDEAf7vVf8Ax0rQBYmmjgjMsrbVXqcZ+gx3PtQBUkkur4GK1D28R4aZ1KuR3CKeQfc/gDQBbijSGNYolCogCqB2AoAfQAUAFADJpVghkncErGpc464AzQBzcMw3S2sTyLCkqNcTA+UHV3ycocFeuMjHA9gCAaEOgJb3hljlPkPIJfLBKlSCCBxwy5HQjjseuQDXoAKAIbu2S7gaFuDwVbn5WHIPBB4PvQBNQBBNZQTyea/mq+0KTHK6ZAzjO0jPU/nQBGdNhL7vtF3jGNouXx9euf1oAsxxrFGsSZ2qMDLEnH1PNAFY6ij4+x28t1kE7o8BODj7zEA856ZoAhistQikN2LiIzPjfCqlYm9fU7v9r9KAPknUPhDrXgPxZqlrZan4G07w5LFby+DPDVpJbWmr3+o2EiMLXNyD5zzW8t3YzS+cWkiuwW27sDxMfPMsVbBYrFXpRhyUIuTvBxtycqeq5WlrF30i+it+lY+ORZ7kE8bl2X1HmPtZVsXWUE6cvac3O5yhZJTaUkpQSi3NJu7b831n4Zr4I+PHgLwz8J/Fz3OmXmtWPijTvD93etb29tbSsoE/2maT947+TcIFSN5RFEgbc0ih/d4L4mzjO8nr1M3pwxEcHB026sIyqRnOXJSSqyfNBJzlNXUn+7lCLXNBL5yrDKakJNSq4TE4uo25Ydzp0asKUHWrc2HpwVKb5YU6UkpU43r061SL5Kk5foWlvBG5kjgjVz1YKAfzqT5gkoAKAKd5JJNKthAzqWw0zrxsjOeh9SRj16njrQBajRIkWONQqoAqgdAB2oAdQAUAFAEc0ENwuyeJJFByAygjPrQB8y6d4V8N/Gr9q6XxlZeN49X0/wCED/2ZN4fubOYpa3zRvtnjkkZ45GE4kywEbq9onBCo7/NKlTzLNfbqd1R05bPR669U9b9mnFdk3+2VMfjOCOAFllTDOnPM/wB4qynG8qacbwaSjKKcOVpNzjKNWWqcpRj9PV9KfiYUAFAEV1AbiExLJsbIZWxnDAgg478gcUAQR6jGki218RBOeAG4WTpyp6Hr060AXKACgAoAKAIbm5EAAWNpZH+5GmMt6nngAep9vUUAV2t7+9idLuVLZGxhYCS64wfvnjrnovpzQAybQrCeIxym4csMFmncnPrycZ/CgCyi22mWYBbZFCnLEcn3OOpP6mgCKOyW4jaS9iHmSSeYADho+AAAw5BwBnB657UACrf2ZwP9LgAGMkCVfb0b8SD9aAJ7e8trosIZMshwykFWX6g8igCagAoApf21o/8AbH/CO/2tZ/2r9m+2/YfPT7R9n37PN8vO7Zu+XdjGeM5rf6rX9h9a5H7O/LzWfLzWvy32vbW29tTL29L2vsOZc9r8t1e17Xtva+l9rl2sDUKACgAoAq6ppem63p1zo+sWFvfWN7E0FzbXEYkimjYYZHVshgQcEGpnCNSLhNXT3TN8Nia2CrRxGGm4Ti04yTaaa2aa1TR8gTaZf/softJ+DfDPgiyMHw+8fmHTV05bqWXdceYkLyOZpXPmxvPA+9VRTE3lgZDMfpcHUrZ1lEsqacquCh7Sk73vh04Qq05NtfwnKNSDfM+W9OKSWvpZgsJXq1eJcTOMHjJunX91RtinGpVoVYKELWxMadShUgrWq8tepKV7L7Jr5w8YKACgAoAKACgAoAKACgAoAr3sEkqLLAQJ4TvjJ6E91PsRkfr2oAkt50uYVmQEBh0YYKnuCOxB4NAElAEVzbW17bS2d5bxzwTo0UsUqBkkRhhlZTwQQSCDQB8ZfEr/AIJH/sh/ELX31/TNO8UeCWmaSS4tPDWpxx2ssjuXLCK5inEQGcKkWyNVAAQYoA5Dwrp3wP8Ahn8afhF8M/2RNNhii0W+mt9e1O1iaeTVI5nQXHnzspa5Kwxzv5nMaK6+WVVAE/bcl4axWU8I5lLPIckJxUoRk7NSSfLK1/dcpOMbO0ny8rVmk/y3M88oZjxHgo5VLmlFtTklo4tq8dtUoqTvsr3T3a/QGvxI/Uj5A1L4FfCH9qDx38edBfQY9O0bU7PTfDl5q2j2sdq1/fRzG6nnaURAyTRXEFuMsXDCNN29GVa+7zzBSybhvA4Sokp15TrSX2kklGnfV6OLk1dKzbVk1K/yWVYqOZ53i8RBtwpKNOL6N3bnbTdNJO29k9rW+ALP/glj+1J4C+N+k6XBoml634dt9Qjki8R219CkHl78I728jeerKdjOEjkCZJDMFJr4mhTwtarGnjXNUm/edNpTS68rel+19D7PD42vltWOLw1OnUqQ1Uaqcqcn2nFNNx72Z6J+yx/wSj+LevTRr+03Y6boHhfTNahuW8P/AG5bm+vVRVaTE1nIUihlRvKOJfMBDMFXavmdGA/s6iq88VRcqkopRadmmr2u9Vy6ttWvJqK5opE43Mc2r4ShltGvbDwqObg0mrySTtondqKS97lV2+Vtn69VxGYUAVdU1TTND0y81vW9RtdP07T4JLq7u7qZYobeGNSzySOxCoiqCSxIAAJNAH8+n7fvg3wN4a/aW8VeL/hb8UfD3jTQPGOozeIku9I1m2vHsr25laW4tpPJbjZKzMjAEbHRSxdXwAfUfwJ/4KP/ALQeo/FP4P8Aw08PaFrfxB0G18P6bpPiq2jWPVNU1K7lXfNqAuY4kZXgjx8khPEUvnyM5MyqrKnTqKHOne1rXT2TatJRd4u6drxdrqTTPQll9SdB4jDwnKMIp1HyxcYOUmo+9Cc1yyXLZz5JczcHBNa/rlTPPCgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/wTh/5Mm+Fn/YNuf8A0tuKAPpOgAoAKACgAoAKACgAoAKACgAoA+Nv+Cr2neOh+ymPGngTxVJoE/gfxTpPiG7uIb2W2neJGeCIQtGPmkW6uLWUBioHlbgdyqCAd9+wH8b/AImftCfs0aB8R/ipoVvZ6rJPcafDqMEyFdbhtmERvzEnFu7SrNG0fALws6hUdFUA+i6ACgAoAKACgAoAKAOO+L/wm8E/HP4b638KfiJYT3nh/X4o47qOC4eCQNHKksTo6EEMkscbjOVJXDBlJUgHwRoX/BJjW9R8f3ngn4n/ABa1jWPgZoTy3ngzTrTWpo7+wkklWRrd7aSJ7VEYSTiWaLEkjxxyDy97oAD9BPh38OPAvwm8H6d4B+HHhix0DQNKiEVrZWiYUYHLuxy0kjdWkcs7sSzMSSaAOjZQwKsAQRgg96APlX4n/wDBPf4b614CvfBvwD8Uap8E59S8R2/ii+uvDSbkvb2CRXg80F1mEUDBnt4IZooYZX8wRllXAB5j8P8A9vz4qfD/APaKvfg9+2z4M8J/B7QbnQJdW8Os13Lf3J/fhbeO4v455YZcxR3QaTZHukixhSwQgHv/AMAP2yfht+0d8QPGHw/8DeG/F9tL4Rjhujqeo6SYbDULOfBt7iGUMdqzIRLEsoR5ISJFBAkCAHvVABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9ABQAUAFABQB8A/Bv8A5TIfHb/sn9n/AOiNCoA+/qACgAoAKACgAoAKACgAoArS3bCQ29rAZpV+9ztROh+ZvoegyfagB9rb/Z4thbc7MXdsY3MTk/h2HsBQBNQAUAIQCMEZBoApx217ZgR2kkUsK8JFLlSo9A4zwO2R070ASQX0Ur+RIDDcYyYXI3Y9R2Ye4oAW5ujE6QQxebPICVTdgBRgFiew5HqfQGgDK1OGRUdtSxcB4jt2sVRGGd2F6HCksCcn5T04FADobaC3hmvEubiMxzNHsjCyMCG2qBvBOSNvfHzdhQBZuLa4+ywSXObiSF90oX7zITnAwBnHyngDds6c0AT/ANpRiby3t7lELBVlaIhCxIAHqOT1IA/SgCO9EtkJb62lUBsF4nUsJH4VdpBG0nAHcdOPUAfcDzNStI2GVVJZcf7Q2gH8mP50AXKACgAoAKAGSs6RO8UfmOqkqmcbj2Ge1AFW3sfNjWXUA0szp+8R2yikgZUKPlx79fc0AMlhhsr9L1YSsTo6yuvRWO0hiOwwvJ+mfWgC+CCMg5BoAWgAoAKACgCC5vIrYqm1pJXICxR4Ltz1wew7k8UAR51SYKVW3tsg53ZlPt02gHr3NADZNKhnB8+5undhgt5xXjGCMLhf0oAuKqooRFCqowABgAelAC0AfL/7evgHVNS+Hmj/ABe8KzyQa/8ADfUU1KF413sIHkjDuE2sGaORIJMthVRJSc18zxPhpyw8cXSfvUndemn5NJ9rJn7f4GZ3hqGb1+HcwinQx8HTlfT3kpWTd00pRlOOmrlKNrHi2txah4s03w38UvhbYX7eIftk2p+D5tRuTIL23uJLye50yHzgElvNOu1u2jhAVDZRxkeeEdV7sn4qw+SUMRDG05PLsdySqSpRgqtOpTa5XeUbSg5LWMmryu4yjeSqfBZnwhick4o/s3E4inHEYT2kaftvaPDyVaEVJyVKanSdWjpzxU5K8YyjdKdL1z9kD9onxh4z1TxZ4K+N3jfQxr2manBYafa3ctnaai10Vf7Ra+RHs3+XthYYjz+9PzMNoX38a8C40Mwy6pfC4mClDnlFyjUTaq0naMLum+W3u6xkpc0k0ZZnlE/ZVMNSw/8AtGEk1WlRjUdGVCUYTwuJvKdXl9vFzuvaJRcHF04NO/1bWB8kVb+5khWOGAfvriQRpwDt7liMjIABP5UAS29utsjKGZ2di7uxyWb1P5AfhQBLQAUAFABQB5b8afizY+E0tfh14Z8b+HdI+I3ipUh8OW+sJNJCXeVY98nko/l5y4jLgK8i4+bawHl5jjo0LYelOMa0/hve29tbJ28r7vvqfdcG8K1c1c84x2Fq1Muw93WdPlUrKLlaPNKPNbRzUdYwd/dvFml8EfCviPw54Dsbvx9pmiweNtXjF54judNsYLc3N2xJzMYQEklVSFZx8pYMV4IrXLaFSjQTrpe0lrJpJXfnbd939xxcaZpg8xzWpTymc3gqb5aMZylLlgrfDz6xi2rxi9UrJ6o9ArvPkgoAKACgBrokiNHIgZWGCpGQRQBBp8pktgrMWaJmiYlsklSRk+5AB/GgCzQAUAQ3U/2a3ebZvKjCr/eY8BfxJA/GgBtpaLbjzHCvcOMyyd2Ppn09B2oAsUAFAFL5r+7zhhbWze2JZAf5KR+f0oAu0AFAFa70+1vcNKhWRPuSodrp9CPr06UAQpc3Fj8mqSIyfw3KrtXtw46Kc9+n0oA+ef2z/ij4p8P6X4d+FXw8uL2DxJ4zvFjWa0lMMqwh1VYkkBG1pZWVchvuq4PDCv0vwxynBY3H4jMMxUXRw0OaSlrq72bjZppRjNvqnytJ9PjOO8XisJl1Gjg3JVK9Tki1ZX5UnJX5k4u8qaWlmnLVWs/ItW/YD+JGn+Gp/Ftp8RLS/wDF1uGvTYQwyASzKxb93dswYynAILRqN5wWA+avvcP4v5XWxSwU8K44Z2jzNrRNW1ppW5V1Sk/d2Teh8XW8OMfSw7xMa6lXWvKk9XvpNu9+14rXqtzqf2QP2kPHWq+NJPgp8Vri+vb9lmTTrm9Qi8t57dWaW2uMruY7UdtznerIVO7cNnkeJHBOX4fALPsnSjDTmUfgak0ozjrZatK0dGmmrWd/S4J4pxlbFvKMyblLXlb+JOO8ZdXom7vVNWd7q32TX4cfqp83eK/21vDx8ST+Evgv4C1r4oX9kvm3cmjCT7NHGGZZCrpFI0m1vJwyoY2EvD5ADd/DmCXENaU1NU8LB2nXlpBStfkh/wA/KlrPkj9l3utL/Q8T8O4jhDAQqZq+XG1lelhVZ1nFtJVKiulRo35ouc9VOPLyPVx0Pg9+2v8AB/4oxSW+r6hF4P1K3hEsker3cUdrJkkFYrhioYj5SQ6ox3fKGCsR7cuF547DfX+H6qxtBvlvSjLni7J2nTa5otp+durTaR8nmtTG8L43+y+KcNLBYjl5lGo48slzON4VE3Gaut01fpdalL9on9orxx4U8caP8GfgloFlrHjHVI/PupboCSO0jKMUQJvUCQhTKzSMESNQSGD5TzMq4fxmcqeJ51QwlL+LXlFzUW7csIwi1Kc25Rvsoxd3dtJ+/TxmR5LhlXzWMq+JqfwMLCapSqxTftKs60oTjTpRUZxjZOdSonGNlCTOa8Jfs/8Axw8dePvCXxD+OXxOttak8KzJe2llZwRrHFKpWRUZ4kjjBEixlyiPvEYTfjDL6+LzbL8nw9XL+HHNqrpUrVeV1JQ1/dwjGMYwhLRylbnkvdklaLj49OrLNrYnOcPTpuGtOhR9oqUajSXtqkqlSpUq1Ix5o04cyp0378b81SM/qy1ufPVo5AFni4lT0PqPVT2P9QRXyJoT0AFABQAUAFABQAUAFABQAUAUJGNhfowUC3vG2vgfdm7HOf4gMdOoHrQBfoAKACgD5XPhX4q/s+fGTxx4y8BfCI+O9A8cSx3scsOoxw3dlOzvJLEfkJVPMeQ4CFSvkZcsrCv17+0Mn4xyPB4HMcb9WrYZOLTi3GUUkovezdkteZO/P7tmmfnH1PMuGs1xOLwWF9vTrNO6klKLu21ttdvS1rcvvXTOv1q6/aa+LGmz+Dv+EFsvhTZ3iNHfa3LrcGr3DQMpVoreKELtkOfvsy4AJVg2K8XC0+FOHqqx31h42UdY01TlSjzJ3TnKTd0uyTu901c9SvPiHOabwvsVhYvebnGo7bNRUbWfm2rLZp2PVPh18PvDfwt8Haf4I8KQSx6fpyEK0z75ZnZizySNgZZmJJwAB0AAAA+QzrOMVn+OnmGMac59tEktEkuyXq+rbbbPo8ry3D5RhYYPDL3Y9929235t/LoklodBc20d1GEkyCrB0YYyjDoRnvXlHoFCK6lW4JmixcxoFuI1yTIgPyyJ6gZbjrye4AIBpI6SIskbBlcBlI6EHvQA6gD8xv8Agsd8RrrVG8EfAddb1Xw9ptwtx4hv7m700ppeqSRjZDDHd7gzywAyO8QUoftEBLbwi1zYqtUoQ56VNzfZNL56/lue1kOW4LNcV9Xx2Ljho20lKMpJu6Sj7ibW7fM7RSWr2PkXwz/wTI/as8TeF/D/AMQfA+keFvE+lavdyrG+ieKrC4MMMUuz7QZfNWB1Yq2BFK7gqQ6owxWsW6lO7Vm1t2+7scFWMMHi3GElUjCWkle0knurpO0t1dJ2eqT0Pqr9mX/gnP8AtKeDv2r7/wCJPjHxrf8AhjwZ4a8W3OrWdzaauovPFEXnuY2MMMkgihnRVE6zsHMchj2tlmXCjhIU1CU0pTikuZrXRd9Xrr16nq5nxFisZUxVLCylRwtapKp7CMn7Nc0rpcqtF8qUUnyr4VorK36k11nz4UAFABQAUAFAHkX7X3/JqXxi/wCxF1z/ANIZaAOJ/wCCcP8AyZN8LP8AsG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFABQB+d3/BT6Lxd+0F4i8Cfso/CtvCWv6t/blvrepaNDrZi1yF1tLs75EeP7Pa2qW7OxkkkaR3ngCxAYMoB+gHh3QdN8K+H9M8L6LHMmn6PZw2Fos1xJPIsMSBEDSyMzyNtUZZ2LE8kkkmgDRoAKACgAoAKACgAoAKACgAoAKACgDy342/sv/AT9ouCxi+Mvw207xDJprhrW6Ms1rdxKA37sXFu8cvlfOxMZfYWwxXIBAB4v8fv2JfA0fwd8JaD8INb8a/D/QPhRqs3i7+xfBfm3t9rPlD7Q0cMcsuZtRMkMYtp5WkMbMVCsGAAB6N+yH+1p4H/AGu/htJ418NW8ekaxpty1prWgPeC4uNNcs3kszbULRyxruSQKFJEiAlo3wAe6UAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgCC7neGMCIAyyMI4wemT3PsBkn2FADBZylSs2oXEgYcj5U/IqAR+dAE0MENvGIoIljQdlGKAJKACgAoAKACgCK4toLuPyriPcucjkgg+oI5FACW1na2abLW3jiGADtXBOPU9/xoAr6xFDJbI9yjNDFKrybeoXkE/Tnn/ZzQBFeWrNvubRPPiuVUyosnJxjbJGTxuA+mcD0oAsabepe24cSBmT5WIGM8ZDY7ZBBx2zjtQBYmhjuI2hmXcjdRnH057GgDNs0LGaW+mldbKVkjVsngAEMQPvNgjGfYjk5IBDc3Gqxzw6klsgSQeQttI+H5OQfRSce+OM+wBow6layv5TM0MoCkxyrsbk4GM8HnjjPNAFqgCK5uYrSPzZjhc4/wAT+AyT7A0AS0AFABQAUAVre3ktpXjjEYtm+ZFUbSh7jA4IPX6+vYAs0AFABQBXunu8CG0h+ZwR5pYBY+OuOpPtj8RQA63tYbZSI1JZsbnYlmf6k8mgCagAoAKACgCC+sbLVLK403UrOC7s7uJ4Li3njEkc0bAqyOrZDKQSCDwQamUYzi4yV0zWhXq4arGvQk4zi0007NNapprVNPVNbHyT8YPgho3wVh+IvxfNh4g8b+FvEFjbWt94PS8W3hhHnALL5qjfDDaIIxbmBDLD0DJGGYfM4nB/2NKrj6d5xkknDS29tf7sVokk7LTa5+35TntPxQwmA4NzBUsPVhKTVdxk5P3b2Sur1Ksryquc4xm1za1OVOp4p+HPw01vS4PiD8V7XxdDP4Ru9Mhe61XXGjvotPu1idEugke1DZm+blR5shtFaSV2eTPDlebYvhjPKWFw03HCVE6sqMvepp+zkpc1OTcJO0bXa0TtGzjFr86nSrSdbJMFGnVqyn7KFanCKrP94nH2WIio1lGUkmlz8rTs002n0P8AwT88ZSa78PvFPhnUfFN3rN/ouumZPPlnlEVlPCgiMbSgbUaWG6OwYKncSq7hn9M4iwVHL85xFPCw5KU+WcI32i48srqyUJe2hVU6SUVTmpQUVFI8/Ma2Ix+T5djcXaVZQlSqySSvUhNzj715OrH6tVwzp13Kbr0pQm5yk5W9D8J/tV/s/wDjTxdc6DofxIs5b2G3mdPPt7iCHyoEaWeUTSxrEFVFLE7vuxluQM18nRzzL8RU9lTqK+vRpaavVq2iPosz8LOL8nwix2LwUlBuK0lCUrzajFcsZOV3JpJWvdpbnsYIYBlIIPIIr1j8+atoyC61HT7Ga0t72+t7eW/mNvapLKqNPKEeQxxgnLNsjkbAydqMegNTKcYtKTtfRefX8kbUsPWrRnOlBtQV5NJvlV1G77Lmkld6XaW7RYqjEZNNDbxPPcSpFFGpZ3dgqqB1JJ6Ck2krsqEJVJKEFdvZI8V+Mf7Xfwf+Eegxah/bsPiPU9RsTe6RYaVKJkvULvGr/aFBiSPzI2UtkkbW2qxG0+NmGfYPAU+bm5m1dJa36b7Wuv8AgM/S+EPCfiLizFuj7J0KcJctSdRWcHZSa5HaTlyyTSsk7q8op3MD9l+y8f8AxUsR8cvjr4W0JdZnuRN4SmTSYoLuy09onBCygmU27+fJsSUlvvPlg6Ec+SxxGOj9ex0VzX9zRJpWfXezu7J+vVHreJlbKeF6v+q/C1ep7FRtiE6kpQnUUovWLSiqkeSPPKCS2hZOMk/oyvoj8cCgAoAKACgCCGd5bi4j2gJCyoPUnaGJ/Jh+RoAimZrW9hdSoiuWMcgOB8+35W+p27fyoAuUAQ3dwLS1muWGREjPjOM4HSgCK2s5FZbi9mM04zjHCJnsq/Q4yefftQBJPfWts4ill/eNyI1BZyPUKMnHB7UARNqSZVYbS7lZjjAgZMe5L4H60AJLJd3K/Z0tJ7cSfK8pdMovcjaxOew9M57YoAtxokSLHGoVUAVQOgA7UAOoAKACgBCAwKsAQeCD3oA8V/aU+E2sePfB0E3gZIYfFXhy6TWvD85AWSKeJg0kMUpB8syKFZV+4XhTdgDcv0XDOcRyjFyjXV6FaLpVdWmqc7KUotJvmj8S0d7cul+ZcWPwUcbSTu/aUpKrTVk4urTu4RnGTinCTvCT5lyqTn73LySf+y18fIfjp4EM+p7Y/E+iFLfWI0i2RyFt3lTx8n5XCtleCrq4xt2s3FmmS18o5JOaq0aivTqxVoVI90rvlavaUHrGXdOLfq42thqmLqU6VKVCpBtVKE3epQmt4Sdo80ftU6kU4zg4u6mpwjB8df2YdB+Kmow+OfDF7H4c8c2DwzQaosReO4aHmJZ0zgkEKBJgsFVQQ6qqj6XhXjatkVOeAxsXWwk04yg3sne/LfRXu7rRSfVPU+Oz/hqGayhi8JJUsRB80ZpK7atbma1drKz15ei1aPn/AOIfwI/bH8YeGdN+Gmv6nqmuw6dfMP7Zh8UQJY3NlLFEDFcQusdzO0bqx82Uu2GkUK2Qx8/MuFuCs2nDEUsyrUaKd5UJQlOTjfWCqJ2V0rRlOVRxveTey/RuFfFfibhOdebyTC1sTOCjGvFqmo1Eny1fZtNJXd6lOlGjGfLHlUba/Rv7Nn7Ouh/AHwo1sJY77xHqaRnVb5C3l5XJWGEHkRqWPzEBnOWbHyok8R8QU819lgcBSVHB0Fy0qa6L+aT1vKW71du7blKXx2CwmLliK+a5tWdfG4iXNVqPrLslolFbJJJWskklGMdf4p/s6fCT4xSnUPGPhlf7WW3kt4tVspWt7uMMoAYsvyylMAoJVdVOcDlgfCyzMMZkuL+vZbVdKrZrmj1TVvei04ztvFTjJJ6pJ6n0FbF/Xcv/ALIzCEa+F5lL2dRXSad/ckmqlLm2m6M6cpx92Ta0IPgv+zh8OPgWb658KRX19qOoYSTUdTeOW5WIc+SjIiKiZG4gLljjcTtXb6+d8WZzxFFU8xrucE7qNoxSdrbRSv5OV2ru1k7Hg4PJMpyyXtMvw0aUrWbTnJvVvVznO26Vo8qaSum1d+pV86ekV7i2ZpFurfatwg2gnoy5yVPt79j+IIA+2uI7qLzY8jkqysMFWHBB9xQBLQAUAV7yS5gVZ4IvNVCfNjA+cr6r7j07898UATI6SIskbBlYAqQeCKAHUAFABQAUAFADJoo54mhlXcjjBHtQBHbzEs9tKSZYcZJH3lP3W/HBz7g9sUAT0AFABQAUAFABQBXvLUXKKyNsniJaKT+62P1B6EelAFOB2swl6uVsrkCSSNzzA7c5B/u5PI7HnpmgDyT9rP8Aau8CfskeA9M8Z+NtP1LUjrWqx6TaWemGA3PKPJLPsldMpGiHOM5d4lO0PuUGtXqfi3+zp8HfHP7XHxt8LfCnVPFXibxT4Str1rvWdbginmbSbSRpri4Uy3AKwGaXzipbcplmLhXdnVuLDRdSf1ipTcJ25bNp6J3Wza6vs/wPpc6rwwVBZNgsXHEYZS9qpRhOFpyioyTU4xle0Y/zRsk003JH78+C/Bvhn4eeEtH8C+DNJi0zQtBsotP0+zjZmEMEahUXcxLMcDlmJZjkkkkmu0+ZNqgAoAKACgAoAKACgDyL9r7/AJNS+MX/AGIuuf8ApDLQBxP/AATh/wCTJvhZ/wBg25/9LbigD6ToAKACgAoAKACgAoAKACgAoAKAPzi/4J9+C/ilr/7Z3x6+MP7RXh3U9F+IUFrb2cFnc2whtjZ3NzNH5lsHBaWGNdLihhmjdkMYfLSFg1AH6O0AFABQAUAFABQAUAFABQAUAFABQAUAFABQB8M/Eb4AS/stftSyftj+AfiDoXgH4Walp8r/ABPtL7T/ADrYASwIsdpBAPMaa6laNlKgGOVJXLSiUW7gH2T4F8c+E/iZ4P0jx94F1y31jQNdtUvbC9gyFliYcZVgGRgcqyMAysGVgGBAAN2gAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/ALGBP/SyzoA+n6ACgAoAKACgD4B+Df8AymQ+O3/ZP7P/ANEaFQB9/UAFABQAUAHSgCmdThkylnHJcyDnagwACMgljgAH9e2aAJQ16Vy0UIOegkJGPrigCGyieZhqNxIrvIg8sL92NDzgepPc98DpQBdoAKACgAoAKACgAoAKACgCK7Mi2szRKzOI2KhepOOMUARacT9kjT7PLCkaqiCXG8qFGCQOh9qAJobeG3QJCgUABR3OB05PPc/nQBJQBWuNPs7olpoFLn+MDDdCOT3GCeDxzQBXjh8mVNJmlM9vJAxHmffG0gYyMZ4P1GKAIbzT4S8kdzHO1syoVm83eYWBOT8xJX+HkDHrxQBp27xPAjQzCZMYDhg27HGcjrQBFqFjDqNsbad2WMnJ24zx9R+P4UALp0iTWFvJHMZQY1+cnJJxzk+uevvQBYoAKACgAoAKACgAoAKACgAoAKACgAoAKAOf+IHhT/hOvA+veDl1KTTn1nTp7KO8jDFraR0ISUBWUsUYhtu4ZxjPNc+KofWaE6N7cyav28/ketkOaf2JmmHzJwU1SnGTi7Wkk03HVO3Mrq9na97HxZ4Hk+GkV9rnwevNYHidNMhtvA/i6+1SCSzvNTin1SUafdWqTO+x7LUNSlt2RgrH7TFMkpMUcLfnOa0KeIwbp0KjdXCczu01zRb99WvdW2fSy7PT9a48wGbYLG4XjmFGNCGLcJQjBqapThGPs7yUVCXtIxVSNl/MmvdvLN/ZU0L4ivr3xB+AN94gtp/BMMep6V4oSKEidp5YpLMTWD7G2ys0AwspKeWrnaWGa/Z+I8wwnFOU5bmka9WVfE0LVozaaioJ0eeOj5XUqwm0r8rSlJwjJu/57lEXwhjMXWlgqNOGHxFOrg50ouPM6vJjPZVlzrmhRoVqcW0vaRk6dNVZ04q3p/7MHwn8DeB9J8Wfs7/ES58JXvxAvbG8F+dGiY3i6FdxQqYZLsxIwbexbyyxIVomxjBr88yfAUMLCpl+IcXVad+XfkdtG7L1t2sz9P8AEji3Nc+r4TjHKIV6eAhOHJ7VrkeIpub5o01OSasuXnSs5Kcb3ujurH9lT/hFI/h1pvw4+KviTQNK8DX9xdXlu7tJLrcM11DPJbzPE8Kqh8rZjYy4bJUkEt2RyP2HsY4arKMabbf95NptOzWmltj5mt4o/wBqPMq2c4ClWq4uEYxdklRlCE4RnBSjUbl73NfmTutGla3kHx1/Yq8cQ+Btc1H4d+PfEvinUZ/FJ8RDQ7+/Jg8kxugKCUu094gZQZXkXzI1I2lgoPkZnw7XVCcsPUlNuXNyt6W+d7yXdvVdL2P0TgjxlyueaUKOcYSlh6aoex9rCHvc109eVRUKMmm+SMHyTad1Fya7nwJ+1Z8ZtT07WD42/Zb8X2N7o+gm+iFrY3udTv8AzYolgiie3zEjmR3J3SFEjc/PtNd+GzvGzjJ18LJOMb6KWrulZK2m993ZLqfK554X8NYavRjlmfUJxq1eV806X7unyzk5ykqlpNKKivdgpSkl7tylrngb44ftNa/oV34qvNR8D/DjXfD0sPiHw0j7L60nS53JG6zwkPLI8cMiy+WmyJJEwu7M8VcLjs4nCVVunRlF80eqafmt3o07Kyuv8XRgc+4W8N8NiaOXxji8yo1oulXtenKMoWbThPSME5xlDnlzTcZXdrUvVvDn7LXwT8Ojw3M/hP8Ata/8KWCafpt9qdw88scayvKGK5EW8SSyOGCAqW+XGBj1aOTYKj7N8l3BWTevVv0vdt7HwWZeJfEuY/WoLEOnTxM3OcaaUU24xi0nrPl5Yxi1zNNLW93f0xLXUIsRx6grRrjBmi3ueMYJBGfr1r1D4MVrm5tdpvI1ePndNEDhf95TkgY75PvgUAWkdZFDowZWGQQcgj1oAdQAUAFAFbThH9kSaNSPPzMc9SW559+cfhQBHqAVnginIa3nYwuh4+YjKsCOQcrj8c9qAJNOd5dPtZJGLM8KMxPUkqMmgCveyz3v+i6a8ZaORTLI2di7SDtyOp9R6ZzjIoAiu/7XZltYL22M0iHcgjZQi/3s7iRzgD8SOhwAXLD7MYy0NqkEmcSxqoBV8cg+v17jBoAtUAFABQAUAFABQB43efta/BPTfiTcfDPUvEM1pd2jPb3Go3EHl6fFdpKY2tmlYgq4IJLlfKAH+szxX1dLgbiSvh44ylg5SpSipqSlB3T1VoqfO21rblPHqcS5DSi4VMbBVlNwdNxqqStvJydP2XLfT+JzX+zbU9Z1PekC3KEg20iyt/uDh/8Ax0tXyh7B8meL1sfgh+2/4e1/RrW4i034k26WmpQW0UJDXVxL5JKoApRTPHazyOSWLNM2WyVr9DwPsp+H+MVVyfscRCUdfdi6jhTtFdFeU5yWnvSctW2jxsQ8VnHGODpw9mpPC1YSb5lOUcPCrXUpy15pckI0ae9oU4U2oxjGR9fV+eHshQAUAFABQAUAFAFG8imtphqFnEXI4niXrKmOoHdh29Rx6UAXI5EmjWWNgyOAykdwaAHUAFAFOT/iXuZ0XFs2TKAP9Wf74Hp1z+fqaALlABQAUAFABQAUAVL6CUmO8tVDT2+SFLECRT95f6jPcCgCxDNHcRJPC4ZHAZSO4oAfQAUAFABQAxJA7SLjHltt+vAP9aAH0AU7dVhurizYfJIfPQHkEN98c/7XJ/3xQB+cf/BaLwH4i1f4Y+Adf0Pwtf6pYeHtSvnvL22sJJhpdvLHCv72VVIiid1jA3EAsqjnAxMpxgrydl/novvZtQw9bFT9nQg5Ss3ZJt2inKTsuiim2+iTb0Rv/wDBFDS9Mh/Zs8Y63Fp1qmo3fji4tbi7WFRNLDFYWTRRu+NzIjTTFVJwplcjG45oxP0JoAKACgAoAKAPKNTiufH/AMTxf/D3W7bS5PB8Utjq+sx2hn+2XLZMelt8ypLFE2ZJxnehdEjeJ3lZQD1SEzGGM3CospUeYEJKhsc4JAJGfagB9AHkX7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/AOltxQB9J0AFABQAUAFABQAUAFABQAUAfCP7T3/BTjVPgT8aPFHwP8G/s96l4z1TwxbWst1qCaq0UUZuYLeSJ/Jjt5GKB7uGM5ZMuQoOWFAGj/wTX/Zl+I3w5svGn7RPx80yO3+JPxXvDqEsFxaRR3ljaSyNcSeaqqv2eS4mk8yS3AAQQwBgrqyIAfbtABQAUAFAH5JfszfEj/gqJ+2HoOveNvhr+0l4Q0Wy0jUvsE9tq2mWsBDtGsgEYi06bKBWAyzbuOc9SAey/wDCjP8AgsX/ANHafDL/AMBk/wDlRQAf8KM/4LF/9HafDL/wGT/5UUAH/CjP+Cxf/R2nwy/8Bk/+VFAB/wAKM/4LF/8AR2nwy/8AAZP/AJUUAH/CjP8AgsX/ANHafDL/AMBk/wDlRQAf8KM/4LF/9HafDL/wGT/5UUAH/CjP+Cxf/R2nwy/8Bk/+VFAB/wAKM/4LF/8AR2nwy/8AAZP/AJUUAI3wP/4LEoAX/a3+GKgkAZt0HJ4A/wCQRRsNJy2F/wCFGf8ABYv/AKO0+GX/AIDJ/wDKigRV1b9nb/grvr+lXuha7+1F8KNR03UbeS0vLO7sIpoLmCRSskUkbaOVdGUlSpBBBINAHlnju/8A+Ci/7Ot94R+Hfjn9sf4TeCoNbvItA8M2i6dCloYY4ogjosGkFba1RpI4N0gjVWHQRjfQB67/AMKM/wCCxf8A0dp8Mv8AwGT/AOVFAB/woz/gsX/0dp8Mv/AZP/lRQAf8KM/4LF/9HafDL/wGT/5UUAH/AAoz/gsX/wBHafDL/wABk/8AlRQA/wDYM/aC/aj139qT4nfs2ftFeP8ASvF8nhDSpLn7Xa2EMQhu4LmCJhDJFDCXidbglhJGTmNNuz5w4B+gtABQAUAFABQAUAFABQAUAfMH7U3/ACXL4Ff9jAn/AKWWdAH0/QAUAFABQAUAfn58I5mg/wCCxvx1kEEko/4QGzDBBkqPI0LnHf6DmgD9AIZoriJZoJA6NyCKAH0ARzTw20TTTyrGi9WY4FAFaOTU5SJljgWJjxHIGVwuepPrjnGB1xnigBfsMk4/4mFx5y5B8pF2R/iOSfoTj2oAuUAFAFGFYrW9+zxTOkaQ5Mbt8vLfLtz6YYfivoMAF0EEZB4oAWgAoAKACgAoAKACgAoAKACgAoAKACgCK4torqPy5QeDuVlJDKfUEcg0AZry6pa3lubgAxMRHJKr5R+wJUj5D344JwPcAGklrBFO9xGm15AA+CcHHQ46Z96AKmryeZbvYQxNNPOpwi9APVjkYHGOvPagCS2ubcTPEYJbeWZtxWVcbmwBweVPAzwf60AXKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA/Pv4rXNr4/wD2n7jxP8AdQl8R6d4igs/C/jK70uG4uFsWlyi3aSxGMxrHHbQyx3EMyhJrUEuu5d/53meY4WjmbxVGpFwkuWd9U1s7LTmVraRvtrvr/T+FwlHLvDn+xON6c6Mk51cMvhnJpcyjop8knOUov2sFaNRW1Xu9L8F/EnxN/Zt8a6jD8W9E/wCEj07xZeCDUtc0mOO5k0ea3WeaSW7ljg82UPDJHc7JpAY7cF0QN5kdfqGVYnL+LsPhp5RVcaijSoxoVIclvZ0oqSpVbyhNOblJRm6U5SdRxUpNRf8APmY0qGHo4ycKfLhqUq1d1YTUoyderePPhny1KMoU/Z0JzoqvR5qdJSlCnesd/wDDiOHx5+2Z488R3/wptdKTwdarbWmtXNpd2+oXFwUW2Eh/emCSJ4ludjbAzReSR0bHxuEtis4q1JUkuRaSaak3t3s01e2m1j9U4hcsj8OMvwlHMHU+syvKlGVOVKMU3UaXuKpGcZOnzrmaU+ddVf6gr6Y/DwoAz3tre81SYXEUcyxQRgK6hgrFnzwehwB+lADoFXT7j7N5arDcPmIqOjbeUP4KcewxxgZAL1ABQAUARxQxwBliXaGYsRk4yeuPT8KAJKACgAoAy7yVtJYyQSiQXEmVtWBJZj97YR0yTk5BGfTNAC3kt5eR7IbGVYMhmckLIwBz8ikjB75OCPTOKAG21rPdiWKUtHY73KIEMbybjuO7PIAJI4xn6dQDRJgtLcthY4olLEKuAqjnoKAK+nwSL5t5cAia5bcVP8CD7q/gDz7k0ALcWZaX7Zav5dwAAeflkUHO1uv5jkfpQBJa3cd0GAUpLGdskTEbkPvjseoPcUAT0AFABQAUAFAH58fs4fAvwN8evA3xMtLq0Nlqn9oW8miXgznTXCztEFJLOyMWKyozNuULzvVXH6XxrxRj8VPJsXCTjWhh6WIUovlvOqnzJpfZag4yi7pxk1bvz8MZHheE6+dYGUVVwlXE4nB1Kc1zN06Ljyyu7L2kXUU6c0oyhUipXtoeh/sw/tZ+HbLwPceA/jN4hj03UPDlu8dndyxMPtNnFEf3RIB3ToEKgYDSAoAGfO72uPOAMZKus1yqjzKqk5wg+flm93HRNwbe9tNW1GNkvgOE+LMI75Xi61uSTjTnUSp80FpHn96UYyS3XM105pNNtvwO1K//AGhP2lIfjZZw6nbaB4P0g6fCt3FCojmZZoY4FZCTLujlkuGJ5R38skqEZ+biWjiODeF6fDldQ9rXqSnNwk5Jwi003zKLjJvkSSi42hL3rvXsyOWB4kzutndGUnClThCCnHkkpyT57crnGUIrnV5TjNupBqFk1H7Gr8jPuwoAKACgAoAKACgAoAoTl9NlN0i5tHOZkUcxsesg9vUfj60AXgQRkHigBaACgCpHmxkETMTbyNiLjiIn+D6Ht6dPQUAW6ACgAoAKACgAoAoqTYXTI5H2e5fKHp5chHKnnoxyR7kjuKAL1ABQAUAFAEMH+tuP+uo/9AWgCBDc3xMsVw9vACQhRVLSc/eyQRt9OOeuecUAQXaXVrPaTtdpKqzCPDrtkYPwRlcA84OMD7ue1AH5e/8ABTz9rP4Z+P8AW5f2cvCXxA8VeG9d8J6sLfV9ZtdQkHh+4DJE0lvPHZySSXDQyhQ2+HfDJBKqBiSrcWMjUqckFTU4N+8n272ejs7P8u6+l4br4TCPEYqpi6mGxEKbdGVO+s9E4OUXzRUouUbqy1vJ2XLL6h/4JqfBvVv2fv2YtN0XxyZbHX/Fep3Pie9triRWS3M8cUcKLIvynMFvC5G4kNIwJ4wO1K2iPm5Sc25Sd2z6gsfE2japFJPpdxJeRxTS27tBC7gSRyNG65Axw6MPw44oEW4bySWQRtp91ED/ABuEwPyYmgCw7pGjSSOFRQWZmOAAOpJoA4G6+Ovw2tZ5NNGsTXWsJPdW66NY2sl5qDtA7ISLeEO4VwodGYAMjK2cZIAK8kPxO+I4ktryG8+Hvh8syP5dzFLrd7GU4w8ZeKyXLHlWklO3gxHkgHceH9A0bwtotn4d8PadFY6dp8Qht7eIfKiD68knkkkkkkkkkk0AaFABQB5F+19/yal8Yv8AsRdc/wDSGWgDif8AgnD/AMmTfCz/ALBtz/6W3FAH0nQAUAFABQAUAFABQAUAFABQB+U37JXxNn8G/wDBTj4qSftMR3nhbx746jm0bRIbu7je0US3FvJa2O9UxIWt4LVIJd6qwj2Yd5UAAP1ZoAKACgAoAKAPzh/4Ij/8kU+IP/Y0x/8ApJHQB+j1ABQAUAFABQAUANkkjhjaaaRUjRSzMxwFA6knsKTaSuyoxc2oxV2zP8P+JfDni3TV1nwr4g03WdPdmRbvT7uO4hZlOGAeMlSQeozxUUq1OvHnpSUl3TuvwOvH5bjcqrPDY+jKlUWvLOLjKz20kk9T5l/ax8J+Lvi5468P/CfTvitdeFdD1Tar2N3oNwlneahEk08RjvlQJM7DaDAZAFMQcBnAA+azyhWx9eGEjV5Ivo4uzau1aVrP0vpa+rP23wrzXL+E8qxOf1sAsRXp3tKNWDnCnJxhJSouTlBJ3aqKDclJxbjG7cfw4+EP7V2iX3iz4d+IvirrMuj+Zp2qaB40u70XUq3cM0LzQC2adpngkTzUaOVgh8oHGJGBWEwGa05VMPUqvl91xm3fVNXVr3s1dNPTTzZXEXFvAONpYPOMHgIKtarTq4aMeROEozUZ+0VNQU4y5ZKUE5Lmte8ItaXi34S/F/8AZ+jXU/2T5Tqem6trjXN94K1G1tpLRZJoHV7gXTtHMka+VbgR+ZweSxUla0r4HF5V72Vapyu4NK2q3vo7aLS/4aHHlXFfDvHsnQ4+Xs6lOlyxxMJTU2oyTUHTSnByfNNuXJqtElKzPjX9uvwx+0R8QvCWiwftI3Xh/S2uNSurnQtJso7J7vTmWMBnWSIPJ9mbeqlDOxYxqWAKo1eTiM2zfLJxrYzltJv3NNvJq79Lt+aP0DJ/D7w644wtfLuG/a+0pRi/rD57OTukpRnyxburyUacLr4ZLW31h+zz+3L4c8aWngj4Xax4a8V6n43vIrbTLy8jjsVtp5UXbJeM5miABRWmdEjBHzJGjnYretlfEtHHzhh5xaqS9OW6V+99bdvLzPgOO/BHMeE8Nis3w1aE8JTaaTcva8spKOqUORuLerUkmlzWXwr6Hs/jD8Mb/wCIFx8K7Xxppz+K7VSZNMLlZCQgdlUkbXYIdxVSWADEj5Wx7Ucww0sQ8KprnXT8T8xrcI55QyiOfVMNJYWW0+mrcU31SbVk2km2knqr9jXYfOBQB+a/7H//AClY/aL/AOwXqP8A6X2FAH6UUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QAUAFABQAUAfAPwb/5TIfHb/sn9n/6I0KgD7g1DxJonhaS6bxBqtvY2zbrhJZ5cKFWNnl5PACrFJIewXJOADW1DDVsVP2dCDlLTRJt6tJaLu2ku7aW7Mq1elh489aSitdW0lom3v2SbfkmyXRvEdn4r0yHWvCeoaff6bc7vJvorhZ4pNrFW2eWSGwwKn5gQQQRxVYrC18FVdDEwcJreMk01dXV07NaO/oKhiKOKpqrQmpRezTTT6brTcvx2KFxNeFbiYHKsyDCf7g52/XqfXgY5zYtUAFABQAUAZ19bx6hdLYToojWPzclAWfkghSfu44yRz8w6UAXookhiSGJdqRqFUegHSgB9ABQAUAFABQAUAFABQAUAFABQAUAFABQA2SOOVDHKiujcFWGQfwoAz0nt9LuntDKiQOokjjGdysTjao7gkEgDnr7UAWLESsZ7iWJ4/OkDIr43BdijBwTjkE496AJ5YYZ0MU8SSIequoI/I0AVGkvrJSPs/2qFCcMrnzQv+6R8xHTrk0AWWurZYFuXnjSJgCHZgAQenJoAejpIgkjdXVhkMpyCKAHUANkjWVDGxYBuu1ip/McigCury2s3l3DtJFIwEchx8pPGw4/Q984POMgFqgAoAKAK95eJaR52tJIwOyNVJLEewBOOmTjjNAC2l5BeJuiYhlxvjYYdD6MO3SgCegCKf7UMNbCJsdVckZ6dxnHfsaAITf+TgXltJDyRuALpj13DoPrigDk/i3qviDS/htr/iLwR4g07T9UtLCR7W6vp4Us0cdGkeUiNACfvMceueMcmPnUp4ac6TSklo3a3zvp959DwphcHjc7wuHx8JToymlKMFJya6qKh7zfpqfH/wAMbvxL4hg13QDr+j6VrfiTWtPn8Za94Tu7RJr2K9uYrKEWNxbFkRl8yWSSRANkuTnzJpGrzsoybLaMaGeZ3h44mVaUqdOKb9l7SCdSc6vJZP3XCMIKXvv2jl8Gvd4rZpisXxFLh3LK9ajhcNCDjGrze0hCcYrkhGteUbyUm5OOkFTUfd5UtT9pjxJN8Lvgp4g+BVnf6LLeS+KITEkFhFYXVzpFwrX/ANpaODZE0pu43gllVAH+86K8oY/rWDyKtmGOy/M8iwUvZ355Qg3yKrh1OpGmpyuqcasqdJJzbUfaNJu2nwuQZrluXUsywnEONjBqk6cJyScvZ4qVPDzqezjrWlQpVq1Xkp8spOim7K9/Xv2H/hnZ+AvgzFrJ0W/07UfFVydQu4tStBFeRKiiFY2barNEWjknjBA2rc4+bl3/ACrJ4OVOeMqR5Z1pObTVmm3t3te7jfZPru/13xZzh43OKeV0q0alHB040oShLmhJb82jaU+Vxp1GvilTvaKtCP0LXrn5aFACYGc0AVbuVpHS2tSrTbgxPURj1b+g7/mQATxCONRCshYqP4n3N+JPNAElABQAUAFABQAUAVZd/wDaVvjG3yZc/wDfUdAFqgAoAKACgAoAoXrPY3I1JQzQlBHcKBnaoJIccZ4yc+x9qALwIYBlIIPIIoAWgAoAKAPK/wBpf4qw/CT4R6xrkN20Or38TabpHlthxdyqQsi5Vh+7XdLyMHYFyCwr6jg/huXFOZxwcm400nKcluortfq3ZJ62vezSZ4ufZ+uHML9cUVOd0oxldqUuzS1a3bV1daXTaMv9kb4bar8NPgzY2niGC8t9Y1y6l1q/t7r78EkoVEQjAZT5UURZWywdnBx0HpeImdUM7zycsK06VKKpxcdmo3bfZrmckmtHFK3c87gzLKuWZVFYi6qVG5yT3TdkvO9km09b3N7xr+zf8EPiBO174k+Helm8eWWeS6sg1lNLLIcvJK8BQysTzmTdySe5z5OW8X57lU4yw2KnaK5VFycoJaackrx0tZaXS0TVz1MZkGV46lOlWw8Pfak5KMVO6vrzpKavd3SdpO3MnZW6Pwx4O8N/DDwjZeFvBunjT9LsJVCRb2kbEkuZCWcliSWY5J74GABjzM1zXF51i5Y3Gz5qkrXdktlZaKyVl/Vzry/L8PleHjhcLHlhHZb76vV67nT15x2BQAUAFABQAUAFABQAUAUIg2myeTI+bRyBEx6xEn7h/wBnptP4elAF+gAoAbJGkqNHIoZWGCCOCKAKkEk1pMtlcszxsMQzsclv9lv9r0Pce45ALtABQAUAFABQAyWNJo2ilUMjgqwPcGgCtbyvbTCwuZdxfJgc9XUfwk/3h+o59aALlABQBBc3PkYSOJpZnBKRqQCcdyT0Hv8A14oAxL6W5eWW3uJyq3JG61tjvkUbRnJAzkgdMY98ZyAclrHxa0aQX/hvwrrMd7rCQSW9taaDbDV7q2mHyKZVjbyoNrMvM7JHngtw2ADmfDmufGj4q6LBqUGlaL4VYx3FtPJeTyahc208UjwS7bZBDGjeakm3fK/+rw6kH5QD8Pv2ofDPxF+CP7S/jnQfG9ja3WqJ4puNehvNT0i0lTVYZblp4LrYVeMxTKVZoQSgJeN1yHWsnSi6iqa3Xm7fdez+aO2GYVaeElgko8kndtwg53Vtqji6kVptGST1utWfqz+wV8a9V/bH+HGpa1478T+M7DWPD90lleW9p4jt7a3u5DGrG5hS0SK8iTLMuyUtFyNryureVSqQcnBNXWtuplLCYiFCOKlBqnJuKlZ8ras2k9m0mm1urrufSPhT4IaRo2nT22qeIPFk8sl/e3CeT4v1WNBFLdSyR5CTpl9jpvJBJfccnJJs5yxe/s//AAp1O4a81bQb/ULlgA1xe63f3ErAcAF5JixwOBk8UAXofgj8HIIY4R8K/CcgjULvn0e3lkbAxlndSzse7MSSeSSaANfwt4G8MeCX1BfCek22lWmpTJcS2VpBHDbpMqBC6IijaWVEBGcZXIALMWAN+gAoAKACgDyL9r7/AJNS+MX/AGIuuf8ApDLQBxP/AATh/wCTJvhZ/wBg25/9LbigD6ToAKACgAoAKACgAoAKACgAoA/Lz/gqXdeIvhx+1Z8BPi/o3xC1PwyjounPetFJ9h01be/SSWdim/zQ6XWJYvLO6OBARIG2gA/SH4f/ABE8DfFXwpY+OPhz4q07xDoWooHt72xmEiE4BKMOqSLnDRsA6nIYAgigDoqACgAoAKAPzh/4Ij/8kU+IP/Y0x/8ApJHQB+j1ABQAUAFAHjf7Wc/xE0v4M6l4n+Gvii/0TUPDsq6tctY2qTy3NrEr7osOwCoGaOR25xHE42tnafGz6WJp4KVXCyalGz06pb/ctflY/Q/C2nlWJ4moYHOKEKtKven78nFRlLaSsneWnLFae9JaxdpLz39h/wDaN1f4q+C9d0r4l+IUuNe8P3sbfbrlYYFntbhWMa8EbnV4pgflACmPknOODhvN546jOOJleUXu7K6e3z0fTax9l41eHmH4XzPD18ko8tCtF+7Hmlyzg1zPVO0WpRt7zbkpbKx2v7Tnxb0rw74B1Twn4d1QXfiTVrAypb2ulRawkFmcbpb22JO2zm4tzKVYAzghWxiu3OcfGjQlSpu82uiUrLvJfyv4b2e5814a8KV8wzalmGMp8uGpzs3KpKg5T6RpVNL1qf8AFUFJNqDTaucZqn7G/it7Kz1DwB8cfEPw51OeM3Op6RoNxO2hw3kiq06WMKyRPBAZgzBWL8FRgbQK458P1eVSw9d0nu1G/Jd78qumlf1PpMN4v4BVZ0c2yunjqafLCpVUPbuEW1B1pONRTmoWTaUdbu7u2SfDb9k3xzf+M9N+Jf7SXxQufGGu6DfRX+kWVndSiytJozkN8ypwWWFykaRgtENxcEgvB5HXlWjicyq88ou6Sbsmvu8nZJarW5PEXipldDLauScF4FYahWi4VJSjHnlGXTRy1ScoqUpTajJ8vK7NepfHX9oXwJ+z5pGmat41ttWu/wC17l7a2t9Mt0llOxdzufMdECjKj724lxgEBiPTzPNaGVQjOsm+Z2SX/BaPhOCOAM14+xFXD5ZKEfZxUpObaWrsl7sZO71e1rJ3adk/kj9sX4v/ALP3xps/Dep6F8XtSnGhmVbrRbHSrpZL2KZoidkkypFG6iM5L7uo+UldrfJ8QY/L8xUJQrP3d4pPW9urslt1P6C8IuEuLuDamKoYrLor2tuWrOpBqDgpLWMHKck+bRRt6pO69E+Bn7FF7pureEPH3xG+J2oeJbHw6ftmhaFPaXEUVrGSXtyVncPAwPlSNAI1Cuu0lwCT6OW8OyhOnXxFVyUdYxs9O2706Nq2j01Pj+N/GalicPjcpybAxoTr+7VqqUG5PadnCNpr4oxqczvF8yUW9Iv2mf2Cv+Fha1J4++CmrWeh+Jr69kvtRt9TvriK0uLhmaX7QksSSSxOZcblAKEHIC7SH1zDhtV8QsThJckm7y+e8o9pdunVNNHn8H+NVTK8nqZJxBR+sUY0+WkkkvhSUaVTVKVJpJSdnK14yjUjKy+YF+HX7YHw91TX/H8nwZ1nXdUvYrjR7zX9SQXt7pErxq41O0O9pnuIwq+XcIkkancGYYIrwMFgsxwc6mOrU+aUduaLk29NVZ9Osr6La5+ucTcS8G8S4fBcLZbi/Z0azu3RqQoQhTSknCfNFp87fu0eVOUknJxVm5vB/wAeP2rvgHqXhvwz458c+M72C2KapPY+Jgl7qNxYtM7SLOZl89cqsgAdkZU2kFRtaqxOd5thsTB4j3U7PlSXw/i+jvd3XkY5H4XeHueZHXhkz9u0501WlOSaqWVrNKMWk5R5XGDjJW+K7v8AqvYXsOpWNtqNuk6RXUSTItxbyQShWAIDxyBXjbB5VlDA5BAIxX6JGSnFSXX5fg9V8z+Na9GWHqyozabi2nZqSunbSUW4yXZptNaptH5v/sf/APKVj9ov/sF6j/6X2FUZH6UUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB+YN9qvxK0D/gql8f/ABB8LdK0rUtU0zwFaXdxa3/nkyWiWmimQQiF0Zpc7CFLYIDAZbaD9FwvhMpx+YxwucTnCnPRShyq0m1bmck0o2vd20dul2eLn2IzHCYKVfLYxlOOrUru8UneyTV5bWV9r9bHc/CL4ZeOP2v/ABLN8QfiH44gfQtI1VRc6RmckA+SzwwRghIUkiUqZA5k3RgsGJ3V+3Z/nOXeGeDWV5Xh37WpDSp7ur95KU3q5SjJ35XFRs7RaXun5ZlGWY3jnEvH4+svZwlrD3v7rcYraKa0unzXV2m9T7M+DPwm0P4LeA7PwRodzLd+W7XF5eSqFa6uXxvk2jhRwqquThVUEscsfw/ibiHEcT5jLH4hKN9IxW0YrZX69W31bdklZL9VyLJqOQ4KODou9tW31b3dunZLoktW9X3NfPnsBQAUANd0jQySOFVRksTgAUAVxqemkEjULYgDJ/erx+tAEdtKb67W8jidYI4mRGcbTIWKnIB5x8o5PXPtQBeoAKACgAoAKACgAoAKACgAoAKACgAoAKACgCOedIFBYFmY7URfvMfQf54GSeKAKdgpW7mkvIUS8lGQVyR5YwMKT6HqOOecYIoA0KACgAoApR2MqToTcBoIpDLGhT5lJUjbn+6Nxxx7UAOeGa1ZpbNQyM254cYye5U5AB74PBPpkmgCeG4jnDbCcodrqRgqfQigCSgCjqSy3DQWMEvltI3ms+3OFQg8e+4r+tAFuKJYYxGrOwXu7FifxPNAD6ACgDO1O3Bkjun3bEBUsCQYs9HBUg47EA9Dk9KAGaXYS/ZoZdRmS4nQ7o5lPzBe3zg5YHrz680AalABQAjMqDc7BQO5OKAPOfj5deBdL+EPizV/G2nade6YNMn863nvhZG+IQkW6TgbhI5XagGSWIArgzSdKng6kq+sbPS9r+V+76H1vAmFzDGcR4OnljcavPG0lD2nJrbncdnGN7yvolds+KPA/izSje+F/FXgjwTJ4O+Gvh+50jWLy+uUvNRW2uX1aeO9097gMnmoYbK0u1JR/KnMCnd5kcddnBVTDYvBzwmAp89evyQp0G07cs5c1aDcbupSjNpqMo/uqtScrwpztj434DM8l4x+sZ5iOflXNOuoqClF04qMHCLajGTjbVO86cbWlON/s3w38RofHd5rWg+JPBU3hfxJpdr9vsbDU5YZLubSpSNt0uzOzLR7ZI1ZvLdVVjkivqc3ySeVYWGIwmIVahN8spQUlFVI7wd99HeMmlzRbaVrn53l2aRzCvKjiKPsqsVeMZWcnB7SVttVaSTfK0k3seoKVIBUggjII9K+ZPdFoAytS1C4aX7BpxxITtaQf3sZ2L745JPCj1JAoAo29lA3z3F5bzHkJNenzTIoPJVdwCrnPOTn6YyASab/AGlBcX2m2wsUa3bzBiFgHL5IHDDHGB3x74oAv29mZLfzHje1n8yR1ZSu8AuTg9QQeMg5H5CgCOy1eTYE1SDyJFZkeTGI1YdASehII56HsewANMEEAggg8gigBaACgAoAKAKcYaXVZZQ52QRCLHbex3H9Nv5+1AFygAoAKACgAoAQgEYI4oAoWz/2fcLpsgbyXybZyc/WM+47e3HagDQoAKACgD49/be1ez0P4p/BvVfE0ayeHrLUnurtHgEytDHdWjXIKAEv+6C/Lg56AHmvvuEcfUp5Rm2XYKPtMTWpR5adm3KnFuNbls1zTUKl4w3btyxnqjz8VkdPG5hl2aYufscPQqyjKspqPJWnBywqm2moU5VqVpVNIxV+edP3ZH1Vo/jHwl4ijt5dB8UaTqKXaloDa3kcvmgAkldpOcYOcdMGvicXhMRgKipYunKnJ9JJxenk7M7cNUhjacq2GanCNryi+ZK+iu1davbubFc5ZW1Nd+nXS5AzC/J6D5TzQBNFJ5sSS7GTeoba4wwz2I9aAH0AFABQAUAFABQAUAFADXRJEaORQysMEEZBFAFa2eS2cWVzKXJz5Mh6uo7E/wB4fqOfXABboAKAI54IriJoZkDIwwQf88H3oAht5nSY2E5ZnVdySEf6xBgZJ6bgSAfwPfAALVABQAUAFABQBDdW0V3EYZgcZBVgcMrDoQexFADLSabLW13jzo+jAYEq8fMP6jsfqKAMTxh8RPB/gWOIeItbggu7rAtLFN0t3dHcFxFBGGlk5IztU46mgDzXw58VPif4vt0i8MeBba2ul8uLUtV1vfbokoOH8mwQtOI9pEiLPJCSHGT826gCx/whUniENN8R9evfFc4KB7SWYWul4WUuqi0g2rOmQvyzvOfkyO4IB2XhuwsfDmmzaXpFtZ6VaRnzLez0y2htYi7tg4QINuSV5IHUnnrQB02jaTaaNYLZ2lrDBud55vKXAknkYvLIfVmdmYk8kkk0AcF8bf2b/gn+0VpFvo/xg8AWGvrZbzZXTF4bu0LDDeVcRFZFU8Eru2sVUkHaMAH4q/HP4R/8MT/tUaf4J8U+JfEF74H8PX1l4u8PpZ3f2a/1Kw+0+ZGhmg2+TOskEsRmwhDxeYioGXHn16MqNT2+GpqVSVk23a0fxfyS83e1n9hlOY0swwf9lZ3jZ0sJRU5whCPNeq07ae7F6vWU5XSvGLipOS/ab9m/49eHv2lvhNp3xe8LaNqGl6bql3fW8FrfhfPVILmSFWfYSoLKiuQrMFLFdzbcn0D489PoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKAPgr/gsP4b8N3v7P3h3xVrPw11XXX0bxTZ+frOltBBLpdi6yLMks7wyukUxKRqAoj8/wAhnJKpFKAfRn7IfwV+EnwP+CWk6H8GINbXQteI8Qyza3KzahPcXEMYLXCbVWGVUjijaNUQKYyCu7cSAe00AFABQAUAfnD/AMER/wDkinxB/wCxpj/9JI6AP0eoAKACgAoAxvFmp+F9O0dofGD2n9m6rPDpDQ3UXmx3L3UiwJAUwd4dpApBGMEk4UEjGvOlCFq2zstet9LfO56OVYbHYjE82XJ+0pqVS8XZxVNObkndW5VG6d73SS1sj81fhn+yoPiR8RrqfxKbPwx4WvNZ1e2jso9VFve2a2t/Db+UFeOcby1wkaRSMHbgk/dEn5hgMuo4/MKmHnJRUXa17PW7tFNO7STv+bP7A4m8ZqXDGXUcJRjKpi3ShLnlBSpz5o6Sco1Kek9J80E1Z8vLGV1D64+BP7Efw4+CniRPGkms6l4i1y1LfYprkCGC2DLtJESH535blyQMqQoZQ1faZZw5h8uqe2bcpLa+iXy/z/M/EeOPGjOeMsE8tVONChK3Mo+9KVnf4nsttIpN6ptxdj6Lr6E/HAoA/Oz9rP4iwftB/Hnw78ANPuNM02w0nXl0uPXEna7Waa6WBCNkYwGSTdHtz97AZkAYj89z3FrNcfDARaSUrc2+rt27PT13sf2H4VcPS4B4UxPFtZTqVKlJ1HSaUGo03NrWT1Uo2le3w6xUrpH1p4I/ZP8AgN4GOiXlh8P9Ou9W0P8AexaldqZJZbghd07qTsLbkDKAu2NifLCV9XhsjwGG5ZRppyj1ffv29O3Sx/P+deKfFed+3p1cXKNKro4R0SjraKdua1naTvea+NyPX69c/PAoAKAPz21HUovgx/wUo/4SH4ueHVJ+I9zBpvhrU7CMzLL58S2do0wWLCMuGtmDMrYJk3Mgw3zEoVFnilPWPLpdpct0leOrbu1Z2Ss32ev7lSxODn4WSpYZKFf2iU3CNSTqck5TSqtQjCDjGopwcpTUowsnzRtT/Qmvpz8NPzX/AGP/APlKx+0X/wBgvUf/AEvsKAP0ooAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv8AsYE/9LLOgD6foAKACgAoAKAPgH4N/wDKZD47f9k/s/8A0RoVAH3lp+j6TpLXT6VpdpZtfTm6ujbwLGZ5ioUyPtA3MVVRuPOFHpW1bE1sQoqtNy5VZXbdlq7K+yu27LTUypUKVHmdOKXM7uytd7Xfd6LUtsyrjcwGTgZPU1iai0AVWu/MufstqVdlBMrHJVPQcfxH09Mn0yAR41mUbGa1gz1kQtIfwBAGfc5+lADxplqVCziS47nzpC4J9dp+UdewoAtYHoKAFoAKACgAoAKACgAoAKACgCvdzyxCOO3RWlmYou4/KvBOTjnHH6jpQBMgcIokILYG4gYBPsKAGXEzwIJEgeUA/ME+8BjqB37cdf5UAR2N2t7E8qgbVkZAy8qwB6g9x/UGgCzQAUAU7m6aRjZWLo07Ahju/wBSMfePvyMDv+BNAD7bT7O0dpbeBVdhhnySx/E0AJel4HivVXcIsrIMZPltjJGOcggH6Z74oAsRyRyoJInV0YZDKcg/jQA6gCC5lnhCyRwiRFyZAD82P9kdz7UASRyRzRrLE4dHGVYHgigB9AFe4soLhxKd8coXaJI2KsB6ZHUZ7HIoAqy3WoWcyWyxpeGXJQ52MADzuwCMDI54+nqAS2j3El7M1xAYiIYwF3bhnL5IP5eh4+lAF2gAoAKACgDPurXR9Ptpr+4eOxt4I2knlExgjRFBJZyCAABk5PQZpSkopyk7JGlKlOvONKlFylJpJJXbb2SXVvojBl8ffDnStXj8O6l8TdETVJ4klhsbnWbdLhoym5XWPcGYFRuzg5AzXPLGYaE/ZyqRUu11f7j1qXDec18O8ZSwlWVJOzkqc3FNOzTklZNPS199CabxT4EtNOuNW1Dx7pwsrPH2i7l1iOOKEEEjeysqrna2M+h9Kp4mhGLm5qy63RjTyPNKtaOHp4ao5y2ioSbe2ytd7rbujybx9+0fodh4s1v4YfDLwpN4k8d2ugNrelyT27zWF4wgSdESWPdJITCSy42o7KsYkVnyPJxWdRjVlhcLHmqKPMv5Xpfpvp8m9L3Z+g5D4Z1a+BoZ9nlZUcFKsqU7NKrD3nBtqdlG00k7tyjFuo4OMbPG1H9m2L4pnwJ4v/aAmmuPFGn6cLfUtBtdQB0uW6ycTtFGqlXcBfN8llQsigblX54/sf8AtD2VbMW3OK1in7t+/r3tpddt+n/iJD4QePyzgyMYYerUvCrKH75R/lu27xTuoOac1GTbtJ+7vfEzwt4Y8Sy+GvDt94H07/hEdQ07UfBWq2a3iWUFnpt9DG0bIq7CSZ7W2to0jAcNd7kI2EHk4rpVcNl8cXgnyToNSjyrp8LWnSz16WVmrM/LnXqY/ESqYyTqSm7ycndt3vdt3bd9bvW+pJ8AvE+ofEbxz4l8T+Nsr4q0LT7LSWsrPVba80vTUkUJfQwfZ5G/enULG6DmYCUJFbqQmGQfquNq0/8AVnL54NxdKvzTk1GSlKpHZNyS92FOpGKUPd53U3ep8fgb1s5xTr83PTUYxTceVQlvZRb1lKDb5ve5eTbY9qtpFtbt9OYBI8BrcE/eGDuUZ9MdOwI7V8qfRkeq3s8Ukdpbts81W8yRYmkaMHhcKvcnoT6HrQBnSz6ZBAtpNfFETIimUbZ4W6MGUDPPPOOc884JAJ7WSK/mmuLNALa3jRY96FY2KbivJ7Akk8cYU+1AFfSrfUJria8t5yiuy75pCGDsPvYAHzLnIByAAeOegBrDV7H7S8bX9qIwilT5q8tk5HX2H50AVvtWnxLNBpkzy3E38QZpC2B/CzZBIXOBntigCvDceQPNsHny8khSBomWFlGTtUkYUlQSMd85oA3UdZEWRGDKwBBByCPWgB1ABQBFPOtugYqzsx2oi43MfQZ/P2AJoAZYwNBBiUKJJGaSTH95jkj3xnH0AoAsUAFABQAUAFABQBFdW0d3A0EhYBsEMpwykHIIPqDzQAlq1w0C/a4wsoyrYOQ2D94ex647UATUAFAGP4q8H+FvHOjyaB4w8P2GsafIdxt7yBZFV8EB1zyjgE4ZcMM8EUK8ZxqRdpRaaa0cWtmmtU10as09jpw2Lr4Ry9jKyknGS3Uov4oTi9JQltKMk4yWjTR4RP8AsEfAmWxNjC/iS3xqcmoJNHqCNKkLgD7HueNt0C7QVLZlBJ/ecmvpMFxpxPl9KNKjmFRpXs5clR2bvZupCTdns5NyS91S5dDDMMFw9muJlisVlGGvKMYtQjUox5orl9oo0atNRk18UYKNKT950+b3ji/gM/jH4A/tH6n8Atat7aTQPGJudX0RLa5kkS0hTz2hcb2JXdFbtG6tuYmOI7iAS30OYY3E8Y8NvNsdFfW8JU5ZyVkp0pt8krKPxKTUeW9l70r+8kvEr5ZlPDGcUsPk9Sf1TFUk4wkm5QxFOMFWhzc2sGm6inZXvGnyrlbPra5BvZ/sSuRFHhpyuPm9Iz9ep9setfnR7JdoAKACgAoAKACgAoAzZLxLyVFtJnjkRmMLMcRTleGXg8j3x2yM4NAFy2uVuUJ2NHIh2yRt1Q+n/wBfoaAJqAIbu0ivITDKCMEMrLwyMOjA9iKAGWk7nNrctm4iA3NjAkHZx/X0OR6EgFmgAoAgu7c3EY2PsljO+J/7rYI/EckH2NAC20/2iLcybJF+WRCeUbuP/r9wQe9AE1ABQAUAUNZ17RPDljJqev6vZ6daRAs891OsSAAZPLEDoDQB5PZ/tGxeIBeWPgfwXqXiS+trm9t/tFpti05RHKRblrmYoG8yJopCIg5CvkBhjIBBPo/xQ8Z31rf+K/HMOg2ieYf7N8PFlkKkgYe8fDhdueVjgYb+HO0NQBr+F/A3hHwqb6bR9JiS7vZXkvLyWZpp7qQHIeSVyWkb5mPLyEksdpyTQBqxWMdveXhs7JIJZ5WupGRQpZ9qx73woAIWOMBiFIVVGQAMAF22SOS8y07QJP5kbzsSVODnBOcc4PVnByM0ARXKNOv2a1hDqJiImLKPv8YwAACTg46gAcDBJAOzsbkXlnDdAr+9QMdpyAccj8DkUAT0AfDX/BWnS/Bmhfs8t8Sbj4JaD4s8T/2ja6FBr93Yb59CtpEuStw8qLvaJZGKpFI4h8+4jZlfHlvjWoRrxUZNrro2vxTTPRyzNK2VVZVqMYSbVv3lOFRLVO6jUjKKem9rpXXVnmX/AARV+MvivxD4M8Z/BHWGS40bwjJBqujyszeZbLdvJ59vjO3y/MTzVwAQ8spJbcNux5x+mVABQAUAFABQAUAFAHkX7X3/ACal8Yv+xF1z/wBIZaAOJ/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFABQAUAFABQAUAFABQB4d+2p4T+EfjD9nHxTY/HHxDcaL4PsPsuq391bTxxXDC1uEmEEHmEK002wwIvJLSqFBbFAHkX/BLr4qfF34sfBvXNW8aeHrfSvAumaqulfD6EIwkttKgjEf2JZCim5gtwsUSXLl5ZHWcSMWSgD7OoAKACgAoA/OH/giP/yRT4g/9jTH/wCkkdAH6PUAFABQAUAfm78L/if4s/aP/aTk1zxLpfiHxL4d0e7Oq6NpkeoparoIW8iFtdiIPHFM0IYb1JLugkIEzKI3/G83zjFYqo8SqjilK8b7LdxWiau7WT77tK7X9f8AGmR5d4a8FQwOAlCli6yVOo4xUp1042rK87yjDXmutIvlikudHtGm6Fq+j2PiXwvHZXunWUN/oQ8GPfWGoxHy5LS00wahfXEE8Mc8gmZpFsi0M8TWsc7bHeF4eKVWnFUcZTqXq/vOdRavHlftFy3TavquZXXTVXT/AJXs3eLWmlvyPp/wZ4og8Y+HLbXorSW0kd5ra6tpVYNb3UErw3EOWVd4SaORBIo2OFDoWVlJ/bKFaGIpRrU/hkk16PVHktOLszwv4rft2fBz4cz69oOmNfa94k0OeSybT47eS3ie6jk8uSMzumAFIbLBWB28ZyK8LHcTYPCOdON5TjpbVap2evkfsnC3gfxHxDDD4uvy0cPVSlztqTUHHmi+RO75laybTV9bHht7+1F+2rJ4c8XfEOXwDaaD4Ta1hkt7vUtGliTTI5UZIHtGYq9yzsUJdlmjDlCwjjbFeHLOs6dOpiHTUadlq4vS+3L3v3s1e2yP1Kj4ZeGkcZg8nji3WxXNJOMKkW6ji05qqkmqair2ipU5uKkk5zVzpf2K/wBlTwjq3h/Qv2gPHb6hqOuXlw+o6bZTKYILSSK5Pl3BCndKxMYdScJh/uNhWrp4dySlUpwx9e7k3dLZKz0fntddPLqeJ4yeKOYYXGYjhLKlGFCMVCcl70pKUFzQ1Vopc3LJK8rr4lrE+3K+1P5jOJ8S/F3wn4b8RWPhTytU1XU7y7a0li0uxe5SyZYBcOLiUfu4nEDLKIS3nyIwMcb15uPzbB5av9pqJPTTrZu17b2v1NIU5VPhRgfFr4irpOl6XeaNqdylldRDUre7tAZbTWYTGy/ZI5II5JWkbzIZI0g2zyYDQLciKa2fyuIMTWUY0L8lGomvaJ6xlvG/aDSak9rPVxSu9KMVvu10/rqc5oviDxW2hyfDz4cSQ3E2jQfa7e3sVtLRbS2EtyLfTJZxJNHbKkthLYSvFBIyq48pUaJ5E8fJs4zbM8PHCYeKUoqzqyu9LKzS5VeTUk43etm33NatKnTlzSfyPnP9nf4ftrX7cfig/E7Q9EbX/Dem/wBrPFpwlltP7QiSzt45S9zmW5dUkDfaJMPJIqzYQ4RayrCN51OniZuo6abTlq73X3K8m0ltp2P6LzrFVOHvB/Bxy1qH1qpy1HHRyUvaSkm1q3aEabbveC5XpofoJX3p/M5+a/7H/wDylY/aL/7Beo/+l9hQB+lFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/wBjAn/pZZ0AfT9ABQAUAFABQB8A/Bv/AJTIfHb/ALJ/Z/8AojQqAPvx3SNC8jBVUZJJwAPWgChHbQao73d3AJIiNkCSIRhe7YPQk9/QCgCU6bG4Mc1zcyxc4jaTj8SPmb/gRNAFmKKKBBFDEkaDoqqAB+AoAfQAUAFABQAUAFABQAUAFABQAUAU4dTt5ZTbyboZgzLskUjODgYPQ5HPBoAS8nW2u4JblwkB3KGLABXIJy2e2AQPc98jABKNQsCyoL23LPjaPMXLZ6Y55oAdb3UdxvVQySRnDxuMMv8AiPcZB7GgCG5t5oS11pyr5nV4icLL6/RvRvz9gCxBMlxDHPEcpIodfoRQBDqkxg065kWURsIn2tnHzYOMe9AE0EENtEsEEYSNBhVHagCSgAoAoMI9NuTMqBLacAOEXiNxk7zjgAjgn2XPsAXlZXUOjBlYZBByCKAFoAo3AbTy17D/AKgndcR4zj1dfQ9z2IyevUAuO6Ro0kjBVUFmJ6ADvQBSmnmv4WgtYLiIO2xpXXZtXIyQD83Izjjr6UAWbeztbRStrbxxAgA7FAJx0z60AV7glNWsyqj54pkY47fKf5j9aAL1ABQAUAFAHmv7SPiDw54Z+BfjLUPFz6omjz6c2m3baXFHJdKl26226NZHRSQZgSCw4B69D5ub1adHA1ZVr8trO2+unW3c+08OsBjcy4pwVHL+R1lPnj7RtQbpp1LNxUmrqOlk9bep88eCv2I/gP46+Efh3XtPHiBF1SBdROtGbytRML7mVJIC8lsAFKg7F3fKME858PCcN5di8HCa5rvXmvZ/Nax+5H6pxB41cZcP8RYnD1HScabcPZcvNTTSSbjK1Oq7tN+9K2rVrJW6bwx+x18D/DGhar4Zb/hJvEGl6td2l7Pb39+sKzPaw3KqytBHGwAN07bc5yic43K3bQ4ZwVGEqT5pRbTab7Xtsk+v5HzGa+OHE+Z4qljYKlSq04zhGUIN2VR03LSpKcW/3aSutE5aXs17r4RFl4S0bT/DWkWarYaZZW1laQM586O3jQCONXYnzNq4xk7jn1r3aVKFCCp01ZJJL0Wx+VZhmGJzTE1MZi5c1SpKU5OyV5Sd5OySSu+ySIPHfj7wpZpaaFAs2teIdRUyWGkab5cl6Yxy87h2VIIFC4aWVkTcUjBaWSKN+XMMyw2V0XiMVLlj+LfZLq/+HehzQpyqPlifOvxAu/iL44trS5n8V+GdI8NeEfF3h3UdduIzFDpb3Nrq1q91pUd1dKTetA6zf6XGtptnihtgryG4jtfgsbxbUx1OeFjSsqkJpRs5Ts4S5ZO2kb6O1pPlvK6jyufZDDKDUr7Nem/9f1ttQePNJ+FHx18Wap4f8HXsmhO9lo+vR+HvBurO0epTrJfC6kNpayQXsrm7XfIJFkBnVShcr5v23DedY3M+B6OVYaLeIoSqzpudp05Q0vTUvaRnSatJqPJKEml70eb3fm8Vg54LPJY+y9lVjCErNqSld2k1ytT3Ub8yaXR217aD4x+LNYumvdNgtNStLuW5TTvD2p+HtQ8K6/J9niaWSSzGqMq3zASW0YUx20OXkZ7hApjr5+PE2Ny+SjnWFcIu3vx96Ku7a2vbq/ictvdd7n0H1eE/4Ur+R2OmfELwh4p1yXQYrknVmW9c6fcwtFLHNaG1ikSRWwRta6iZT9ySO4SVGaN1c+/l+c4XM6s6eGu+VRd7aNSvt6Wad0mndbp2wnSlTV5HS2uswQxRXDTo0s0JDQs4Us6nCsSeFJXqSecLjNesZlmOK71JvPu5oTaK2duMowGOxHI6/MSR3CjjAAv2tdTvTZyFo7bafL+YAXHbrnleCcDII6+lAE++9sXZpxNdxMq48qNf3bZOcKOcYK/3jwaAEu7gXcRRbC5lhX53YK0bqRyCgIBJyO369KAK9rK91qEG0yzi3L/6QU2qUZeh4HzhsDGOgOeegBpWVubWDyTtwHcqF6BS5Kj8AQKAJ6AKd5PcNILKxwJnXc0jDKxL0zjuTzgex9KAI9NhiSRvOEn21F2yGSQuSuc5XPG0n0A9OooA0KACgAoAKACgAoAKACgAoAKAPF/2iv2jI/geNB0bR/C0niXxH4lmeGy0+KcqyjhEYoivI7NK6KkaqN+JMMCuD9BkuTUMdSqY/H11RwtFwVSTUpS9+6ioQjFuUnJJW0snza2s8JPFYivHL8uoyq4mrGcqcbwjD93yyqOpUqThGnCNNym5O+qUWkpc0eB8K/tsXWn+NtN8A/HL4YXvgS81OGGaG7uJykarMw8p5I5VUxxY3h5N7bGRgyrhtndQyXJ88VSGQ4xzrxXMqFWDp1JR5W26er9o9H7sVdWd7O3NeaZdxBw5h4Y/NsNGWDcnB4ihUVajCako8tVpL2d+aLUpe7NSi4OSbt9M6nrWjaLbJeazq1lYW8jiNZbqdIkZiCQoZiATgE49jXx1SpCkr1Hb10OzC4PEY6fs8LTlN2vaKbdu9lfTU+UPhtqifGn9svxN8UbHUkufDHgKwGl6ZeFohFuZHhUIUYeYkkj38iSHeCu37uUx93HD4jJeEZ/W1KE8XWTjFpq9GlFe9ay3qO65m+aLhKK6nmZtiMHic6wuBwfLP6rQbqyi+a2Irz5uW7bs4UY04SUFFwqRqQk2+ZP6y0xMWMMjLiSZFllJGCzkAkn/ADx07V8OdZaoAKACgAoAKACgCrqHneQDGxVAwMxXO/y++3Hf9cZxzigBZbO1urRYAAIwAYmjOCmB8rKexHY0AVz5pnCMUS+RT5bkEJOnofz5H8J55B5ALltcJcx71GGU7XQnlGHVT/n36UAS0AV7u1FwEkUhJoTuifH3T3B9QehH/wBagBbS5+0xZeMxSodskZPKN/h3B7jFAE9ABQBTuke1dr+3iLnAE0a9XUdx/tAZ+o49MAFpHSRFkjYMrAEEHII9aAElmigjMs0gRF6kmgDzLWfi/d67qs3hT4P6fZa7qMNwtrc6tdTBdJtJfLaRow6sGuZlVQTFFwN3zyIRispV6UKipSklJ7K6u7b2W7sOzauN8NfB7TZtTi1b4naxL408S2gSaG71BY/LtT8pzbQIAkBDoGyo3cjLtgY1EdPqug6fo90+oaNpdvbSahkXskZ8szMEVVZgD87bExyDkKAQcCgCiFJd7eR3RoWZkAzmMFhyARheeekfUc8jAASN88wYEx3WV3AsQzKc9d2D19ZDkd80AE8TXHk2ttEzyI0kYj9Ce2Dwvrnan3T1PIALF1azWf2g3TiMTAGQOB3ZtnPzE84JGSTsbOeCQDPupJGidGWVBH8mCxZlQM2FYHpggDGR0BwetAHc2EFvbWcUNp/qguV5JznnPP1oAsUAfEP/AAWCi1CX9kB/sT3oiTxPprXYt9WjtFaELN/rYXG69QPsPkR4ZWCTk7IHBic4wspNK+i832OjD4WtiVOVKDkoLmk0m+WN0uZ22V2ld2V2lfU4j/gjh8IvCvhz4deLfi74Y+IcXiI+JrqHRr6xbRZbSXSLiz3yeWZXkKy747uFzsUqPlAckMBnh51alPmrR5Za6Xv+Nlvudeb4bAYTFOlltd1qVotTcPZu7SbXK5StytuLd2m02rqzf6K1ueYFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQB8Pf8ABWI3fiH4O+Cfhfa+EviFqq+KvGNn51z4UsvtYgihDb4poBj7TI6yloLctGHkh3mVfK2uAfYvgjwh4X8A+EdJ8G+C9Ft9J0PSLVLays4IREkUYH93A+YkksSMliSeSaANygAoAKACgD84f+CI/wDyRT4g/wDY0x/+kkdAH6PUAZHizxZ4c8C+HNQ8XeLtXg0vSNLhM91dTk7UXIAAAyWYkhVVQWZmCqCSBWVevTw1N1qztFbv+v6Z6GVZVjc8xtPL8vpupWqO0Yrdv8kkrtttKKTbaSbPmLxr/wAFEvAPhzUnt9C+HviXV9Ptrm9srjUJglpEZ40zb+SDu3pKcEl/LdEO7Y5+SvmcRxZQoytTpykk2r7ara2+/nZpa2ex+4ZN9H3NsxoqeKxlKnUlGElBXm+WTtPmty2cFty88ZS93niveF+Hv7d2peK9bRfFPwI17w74ZggludT1+KS61CLT4ViZ1keOKzyVZgi7sgAPuPAownE0q8/3tBxhu5auyt2URZ/4H0crwreAzWnXxLajCk1TpupJySaUpVrXSu7WbdrLVnO/AHQPFOh6jr19rHh7Wpvhhqx0jVvDVjfR201hEt5qVtLpTRW7KqxXNvmSWZ4ZTh2RyjyLGW8VYGssDjmnKNGcHONtOjktdtUlGSStbS60OPxTxWX1qOAp050p4ui5Ua7XO6rqU+WE5SnKTlOm2v3bqJSXL7raclH1TR/g74x1ZzfeJ/HGutf20jxpdW3iG+jtodOhvY3jtfskLwRyvNAJx9plWS4hDrG8135Ymk8TCUqOd0a1eio04UKabXs4XlLklduSimkpK9rO/ZbL8fk3SaT1bfd9ze+Afj2LxJcatpkOv22sabcxRarod6l7cSmW0DNavbBZA6q9qbeKKfFzLIbiSV5I7YSxI32PCONnPDywde/PT6O21ltt7vWOiXK0ouXK2cuJilLmWzPLf2bPg/oei/G/4r/8LMHg/wAQeLDrcWu6cksMFxqFlE1zcul5tCsts0u6GQIj70zGXVN0e7TJ8BCnjcR9Z5ZVObmWza1bv5X0dk7rS9tL/u/iNxbisbwxlH9ie2o4X2TpTaco05tQpp073TqKFpxcpR5Ze8oOVp2+tOvBr6s/n8WgCtqGpadpNo+oarf29laxbQ89xKsca5IUZZiAMkgD3IqZSjCLlJ2SDc+fdK8U+E/FXjC5v/E3h2PQpdVt9Y1C3Mk11aa28iu1nbXVrpjQiWS4aw04yCYr9ohDCONSksgH55HOMIs3rV68eenNwgp6OEEknfm1STm4u2iWsm7pX7nSl7JRWj1dur/pEtjNBr3i68sNP0WDRtcivb20hlgviXjsVuFkllGbMmz/ALRlihaZYV2GIw3AmWZ4N/HnWGr4urTw9acadOlGEffvGEqj0vCKsmoqV0+VJWtJcpVKUYpySu3d6b28z2/w5D4esbefS/D+n2unrFcT3FxawW3kATzTyPNMV2ruMk3nOZMfvGZny27J/RMLisLOc8Jh2r0rJpKyV1olpbbottmcUoySUpdT5BPjjxV4h/4KQ6h8OfhZNDpMHhyztdS8eCRJZI9Rt20+LZLJgxh3Ans4IYzI6xMJZwjFpkfy/q+IWce0iuWDV9177SSu1dOyTSW6T1+07/rv9rZRLw4WDrS9vio1OVJwmvq8ZSnNRhPldPmqSjKctYyqQbg2/Yw5ftWvoT8fPzX/AGP/APlKx+0X/wBgvUf/AEvsKAP0ooAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv8AsYE/9LLOgD6foAKACgAoAKAPgH4N/wDKZD47f9k/s/8A0RoVAH3lehrmaOxUIFyszljyVVgcAd+QAfTPfNAFygAoAKACgAoAKACgAoATcoYIWG4gkDPJA6/zFAC0AFABQAUAFADJYopkMU0ayI3VWGQfwNAFdNLsY5EkEJYx/wCrDuzhOn3QSQvQdKAJpbaCaOSKSJSsvL9snjBz68Dn2FAFJI57LUImnu2lhmUwoXwGDYBAbGAfutg4z82KANKgCt/Z9tuZ181dxJISZ1XJ6nAOB1/PmgChFpSz3++4nmmjs2UJ5rZLPgH8ABj3JznOBQBsUAFABQAUAUzYSQuXsbtoQxyYmUPHnvgcEevBHNACg6sjEGO0mHZt7Rn8sN/OgCtqEupvbPa/YYWa4R4wEuCSAVOTygGPx6ketAC3Gox3cE9lBDN9qKFPKaM/KxBxlvu449cEetAGnQAyUzBCYER37B3Kj8wD/KgDPvpp2t2EttLFcxZeFolaVS4HHIHQ5wcgdT9aANGORJUWSJwyMMhgcgigB1ABQAUAeeftAr4hb4Ra+PCvj7SvBWqf6L9n1zVL77Ha2n+kxbt82Ds3rujHByXA715+a+0+qT9lUUJae83ZLVdfPY+w4CeDXEOHePwk8VS9+9KnHnnL3JWtG6vyu0nrok30Nr4er4gvvhvof/CVeJNO1zVrrS4mu9W01le1vHdM+fCVVFZGBDKQoBBGBW+DdR4eDqyUpWV2tn5rbQ8niOOEhm+JhgKMqNJTko053U4JO3LJNyaktmm2092P1iF7vS7G5S3UxxxskihS3lnAGfXClSDzn1zyK6TxTzD4i+Ktb0K3hj0/VdP0K0unS2n1m6AuriPcpRYNPslRje3sgGyKMqVDOrbLkqbeTxc5zOtl8IwwtF1Kk7qKS91P+8+i6+dnqlqtaVNTd5OyRyGt+APE1nf2Hw80+4utDh8T6pJf6l9n1JxrWr2kAhW71DUNRhljkgAV0hhhsyzIx05S8VqJrVPjMzwH1DDTzbOp+0xElywjZOMW9lbVPl3d/d3+JtN9VOfPJUqStHqdb4V8B2vj6G5uPF2h2V9pay3kOgaHcQRG00zSJLUWYVYSiBmmgR3IljLxLfz228Rlw/1HDWR0srw0a04/v5q85O99Xe2rdrXs7Wu1dnPXrOpKy2Wx578QNIHhHTT4u+J0mtT6j8PdHvvEUfiHRrp7W68S6HpkTrLbXywyQOl4i3s4RkdUEsjzL5UVxcWR4svxWP4BzRf2bOLw+KahKE4xmleUXopxlHRqLTteySeykZYzBYfN6SjiE+aHvJqUou9mt4tPZtb9TqfDGiWvxR+JOr+F9F8d6pfeC/AlzBFq2kamtxfQ3mpl5SwiubiT7Qj20tvBLHLvbbKFeIJtDN+6Z1SpYXIYPNsJTlWxabjKPLDlprkcW4RjyPn5mmkovl+Jt7fFZbKWKzWcMBiJqjh3aUZXlzTfMpJSk+b3bJ6uSvskt9L4X6F4ot/GHhPWdctk1Frnw/rMtzfFGivLtUu7FdLmuYyEiF01lj7SVVf30YVB5SKq/gvC9OnhMyxuFpJxgnHlUt7JyTt1cVLbyavq3f77ENypwk9z29F1Ir/o0Vtaxg/LG6bjyeSdpwO54zn2r7g5B/2KabH228aVQcmNF2I3GMEckj2Jwc856UARXF/p85NtcrHJExVWDYypJI+ZDyBnHPv2xmgCG4ElnPCbLVDid1HkSt5uQcAMuTuwOp59+2CAacInCkXDRsc8FAQCMDsScc570ASUAFAFe5vI7ciJVMszY2xJyx5xk+g9SeKAC0t2h8yWUgyzv5j46DgAAeoAAGe/J4zigBbq1FwoKSGKZM+XKoBKn8eo9RQAkFwzOba4ASdRnA6Ov95fb1HUfkSAWKACgAoAKACgAoAKACgAoA+P/wBsLSRZftBfAnxbc6lJHaHX7SGVJp8W9utvfQO0uDwhIm+Zs8rEucbefcqYWrnPDcsvwqi6tHEUq/L/AMvJ01GcJ8q3kqd1J2Vopybep7/BWZ0OH86xuOxvMqWIwGJwynZ+zhVnyzpKctouryShC7vOajFI+nPGXw48BfEOOzi8c+ENJ1wadMs9ob21WVoHDo52MRlQxjTeoO11G1gykg+A6cXVhXtacGpRktHGSaacWtU00np2OLA5xj8to1sPhaso060ZQqRv7s4SjKLjOL92S5ZSSuna91Z6ngUH/BPr4SW0FzZf8Jp42+w3DRTyQ/bbVQ88YkVZXxb7WKrKwX5cqGk5IcgfbYbjnMMNev7GhLFO/wDtDowVbmakubmilDmjGXKvctbSSld38DH5fRxzhQ9pWjg42Swir1nhuROE3TUZzlUUZzgpztVvzWcHBxjb2HwJ8K/Bvww8Ar4a8Jac0NlawvKkkp3TzTbctcSvwWlcqpJwAFCIFVVCj5bF4/GZhNVcbWlVmlbmm3J23+Su27KyV9EjvqRw6qznhqEKMZO/JTioQXSyS7JJXbcna8pN3b9ArlJCgAoAKACgAoAKAKc0UttM97bqzh8edED97AxuUf3sYGO4A9KAI4JIrfN3BMGsZvmI6CFu59gT19Dz0zgAuTQpOgViVKncrL1VvUf555B4NAFDM5laVIwl9Evzxg4S4Ttgn9D1U8Hg8gF63uI7qISx7h2ZWGGU+hHY0AS0AVbu3lLrdWuBPHgEE4Eid1P9D2P1NAEttcR3UImi3YOQQwwVIOCCOxBoAloAryXcIZ4Y54hMqnhjwpxn5vT1x1wD6UAeX+Jvi3pem3t34a8BWt/4q8TwuXuIdNH+i28hVzi4nbMMI+TG0kv90hWPBAPMPiX4Y+JHiK6sNQ+J+oW0+j6lDNpNzbaWtxJZ6HqDOj2F7NZl915AsjFJmYoijyXkjWNJZo/IzyvjMJg3iMEk5Qak0+sU7yS7O3rpeyvY1oxjKXLPqXru8sbho9G1NV8IyPHp/hnUNNCR3cGgayrtLo2rWZuAtuYHnXy4Jfs7+dOtnEywy280A+WzutDMcLR4hyzWdF69+X7UZW106625XJ6ppnRSTpydCpsz3bwD4gHiPw8l1cafDp2p20r2mrWMczSi2vUwJFDsqO6N8skcjojSRSRSbVDgV9rgcbSzHDQxVB+7JXX+T809H5nJODhJxZd8Sh/sMTxkblnXjg5BBUjBBz97pg/Susk52RQkgZd2Y25G0YjBfgZ4Cc89EPPvkAEcymQlRCyvId4Zxlfv/eJPbAPJLjr9QAdZp+jQ2LLK0rzSKCELABY89dijhc96AJ7yyjvEBOElj5ilxkxt6j8hx3oA5m10O8vpvPjQW8Yx+8JG4OpIOAOcqeM5GSu45oAydR+KEWm6NqFr4ZsrTVb/AEKCQXc95eNYaTbGJ5UcT33lyKpXyJA6xJK8Z2+YqK6sfBzXiPA5RJUqrcqj+zHWXzXTfS+r6Jm1OhOrqtjw79pH42XXwg+GviD4s+JPG2qTSWFlEmiSW2ppoemTahIDth0+NYrqW7uHTbKBdRXdr+6J3Rxifb8hgeJs2zjMIUaFoRu7xUeZpd5tuOiemkovyd0n0zw9OlBt6/h9x+a3wh8B/H7/AIKJfFbx7408P/EbQNG8TiC2vtQ03V7mU6fcwqogjUQuZ3dAGmJUQtDGX2/uxLGlfeSo1JzhRqx54r3udtJqSelopL5PTS979fYyzFUcty2vjMNjJU8TO9L2UYNxnSnG03Ko5WS3XLyt83JKP80P2e+BPwO+H/7PPw5sPhv8OdBtNMsbcm4u2t1kzeXrqoluHMrySFmKjAZ22qqIDtRQPRPmD0GgAoAKACgAoAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP/BOH/kyb4Wf9g25/9LbigD6ToAKACgAoAKACgAoAKACgAoA5n4l+DJPiJ4A1/wAEQeJNX8PT6xYy21vq+k3ktreWE5H7q4ikidH3I4Vtu4KwBVsqxBAPibxZ+zV+254V8J/Dv9kP4DfEb+yPhraaZDP4m+JjXK2+rw3jX11PdW8CJP8AaBFtaAxpGoJ+WN7gRmSgDy/4MftkfEf9lPQfH/gf4ieO77476xYfEyXwN4c0uW6vf7XglhmnimuLuZoLgxxT7IDbwq8zuwnWMHypdgBtfDL9vD9rT4J/tGWnwe/ba+H8htfHWpW0WlS6bZQoujm5ZQiWrW4Zb+3R5Y43HmSSptb55HXY4B+mtABQB+cP/BEf/kinxB/7GmP/ANJI6APuv4m/GL4cfBzTbPVviR4kXR7XUJzb2z/ZZ7gySBdxG2FHYYA6kY6c8iuPGZhhsvip4mXKnotG/wAkz6ThrhHOeL608Pk1H2soLml70Y2V7bzlFb9L3Pl3SfEkn7d/xBFjP4Y1fRPAHg+W8ktdU2RzQ3/mRpC0E0U0Twi5KyCWN1O6Bd+AWdJF+Yp1nxNiOVxcaUL2e6d1azTTXNrdPeKv1aa/c8Xl0fA7KPaxrwq4/EqClC7jKHLJzU4yhOM/ZJxcJxa5asuW9oxlCXZ/tD/tCaf8BvD2m/Az4FaT9v8AGa2cGnWNnao12NEt9qJC0inc0k7Bk8tHzksHkyCqy9OdZ1SyWisJhbc6VvKKt1v1tsn6vz+b8P8AgCtx1jKvFPFNTkwfNKc5Sah7aV25JNWUYJp88o2tZwhZ3lDD+Ev7P3xm8TaPbeP7n9r7xBeyarp+o2txZwzTXlraXksM1rII5Yr3y2a2nZipUAB4RgKRxnl+V42vSjipYqXvxvbXTmj/AIt43vstV0OjPPEzhJylgcBkNF06dSDjUXJFzjTqRlqnRbSqRjyyTbfLJp31OU8U+J/20P2ctJvtU8TfErwJ44sba0aa70y/uVknt4WkjT7TtkW3mkXe6RAK7rulxsyQRxV6ubZfSqe1qQqxjpKLadubunZ6rZXd09j6HLcP4W+ImPp5bg8LWwleT91xVueybcUk6sFZe85OMfh+LdOp8Ivjd+1h4v02TxJ8KP2dvD15cahEqXWvX73RS+ghllWOGO4vLtWdInefEYlcK0kxCqXbKy3H5lKjbL8JCMXrouWL6aapX0s9XsGf+GvhrwzipUc4zarGWi5E4ynHRP3lClNpNO6vGKs1qy14z1X9qrQvDttq9t8PfCfhz4gTapqVuNP0iXTopY9L1UW8bvEWuGKmbU2S4eQSbzcRRs52Flfx8VjMzyzMamIrx9m60NXq4+6t1Zy96KX/AANT5jDZf4YYTNeZYyrWwtOMJxTi7zqc8uanJOlD3HFRv8N7tc99F4LbX/7QnwX+KWp/GadYru/0G5tI/E13ps9tLaT+ezRvbXht8p5jtbssu4eYspjkYiSSJ38/D5pXw2JjjVL3peVuZaXurK6d97au7TbTa/cMFmvAniNlH+qWFk1dS9nCalz03Fy5ZU5SbV4rWMIzdqfuOKgpRX3nN8ZL74j+B7XW/AuqX+nrq+nyXVuvh+0i1HU1ZbRLkRGadGsbKZWEsLC5V4yzRjfG7gV9VX4pxuJUv7NwztHl5pT6KTevLH3nHS/Mul9D+Ns2yb+xsxr5diJpypTnBtbNwk4tq/e3U4fUfjTY+JvGF5B4A+JmjXHkadaHxO0dzqWp2lu9zGwWPSzb3UYuZzMLWFRBBGd0xxJ57C3fxsVjMXUjSjHF1KledrQgo00r2dpNXV1d3321SSuvSx/C2a5Pg6WY4/CulQq3UZSW9vLdXWsbpcyu43SZ6fo3gDx7q9lpE+uas1jcaZdGaP8AtfbqtyIBdTqkavG0aQSfYZpIjMvmTEyx+ZNP5Lmf2pcP5nm8JQzWu0rLlUbWvZXckvitJddHrblvZfOe2p0nemjmrzwVPe+LrL4Y23jC4F94Vj0rWtCuNS0ZLqdHgbZfahLMpIklvEu5rZ2YQZBnMPzRyOOHH5O8RmEcFTg6dFR5W4qTUnJKz5UrK0oq8m3FKCU5aRiXCraHO3d/5f1+OhZl8vwibS+8JaimieHNAmltrfS0tItIsJzCLuOaFoLiFTFFGi21z9pjZy8dqzQqI2ZZ/KxSeBVGvgsR9Y59btuTVSyUZKF90moxUlpJrmfw20j791OPLb8vU3/AWqaV4DsprDV7XUdIttPaPTb2XVZ5EjjZN7i+Ms87ieO4aVXM4eScyS7bhjIkiwfQ5Diq2U4mWDzWPLUrNzU7pqWivFtWScbbLTXRJW5sa0VUipU3otLHyp+0N4n0f4efH3wp+1z8O9Lvb281DRWe60vVJbq2hmtGW3hi1IwoymKWSGb7OsVyAV8tpBbl45HTPF8Q06GNqV8DTi0moym95NaOMddkkrtJrZvpf9p8Ocqw3iBhP9VM5xdWHslKeHhDl9mr8zk5rkvKScrpyknZyjFx6/dnhHxLY+M/Cmi+MNLinis9d0+21K3S4ULKkU0ayIHCkgMAwyASM55NfdUK0cRSjWjtJJr5q5+NZtl1XJ8fXy6u050Zyg2r2bhJxdrpO11pdJ26I/O39j//AJSsftF/9gvUf/S+wrU88/SigAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gAoAKACgAoA+Afg3/AMpkPjt/2T+z/wDRGhUAfeN3uivbS6JxGN0L49X27fwyoH4igC7QAUAFABQAUAFABQBWv7ia3gDW6Kzu6xjeflBY4BOOepxx60AQ2f8ApF/PdvbmN440tyGIJBGWPQ4xhloAv0AFAFe6vIrXYhBeWQ4jiXlnP9B6k8CgAiN+8gadYIowOVQlyTz3IGO3Y0AWKACgAoAKAI5oUuIjFJnBwcg4IIOQR7g80AUbm+u9Mt2e7jSRVRtswbG5h90MuOM+oOM+mQKAEkGpS3a2TX6BdhklaGEqyjOFGSSBnn3+WgCYaTaqNyNKkpJZpUcqzNnOWxw3PqCO2MUALu1WNvLEME69pDKYyfqAp5+n6UAWk3lB5gAbHIU5GaAHUAFABQBmGKexuzKZ7uS3kJbjMu0/3SME7fQj3z2oAkhuVuNSDKkhQ252F4mTadw3feAznKf98/mAWLmzS4ZJPMkiljzskjbBAOMj0I4HBBoAbYzSSRNHO2ZYHMTnGNxHIb8QQfxoAs0AFAFOOJbfUXWEKqXCGV1zj5wQNwHuDz9B68gFygAoA+UfG37ZWr+GfjxqfgCxsvDNz4L8P3NuNb11pLhn0+EKv2lX2AgzK/mRoiBtzlE++SB8tiM/nRx0qEVF0425pa6Lrfzvokr62W5+8ZP4R4XMeFaWbVpVY42upeypWh+8ld8jjfVQcbSlKTVoKU/gSby/22P2ifhXq3wPn8BaJrc2o6n420zT9X0wwWziIWgvYpFeRnC7CwhlATG4FMMFyM5cR5rhZ4F0IO7qJSWnTmT1+5+fc9DwY4Az3DcULNcRTUKeEqVKdS8lfn9lKLUUr3tzxbfwtO8W9be7fA7xFoVh8FfhjYX2sWlvPdeGNGt4ElmVTLKbOPEa56ucHCjk4PFe9lsowwFFydvdh+KSX3s/IuOteKsz/wCwit/6ckL8RdV1bRTHotnqGuS2+qzIkVroenAXobzPMmV72RvIghaLepZjHIMkRSiVohXFnM84vCllcY+9e8pP4e2n5aS813+apKlq6hxtp8R9c8N+GJF0Y6Xpdhpl99jXUJrXUPEc+ppE5zb20cbR3V/cvCru8oLmOXeu25KSMvz9POMXlUv7Mg3isTJtvooXW1+yf+GyfTRGzpRqfvH7sfzOWOsah4zsox4k8VW2saL4y1y5sNcv7K2jeysrS3cR22hy284DW011uIuBcRuwd7m03xvJZlOXCV3neewWZL2cqUU403dXnvJrdNJq611Si9UpFSXsaL9nrfd+R9CaDGl9bTQS2joEk3CfIVllAGQB1BHHPfnPUiv0c4Txr9pTTPiD4C03SfjD8Oo9LvL7wo90NTF/CzmbSbmEi4hHO4ZuYdPuGZWQkWmM4Ox+zLeFct4zx9DJ82nKFKcvii0pKVny2upL3n7uqa172a8vOszxWT5fVxuDipTgr2e1rq97NPRXe/Qz/hF4UsNI8Ia1491nQHi+I/hHR73SJtVu9eutad2hizDdxyXDMgFxB9nuAqqDGtwYm5Ditc844xObZRiIZdJ0sJ78oU+SEHHl5uVTUVeUotWfM5JyV7y0Y8h4cwtDG0K+KXPXfLGU3KUr3tzON3on0slZOystDz3/AIJ5ftU+J/2ifDlt4K+PE18PiX4bi/4SjT7zatlFr2j3IkhjuljttkUqxGZ4XQp5YYQPgzKxj5KuH9m05JcySvqm7SSkvk007el7XSOylXjU1T0baWjWsW4yXykn662vZs9S8E/Fbx9q/wC2/wDEj4Qajr3m+EdA8IaTqmnad9lhXybqZwJJPNCCVsjszlR2ArKguelVlLeMopeji2/xOrEpU50FH7UJN+bU7L8Dd8XftpfsreA/H0fwy8W/HHwzp/iJpXglt2nZ4rSVCQ8dzcIphtnBBBWZ0OeMVKfM7ImS5VdnofjHx98N/BumWesePPFugaRp16zLaXWp3cUUMpWF5m2O5CnEUUkhwfuox6Cm9J+zfxWbt1st/u6iWsVNbNpX6Xe339D5m+K3/BQX4K+G/C3w28W/Bnx94K1bT/GPxA0/w1rE16z27WGk72+33bQsYpIzEvlfPINiidHIZWXK3aV+z+TuvzT+5jnFwjJ27pdrr8/l3Pe/iB+0T8D/AIW+BdP+JXjz4naFpXhvV7ZLvTL1rjzf7RiZFdWtY4w0lxlGVsRKxwwPSlKSi7McYuaclsv6/Rmj8KPjP8LPjl4ZXxh8JvHGmeJdKLBJJbSQ+ZA5AYRzRMBJC+CDskVWwQcVbi0rszUk24rdHZSSJFG0sjBUQFmJ7AdaRRW05JTCbq5QrNcHeynqi/wp0HQfqT60AW6ACgCKe3juFAfIZTuR1OGU+o/zj1oAjguj5n2W62pP1XHAkH95f6jqPpgkAs0AFABQAUAFABQAUAeGftDftPWHwavtK8H+GtAHinxhrkght9MiuQpty42wmRVDOzPIyBIwF3AOd64G7uwWFo1o1MTi6vs6FKynK3NLmnf2cIU01Kc5yWiVlGKlOUopK/r4HJsXmHJTw9NyqVOZwi7wg4U3F16k6zThSpUYO8pvmk5uFOFObk+Tw/4p+Fv22vjpaaX4S8dfCzStL0GPUI7m4l06fTnkhP3fNVZLxmZkR5MKrpu3FS2DkfR0+Lcq4axEcRw9l9WpW5WlUrVKcfZylFq6pwbU7XfMnKN7Wi1fmOfAcFYXN8NVp8SZ/Rp07q9GhRxMvbwjJT5JVp04+y5nCPLJU6nLdSlFtch9i+AfC3/CD+BfDngr7d9t/wCEf0mz0v7T5Xl+f5EKx+Zsydu7bnGTjOMnrXw+FofVqEKN78qSv3srG2e5n/bea4nM+Tk9tUnU5b3tzycrXsr2va9lfsjVvwZIBbhgDO6xnOOV6sOf9kNW55RLPH50EkWB86FeenIoAh0uQy6bayFy5MKZYnJJwM/rQBaoAKACgAoAKACgBksqQxPNK21I1LMfQDk0AV7GBjBK9xCim5cyNFtGACAMH1OBz7k0AMiD6dIlsQWtXO2JycmMnoh9uyn8PSgCa9tBdRrskMU0Z3RSgco307jsR3oAqK8wm+1iMLNGoS8iAzuXs6+uOcdyMjqMUAaSsrqHRgysMgg8EUALQBSuY5rWb7ZaR71Y/v4h1Yf3x/tD07jjsKAOY8beP9F8LWA1jWLxodFhl2XU8DAyD5SVIUHeVLhY8IGcs6gDGSADlYfCHjb4sxW974v1PU/DPhSVRIuhW8hh1HUV3Aq15MMNbqyAAwR4fDne6tlFAO8TRfDvg7RbTTtA0ux02yslMUNlbxCONo2bLoFXqSTuzj73J6mgAm8I2EmjzWF3bJdi4iEVzFIoZJIyQZE24AYMMjDZ9PWgD518ReENZ0XV18G3Gp3WozQ2lxH4UmnvZXPivRWhZp9DvZWaNVvoFLG2uJJDOFUT7ptuoq/wOOoy4VxjxdGN8JVdqkekW9Lpa6O702+zp7tu2D+sR5X8S28zqfhf8RdJ1k3PjDSbxrODXNQbw/riS6dLaINWijCW94+9VeKaSLyIHjcPIsn2a1bZLA6VXDtWWUY+eTSlzUpLnpO97xfRW7q76K6bt7wq69rBVeuzPSBE7ykLbZldATuAY788g5DfMcgfw8kZFfeHGTRwC8eK0O5ACYjuXcItzYGRyAcjHRDkjr1oA0tA0mSWYajfpl4yyqGVfvhsE5HJxjqecn2yQDo6AOc1Hx3oVpq0vh2weTVNXhGZ7SzXeLXmH/j4l/1cDbLhJFSRhJJGHaJJNjAeTmud4LJoc+KnZ9IrWT9F+rsvM0p0p1XaKPHtS8aaV4y8PaldePtd8Pan4et2kk1xbW8a38M2dscwXFveX0p2agYvKuo3j2pGxKCe3gLI4/O824qzLHy9hhYukpbJa1JdVtrFO6atbrZy2O6lh6cNZa/ka3hXQPFfxCjS91iM3emi6jvYdR1zSWtI1lhntGC2OlPiRFBt53juruQyxTFZI1ngdQOnKuCa1a1TFP2MduVO82ne/NLZXTs0lZrRpPUmpi0tI6/kfD3/AAVb8I6Tf6V4G+F3gDxd4c1vxi2po+u6ff3ccvirVZ2iYWDKQg2wBri9It0MUQe5XyogMhfvYU8JklGNKjTcYX+zFv5u132V3ds7cnyPFcRVKkMPUpqUVe1SpCnza7R53FN7vskndrS/sH/BK79kDxR+z14O8VeOviv4W/sjxx4h1CTSo7afy5JrPTbWQqQrrnAmnDudjMkkcVs4JBBr1Dwj7voAKACgAoAKACgAoAKAPIv2vv8Ak1L4xf8AYi65/wCkMtAHE/8ABOH/AJMm+Fn/AGDbn/0tuKAPpOgAoAKACgAoAKACgAoAKACgAoAKAPNPB37NnwN8B+IfFPivw18OtOi1XxprkXiTW7i6eW8Nxqcc8k8Vyq3DusLpLNI6eUECs2VAwMAHgv7Xvw6/a0l+L+h/GT4LQ+EPFXg/wjoa6rL4P8Q2r33m63YJqMkNxawBPMS5ZLxY4pLeRHMqRCQFEWgDkPH37dH7UHwF8B/CD4h/HL9me4i0nX7e9uviFPpVm6ppCPeCKxjhJuZPs1x5DxSvFd7RI8qxI8brKIgD64+Cfxv+Gv7Qvw/sviV8K/EMeq6Pdu0EgKmOezuUx5lvPEfmjlXcpweCrI6lkdWYA/Fr9hr9pX41+DfDOq/s1/AT4e3ereJ/HutxXv8Aa9ndAT2FusaRybEZPLjAC5ad5AqqW+6cOOPH069XDThhpcs7aP8ArvtfpufRcJYvK8BneGxGdUfa4ZS9+OuzTV9NXytqXL9q3K9GfTXwD1P4FeLYH+Ev7SN5ceFU0mxOm6bcTalJp9qbsXd1L5l1uwiXMRu5Y4jL+7w8ilSzAH4DJoYXHS+oYxNNKys2tbyeq/mV3a+lr9T+uvEqvn3CtF8V8NThOnKanUcoxk1BwpU0oNu/sqjpwc+R8zkoNPlTt9a/Ef4raR8NfCmpfAX9l6y0mfxv4e01TZ6NBl2tbfc3nvCpDLdXif6xoXbe3mGRhKVdG+oxeOhg6UsBliXtIrRdl1t3kt7PV3vrqj8K4d4WxHEePpcV8cymsFWm+ao9OaVlyKTTTp0ZfCpxXLHlVNcl4yXkHwZ/Z4s7O31Txp8UPE1pqmq+PLGSC31Rpre/h1Wz1LT1ecWkzO0a3qyNPh3WWOaAfICJZPL+CwsKWOhUrKtbE05KfLNcqvGWvNJ7300fLaWj01NvEbxKnxFGGSZTT9jl1P3YqL/iJP3JNWXLFJJxhZ2erbaXL7T4M1zxj4TvNb0W91q5tNb1G/hMep6toM91p2qF/wCz7K2u3eGK2jhndYjG1qJFPmyPIuYVjrqyXOq2DqRpYGn7SE1aNP2ivGV5Slo7u2/vaLltzXaufkdWkpq83ZrrY4j9suBPiJ8Otb0qPSVsn06BvFtn9p1vGoytaMLeSQ6cUbZZG3lzvWRHWaSISRJukats7zDJ8wnLEUIS53aKqWahJrXlfd22dr6LXlP1LwVzmtkXFlCnK3JXTpST3SlZxa1Vnzxinvo2krtHn37Jvjv/AIUr8KtOu9DkGsap47vrq9ubXxHqkuhaJpVtZx3QeS3uGt5vtdy62+6RIEdljUGTy1g3P6WQ4+hluEbhzVakndwguZxs7Xa6XWt3a6svX1vH3OXmnE/9nOlGCw0VHn+1PnjGpq/5Y3tGOtm5yv71l6d40+LP/Cf32o+GNa1Lwh4e8beHtP0zU201Gm1VdCtpNTim+13/AJsti8kEotrNmhEcclsg3ytieNK8jPM2nnDo1YUpKinNJ3s5ycbNRajJJrVJ6qTuls7fi9KkqV03rp8jwr4sXngXwX4Q+IvwN8X2jt4km1+68TxxW+lRTXM2sXBMrX8BRTFZWTLHJtje5urxbe6EcjRiMRvwZzQxOFxUKdRr3IqGj05Fqk9nKWqd3GMbrRPp+z+DXDec186oZ9hqSjh6cnzTqXjF8ydOSh/NKzfLa6UrKTV0nofC39mP4/8Ax58F6D4f+JGsHwt4H0W686CK5to2vppYke1ysX+sSSONDB+9KBU2lUcl8+5lmT4vNYQxEpKEOXl5la8o9tNWtLe9olayep91xRxtwBwjmlfMMow0MXjqkufmWtOE9HzKTvFNv370k5OXMpSjpbt/2JvC3hP4XfELxH8LvFtzBb/EWB4nF1Z307W9zCYklk04o6CFpoTGZCyBmKySbHKo9bZRleEddYXFfxqc4zTT6pX5dV03aW/f3TxPGrFY7irLcHxJl/M8AlKPLJRTTcuVVnaTly1PdjHmS5XFJqLqWf3BX6EfzQYviHxR4M0Sey0TxX4i0Wwm112tbK01C7iia/YlVaONJCDKcyICqg/fUdxUznGlFzm7JatvRJLqxpNuyPFNA+Gtpq+uWEl9JrbLoQmSwe+iEVvPrUF7M8mpPZMy25nl8157V4ovJRndnUmK1RPzvJa2Eq47Exwzg67vKlK3u8rStGySacUkmt1q0nZ37aqkoR5r26m34i+HV1pNvYeLLaG9HjuBL/SPD8tx4g1PVLC1MySGN7pZJovMhZYIZZoyud8aKrO8cUlZZ9leNw2XurmeL9pDmi3Hlim22rqEnqna9rK1r3SVx0akJTtTjZ+v5nzh8TNI8O3PwG1jwx4f8O6joHgvSrDRLrTblL/UtRurfQ4bq4BuLyOVlaGKC1tLt1s5sqjtD5jW8qsIfGqZdUeOpwr1OapNSc7QV1KyclFLSUr2vKLWia6NH3nhvm/9i8SYbHQUPc57e0qeyhrTnH3qjUlFa6Xi7uy0vc+h/wBkX4kfDHxT8FfCGh+Cfina+KpbHTjAq3E9vHqCRxSOgSS2jkcxqgXYvzMNiKdzdT+o5Valg6VKTd7faXLLTy8vnpbU8vj9zx/EWOx1KEORzTboz9rSvNXuqiSTc3du6j73MuVWaXyT+x//AMpWP2i/+wXqP/pfYV6Z8QfpRQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QAUAFABQAUAfAPwb/wCUyHx2/wCyf2f/AKI0KgD7vMyajcRxwkvbRHzXkX7ruCNqg98Hk47gD1FAF+gAoAKACgAoAKAGuSqlghYgZCjGT7c8UAYq63barqc2g29zYfbLJ4Zrm2+2o1zAu5XUvEuSu4Yxk8gg1vPC16dKNecGoS2k07O29ns7eRlGvSnUdGMk5LdXV1fut0fM3xb/AGyf+FbfEDx34Jg0iK/exFtBpU1q4aMzm3Uzee2/5Srtswi5+QqcEZH6vw/4XSzzLsJmHtXBT5nUTWvKpNR5NOsVe7dtU1daH57nHHyyrG4jB+zUnCyg09L2u+bXo3ay7NOzM79lvxJ+0/8AFLWIfEXim7sJPAssVzBLeXelWkDSSKAB9mEKJIzbyBvP7oBZRkuoWurxAyng7IsNLCYKDWL0aUZTdk7/AB8zlFK2tl79+V6RbZhwfmPEubV44jFSTw2t24xV2v5eVJt36/D8XVWPrr7XdKojawlMx+UHI2E+u4dB9QD7V+Ln6cQQm4sHe61IRnziBJOshwgz8o2kDavPXJ5OT6gA0uvIoAWgAoAKACgCJLm2kkMSXEbOMgqHBIx14oAe6JIu2RAw4OCMjigCtYfNJdzEAF7ggjuNqhRn67cj2YUAW6ACgAoAKACgAoAKAKVqwur2W8RWMQjWOJyMA8ksV9j8vPfHtQBNd3lvZR+ZO+M8Ko5Zz6KO5oAx5JLm5K3I8yzmf7xS0nZlA5APG1/TkY64oAux3OrIhneCO4hXoFRopSOMna2eevHGaALlreWt6nmWs6SDvtPI+o6j8aAEurZbhMgKJo8mJyPuN2P09R3oAiXUggUXttNbM20fMu5Mk4xuXI6+uOooAteZGJBCXG8qWC55wO/6igD5R/aE/ZR1a88a3Xxp+EOo+GdLu7mEHxJo+t22dM1KIMZJppgQ6HdsiLoUALIZQ4kGW+WzTJJuu8bg3FN/FGS92S3be67XVul73P3ngTxOw0crhwzxFCrUjF/uKtKVqtOVuWMYNOMla8lGXM7KXs3F03Zcr8DNH8G/tCeC4PHNt8M/D0upeGLW28JWVrrNgkun29wAs13dxwqXkmhVLqPy4ZXGDbusZthM8rfI4ivHPsZhMtptU04vmajrpzOyb1tpprbW7TtZefx5PiLgPPsVhaWNrRhOXtItVZLnUko88uTlXPpyybim+XqrN9WPg1P4YM2s23wu+H+pMEf7VN4Gt5PBniCGNP3higlS4P2rzpEiVo3urWMbCXMgIVfaxfB2IVJRwuKcrfZqWlHstGmlZXs+V+Vj8nq5jPFVpV8TeU5Ntyu3Jtu7bb1bb1bb16li21y7stFtPCba3451SysLWDTpdEn8MTr4ivrtVErCXVN6WTRuGaN7tQsRdn/0sEk0PFcR+y/s6nhowlspp+4o7aLWz7a3t9ky5aF/aOV/LqeneGtPtPA+i3/xV8eS2+mWWg6TPJ5NrC7Wuj6XDGXaKCJAS21IwXkVd0hRQqhFRB9BkWRUclo2XvVJayk92/8AL/h2Y1qzqvyPMLePU4vHHw50vW9H/s/+1NVkOt2yao7Wp8RXWl3mpzPPGVYXC2n2GGOBTM8Si7UhQ1nAR8lw1jI5lndXFVbSlJScXb4YxaiuXXS6bT0T0395nTiIuFJRR9PaZZ/YLKK2Ztzqo3tnOWx6+g6D2Ar9KOAo+MvCWg+PvCOt+BvFNl9s0bxDp9xpeoW/mNH5tvPG0ci7kIZSVYjKkEdQQaAPnb4N+H761+HfxD8RR6kws5LLXPDlzp3/ACxS90e8vdPe7gUYEaXAt9/lYPlKIoldo44wvg47PYY3C43LcRTviaSlJ1k9asZwuvaR29pHZ1FZ1buVRc95SeTZdPBY+lOlP9zJxtC3wNS15XvyvpF6RtaPu6LwP4f/AAT8VeJf2H/gX+0J8FLSFfi78JtKl1TRP3LMdZsfOm+1aVKEIaRZYzIEUc7mZVKeazV9Xm7lSxcK0dUoU1JW3Xs4/it1v1sr2a4clTr4SWHbt+8qta2Sl7WXys7JPZaK7smN+Dvx/tPjF8fPjr8dvgzvnv7n4LWd9p1myF5rbVYYn/0WRSBl0nTZ0w2Ay5VlJ86talgcXNSaXMmnbW3s3rbXVfPXuexSUK+OwVOsre61JX2/e6/hr0duh7j+xF8HfgvrP7EXhLR5vDei69pfjjRFvvFE11bxytqV9I7tcfaJMbneGbfGrMdyeUuCCoNdONp07Kna0VGOz291Xa7Pr5baWPPwlWpJurLWTlLfraTS33ukvJ79T5S8K2sfxB/ZA/Y98P8AjFjrukSfGu30pEv1E6Xemw3l/HHA4fIeLy0EW0jGxQuMCs8PD22Oouej9lUl21iotfki6kvY4WsorT2kEvSV7v72z6V/4KFeCvBuoP8As/XV/wCEtFuZr745eF9OupJrCJ2ntZhOJYHJXLRuI4wyHKtsXIOBQtZJv+tW/wA236tkz92m1HT/AIa35JL5I84+JGm/GO//AOCg93o3wg+Hfw01Cf4d/D3TV8KaV4tea3s7KweYebe6fDCpVJEmxbl1C7UVVHHTPCSb9vPS6lGN9mlyXsu6u22+l7d79GLhTSoQjezjKTXTm52tezslZddXurnon7Pvwn/aK0P9rjXvi38S9G+Fvhe28Q+EvsevaL4N1Scm/uvtZkttTuLaRRvl4uIvOY9NwHJbOlKMIRmu9reu3yVr6Lrr3MqvtJckktm035W2+Tt528rI+vza3E0xe5uiYgflhRdqkZ43Hqe3HA9qQy1QAUAFABQBDdWsN3H5coIIO5WU4ZG7EHsaAG21w7O9rcYE8YBJHAdT0YflyOx/AkAka5t0kELzxrI2MKWAJz04oAgvbu4iPk2Vr9on2FyCwUKMHGc9yRgDjPPIxmhjik2k3Y+LfBnjb9tz46trXiDwH478M6TpOnX76Z/odvbfZJJVGSYTPDLORtaNtzlc7xjkMB9vX4Ry3IMLh48T4mtDF1Y80oUFTcIJyai26kLvRWkk2+ZStpZup8Z5fmGNxEOFMto1sFSklGri54iNWpaMeZKOHrRhG8m3BuKXK483vXS6az8Sf8FC7WKJB8OPD8uxQD9ourNy5xyWIuweTzwQPTAr2J5TwDXwa9hmFWnVcdHODlZtbTjGmk7dVCaT6S6nx0c04nw+ZSeJwNOrQUrNUpuF4p705VJTlHmWsXUpyavrC+hr+Av2znXV7nw58bPAeoeCLmxW1hkurqGcRvcyZwrq0Q8kOFeRCxKFEk+f5Ru+ehwrDNKc6vDuKjjPZr34qMqVVWV21Rqe84PTllFvmk3FJtM+jzTE18hdJ57h3hVXb9lL2lOvRlG6SX1ii3T9pF39pCShyLlm7KaS+i49f03VNJ+36JqENyLi28+2aIh96lco49Qcgg9CCMZBr5SpTnSm6dRNSTs09Gmt013OmMozipRd0z5c+G0Nlq3/AAUE+IV/c2UE0lloG+GR1WQwXCRabAXQ4+RihlUkc4d1zgnPv4zB1aPDuDq0k3SqVq85veKqKNKnDyTcIzte7+Ozs2j1XmVOaxGFrzjGvTpYWnTS0nKhKeMxFW/VxVWdJu1o3VJtXUWfXFfOnmEc9xFbJ5kz4BOAO7HsAO59qAIII7ieYXV2nlhMiKLOdoP8Tdt2PToCeeaALdAFTSkSOyWGMELE8ka5OThXYf0oAt0AFABQAUAFABQA10SRGjkUMrAqwPQg9qAKtoZLRhYTtuXnyH9VH8B/2gPzAz2NAFp0SVGjkUMrgqwPQg9qAKNtcvaXY0u7lDllLW8hYbmXP3WHXI9e+PXNADtV2wxLerL5c8OfL54fjJQ+xxye2M8YoASxM9qVtLpVXzdzxBT8qc58vPsOnsDwMUAS3N/DA3kRlZblvuQKwDMcZ59B7mgDlPHPjq18H29tbajPJdavqkhi0vRtMAN1duMk/M3CRqMF5m2Ig+8wyAQDmfDfgLS/FOpXni74s2ularfRbl0/SpI/O0/SbRwuQiyj9/M5A8y4dQzYUKsa/KQCT4keLfE3haz0Lw5pF1/wjdnranTLDxDeM1+9hqIaIWMd1FKMSQ3B3QuzTJIZHhiR/NnWSPxc+x2Ky3CfWsLBT5WnJf3OtvPz1sru2hrRhGpLlk7f5mv8NvGFj4q0d7jUNFNl4osLltM13TXu0umsL0IrFDMAAYnjkiliYqjPDNExjRmKL34HG0cxw8MVQd4y2/VeqehE4OEnFnYaYHs4YtOnuftEyABtijES4+UH06cZ5P8ALrJMrx/4C0v4g+GpvDt7e3+mSCVbqy1HTZvIu7C7Q7kuIXwRuBzlWDI6s6SK6O6tjiMPSxdKVCvFSjLdMcZOLuj5h1ieTwR4u8SfEXxNetasLC30j4taLpySWpSBWeKw8U2ZQmeGMRJIXZZm2W6v+8M+nsj/AJHj8uxOV4iGXx+KMuahN9erg+jd7W0+PpyzuvThONSLn3+Jfr/XT0PZ/CPiHV9U1nU/DPii0jt/EOhXCyl7VkaK8s52la1u4lJDLvMUisjkFJopUUPH5csv6VkWcUs6wccRDR7SXaXX5dV5ed0cFak6UuVneaNpF9BcxTXqZjii2Ksh3FGyDleWA9MjHTpXsGQvinxtoPhEQW9/LJcalfK5sNLtFEt5elWRW8qPI+VWliDysVij8xWkdF+auXGY3D5fSdfEzUYrq/yXVvyWpUYSm7RR5dqHjLxh4wuZrS8Weyaxmt2k8K+HNSVtQjYsGX+0tQDIlt+7ntpjbQsrEQShJryNzEfznOeNK1dezwH7uDv78lq9/hWvZq+tm1flep3UsIlrPV9jndB8Pah4vsLfRPC2maXfaTdWnnNaWFsLfwXaLcySNJIHVFk1l2S5n3Ro32eVoAZVspHVzx5Xw1j80q+3rc1ON9Zzu6srW2Tfu7Kz3V7KUloXUrwpqy18lse06N4BsLXULXxF4huDrWuWqKYrmRSlvaS+W6O9rblmWAsJZl35aYpIUaV1AA/Q8pyHBZLC2Gj7z3k9ZP59vJWXzOGrWnVfvHU17Jkfgv8AtbfFT4MfEz9tvx5L8ZPh5q+jaBpXiS70TU9T8I3pm1q8TT4xZI5W8k+yfM1qhASOPYrkEysu5sJwqupGUJ2it1a9/no0/vXkephcVgaeDrUMTh3OrKzhNTceS17pxs4zi7rT3ZJpWla6f7m+CPCOj/D/AMF6B4C8PRvHpXhvS7XSLFHcuy29vEsUYLHJJ2oOScmtzyzboAKACgAoAKACgAoAKAPIv2vv+TUvjF/2Iuuf+kMtAHE/8E4f+TJvhZ/2Dbn/ANLbigD6ToAKACgAoAKACgAoAKACgAoAKACgAoAKAKuqaXpmuaZd6LrWnWuoadqEElrd2l1CssNxDIpV45EYFXRlJBUgggkGgD86PiR/wSNk0bWLif8AZe+Ll34P0vxZdxWXiSx1kfahZ6St5b3qGxlVfOMkNxaQMiu6u+1Q06AOZADi/wDgid8IvDNyvjf46XE92+vWMh8L20JKfZ47aVYLiSTG3d5pZFUEMAF3DB3ZAB9sfti/s46J8evhTq1pp/gfStY8Y2y276TNcFYZG2TozxNISFZTH5gCSEoGIPBAYeRnGBliaDqYZfvl8L2fmr+auux+i+HXFNDJczp4POqjeW1G/a02ueD0bhJws/hmoSbSvZddn8raJ/wT6+OsWh6Z410fWrXwl4q0vTr6QWQ1R5ria+zOsS5gKQrHJC0aFWmcDJYkbii/LYbhjFRpc0/dbTTV1d76J2ajdWW70ve97H7jxL408N5jiZYDk9vRclyzlCapRjKMIylUpc6lW9m3OSjKEbtRUOVpVHlX3x8/aN8HDw547+MvgGXw+/imH+y2u5rFtP14WunXhMm2PeFicvKJgGjMEyyRhlkj3IPmMyyKtlChSqWcXdpbxbsk79dFZX0a3i09Tg4c4D4I8RMLjK/D8q1KrDaM5K0Obm9m37sm4txd1zynZO+8b/fnwt0/QPEXhLQfHChNTj1CODXNHkvLJUl04T2axsIgWfySQ82QjYCzMgJXk/o3DmVUMvwsZ07SctVLlUZcr1SbV27ebZ/N2eYGvlOY18uxNuelOUHZtxvFtNq6WjtpotOhteI/hr4B8WaPcaFr/hLTbmzunmldVgETiWa3e2llSRMOkjW8skRkUhvLdlzgkV688DhqlB4VwXs30tZb36ba66dddzlwOPxOW4unjsLLlq05KUXo7Si7p2d07NbNNd0fnp8QLX4lfsN6/qGhaLa23ijw3qJkufDN5rEm1hdRw7grpDIBhZJYi4ePy3eKKVEV412/mOYU4YLE18ujJqk5Q57LVJ3ko3fl53dvVH9ORw+X+N+TQxFFqnnGHhZqTSVRXinOTjDWL1sopezlLla5XFuL41/tBeDZvCdxpPgXxXceLrrxPoN7od/LqMsxNnpt+pa4t5Yoytul5HiCMTwMxcrOJ1MkaSP4VF1KUlJx5HFxkrJbxtZpu7aer5XotHF2bR5vAvgpjMxzCVbP0lhacpRtGSbquMpQlFWd4xUo+9JqMmtIq75o+/fsbfs4+C7z4ZJ8VPiT4ePiLxH46huZbr/hILUT7LWV5UJVZQS32iNt7SHJdJQPuk7v0vhzK6caCxtZXqTu7vpq9Vfvvfrc8Pxp45xmIzSpwxl01TweHtDlpuyk1GN4ytpam/cUNouLvqlb1zT/ABLrHgLxRq7eLNQtjaX7WtvbaNDqEDNBcPc3EVvdo0zR7RfIIU8g7FS4t3WPzmleU+bhcxjwnOWWYqMpU1eVOSSfu7tNafC7tvzvZKx+Iyh9ZSqR36+pynxg+B/gD4+araahZ3V54K+JlrpVtqVjfmBlubUC4ibcWhkWO4mgMRiJjmkEP2hWP34GPoyhlvEydfA1f3kftJNNapptPlfSye29tVp+hcFeI+ZcFReAxEPb4KbfPRk1aScZRcU3GaUW5KUkl7zik2k5X8Y8XX/7bv7K+i2vjfxN8Q9N8ceGIdSVL20IlvHCuhAaaWSBZYY/lABWXaJGXIO4huGvLOskgq9WoqkL6rV/e2rpfPf1P1rKaHhj4n4meV4LBywmJcG4y92Cune0YxqOE5attOF3BOzXKmvozwXeaL8fPBkPxc8EX15pY8RWd1aG11rT4rmFjGxgUSorCQpGyTlEiuEQ/aZiwLN8nXXyXBcTwWPlKab21Xu2utFqtXq/Rao/AOJ8ixnBmbVclxbjKdO13G7T5oqS3SezWjX3qzKfjH4U2GneKLvxPcat4muW8RXiy3cqPJcWtnBb2kaW1tDbhmWKYXUa3kM6wlxMroJFMqI/n4rh6WU5bSjgoOpiITTjJKzu5fa1+G3uvVrZvRaeHGv7Wo+fSLRkfE/VPHd5oMejePtbsfD88UOjQ65/Y93HNY3cV6rW8sca3UUcn7y+Jh8pX89rdENvIlxMYguLcViaGMoRcFOnZzjF3tzw1bdtbKL0v7t3eSsm0YaMXF62e3yZ6d8RfhtZfEKxaK5ktj5lssRtbuyt7m1kmS4hntrqVZI2eR7aSJnij3rGTI+9SSjR/UZnlbx1WhiKcuWdKSd9dYvScfmvJ7W0TZz0qvs013/Poflx4w8VfAH9kv8Aak0jx58N/Gni288PaRq1jBq9951jdCUsGhuorY/ZmMlsLeSTcMhy4lMToSHb5/AfV45pTw2CjzRpqV5N9Xu9NHb4dtdOiTP6e4rWc1uBMZnfFFZUK2NnS5aUaabcaa9ym+b34KTTrNc7ULSaXNUqQfqv7H//AClY/aL/AOwXqP8A6X2Ffbn8uH6UUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QAUAFABQAUAfn98IYY5/8Agsf8dFlG5R4AsyVzw3+j6EMEdxz0oA/QBVVFCqoCgYAA4AoAWgAoAKACgAoAKAPB9c/bY/Z9sNF1C+0bxl/amoW9rLLaWP8AZ17B9qmVCUi8xoNqbmAXceBnJ6V+h4Xwu4krV4U69Dkg2lKXNTfKm9ZWU7uy1stXsfGYjj3I6dKU6VXmkk2lyzV3bRXcbK70v0Pzrvr6/wDiN49uNT1K703T73xRq7z3E88nkWdvLczFmd2Yny4lLkkknCjviv6bpUqeS5dGlSjKUaMEkkryahGySSteTS0Wl2fhVSpPNMa6lRqMqsrtvSKcnq29bJX+SPtf9n39iTw14dlXxh8Tr7RfFxmiil0u0tC02nBGVX89iwAnzkhVIMe35juLDZ+D8Y+KWKxsfqOUxnQs2pylZTum1yq1+S27d+a+miT5v1vhrgHD4V/WswlGre3KlrDo7v8Am8la1tdbq31ha2ttY20NlZW8Vvb28axQwxIESNFGFVVHAAAAAHSvxypUnVm6lRtybu29W29233P0uEI04qEFZLRJbJEtQUIQCMGgChNpNtBbTNp9ssdwUYxsh2tuxwM56Zxx0oAtWtzHdR703AqdrqwwVbHII/GgCagAoAKAK8thaShj5EaSNyJFQbg394HHXPNADbaaUTvZ3MiPIiLIGVNgYEkdMnpjn6igBLT93dXcDZJLrMGJzlWGB+RUj6AUAW6ACgAoAKACgCkb6aaQrp9ulxGo5lMu1CfRSAd3vjpQBAWudR3pJp9q6wSbDm5baxAGeicjnGD3B9KALCpq5OPNs41xwBGz7fbqM8d+PpQA6CzdJvtN1P58wXap2BVQd9o7Z78npQBaoAKAIJ7KzuWElxaxSMBgMyAkD60ARf2csQzaXE0T5yCZGkU+xViRj6YPuKAEN5DGrW2qvBGzAjLHakq+oz+ozkfTBIB8l6/+1tonhzxpqthrfxT0/SJhc6lpmnQReG7i/GmfY7ua2WTU5PNV51nNssiRW8cLKspJkdHjnPwmccVYzAY2WGo0E4wavd6yTSeltI77vm9L3R+lcM+FXEPFeVyzXL6V4XtG7UXN3tLk5rJqPVtxV01FuSaS+M/29PBn/CN6yNPfwdqcF1b31pYxx3t/dXUr+XiF59PksoAImZ03q1ygZRMI5HZCKJcZxnS1w0rvTVrl81fW9vTte1z1sm8GuLK+cRwM4KjKnyTlLng+SMnLll7sm226ckkrtNK9k7nPfBnQfilYfC9fGc+g+GtR07WfENnd6h4TstCs5LOxsYL4R3Txw25jkXULcRmQhvtMpNrHCY1nSvk8TisJOTjiOajioX5XFKC1jeKezWrevZ6ytqsvFDHZfjs+ccpqyrUqcI05TnUnUc5xcuaUXJv3dbWjaDacoKzTf0X8CNRsPGXge+e/8yafT9f1TTzb3cE1ve2VvFdP9jinimCzxMbM2sqiQAtHLFJysik/qmS154jL6NSpJSlyq7TTu1o9U2m77+Z+UVUozaR3cnhWyyXtpZIzxhX+dAMg4wevTua9MzPPPG/xNimvZdEttIlu9J06cQyS2F4PN1bVI2Yx6TZJlPNdTC7XDO6RRhBG5dTdG3+I4vzlUqLy3DSXtJL330hDq32veyVm7PRXcb9eGpXftJbL8Wcl8MdFsvEcrfEPxIsV5c31wLqzvbby3sZJpYAsuoWrJDC06+U0djDcSoXe0sUeNsXUjSdnCuULAYb21SNpy0V91Domruzk7zkk95W6JKcRV55WT/4f+tD6D09dthbrs2YiT5c5xwOM96+rOYsUAfNHwo1TR9K8Fa4ni7WU0vw5q3jH4h6RdXTSOqxXFx4pv1iLHY0calRMPNkZVVii8mTFeBkmAjnWb5rlVGm5V5xpyXKo39nGm41LXfM5XnBqMYyuk5OyhrGKx6yqWHxlWSjTTau725nJON7KyWkldtWbSWsjsvB2meF/2afhZ8M/h74Iku9a8HxX1voo1m91CFmt7W5WY207PGipOZbuS1tkVAuTdI/3UbPo8UYvF4WH1pxalRcVONndxhHkkmm1Zpe87/ajsTl1CjCny0neM3KSd76zk56NdLvTy6szfgn+zh8E/Anxd+IHx7+F8+rw6j43uZ7DWdOlxHY291DOfPaGF4llQvKjSZLsjCTdH8jIa2wGIo1cGvqrvTlaS/HRX2Su1bptpaxtWi5VlVn8STX3219dN+u7ucHq3/BOb4dynWdD8IfGj4u+C/BHiCaaa/8ABXh/xGsGjETf6+KOFomMcUmTuTJBDFRhcAbQjyxUJO6W1/y/rXu76hKT5nOPut9Vpr39f62PV9c/Zd+FWqeHvhj4S0uzvvD+i/CTXbHxB4esdKlRY/PtVdY0nMqO0iN5jM53CR2JYvkknWNSSxCxL1lyyj5Wkkn9yWnRdiJRUqLodHKMvnF3/FvUuftDfs/eFv2jfBmn+EfEuva/oU2ia1a+ItI1bQrsW97p+oW4cRTROysAQJHHTjOQQwBGbTumnsWmuVxavdW/r+tjjviP+xr4N+JVj4L1K/8AiL490fx74G05NKsfH2jaqtrr1zbBCrx3UwQrMrlmdgVB3M+CA8gam2puUHa6s10fr89fX0VpirQ5Ja6t+av/AFb0ND4HfsteCPgb4v1Xx1N4z8Z+PPHPiK0WxufEfi3Uvt99HYxlW+zRMEURQ7wrFcElgvOAABNRi4xSV3dvq/n/AF0vsrJx5pKTeysl0/r+lu7+40igoAKACgAoAr3F/bWz+U8m6UjKxINzt9AKAPCP2uviZc/Dj4YXFzaeILzRfEOuyLYaN9g8ppo1UiSd33MDs2L5ZdNxjaaMjJOR9Lwjw9PifN6WBs/Z35qjTtaEWnLW6fvaQvH3k5Jq1m1yZlnNPhzL62a1Ixk4RcYKSbTqzTVPaLi3B3rctS0JxpSi73UZeV+G/wBgy88VeFLDxl4u+JWtaf49v0gv2uZIhdC0f5GVZCzLLJKqDG4SrtbGMhfm+izzi/BYbG+xyPA4f2VK0VKdJTlPlsuZyupWaXuu/N1k29FxcP0MfPByjn2MxMva8zlTpV3RhDnu3GMFGVNLmleUeTkteMVFamjaeN/2v/gJZTeE9c+Gi/EG3MMlro2sWEc11IZFjTymm8nMjRoM5SRI3ch8SkDcdnl3C/F1sVh8Qsvqu/PTmueDk9VKEueCUU+ZNe70tGCtzcHt8z4bl7KrSlmFFcslOElRmoJuMoTi4VP3klyOMoupGKbu6jbUO/8A2IPh74t+HPwcuNM8ZaPcaZeX+szXsVvcqUmEPkwRAuh5QlonODzjB714/HFbBPE4XBYGv7aOHoU6Tn/M4X1vre6avZvW66HZk+IxWYVsbmuLoKhLFYirW9mtoKo78q0Wid0tFpZ2PoOvij2zmPiL8N/B/wAVfC9x4R8baTHfWMx8yM/dltpgpCzQv1jkUMwDDsSDkEgilOElUpTlCcdYyi3GUXa14yVmnZtXXRtbM7svzCpl1VzjGM4yVpQnFTp1I3T5KkJJxnFtJ2a3SatJJr50/Zs1bXfhB8c/FX7M+v65b3OjrG+p+Hw0jKI3IWYwwK+X+aGRnZC5CtbuRks7H9D4xqxz7K8FxM3FVJ3o1kmv4sU3F8qWjlBSlJOV4x9mldXk/lOHsHDLa+MyWjTqclPlr0pSi/4E3yT5p81mqdZwpwlGC55Oq5OLUYK9rUWsaJ+334fnOrWSQeIdAlX7Nbho5WtUtpcCYiMLKTPAzDLMQsS5ICotc9HDYrF8DVJc1oUsUpaveLpKHKut1OXM72Vno29Fs8bluH4iVJUm6tTB8l7XtUWJ9pz6vSLox9n7uvNdONnzP6cuLvynWCGIzTPyEBwFH95j/COP8Aa+EPXEhtCHFxdOJpxna23AQHso5x9ep9egABZoAKAKGleYpvIpGJEd3Js4xgNhsfmxoAv0AFABQAUAFABQAUAR3ECXMLQuWAboVOCpByCPcHBoAyxqVzcTHSYJoUuoxtlmYgjp1Rc5J7kdByOaAMGWxu5TLEglmuLd8tLHliz+7eo+o4I4BDAgHS6fbX7mO61ZozMkexUQDAz1Y/7R4HHHBx1oAg1HSLJljjgsd8xcFcltqoCNwLc7RjIAHQnIFAHld38Tbu58S3nhf4feHbOXXbK7ktL69ubk3FjYIGG6R2DBpZ+VX7OpVg3mFnCBHcA0/Cng3TNCXUdSuby61PxBqsiy3mrXIDy30o+VY2CqPLUAERxqFRR0A5LAHZ+HbZbmRJ7xdzopkjZmJMp38sc/3SAMdiSe4NAGn4k8PaT4s0O98Na/YwX2lanC9rfWlxEskVzA4KvE6sCGVgSCCMEEjvQB4CmjeMfht4hisxZ3/iPX9DsktbC8udTSKTxroeJW+xTSSBYv7StX3SI0o+bAYXEKXl55PwNVPhDMPbRv9TqvVJfBLv6enS6s3GN+1f7TC32l+J714S1jRvEPh2x1/QLqe5sdRiFxFLcQyQznPUSxyKskci42tG6q6FSrKpUgfeQnGpFTg7p6prZo4mraM16oDgfin4I1vWhY+M/AhsY/GPh5ZBYi9OyC/tZGja40+WQKzRRzeVHiVVYxyRxSFJAhjbys5yijnWFeGradU+z6Pz811X3mlKq6UuZHhXhV9E0uHwrqmjWd1YaLbveT+DJmQwXWloI3bUfDN3EWJVI/s0skSKREhsooyqNZQPP+b5TiMZlubcktKjlGFVPaSbSVRba6q7et3za88lHvqxjUpXW26/y/r9D1vxd8QPFVx4gbwjBLD4MgmmlhtruV4b7WdUSMBJJbCyQSokSPPbObiYSFQJFktkVkmH0uecYLBudDAw5pxdpSaajF66O9rvR22T6N7HPRwvPaU3oed6YPFfjZrrw94Fg1CCztz9g1m1t9ZDXyXBtnHlatrzSTSRyIEMLxWP2i7hb7JJ58cbgV83gsmzHP6v1qu+b/AKeTvy2vf3KenMuq5koO7TjfU6J1YUVyr7l+r/pnqmhfBTRYbI6f4tmtdXsEnuJIdGtLL7Do6pK1xuEtoHf7UzrcZl+0vKjTIJkSJun3WWcMYHLZKs17Srp70tXpa1lsrW06paXOOpiJ1NNl2PSK+iMAoAKAP5+P2ifiN8Rv2cP22fi1q/wp+JNrFqJ8X3+pJqGmeVdRwSXNwLp4f3isqyxlvs0wAydk8LZRnVspUKU6irSiuZbO2q+Z6FHNsfh8HUy6lWkqFRpygpPkk0003HZtNLW19F2P3d+FPi3U/H3wu8HeO9a0Y6RqHiPQNP1a7087s2c1xbpK8PzAN8jOV5APHIrU886qgAoAKACgAoAKACgAoA8i/a+/5NS+MX/Yi65/6Qy0AcT/AME4f+TJvhZ/2Dbn/wBLbigD6ToAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA/OH/giP8A8kU+IP8A2NMf/pJHQB+j1AGD458c+Fvhr4WvfGvjXVP7O0bTvL+03PkSTeX5kixp8kas5y7qOAeuTxk1hicTSwdJ1qztFbvV7u3Q9XJMkx/EePp5ZllPnrTvyxvGN+WLk9ZNJWSb1f4nwdd6P+0D+3f441O2svixpGk/DrwrctZOmiR6hb6bqkFxMZoZJbedFe7uoY4oN6yNHGki5jwJCz/LV6tfiN1MPhpRVBOPvWkm9LvTZ2fR26ep+8ZZgMp8F4YLOc7pVZZnONb9ypUXCPvcsW5JOUFKDVpRc23zrZOL+9fBvhPRfA/hmw8LeH9K0/TrKyRttvp9t9nt1kdi8jJHltgaRnbG4n5uSetfWUqcaMFCKSS7Ky+S6H8/ZhjauY4qeKrTlOUnvOTnK2y5pO3M0rK9ltstjE+IfiLxZpMmm23g59EjlFysmotrAkSGS3dJUhgilQ/JPLc+QoYpKFTzCUZjGrfOcTZ9DKKHs6cv3strK7S6ys9LLW1935JtZ4ei6ru9j4K+JnxV1nwncw+DNZ8PWfxG+JmrW8l5pPie/wBLtprzSjqUtwY7JLSS3+0RzQJdTQrazqV2vC2xQzWy/n2YY6eYVZ4hzcYya5oJvllypK7afK07Kzjtqr6KT/cPC7w2r57/AMLeYVHh8HSTaqJqEpNXvyyfwxjrzzemnKteZw5bTvgr4I8IXU/w+8dak0XjC702KXUdKguYrjWLKK5YrAbaygllW/Ec1t9paOAm5ktwW/0YhbXUN8Nl+IxuHvQi5Sk7xdvddldrm05X9nVcvNeOrV4evxL434yrxFQxWXK2Fw0pLl5pfvk2480k4q3NDWKcZOm25Xb0Ppz9j74o+IPBOoyfsu/FoGDXtIubmDw7OXZ/tttEhlaNVKhljVAzxSvhXQlFwYsH6rhLOo4ilHCVHrry+lrtPtbpf0WxxeLfDGEzmiuOuH9aFSMXWVkuScnypt3s5N2jUhG7jL3pXU7nqvxeluvA/iV/if4c8Prq+pyWGmaPcaet4befUJW1IR2EcMjZijZTe34w+xJGnjWSRIwXTbiWNSWOwcMLPkrN1OWW6Xu6ppp7uyv01dmfg9C3JJyV1oc8v9lwa/oE/hXRy9n4W0tNcbSJ7aTSv7PvLtLiKy1GVEt42t45lGpw3UfluYnZHa3UeZJXnqrXxMVnGFpOOIpz9nWjGz5lG3Oktbvaz3S0u7Jl2UX7KT91q6/Q9Tmbwx8WfDOt+CfEujzRLdWzWmqaTeMgnjhl3KkoMbMu19rNHKjHDIwyskbqn12FxeDz3B+0ovmhLR90+qfZr/JrSzNMBjcVkmNp4zDPlqU2pJ7rTutmns09Grp6XPjH4W/E/wAV/sc/EvU/gb4vM2o+DL6aT+xtR1K7eO0S+2LumjmEbeTbySkCaJRIIJGdsuwkaX4+lm9bhutPA1n7SEbWSsmk9fPo9m9dLWTP6Kz7hzAeLeRT4ryOmo5hGT9rTjzO6VlGLUmryjBJqcIpTanGzlZR+j9e+MWsa+2taPoS65pE2lXMtsy6VpYutSkVI7KVrjbPGyWqoLiUGCaB57iMJLbo2+IS9GK4izOMYwlShRb1cqkrJJ3aSWjk+Xlu43s3ZqL2/nCNCnvdv0POLLxT4dsYtO8T/F7x34ss7hbPTPEuq6bqVnLY30d5MbPTFvcWrqllaxmGXzLZZHVjeTF1OY/M8zEYeGdRqYvF4mCmrRXIlNKHNvK7Sbbkm2knGy2b5T6PIuGs0zyrGjl1CUk+ezd1GU4QdRwUrWc3GL5Y3u73do3a6KLwF8VPjVb6z4M8fQ3GkeBZrrVdD1XSLpIokVIJUbS73R5I4UmdVaNGc3LGNudqFSFT6TC5fisRh/qeIlJ01zRlzO8n714zi7Xfz0stEfY0M1yLgSVLMMon7bGWo1aU19nmhy16GIjzOEU05cqpp1It+9UVlfmfjx+yP4q8Kfs4X/hX9kHxvq3gbxZp99Frd1fw3dyL3X44YJY3tJJoPnUkSeYixxlDIgUInmGRfby/LcPllP2dBb7t7v1/4Gh8XxdxrnHG+LWLzaony3UYxVowTd2or83JuTSSbdlb5U/4Jj+P7f4pft6/F34gWd1qF1BrXhi9uIrrUUhS6uUF/YqJ5khVYkkkCh2SNQisxVeAK7z5M/WWgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gAoAKACgAoA+Afg3/AMpkPjt/2T+z/wDRGhUAff1ABQAUAFABQB5j8fvjpovwG8IQ+Ib7T11TUL25W2sdMF2sDz95H3EMQiL1YI3zNGpxuyPrOEOE6/FuNeGpy5IRV5T5XJLsraK7eybWik1e1j57iTiGjw7hVXnHmlJ2jG9r9310S62erS63Piv4n/tq/FDxfd67YeFdSl0bw/rVraxRWrRR/adPYQoLhYriMKzB5fM+dudpGBGeB+75D4X5TlsKNTGQVStTcm3d8s/efI5Rd0rRtotL7uW7/Jc248zHGyqww0uSlNRsrK8dFzWkrPV31ettlHY8v+FHwh8S/GDUNY0vwxLbx3Ok6XJqQFwxRJ2EiIkAf7qO5f5S5C/KckAEj6viPibB8MUaVfG35ZzUNNbXTbk1u0ra2u9dEz57JMjxOfVKlLC25oxctetmla+ybvpeyOevNMl8I+ILvSvEempJqWial9lu9NmO6CRonZZopJIpFbG5Av7s8hmIdcAn1KddZjho1sLO0KkLxmtGuZJxaUotbO/vLRpJxd3bgnSeCryp4iPvwlZxe2jfMm0091bR662a0v8Aozo37Yv7MsWhWNpZeMTokSWkUcNgNEul+wqEAWHEcTRDYMLhCU+XgkYNfzBi/DXiydecp0PaNt3l7SD5nf4vekpa76pPXVXP3jD8ccPRpQjGryKytHkl7um2kWtNtG12dj1/wVr9p4q8J6V4l06WWWz1S2S7tnlXa7Qv80ZYdjtK596+GxuEqZfiamErfHTk4u214uz/ABR9ZhcRDGUIYil8M0pL0aujbrlNwoAKAKM0T2Mr3tspMbZa4iHfj76/7XHI7/WgC4jpIiyRsGVgCpByCD3oAdQAUAFAFa8t5ZVWW2dUuIsmNmHB9VPsf8D2oAoxX6TXkF8trNsNtiVkQvtLbWVcDk4Geg/iFAGpHJHMgkjcMpyMj1Bwf1oAfQBUikuI79rWZxJHJGZYmIAIwQGU4643Lg4H49aALdAFB5p79vJtkZLcn55z0de6pznnpu6dSO1AF4AKAqgAAYAHagCndxJbOdTiwjLjzzzh4xnOR6gHOcZ4x3oAu0AFABQAUAFABQAUAfB/7G3hjwN4w8Sa/e/HTwbaS+PPGlvJeQ2Wu2DK99BE8X266ezlUJBLJdfvNpRDuScQKscMix/A8OUqWMx2LWMinO+qdna8pOSS2dny6620111/o7xM4kx2UZVlmE4ZxtWWDpQilVinB83JenCVSMYe8qMv4ekuWzqRctT6xk+F/wAMbGxg8I2fw68NWuiw3g1T7P8A2PA0CXQQr9oRGQorqAql8Zw4GRX2ccBhYw9mqceW97WVr9/XzPxGrxZn1bEvGzxtV1XHkcvaT5nC9+Ru93G+vLtfWxB4g+CXgDxPrV34huPDFhb6xqkEcFz4m0lm0zWpLdWRvJN9ZmK4KEonyiQLhcEHAzeJwlDGQ9niIKcd7NJq/wAzwYycHeLscPf6tr/gPXZNV8YasuhRabNa2d14oFhDd2Gv6ZD5sxGpTRxRyafLAgumLF47SOS7Vg0297ZPh44DMeFK1Spl1P22Hnq4XalHslu3utUm2lqlZM6+eniUlN2kupe+IXxc1q1Wx8M6ncxfD66u/MkvddvZ0fT4LWC1Nzdy2l0+2KWRUWVUSdY3UQz3LwPDBtl7K3GOGrYB1sBFyq6JQtqm3ZN2vdXttu2o6N6QsLJTtPbuc14E8HXHxa8RP/b2lSSeCfD6zadBbXuZ4iQfKe1Y3Ad724li80X08mBEJRZRmSX+0ZDwcM5HLET/ALRxet3zX6zle99NoRfwr7TXO9ORK8RW5VyR/wCG/wCD/wAN3PQvGPj9tG1a4Twp4L17xdfQvC0tlorWatb8OfNd7u4ggyegj83efND7duWH12Y53gMpcY4yoot7Kzb+5Ju3mc0KU6nwo09N+IUr6la+EBp8ljrTWtxcwadeRqZp7O2mSB7lHikaN0LPE20NvRZ4fMWNnC0ZVnWDzmm54SV7WumrNX7/APAutNwqUpUnaR6BXqmZ4F8Bb3Rl8I6zo+sCxnjv/Hnj0yWlyqv5sCeKdR3sIz95VZowTggF19RXxOVTq0eMMVWpNpqC1V1Z2p9Vt1OmvThVwihUSab2evcs634L8Mp46sfAHw9002emeJopb7xRpdpttbOxggQeRfxIuyW2vWnNuIpYSu7yGcgtCHT9mq1nnmR16udv2nJaFOUrucpSd5U3J3UqcY80pKWsXKKi1z2fyCoRy3M6dHLVyqd5VIqygopWUrbqcpWSa0kk7p8t1meGfGV5pvju+0nw74m8Par4u0y1b/hLtJgeZodZS3byI7yF43MNldjOJI2ikkIEdu7bI4J1/GJ4LN+AI+0xuGqPAVZL2dRppJyTlZNqzulfdJpOS6n1NLHYLMqjo0K0XVjfmipJtWdndJ3Vno9NHoz6EgnhuYY7m2mSWKVQ8ciMGV1IyCCOCCO9fbkD6ACgCKe6tbUA3NxFFuzje4XOPTNAENkpkea8ZWBnYBAwIIRRgDB6ZO5v+BUAW6ACgBrukaNJIwVUBZiegA70AfNXi/8Abz+FGg32raf4f0bXfEI0ogPf2sUS2LjcqF1kZ92wOyqGKBWJG0kMGP2WV8E47GYT+0sdOGFwzUXGpVnGMZqV7curfb4+W6aceZHnZjmUMNjYZTl8J4vGtzUqFGFSdSm4WuprkUW7XbVN1HDllGooSVjBu/2uPi/FbS3ms/sreL4bWCNpZSs91BEiKCWZ2+x5wBkk5AwORX1WH8NsqrKEHnFLndtI8stX0X7xN66LRN9lsfG4rjbHUZzdPLarppu0pXi+VbOS5JKLtq1zNLu9yrF/wUO8B29iZLf4Zawl66F3iW4hEZlxwPM6kZ/i2ZxzjtXTT8F8ydRqpiYKN9GlJvl7tWST/u3a/vGFXxOy9Qi6VGblbVPlSUuqTTd1/esn/dQ/4Y+GfiX+0p8XNM+NfxV8NS6P4O0GPz/DulyzNsaQtmNlRhl+QHeUqm8pEBlRtXhzHH5XwfkdXJ8nxSxFfEP95UilyqnbSKabWzt8Ut5v3bxS9GGEx3EWb0sbmWEeGp4dWVObfO6l/elJOMWveWi5YpKMF77U5S+vK/KD7kKACgAoAKAPCv2jfgT4i+IWr+HfiV8OPFOn+HPGPhMn7Nc3cWI7pDIrIkkoDFQh80hSjq3murLhiR7GTZ5VyaVSDowrUKqSqUp6c3K7xcZpOUJQbdmk1q7q/LKPQ8NgMyw6w+NqVaNSDcqVak1J03KPLUUqUnGFWFVKCmnKElyRalbmhPhvhP8As9/Fiw+M8vxn+L/i7TtT1yO3ZbKWyDbZJni+zFnUxRqqrG+AFXBLbs/KQ33fEvGuT4vIYZFkmHlTp3u+fpZ82nvSbbk9W3oumun53kfDOZYTN55pmdeM5NWXL16a3jG1klolv17/AFTb20VrH5cYPJ3MzHLM3ck9zX5YfeEtABQAUAULJ3GpajCysAGikXI4IKAZHryp/KgC/QAUAFABQAUAFABQBR1GG9mjm8t8RJESqISHkfk4JHQdOnJ9RQBBBY6JbWcc1ytsyyAESTheSRnAzwB7DigCO8s4o7uyhsmaBJ2cHynKgALnKgcAkAj8fUAgA8a+JH7Tnwz8M2d5B4D8SWmqarFaXFz/AGjaut7YW3lHEjzkSr56xqtw7bXWNPssiSz2zFCeDF45YZWguaX3JPonLu20kknJ3VosuMObc5Hwzrvxk8VnWD8U/EfiF9L+2xrqnhix0s6TfQ2sLtFPPaFYPOmtHkTeYYbi4kaNsR3UpxFP4uI4jeW4qnRx8HGE9p7RXZdXpezb5X15Emaxoe0i3B6rp/X9eZ9A2Phnwdqfh7Sb3wZb6ZDYx2Yk0ifT0QQfZpV3DyynHlurZyOuc89/qE01dHOUYbGW51NNPKeRMR8wBI8ojGT054Xjr1ByTk0wOvlsIzapBbHyWhA8lxyUI/mPX1FAD7OZ7i2SWRQHIIbH3SQcZHscZHsRQBgfEDwPZePNBOnPcvYanZyC90fVIQfP0y+RSI7iPBGcbmV4ydksbyRSBo5HVufF4WljqEsPXjeMlZr+uq3T6PUqMnB8yPEfAXj3UPh74g1S+8Xva6PBE6/8LG0xhIING1F1VINesx85TTLtYnMrl2ij2ebIYZYNSJ+QyfE1shxf9i41/u5P9zJ9V/K3pr0Ste+mzidNWKrR9rDfqfSlfbnIcP49+KWi+FLo+E7KeS68W39lJPpemW9m15M55VJpIUdCtuJNqtNI8UQZlVpULCuTG4/DZdS9tipqMfP8kt2/JalQhKbtFHhV0LS58R3Hg3VrY6a/i/Tf7e1XTrq/s77xZaapHcL9k1lbK382IpDJDDGssXngSR2ahIo7dyfyh4zMM1rxx+Hi516ctJQjJQcdPcvZPS7+K14tptto9LkhTXJLRPvv6nb/AA4+GnifVIIn8RI+m28unQDW9RNv5V/4ovXhLtu3/vrSwgeeUJC4SYvlAsMEeLv63A8PVcdi55jnCu3K8IXTUVsm7aN2S01Vlrd7c066hFU6XzZ7Ho1pYaDY2Xhyw0q002zsYI7SxtrOERW0cMabUjjRQFjVVXAQcAAAZAr7U5DToAKAI7edLmISoGAJIIYYIIOCD9CCKAJKAPxL/wCCiPwR+AXhX40+KvDPws0j4u+LPjB4p1ldc1CFVhbRrGXUp2uPJijS08+5dt+1ER9o3jMrvG8VZVK9KlKMakknLa7Sv6dzvwmV47H0qlfCUZ1IUlebjFyUFq7yaTUVZN3dtn2P2Q+Guu33ij4deFfEup+Hp9AvNW0SxvrjSZ4DBLp8ssCO1u8bco0ZYoVPIK47VqcB0lABQAUAFABQAUAFABQB5F+19/yal8Yv+xF1z/0hloA4n/gnD/yZN8LP+wbc/wDpbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAH5w/8ER/+SKfEH/saY/8A0kjoA/R6gD5z/b90/wAaah+zZrg8H2s11HaXEN9q9tE0CmawgDysMy9MSpA3yENlR/DuB8TiClUr4GVOFrNrmb6RTu38rbK77I/T/CDH4PK+KaWNxXM5xjP2UYrWpVlHkjDZpcyk/ebjFOzlJJMt/sIJYL+zL4Xez8P/ANlzStcyXrCaOdby5Mrb5llRV8xekY3KHQReWc+XknD0KcMBGNNaXertrrvpf03drWu7XDxgxGMxXFdarjZpycadox57U1yL3PfUXo7yvyxU+bn5Y83Kvd9U1TTNE0261nWtRtbDT7GF7m6u7qZYoYIkUs8juxCqqgEliQABk17E6kKa5puy0Wvduy+96LzPzFJvY+OP2rvjTr/hLxnd+FfBmoQ3XjPVhYwwaTcWlxdQ2kNtMZLQ2sTqYJL+aa483zMEbLaOLa0ixu/5VxLipYzHydWm4KCcVsm07qTk078trqK6qV72bT/Z/DPw9jnqWcZvdZfCXvtPeyvrqnGmnZVKivy36JTqUuK+GXguy+ANhL8V/H32nSfFCxa5ba1rPiCxm1E2sqw30r3NrbIYpbiPyrRZ2uLaeedoZJ1eKFHee348sftoVsNh489ScY8vLbSLtzpt3UG4ycbyikrNJu6vr4keJuJ4nl/ZeXv2WXQaUYJcrkor3XJL7KavGCskuVuPNFNb1hrXxZ+Fdt4sPi9vBvhLTbS+Grp/wr9bGxur52ktLeVtVTU/tFvDJ5N3ZuD9ojAaB1aYw7Jmyjh8Lj6lGNFSqSkuX965cq+K3I4ODavGSbt12umj8n5pQTvp6fre5y/7Sfw01mw+Fek/GmO18T6B4p8L66Rd3+ui3bWb+a4l81Jnn0/zLdFgIgWArKsaKXiAjZER+qGXyo4FZnGpFJyioKPMkuXmTtzJPmur/KTu2z918C+IuXNqvC+Ljz4fFQleLSa5lG7ur25ZU+aMtG5NQWyZ9fX+mN8X/hz4a8aafNe2j61oUdxN5M0kNytvdW6TK8CrJ5KXcUy28kUzrIY9kioV81mr73MsDUznCUMThmo1YuM4N7dG07Xdmt0t2kfh+b4L+xM1xWWz1VKc6btr8MnG6vbt1S9DB+H/AI98MaX4qXw5J41N/qOoaheWkM2oXn77UjCfMn+zJki58mZrpJ1iCG0lXyvJig2BflMvrV8LUlm06saWq9vTcZK13bmsrvml8S0WrfRu/JNKS9mlfsyPxSnh5bqWwtLm/wBIewuLXT4dNk1m1tons9RvBZx2zRNuiS0nlto2icFrtQ0sUCxsRA2GOx2CzLFKhk8Gp1ppup7yXNBaOMY63XNzNtJX1lfdVCE6cb1Xolt6nkP7S2k+C/FPwZ8Xan4jQeIdZ8MzzKmtW2o29npeg30c8UQ0m2kl2m4YfafLfZETcSwNE7xTRwQRdWYYTJ8mw39mwTniHZtpPmvZtyk9bRSTfLfRa/3j9N8IOIsdlPFOHhTrezo15KnNNOUZXfuqya97maUZ/Zcne8eaLzY/ir4z8OfsP+B/iob/AFfWdY025u7BHJeSBWee6to5L8ggyQpFtVEc7DMbcsHIXGuJrYmWU4fEShCpFOSlzx5mtWotX0S9eqitbn2eO4HyzPfFbE5NK1Gh7tRxi+Ry/dQlKMFbWUpNyaVrQU2mrHo/7Kv7N3i/wbr118c/i54nvr3xt4jsyr2aTMkdtDMI3KTrtXdIpRV8sfuo9uFDFUK+3w/kUsA/rVbScl8K0ST11SS18tl91vnPE7xDy7NcHDhTh2hGGCoS0k0m5OHNFODu7Rd2+d/vJ3vJq8lL6dr6o/ETn/iB8QPBnws8Gat8QviF4htdD8PaHbm5vr65J2RJkAAAAs7sxVERQXd2VVDMwBAPxl/ZNvoPAn/BT2Pwr+y/rGkN4M1rWL/TolmvJbi0ufDwha6mjWUb5HdY4N0JY5MsUQkbaXNAH7dUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gDO1bxDoWgy6fDrWrWti+q3a2FkJ5Ann3LKzLEuerEI2B3PA5IFdOHwWIxanKhByUI80rK9oppNvyV1ftvsYVsTRw7gq0lHmfKr9X2Xm7GjXMbkV1dWtjazX19cxW9tbxtLNNK4RI0UZZmY8AAAkk8AVdOnOrNU6abk3ZJatt7JLuTOcacXObslq29kj5n1j/goF8H9M1uTTLTQPE2o2lvPNDLewQwBJAhISSFWlBdWIz82wgEHGeK/VcN4PZ3Xw6qzqU4SaTUW5XV91K0dGvLmV+vU/Pq/iTldKs6cYTlFNptKNnbZr3tU/O2h8U/Grx5H8SfiVrfi2z1XWb3T724Z7BdV4mtYGJcW4USSKqIzuFCtgjnC5Kj954Xyh5JlVHBThCM4r3uTaUlpzX5YtuSSbur30u7Xf5Jn2YrNcwqYqEpOLfu826W/La8kkm2lZ+dlex9TfCT9jj4Ty6Dp3iDxD4mt/HUurZa1Njevb6cFwFYExkSsVkEg3b0yQAUBDA/i3FHilndHFTweEo/VnHR8yUp907NcqTjbS0u6k00fp+QcA5VUoRxOIqe35v5XaHZrR8zs763XZxWp6vqfwOl8H+JpfH3wRtdN8M+Iby3Wwv8ASbi0LeHr+PkfPHEBJA+RG4eLjKYZfnkY/IYfi9ZnhI5XxIpVqKfMpxf76D8pSupLdNSV9bqXuxS+krcOfUcQ8fkjjTqNWcGv3cl5paxezvHtqvebOr134ZeDviF4b+xfFDwho39u32kR2Ml4ttFc3Vg00bbo7a4kRnBjlMxQ9BwcZzXkYLiDGZBi3/ZVefsY1OZRbkozSenPFNJ8ySUl20uejisnw2b4f/hQox9pKFm7JuLa15ZNN6Ntp99T83fhnoulQ/HHw74Xvo9K8Q2D+JINKZzve0uke4EImUAqXX5hIob5ThQ6spZT/VOf4mtV4dxGKpOVGfsZTWynFqPNZ72f2XbVa8rTs1+AZRQpU86o4eoo1I+0UerjJc1rra66q+j6pq6P1i0xLaCyhsrW3jt0tESAQRLtSHaowij0AxjHGMV/G05yqSc5u7erb3bP6XjGMIqMVZIfdXP2Z4WeSNI3dlctxgBGbOc8fdqSiSCeO4jEsRJUkjlSDkHBBB5HINAElABQBm2uoWttctpE0yJJEQsQJ4ZD90fUZAx1OM96ANKgAoAKAI7h3jgkkjCl1UlQ3TPbNADLO1Szt1gTHHLEDG5jyTjtk9qAGS2mJDc2hEc2csM4WT2Yev8AtYyPccUAKLqaOVY7uBYxJwro5Zd3HBOBjJPHr7HAIBFqqAQx3Rh3i2cSttxvCjk7SfcDPqMigBoLaqSY52SzHy/JlWlIPPPUL24wTz26gF9VVFCIoVVGAAMACgBaAGuiSI0cihlYEMCOCD2oASFHjjVHkMhUY3EYJ9M+/r/SgB9ABQAUAFABQAUAfC9zP4V+FX/BQnUdS8Z+ENQjuvFN/p6+EZLBbb7MIr2yjtZ7yUFi/mNefa0OPLYKrsRIJBn4fGRWA4jo1IaRmtUu8rxXbS9m731uz+hstp51xp4XVcPen7LAzm7z53NxpxVRKL1inGM5xXePLG0EnKX3DdwSSqskBVZom3IWHHuD7Ecfr2r7g/nkp3HnWemPBCzx+XIsKv1IRnABX1wrAc9xQAWcVrd24tniRhaExtCoJhLDIByRz64ycZ555oA8C+IvgDUfCWu+HfC/w90rT7rw14w1O40zxJ4W1S3N5oUdmdNu52KRlf8ARd72tvAdmYd15JI9vNI+a+H4vyzA0sNLMrclWLVpRdm3e3za36PT4kjrw1Sbl7PdB4cvPjBe+HIfEFp8MPAc2l+LpIbjULd/iXqV1ai1ltYShgjk0sxFWiyWiURxs+WJZpXesKPDGaTowisfOMVFWXK00t7NKfTbd220SRTxFNN+4v6+RseGvg6W8J2PhDxjD4a1bRrWGaG38PaJ4eGkeHnj5ASSxaWcT/K8wMbTPCSVfyleNXHu5fwxgsvrvFNyqVH9qb5mvTRa+e/mYzxE5rl2XkSXXwvaKa1FjbaB4n060uLm4stA8TQtI2kzvlQ9jdCKaSOMI1wpiZJMLMqRvDFCIn5cw4UpV6rxGX1Xh6j3cL2a9E42e2zt3TbuVDEtLlmuZeZzWseN/A/wn8V3Nj4+8baP8Mb2OeIWthfeMGg0PWdNZDua3S7iS3SWOSV5JI7VEnEkVt5soiuB5nkSqZ7w5iYwbniqLT+y279r+9JdN7ppuyutNbUa8b6RZyei+JND8A+Fx4+sfiD8ORoi+IfEd34f1/U/FkEGg6/DrOq3l81il6jM8F5byworsI50McbhNzu/2Xz8HnlWjnNXFfV5uVRLnpqL54cqjrtZxfS9n3tpzXKinSUeZabPozufFdgNQ1OTUfFHhvx54butMW3sIdc8MTyStqKzqXeGP7Az3T28UoO43MEKqxLJgO5r9I4e8TsDh6Tw8ZwcJPm5K0FKKktOZc14qTWl1LVWveyt4OY8PQxs1WlzRmlbmhJxbW9nbdX1Sez2td3hXQvD/geaz+N3w1vdNu9D8P2knhnxDaRf6U9naRXLtcT70Dyie3mZzcRPlnVXZgsqDP3uCzCPGODnk+NanOrP21KV1yyqciioNNqHLOKUabVuR2S9yTS8LF4GGQV4ZnhlanCPs5xtdqHNzOaesrxbcpp35ld/Elf3jStVE2jWer202n3GnXMUUkEtocRGB8bJEIJDIVKkYxx+VfI1qNTD1JUasXGUW009GmtGmu6Z9PSqQrQVSm7xaumtmns0atxdW9qFM8qoWOFXqzH0A6k+wrMsrpJd38bDyXtIm4DMcSkY7D+HnuSTx0FADZ9PjhEdxY2yedA/mdPmkBBDAt1JIPUnqBmgCzaXcN5CJoSeu1lYYZGHVWHYigCagAoA+XP2w/HviHX7nRf2cvh8qyax4xUy6g4leNo7NXICZAwUcxTbyCflhdCp38facE5TlmPxFfH5zZ4XCxjOcXd8zm2qcbWtKLcZXTdm1GMoyjKVvOz3MszyPB0a2U80cViJzp0ZpRtDkjGVaak25RqQjUp8jULx53UhOFSlG/rXwc+Eeh/BzwlH4a8IaXEs8qRSajqlzuWS/uFQK0pj3MyLwSse4BNxwCSxPj8RZ/iuI8fUx2IbtJ+7FttQWyjHotEr2S5neTV2wyjLaWUYGlgqSXuKzaiouTu25Sa1k7t2cnJqPLBPljFLvZLfUXjYrqCrKAdgSIKhPbdu3H8iK8M9EoWlvp5lD3KNcecTFuuX8wpIpP7sg8DHOD7n1GXd2sFuptAAAAAADgAUgFoAKACgAoApm9e4X/iWxibJI81uI198/wAX/AfzFAD0sVMiz3UjTyqBt3DCofVV6A+/J96AJZYIpyhlTd5biReejDof1oAkoAKACgAoAorIya28JT5ZbVWDZ7q7cY/4GKAL1ABQAUAFABQAUAMmmjgjMsrYUY7Z68AADqc9qAKjvqly2LdI7WLn55RvdumCFBwB16nPsKAOX8TeM/AngAf2bqVwdQ1qa3ea10azi+16pephyFhtkG8qfLcZwIxtJZlAJoA8H8fReJvjPrekR2tlcWWkfar7QrLw9a6st1PqTLJ5V9PqE0LS29pb2piKsjiQpMuwAXTWu35nG5jUxuOeU4RPmik5yfwxWjWl05X2tdLvzR5kdEaahD2kvkeweBfgr4Y+HdsdZuMapr8sUAubpY2htvtKgjzobYs4WTfJKRNIZ7oq+x55gq162Ey2hhEmvektpS1av0XSK/uxSiuiMpVHIm8d6dretaFY+K9J0Y6jqfh66juraKB0iurmEOv2mKJj8u6SMMqqWRHZVDMinK455l39q4CphY25mtL9GtvTtfpcdGfs5qRyngrxuvhTw/PD4Vs21nQJLrUdduXVZ49dszeahLczqdKeASSCI3Dtt3LMVARYZJNok+byviGeV06OAzajKk17il9l8qSTv+bV11va9uipQVRudN36ntVjDZpCs9mUkWdQ/nKQxlB5DFv4uvB9+K+6OMs0AU9NCwrLYqxP2aQqMknCH5lHPoDj8KALlAHlvxy0a20/TYviXp19YWuu6NC2nQ21/KFtddguZY1OkyKzBTLPKsSW8gBeOZlCiRJJoJvFz7KsPmuDlCu+Vx95S25Wut+3fy7NJrWjUlTldHnXhvxr4m8O2Fr4Ds9R/wCEN8L3dz/xSSyaWG8Sz6Y6xAW9npSpIIbe2lurcCaaEPBbr5M9pF5YvH+TwPEuZV8JHB4Ol7XER0ck7wVr2cpOycmk/tcrdmpO/KumeHpqXPJ2j+J33w2+Ft1p9nJdXumP4Ys9REF1PpyXrXesXc3kPG7arqTPI80wVrdf3UjMjWvFzPG4RfVwfCiq1Fi85qe2q9vsLrZLS/XTSOvw9TOWJsuWkrL8T0Tw34T8LeDbB9K8I+G9L0SyluJbuS306zjto3nkbdJKyxgAuzcsx5J5JNfYRioJRirJHKatMCveQtLEHjUmWFvNjAbGWHbPoQSPoaAJIJ47mFJ4j8rjPPUeoPoR0IoAkoAq2WFe5hXO2Oc4z/tAOf1Y0AUDcCa/eK4vy0YuTbqkcvlhMoGUnack5DLyevYYxQB+Zf8AwV58Jy+G/HPhP4nXv7QXiLw/Bqel3FvpHhm2S8uP9PtZYDNcW5WRYLdHiliDklX82KHCyLJI9vxYyndRnGkqkk1a9ly+d3dq1uib20PpuG8X7OVbDVswnhKE4S5+RTl7SyaVPki4xk5czS9pKMEnK8ls/qj/AIJpePvC/jb9knwzpvhfxd4g8SJ4OuLrw7c3uuWAs7lXjcTRQCMTzqIore4t449srDYijCEFF7Fe2p83NRUmoO66X0dvS7t979T6lpkhQAUAFABQAUAFABQB5F+19/yal8Yv+xF1z/0hloA4n/gnD/yZN8LP+wbc/wDpbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAH5w/8ER/+SKfEH/saY/8A0kjoA/R6gDK8U+GdG8Z+HNS8J+IrU3OmatbPaXcIkaMvE4ww3KQw47g1lWowxFN0qivF6M7stzHE5RjKePwUuWrTalF2Ts1s7NNP5po+NfhL428W/sefFix/Z2+KGtaCfAWt/atS0jU2vJwmlJNLMyDfOWZI2kRlaJ2IRpQwl2qd3yWFxlfJMYsDi3H2ctU1oo72++2t+rvfe/8AQ2f8NZX4n8N1OK+HqdZ42k1GpCT55VX7rlre7cVK8HG3uR9mqa91RZ8Tf2wvE3xU8X6Z4D/Z402G3Dau+kf8JHr1nD9lgu5C6W89vKWdE8yKK62CRd8iSFFiMmFrweIs8+tqvhHyzpe64tbrltzW73l1Wijfo7rl4X8J8JkeDWe8acyioTmsPBSdSSja7ny6xSTXMk0ouUfaTp2cXzPwe+G1t8JPhfL8RtI8L+M/Gnjrwr9sOoeD1XT9S2axbwsRarJELt4pijRSCeGUyM0Co5V3NsfFp1K+Z4uWBjOMlWafPaV7N3cor3bbWkrL3f7vvHzXHnGuPzPDUMBSwrwmDhCPs4Pm5qlJWVPnk/4kE4uULe6ne7nJcx9GveaX4EsdK8GWmsWmk65r2p6n/ZcUUUUD/wBmIv2yY2ektdBpXgtYo7bMcMjrcuZ2tjHJMG9OjhaOMwEajw0KkqKl7ZSnKNXmTf2rLRJX95vS8VrHX8rcnGduZq+2mhieL/DVxb+NdY1XU47yHRLSTwxLbJb2cjRLFHqlu8aWpgcMJlktpzcR+U5njk05d+ITFXi5dWhmMKGEqSUOVVU5btLlnJuSe6973HdOL59NUzaacHKS8v0/p/IX426l4J8T+Gx8ANd03QdV1DWbWCbT7Yaqmnatc6lMzst9BZiGV4YfNE8kspy6xw3reVKsbB/s6md5W8C8HSpzlhoRS50rK61Su7avTXrJpW1PQ4cznH8L5tRzjBOPtqbbSkrpqScWmk07NNrRprdNNHivhrwF+1f8NNCli+C3xgvda0uytT5vh/WNPeO7Se0ldZ7CCGVZ44yFIYGGeMzo2+EyKqSHxslx+ZVebD4SslJK6i2nzK2jhzK2mt1o1bVXTS/bcXxv4ecZ1ufiXLHh6km/3tN33teVRwUJSeiSvCpu2rXZftP2zn8Lavdn4o/AvVnvJtTu10qZbqCC2F/AZbNzHJPFGYyuRDPK00jL+9+Vf9Uc8EsrjOVHNqU5T+1KUp83dJxTWnXq729V5OM8IXmOWf2nwnj6eKcYxcqUeVyi3HVKSbu73tGUYOytrJWfrvhjwHpureDW8UfDS8TVvD9rpV3a2Fz4Z1KxtbiTULd5ra8uLKNYFtY7qcRtbCZrhRCi7IfswaUt6GByrMsLgv7WpVownKLk1JOVobpc15PRLm0ipNv3m7I/FcVB0K8sJiIOMovlaejUk7NNO1mno09jz/4xa3d/8Kc0zx1408NXAvYIbiw1Uz6e+kSabBqNo1nFaRwN5uLcfaBNcmGe5kE9pEFSaPY9vxKrlOGy/wCq4KparN3m7ttqN27NJRae0U+W6d5Wd0e9wpg84xue4b+yafPXjOMoK2icWpJyvtFWvJ7WRgfsB+AfF3iDxTf/ABQm1LxPo/gvSVez0DTGuttrf7i6MJPLEaTCIKC7CJRJMd5IZWU+9wvhatWq8U3KNNaRV9H62snbrprLXdM/efHbPsvwGAp5HGFKpjalpVp8t5QtZrl5uaUOdv3U5twpLlScZRa+xfiz8S/Dfwb+GniX4peLp1j0rwxps2ozr5scbzlF+SCMyMqmWV9scakjc7oo5Ir7o/lQ/OHwR/wVv+OOiR2Gn/GH9nFdT8ReMJ9Nl8IaLoNjf6VNe6bcCUfa1Nz9oN2JpDAlssC4kK3G5lxGHAD9k/wD46/4KA/H7xz+0/8AtAeDbm2+Fmq6JPoGkaBdTTS2V3E5WFYLV5UJMcBt3mknt2gYXux02nzEUAtfsG+DvDXw9/4KW/HTwN4N0mLS9D0Lw/eWGn2cRZlhgjvbBVXcxLMcDlmJZjkkkkmgD9QaACgAoAKACgAoAKACgAoA+YP2pv8AkuXwK/7GBP8A0ss6APp+gAoAKAEDKw3KQQe4oAWgD85PCXjjwz8Ov+Ctnx/8XeMNQex0my8A6es9wtvJNsLx6Ai5WNWbBZlGcdxXo5VlWLzrFRwWCjzVJXsrpXsm3q2lsmzjzDMMPleHlisVLlgrXdm93ZbJvdnsfhD/AIKA+FDrHiDT/H+jyJZ295ctot/osDvHc2iBzGkqTMrrI21ArYCkyjcsIUk/q2Y+D+M9hQq5bP3nGPtI1Gk4ydrtOKacVdtq7aUfdc20j89wXiThva1YY2HupvklBNpxV7JqTTu7Kzsk29VFJs8R/ab/AGnfD/xx/sm38PeDL7SpfD169xp2rz6hi4MTovmIYEG1CXSJg3mMR5Yxjca++4F4ExPCntZYmvGaqxSlBR926bs+Z6tWbVuVJ31vZHyHFnFlDiD2caFJxdN3jJy1s1quVaLVJ3u9vNnL/DL9qf4w/C66v7jT9e/tuPUpRPcwa7JNdo0gDAuD5gZWO7LEMCxRM5AxXr57wBkmfQhGrT9m4KydNRi7aafC00raJrS7ta552U8X5rlEpShPn5ndqd5K+uu6abvrZ62V9j61+DP7a3gv4n3+i+BPF/h+fSfEGsbrSWTMR0uSXyyQA0kgdfNI2rGVY7nVdzZzX4zxN4X4/IadbMcFUU6NP3kve9olfso2fKtXK60TlZbH6bkXHuEzadPBYqDhVno9uRu3du/vbJWerSu9z5Z/at+C0Xwi8fyXVjrmkXWneJrm71CxsbNBDLYQmQERvCvypGN+xGU4bym+VcYr9d8PuJ3xHlqhUpyU6KjGUpaqbtupPVvS8k9VzLV3ufnPGOQrJca5QnFxqOUklo4q+zWyWtotb2eitYn8D/sl+MfH3wnX4oaL4k0cPcx3MtnpTmQTTCGR4yhfG1JHaKTYDwflywyduGb+JGXZLnX9kYmnJWcVKellzJNO17tJNXe+9k7K+uW8EY3NMs/tKhOOt2o63fK2rX2u2nbptdq+nuXwb8R/G/4Yfs5/b/8AhX1lF/wieoXa3Wma5Z3Fnc3ekJ/pVw8TOwAkBmnwxQoViwFZ12v+d8UYHh/PeLVTliW/rEIpTpuM4xq/BFSST91qMbq6d3dtRd4/aZDis4ynh3nVBL2MpXjNSi5U/ik1drVNvWzVls2rP6H+GHxD0b4r+A7PxV4cjvF029kljt/PRPtFq0MhULMiOwByisME5UqSBmvzDP8AI8Tw7j55fi7OcbO6vytNJ6NpN72em6aPu8ozWhnWEjjMPdRlfR2urO2tm/XfZo6eKK11gyC+V4p3CsIcsjogBHcDP3nyenOO1eMemeDftJ/BXxX4w+I/gH4kfDGwsrnX/Dt/aRXUd05W3jt45/Ohll+cHy0ffuWNTIyucZ2jH6ZwNxTgssy/HZRmsmqNWEmuVa8zjyyS0fvSjazk1FOPmfC8V5BisdjMLmOXxTq05RTu9LJ8yb12i73suZqXkfQr7ItVjYHaZ4XU+jlSCPxAZvwz6V+Zn3RaeOOVdkiK65BwwyMjkUAV5NPt3dpY2khkYglonK5PqR91j9QaAGmTUrd2DQC6jOdhjIRh6BsnB+o9OlAEMmroiXMUjRQ3EW4IGbKu20EYOBk8jI60AJNocLwiGNwQfv8AmpvLnOdxYENuzj+LHbFAFSO21GKVbU332eVmV442dpI5AoXIUk7hznKnseOhNAE8mo6nacXkVsCi7mC7sS5YAbD2wSoOR1PYcgAsrqMzReZ/ZV3wSpA8s8g4P8WTzQBFe31x5Ck2FzDEzr5sjbDsTOScAk9OOnGc9qANOgAoAa6JIhjkQMrDBVhkEUAZt1Z+XClmt7cOs8ioImccpuy43Y3Y2hup6cUAWtNia3tVtmgMfk5Qcg7x/e49epzg5zQBaoAKACgAoAKACgAoAKACgAoA/Pz9r2Hxdpv7YHgm4udelutHnuPDuvWtg7LZxFdMv2n+xI7OsU1w7xziJ52RFe+VXeJP3g+FzfERy3PKWMxLbgo9Fsmmn62fvPrZ2Sdkf0Zw1l9HPfCXHUMvw6eJpVHdp80pLnozlJJq8FyRjFxjpJ0ubVto+8/D+vaX4o0Wz8QaLNLJZX8QmiMsEkEqg9UkikVZIpFOVaN1V0YMrKGBA+4hONWKnB3T1TWzXdH85tW0ZekjjlQxyoro3BVhkH8KoDKvYIbG+tb+SVUhV3DAxqAmY2xggA9sYOc5HtQB4p8TtTv4vGeteNdHsV+1aXY6T4e0y8KnzrGLVNRij1K8UEPG6LFDZSgOuN1kwYqrMa+G4mUMwzTB5XXdqbblLs7Xsuju7NaP7Xc7MPeFOVRbnbabbadpMVnp9rp8dlaWSRQQxQfu44oFXaqKqjCqFCgBQAAMAdK+5OM3tK0xZ9SltyZBbeWZVMbDCsRtAJHAIyw46laAG/bPJsn8yC4SR8yqVYp5cyllYsc5HylBgeuBjNAG3oMEMcckhnSS5c5mAbLJ14IydpzkkepPoKAOM8RfCbw1Lrmq+MvCuu6z4W1+4iRtRl0e7Bt7vaqANPZTCS0eYpDFH9o8oXHlxpGJQgC15WZZJgM2X+1002tntJb9VrbVuz0v0NKdWdL4WeY6L4G09lh0m18N22javY+IMeI7rwAtxpSSaoE+0Wt/KiMnn280dyskkEq3kayXKLKZUhmmHx2BwVHDZnUyXMo+1jJXpykryStZpO11orXvFJxukrnVOblTVWnp3KHiP4keC/hH8QYrb4ieO9E1LXtIaz+xaq0Mq6m1hKWS8stSWwhMTlEZriJNqRO8kGYlaAXEn3eQeFvEv1ynjuH8POWEndTTnGKXRSj7SUXJJ9Y8zXK1fWx85mfF2T4BvD46slUXRJyfz5U7ejtvexf+AH7SHwX/ALL1rwhd+MNK0WG28R6u+kx3q/Y7eTTZrmSeBkZ1VI12yFRGxDDaBgZUV+v8YcE566tLGwoSqOVKkp8vvy9pGChJNJtt3V3JJp3vfdnyvDXFOUqnUwsqsYJVKnLf3VyOTlG10klraz1VrW2Pf/DWqeFdcsRrHhLVdN1OzmLBbqwuUuImwcFVdCRgEEYBwDmvzTF4LE4Co6OKpyhNdJJxf3OzPuMPiaGLh7XDzU494tNfejXrmNwoArTWW6Y3NvM0ErKVYqAVf0LAjnGPY9s0AJb3oa1M1yVV4mMcoXJ+cHGAOvPGB1OR60AMYaldDbhbOM5BOd8uM9sfKp/7660AfKXw9stT8Rft3eL9U1LSL9LDw3pxsrNZw8kVo3lQpDsZhiMSqLmZV7l5GG75mP1OY06mC4by6FO/JiJ4irO+tp03CjBJ2VouHNLld7yk3c2oV8LXr4+MnD2uHhhKMHHRyp1lXxNZzV3zThV9lTc1a0YQg0mfXVfLGIUAUdQspZI5pLNlEjr8yMMq5H3TwQQw4w2ew9BgAEvJrZP9PjfZt3CZUJ4/2wPukDqenuOlAFqGeG4TzIJkkXONyMGGfwoAkoAhubqK2VS+5mc4REGWc4zgD/I9aAIPss1782ohREcFbdTkAjn5z/F9Pu/XrQBcAAGBQAtABQAUAFABQAUAU5Si6tbZwGeCZQcdfmQ/40AXKACgAoAKACgAoA5rx74z0TwZpEdzrF7DE95cw2lvB5pFxOzyKrC3jVWeWVVYssaKWYqBxnIAOTXUPiL8W9EVNEWfwN4e1CJc6rKd2sXUJ2FjbQj5LQOhkCzSM0i8HyVOGAB0Nvonw7+D3hzWfFkkEGm2lhZTajrWs3TPPdSwQq8sktzcPull2rvPzMcDhcAAUAee/A/wvLB4pivNZ8IJperaD4faW+urPV2ubVta128bUNbtdhH3o57a0mViQNl6AiIuAfjeFJPHVcXmjd1UnaN1b3Y7fg0vWOrbOrE+4o0+yPZtUWOS2ELlt0jhYwGwGb0PBBGMkggjjoa+yOUw7DUNStZRokeHmCFVEnWI545GQQBzjJ4HX+GgCv4q+GWj+JxpF0uo6hpWq6Jei9tNUsXT7QGZSkyOJFeOWOWN3RldGAJV12yRxuvHj8Bh8zoPDYmN4v8ATqn0/pbFQnKm+aJ16qFUKoAAGAB2rpp04UYKnTVopWS7JbITbbuxasRR1SaHT4JNZlnigitImkuHlcInkqCWLMeF28nJ4HPIBJoA4O9+MOnaz4bfW/Bd3bW+nX0ccWleI9VhP9n3NxPJNb2/2eAOk95unS22KvlJcx3UTW80m4V8rmXFeHwlf6nhIOtWvblj0tvd2eyvsnazTsdFPDSkuaTsjA/snxD411eLxvZ6RcaZHJCkNprniCGQajBA7pKEsNKdFW2Z4rq5t2ln8mcPbQ+fBdRxpnzlkWa561POqvJTvf2cP1d32TWsnZuzizT21OjpSV33Z3ngn4eaH4RkudVhs/M1fUSXvL64fzruc73cebMfmfaZXCqMRxKRHEscaqg+ywmEoYGkqGHioxXRf1q+7er6nLKTm7yZ1tdJIUAFABQBWiDQXbxZ/dzAyIAPusMbh6c5B+pagCd3VFLuwVVGSScAD1oAy/7SijnlETpELhvME9wdsZwqj5c4LdB0wPegDPaOON2t4rG9k34Et3LbuzEFsnYMfKcsTnHboTigD84/+C1Xg/Xpfhr8KfHEd5dPpenarf6XfJNKRm6uYY5YDsJBOFtLrBwcBuoyMgHT/wDBF/XtT0j4Z+PfhJ4j8OXWj6hZ6rZeLrU3u6Ga9stSs40jkSB1DGILZo6ygsri4XGMAsAfo3QAUAFABQAUAFABQAUAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAH5w/8ABEf/AJIp8Qf+xpj/APSSOgD9HqACgDgfi78Dfht8cNLs9L+IWhtef2dI0tlcQzvDPbl9u8KynowVQVIIOAcZAI4MfluGzKKjiI3ts9mj6zhPjbOuCq86+T1eXnSUk0pRla9rp9Vd2as1dq9m0/HPBv8AwTy+B3hh9cg1jUPEXijT9ZEcYs9VuIFW3iWSKQqjQRRtuLRMu/Iby5ZEzzmvNpcM4Clsm9Et+qd7+T6aW09T7HMfGzijMZKpN04zjKbjJU1dRnDkdPW6lTvafLNSvNRcm1GKXjvxc+DmqfsNeLNP/aG+FerXdz4Lg1AQ6vpE8hiSztJAqiGafLK8LsXVJJEHlv5H+scgnxMZlFXJcVDHYNOceb4bXaurb673avbS63ufpOQ8b5N4q5RV4e4rVKhiIUn7Ou+VK6avKKfLyNcsHKEZ2qRUvgirL6Q8Sa18LdI8H+E9e0LW9Y8KWPxX8S2dxYXVhdQwr/aF5bvdIGiuSyRC4kg+eKBCZLidmdT5txIdeJsuwWLy+ebUqKlU5YtPXbS7aTV2ot6vVWWuiP5xxeDxOT5hVyzEu06U5QlbVc0W07PtdadzB8CabqPiPX7rwLcXE194Y+xaaLTRtT0yKK300afekXtrukZLy63JJaeW01usTQfZ3Dy+c7N85w/gKlTMaVaNJNxbc7uzlCpH3ZcusbWbuot6uzSVjGtNKDV/T1XQf4n0+3sfiv4m8I6F4Xs4LTTtF8KGx0xLqBNKuIr7UdRikWS3ezcWxeWEec0fmNKtva8oUNfQ8SYaviMywWHw0lF++46P3XFJ3snZ6KyTVt73TssKElGnOUvI0Ph98JPGVz4uj8davfJHpy2uiT6Z9tlnTUbmaNJftkuqwQLbxXM3lyQQwmbzBCYFlCB1IkwyfJcXL2FRr2SpznzLq9U0oXu4wdmpK6unLdS1qrVirre6X9PzM/4heAvh74ctNX0H4pWljL4DfUrzxFdy6q6MLiO8uZ55DBIjRy29zb3t4yqU4a2uVjHnSufL0zKFHLM1k8clLD4m3e8ZQVlsr2adtO+6Sd/o+D8dn2FzCm+GZTWK1ilBX5k9WnF3i4q3M+ZOKtzPa6+Rvhuvi8/GKXTv2Qo9e8TeHNCuWu7J/Edl/o8Ewt5wyvKm1YY7lVlRN/k7y8e8LJCkieAsvjjcTOjl8XOk91K6TtqrtWtdr3W+V620u7/1LxflWTY7JMPmHiN7PDY3+ahJc7i5RTSjJSc/ZqS9ooqry2cqbtKz+odT/Yut/iBrMg+J15ptzpmn6xFf6TqNorjW59NNsIm0y/uDzc7PJtALuV5rh1hfLKZPl+0lw9HEP2dblVNSvFRXK7O94NJJWT2lq99rn4Xk/iPguDqKrcNUHDE1KPsqvPaUHUjJcteDbc7yjzc1L3KabjpPk1+m9J0nS9B0y20bRNOtrCwsolht7a2iEcUMajAVVXgAegr6SFONKKhBWS2SPybFYuvjq88TipudSTvKUm223u23q2fllqHwVuP+Cjf7cnxBn+JGqXx+Fvg3SP7P8Par4P8AEem3lvEqTotsouYoZo2e4zd3LwuBNEHVGfEaK1nOfp/4N8IeHPh/4T0fwN4Q0xNO0PQLGHTdPtFdnENvEgRF3OSzEKBlmJYnJJJJNAGzQB+a/wCx/wD8pWP2i/8AsF6j/wCl9hQB+lFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AeI/tq/EHxT8L/2YfHPjHwXqa6ZrMNvaWNtfkEmyF3eQWr3K4Iw0aTvIpzgMgJyBihUlXnCi5cqlJJvaybV9emnVa9rPUqMuS80r2Tf3Js8V/aB+G+j6ZF8EP2FPBuva/8A2N8Q/EF5qniu7vNVmur3UdGsI2u7+K5uC3mK11K6jepVQylQAuVohy1MQoyS5IRbsuj+wvOLd763WlttJadOjKcW+ebSv/6U+uqSWlrPW+50vwd+HHgb9nD9r1/gj8GZp9H8H+IvhxceKdR8LNqE93Da6hb6nBbQ30f2iR3iMsc80bBSFf7OpIJQYmFSblOnJe6uVp66P3rpa2s9G9L362dhzhFRjOO7bT9LJpvr3R9Z1Qj8Zf2o7q5t/wDgpL8YooLiWNLnRNKimVHIEqCw0pwrAfeG5FbB7qD1Ar9Y8HKcJ59UlJJtUpNeT5oK67aNr0bR+e+JU5RyiCi7J1En5+7J6/NJ/I+sdd/Y1Wz/AGa7LxFonha8vviI1tb6leQtJMkyxMWeS3jtyQDKkbqrIRuJiYL8xCt9XhPEz2nFU8LiKyjgryhF2i1dWSk578rabTvZKSvorr57EcDKHD8a9Gm5Yqyk1rez1cVH+ZJpNWu2tNdH7l+yP4E0K1/Z98Kz6r4V0o318lzeTSvaxPJMJLmQxO7AEsfK8ocnIACnGMD8/wDEXNsRU4lxMaNaXJHlileSStGN0l096+2jd31ufYcF5dRhkdB1KceaV23ZNu8nZt/4bfLTodN42/Zj+BvxB1F9Z8R+ArX+0HgEBuLOeW0OBnaxWF1R2GcBmUnAUHIAFeVlfHXEGT0lQwuJfJe9pKMvVXkm0n2TWt3u2z0cfwnk+ZVPa16C5rWum4/+ktJvzafbY+Lf2jfgnYeANXtfAnw/+Dnil/Ku4IrfxQ9zNeDWWlgDeQsSRCJZA4cAIdx2MduCMfu3BXFFTOKEswzLHU9VJujaMPZ8srczk5OTi1bV6K61unf8l4oyCGW1Y4PA4SejSVS7lz3Xw2UbJp3210eltvGfDPh8N4wGjeJ9Lu0TTTczapZM4trhY7WN5Z4v3mNkm2J12nndwBnAr7fMcZL6g6+Cmrz5VCVuaN5tRjLTeN5J32trsfLYLDL637LFRdo8zlH4XaCbktdno13vpufWHw+/bx8AeG/D7eGbz4R3ek6dYWwj0+3027juUkbn5Zd6x7AePmG89eK/Gs38Icwxlf6zDGqpObvNzi4v1VnK78vdXmfpmXeI+Dw1L2EsK4RirRUWn8nflt66vyPB9X/ar+O2t6ZqOi33jK3FjqttNZ3UEWj2Me+CVSrpuWEMAVJGQc++ea/RMJ4dcN4KtDEUcPacGpJ89TdO6dua267W8j4vEcaZ5iqc6NWteMk01yw2ejXw3287ntP7AN3JpOsX0Uvhzx1L/bMklql/bxFvD8ISJZsz8AJcfIFV8tkTBcLklvh/F+msRQg1Vor2aT5W7V3duPu73hrdrTWLd3ay+r8N5ujVknTqvnuuZL90rK/vdpaWT1+K1lfX7ZllM+oW8HkPG0LvKWccMoUr8pGc8uPpjntn+fT9iHTwW0V/DeNbx7n3RGXAyGIGMn8NufUgDrQBYubaO6iMbll7q6HDIfUHsaAIbWeU3M9nOQzRbWRtu3chHp3IIOSMD2FAFugCC8ultIDISpdvliQnG9z91R9TQA61hNvAsTNuYZLtjG5icscdskk0AS0AVNRMUiJZMFL3RMaZxx8pJbB9Bn8SB3oAzIViuo42tJJZGSX92vl/u40MyvjIHZQvGcgcYFAG1bwJbW8dvHnbEgQZ64AxzQA9lVlKsoIIwQRwRQBUsCYjLYOSTbkbCTnMbZK/lyv/AAGgC5QAUAUrMJDcTRTJid3eQOefMQnjB9htBHbHuKALtABQAUAFAEF5M0ESupwTLGn4M4B/Q0AJczFJLeBG2tNJg8jO0AsePwA/GgCxQAUAFABQAUAeV/tC/s/eFPj94RXR9ZjFvqunF7jSr9AqvHNsYCOR9rN5DMVLouCdqkEFQa8zM8ro5pTUKq1Wqa0fpez0fU+24J46zTgfFTrZfO0ai5ZJpyW699R5opzjrytu2rT0bPlv4G/GH42fBv4iw/Br4pW8Vpf3k6Xb6dqcLv8A2qsm/wAya0vVm8qK5kOWwscsd3PEVc21xNPdS/HYDNsTw644PMIfu76O6vFd7LeN38tUr25V+kcfcC5NnWFq8VcES56CXNVgoySg27e4nFWtZyqU2704uM7KnKNvviv0Q/ASO4t4rqFoJ1LI/DAMRn8RzQB478UPhtq+oX93qHhK30TUL6fS30280bX4nk07XtMkfMlndMisUIJlEcjRzLGLicGJ/NYHxs6yWlnNKMZScJwd4yW6f+Wz0ad0tTWlVdJ90zmfBXjiXwxLfwppOu67pukxWZ1Pw7q6NceJ/D0LjZ5sYBk/tW2O0tvWSSUvBeiOa8k220XhYTiDF5VWjgc+jZv4ai+F+u1vWytdXSWr2lRjUXPR+4938OeI9A8W6HDrvhLV7PUNPuDLHFcW7B4xJHI0ciMBgq6SI6OhwyujKwDAgfaRlGcVKLunszk2Oa8TeJbKys5NZ8Q6hZ6dpumWjXlxcXM6xW8IRA0skrNkKiDdktgYVucgiqA5ay+M/i9Zmkt/2dPHd1ZfMttdWx0W18+Pd8kgiudSjnjVlAbZLFHIuQHRGBUfLz4yyWnJxda9u0ZNffbU6FharV7GHrvi68XU9P13xHJ4o8LR3SSz2XhGw0aHWNXnjtyxuprpbJbsfZ9rQKv2fayNKgaZ5rmGKPzKvFONzLEOjkFFVIxV5SldLXZK7jb56vWysrvRYeFON6zscLd/G/W9CS78I6Hdx698XdchjsNHNt4E1DQUt7N2bE0o1B5N6wtFcuW8xlG5d0aorMf1nw8yetxA6ma59Q9hhMO/fk5K0tE+SLVm5Sdk0raNWfM4o+R4nzh5dGOBy98+Kq6QSW3eb6JLWzel07qylZmk/CXw98MrnRJ/DVzo8nizR5YfEXinxZr2osY7K0ZZ4rp2T5Hbz1a5WJHKRgI80sjSQJHJ4viv4gYjPsLLLZuSo1Hy06NPRWjJOPMldSs1HTXX4Eum3DHCuFya1eylW3lOWru078reyd35tfE2Hi/wL4F15dB8K+PPCXiXUvDd5dQxaZ4guYtN0G18MmdHtrXT7e3i8i+K+a1rDFBcQzJmSAs7mPj4rA8ScecN4NYzBYh0Y0kvcT5nNK2s0+aL0bbvbZuy3PZx2TZRmz9ni6Knfq1Zr0as1sthngf9jv4c+M/COmeIxPrvhzxJpouNN1iLSbhoIpNTtna3uJIBceZIlvJJG0kOSC0MsbH7wx+5ZR4xZ5isFGrU9nWjNJ+/BaeXuOKutne+q9T4zFeHmUOrzUuem1/LL/5JSf3dybx38M/jv8IrW11/4F/E/wAb+JJLG1SC98P6vcJqJjhUku8Ub4XHyQoqRxmTDMVbBxXvZJxJw9xNVlg+IsHRouTbVSCdP3tElJq76yk5Sny6K6vqebmmSZzkdNYnJcTVqctk4Tanpq7pPTokko82rs+h2Xwc/bF8G+M2Xwv8TFi8F+LoJTaz213vitpZVHzbXcfuTuDDy5DkHChnJry+JPDbG5avrmUv6xhmuZONnJJ7aL4laz5oqz1dkjvyTjfC45/Vsx/c107NO6Ta31fw6/Zk79Ltn0PX5ofcmVLbzQ6mk9zLG1tPKMIARiQKdhbJIPAxxjkKcUAatAHyp+y1LJL8cPjrPNM8gTxcELuSdo8/UkUZPYfKo/ADtX6VxhRo0OG8ljQd1yVXoraycHLTyk2m+r16nyWUV6+I4gzKeJjyyXsVq+b3YwcYu/nFJpfZT5eh9V1+an1oUAFABQBXmsoZiZBuilOD5sZ2vx0ye49jkUAJ9gjbmSe5dsDLee65/BSAPwFAC21ja2hZoIgGbqxJZj7ZPOPagCxQAUAFABQAUAFABQAUAUryMfb7CcMQwkePHYho2J/VBQBdoAKACgAoAwPEfj7wX4S0GPxR4i8T6dZaXOoa3uXnBW4ypdRCFyZWZQSFQMSOgNAHKNr/AMWvHouLbwroC+BtLdQYNa12Fbi+lVlYZi09WHlEMFOZ3BAIzESSFAN3wx8MfC/hnV5vE2281bxDcx+VPrOq3Bubtk3M2xCcJAmXP7uFY06fLwMAHUW1tbWVtFZ2cEcEECCOKKNQqIgGAqgcAAcADgCgDkvi4l5deCJtKs9Ks9UTVr/TtNvrG7hWWG506e8hivkeNkcOv2R7glSuCAclBl18nPsT9UyyvWvZqLs1o02rKz9Wv0NaMeaokVfg9AtxoeseLJNDj0y58T67e38nlzxzJeQwsLKzvFkRQGWeys7SYZLECQLuIUVjw1hHgspoUnvy37fF71vlew68ueo2dzLIkMbzSttRFLMfQCvcMSnp9r+9l1GeBY55yQBtAKx54Bx1J6k/h2oAv0AFAFDUNXt7LMUYNxdEHZbx8sxx3xnA9/T1oA8J1Twx48+JniaS21zS5tUutMld431mxntfCujTgrLA0VixSXWbmM/ZXEznyg8MzRTWUhMB+LqYbOs/qSp4h/V6F2rJ3nJbPXonr5We0lqdSlSoq8fel+B3CaL8IPg/fReL/Hni/SV8Q38zWUXiTxXfWkV9J5wGLO3kYRpDERApFvAqIzRtIVaRpJG+lwGXYTKqKo4WKivxb83u3/wy0MJzlUd5Ho8kdvfWxRtssMyggg5BB5BB/UGu8gis7ltslvdtia3wHcjaHXs47c459CCKALdABQAUAFAEc0CTGMvkGJw6kHGD0/kSPxoASW3hmYNMm8L0VuVBz1x6+/bt3oAloAKAMHxj4Vs/FOloraboc+raZIdQ0K61fS1v4dN1NY3SC7ERZG3J5jfckjcqzKHXdmgD8lfCfxs0T9iL4++O9T+NXhvxL8Zf2ntTvI7XUL+K8itNCtrK5ghnRbSd1Mu8xmEEm1jSOMeVEEjRmmzrVoUIOpVdordnZl+X4rNsVDBYKm51Zu0YrVt/1q3slq9D9etH1rTNetPtml3tvcKpCSiGeOXypNoYxsY2ZdwDLkAnqDyCDVpqSujlnCVKThNWa0ae6fZl6mSFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB+cP/BEf/kinxB/7GmP/ANJI6AP0eoAKACgAoA5n4kfDnwh8WvBeo/D/AMeaX/aOh6qIvtNv5rxlmilSWNgyEEFZI0YdiVwQRkHKtRjXpulPZ/I7sszCvlOMp47DW56bTV0pK67p3TR8d/8ABPTUG8JWHxP/AGb/AIircatJ4e8SyXSy307ahFKXkS2uIEiEIjhgingV15w3nSPtRUZj8zlub4XC0nhMa403Tdu0d9NW3d3XM3p36Nr9W8VOGasatLirC1J1cPjEmp1ZRdRycVL4VCHLBRfJGK5kuTdRcE/p7wz4vurfxvN4YuzaXGoakZbuVLS1jj+2rBBp0U15DIWXMET3KRsH82TJChsRhKjLcyx88xlgJqNRQb5qie0XzcsWlb3k9H2V1b7R+SVKcFDnWl+hv+N9W8F/Dy0v/ihr9hbQywQW9pd6gERZfsyyvsDuxG5IjcTuF5YCSUIrM+1vczGeFwcf7QxEbumnZ2u/esrLzbsr/ik2bZZl+LzfFQy/Awc6lRpKK3b/AOBu3slq9j4y+EPjD40fHC4l0/4O67a6WrDVbDxXr1xbPHItvdNdXFisPWWKK0e7uIrRIrhnXYrPHGnyn4zK3jcwxNergZckKjfNHpG8bc11b3pPXTW61utT92qcH5P4ZUfa8ZUo16slSnRUJXvKFRupTcZJxcHHl53OPK4txh72p9Ga9+zjc/ELRNO8JfFPxpc67oNt4e0SyuYcObmXVrGSVpL9J5GbyjOkrRyYUyOpH7xdvP1dXKHi4Klip80VGKffmje8ru9rp2el33PiMD4hwyDFVMwyHDKjXlWryi9OVUayio0nCKXN7OUVKF5ckXf3HfT1Twf4L8KfD/QLbwv4L0Cz0fS7RQsdvax7QSABvY9Xc4G52JZjySTzXqYfD0sLTVKjFRiui/rfz3Pg83znH59i5Y7MqsqtWW7k7+dl0SV9IpKKWiSRnaB8WPhZ4r8Val4F8LfEvwprPiTRvO/tHR9P1m2uL6z8qQRS+dAjmSPZIyo25RtYhTgnFbHmny7+2t8U/itr/wAUfhh+zv8AswfF3wxo/jm58RQaj4h043rpqMNpEn2iJpFBEb2gjjnluLcnzpEEAVGjdwwB9BfAf9n34dfs6eGdU8MfDuxkhi1zWbvXtRmkSKMy3U7DISOFI4YYkRUjjiiREREHBYszAHpdABQB+a/7H/8AylY/aL/7Beo/+l9hQB+lFABQAUAFABQAUAFABQAUAfMH7U3/ACXL4Ff9jAn/AKWWdAH0/QBxHxt1DQdN+E/ii48U/D7XfHOjyae9tf8Ah3Q9P+3X2pQTERPFFBuUudrknDAgAkHisq8I1Ics1dO23rv5W3utVa6NqCbn7skmk3rtom7ed9kurdup4n+x18M/gtDc6l8UfBXwl+LnhLX4rNPDqL8TYr9L+001SHjtLQXMsii2UquPLY4wAT2rpaapNJr3t/5tO/8AVn8lbkUVKabT93btr26ffqtdk9fTPgb+zd4I+BEmtatpGu+K/FXiXxGYRrHibxZq76lqt9HCGEETykKoSNWKqqqvGM5IBqE7R5Yqy3fm9rvvoW4pz53vsvJb2XbXfv8AJHq9Io/HX9obQ7zW/wDgpt8XvJ0jVL+zsNC0u+1P+zrcyy29kmn6X50xwCEVQfvNhQSMmv0/wnxVPC5zV5pxjKVKUYczsnNzhyx7tt9Fq+h8H4hYeeIyynaEpRjUTlyq7UVGV35W7vTufr14e8QaP4r0Ow8SeH75L3TdTt0ubWdAQJI2GQcEAqfUEAg5BAIxX5zjMHXy/ETwuJjyzg2muzXpo/VaPdaH2uGxNLGUY4ihK8JK6fk/69S7FFFBEkMMaxxxqFRFGFVRwAAOgrnlJzblJ3bNklFWWxneKZ/ENt4Z1a48I2drd65FZTPp1vdNthluQhMaucj5S2AeR9R1HVl8MNPF0o42TjSclzNbqN9WtHrbyfo9jnxkq8MPOWFSdRJ8qezdtE9tL+a9UfNHw9/bk0u31fU/Bvx80SLwtrWkXE1rPeaeklzaGeOR1eJo08x0K4UAq0isdxygwD+q5z4VVp0aeO4cqe2pTSajK0ZWaTTTfKmnq7NRa0Vnrb8/yzxBpxqzwmdQ9lUg2m43cbptNWXM1bbRyT1d1ofFvjbVr34p/FHXdb8O6Ne3E/iPV7m5srGGAyXDLJIzImxNxZ9uMgZ5ziv3bK8PTyDKKOHxU0lShFSk3aOiSbu7WV9rn5Nj6084zGrWoQbdSTaSV3q9FZX1t2Mjwp4ZvPF3irS/CNpdWtnd6texWEMt4zJEksjhF3lVZgNxA4BrtzDHQy7B1MbNOUYRcmo2baSu7XaW3mc2Dwk8biYYWLSlNqKvtdu2tk/yPoD4M/sy/Ejw38bNE0n4lfBr+2NFwLi6luJPMsYY85Enmxv5TurKMwPu3KWBQghh+b8TcdZXjshrVsqx3s6uySVpt9uVrmSaek42s7Wlo0/tsi4Tx+FzenSzDCc9Pdt6xS73T5W0/su91f3dUz9BpItOa2WSIrClhuCPEoBhCcMoGOBgYxjp+FfzLKTm3KTu2fuiSirLYgtHuby/Rr6NIWto98cf8TbsjeeSAMAjbk4PfpSGaE0cFxA0cwV4nGGyeMUAZa6jfWmnS3rrHcwJJIEcOd5QNhScDB+o7c80AS2dpqZL3s88CzzDjdCWMa9lHzDHuPXuaAJ49MSJdqXd0Ny4kPmkmQ4xkk5IPuuP5UARyWlvY3NtcxRAAs0MjnLN8/IJY8k7gBz/AHqANGgAoAjmt4LlAlxCkig5AYZwfUehoAppd6XZSPGL0s3Rw0zyhMHHJJOzk98UAXBcQMzKs8ZK8MAwyPrQA2a7tbdPMmuI0XtluvsPWgCoI7y5u2vLZ1t0VfKAliYmQZzkjIxgkgfj2IoAfYvdNd3S3Uys0exVVFKrtwTuwSepJH/AaAL1AFa+zHGlymd0LhsAE5U8MMDk8HIHqBQBZoAKACgAoAqXO6W7toEYgIxmkw38IBABHuTn/gJoASdA+qWmc/u45nGPX5F/kxoAuUAFABQAUAFABQB8e/t12divifwbrMXgDTfFeoWuk6yLjS9Qsy8N3pYjja7ufOjkWSJrQKoUuNobUFaPdIvy/C8aQl/s86as7tc3W+nLHzTd3t06Xd/2Pwrx+FlhsxyrNMdUwuGnBScoSSXMpKNmnCUm5KWqhKDlCMlJSjrD7Cr7o/HAoA5rx1d2lrpsardWMWr3DPFpEV1dfZ1uroRs6w7wrMAQpJKqzAAsFbGD52aZnSyjDPFVk3FW+FXev3JLzbS6btJ3TpurLlR5fcjwf8YNL3MNS0LxRoUgTzjcRxax4fvsqxTIWRU3bVJBL29xCwz59vLlxfUs8wd9KlKa/rzTXyafZh79GXZo7jwa1zomlx6fHa2sup3AS41C5tdLWH7fc7ER7mQI4AZtoznOAAucAVtgcFRy7Dxw1BWjHa7v5/n/AMDQU5ucuZnF/FvxN4hTxJZfDSHwD4W1z/hKPC2uancWWvag1tYXCWtzplvNFNIsFwFikh1KYsphkDGNQdoLNXz/ABfjamDwMeSbgpyUXKKvJaOS5feju42euzZvhYKc9VexLD8KvDfiafUPFnxG8NeHvEWo6gbeaEX1hFeRaQEihRrGKWWMM0W9ZpAzqgZp2O1d20d+TZFQyjCLDaTd222krt/f0st+hFWtKrLm2Nbwx4H8EeE7q7fwr4M0Lw3dXvlrdXOmadBZSFImdwJfLVdwj3ylckFdzAcMRXt0qSXuU479EYylZXkzx39mvS08f2+vfH3xRptqviDX9YngsZXMZWzsYYUjSO3AG5SfMeNmPzMsSk9WZv1XxFrPJFh+GMDNqhSgnJarmnKTbctbPZSS2Tk7bJL8/wCC6X9qutnuLinVqTai9PdikklHqt3FvdpLzb7LxnYXcWsWVgttFYprGsJqema8qzvbLr0MEYtrfUrYfu54ZFiZdzugDpAsey5FvPX84cQYTFZXjVxBRfOo2Tg0tI7PlfT5K6u943R+mUZRqQ9i9PM6ByvxO+H1xpF3eaz4VutT0+4sj5e2K90e++aIlGwUEsMuWWZd6MY1kjd0ZGP2NKrh8ywynBqdOa+TT3T/ACafozlalTlZ6NFP4Q+L76e8fXjpkEDeMLT7Vq9pbieWCw8Q6eVsNSiNyy7X/wBVBDGoChxYyyIG3sa+V4SqSwU6+S1n71KV43trB63t+L1fxJHTiVzqNVdfzO9OrXV5qDy208sE8qeWFOCM9kAPvjnjue+B9och578UP2dfBPxtRbbV7FtM1i0idYtXtECzxDaRGsykASpu2nadrYD4K7tx+v4W41zLhWpbDvnpN6wlez7tfyyt1XldOyR83n/C+B4ghesuWotprf0fdeT+TVziPhz8UvGf7NOvQfBz9oKW6uPDszuPDni5i0kIiBwsEh5IUYPDEvFuUEGIo6/Z51kGB44w8s84aSVZW9rR2d+slsr+iSnZtPnvF/M5Xm+L4VrLKs8bdJ/w6u6t0T8vV3jovgs19QpPZ65pUV7pV9Bc213ElzaXMEgkikUgPHIrLkMp+UgjgivyGrSqUKkqVWLjKLaaas01o009U090fo9OpCrBVKbTi1dNapp7NPsWbedLmFZ0DAMPusMFT3BHYg8Gsyz5c8BQSfD/APbS8d+B7tfs+jePtNXWrSF2gP2u4GJDINuHXa51ABRhjjcwbhx9/jZTzjg7DYi0nLB1J03ZxtyVEpqckldJPlpx11d2+ZtNeFGFHL89qxvH/aqUJq6qJxnScqTpRbk4yco2rT0fKnGMHBKUZfT1g5a32M5doXaIktljtJAJ9yMH8a+APdLNAFNNREl+9itu/wC7xlzgDpnoecdsjv8AnQBaZ0TaGYAsdqj1NADqACgAoAKACgAoAKACgAoAKAILmaWDy5FUNFuCy8cqDxu+gPX2ye1AEGpl1Nm0Wd4ukwAM5BBDf+Olj+GaAL1ABQBneIPEeg+E9Jn17xLq9ppmn2wzLc3UojRc8AZPUk4AA5JIAyaAONHiv4i+Nknh8D+Fz4csNxhXWvEsEkcxIZgZINOwJHGFGDO8H3wdrgEEAs+BPhbZeE9Rn13WLpde1wxx20OsXUYE8VsqDMEcagRW8YcyEJCqKVKhgzKXYA7qgAoAKAPFv2iPE/hjwveaB4n8UWOsTQfD/TfEHxFjbTkU7/7P09rOWBt4CMXh1aZkUyRkvEpyVV6+R4ylUq4GGCpNXrTjDX1vf70r6PR7XsdOFspub6Js9L8A+Fl8D+BPDngpLyW7Xw/pNnpYuJXZ3mEEKx72ZiWJO3JJJJJ5Jr62MVFKMVZI5i34iWV9LeGFGZ5XjQAY6lhjOT64H40wL8Ey3EEc6fdkQOPoRmgCG/voLKEmRsyMCI41PzyHsFA560mroDjfAvh3xfZPqP8Aag07S9MnaNbeG2ea61C4MamIXN3eTENJI8Edqu0oXQxMDLKCpXycowGKwFOSxdd1ZSd7vZeS3svLZdEtb61Zxm/dVjtrWztrKPyrWEIpOT3JPqSeTXrmRy/xS8VX/hLwmbrR7eebU9S1Cx0ayEEQlkhku7mO3NyI8HzFt0ke5denlwSElVBYcWZYxZfg6uKdvci2r6Ju2i+bsvmXTjzyUe5wvw58B3V9pusz+GNctvDmjNq0ltYtY6SDqsrWc8kN49/dXUkwvHnuUmcSlFkKvuZ2kdmHxeW8K0s3w0MfmlSc6lRcz95W12tpppbS9ltax1VMS6UnCmkkjM8OeHfG/gZ76zh8Ej7fd6jFFO/gSeGytryLYzPez2N9IsNq26TY7xSzTyKkJ34URx9X9h55lzdPLMUnT6Kpq16Plf6Ly6k+2oz1qR18ipoPwN8dONM1LUfDXgey1LQ9T+0Wd34h1TU/HF+LYxFHEF7fNBNZyNnACtKgBckEyNjpXDeNxM+fH42cvKNoK6d1pqn/AOA9uxPt4RVoQX5nv9rdw3kfmREgjh0YYdD6MOxr645iegAoAKACgAoAKACgAoA/C7/goz+1VH47/aF8b+GPh/4J07wy2iSN4P1nXhAv9r6wbC7JcGUEiGETRAJ5YErpGvmOVKwx51aVOtHkqRTXZq6OvBY/F5ZXWJwVWVOor2lCTjJXVnZpp6p2fdaH3X/wR08HeI/C37JV1qWvaa9pB4n8W32saWXIzPZ/Z7W2EuOqgy20wGcEhQw4YE6HIfc1ABQAUAFABQAUAFAHkX7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/wCltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfnD/wRH/5Ip8Qf+xpj/8ASSOgD9HqACgAoAKACgD48+Pf7IHj1viqvxy/Zu1DSND1tzJe31j8ts0t+2Q88XyGJ2mDsZVk2hm3sxcysB8bxBw7WzCrKvh2nzK0k+ttrdPys1ffb954C8ROG6XD64V4vwznQjPmhKKvy3bk+blcZxabdpQ5nKMnBpRXvcT4h/aG/a1+Atnonjb4k/s0rqkEGoyaJq2qWzwF7j7W8IikL23mG0jQxRRmVleOU+Wm0MEavKyLJ8dkNaeKlH3OV3TfRa2Vm9W9VdWtfXUxznIvD/NcX9VyPG1OetUo06UVF8sXN2nObnaUoJW0TjOM3rFxtbwP9qz9ta9+M0SF9Gu4fDvhLSrHWdY8M6XfzzGWaS4gimMl4tmUt2Q3QiSSePyt0bFC7Soj+riaOL4hxCpyTp0I2ve6u99E0tbO2zS3u72f0WS5lw/4OZRPFUqkMVmlbmUeVxlGnFPltKUJySjzJydpRnO6g4x5XKP25+x1+1f+zd8YfhRqsnwptZPBOifDyFV1TTtc+z2psbURGQ3ryLK6NCSsxaZ3DFo5GcDIZvrMPh6eFpqjRVorZf1/TP57zbNsbnuNqZhmFR1KtR3lJ/kuiSWiSsopJJJJI8U/a9/bt+JeteNb79l/9iHw1q/iX4h2V3EuteINLtIdQt9OhDQ7lgKl4wRLMsM81wFjgIdD87b4tjziH4Sfsh/8FCj4G8Q+CPib+15qGnaZ4l0iyurG8i1m9v8AXdC1j7TYSzAzfI5jWCK9t9kd4YmaQPtyxKgGvrX/AASl8MXfgnUX0z4xa+vxS16O0g1fxvMjx/adly0lxObSGVC8s6FFkMsz+Y0KyMfNaSRwDrP2N/8AgnRpH7LnxB1T4s+J/ihe+PPFd5ZTaXaTz6ctvDa27vGTJh3lkM5WJV3iRQqSSJhgd1AH2PQAUAFAH5r/ALH/APylY/aL/wCwXqP/AKX2FAH6UUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QBwfx3074a6n8HvFsHxiuZLfwVDpsl3rcsVxNC6WsOJWKtARLn92MBPmb7oBJwcqrhGN57XX33Vvvdku+xtQlUjO9PezXyaaf4X16bnzH8H/F/wAWvhd4bFn+zl+wP8Q7jwXqSxXlm/i74nW1reMrKCSLG9uJ2tclj8qldwAZgDwOmcqllCp0+/orPr026a26nNDkfvw1v/X9PqfQPwZ+Ptv8UdX1nwN4n8A6/wCAPHnhu3trrVPDmt+U7m3nBCXVpcQs0V3bGRJI/NQgh4yGVCVzEVzwc46pOz8nvZ+utu6VxuSjNQlu1deaWjt6aX9Uer0ij4A+Dqq//BY747o6hlb4f2YIIyCPI0Kmm07oGr6M+9dL0rTNE0+DSdG061sLG1QRwW1tCsUUSj+FUUAKPYCta+Iq4qrKtXk5Tlq2222/NvVmdGjTw8FSpRUYrZJWS9Ei1WJoU5NX0mGG7uZtUtEhsFZ7uRp1C26qCWMhzhAArZJxjB9K2jhq0pRhGDbl8Ks9b7W779DJ16UVKTkrR3129ex+Xf7T3j/4efEj4oS+Ivhtof8AZ9h9m8m7l+yR2/2+8E8zSXWEJ3bw6fM2HOPmANf1vwJk+Z5JlKwua1Oad7xV3LkjyxShrtytPRe6ujP514tzLA5rmLr5fDljaz0S5pXk3LTvdavV9T2D9hT4s/DXwlJqHgfxLbw6brmsX0MlhqcqhhdM2IlttwTdGQTlcsVPmP8Ad/i+J8WOHs1zFQzDCNzpU4tSgvs295ztezv1sk1Zb9PqfDzOcvwTng8QlGpNrlk+t9FHa6t0u7O726+8/Gj9kPwB8X9e07X45o/DVxFJM+qSaZYxrLqfmOrFnbgCQYk/eFWJ385wBX53wx4jZlw3hqmGa9smlyKcnaFk1ZLfl291NJW0tc+0z7grBZ3WhXT9m1fm5Uryu09fPfWzeuux7nPCZlAWaSJgchkIz+RBB69xX54fZGc1ldz3EtrLKywyxHzZY8AyH7oBBBAOM5K+g6cCgC3eWFvd/PKzp8uxyjY3p1Kt7fr155NAGe2nQ3zL9jtVgttwbeeFf3WIgqeONxHfIzxQBcn093jjxcyyvE4cCR8LJg5wwUAfpwQDz0IBYtbmO7hE0YZeSCrDDKR1BHrQBNQAmAetAC0AFABQAmB6DnigCs+mWTqiCIxqmcLE5jHP+6RQAtna2UKmazVSJcEybi5b0+Ykk0AWaAKlzBMk3260G6UJsaIthZFByB7EZOD7ke4AJ4Zo7iJZomyrdOMfUH0NAFbV2eOyaZVVliZZXBbGQpDYHB5OAPxoAfqBaKJLsdLZ/NYeqYIb8gSfwoAknvLS2aJbm6hiM7BIg7hS7HoFz1PsKTko7s0hSqVU3CLdtXZbLuyamZhQBQ1O4tbJ7W8uJ0hxMIgWYLvDgjbyeecN/wABpNpblRhKd3FXtq/JALmCfWVhhmjd7eCQSqrAmMsyEBh2yASM0JpuyHKnOEVOSaT2ff0LF3fWWnxia/vILaNm2h5pAgJ64ye/B/KlKUYK8nYqlQq15ctKLk/JX/I8s+JH7SHgnwTceJfDGhyJ4h8beHNM/tV/DkcjwSTQhFlfbMyFCywsZSq7n2jhTXmYvNqOHc6UPeqRV+Xa633tbbW2rt0PueHvD3M85hhcdik6OCr1PZqs0pKMruKvFSUknNcik+WPM7OR57D+2Zq1r468GeGvFXwN8W+HrDxRaH7XPqFpPDNp90jP5+FkiRZ7eJBHK8qkbYpNxUMpjrgXEElXpUqlCUVNa3TTT67rVJWba6O9uh9dLwhoVMqx2OweaUa08PL3VCUJRqQaXJdxm3TqTk5QjCSd6keVNpqZ6V4P/ah/Z/8AHV1JY+HfirobXKTR26w3sjWLzSOSFWJbhUMxJGP3e7kj1GfQw+dYDEvlp1Vfz0+69r/I+Nzfwz4uyOmquMwFTlabvFKoklq3J03Lkt/et17M9Sr1D4U83+Kvxz8J/DOSTw6sg1Xxlc6bPqGk+HoWKzXwRZG++RsjX91JyxyQjBVdsKfOxuZUsI/Z71Gm1Hq7X67LZ/pd6H2PDPBWP4iisa17PBxnGFSs1dQ5nFfCvek/ejolZXTk4xvJfMOhfCv4q/tb/ErSvif+0B4BOg+BrPSXXTrKxmMUt3FIzNECRL56tiQO0gCZEahVUtmvm8Pg8ZneMhjMbDkpxSaS663Sd7vXrorra1z9j4izvhvw64YxfC3DWJdfE15ONWT+yuXlqWaio2snFRTcouTbk7I+2F1rTnDFJnbaNx2wucD14Ffan81lf/hIYJ2WPT7We5dztQ7diEjkjcenAJ6UAcv4j02z8VWd1Ya5CJo7pD+4LFSgUMwZJFIeNoz8wcEMHUYwUzUzhGpFwmrp6NPZoE7ao8z+HOo6de3Gh6NcXuqaprFreeKbO2v9QhIvF07TtZ+xG3kc587YZLVFkkbzJfKErZZnI+HyPCLCcQYynhVy0YqOi25pKL/+S9NtNjsrS5qMXLc96037JZaRFcuYoIxAs0sjYUAbclmP06k190cZ8+eM7/UvitcXfhm+0qb7T4sgj06TRrizDLpPhN7t1uri7fCz202pQRvEked4kWECHNpdyr8F7WXE+cU5UVfDYd35rv3p6NW6Ozt8ru/vJHZb6vSafxS/I9VjdRI0iSKzhAquCQoGMAfOPnHUFcZ4GOOK+9OM5H43a/pnhn4D+OtVvFkkuptDl0zYz8AXWLdNpAPAMyZB5G3HHWvquB8HLHcRYOlB2ampfKHvv8Inz/FWJjhMlxNSS3g4/wDgXur8WHwK0i28H/AfwBo9tcxyvLpcWstK8QDKbom5KqM8lTKQDkZ8sZ64quOsdLMOI8ZVkrWm4f8AgHuX+fLf5i4TwqweS4amne8VL/wP3vwvY6DXvD1n4n0S88Ma5I8cFzAw89J2gltnDbopo5PvpKjgSxlMMrpG45GR8hUpwqwdOorxas09mnuj6JNp3R56niDWvAF/qPjDxBLDC+madG/jqzS2htpZFihZV8Q20dvJJMYZBCUaKZmkaG2jCeW9s8U/wmVYipwxjf7Ixf8ABqNulLV2u7KL6ettpO7upXXZUisRD2sd1uYOp6r/AGJr/iHW7jQ7mSxWNPiN4fjltHvrqYWdvDb6utjBG4MHmWdxBHCjFZTcXd27I68NyYrOMG88o4/ByvG6p1H8K96/K9bN2s229PdjqVGlL2LhL1R7k0kd2sd1drJDHMQ4kkhLo4wSp3Aj7y4HyjBwD9P0k4DodIvLa3tX8x7l2LszuyNIQBwAxUEAhQuQelAGP8Svh74P+Mngy98GeIliubW4+eGeIq0tpcKPkmjbna65I91ZlOVYg+zkOeYvh3HQx+DdpLddJRe8X3T/AAaTWqTPMzfKsPnWElhMStHs+qfRrzX4q6ejZ4P+yt418W/DzXvEX7NfxNJk1XwzDJf6PN56tFNaAKTDG5IZlIZZYxtJCmUME8sLX6H4h5dgs2wlHizKf4dX3Zq1mpa6tapO6cZO9m+Vrm5rnxnBmNxWXYipw9mHx09YO+jjpourWvNHS9rp2tY+orOB7eDy5HDuWd2IXAyzFjgZPrX5Gfox8qftV/8AFL/tF/A/xpphvF1C9v20+4+y/wCtltorq3HlLtwzb1vJ1Kk4IbGBls/b8F4pRo5rgakVKFTCzqO6u+ai1yW6b1G9r3UWmrO/HxBgHjctwOKjNxnRxtKnHW0eXEwn7Vy66LDQtZrRzUlK65fqiy5lvHAO1rj5TjGcIoP6g18QdhaoAyLyd4b51S1UOV3mZpfLUIABktg92Ix7Z70ATabFLKRc3MrzBBiJnxjJ+8y4AyDwASOmccGgDRoAKACgAoAKACgAoAKACgAoAz/7Tt5LyXT54iEAKFnA2k7ckH0BXkZ64b0oAy9N1CG6uLeW8uk/0C2zln5d3HUD+LC8Hvk0AXtV8SaXpOnT6ne3X2W1gjMkl1cRtHBEuPvO7bVC+5IHuKAPM9O+Lfjbx7qq6V8M9DsRZ3ire22u67az2tuLQGJZfJtiyy3zAsT5iGKJfNiUtnBcA6nw38JtG0vW08Y+JtRvPFXihVwmqaoVP2QFVVktIFAitkJBPyLvO5tztmgDuaACgAoAKACgDxvxfBq+h/F7X/GWvaxotr4XHhTRUt21q/VbW1httQvpNcm2M37ofZZtP3ykBGMcAckRjHxPFH/Iyy7mvy+06d7w5f63tex14f8Ahz9P8z1nVNV0vQ9Pn1fW9StdPsbVDJPdXUyxRRIP4mdiAo9ya+1bUVd7HIed+M/im9iuntaXVnoNnc3ZVLjWIZDe6iIo47hoLCwXE00rxLdLhtsqNBuEEyEV8fjeLYTqPC5RTder3S9xa2u31SdtdI2fxHVDDNLmquy/E6Xwnd65qmg24FpdaeEaSNpr8Qm4m2yMBKEhJjAfG8DjAcAqhBUfSZc8W8LB45JVba8u3/D97aX20MJ8vM+TY6C1021tHMyIzzMMNLI252/E/wBOK7SC1QBVvNU07To7ifUL6C1itYGup5ZnCJFCuSzsxwAoAJJJ4HWgDxXxNqviXx34t0PUtJjtraXFwfBum6layZjlMLJca3fxACZEjhkMMUAMXN3smdJLqMWvwOY4t8VYv+ycG/3EWnUmuttoxe2+z6tXWkdeyEfq0faT36I9m0LSINA0TT9Btrq8uYdNtYrSOa9uXubiRY0ChpZZCXkcgZZ2JZiSSSTX3dOnClBU4KySsktkl0ORtt3ZeqxBQBWubJZ3E8TmG4VdqyqOcehHRh7H9KAG296d62t6qw3JHAB+STHUoe/06j9aALdABQAUAFABQAUAebfHr9oX4Xfs1eD7Px18WtYudN0e+1KPSYJbezluWa5eKWVVKxgkDZBIc9OAO4oA/Lj9i74jfFf4+ftYnwrr+m23xr8Baf4nvdTbxj4s8Mtc3WixAmezuYbrBfTDMdPjEdruEOXkQRnkqAfshQAUAFABQAUAFABQAUAeRftff8mpfGL/ALEXXP8A0hloA4n/AIJw/wDJk3ws/wCwbc/+ltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfA9//wAEXP2U7y+uLu38X/E6xinleRLWDVrExQKSSI0MlmzlVBwNzM2BySeaAIP+HKn7LH/Q/fFX/wAGunf/ACDQAf8ADlT9lj/ofvir/wCDXTv/AJBoAP8Ahyp+yx/0P3xV/wDBrp3/AMg0AH/DlT9lj/ofvir/AODXTv8A5BoAP+HKn7LH/Q/fFX/wa6d/8g0AH/DlT9lj/ofvir/4NdO/+QaAD/hyp+yx/wBD98Vf/Brp3/yDQAf8OVP2WP8Aofvir/4NdO/+QaAD/hyp+yx/0P3xV/8ABrp3/wAg0AQWn/BFH9mVImF/8RvifNIZZCrQ3+nxqIy5MakGzbLBNoZs4ZgWAUEKACf/AIcqfssf9D98Vf8Awa6d/wDINAB/w5U/ZY/6H74q/wDg107/AOQaAD/hyp+yx/0P3xV/8Gunf/INAB/w5U/ZY/6H74q/+DXTv/kGgA/4cqfssf8AQ/fFX/wa6d/8g0AH/DlT9lj/AKH74q/+DXTv/kGgD2/9ln9gn4J/sjeIdb8VfDrUfFGq6rrllHp8lzrt5BM1vbq/mMkQghiADsIyxYMf3SbSvzbgD6QoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv+xgT/wBLLOgD6foA4D4+fDzwv8Wfg14v+G/jLWI9I0nxBpktnLqEjhVs3ODFPyyg7JAjbSwDbcHg1nVjzx0dmmmvVNNfijfD1JUqnNFX0afo00+/RvW2m55F4b8Y/tz6do1tYp4Q+DHxKSPMEfiLSvFVzpy3KKSEmntzbSKkrKAzrE7IGY7eMCtZSdR3ceW/zS9Otl56nNFKEeVO9tPP59Do/g18GfijD8Xdf/aG+Pmr+FpvGOpaJF4V0nTPC63B0/SdHjuXuChmn2vczSSMjM5jQKUIX5WwNIVHToypJ35mm9vsppJdbe82731fZDblNq+iV7K/e12/PS2mll1Z7zWQz4B+Df8AymQ+O3/ZP7P/ANEaFQB99yyxQRPPPKkccal3d2AVVAySSegFOMXNqMVdsUpKKu9hv2u1+y/bvtMX2by/N87eNmzGd27pjHOemKr2c+f2dnzXtbrftbuLnjy899N79LH5dftT6t8PdQ+Mut6p8LNaN5p+pATam9ucWz3+5hMYmAAkRsK5bkM7uQSpFf1twBh8zo5FRo5vT5Zw0hf4lDTl5uzW1tGkkmr3P514vrYGrm1Spls7xlrK23Nre3dPe+qbbs7HnXgfwnceO/Ful+DrPVdP0651e4FrBcX7ukAlbOxWKI7Dc2FHynlhnAyR9PmuYxynBVMdOEpxpq7UbOVlu1dpaLV67J2u9DwsvwcsxxUMLCSi5uycr2v02Ter0Wm7PqXwL+wB8TdK8Q2utaz8RvD2kSabLHeWVxp9vLfuLiN1ZN0cqwrtBGc5PTGOa/Is28YMpxGGlQoYWdRTTjJScYLlaadnFzd/kvU/Rsu8N8wo141ateEHFppxTlqndaNRX4v0PuW1W5S2iS8liluFRRLJFGY0d8fMVUsxUE5wCxx6nrX8/wBRwc26asuibu7dLuyv62Xoj9hgpKKU3d9bafhd/m/UlqChryJEhkkdUVRksxwAPrQBQmu4rq7hs8SCCTdvYxMEkIHCBiMEEZPHBC45BoA0aACgCBrK2af7QYhvOCeTgkdCR0JHYnkUAT0AFABQAUAFABQAUAVLENCZbEj5YCBFx/yzI+Xnvghh+AoAt0AFAHifxi+Ptp8KPHmgfDqxjsBqHiyx1C6jvNTmkistLljicwSzFUO6KSVSr4dfLVWckA8+biMbUWMpZfhoc1WrdRVm9fsqyTbu9LLVH3vDfBkM0yTG8S42q4YbCOHOlyc0ouS9rZyklBwg+ZOSak7RWtz5q8efGT9qL4qfC/wxrGuWVx4C8P6nqr3N14n8Mw3flJp64hYziGV5ogjtIdrYWbKbceWS/wBTivDniHHUlgY4yhDGRkuejGcoztKMXGMZSShOavLnjGTirL323YeR+JXhlwvmtfHRy/E4rAypNUqlanSnB1IznzScEuaFOaVNU5ygprmnzU0tVe+Efwy+Muv+M/Gus/D745nx5bjwyPD9t4m1bUdQtWge9QTD7O+2ciaDarMgYFGlXO1sgZ4Tw/p8NtV+K8TWVSpzRjCmouahFpOTcqnu8zuqbSknyuV2nYfE/jZguLMJQwPB+T4elTo1IVpuf8KdRKVqfLTpx548vL7W7jLVQsrXOtn/AGXP2iNTsfBeq+NPHGgeINV+GGoHVNEQ397Ncasz3Uc8kVzdTAeWVEKJGVTpwxUAMOiWScFVp4eE6mLfJKT55Olpd3heKjJzS0UrSg7JuK1UV4VLxM4xwUc0+o4TBUoY2nCE6cIVVFctN05um1OCpuXNKS5o1EpNKTdnKWZpPxy/al0PwTe+AtShsD8Vb/xHNDp9t4hghsF/sqSPy1eylmeKC5kW6IVF3ylg3CyIMr5+a8H8R5bl9TF5bOGMp87tUp2fLTcXJOcLp02lq1K6js29D3cm4o8MOIOIqFLNqdXLqKoR5qc+ZueIjUUZRjOMZ88JRdlKCpuVuZKEm0dT8Jv2SPifH400L4wfGn4zanq3iSykNw+mI7zRQqTIfIE5cAKPMJ2xoqKdwXcMNXxWAyLFRrwxmMrOU103XXS9/PorLofecWeKuRVcrxHDfDWWxpYaatz6Rk7cvvcqje75bXlJykrOVnobl7+w38K4fhfH8LY/FvjFNFXX/wC3iTeWpka6eFLcbj9nxtVAcAAHLncSMben/VrC/VvqnPLl5ubdXva38vY8deNufLPHxB9Xoe3dJUfhqcqgpuei9ru5PVtvRKyWt+wuf2V/h3deNfG3jo6r4ijvvHmhXHh7UYY7uIQw200cUbNCPL3B8QqRvZ1yTlTwB1yyahKvVxDbvUi4va1mktNN9PM+eo+JWbUMqy/KI06fs8FVjWg7S5nKMpSSm+ezjebukou1te/k1t/wTS+DhlEt14w8aCJoIwYVubTes2TvO/yCCuCoC7cjBO45AHk/6n4K/wAcreq/yP0F/SP4l9mksNQ5ru75alraWVvaXve93d3ulZWu/o7Q/hJ8OPDvie58baZ4Q05fEV2sSS6rLEJLrEcCwKEdsmMGNFDBNobkkEkmvoaeBw9Oq68YLnfXrorfLTsfjeL4pzjG4CGVVcRL6tG9qadoe9Nzd0vifM205Xa2TSRueIfDPh3xbpcuieKNDsNWsJs77a9t1mjPBGdrAjOCeevNb1aNOvHkqxTXZ6nm5fmWMyqusTgasqdRbSi3F991+R4f8S/2J/2fvFnhiWy0zwfF4a1KC1+z2Go6UXWSN95KF4y2yclm2kuC5U4DrhSvi4vhvAYmnywhyO2jX6rZ/PW3VH6bkHjVxbk2MVfEYh4im5XlCdmnpZqMrc0NNUo+6pauMrtOOy+D37RugxeIfBvhr4v2tp4Mh8KJpXhNZCr6jaahHFbhZJ5jbFgjMlwrMrsVSUeWiEJ5YsBj6XPRpVbUuS0P5lJJat8u297PRPRLSxU4t4SxywuY4/L3LGvEOpiWk1SnSlKo3GEFWS5knDl5opSlH95KScufq/hT8CYvDkPh7xb8Urq08XfEXRdOXTx4ikErSpF858sGRyJGUzSqJ9iO6FdwzknpwWW+yUKuKanVirc2u2v37vWybW54nE/Grx0sVl+QxlhsvrTc3RXLZy91X0V4pqEX7PmlCMr8ulkvXK9U+AI54vOgkh3Y8xCucZxkUAciq3Wk3620M4nezb5Y2baG3IWJC9+CRwSc/XFAEusIG1SZLWJ4wuA7IpIZn2q+B0Jw6jpnnr0yAeOfs8Pr/i2Dwxqeql7+zPw48NapZas9nEgmvbz7VJqJS4jjHMpi093jQhFxGwVcrn4zhPknXx1R/wAR1Xf0TdtOmrl6/I6sTdKC6WPZfHXgG18c+BNd8A6hqlzb6f4h0e70W9kiI3rBcQGF3j3ZVHCsSuQVB6g19mcp5to/ia4/tTTZtZS0S41+2m025m/eW23WbMHzLaCK5YyvFLEtzPEI0VfKtpZSziZWr4bhN/2ZicTktRawlzRfVxdld2025X31t0OzE/vIxqrqdvqtxY6Vp934t1eRrPSNOglu9RuZCSFgRCzlQi72ymTwMtkDBPJ/QcJhK2OxEMLh4805tRS7tuy30Xq9O55uJxFPCUZV6ztGKbb7JavbU+XvF/xC8e/tgBfAHw/8O/8ACMeArW9Md7rV6TILpYmRkX7oCuFKP5IY8lCzqvzV+2YLKco8MEsyzSr7bGOPuU46craafV6bx52krXtFvQ/LcVmOY8eXwWAp+zwyfvTet7NNdN9nypvW12lqfSWi6OnhbwBpfh6Mte33hnRbTTlhjRI5dSMECoNm9wgLlRtDPtHBZscj8Uz3NYYnE4nNKi5YylOo1vZNuTXS9vTU/TstwbweFo4RO/JGMb7Xskr+VzzTUPGXjfXdZn8PPN9jvoZnx4c8KBdX114VjW6ijvL2QR2WkSyrujbzSVO4CG7DMjL8E+IMfm/uZLQfK9PaT0itbXS6236vvHv6nsIUtar+SJ7P4P8AiLRJ18d6b4R0HQvE2m730wSX91qms6zb4iAt9U1RxuVXjzCYD9rjiljtJUlkFukdTLhTE46jP+0sVKc5LRL4Iy7peT6pRdm11Y/rMYNezjZfiZhi8K/D7Tb74h+FtD0uy0TRDY+JotZtbsQQX3heSNkuRM6DzZksYpp5obba0KRrYpHtY4j83A0/7eyCtgJx/wBopN3VlfmTbT9ZWcW3re7fnpN+xrKa+FnoHwf0SSy0e++Hlo2yDwLqD6LaYiZIodPKRXFjEhbLzGK1nhiLMx3SRMSxO7P13D2YvNMtpYiT961n6rRv57/M5a8PZ1HE9csbVbK0itVIby1wWCgZPc4Fe0ZBcWVndc3FrFIcYBZQSPoeooA8k+OPwYuPGg07xj4O1s+H/Gnh1t+k65li8a4bNvOwyZLdyxzuD7NzYBV5Eb6/hbiiOS+0wWPh7bB1dJ0/PT3432krdGr2Wqai4/N5/kLzTkxWEn7PE0/gn/7bLvF37O13o02n51bfFn9tdNIg0eP4HaJc6vZRRPdancXkRtrpGLqCqpcInmHYCwSQ467FV0x9bPIOAZVXiP7RmqUm+WKT5ovR6twk+XXS8VfbmbjI+ejm/F8aao/UoupFK8m1Z7rRKSV9NbN23sk0cZoOqeI9T/aL8Fx/tTeJbSPxI8NxqfhPw3ZRRrb2Nw8xWFLidRsG5ocwqJJXZkUSMhwkvzeb8Z5Bw5TqcNcO80niF79ab1lG8rRirRS3cU+VNq6abamfouT+GnEvFmVPjbMIQVHBtJUqd3Z8sOao7ynJtaTqK6hG6lB8inGP2paQG2t0hZ97DJdsY3MTljjtkk18OAtzcR2sLTSBiBgBVGWYngADuSaAK8Gnxs/2u9iSS5chssA3ljsq+gHqOpyaALtABQAUAFABQAUAFABQAUAFAEVzcR2sRlkDHkBVUZZmPQAetAGFfz6XHPMNe1GCC6+xS3v2aSQJGtvERukycbxGXXc2cL5i5A3LkA49vivr/irSpG+E3hU6oI1w+vahm30dCA4kMRz5t3tZMful8s/89RjkA1dK+Fkd5fw+IviRrVx4q1eC4F1bxTEx6ZYyKX8trazB2BlDcSSeZKCM76AO7MaFxKUUuoKhscgHGRn3wPyFADqAOT8I/FXwB478TeKvB3hTxCl/rHgm7isdetlglQ2U8ilkQs6hXyqk5QsOOTRD95TVaPwtyjfzjo18hSahVdGXxJKVvKV7P52Z1lAwoAKAI7i4gtIJLq6mjhhhQySSSMFVFAyWJPAAHJJpNpK7Gk27I8L+PWp6ZfeGdK8YaN4n0a+0jxjDbaFaalqllban4YsdPnD3Vzc3ZDR+bbXkMMdt80xR5GtEjMRleRvjuKaOIjXw+MS54UndQV+aU9OVLe/eyWiUnrojow0ouMo3369PMu6Z4f8AEHjnWdF8WQadHfeTK16mueKLGSOeyST5hHp+nlUNqTDc3Fu0rmOdRDGsy3Od45lkma583POKjp03tTg/zeq3SevN/wBul+1p0dKSu+7JvGEnhL4Zw3ptraTxH40uNIe+uGlO/U7vTLWaP7S0QRCW8o3W9YIlADz7UVDKufcq1cv4apU6EIckJy5U0tE31k2/xbbt5J2xSniG23dot/BrXFsLLT9Mvri4sodYQwwWGqapd3tza6xAJBe2aXN7J5txHsiLwiOIKUt55gSkiY4OHcxxX1qvlWYS5qtN3T0V4aa6eqerv71uhdeEeVVIbP8AM9ar645iheXU9hcrcytusnAWTgZhbPDe4PQ+lAHkfxC1vUtd8SWelz6HFqlwLqQeGfDSXUZGpTQtGW1O/lVXFvaQM0bA4cJuiYpLdS21snxOe1swzXFvJcFFwjZOc2tOV9F3T1WmsmmtEpM66KhTj7Wer6I7/wABeA4/CEV3qmqaj/bPibWfLfV9XaHyvPKbvLghj3N5FrFvcRQBm273d2lmlmml+myzLMPlOHWGwyslu+rfdvq/+GWhz1KkqsuaR1legQFAFO31jSbrUbzSLXVLSa+09Ynu7WOdWmt1kBMZkQHKBgrFcgZ2nHSldXt1AuUwK9+lo1pK166RwxqXaR2CiMAff3H7uOue1AHEfDT4kah43FnaajoE2lX7aFaa7f2d6rwXdil48gtYJItpXzQtvcLMBJ8jxgAMHyvh5NnDzeVdxjaEJcsZJ35rdfye7vc2q0vZWvuzv69wxCgAoAKAE6daAPz0/wCCuH7SPxf+BVl8LtG+FniqTQotevNQ1DUnijVnufsT2jQwPuBDQMZn8yMgrIvyOChZWAOZ/wCCH/gvVLH4efFH4iTXdu2m63rWn6LbwKzebHPYwSTSuwxtCsuoQhSCSSj5AwCQD9MqACgAoAKACgAoAKACgDyL9r7/AJNS+MX/AGIuuf8ApDLQBxP/AATh/wCTJvhZ/wBg25/9LbigD6ToAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/ALGBP/SyzoA+n6APPf2gtS+GOi/BXxhrXxm0iPVfBenaZJeavYPGJPtUURDrEqFlDOzqiqCwBYqCR1rKtKMI3kr6q3q2kvxsbYdVJTtSdnZ/dZ83yte/keAfCrwt+1T4Q0CO6+Cf7LPwA+Emk6n5U1xo2pazdnUpAq/KbmWxs/LaTDHG4uVyQe9dE+ePuSldK+i2T/LotVvY5oNTXNFb9+35/J7HrXwa+Oni3xV471z4M/GP4e23gv4gaHp0OtxW9jqy6jYavpMkjQi9tpQqOgE8bo0UqBlzGctuO1QjKpTlVS0i0n6tNr1vZ7X2a3Kk+SSi+qbT6aNX9GrrR663PZ6kZ8A/Bv8A5TIfHb/sn9n/AOiNCoA+7fEWj6T4g0DUtB16MSaZqVpNaXqGQxhoJEKyAspBXKk8ggiunBYmtg8TTxGGdqkJKUdL6p3WjunqYYqhSxNCdGt8Ek0+mjVnr6H5RfEOy0b4ceMNb0b4U/FGTWtEvFmsXuLGSeB5LYthre4O1I51yPvRlo5AA3Gdo/sXJqtfO8DRr5xhPZ1Y2laSi0pW0lHVyi/KSUotta2u/wCa8zp0srxVSlluJ56bvG6urrrGWiUl5q8Zb6bI+Cfwg1z42+OYfBmi3kVkoha7vLyVC629upUM+0feOWUBcgEkZIHIOKOJMPwtl7x1eLlraMVpeTvZX6LRtvWy6PYMhyStn+MWEovl0u2+iXXz30WmvVHqniz9gn426HJK/h19F8SW/muIRbXgt52jDAKzpOERWKnJUSMBgjJ4z8fl3i5kGLSWK56Lsr3jzK9tUnG7aT0u4q+mi1t9HjfDnN8O26HLUWtrOzt0upWS9E367X/QLwfdeJL3wrpF54x063sNdmsoX1K1t23RQ3JQGRFO5uA2QPmb6nrX83ZlTwtLGVYYGTlSUnyt7uN9G9FrbyXoj9uwU8RUw1OWLio1GlzJbJ21S1fXzfqzYriOor3F4sUq20amWdwSsY7D+8x/hHv+QJ4oAhksJrpA95OBKhDReUCFjb1wfvHtk9uwycgEkWL60aK9hQnc0ciY4yD1weRnhh9R9aAGrbX8OVhv0ZM8CaIuQM9MhgT9TnpQBZhMxiU3Cosn8QRiV/AkCgB9ABQAUAFABQAUAFABQBVvLednW6tXAlijdQpH384OM9uVA+hP1oAnhljniSaFwyOAykdwaAPI/wBoH9pjwT+z/aafDq8E2r65qkiG20e0kCzG23gS3DkghFVd23P33G0YAd053Vq1cVSy/BUpVa9V2jCOsn3fkkk227KybbSTa+u4e4SnnODxWb42vDC4HDRbqV6mkFK3uQXWUpScVaN2uZWTlKEJ+c/A74WR/E/xh4u+J3jzU9Q8XeDtR1iS/wDDVjr0EohaSWILJKbWX926RIRbxMVI+R2VUwtfbexrcDYR4KhVaxtaTnVkmuanG65KMZRk7bc0+XlveMXdJJfF51neH4+xGHqrDQp4PC0o0acUnarNXdSvJSjFvmcrRU+blfNJWlJt/UgAAAAAA4AFfI7mwiIkahI0VVHQAYFNtyd2JJLRDqQzA8beBPCPxF0KXw3400G01WwlJYRzpkxPtKiSNh80bgMwDKQwyeea9HK82xuS4hYrAVHCa6rqr3s1s1otHdHFj8uwuaUXh8XBSi+/R9090/NanzT4VsPGHxA074bfCr4kQ/EjRvEfhe/1S21K8ggls7eS3itLqO3vob2NdsjRMbSNJN2GMr7hKHDH9VzCtgcnq4/OMreHqUK0abjFtTkpOcHKnKm3dKa55SjbRRVnBxsvz/B08VmVPB5bj1WhVpSmpNJxTSjNRmppWbi+RJ31bd1JO77jxH8FvH/gSwsfEPw38b+OvHOp2WqQz3Wk+JPFr+VeWXlypJAgIWHcWkifMg48r5SDw3zmG4ky3O5Twea4ejhqcoNKdKirxneLjJ6udrJxtF682qtqvar5JjsrUMTl9arXnGSbjUqu0o2aaWije7T97tp2cmo3/wAf/i+2qar4Im1b4W6do9vjR4NY0y3e71q+IcP9oRmfyIEKqi4Vt3mGTLYVFdGjw3w37Ojj1HGzqP33CclGlDS3I0o883dt3atbksruTKtTO8756uDcsLGC9xTjHmnLW/Mm3yxVrLR3vza2SXI+DfEX7RXx8GmRaT4h1P4aJ4T06K21u8vtEV5tU1rfiQLE+1DEEjVyuFKNKUKuCpX2szwXDHCHtHWpRxjrybpxjUaUKNtLyV3zNtpO75lFSTi00/LwOKz7iPkVKcsN7KKU3KF3OpfXR2XLZXtZWbs01Zrotb/aJ8bfA6OO3/aL8HwzJf3DR6Xq/hJfNtplVV3LNHPIrRuM7gc/MCwCjYS3mYXgzAcVNy4YrtOKvOFbSSu3ZxcItSXTbTS796y78RxPi+H0o59STUn7sqWqdrXupNNPr562Wl27w38Yvjz8aLabxJ8F/AnhvQ/DEUnlW194zkuBLqR/iaGO2ztVD8pJLKTwGJV1Wcdw1w7wxJYXPcRUqV2ruNBRtDspOe7e6Vk0t1Zpt4XPM6z6LxGU0YQpLRSq815d7KOyW27XZ3TSpW/wt+J/xr8S6Zc/tLeFvDltpGl2V9bWunaPqFykr3LTQt9rzG5AjMcYQKZdwLE7Ru+W63EOU8L4aVLhStUdWpKEpSnGLSioyXJqt+aV21G3aTtrFLJsxz6tGpxDSgoQjJKMZST5nKPvaPayslzX8tdPTtR+BHw91j7L/a58VX32G5S8tftPjHWJfIuEzslTddHa65OGGCMnBr5ihxbmeG5vY+zjzJxdqFBXi907U9U+qejPeq8O4Gvy+155crTV6tV2a2avPRro9zF074K/EC3uTZ337RvjafRbaMR6fBHBZpex9Mm4u2ic3J4OCUUjPU85763FGWzjz08roqq3eTbm4P8AwwUlyfKTXkclLIcdGXLPH1HTXwq0OZf4puL5vmkzkPHPw3/aC8P69Hc+CviB4x8W6HNblWtf7b0/Tr23uN6AMZZLRo5I9pc4CK3HsN/tZVnfDWMw7jj8NSoVU9/Z1KkHGz05VUUlK9lq2vv08vMMrzzDVlLCV6lWm1tzwhJO66uDTVr9E/10vhb8cLnQ/hvqEnxYuLu81jw94vu/Bay2cTXk+rXSPmMRJFDGXbYxT7gLeSWOGbaOXP8AhWGKzSCyZKNKrQjXtJ8kacWteZylKyur/FZc3KtFc6Mo4glh8BN5m2506sqN0uZzknpZKKu7aba8t93Y9P8ACfiDwv490ey+IXh+5lk065SQjzrd4WEkTtG29GAYOjLIvI7cHGCfiszy3EZPi54LFJKcbXs01ZpNNNaNNNP59z6jAY6jmWHjisO7xlfdNPRtNNPs00Zhu5JT56JEXXbIzgPw27czcDgnAyfQYFcB1nm/w4utS+H0vhj4X6ZJaaW3haMaRYaReylbbWdA3KtvPaXRRnkuba1gHmRdfMV1lCRzW91X53VrVeEszr4qrTc8PXd+aO8Xq7PZbye7V1Zp3TR3JLE01FO0kaXjv4hfGvSbq4mm8IfDC10L7WYrK41Xx/dWRnX5inmIdKdRIQN2wSMo2n7wGa7KfHuX1Xy06VRvyjF/+3EPBzW7RznhyE+I31K3i1DQ7y91XxBpviXWrrQjI1ppjW9vD5MCXnlbL+5L6dDG5IgdYJVkZI8RLNll0cTnudQzd0nTpU4uK5t5brbp8V+qurXb2c+WjSdK92zA+MF1rfx98f6Z+zR4N1RrTSNOQ6t4vv4pWIjRX/dQENtEgUGEgAsC8kZIXyWI/pnhClQ4PyifFePhzVJvkoRfW+8r62vZq7SfLF2b50flvElStxJmUeH8JK0I+9VkunaNtL20dtfeavblZ9F+Cvhp4T8BeCdL8A6FaSnS9JjKwmeUvK7sxd5GYY+ZnZmOAFycAAAAfm2c5tic9x1TMMW1zzd3ZWSsrJJdkkkr3emrbuz7jLMuoZThIYPDL3Irrq+7b822327JLQta/wCBvDfibSrrRNZ06O8sb23e0uLa5Anhmt3Uq0LxyZVoyrEFSMEEjoa8w7iKx8N2/h63itrHTIWs7ZNsS2YNvJEoB+VVUhSAMDAx9KAJbx7f7E+p6Vez+dbj5leckqCw3BhJnH3enfHegDwvxNp9noMusaU19b2cECXHiW1nsVb7dcaRNdpJ4isliEM88qFmEmIUUu11bRx+S8KSj854jhismzH69gpNKunB7WU7Wi7tpJ7NNvS0nqro7qDjVhyT+zr8j0X4LNY6pdeKvEd08SeIF1abQbuzjmjIsLCzmlbTotkbEL5ltcpeAt+8Ivhnagjjj+h4RwtDD5VTlQd+f3pP+9s18mrfK5hiZOVR36Hp9fSmBVubk7xZ2zqbhx6ZEa/3m/oO5/EgAaulWJ+aeEXL4wXn/eH9en0GBQA9tN052Lvp9szMckmJSSfXpQB4l+1N+zfY/GnwhDqXhpRpvjTwysl1od3b4jaVgC/2VjuUAO4Uq5OY2+YEAuG8PO8pWY0lOnpVjrF/p/k+j+Z+o+F/iDPgvHyw2MXPgq9o1YvVJPTnSs7tJvmilacdGrqLTP2W/jfq3xT+HsjeJjL/AMJV4VkOmeJ7K5iWC4hnUttnCcEB1VtysFxJHKqgBRV5JmTzDD2q6VIaSW2ve3n8tbmHihwXT4PzdSwPvYPEL2lGSfMuV2vHm68t1Z3leDg222z2uGOS7uRfTq6RxjEEbDBBI5dh69gOwz617J+al6gAoAKACgAoAKACgAoAKACgBGZUUu7BVUZJJwAKAPM9X+N2nX1y+jfDDS38WX5+UX8T+Xo1u+U/1t7yjECTOyESNlWBC4JABmW3w+fxvqNtqPxM1i28S3SSJLb2NzaEaNbbWYSLHY78O7QvIolnaYq6h14BjoA9JOn266oLWaPfbSxtNHG7kqsgYbgF6YO4H8/U0Aa9ABQAUAfIf7Hf/J0X7Wf/AGN2k/8ApNNRg/8AkWQ/6+1v/SomeJ/5Gk/+vVH8pHtX7TXxxtP2dfgvr3xTl0htXvLHyLXTNNWQRm9vriVYYI9x6LvcMxHIRXIBIAMSk3OFKNuab5Y376v8k3rZabo3ikoyqT+GKu7dv+HaWz32PmDxR+0H+0/+z5o9v8b/AIrfGP4SfELwu1zbjxX4M8NwJDdaHbSzJEH0mfzTJeMpkDOtx2UhSATIttxpzUG7pv4u179O17LZt6aLVkJTqRbirNK9u9t/nbzt+CO3/aQ/aJ+INv8AH3w9+zf8PPix4D+FNtrHhMeKJ/GviaOO6knaS7NvBZ6fBM6QSTExliJGYsjsVCmP504Sc5Qbs4taW1ff5dO9xKacIzgrqXXsv8/wt52M/wCKmsfteT/sj/H3w38UNA8Cyanovhy7TSvFVpK66d4j0OSyc3sq2ySPNBeJAs2A4WEzSRhQY1ZipNuMGlZ3fMn2to4vu30a0Wu7siGkpJu6srPre+qatsls03d+l3y3w++O3xe+Bn7D3wkn8WaJ4A1Xxd4vbQPDXw1ht5LiPT7ezn0+E211q7zMrI8SJO0xg+U/u1UqGZ1ptzqxpRaTeuv8qSu/y+/S+zilaNOVT7KdvPmben3/AIJ3aNmx+Pnx8+AvjjwSvxo+M3w1+KvhH4g69aeH71vD9vHZX3hnUbssIvKCOVuLBGXa0soEvzKeCNrVTSlUjQ1beifVvord30t536Iqq3GlKvolHWS6KPdPyv8A1uvoj4ieG7m11Gy8SaO8kmp6NcG8tkAZ3uLeTi7tcedCrmWNcIJHMazJbyyBvJUHx88yuGcYGeFe71i+0ls9nZdHZXs2b0ajpTUjxnSo7jQfE174X0bxW9po/j6OHXfC3iCCSCOS2mWOFLS5bHlPcCM/ZLKSNyyywtpcbCZp716/PHja9ejDNo/7zhWoVIu/vRu1frrupf8Abz0tE7eRRbp/Zlqj6a8DeI7nxX4VsNZ1GwSw1J4zDqVkjSOlrexkpcQo7ojSIsquFl2BZFCuvyspP6jhMVSx1CGIou8ZK6/rutn2Z58ouDcWbjokiNHIoZWBVgehB7V0EmTEn9h3bmVy1lctnzXOTE54wx67TgYPbjPrQBrF0CeYXGzG7dnjHrmgDlfE/wASfD/hPy5tUaeSKf5LaK0t5bm6vJdwBW3t4laSYLkF2RSqLl3KopYYYnFUcHTdXETUYrq3YcYuTtFHB69498Ua7PFZX9jq2iRXCLjwzpMsMuuTRT2kvzX11HJ5GnRh47sK8U4DSWsJS73SG2PxdbiHH51UeHyGHurepJaLXomn5bpys37qtc61QhSXNWfyNL4e6L4q02xudY1uXRtH+03lwR4f8PQBbMzXEkU08s9xLGslzP5ouX85Ut1IuZPMjdgsg9zJchWUynXqVpVKk0lJybs7baa9NLttrpZNoxq1vaWSVkj0jRsjTYInCB4l8tgrBsEfQkdMH8a98xON+N72uoeCD4FmKO/jm6j8NeQZ3gee2nVmvxHKnKSLYR3siHI+aMAHcRXlZ3jnluXVsVHeK09XovxaNKMPaTUTM+EN9fXF94g8RarBcWo8R6xO1rb3DKxgtbY/ZLdkYAfu51tvtIU/Mpu9h5DVycLYFYDKqMOslzP1lr+CsvkViJ89Rs9Tr6AxCgBqSJIN0bqw6ZBzQApIUFmIAHJJoAzNUv4pLSZLVnkkQbleOJnVXUggEgEZ47/jQB+T/wDwWBs/g/rfxq0CO/vfHy/ENvBlvBpen2OjW82mXqPqFwLYPI8yXEcgf7WGCxSh90G3aVcMAfbn/BOT4XeM/hF+yL4N8K/EDR59H1udr3Up9OuYBFcWiXFzJJEky/eEhjKMVfDoW2MAUwAD6YoAKACgAoAKACgAoAKAPIv2vv8Ak1L4xf8AYi65/wCkMtAHyJ+xT+2J/wAK5/Zd8A+Cv+GWf2hfFH9l2U8f9r+G/A/23TbrddTPugn89d6jdtJwPmVh2oA++fAfix/HXg7SfGD+Fte8NnVrZbn+ytetFtdQtA3RJ4VZhG+OSu4kZwcHIABv0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QBy3xR+HHhr4vfDvxF8MfGMMkmj+JdPm066MRUSxq64EsZZWVZEba6MVIDKpwcVE4Kas/J/NO6/E1o1ZUZ88fNeqas16NNpnjun+A/wBuTwRplh4c8N/HH4X+NrO3LI2qeMPCV7b6mItx2K5srxYp2Vdo3lI2bktk8nTnlOd5pLu139Nl8mYcqhG0PxfT16nTfCH4B694N+IXiL4y/FP4knx7481+yj0aG+j0aLTLTSdISZ50sbSBXkcIZJNzPJK7P5cZJyCWqE3TpOlFuzabv1aTS9LJvRdW2N805KUui0SvbW1/Vtrf5LQxvip8G/2r/FnjrUdf+GX7Zv8AwgXhu5EIs9A/4V1puqfZCsSLIftMziSTfIrycj5d+0cAVAz849U8SfEj4H/t0fHPw9488aQ/EXxTr3gSPw5f+IptKi0pZY7q30t/MFpbkorJAohUBgCwEjZ5Q/oHhnktPOs/g6knFUV7XTq4SjZeSu03o7pNK17r4/jnNJ5ZlE+SKbqv2evRSjK783ZNLzd9bWf2J8Df22vD1l8Orzw58cri91LUNNiMVrMlp9ok1W2IC+VKSdrSj5gXkKh1xuJfcW+44r8LcTVzOGK4fShCbu1zcqpy3vHqovSyjdxeyUbJfKcP8fUKeAlQzhuUoqydrua7Ppzd27Jrd3u38VV+8H5Kem/s6/EPx38Ofijpl/4A0c6zfamRp0+k4/4/4HZWaMNg+WwKBhJ0UrlsruU/KcaZNl2dZRUp5lP2cIe8p/yNJpO32lrZx63srSs19BwxmeMyvMYTwUOeUvdcf5k+l+m10+ltbq6f6vV/HR/SoUAFAFO141C9GA2TGd/p8uNn4Y3f8DoAuUAU52FrewSghUuWMUgzgF9uVb64Ur75HoKALlABQAUAFABQAUAJkDqetAC0AFABQAUAfJ3xl/a11vRtel8Nfs7DQ/FGsadrE2k61pGo2V09z9o/hayjR42mQPHOshXfhthA2sGPLgaOZcQ4mWGyNQfsn+9lUfJCmm7czk2lyp3va8n9mLP0yhwzkHCmXwzPxBlWowxMOfDKhyzlVaXM6biozanKLg4c3JBJy55xasvNPhR8JPjb4z+ON18cPGvgzXTqCeKLCaCbUtHtbSFrFZAkxkt7mYSRGK38sxGFZyHjxuLKHr77IMro8HUsyp5lisNWqYmm1GpRlVlUSSfLC6hyKMpKPNDmjdfG5JRR8N4gcZ4TjjAZJgOHMLi8PQwTvUo11SVJzk43qr3+ec0pVLTcNL/u4U3KZ9+V8WeYFABQAUAFABQAUAIzKqlmIAAySegFAFZdV0tpY4V1K1MkrbI0Ey5dsE4AzycAn8DQAn2/zC32eyuJ0VipddoUkcHG5hn6jigAbUYYiBdxyW2RnMo+Xv1YEqDx3NAEUdxb3WqxvaziZY7dw5Q7lUlkxyOMnB49qANCgAoAKAPCviT8FNF0B734g+BbDWb/AFmxvJPFFn4W/tGb+y73WE3n7W0QR5Fl/fZ2RvGjlI0OASa+/wAm4srYnkyvMZRjTnFUXW5F7SNJ29zmuo8um8lJxTlJXdkfIZnw9Toc+PwUZSnF+0VPmfJKor+9azfNrtFxUrJOy1OV8EfHZvBXgS08K6/8J/ileatdS3d7fyWfhF4oUubu5mupYEWWbcVXzHUHOSq7sg5A9POuFaeb46picPj8NCmlGMVKum+WEIwUm4xt71k3tZu3ZvgyviCeXYSFCthK8ptyk7UmlzTlKbSvK+l36pXNQ/FP4lTC413V/wBnnxgdEETTWc1tPFcao6mRFjV7Bn3qPn3Mqs7L34yw8SXC2VTlHDUM1pOvezUlONNWTbtVs4vVWV1FS6O9k/Ujn+YRi69XAVFStpZxlPdJXp3TXnZtrrpdqS18ead4vubr4U/GTwMvh3VtVgkEvh/W1hvrDU4m6iGZgYbrh13AncvzjDbCa4s14XlhsO8wyyssThlo5xTTi/8Ap5B+9DVOzejVndcyR1Zfn0a9ZYPHU3Qr7qMmmpL+5NaS0aulqndW0bO/0Xw3o+gaZbaLoGhW9jZ2dstnbafZW6wwwRIo2osUYXamFwF2gAEc8V8qfQHmX7VfxKuPhb8PL2C21s2XiTX1NlpsULg3CRFsSTkht0YWPeqyBmYMyKOMkfoPhzwxLiDNoVa9Pmw9L3p32bXwx2ad3ZtPRxT8r/Hca56sny6VOlO1appG26XWW91ZXs19qx6D+z58DdP+C3hRormZL7xTrCpL4g1NJpnW8mV5XTAkY8IJmTcFUvjcwBOB5vGPFdXifGXguXD07qnC0Vyq0U9l15U7XajsnbV93DXD8Mhw1pPmrT1nK7953bW76Xteyvu1c9Ur5A+jCgAoAxtesEEbarbgx3EI+d0B3FMYJ4I5A569sHigDyj4uXL6FZ6b8UG2RzeDr1NV1AwHylfRbiMR6l5zbWJihhYXpiUbpZrG3A6CvD4jy3+1MtqUEveS5o/4lqrdr/DfszahU9nUTMv4dNp/hb4rS+Grm0J0/WQ/hx2lWVUTVLCAXVjsMu+W7nuNJmInu2lZc6SkZIYhF+a4HzBzjPCTe69pH5vlnotIpSV1Gy0d9Vqb4uFrSXp/l+B7wNJsxGIh5/lgbdhuJCuPTG7GPav0E4iG4sotOQ32nxrEYUzJGowsqDk5H94DOD69eKANBHSRFkjYMrAEEHII9aAHUAVZNTsI22faVd842R5kb8lyaAPk743eAfHnwi+NGl/tEfA/wpcaomuyf2d4p0Rdyi9Dne05YlvJRlQbpCqrE0SOQwZxXymZYSvl+MjmOBjzc2k49/Py9dk0nqmz994L4hynjDhqrwZxTX9l7Jc+Hqu3u2VlBLRyau7R1lUjKUU4yjBn1ZoWv6F4o0qDXfDOtWGrabc7vIvLG5SeCXaxVtsiEq2GVlODwQR2r6elVp1oqdKSafVO6Pw3HYDFZZXlhcbSlTqRteM4uMldJq6aTV001ps0y/WhyBQAUAFABQAUAFABQAUAed+JPio92Z9G+E2mjxlrtrJE1wlnPGLK2QTKssc92zCOOQqHAjUvKMhvLKgmgDlG8KeIPiEbfWfiN4hk1rTr20iurXw7Z2clvpkZdIpQs0TfvbplZcj7RtA5YRIcKADrLW1stOs7fT9N2QJZJHFbw2qCOKOMAbFQKBheF7rtJOBmgCWC4YDzo3YbWEiJvGwSLgglQvXGeSF6H5jzQBtDUbu9m0+UXClzLG3lRoMFGBDPzk8fOp57A8UAdJQAUAFAHwB4F+Kni79m79pj9obUvEH7Mvxy8Wab418SWV5pOoeE/Bkt/ayRQwOrN5rvGpBMgwU3Dg8jFRhJ2wSoNNNVKsvlKSt+V/RoMTTbxsq6aadOlH5xTv8Anb1udL8fbrxL+3f+zn4w8KeCfgF8SfCfiXwjqGk65pOm/EbQRosGuTJK5e3iJlYSjyUmVgWQBpYcsoYsLnBU/ZYl6qMm3HdtJdVvZ82ltXZpI1p1JclSjF2548t+2qennePXTueNx2f7Lni+x0zwz8L/APgmj4suviNez20F5omvaBdabp+klpEE7Xd8xIjjCF9kmzk7dyrnbWjgpVVGm/dvu9LL0118lfra7sng5qEHKad+iW7fT5eb+dldr3L9r278Gad420DwR8e/2Vbrx/8ABgeHJF03xD4f0SfUNR0LUxLGklu8kMiy2kDQpEd6bWdtgG7y32Zzm6tSUqr1bun37387/L9Lp0lSpRVJaJWt27W8rf1tfzT9nn4O+J7n4aftR6N8Gfh94+8L/Cvxl4Xn0z4f+G/FzyQz3Gpy6bcJPNbQ3Dl4YZHlhUSOT5g2bnJjKpUqjp4aXtrtptrS7UdW1pvr8KWvld3YoqtiIRpNRTVpX0XNdWd3rZL4ntd6aLTG8TeFdY/aB/Yq+EFz4Z+CviDxDrX7P19pFt4q8C+INPm0651m2s7BYb+3tVKsZXbClMfNhXARpNsTP2cI1qeIqO8GnHTVq6jd2unp02TfWybWdKclTnhtndS/GVlfz1ut0vNo0fB+h/sr/ED4heC/DvwP/wCCfPiOS8l1qA+IdT8SaBc6PaeHbNAzSyPOzsrXKMqlYhkOVZQ2SKug6lOqqtN25db+a2t3d/6tdqcRGnUpSpVY35vdt6738rXX/Bsn+gviWFxDDfrki2Y7h04OMNkcjBA6YOCcEdaxNT598e+E74LrfgnSI3ksr5ovF+jafa3It72Ux3KjWIrVEEIDnzo7i3meZit9eeYZYxFEy/BZ9To5Rm1LMpr91VTp1NLqzWl/VbrtHZu52UW6tN01utUb/wALPHWfE2k6ta6sNYsfFSxaHrklnYJFFFrcNn9pt9TZYy3kw3lnsG6WV2w2lRqFLPnThucsox1bJKnw6zpu+8X0V7X010W6mxV17WCqr0f9f10Pfa+5OQ5Pxj8SfCXhC6t9E1J7vUNWvzGkGkaZYy3126yl1SSSOJW+z27MjIbmcx26tgPIua5MZj8Ll9P2mKqKK83vbst2/JXZUYSm7RVx+iXvhbxDqWpabpdxK76Bcx29/YzRyqsU0kCToVD4V1KTL86bk3Bk3bkkUGBx+HzKisRhZc0X11W3k7NfMJwlTfLLc8f+McnjdfHE2g6ellouueIobWbw1rGn27xf2vDYeZNc+H9QvDG/k+ZHJdvDcIQ0YuZnjh32pa4+O4twqp4mjmOJh7TDw92UduW7+Ja630TXWyT306sNK8XCLtJ7HZ/DPRJZtPtrWPwLqXhO0AluXtryS0llinkdZZTI0NxMHlkkmndpS0hZ1ld2LOrP9Bk+d4HNHKjgE+Wmlra0dbpJLfZdkkvuMatKdPWfU6+98MR27W6WczOZZSuJj0OxiecY7f3T0HIr3DE1tE0+bTbaSGcxkvKXGw5AG0D0Hp6UAeOfH3xbq9nfalb+GPEE+naroWgLb6bFc6aJNPm17WrkWOkTNcbXKtBLFKkibCqxX6yPwFr4zipfX8RhMqSv7SfNLWz5Y7/em3/27ZHVh/cUqnZHc+FvBOlaP4f0zwj4atoLTw/odpDpliiqxMFvbxpDEiMxJYCOJBkn+I5yRx9mcp0UOlfaWlkj1G/ghWQxxRxzkYC/Keuf4gce1AFsaNYEKZo3ncLt8yWRmYj6k/XgccmgBx06KF1m09YbWQZB2xDa4PYgYz0GOf5mgCrPBqL3IN9aw31sPupGFXacH5irnk84+905xQBdt7+1uG8pJCkvXypFKP8A98nnHv0oA8d/at8B+PvFnw3udX+CE/hjSfirpEb/APCNa9qumR3FzYRSMgvY7Wdo5DavLArIZNpU5AOzIkRNXVioTdOSmt13Sa+aej9HofDv7Nvx4+GP7J37SFv8LPF/xt1b4veNvipcWNn498YLqok0TRdRWLZZW0Dujy3rrJIYZJ2kijijaPKK0ckUbJP1NoAKACgAoAKACgAoAKAPIv2vv+TUvjF/2Iuuf+kMtAHE/wDBOH/kyb4Wf9g25/8AS24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/SyzoA+n6AMLxz4WPjbwjqvhNfEWt6A2p2zQLqei3Ztb6zY8iWGUA7XBAPIIPQggkVM4uSsnb+v6uXCfI72vvv5q339vM+Q/g9+yR8YNXs/EGifGD9qb9oqw1zw/rM9lBqOmeLo4dP1uwOJLa9gSS3kaIlH2SRGVykkb/MQVq42lTjPZ63XZp9O6as09H0toZJyU5Re2ln5NddFZp3T3WzT1097+EH7PFx8I/El14il+Pvxe8ci5sXsv7P8AGHiGK/s4i0iP5yRpBGRKPL2hsn5XcY5yLU7U3Cy1ad+ul9F5O+voh21uevVmM/J34gaf8JdY/wCCrPxd0j4y6pqel6Le+HdNigv7AgG2uvsGksjSEq2IyqyITsbBdScAFl+44CrZzhsfVr5HCM6sabbjL7UeaCaWq1Ts91dJpXdk/leLqWWV8HTpZtKUabmkpR6S5ZWvo9LXWz1a2Wq3P2hf2fIPgvJYa1pXjbTda8P+JJXk0Dy2d7qa0CI5kdkTySq+ZGu5X+fcrKoBYL+/8G8Yy4nU6FbDyp1qSSqXsoqV2rJN86bs3Zx92zTbdm/x/ibhqOQuNanWU6dR+5vzONk7tpcul0rp63TSWqTf2VPg9o3xn+KaaF4llk/sbS7N9UvYI3KNdIjoiwhhyoZpF3EYO0MAVYhg/EDiSvwxlDxGFX72clCLeqi2m3K2zsk7J6XtdNXTXB2R0s9zL2OI+CKcmu9mla/S7evW17NOzP0C8Afs8/B74Xa63iXwL4PGm6k9u9qZzf3U/wC6YqWAWWRlGSo5Az78mv5uzjjLO8+w/wBVzCvzwunblgtVe2sYp9fQ/bct4ZyrKK31jB0uWdrX5pPR+ra6Ho1fMHvBQAUAU7UPHf3kLHcG2TgnGfmBXHHpsoAuUAMmhiuImhnjWRG4KsMg0AOVQqhRnAGBk5P50ALQAUAFABQAUAQ3dtDdQmObjaQ6uOqMOjA9iKACzna5tILllAMsauQOxIzQBNQAUAfH/wC1P+0xc3mt6l8BvhNrupWevwxxteazpO+RkuQ+DpqmNS8cjlo1EsZO2VkibbmRo+bK6EuJ87p5Fh5SjG6dWpFpclOOtRuT0jaN7S1960bNs/U8FkcOBuFJ8f53Rp1FZxw+Hqp/vak1alJQTvUTnvTai3SU6qdox5vd/hP8EdH+GWoax4qu9Vk1/wAW+JPLbWNamtILZp2RQMRxRKFiViAzDJLN8zMxAx72NzGnWoxweDpKlRi+ay1c5NJOdSWnNNpWulGKV1GMU2flEpYrE1vrGOrOpJLlim3y06fM5KnSTcnGCbdk5Sk9OaUmkek15RoRXDyxxb4YvMYMvy+24Z/TNAEUWoRvci0eGWKUqW2uAOBjoQcHr2z05xQBaoAKACgAoAwPG3jvwl8OdCl8S+NNct9L0+LK+ZKSTI+0sERQCzuQpwqgk9ADWdWrCjHmm7fr5Jbt+S1OzA5ficyq+xwseZ7vZKK0TlKTtGMVdXlJqK3bR8wr8dP2hvj5qtzb/AHwVbaZoEUiwJ4j1ZFKwShuZE3logRgqVWO4kAIYqmdteS8XicVJxw6strqzd/OWsVtayU5a3aWx9/T4fyTIaVOtm8nKo/ecZKcIuLSaUKSUK9RtS5lOcsLSbi4QnPWSXUf2Vv2i/Ek2lan4v8A2kdcl1G9vpGv0sZpvsmlDypys8AWa3DbsRxFUiQj7Q3VVbddLLqvK5VaknKX96TS16cvs/yX3Xvjj+M8B7WnRwGCoxo0no1QpRnNOLT9p7b62203pepJNK7XNyuF69+Cf7ZfgO3+x/DP9oKDXrJYU3DWVxc797ZRPtUd0NoBB3eYueRjCrnOrhcdRVsPK6/xO/8A5PGp+DXn0OrA57wvmMufN6KjNv8A58xcErKzX1argmm3dNShU2Ti1edzxB8bP20PAVzs8SfAfS9dglk228ug2lzcKVXBct5Es7rncApeNOjcNg4K2LxtB6xv29xv1u4Sm/T3ULLeHuGMziuWvytL3v39OLu72UY4ilhou1rytWlZNddBNN/bv0TRro6R8SvhT4h8Ja1cXsTPbzyGREtGdEedxJHHPuVRLiNYGDGIDcCx26Uc1UpclaPK99HfT/C1Gd29ElBnJmfAM6NF4nLazq001FuVNxXM97Vacq2G5YxtKU5YiKSvppd+q+Hf2r/2evEzyR2nxP0uxaLbu/tdZdNB3bsbWuljV/unO0nHGcZGen+0sKvjly/4k4/dzJX26Hjf6lZ5O31Wh7e9/wCBOFdq1r8yoyqOPxL4rXvp1PTtK1nR9ds11DQ9Vs9RtXyFntJ1mjbBIOGUkHkEfga7ITjUjzQd15HzeIw1bCVHRxEHCa3Uk01fXVPXbUuVRiUVMf8AbMgf7/2ZfL4PTcd3t3SgC28UUhUyRq5Q7l3DO0+o9DQBXifzNTuARxFFGq5PQksTge+F59vbgA8++MNvbeLb7wv8OLKys7vWLnV7PX987AHTbKxuYpJ7ofKTubK26gbSxuDztWQj6rhyUsBSxOZ1ZNUlCdKy+3OrCUYweq0WtRt3SUFpzOJ4GdJYupQwNNJ1HOM7v7Eack5S2er0gtm+beyZ6PBbW9rH5VtCkSZzhFxk+tfKnvnxd8avCXiDxR+3J4Pg0K5n1w2Y0jVbuCPZjR7SC53Sq7Z+VcL5vzYJa4VQCWQH944XzHDYDw/xUsQlT5vawi3f95KUbRa7vXl00Sg27Wlb8kz7BV8Xxhh1Rbnb2cmtPcjGV3ft/Nrq3JJbq/2tX4OfrYUAFABQBR1ybydJun27t0ZQD3b5f60AY8mkyX8lxFBaQyWpzautxgo6KnlspHJIOXBB6/KecGgD5yubfxHoN5pllp3m3+taRI/hOTy7lZr2bU9ILajohurnAjt4ruy+0LcswVmbUoUXZuyPySslw9nMntGElNdvZztGaS3bV1y20XLJu9j0l+/pLz0+a2PqbRNa0vxJoth4i0O+hvdN1S1ivbO5gcPHPBKgeORWGQysrAgjgg1+tnml2gAoAybbxR4W1PW7/wAKWXiLS7rWNOjV77ToruN7m2RwCpkiB3ICGUgkDII9ayjXpSqOlGSclur6r1R3Vcsx1DCQx9WjONGo2ozcWoSaumoytZtNNOz0s+xa0wJFDJZpGE+zStGFHTafmU/98sM++a1OEg8QaLpfiPTbjQNbsku9P1K3mtLqB8gSQyRsjqSORlWYcHPNRUpxqwdOaumrP0Z04PGV8vxNPF4aXLUpyUovtKLun8mrnzL8L/FHh39mL4yQfst/2FLpvhjWLb+0dH8QapqIkm1TUpQNxkPyxQodht0jVA2+FThjNmvmcFWp5NjFlfLaEldSb1lJ/cl/KkluvM/ceJ8sxviVw3LjpVVUxNKXJUowhaNOlG9rfFOTV/ayk5Ncs2tFTsfVNvL58KSldpI+Zc52t0Iz7HIr6k/BSSgAoAKAI5pkgQO+cFlTj1ZgB+pFAEd9eRWFu1zMrsq9kGT/AIfnQA29vTZWLXhtJpWUAiBNvmMx6IMnbuJOOSBnuBzQBwmqfG/wvK0umeAYZfGeti3WZLPTGAt4t6O0f2m7fEFsD5ZyHbzORhGyAQCmnhDV/Gnl3PxZ8dWz248xH8O6BdvbaZJG28bbmQkT3eVYbgTHEcYMR5LAFy8+Kfwj8CwXXhPw/qulNc6FFG82g+HrVr66s45WO0iwslebDEOQFjPRmOFDMOfEYzD4Nc2IqKC82l+ZUYyl8Kuch4g+Ieu22hahrOneDntLZbWzvLXVvFmpQ6Jpk/nlWeOY/vbu2dSQCs1nneY0wMMV+dr8ZZXSn7Oi5VZaq0It7etk/VN6G8cLUau9PU42P9oDxYLSe0mT4CyiTJjLfF+UCIkHkKNJ2jkk8Adaw/1yo/8AQNW/8AX+Y/qr/mX3kuseNPGusiXQta8WaZ4c1k3n2WbSPh6JPFGqxSuY2t5mubq2WHT0dlkjka7tTCN6H7REcEYriXMM0tHJ8K2v5qmkd9dnZ9NpX390f1eFP+LL5I1pviz4l8Haq2teLLfXvA9vfl7h7TxvpdpcafaW0ERMiDVNLmkh05HZfMM17LKGeXEasVMa3HG8R5XH/a6Ma8Vu4O0t+1lffpFbavqHJQqfC7ep6p4J+JE/iKaw0vXvD/8AZ19qGnrqFtd6fd/2hpF4vyl1tr0IhYqrxsBLFC0il2iWRYpWT2coz/B5y5Qo3jOO8ZK0l0vbXrvZ6dbXRjVoypavY7evbMjG8QeMPDnhiextNZ1IRXepzLBZ2kUTz3E5MkcbMkMYZzGhlQySbdkSEvIUQFhzYvGUMDSdfEzUYrq/07vslq+hUYym7RRxUnxT1+PUYFn8MXKPdrEtvoNtCt3qMJeISGS9nST7JaBdlwu0yMjmIeXM7uIa+AzDjqcpNZdGKgt5Tvr/AIYppvp3euqSVzthg19t69kZWn/Fnxt4gsIIfD2l6Nf6jfR20pvbKSaXQ7BH+WXbfsitqJVo7ghbeEHcsUc32YOJj6OCxXEGcNWtQpJ/E4WlJa7Qk5JX039U3sZzjRpeb7X/AFOg1Lxf4m0M+HPDl5qOn32oa9Zz2Taxa6dMsEWrRwGUFrQO5ihdEuHHmTjaYki3s8qmvT4gzDG5LhIYqglOMWlPm3adldNWSd9HaL3ulZMzoQhVk4y07F7w343bXdJt9ctvNMVwrGS2mEQltZEciWCQJI22WMrIjAnh4yOTzX0FGrCvTjVpu8ZJNPunqjFpp2ZfHiG9kuIkQq4BAkjt4y5JBbOD0OQmR7OeuM1oIii8RandKrS4t0YjYY4+ZMcEAtngEjopP60AHivUvGE3gfU7nwKNMPiR7K4GlfbhI1p9tEbGETCIM/l7wofGG25xg4FRUi5wcYuza3Vrrz1utPNNDWjPNPh54v8ACyubPQdXuWsPHmo3mtaIgt44rS2udkY1DTcxEg3f2qO8upoZFMrPJeYB8iYx/K8K5hWnTnluOb9vSbvd3bTd09dWtbX2tyvqjoxMEmqkNmbni7wfeeN7GGPTxFDr2h3EeqaPqEi+YLa9iACsxDK+x42khlRXDNDPNGCgckfRY/BUsxw08LW+GSt/k15p6rzMITcJKSPLLrxJ4a0Px9pNm/iK6tdI+JkbSaRpsfhlymh6hFcrML+4lmd7WC4TU7lUIkgEr3V7aI27yRt/Iozx6pRcaX73BP3pc2rjzaRaSu4pX1vblT7o9O0L76TPTNY+JHim6E+gmEHXbNrNp9G8HTrqWoMTElwRNLcxxRWUMphvYA0wTzECNHNFM6xr9auI8yzu1PJqDjFrWc1ontpa6bWn8z/u2Rzewp0tar+SM3RvAOoXEc1rqN3FoOgvczSWej6BdzW7z3LXfnte3l+jRzzTyFUaRBsUm6uRMbv5ZR25dwnQpTeKzJ+3rPdy1itNknv8+ysokVMS2uWnojB8K+HNN+FOt6fonhTQW0qfw3okj6QugW7zQa/4ft2BOnXiSMcXkMlyDGzy+YZZXmjkKTX1vXn4yWJ4XzL63z82Grz95N/BJ9V6JaWXwrleqizSPLiKfLb3kvvPWbiTwz410q0n1yOy1/S7tra8tFjKywGVHWaCeIg7SVdUdJASVYROpGQR95OEasXCauno09muzOJO2qJvA3hDwv4dv9U1nwl4P0fQv+EguXvtVbT7CO2+3XTHPnSFFDSync+6RupJ69SQhGlFQgrJaJLZLsgbvqzqLrDXdmikFllaQjPIXy2GfplgPxp8yT5b6geY/FT4pNp1hdw+ENZ021/s8OmoeIdQuI10XRJRL5ZN4/mxtKybZc28bBtwjWV7dZFlHh5txBhsr/dL36z2hHVv1te3z1fRM2p0ZVNdl3OL+F+iatqeop4p0Q+IbXTLzUIten1XXm8nU/F99Lp32L7RPaoI1s4IoVsysRhiZpI1IggWIPP5+UZRjqmM/tbNmva2cYxSVor1111fV6PWTvZXVqwUfZ09j32FV0y2h0yzj82VUyBnAGTy7HsCc+/XA4r605izawG3h8tnDsWd2IGASzFjgc+tAE1ABQAUART21vdR+VcwpKmc4dc4PrQBVNi9pKbu0aSUlQjxSPu3KCT8rNyDyepwenHUAH4bft+/sYeJvht8dbnXvhT4d1fXvCnjkX3iCA2tivl6VMs7Nd2zeXxHDEJIZVkdY1WOdUyxidznWqwoU5VajtFK7OzLsvxOa4ulgcHHmq1JKMVortuyV3ZLXq2kurP0u/4J7ftUWH7Qvwii8K+IPEK6h8Q/AEEGmeJWEcm27HzLBeJI5bzi6xkSvkHzklO1Y3iZ3SqKrCNRbNJ676k47CTy/FVcJUacqcpRbi7xbi2m0+qdtH1Wp9VVZyhQAUAFABQA13SNC8jBVUZJJwAPWgBIpop03wypIvqrAj9KAPJf2vv+TUvjF/2Iuuf+kMtAHE/8E4f+TJvhZ/2Dbn/0tuKAPpOgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA4z4qfGX4W/BHw2/i34r+OtJ8M6YofZJezYkuGUZKQRLmSd8c7I1Zj2FROpGDSe7/wCG/XV7LqUot6o+Pde/4LO/sqaVqlxp+l+GPiLrUEEhRL600q1jgnA/jQTXKSAH/bRT7VSbe6E0lszrfhZ/wVi/ZC+Jeqx6LqOv654GuZ5UhgfxRYJBbysxxkzwSTRRKO7StGoxyauMebZ/1/X/AACJS5d1/X5/p3PsGxvrLU7K31LTbuC7tLqJZ4J4JBJHLGwBV0YZDKQQQRwQaTTTsxpp6onpDCgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/SyzoA+n6AOK+NWueO/DPwm8V+IPhlov9reKNP0uefTLQQtM0kqrnKRL80zqMssQwZGUICN2RMruyvZNpNrVpNrmaTtdpXa9NnsaU+W75+zt5tJ2Xo3ZN9E7nD/s5+LPG/iTUvFlvqfibxZ4q8JWn9nvofiDxV4XGhajPcyJJ9ttvIFtaiSGLZbssv2dPmnljLOYsiotyg5NW1aXmrLX7210TS0XV425ZWvfRX8ndq23ZJ2u7X31svbaCgoA/I/4up8Lpv8AgqD8abf4sTa5b6XL4Y05bW40dUaeG7+w6QUYq/DJsEgI/wBoYweR+geG7zeObTlkyg6ig7qd7OPNG6utU72a9Ouz+P42WXSy6MczclBzVnC11K0rb9LXT/pnP+MPDtr4Z1qSx03X7HWtOlBnsL+0lQ/aLYswjeSMMzW8hC5MMmHTPI5BP9Q5bjZ46gqlWnKnNaSjJPSVldJtJTSvpON4y6PdL8Fx2FjhKzhTmpxesZJrVdG1duL7xlZrqfU37C/i7wL4Iuo9L8R+HtSsNf8AG11La6VrtzbBbK6ihCf6JDMT98yM24KMFvKUnOwV+Q+K+XZhmsHWwtWMqOHSc6afvxcr+/KK6ctrX1S5mtLs/R/D3G4PL5KnXpuNSs2oza91pW91Pvfe275U9bH3VX8+H7GFABQAUAU7QGW9u7pSfLO2Fc9yhbdj2y2PqDQBcoAKACgAoAKACgAoAKAM+5Zr+5+wRbvITP2phxnjiPPfOecdu/NAGhQAUAeL/tI/FuHwnoL/AA48I+NtE0f4h+KbVo9Ei1Cd4iFZtpkEiqUidsMkTSsimQcFthU+Pm+OVGH1alNRqzXu30/HZN7K9lf0P0jw84WlmeKWdZhhalXL8PJe1cEn52cW1KUVdSqKClJQ3S5kzzT9kn4B/D34catfaD418Oxv8TtD23jm9KyQ/ZHf9zdWIztdcoA0uN8cm5PlBAb9NxsZZFkOHwmQvlwNRJTlFcs51lFKp7bWTUndtQ5nDkty3S0/Ls74ixXHXE+JzTiGTnioyl7OLfNClRcm6caPuwTjFWTm4Rm5Xc7Sbv8AWdfFHSFABQBFcW0F0gjnjDAEMOcFSOhBHIPuKAKjQ6jDIIrKUmPIJa5O8KOeFx8xP+8fxoAkSbU4x/pFjHJgdYJeSc+jAdvegCW3vIrhjFho5V5aKQYYD19x7jIoA82/aA+PHh74DeEU13UEt7/Vr6UQ6XpDXJhkvSGXzWDBH2JGjb2crtHyrnLrnjxeNp4RJP4pbL82+0VvJ9F9x9Hw7w1i+IZ1J0k1RpJOpNJOybtGMU3HnqTfu0qaac5WV0ryXhPwT+Cvif8AaJ1A/G/9o7U5dWtJbmUaPoMc5S0RUdo5I2hGfLhDxoBGGDyGLMxYEhvOwmCq4lyqYx3vp5NdUl0ht5zteTs7H2nEHE2ByaNHB8OwVPkSkrpOUJWXLUnL7eKSc0204YbmcKMVOPtD6/sbGx0uyt9M0yzgtLO0iSC3t4IxHHDEgCqiKuAqgAAAcACvbjGMIqMVZI/La9eriasq9eTlOTbbbu23q229W29W3uT1RkFABQBk+IvCnh3xbDbW3iXSINRgtJZJo4ZwWjLPBLA+5PuupinlUqwI+bpkAiZRjOLjJXTNaFerhasa9CTjOLTTTaaad001qmnqmtUzg9f/AGXv2f8AxHFHDefCvRLRI2DgaUj6bkjP3jatHu+8cg5zgZ6DHKsBh4rlpx5Vv7rcf/SWrnvT4tzivN1cXW9vJrlvWjCu0t7J1YzcbO7VrWu7Wu7+Taj/AME/fAumoNW+HvxE8WaB4gt232+ou8UzR9Q+BAsEm5lJGRKOpznJB4ZZLT1lF+//ADNK/nrDkk2+rcvzd/qqHibjbxo16dsPrelCcnB7cq5MR9ZpRjC3uxjSSWlrOMXHCvvg5+2N4MghsvBf7QH9r6PdMksE17NK9ysscTXHzCaKYpHvi248/a+QrqFdlDhg8ZRk406j5bO2vNrbT3ZJvR6/xLP00UYniTh3MKVOri8HF1VKPMlT9leCmnJOpQqQheUU43WDTino3Jc8tdPjd+1r4HabW/HP7PNj4itZWMMU+hyyxyEYG1sRm6YREKWyyIMtzt7t4jMKCvOMZLbaUXf/ALd9oreba87dZp5Rwjmj9nha1WjNLmfvUqseXql7X6nJzv8AYhGo2l7vM3aOnon7fvwsmuF0zxh4V8UeHNRikSG8je2juUtnPD58t/NKr3zEGxjC5OKdPN4yfLKDutHyuM7fKLcrLzintpd2M8X4eV6MVVpYqmoSXNF1Y1MPzdlzVoRouUuijVktJNScYuR6Bpn7TP7PHjC3ll0v4o6HBdQsI4hqO+wn8zI27I7hUdxkj7oI6jsa6IZphJy5HOz7SvF/dKzPIxPAvEOHo/WY4Z1Kf89JxrQXrKk5xT2dm07NPZogm1OwtP2nl1S71y3t4f8AhXFxLfW0zFBaJBqSfvndgECnfMuQ3/LJiQBtJ/RaNOpiOEPZwptv61FRa15nKk7xSTu2rR6faVm9bflVScKPEfPKaS+rttPolUXvN7JO769Nehyvir9onxj8Ttc1D4b/ALLuijVb+xlWLUfFdyYxptgjMF8yItkS8+ZgkHcInMaSj5h7eX8GYHIsPDNOLqnJCSbjRV/aTaV7Stbl6ddHJKcoPQ8rGcT4rNq08Bw7DmlF2lVduSPmr3v16a2bjGS1PSfgv8L9K+FHh06NbxT6hrV8/wBp1/XJ2DTajfNl3d3Y72UM7BAQcA5JLMzN8jxNxFW4ixntWuWlD3acFpGEFoklsnZK7W72skkvo8iyWlkuG9mnzVJazm9XKW7be9rt2/zbb9Gr5w9oKACgAoAzfEXGj3BxnBQ/k4NAGXHPKuhPLeXFvY2NiHku5XlCvEIyXYkthUxjOSeBzmlKSinKTskaUqVSvUjSpRcpSaSSV229Eklu30R4B8TfGngjU9asr5PGGkXGh+M9Jhk0afV9WVrSz8SaTdR3+ns1tHIsmXa4jeXc8YH2K3ibDTKD8BxpSpS9ljaUlzRbhJJrmcJK0kt9k2tnbmufSZXw3neN5o4fB1ZJRU9Kc2knflk7LSMuV2b0fK7bGt8M/wBrL4JWeg6r/avxPS8skd/EGn3Trc3c8mm30scqJIqW48qSGe6kt1tfnlSCCJ3ABbb6/D+ZKOXxo4lNTpe5Kyb+FqKta99Gtut2tEW+D84rSpTVJRVazhzzhT5lKMpqS9pKPutRlaXw3tG/M0nb1r9uz9n7S5IksdT1/WFkUlnstDuFEZ9G89Yzk+wIr1ama04NctOpL0hL9UjvwnAOLxMW6uMwlK3SWKotv05Jz/GxjeK/21vhJqdr4x8HW+l+KLuSysr2BL2xsI5rW7UN5TPC7SJkKjNKWcIm2N9rt8m94rGfxKHs5aR30UXp0baWl7u9tmLIuG7/AFLNFi6PvVUvZp1JVY2k7OdOnTnJKbioxcVK7nDRJu3yl4M+E/hvR/C1p8Rfht8WvEGg+NNOnkvNNvtT0h9PspYvLUCEzo0sMZbMqb2lZJCwiZArbz8rhuHKkaCxWDqtVFqtrNdrxlJd1e9ns1bU/fM78aMHVzWeQ8R4GLwU0o1F+8coyu3zONWlRla3LJRUFKPxxk5WivobwP8AFf8AbI1vUPA2uN4M0i/06aOSw14rqWn/AGC+jjc41HzoHklgYL5gdURoyYwUV/MWJPTpYnPFOk6lJW2lrGz/AL102152VtNE7pL4fH5L4WSoY6GEx0uZ2nRajVc43WtFRkowmr25XKSnaTU3H2cpy94+Efx0+H/x1t11nwDqk8i2PmpfWV1H5VxbEsBGzpkgqwDFWUkHDDIZWUe5gMxoZlT9pQfyej6/g7PX1W6Z+WcWcG5rwXi1hMzirvaUXzRekW1fdSipRvFpNJxduWUW8z9p39n7Tv2g/h62gJNa2PiDTXN1ouoTR5WKXGGidgNwikAAbbnBVH2uUCnmznKo5rh/Z7TWsX+no+vyetrHs+GvHtbgHN1i2nPD1Fy1YJ7rpJJ6OcHrG9rpyjePM2uY/ZO+OWo/EHR9S8C+PljtPHng+4Oma3bmQF53jYxC6PJBZmUxybWIEihuBKoGGR5nLGU5UMRpVp6S87aX/R+fqj1PFTgihw3jKWbZReWAxa56btpHmXNydGlZqULpPldtXCTPoavePygKAMnxDq39nW3lxEefMpC88qPXHr+nH4EAyH1LUwn2d5IkKTCV3lbeWwS3p935T90D7ox1GQDE134s6JpmrDR1s7nXdZiCn+yNI3TyxEsoEkzELDCmXUh5pEXAJUkigDgvFGr+JvFMSW/xC1y4trkKl5D4L8JM/wBskjlLRwtcXKsJmQu6oZQLe3U7i7lFLLyYzH4bL6ftcVNRXn19Fu35K7KhCU3aKucn4s8axfDfR9Ts5dTXwx4f0PSL64OieFtHGp6vpiR20dzJczXJ3WNgyefJIYZ0lSQlCkzhhEPjq3F2IxcoxyyilGTUVOo7Jtu1opO76ap3V9UjqWGjH+I/kjVuvhp4g8YMo1rwCuoWmq6VBFqp8ca7PqrLdxK2RcaFb/8AEqdm+Vi8E6AuxYLhEDdayXO8brjcbyK792mraf4vdf3p6ddSfa0YfBC/qdZp/wAMNcm02z0fU/HOr29hY6RBYnQ/DNmuh6bmPdteERRve2wACKsaXoQLGq5YF93RhuDsqoSdSpB1JNt3m23r3WiffVN3Jliqj0Tt6Glp/wAKvB+h6lpmvx+C7ebVdLtl0+HXNTiN5qiWu4/unv7jNxICZHJ3S8l2JPzE19Fh8LQwkeTDwUVvZJJfgYSk5O7Z080EaxXTbVCmVIwRHwvQ9Qrj+Ls2eO9biGSmBplRWXe2xGJ4IZCB1bLDj/cHHtQBvRx2c8UK3GmXN5PJEiSO0bYG0djJjHfp1zQB4P4GPh7w98R/Hkmu+IbLw7o/hb4ppJp1rdTqkJmv/DFiTbQgsFWSa71OeYqgYyTSyHDPKWPwuLnTwvFdKbSSlSbk9rW5tW/SKV30t2OyKcsM15nXeMPizrV5/Zlnpt23g7T9dV0tbi5snuvEV3lhGGstLEb+UFE0M7TXCyGFVYT2qLudKzLjOEVOnlkPaSjvJ6U187q+ui2Un8Leiap4V6Oo7fmcNeap4ju7nSvDnhxdR0+bXYb+S30s6g76reRWflrM2p6tN5z20Mc9ybZktVuLgGSCWCUIsip8lgMBjOJcVJ1J88o2vOXwxTvbkgrXulzK/LHdSjszpnONCOit5f5s9D0v4deHW02+0XWjHdaNcwuBokVokemSee8z3DTRkZnab7SPOM8rK7jeFRmYt+hZZwvgcukq017Sqre9LW1rW5VskradUtL2OKpiJz0WiN9cLClxIfN83O9Q2Cwxzu/iIyuTwRxwwOa+jMDO8U6FNrPh6bSbZrO0umcXFjc3NuJY7e8hZJbeV49yl1jmEbbTIM7cEqc1z4rDU8ZQnh6vwyTT+f6lRk4tSRneDbO8jutSubTSJxpurPFrNpZSEB9PuriJvtcBgSMIi+asdwXLO0k9xc7sALu8fhnC43A4FYXHJXg2ou97x6P87LSytojXEShOfNDqdPLbahZ6el8VRYbgkKm4k/PGwLbRhR8p/DA96+gMCvCg8uOeKMbU2/KqLl24DLls5yO3P+6AaALJ8U6NpXl6bqks8o1Bo1git7eS6lBLRx7zGiuRGrSR75DhIwQXCLk1yYzH4bL6ftcVNRXn19Fu35K7KhCU3aKPFvEDa3/wlPinSdEtb+7udbS01CHQ/D13Dd6voeqw2xmttSnEzJBbrKbXyfJlma2kezix5n2udF/P6lern+Z08wyek4unvOWkZK9muuyf+JpvRcqv2pKjTcKr36dj6Sgs9Ou7SNmsbbDKCVVAQp7gZAPBz2B9hX6Ur21OA5zS/hjoun6e+hXMi3+jXNvfQ6hplxZWxt9Sku5hLNPcIIwC7OZiVQJGxuJMofk2ePhcjw2ExtXHpylOpo7u6SbvZKy00S1vovU1lWlKCh0R0el6To3h3TzY6Pp1nptkkk1wYreJYoxJLI0sshAAG55Hd2Y8szMxJJJr14QjTioQVktElskZN31ZyVxiGaQ2f7yGJXRHYY3qSdzHHUHLLuwBjHINUByHxRu73SdBsPFujabLqsnhTV7S/aOO2kldbF5PIvpo4kDO7pZT3MixxgO7oqKsjEK3j5/l7zPLquGiryautt1qlrtdq3ozWjP2dRSF+GlzNDpk/h+0t7c6fpb7tCfToIotNutHnaSSw+yeUTGY44SLcpw5MLsEEcsbtz8NZnHMcBBNv2kEozT+JSWl3fXW1/vW6Y69Pkm+z2OouPifpugXMHhuK1udZ1mc2/2TStORWuEtpXkX7TOWcJBboIpSZJGAYwtHH5sxSJ/QzLNMLlNF18VKy6Lq/JLr+m7stSKdOVR2ieGeLNc8YeIvEWgaroXjzwu93pvi6S51jUb2JNU0fw7fz2NxpVvp9u32u1lbbOxgdUBlkuLgnZCHeOP87p8nEuZyWOUqSrQtTWqcoxand3TWqV09tNLvU7nehT9zWz1/I9J1Pwp8Sba3h8U23xD8/wASafbRI+my2os9B1BlLmWF4R509s0wdNsnmzyQNDE2JIzNBN9BLgXLPqssPC/M9VNu7T9FZNd1bXunZrD65U5uZ/cZfws8XeH/AA9fWsdtdSaX4Ta9XQ00jVF8m68Ka1IIEi0pgu6P7PKJEEA3mNWkgW3eaC4tlhrIM2xdHEPJs1/jRV4y/nj69XZb7tJ81pJ3K1OLj7Wnt+R7pZYM982CM3AHI54jQf5+tfZHKW6ACgAoAKACgAoA8a/aO8M2fxA8H3HwUj1vV9JPjsFtRutKuRaz2WmQSQveXAm3DaHUx2+3Em9rld0bRCYr4fEOb08nwUqskpSlpGL2bffyW7+692jpwbqRrRqUpOMotNNOzTWqafRp7PofnZ+xj4j0/wAJ/H2PU/2V/wBnm+0X4aeFjdWHxF+IPjjVWivJdLVi9wbiaTbZ2XkFoZvs0MfnOLSMyOqmUr61CVWVOLrxSl1Sd0vnZfkc8rX0P1t0vV9K1uwttU0bUrW/s7yCO5t7i2mWSOWF1DJIrKSGVlIII4IIIrYRboAKACgAoAKAKk2m6c7m4e3SN1JYyofLb3JZcGgDx79rCW9X9lP4vxPBJJbt4E13ZM7ASACxlxvU4PPr16ZAOaAOY/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB43+1p+0l4b/ZW+C+qfFLXbZr283rp2iaeP+X7UpVcwxMeNqAI8jtnISN9oZtqnKrNxtGHxP8Apv5fi7K6vcuCT1ey/r+vvP58/jJ8aviV8fPHN58Qvil4mudY1a6JSMOxENpDuJWC3jztiiUscKvcljlmYl06apq17vu936/1ZdEkE587vay7dF/X39z7T/4JOfsheH/jPq/i34vfE3RI9R8J6VZ3XhmxtJchLu9u7cpcvlWBHlW020ZH3rlWUho66ZQtQbbs5aLul1afTst/tdtefm5qqTV1Gz12b6L9Xs1p3PkD9pH4G+IP2cvjR4m+EniESyHR7smwu3TaL6xf57e4GMj54yu4Ana4dCcqa5KNRzjaT95aP19Lu191fo0dVWChK8fheq9PuV7bN2tdM9x/YN/b28afsxeMNM8HeMNbvNT+FV/P5F/psxaUaQJHy15aAAsm1mZ3iUbZAz/LvKsO2k4zXsp6dn2fn5Pr23XVPlmnH34K76rv/wAH89n3X7zWl3a39rDfWNzFcW1xGssM0Th0kRhlWVhwQQQQRwRWUoyhJxkrNGkZKaUou6ZLUjCgAoAKACgAoA+YP2pv+S5fAr/sYE/9LLOgD6foA8+/aA+Kg+CHwV8ZfFgaU2pSeGdJmvoLQA4mmAxErEcqhdl3MPuruPascRVdKF1u2kr7Xk1FdV1ffXY2w9L20+Xsm/lFNv52WnmeCa4/7dll4n8E/DHU/iX4ZktviO8t1qXjDw54RbPg/wCywefNZxLPJLDOk+BDDcTgMMyEoW2Ab8qu6cumt+/RrbR6qz7J6dTFNuHtUlrpbs3qn3asndWtdx1Wz6/4LeLPib4F/aD8SfszfEr4iXXxAhTwpZ+NfD2u3mmwWt7bWjXL2c1ndtbqkcrCSNZI5NgZg0u4nAC6U6cqlCdWSS5JRjpfVSUmm+zXK1ZXurPS9iKklCpCEX8Sk91dcrj03afMtbJJ6dj6NrIs/L/UPCXgnx9/wVX+PPgbxlcafbza94FtLLQ57wkCLVTZaM1u6EEHeNrnGRvG5Od20/S8KZjj8ozBZhgVJqkuaoo9afNFST30d1rb3XaWlrrw+IcFhMywbweLaTm7Qb6Ts+Vrz39dVrez8o8TeHb7wr4o1bwlfvDNe6Pfz6dM1uS0byxSNGxQkAlSVOMgHGOBX9gYHG08wwlLG07qNSMZK+6Ukmr7q9nrqz+bsXhZ4PETws9ZQk4u2107aH60fCbwu/hP4X+EPDd/pFvYXulaPaRXNvGEIhuxCBOQUypYu0mWBO4sxyc5P8acQ49Zjm2KxVOblGc5NN31jze7vra1rJ7WSsrH9M5NhHgsuw+HnFRlGEU1ppK3vbaXve7669zr68U9QKACgCrqVxLa2rTxbRtI3swyEXPLY4zj0z/hQBNBCtvEsSknHJJ6sSckn3JyaAJKACgAoAKACgAoAKACgCpp33J8rh/tEm84xn5vlPv8u2gC3QBheOvGmg/Drwfq/jjxPdCDTNFtHupzuRWfaPljTeVBkdtqIuRuZlUcmsMTiKeEoyr1XpFX/wCB6vZeZ6uSZNi+Icxo5XgY3qVZKK3sr7t2TajFXlJ2dopvofIPws+Kd58Vvjb4p16b4cWnxJgTVEl8Gaj/AGTEqaPGgZoPOvZfms4wil9nlu5mDsgMg2t62ScHPMcCuJMzxVGOHm26SacqvLCbg+WNldpra+jfNJwWp9Lx1xjDhP2fA+R4fE08ZTpxji0pqNCU6lONT3uWUlK6k052V4JQiqisl9FfDrwL46l8c3vxY+KR0W28QTaRH4ftbHRXle2is0nacySPL8zSM7AYAAVUHLFiF97Os2y+OXwybKOd0VN1XKpZScnFRslHRRSW71bfRLX8jyvL8ZLGSzPMuVVXFQUYXsopuV23u236JLrfT1Gvkj6IKACgAoAKACgDy39oP4w6N8IPB63n2OLVPFGrMbPw3o4DNNfXZKj5QnzBE3KzkEcYUEM6g8GPxv1SCjTXNUlpGPd/5LdvRW6n1nCXDS4gxMquMn7LB0UpV6vSEeiXec37sIpSk5PSLSZ8qaB8IfFviTxJpPxo/aG1q9ik8U3Eun2sTpHINFuLp0XS223jOnki4leJLUxynzprUyKVM6p8lnGEx2HwLzCD5qikpSTvZwTvypbqKevLdXjduzsl9jnnHFPCyWT5FBUsPTiowatzRnZxnUc42jOtOKUZVUmoLmhSbi3Uqfafw08X2vjnwnH4hhtoYLgXt9puoLD/AKv7fZXUtndbGOCyCe2kCMwDFApIU8D7TB4lYzD08RFWU4qX3q5+SSjyycex1VdBIUAFABQAUAFACMyopd2CqoySTgAUAZkKSXSedcyhbKPe2Ogm+Ync3+xjt356jGQC3pyMlmhdCjSFpWQ9VLsWK/hnFAGd4l8E+DPGccMXjDwlo2uJbh1hXUrCK5EYfG8L5inbnaucdcD0rGthqOIt7aClbuk/zPSy7OcyyhyeXYidJytfknKN7Xtflava7t2uzyTxj+yF8D9as47e18HW2ljzIo1mspJYmiRpUDqEVhHJuUFAZFbZuLDP3Tl9SoqPLFNK1rXdrNW+H4X80zt/1mzGdV160lObmpuTjHncoy5rqqkqsbvVuE4t9Xc+av2ov2Xvhn8A/AUfinw9rOu3d5qOqLaQW93LbhIkMbSM+I4VL4EZXqP9YuSdvzfofhdj8Vw9j6jhWf1WEKtarCyV4xhvGMYpOd+XXRtKzlok/D8RJR8RaVGlWoJYycqOHoz56k0p1KqSU51qlScYOLk7RbjGSuoLnlJej+FP2ff2tPgn4cjtPhT8TPD91abl1W90WW0iXz70oglgR5YiWQiNUDedDkAnEZNfG8T5zm/EuYzzOvPlckrQi04xSVlCKkr26t88eaTlK0ebT6rhmjwdkeBhlCw6nGLcfaTVSE2m23VnOlVa5rv3YvDVeSChByqqHva/ir4l/tmeHo7jV9e+GGgabo8OhTvcahpzR3Fzb3K6a8xlbdctGES4QkhoyBgxqZSytJ49OeO9rH2nLyPfR81+W/SUklfv6K+jfRjaHCyy+v8AVXVeJi24NVIulye15VdTpUqjk4O6UU29JyUPfhCe0+Lv7bXiLw9b6n4d+A+h210t5cQXUWpK9u5RUhMbLFPdQkKS8o3hn3YI2psy+CxOOqUlKnHW7veFu2ylUjpvr17K2vrTyPhbB46VDGV1yOMXFxxHNZtzupSpYOsuayjeNly3upTUvdxfEq/8FD9X8dammhG00PRZb2VLMwvo8tnDApIRg8qvcsGAB3NGGJbPlp9xcqqzGVaXI5JXdrezSt035pfO1/7q2O7AT4Mo5fReIjQlUUY8/M8bOo5P4tIqhRuru0FNR0t7WXxvR1b4XftxeJvBMOn6n8YtEtdUi1KWaT7PL9kke28lAg8+2tRuG4y/uzGvOGLt8qpbw+Pq0VGcnzX/AJ0tLd40156W+b6ctPOOFMBmU62HpRdJ00knhpTXNzNt8lXGyaduX31N9lCOrlU8L/s1ftQ6hLfS+Kv2jLvSrHVdOaEw2NzeTtCzFCqLAzxRwAbcFoirYBQYDtQsFjZzm5ySUr7Oo/w54pfK3bZjnxNw1hcLh4YWjUnUpOL96ng4bJp3n9Xqzqb6Komr2k7yii7rH7Fnj3xFpmn6F4g/aY1/UNLgt/s15az6fJItyn2iSU/fumGcOADKJSCoP3QqLUssrTpqk6i5bWd4uXVv7U5fK/N92hlR44y6hiqmOp4SoqrkpRca1OklaMY2aoYek7Oz5vZuk2m1dSvN0NO/YR+FnhaRZ9R1bxPc3DRT2xJa0W2czRPGHUpbh0ZAxYDJO5R1GRToZNToPmjJ312jTjurXTUE0/n+GhnmviTjM1h7KrQTjeLtOti6qfLJStKNTEShKLas04bPS0rSXL+Iv2bvCnw50zVppvAnifxM+jTaPqGkMs2JL+a41R4XtHATaYhBBbedvV/LSed0AJBPzmeyxSx+GyzB1pKU1JtuTv8ADZPSz05W13le+lzxf9Y6k1Vqww9GCkrNKjTktZTl7vtIzcWuflTg4vljBbxTLUXw++Dug+OF8ZwaFoupeGtV1PTZUngaW8srjRddKJYyRxENF5w1W2WKPygIo7S53NncxXzeFs0xEsbHDYyUm5KUWpNtqcHd7u6TjvfeV1sc2OzTE1sPCmlCKprTlp04NprlvJxinKVvtSbl1vfU3PiJ8LPhd4P0/wCLHiyXwJo2n6Zp/gCG6iQaNCILK5s01KSe5hCx5E/zQlivzYihx0FbcXYOnRxmChhoKPPJppJK+sFZ993v3O2jxnxHVjJ1Mwruy0vVqO3/AJMem/DHwPq3hXxX8TNQvNIVbTxH45g1jTpJGif7Tbvo+m27kkMSi/aILhNrID8gboVJ97gzFRxOT00nrC8X5NO6/Bp/M+WxUXGq/M7WUAEWc9vFE1vFKgYKv7wg7clmIHUHoMjtzyPqTnOZufhj4MutQbWtO0KXT9UuJU82+0mSTTLiZpFBdWuLdo3ILE8MSp+U44BoA+d9d+DvxG8LeOdc1z4WfEi8XxXf2uoXZ8NSTrZ3l7aiO4tkullWWOC6mWaW3ZndE8prkSSMz7Un+SzvG0MorxqVeaMZpq8VJJOztzO7Tu5SkrQck1ez6/fZXxSvqVLAYqmqqVSEpOqoVbRi6d/ZpxhUXuUoU3H6woSgnG0bpx1fBXjf9ua+05Nd8I6v4Z+I9jJMYJQ0drC1hLGPnguIZBY3EE3zjfFIvmIUwyoSQe2lUzDkU6b51LZrkmrd7r2W/wA9vM9if+pGOltGlGK2/wBooycn6/2hF8qXeF3Pqo6+c/Ffxd8R/hz+0f4Q+KnxI8Ow+BNYuraAaxd6YGubPWLRYYhNCEOUMygtE+JztH2cqQUWWTzMZCWHzOliqlSNOTte6aUkkrrZxvuvjenLbXV/oPDWJp5xwPj8iweDq4ylBy9moypylSnKcuSSfNTrOD92a/2aN5e15rwbjD7As/2g/C1jrV3ZeMJrzQdOgtrcQ3mraPeaf5lyzSmRWM0aoPkEBVR8xJl42qDX2Z/NBqw/tC/BdrT7VP8AE/w0py3yi/QtjcQPl+90x2/SgDFf4z+HNfIi8F2+q+Kb65bYE0i0MkOS2Y1+1sFt0KAbiGlTlT1J2kA4LxLqPxEn1WTw14x1C/0BdWs5b7TdA8MyfbNXkCNBHLC9yU8uFA89qN4OxDKC1zHGma5cZjsPl9J1sVNRj3f5Lq35LUqEJTdoo2tOhsvDdtF4M0fRbzTba/KKuj6DEz6jDIyJ58l7fiTybaQxOhzvE5MRMNxM7ItfHvPszzyTp5JS5Ybe0nt8lr+UnqrpHV7GnR1qvXsjnbi81DwrqOqeHbrU7LRZLDxTo+oajpelJcS3V/pGpRHS7Frm+8tJjO19Abh5TmTZaCKRjG5kbzFk/wBWzvDQzX997SD96T0503LRN6pKySdk+a9uhp7XmpSdPSz/AAOv8XaRoGg6l4V8X2kFhottperTaNd21tYiU3ttrE6QyQALxGHvn02Z5QCSLZ8kK8hP0vFmXxxOTzjTjrTtKNtEuXf7o3sv+Ac+Gny1VfqdT8GdT8Tw+DbXw54it7i61rRGl0m6uriaPdeG1kMKXr7MrGbiFbe58oZ2faNp6V7OV46OZYOni4faV/R7NfJ3RlUh7ObidjLLNBqhS8vpY5JYE2fZrckNhnyMEP0z14613kCavrNqUSCJn8wSZZGjkQ42kj+6eu3v0PegDEjtwsSmSLD75ZDuXBJQHCglAfvBRgOevQE8AG8E0Twpo0+saze2lla6dbyXd5e3Uqxw20aKWkkZ3IWNFUEljgYGSeM0Acb4o+KxljsLLwdqWj6fcan5skF3r0UqmWKF2SU21hmOe7IYRLkNEm25ikR5RiNvlMfxXRpSdDL4OvUWnuq8U/OSv0u9L7O7R0wwzes3ZHHaF8NLvxJ4svfHfhvQItCn8RCW9vPF2rWwk1zzJbWG2As7OWPyrPMFtY5ZxybVVmtnbLjz6XDmPzmp9ZzypZafu4aKybaUmu131b10ki3XhSXLRXzZ6dY/CnwLYrqTDR3uLnWJBJf3tzdSy3U+JHkRDMzGQRo0j+XEpCRqxVFVTivrqOW4PD4f6rTpxVPta6fm77vRau7OZzlKXM3qeSab4ItvCPjmyk0jwQkVvpWueINPtL+G9iEaaVrKLqlzc+VGVCKNQjitvLCSS/ug/CO7V5GDy7E4POq1eC/cVIR7K0o2ilbfa72S1t0RpOcZUknumenxPcST5htjncBEhYKIzuVOp+Y9EGQVOc+9fSGA/wDs6VrI37TOGJEbpHHyS0fytnOTu3Lnj+I0AI1tBBI8kkShkK7luGALfMx3YJycgJnHZjgUASw6lNYr9mcSQxSBejFFBGVLHC7xkpnAIPWgCIFbaaK0tWuo3ZtzBohFuHJUfeDHnGMt9O1AHK6zP4hW70yz068vIhJLOw0/StOW6vNR8lFuFieeX/R7KJ/KnhYzbVcywhJoZWUt81nONzdVvqeWUbtpP2jfuxu2nvpdaPdu32WdFKFK3NUfyL/h/wCDEUOkS6PdzzaFpl2zPe2mj6lc/br+TbJAs11qpZbqSRoFtG3IySo8G0zzR8HkwfCcJ1vrmbVPb1fP4V1sl1Sd7bRs/hKliWly0lZHb6EPD2naVDZeC9Ns/sD77mIWCIlsxmcyvLvX5WLu7OzDJYszHJJNfXQhGnFQgrJaJLocrd9Wa1nbi0to7cY+QckdCepP55qgItSv1062Ny0TSc4wCABwTyT0HGPqRwaAOdv9V1HUVfT3tQkczbV2xOTwNw5xn+4fu8g9qAKcUHnTBGgNxcYJaJVwy5b5g3B5GGG5jn51PagBGPnsbi+uUj+UYKjn5gCWHuGZWIH958dCKAPLhoui+DPEcXhmQT6LBYw3d34SvbSxaeeC0uBm+0VTiTcUcJLBbAJvjjt0SCRbKQn4HN8HmOTZi8xyiHOquko2uubpJpW9b9HzXa5jspShVhyVXax0XhT4Lzana3Nn4h06Xw94ZvLqG+n0Rb97rU9ZkEKBjrF8XdpFysUP2eOR1aK0jR55YJWtU9DLOGpOssfm8/a1+n8se1lZarV7JJvRXXMRUr6clJWRifGXT9P8P65qVp4et7bTPD9h8L9Vt/EEWiW9p9vtUikiGiLDAyl8Kn9uCBQpi3q4ZSdorm4xaVXBOl/H9ouS97W0vfpa/Lfr26lYXad9ra/1956jqIsWWOOK78xhgSuJMIxPzbFyOmQfmOeWG7JOR9uch5R8VPAy6prT6/caPdSeEfFXhvUPBnjC6soi92Y59gs7oxiOQzRQlryPJBEJvmcgxCVk+W4qy2vjMPTxGEjerRkpJdX3S27J23drLWx0YaooycZbM7jQvixDb+HNI8U+LrnSp7C5tnkufE2hyibRJrZUZ1vCxdmt42RDJ8zSRRBinnyHa7mWcW4PMKywtVOlV25Zaa9k+/ZNJvsFTDSguZao9RDBgGUggjII719Sc4tAFc6haef9mWXfJnaVjUvtP+1gHb+OKALFABQBR1vWtN8O6Vc61q87RWlqm9ykTyuxJwqJGgLyOzEKqICzMyqoJIBzrVqeHg6tWSUVu3shpOTsjxDw/wCFdS8f+LdTj8WW9pPPq621z4zia2Lw21kis1h4cSRTslVRLLNcbnkDiWYmGOLUIli+IyyMuJsy/tWsrUKTtTXd/wAz/B7LWyu+V366n+z0/Zrd7nzJ/wAFgvg78cPF3gPS/iL8P/EWsXvgrQreRPFXhi0nKRBUYzJqTwoB9oWMKQ+8t5KojooUzuPuzjPJv+CPf7RPjy78V3n7NmuQXmr+Gfsc2qaPeNdxRnQJE3NLGnmEPJHMWz5UZcqys/l7DNIkSqQjJQbV3surtvb0Omng8TWo1MTTpydOnbmkk3GPNpHmey5mmlffofrIJdRt28uS1N2n8MsbKrY/2lJAz7jg+gqzmFOoNGyibT7yMN/EEDgfXYSaAIrnU4ZraaKyaV7hkZVVYm3KxHBII+X15xQBONRtlQNcsbY52kTDZhvTJ4P1BwaALVAFGSR726azjx9nix57YPzN12Dt0+97HFAHmH7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/wCltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfj3/wW48c6lefF34e/DUsV0/SPDcuuKAeHmvLqSFs+u1bJcem9vU1ioXrOb6JJd11f3+79yNXNKkoLvd9vL7tfvPz28D+DPEPxF8ZaJ4C8J2X2zWfEN/BptjDu2h5pXCJljwq5OSx4ABJ4FdVKnKrLlj/SWrfolq/I5qtSNKPPL+m9EvVvReZ/Sf8AAb4P6B8AvhB4W+EPhqQzWXhuwW2a4KFDdXDMZJ7grk7TJM8km3JA34HAFFWanK8VZdPT8Ne76vUKUHCPvbvV+v8AWi8rI+Nv+Cvf7MI+JPwotfj74W055fEfw/jMWqLCm57rRXfLkgAk/Z5GMo5CrHJcMc4GOSd6dRTWz0f6Pfvo7K7uruyOqC9pBw6rVfqvu1WvSy1Z+L1bmJ/QV/wTV+IV58Rf2Mvh9e6peC5v9Et7jQJyBjYlpO8Vuv1FsLfJ7nmujERl7tRq3Mr/AHXi36tps56Eo+9Tjf3XbXzSlp5JNJH09XOdAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9AHMfFDUvAuj/DbxVqfxP8Asx8IW2jXkmvLcxGWJ9PELeerRqCzgx7htAJbOACTisq3JyP2m33avaz732632LpxlKSUd/6/Dv5Hzh+z5+0X8L/hxo/g74Qaj8Lfi18M/DGsO9r4M1X4gW+bS4MsjyW+mi6M0rQSCPcsEM5BMcKqGZtu7q5Z1Jckvi893/wfL5eRz88FH2kfhet+n/DefzOv+FmmfCb4Q/tN+KvhD4b0jxJfeNvG3hz/AIWDrXiXWNSa+e4tkvmtILPzJGLqsPmFY1C4EYAJJ5qcPh3TwlovSDinfVtyUtW+r9x383fqzoqUuaX1qW83JW6K3K7JbJe9+HZK30PUEH4w/tUf8pKfi3/2B9K/9NumV+teDX/I9rf9eZf+l0z878TP+RTT/wCvi/8ASZlWCCa5mjtraF5ZpWCRxopZnYnAAA5JJ7V/Sk5xpxc5uyW7Pw+MXNqMVds9Pg+LX7Rvwnv57O88VeLtEu9Ssxug1hZGd4GJCyIlyp28qwEigEYYA9a+Tnw7wxxDTjOFGlUjCW8LWuujcGr9Lxd1tdH0Uc6z7JpuM6lSEpLad9u6Uk7eTWvZn0h+xN4d+J3i7UdR+KPjfxjr93pM8UlvZQXV/M63Uhl/eTAF8AK0bICAcneONoz+T+KuMynBRp5Jl9GEakWpTcYxXKre7HRX1vzPVWSjvfT9C8P8NmGKc80xlWUoO6im27u+stXbS1l532sfXyW9/ENiX0boOFMsJZ/xIYA/lX4qfqBHMLyJGuLq/wBqp0WCIAtzgD5t2SeBxjk0ARroy3GX1CeaXcuDEJm2D9eT+Q9hQBZsJJPKNtOT50B2HJyWX+FvfI7+oPpQBaoAKACgAoAKACgAoAKAKum/8eUaEgtHmNyP4nUkMfxIJ/GgDyv9o39oRP2etF0TXrnwLqniCz1O/wDst1PbSrFFYxAAlmcg5kbPyIQoba+XXA3eTm2a/wBlQjUdNyTdnbp/wey666o/QvD3gF8f4mvhIYqFGcIc0VJNub7JXXuq3vy1cbq0ZXdvlP4U+N/jz+1b8VPEU1xe2jeFjYTrN4Z1oSyaMlk8v7iCQQou64VmDrNhJW8hvm2jZX1+VcHKnlLzzjD21JVpuFPDpQi3BJtzkp+98WifLGSsndxnG0cacbZVw3j6PC3h3GhWrYeEKtXHKU5SVd2XJTlBuPK4ayhz1KXvyjyxqU5N/XHwb+HGu/D65vH8RXXhy3E+m6doul6XokMywWtnY+ey/PO7SSuzXTlieflBJYk41zPFZVHA4fLMopzjSpOcrzabbmoK1oqySUFrdttu/n+Zqrm+ZZnic5zurGpXr2vyppaOTvr3c3okkkklpovUq8E7QoAKACgCv9vsxN5JmCtu2gsCFZvQMeCfYHNAFigDy748/H/wv8BdAj1LXLKe/v7+CdtLsYpUjN3NE0QMZYklFAmDtJtKqqNn5iiPx43GRwdPmteXSN0r99+ivdvotT6ThnhutxHi1ScnToppTqckpqF0+VWim3Obi404aOc2oLVnzp8GPg34r+Nniyf49/H8/brq88saRpMieXFtyfKBibhIEUM0cZOXb55Cxb5vJwOAnipvFYp3v8r22VukF0W8n70uiX6BxVxXhsiw8ciyGPs1Tv1UnTcklKTmvjxMtVUmrxox/c0dOacvefi1BFH4Z0l4gpU+KPDwV1C4I/ta16lYwM8f3j9O9d2d/wDIsxP/AF7n/wCks/H6P8SPqjoPCes/2RqHimJdOkeAa2peRWOAzWNox6jHJb1/pXmcGu+SUP8At7/0uRpiv4z/AK6Hd2N7Ff24ni45KldwOD9Rx0wfxr6c5wur23swPNbLvnZGvLOfQD+vT1oAybHU9Y1O4RI0ijhikBmljG5SBnKAk8/UfywSAb1ABQAyaaK3iaeeQIiDLMegoApiNdWVJpT/AKJkPHGDxL6M/t0IX8/QAEt6PtGLBWH7zmYekXce2en5kdKAC3lECPBcS824BLu3VOcMT+BB9waAJ4ZoriNZoJFkRujKcg0AQ6hG0lthSAVkjfJ9FdSf5UAfLXxM8j9o79pfQvhZp9zaz+F/h3u1PxAsiB0uLkSKHtyjErKvEURyo2+ZPndgKfvchxGGyDIcTmWIipVMWp0KSenuLStPdqUeZxjZpNSja9pM8nOMJjcRj8NhcPKVNYeVPEVJK2k5XeGh8KlTm4KpVTU2pQnB2Tij6rmeSOMtFCZWH8IIBP58V8EesQFbTVrGWC7tFlt7hZIJoJ0VldclXRl5BB5BHIIoAtUAFABQAUAFAEc8KXEElvJnbKhRsdcEYoA8E+Kvhmy0DUtT8ReI7jb4T1c2w8S8+U2mSQMGstfSdBuSSB44I5ZWwI0htpvMiW0bf8nxPldatGOZ4C/1ilqrfaj1Xnu7LrqrO504eolenP4Wecab4f1XQPDyeBvifcQx6fY3934Kln0zUGWG78O+IWRLG5tbWAbrPbfiCwgiYt9nhhmPmOuJT+eYrGQxWLljcFG0pWqWt8NSnrNXe65bzdt24q19DtjFxjyT9Pk9v8jqvFeg/EzxHPovhTxRpy3za18MPE8HiG2tAz6Tc6xOdJjSNjL8pBP25YhLgmMy9Rur7Li3E0q2X4TMI6e/CSfVJxcunotuqOXDRanKHkzrtb+IHg7wN4y1jVviL47sPDWjLpehCGXU9RS0tDO9xqLOA8hCiTy4i3BDER5528Pw9a/s2pG+vtH/AOkxDG/xF6F/UPibomn39xpmgeCfFXiXUVma4gis9GmjhuYiXjKrf3IjtAVKO+HnUkKuMs8Yf6PEcRZVhY81TER+T5n90bswjQqS2ic3H8VfFEuuWXhe/wDD/gzQ/E2pLa3GleG9U8ZQQa7Pp0aubidreGCdfNQxsFWGaSNwpLSx7So8SpxvhE3Up0pyorRzUdE9Lb279Wn5O5ssJLZtX7Gt4z0C88d+Fre51DwddJi0e/tUt7+GLXdPvXilU+RMrCFLld7RCVbgLtd/mKk7+vO+Isty+t9Qx8HJSSb0TjZt7636dmRRoVJrngzyPTfiZe+CvFVtB49vNR+Hni66SZF8TatpSLpusqsTNFDrMcDCymnS3gWTz7S5IjFuwMtiLj7FJ83hK9TLHLEZDUVfD7uld80VfdJ+9a/W3XVStzHRJKp7tZWl3PWte1f4YfGbRG+Hvx78ASadq9q/2g6RcGaYTvF5Ae50y6gCvcwBrmJN6COQB8SxRElK+qWZ5NneEVSrOPJpdSai4vXfVWej2eqTtdHpZDn+c8JYz65lFV06lmrpJpp9HGScWuuqdnZqzSPHfgF4/wBM+Anx28cfAvWNX8S6toers3iDRLu80i5a8knMXnTL5SRNPcs0ZYNLwC9o2FzJgeZlmcYbAYmphZ11KjvGV1ZfaabW7d3q3urJapH63xtk1Li7hDD8cYKhGlWi1DERi3bTlpQkk2lBR5IctOMX7tRNt8rb+idW+MttbWEutQeH7rTdAtrdbu68Q+JZU0axt4BLsm3JP/pSSIoZwJYI4nAH71QwNdWN4xy+g/Z4W9ao9FGKer6a2t9135H4XDCzestEeXaBqHiHx7f3M+jeJPH2uaAy3VpO9q+n6JoyXttdSWtxBEVH9tW8izeZgqxVjDxIVY7+alW4lzSiqtL2dGMtVvKVnqv5o/l6FNYem7O7Ol8OeCvE2r+Hm0HxNK+gw/Zoob6z0XX7+8vZ3ELQtJPrMyw3kjBRBiT9zKrW4LTSq/lrvhuEqUsQ8XmdR4ifTmVorr8N356fDr8JMsS+XlprlR2mlaZpul2SWmg6db2KwtI8dvbAIrO7s8hATli7tIx2BQSxZmJ5r6yEIwioxVktkc25xfxM0fT9bvtJ0PXPtVx4d8X6fqng3VLS3wkG27t/MSeeVCpiAFvJAgjYsZL9ctnbXyHGmHn9ShjqPx0ZKSfle3pvyvXojqwklzuD2aLGlw2vxd+E/wDY/i3TbSJ9e0u40XxHBp9+ZorW5VJLW/tluIzvPlzJdxkrtP7oEdK+roVqeLoxrU9YzSa9GrnNJOLsznfhL4ohuvE8mvG+j0/UvG+km+1HSrWJnS11/SZo9M1ctcFcS7WNjbhQFUixZ0DB2avk+EnUwbxGUVf+XMvdfeMtVp/5Nu/it0OnE2ny1V1PW7zUDqOxdRkRixAjjhiBdGKHpk8jJTIPfOPu8/ZHKQxWxeRoYyhjiR7h9rfcQEZIIAyTt4yCMMDgUAch/wAJ75t5dr4XtrG4h0bzvt+sXhEWmaaYZgkyyTA/vpo9s5Ma5CNAySvblkJ+ezniTC5Q1R+Os9FCO93tftfTu3fRM3pUJVddl3M2TUPEvinVdQvdLk1iK48+KK21fXYAlpbIoZZDp2nAhg43XCedcIrnzVbdcwKkdeIspzfiFxnm01To7+zjdN9ubfyerdu0Wa+0pUNKau+5J4U8H6roviefVpPEVtY2Mc0b3F3FH/aGt+IzG29ftdzcRCK1tw810FtYEIRZEMMlsMwV35Lwv/ZtdYmtUu43UYxXLFJ6ba3bSV7vfdyaUiKuI9pHlSPXE1W/vYvNgjS2i84W7lgWlRiQB8pwByV9evtivrTmMrW7S4j1BLW61OcwXCFt8r4TfzgYAwBnbnA4zmgB80FubSC4iAiglhSSQIMthX2vnAA6Snt/D0oAqOZzIIirSTuvzhgE27xjpkD70zdRxgds0APlYTxeSUnVZgdkasdp4ZlGBjON0OOOgoAbE1vEnmxRxMEYMqsQpIBJ545ysQH1kPrigCxBbPJPLb2iyvNbhRkBW2OrDn5jgEssh69HHoaALV7Cttc/bNUl+0XDqCtrExWNQcKSxJ4UnaDnHfqKAOY13xSPDGk3jQWOq6g1xLELK00iEzSyTyEbYx90LkfKXkZIkwWkZFBZeXGY3D5fSdfEzUYrq/yXd+S1KjCU3aKOK1fWPGPxC8R3fhK40eLxNLYSiG+0KzumttA05JIAQur3zIWu53iZnFpHC6ot3bGW3K+VeH454vNOKo2wP7jDvTnfxStvZJ7dNH0fvbxOrlp4f49Zdj0k3ninwnoumaVNqtv4g124u4BeSLZi2t44iy+e0UQctGgUOyK8kr5OCz8V6+K5eF8ll9W19mtObW7cutrdX5GUb4ir73Un+FXjDUfHXhS41zVILeGeHXtd0oLACFMdjqt1Zxsck/MyW6s3bcTgAYA9jL8RLGYOliJqznGMnbzSZlOPLJxXQ66SNJUKSKGU9jXYSVU0jSkUINOtsKMDMQJ/M8mgB39laX/0DbX/AL8r/hQA19H0qRCjadbgEYO2MKfzHIoAfZ6fZ6ejJaQiMOctyST6cnmgDivi54uvPDujiy0/xHa+HGmtbnUdQ128tneHSdKtQhu7lHaNrYTgSRrGs7Ko3PMUnS3kgfweIM4eUYdOlHmqzfLCPdvrbdpeW7aWl7m1Gl7WWuiW5yeieA11WOL7ZosdppVzfWerw6beQ/aNS1OW0VVgvdSmuyZWlHk2bxRnFwhsofMlxvt4/PyXh6tSrxzPNKjniLPS+kb9Fbtd7e6ruy2ZdWumvZ01aJ6dYJFBHNLdX1w9vHIBE7Supz3UoOc57Hr2AHX645i5aavHcpNNJEYYYmwZWddnQHHXrz2yPQmgDgfEngbR59XvPGXgIW/h7xJeRKt3qEWmqYr/AMuFhAL2Ndj3MaBiQN6yKoIjdAxD+Vm+TYXOqPscSttmt16Pz6rZ/JGlKrKk7xOK8DfEWx8A6SLa/wBIvfC/hfRr+XR9dtZZwbbwtchy8U0TTRqZNKmEq7JkOyCNrfEUMS3H2bwMozHFZbjXk2aT5m0vZz/mW1n56aX1vdXd432qwjUh7WmvVHu/9lWjbfO82fHaWVnU/VSdv6V9ocpbVVRQiKFUDAAGABQAtAGdrOtQaPHEWtri6nncJFb24Uuwz8zfMQqqo5JJA6AZZlU+fmWa4XKaLrYqdl0XV+SXXf5buy1Lp05VHaKPNvEN1rGueLtBt9Q0e2j1Wa8e20n7NcfbE0+1EO+91Fw6J5b7M2qPskCSTQqSFuJVr4SpjlxtjYYOhzRw8FzTvo276LRtdrf9vPojsUPqkHJ6yex6R4f0DTvDWlxaVpiP5cfLyyuXlnkwN0krnl3bGSx5NfotChSwtNUaMVGK2SOFtyd2XLuFp7d402iTG6Mt0Vxyp/AgGthH4cftQ+CfAHwK/bH1TRvhp+0JL8JtJn1W2uL208O2urC48K/abW3a4cLCsaSAiaV1jt5CBHiP5eEHBiI8teFSFFTltze6nFddXrs3ov1Pq8mrKrleKweJzOWHpWclRtVlGtNK8Vyx/dpuUYJym1bR68p+03wu1uDxL8MvCPiO28TJ4jh1XQrC9j1mOyks11JZbdHFyIJC0kIkDb/Lcll3bSSQTXefKHT0AFAEVzNbwxFrp0WNvlO/o3tjv9KAMwWl9KXOnubG2HCwlceb6nHWIH2574B6gFmG9tbJFt7mJrTaduWBMZJ5z5mMHPPXBzmgDzH9r7/k1L4xf9iLrn/pDLQBxP8AwTh/5Mm+Fn/YNuf/AEtuKAPpOgAoAKAPPv2g/iDrPwn+Bvjv4meHbayudU8L6Be6rZw3qO9u8sMTOqyKjKxUkcgMDjuKxxFR0oKUe8V98kv1NKUFOVn2b+5NnK/sZ/GvxV+0T+zZ4P8AjH410/SrLWvEH9ofaYNLikjtU8i/uLdNiySSOMpCpOXPzE4wMAbGZ6/rGsaR4e0u61zX9Vs9N02xiae6vLydYYIIlGWd5HIVVA5JJAFJyUVdjSctEYvgj4nfDX4mW9zd/Df4heGfFcFk6x3Muiatb3yQM2dquYXYKTg4B64NVyu3NbQnmTfLfUj8bfFb4XfDV7KL4jfEnwt4VfUiwsl1vWLexNyVxu8sTOu/G5c4zjI9aS1fKtyrNK/Q0NO8beDNY8QX3hPSfF2i3uuaZGkt9pltfxSXdqjAFWlhVi6AhlILAA7h600m05LZO3z7euj+4ltJpPd6/wBfejH1j40fB3w94si8Ba/8WPBum+Jp5IootFvNdtYb93lAMarbu4kJYMu0BcncMZzSj7ztHUb91XZseKfGvg3wPZwah418W6N4ftbq4W1gn1S/itI5Z2BKxq0jKGchWIUc4B9KV05KPVg9FzPYzbT4tfCrUPGcvw4sPib4UufFkBdZdBh1q2fUUKLuYNbB/NGFBJyvAGaafNqhyTjpLQ0PGPjvwR8PNIHiD4geMtD8M6WZVgF9rGow2Vv5rAlU8yVlXcQrEDOTg+lJtLRgk2fNviH9r/xEP25PhX+zv4Ifwhrfw/8AiB4Tn8QSa1bl7m5eRI9TZfs9xFN5BjJsYhzGx5fnkbacXF2aJUlJXiz33xt8ZfhB8NL23034jfFXwf4Vu7uPzreDW9ctbGSaPJG9FmdSy5BGRxkGpur2Ks9zbtvFvhW88M/8JraeJtJn8PfZGv8A+1o72NrL7Mqlmm88HZ5YUEl84ABOaqScfi0FH3/h1HeG/FHhnxlpEPiDwh4i0zXNLuCyw32m3cdzbyFWKsFkjJU4YEHB4IIoaa3EpJ3S6GpSGFABQAUAFABQAUAFABQAUAFABQAUAFAH4v8A/Ba7Sb+D9pLwfrkls62V54It7WGYj5Xliv7xpFHuqzRE/wC+K56ajCrOKertJ/NWX/pJo4PkU+mq+6z/AFPhDwf4z8W/D7xFaeLvA3iTUtA1uw8z7LqOnXLQXEG9GjfZIhDLuR2U4PIYjvXRczP3/wD+CdXjLxZ8QP2N/h74u8c+JNS1/W78ar9q1HUbl7i4n2apdxpvkclmwiKoyeAoHatq6UZJLtH8kJHx1/wWL+MXxW+H/wAQvBvhbwR8RfEOhaNrfhm6GpafYahLDb3gadkYSxqQrgoSpDA5HFcPxVpQe1l+LkdskoYWnUjpLmlr10ULfddn5WV0HGfvF/wSa8I3Xhf9jDw7qF2HVvEuq6lq6xupUonnm3Xgjowtw4PcOD3rrxNTmhSptWcY2++UpL8JI56HM5Tk2mm9LeSSd/O6Z9jVyHQFABQAUAFABQB8wftTf8ly+BX/AGMCf+llnQB9P0AcB8ffhTb/ABx+DHjH4S3Gotp//CTaVNZQ3YBIt58boZGUEFlWRULLkblBGRmsqsHOK5d0016xaa3T6rXT01NqFV0Z8y6pr5STT/B6GRpPgnxL8ZPgtf8Aw6/ao8BeF2utThfTdVtNIvpLuxvUVV23cDSRpJbsXBkjXl4iqMH3AEb1VCaUo3Tf4O72et9LP52Zz0nKF4y1S/FWW/bXT5XR4x8CvgF+0R4A/a4m8Y/E/Xbbxf4P0b4ay+DtA8VtOg1G6iGqRXMEOoxlg73ax+aGnRfLkWNGY+YzCt6Nb/ZKlOr8blTemz5VUTfl8S0ffRuzN5yioRpU/hTk1fdX5dPlbR/gj69rmMj8Yf2qP+UlPxb/AOwPpX/pt0yv1rwa/wCR7W/68y/9Lpn534mf8imn/wBfF/6TMl8Na/f+FfEWleKNK8v7bo97Bf23mLuTzYpA6bh3GVGRX9GY7CU8wwtTCVvhqRcXbe0k07fJn4rhcTPB14Ymn8UGpL1Tuj7s+EXi34b/ALXHg650n4leGtJvPFmkwyG9gSLyZzA0mVuLJwxkTkIHAO3fjcAJBn+dOJcszbw2xyxOT1ZLDVGra3XMlrGatyt7uN1e22sW1+1ZHjsv43wjoZlTi68Fr0dm9JRd7pbJ2e++jRxPwz8SeOf2QPFt14B8Z6S+peBNR1FZIvEVtCywRPKAivISCE4iw8LEOhDOu8cSe5n2Dy7xMwcc1yuoo4yELOk2rtR1atv1fLNK0rqMuV35fJynE43gbEvL8fDmw05XVRJ2Teid9unvRbutWrr4vt+CZLiCO4jztlQOueuCM1+EH60NvIPtNu0QIDZDKT03KQRn2yBQA+GQywxyshQuoYqeq5HSgCK4tWkdbi3kEM6jbvK7gy5yVYdx+II/PIBHa37SOlvd20lvOV6NjaxxkhSCQcfn+RoAuUAFABQAUAFAHnvjT49fCzwD4ttPAHiPxPHb+JNRtRdWWnmGTNwrF1RRLt8pXZomVVZwxOAASyg8GIzLC4WssPVlabV0tdd+u2tu59XlHBOeZ5l1TN8FQcsPTlyzmnH3X7rb5b87UVJNtRaSu27J2rfAr49eEvj94Tu/FnhXTdWsY9PujZXUF9AodZRGjnYY2ZXXD4HIbjlRkZjLMzpZpRdakmrOzv3tfp/XkdPG/A2P4EzGGXY+pCcpx54uDduVycVdSSaenp2bs7cTp37U9j4l+KbeD/hV4Fv/ABn4f+wy3Oo+ItNeX7NZ3iQzyLA37kofMEUKhvMHMuACwIPLHO418UqGFhzxtdyV7J2btt1suvU+gq+GFbK8jlmme4lYWu5qMKE1HnnByhFzX7xNKPNJtcu0L3Saa8B0fSU/aJ+PumeMPjrbSfDjU1Z7O28P6m0kU2oWTgrYpardZKy72uA8saRKXETQqszO4+o4Y4axmIoLjPEYdylTcvZwS5ow5OVqrV1uuW7cYNJSaVSSUFarjx7x1l/CWDqeF/D2NjUo1owdaumoylKpzKpRpOKUZxqJU05803CLnQ5pyt7H7x0vQ9F0SGO20XSLKwhigjto47W3SJUhQsUjAUABVLvheg3HHU1lXxVfFScq83Jttttt6u13r1dld7uyPyujh6OHSjSiopJLRJaLZadFd2XmW5Yop0MU0aSI3VWAIP4GsDYr/wBmxRnNrPPb9sI+V/BWyo6dhQBWnl1MXMFis0bM/wC8eSIBGCAgH5W3DqwPvgigCy8OoIWMF7GwI4WaHJz9VI4/A0ARXM+pxQYaKBSxWPzEkJIZiFDBSuOp6ZoAuJbwxwi3WMeWBt2nkY989aAPMfjn8YvBnwC8JN4m1JkOp3W+LStJhYK+ozKMlcdEjXILzEfIMfeJVH4sdjoYKF3rJ7La9tXq9kt23okfUcLcLYnifEuEHyUYWdSo05KCk+WKUVrOc37tOnH3qktFpdr59+E/wT8T/FvxIPj5+0QJ73UtSXzNK0Oe2EVu0C/6gCMk4hBJ2wEBmz5jl2fB87L8FOtP65Xk3J+Vr22trpBdFZOT96V9EvseLuJsPluGfDeVUYxow0XvKbi5Jc7m+VKWIltOalKNKLdGjZKUp+weL9XltNCfwxoni6XQ/wCzLBLvUr2OzXV9TitZXeKH7NA5eV7ud4mSGRobkO9vMgjkkdcRn+c1crVKjhafPWqu0U9tN2/vXVb3vofltGkql3J2SOf1azOn+Dde1/xp8J7nxFpSW8uv3Gl6trcniLU3uQPMaCKzZZbVJQqNsht5/KEuEjwrmQeLX4ez3Gwc6+OanbSMU1F+Taceul+Vu3fY1jXowdlA7zwZcXX9qeIYrq3hd5rq3v7e9tTlNSt5bWBRMnzOFHmRyxCMkgrEhIy6s/dwXiaVbKo0YfFTck11V5Nr8Hv3v2IxcWqjb6nQ291qNrMH0xgv2klhGked6rn5gp6A88Z9favrTmNK003S7zFvJrb3Mzkl9hUGQdcMcFiOO5oA6OOOOFBHEgVR0AFADqACgBrxRybTJGrbG3LkZwfUe9AGTrEukeGdMv8AxHeataaLY2EEl5e3V1KsdpDCi7nklLEKiqqklsjABJOM0AYOjfEzwjceJrfwrd315b65qokNuLnS7u3tbl4lZjBb3MsawSyhElk8pJGcpHLIFKIzDjw+Y4PFzdPD1Yykleykm7d9GXKEoq8lY7KW3t52R5oI5GjOULKCVPtnp0FdhBSkn+w37CNXljmw8iRxFmjbGA3HYhenXIyO9AGD8S/FN14b+HfiXxPo9k1zfaJpdzqdvDcW8ojkkgjaVVbABKkx4ODnsOSKzqy5KcpcyjZPV3svN8qcrLrZN22TZ6WTYOGYZjh8JUUnGpOEWouKk1KSTUXNxgpO+jk1G/xNK7PEv2EY/C+o+BvEHjSLxBFqnjLxFqbXPiXkrLCQ8pt1aMHZtbfNKHRVDGRk58vA+78QoexzpUIPmpU6NGFKX81JRupXXuyvJz96KSe32T5rh2tXqZLH2tL2blWxE5x35a0ppTj716kXGEKS9nUlKS0m/wCJr9OV8QemVrNgJLqFRhYpiAM/3lVz+rGgCzQAUAFABQAUAFAGdrlqs1m1yDtktQ0qsAMkBTlTnqD3HfpQB82fGbwB8Pvhr8Mvip418MeELW2SXwPd6rJphaQ28Nzpq3FxAYYnZobcpcM8ymKJCJmeUlnbK/AcW0IrH4FU/dc5tNrrfki797rR91psduGfuTv0X+ZkXb6HpMPh6w13Qb/wLc3XiG7m0HQfF/xNj0Frqa4njnkgt4tJnuI7+A3E+Ft5y+0yNGFWJo0PyVTMcdi8P9VdX2tKnFawpc6Vk1ducYuLSv7y6db3OlU4RlzWs33dvyMvwh4z8FaZL4j8JfBq6sXlm1p/DGn6f8OvAlvot/bXltazzvZS3mrTHT7q4t7ciQptjZTDL+7OWRLoZRjs1rU4TjeTjz/vKt002lzJRtJJvzd7rXq06sKabXpoj07R/h34l1HxDpfizxR4c1CfTtS08PenXPHV3JNHIIFdEl0W2jOklh5YDPE4AcMy79xZvoKPA+LlS5KlaEP8ME2v+33af4mLxcb3Sb+f6bFK/wDhHbeD9KOmRfD7QI7OKe6vrO28FR/8I/cxQIqfZbQwiU216xaMB/Plit2Cx7otoKj0nw5m2CtVwGNk5dVPVN9X1tfpo3/eM/b056Th9xz3i6Dw14gstR8Otf2JvNKjTTPEGtfEH4VX1xb3tnFvY77jbZWzxiVRM0kbG3XG7aoKsJWdcR4V82KwKkrfYev4Of3W16D9lQl8M/v/AKRU8N+BfG2ptovhDXfhp4Hn0XRfAuk2+r+H723hazj1C6+0y3tla4EzRItxbaa+WaSIRxKEV3w8fxNbG4KbrYr2k4VJ1JSg4bWW103HpJpW1Wt+z6lCatGyaS1uYXhbwtoOieCfF/wu8Ia98QvB+k6wNNsLO+uZm1vV9OZWYX/h/TnCXIdba2RghikuRFLcXpRt1rMI6SxOPxdGoqcatT3nyxXLGX8tSS934nq7qN4xjfSSue7CLV7L+tDh9StfGGvftlar4f8AEOt6hb67Y+GrPTU1PRdMWVLO5msrYPI0YxcrbvNcyqzwOk6JPkSworSx60MqxGGrSorDqbh7zpt3bjdO14uzdmtNU+zej/esylHC+D+DlQk4+2rPnab9606qV0+nuR02vFPfU+s9T+A/gS28JTw6Tp9npfjEWF5DonibV3k8RanpN5MvyTxXF+zzy7JBE3llwCIkThVUD9XoZfhlhnR9jGCnG0oqy3WqurX3evzP5yc5c173sWLXwb4ttNI8Ta54a0+0sfE/iFBqkQv7qSa1t9UWyht1XK7ZDABbRDaoTOZHwrNXHw7luIyjArCYiSlyt2t2bvr53u/na5depGrPmiZPw31fU/E/iGUwalPf6JrEM+raZcy28TnSpoZFt7zSrhIYxFHJDLsC75HlZhco2DbFpObIM5r46pVwWOio16b1S0Tj0au728+zi76lVqSglOGzPR7bQnu3lknvWUCQpLEFY5ZT8rHexzkYPIIwfxr6Y5znPGfh3Tdd09vD0OpTNI08VxBJbRp5ttcRSLLDIrEFCUlRXCFGjJXDqwyDjiMPTxVKVCsrxkrNeTHGTi7ojstPFo1xPBp9s9xf3IlvHgiWNru5WKOIyN5ZzI3lQxx7jnCKqg4C1zZbl1HKsNHC4e/Kr7u711/qxVSbqS5pGUfCtp/aMerXVrcajcWeryaxpcLXBgis7t7I2ckcflKNyPHLckrIJMPdO4+7GEUMsw1PGyzCK/eSXK3fpp0+S+71uOpJw5OgaV4/0HURJrGr+K9D0fSru2Or211eMLGaCKJ0huorhZJMrJDcyqshby9jTRRFQ8RZvMyfPnjXXp42KpTpSs03pZ7O7+6+z0a3sdtHLcRja0MPgoSqzntGKcpN7uyV29NTyjTfjn4c+LPifWfhr4C1lfEepaYst3BdSWklh4eNqZTAWlhSUz6g0cV2A0MpWGeSAMpgysq+HXxuY59j54LB1VCjFN3jdSklb7VtLvS6Vkr/ABLf7nP/AA1zjhHJKOd5tGMVUkoKDd5xbUpJySXKlaLduZtXSaTul1qWOu63YJ4n8NNLc6nYtBNp+oeLrUwyXUIk81re2skRP7ODw3FzaPcyQpcqIk3xXKiNz5+WcG42UXUxElR7Rjq+bo5Sv0aUkrtXvblPhqmKhtHU3fA00h1S/jikk1LQNRMmraPdStO01uzTs17ZXAlZijQyb9qHy9qSiBYl+yszfR8K5pVxFKWX4zSvR0d3q0tE/O2zet9Hf3jDEU1F88dmdn5RtybaZWRvLLYRuoXCvnHoBN+Z9a+sOYS31C6itZLcZYXY85t0ZJyCQzA5P93OcHke1AG8+q6XqOnxC+jErHG9V+VVkGejkgDof4s49aAMaUabBK02kyybJYnjmWQfJGGBH3uScEjpnPTPIoAsT6Zq10mLaxkhWR93Lqm3DuwGM5x84/75oAkfSry0ncWcFuz7y6xo53AFiy54wo4TqQPkIHU0AXrLw6w2Nqc4lEeNsScICFVck9Twq+3HvQBsRQxQII4YkjQdFUYFAHN+ILV7vWo40jV/9FAwxIALOUB4B6FgfwoAx9Y0mPUrW40m+muUTU4JLJ49Pu5LKdFlzny5omDxsGLqHRgyMqsCCBWOIw1LF03SrxUou2j2dndfihxk4u6Lujabpnw78OQ+GfDul2dnbQSN5dtZQrBbWoY7iqDG0EklsY6sSQe+qSSshGD4g0F/EunGCHWb3SrsyCe21W28trizmHyiRDKkkQ4kKEOj5DMGG1iK58Zg6OPoSw2IjzQluvnfp5lRk4Pmjudnpul2/hy3s7XSEit7Ka4kklhjt441aaYvJJKdij55JnZ3PVncknJOdaVKFCnGlTVlFJL0Wwm23dkXjrxDbeHvD93eSX2o20kao6vp2myX9wo3j7sMccjODg5wpO0MeMZHLmdTF0sLKeAgpVVayez1V+q6X6lU1FytN2Rwvw0+LWueIbu58P6tqnhDxFfaLFCdXm0WabT762kmDvCs+j3BkkswyAbfMuWZwC4AB2j5efGcMFaOY4apSk9tE07dm+W/TZHQsK5/w5JnW3fxGj0/Wjp1/wCDfEsen/ZUmGrw20V1bmZpdgtvKt5JLrzMHeX8jyQvWQH5a7cFxllONfLzuEm7Wkrfirr8b+RE8LVh0uS6/wDEzwp4ckgivf7aujcXrWCtpegX+ookylAwdrWGRY1UyAF3IQFZASDG+32q+a4HC1PZV60Yy7OSXRPXto09e5lGnOSukV9e+IOny+H7iTwZqlne6xPdRaTaxBUma0vpoUmU3Nu80DYigkF1JAZI5jCrbAWZAcsdm+GwWBnj1JSilpZ3Unskmr7vS+tt3sxwpSnNQPPPD3gzxL4h8W67d2Ud3a6NPeLb6xqGuoZ9T8QiPeDDAs2Y7PTI28yNUEJEzTXUkSQqRdXXzeU5Tic5nSzjN5XatKnBaRit03bq9Ha97Jczey3q1I0k6VL5s9QM06vcafcQJdXE8itvVQwU4xtzwN4Vc9u57c/cHITy2Wn2EKrcILi7JEhTdlnHC4ZsElc4+p4A520ASJos94zXOoNGskoIfC72AI6Ln5V4wOjHj73NAF+6jitrNVRQkcckbH2HmAkk/mST7mgDy34jeA7SXV4/iL4f0CXUNYsrKbS73RbaaLd4h0tmYPaTiZWjYRsXlgDGMCTchmijnn3eHn+TxznCOnGyqR1hLXR/Lo9nvbezaRtRq+ylfp1LPwe8aMLfTvA+p397qUU+ntqHhvW7lpXbVNMDDbHOZiZlvLdJYEk8475gRMCXNxHBjw9nX9p05UMQuWvT0mvNaXXSzfbZ+Vm3Xpezd1s9j1SvojAKAPFPDd7d/EBL2z8d+J9Q8LX174x17R10u3vPsc1/bWz3kFrbwmTM8Pm2EEOobrZo5DzNGyxsc/n08JSzbimrRx0OaNOmnFPbeL6b6ylvddHsdqk6eHTg9Wz0zwn8P/AngKGaDwP4K0Lw9HcLGsy6Xp0NqJQmdgfy1G7bubGem4+pr9BOI36APO7f4palqviCTRdE8Jz3Vo3ieTw5BqsMqz22yCyae5u5NnMSR3EVxY7W5+0RqpwHyPCjnDqZw8sppNRhzSd3dO+21no49rXer2NvZWpe0fc/M7/gpl8Yb/4LftF3ugSfAv4K+IpfE2g2GtQ6/rnhRb/VWX57Y+Y7sI8q1qyqCj4jCck9PdMT9F/2TPGeleMv2aPhVq9pqGnSzT+D9JjnSzgitokuY7WOOeOOGNVSNUlSRdiKEXbhQAAKAPWpp4beMyzyKiDuTQBVaTUbs4tVW1hOR5kq5kPoQnb/AIFz7UASR6fAsgnnLXEwxiSXBK/7oAwv4CgC1QAhAIIIBB4INAHjf7W9o1t+yh8YIo7mQxp4F1wKrAHav2GX5c4zj680Acj/AME4f+TJvhZ/2Dbn/wBLbigD6ToAKACgDxf9tL/k0n4v/wDYm6p/6TvXLjP4a/xQ/wDS4m+H+N+kv/SWee/8EuP+TE/hl/3Gv/Txe11GB4l/wVq1/VrvxZ+z98Kp9L1jW/C3inxLPNrPh7S5vKn1wwz2KR20bAg+YVuJlTkDdKp6gEZ0kqmKtOLairrs273+62rTvaTtuXJtUWk7XfzVv+H7WujkfAPgHx/4e/bU+G3xY+BH7IHxA+EvhadF0HxlY3EKRadcWsn7vz2jicqAoKSMD8pe3jkxv3M29C8q0/aNcjTf/byUvlb4UkkuvcwxDUaUXTTck19zcV+Cu38uxd/Z0+HHgX9qv9vf9o/xL+0Bo9n4sm8CarLoWgaRqAL2cVpHdXNor+QTtYpFbp1BXfO8mN5VgqNKMaE3u29+ut9NPRJPeytfcurUl7aEWrabenLrr3vfs79jlv2TVt/gT+07+12vw7lfV4Ph94V1Y6FbyzSXBRLGT/RrLc7M7iERJb8knEWPasYVZ/2TXnJ8jUt35e0Sk9t7c3Ra9tTX6vSlm1ClH4ZR1t/edNtfe2u/fU8f+B3gLTvij+y/40u9R/ZB+IXxP+IPjq51K7h+IULJOINRBfyGSRnEgCzEvNxulLuGLKEC61aS9nCMbxtr62b79Lafe1Z6mMKjlOblZ6/dovxu7/d0PSf2yT8XrD/gmZ8E7L402erad410nxmtlcrqIKXaxwQ6klqznqW8hYfmJycbickmoqN18fSd7Nxf33gr+r3fmyqMFTw84JaJr7rPT0Wy8jT/AOChvwE+Fn7MviT9nPxD8FPClt4a1a111opb+3Jae8ktprSWGad2J82UOzne3zHdgnAUDpwsk8xpRStFvbpZNK33Np9X1uXOi5ZbVryk3JNK/V80Ztu/e8fl0PR/2k/C+i/Hr/gqX8MPgz8VbCTUPBWj+FJtRttHuJHW3vbgxXczSAKRkM8MKuAcMLUKcjIrhwE3VrYico/w2kuzVoO9tt5v1sk7pBi0qdOjGL+O7flrJW+6K+92tcwrj4KfD34Hf8FgvhRonwz0caPpGveGtQ119MibFtZzyWGrxSJbpj91EfIEmwcBpH24XCjSMpNtOV0tl262+9t/MhqO6Vm9/Ppd/JJfIqeNrb4M/EP9qj4v2Xwf/ZH8U/tG+OXnfS/FN/4n1y3sNF0KdA8ZjtJJIxsIMPkq0jJJi0IgcLuZymnOjJQ2d97X67dbddHtbytpOcYzi3vpstOm+6v3urbnUf8ABI3w/afEn9jH4k/DfxPc3cui6z4i1TRpkjmKvFbXWmWqSrGTnYT5jkcYDEnHJroqr2lGPM+6+Wj/ADbfzOejJ0sQ5R6Wfz1/yX3H3D8Dvgr4L/Z8+GumfCn4fC/Gh6S9xJb/AG6cTTZmmeV9zgDPzO2OOmKmU3JJPpp+Lf6jhTUHKS+07v7kv0O9qCwoAKACgAoAKACgAoAKACgAoAKACgAoA+V/+CiX7JU37VXwVFv4Vt4W8deEJJdS8PGRwn2kMoFxZFm+VfOVEKk4HmRRbmVdxrCpBqSqx3XrqnvotLq2l03ulbmbNabTTpy69dNH6727/J62R+BOq6VqmhapeaJrem3Wn6jp9xJaXlndwtDPbzxsVeORGAZHVgQVIBBBBrcyP31/4Jd/8mK/DL6ax/6eL2t8R8a9I/8ApKEj4j/4Ldf8lg+HP/YtT/8ApU1cEf8AeJf4Y/nI7qn+5U/8c/ygfHP7Ln7Nnjb9qT4saZ8OPCUEsNmzrPrWq+UWh0yxB/eTP2LEZVEJG9yoyBkjvo0ue85/Ct/8lvq+mj77Jnl1qvJaEPjlt1+bWmiur6rot2r/ANGnhDwpoXgTwpo3gnwvYiy0fQLCDTLC3DFvKt4YxHGuTknCqOScnqaynLnk5GsI8kVE16koKACgAoAKACgD5g/am/5Ll8Cv+xgT/wBLLOgD6foAKACgAoAKAPxh/ao/5SU/Fv8A7A+lf+m3TK/WvBr/AJHtb/rzL/0umfnfiZ/yKaf/AF8X/pMz7C+An7FfhTxJ8M7bxr8U5dSF1rkKXtlb2N0kf2W0YZjkZvmDM6kSYP3RtBG7cK+g4x8UsZl2aSwWUKPLSbjJyTfNJbpbWUXdebu07WZ4/DXAOGxmAjisxb5qivFJpWi9n11a18tE9bnifj74RfF79mzxo3iDRv7Uaw0qWG5sPE1lan7JKhZTGZMb0TL4UxSEgnjDKQT95k3E2Scc4BYevy89RNSpSkubZ35dm/d1Uo7Ls07fJZnkea8KYt1qXNyws1US93dWvulro4y3801f75+D3xR0T41/Cy28ZxR2Q1MWzRapbqo/0W9RcOMEsVQkb0ySdjLnnNfzdxZw7V4ZzSpgpXcL3g39qL21srtbSsrcydtLH7bw9nVPPcBDFRtzbSS6SW/V6PdeTR6VbKi28SxRtGgRQqMMFRjgGvmj3CWgAoAKAK97a/aogEcpLGwkibP3XHTPqOSCPQmgAt7ppHMM8DwygE46qwHdW6Ec+x9qALFABQBz/jLx74T+H9ja6j4v1iPTra9uGtYJJVbY0qwSzkMwG1AI4JW3OVUbeTkjOGIxNLCpSquyen4N/LRPfQ9XKckx+e1J0svpucopSaW9nOMFZbyblOKtFOTvotz5Y+Kv7bOoeNYfDfgH9lvS7jWPFPjC3Dmaa3Hmabu3qYfLLbVnQozvI+YURQ+XVt6fLY7iOWIUKGVrmnP8N9PVbtvRLXVar944X8GKOTSxWbcdVFSwuGdrJ6VLcrUrpXdOV1GMY2qSk+W0ZLll7F+z9+z3pPwv+Hfh7SPGNnY694l0uR737ddIt2bCdwgMdpJIu6KNViiGFxkrnvXsZXlUcHh4QrJSmtbvWz00i3stEfnPHvHtfiXOMTiMtlKjhqiUeSPuc8U5Pmqxi7Sk3KTu76Oxxvx5+AnjTTfGUf7Q/wACNTmi8VaIDdSeGhAJLXV5pGSO4chpUWNngUCQKN0nkptxJhjx5nldaFb+0MC/fjry9JN2T6q11vbV2VtT6PgbjrLMTlz4Q4qpp4Wr7vt72nSjFSlBaQk5KNR3hd8sOeXNeF0vJvg18Kfjh4Gv/E+s3+j2vgXxF8XZpNH0fTLW5kh07TUaKW7uLwpCX8qWJFZLWIyiRWeb5AiE19B4cZIsPja+dZvC1GkozdKPK+e80kuVt+7zNKTd1GPNF2co35PH7jzLM5yrL+FuG6zxFSPNF4iopc0bQa0qWi5ScYuUrK05KnJSlyzt6d8P/Bnwi8R/Efw7q2sfD66murxr3UfCviW68W6hqM+oHTbwDzpY32rEX3LcKPnQhyOD8p/X84zPOsFldejQxKUY8ka1JUadNQ9tB+7Fq7ly2dNvSSaXTU/lzLcBleKx9KrVoNt80qdR1ZzcvZz3adkr6TW6d/kfUVfkh+iBQAUAMWKNHeRY1DSY3MBy2OBn1oAhu5b1Fb7Jbo+FzuZ+c+gUdT9SOo59AClLCJIba6udRecvNE0QGI0OXBGFHJ4HcnuaAOB+OP7RfgH4HaDc3Wr30Goa/tAsdAhuFS6unYZBbg+VEACWlYbQFIG5sKeHH4+GBgm1eT2W17b67JJatvRI+r4U4UxPFOJlGMvZ0KdnUqNOSgpO0Uor3pznL3adON5Tk7LS7Xz18Hfgr4o+O3jv/hff7SzC4s5Vjl0vRHjK27x5LQpJExPlWahgyRNkzMxeUn5ll8jCZfUxtT6zi3dP/wAm7K3Smt1Heb96XRH6DxDxfg+GMIsk4ejyVIXV7puk2kpT5klz4uesZ1F7mHh+6ofaqH0p4i+KGl318fDfhDwtqHi+6EksE93aKkel6fKkksLie8kZUZkngkilitvOniYfPEuRnpzTiLLsovHEVPfX2VrLvt00el7XPxinQnV+FHkNtBbeKPFiTX0WkavqXirXYbVhpGtvFLp2k+G5nlKkMIzdCPXHlgkWNFbZqSqxdIsH53B4yXEWe068YyjSoQ5kpJaua02btdNNav4dErs3lH2FFrq3+R7fNvlgNq9wSpUFDNKM+WeRy8gG4EbThf4a++OM8puPBcfhnVdB8P6VoGorBbvev4V1SC7/ANGsZQoeXRpCsTvDp8yRlk3LJChiCJ5LwWKH4TOsPW4exbzvB/w5NKrHvd/Etd9fVS11UpHZSkq8fZT36M9D0TWLXXtJTWdLuFnWfcLhDsYxSRMySQsqnCtDIpjdCeJUKY+U19vRqwr041abvGSTT7p6o5GnF2Z2GgWTRq16+cSKFiVn3lV6nnAHJ9BjgYzWgjXoAKACgCosGo4Ikv4j8pAKwY+bsTljkewx9aAMrWrbVNbsbjw6+o3eizXcMkaanYCJpImK/K8ImSSPzFPzBZEdfk5VlJFZV6NPE0pUaqvGSaa8noxpuLujzXxt8P8AWvDFnPqkmpa/410G7aGXWbGeBLrUbO5ikja31TTkiRVV4DHG7W0UWS0STwKLhZFu/jcz4UhQjTxOSr2danqtX7y1dm299bauzT5Xpa3VTxLleNXVMzbn4/x2lxp2paP8QLDxNomlSz6Z4itdB8Dapr+px3yKxCTtpsjjTZFzH5kc9sSSrgeWSAip5/n2Jg6dHANVFbWTtG/WyfLfytJ27vqOjRi7ueh6jq3jnw/4R8A3fxK8RTzWmmpZrqU3n20ltNtZV8uIwzBXSU5RBG4Vt52kA8V+g5Hl+NzqtQwdOH76pZW6J297a+kdW3rZK55WYY2hluHqYqu7Qgr/AOS9Xsu7Z8r6J+2F+0N8WPFWoaX8EfhNoV7Z22yRUvyzyQxHCgyym4hj3MQ7BQMgK33gjNX6JxTwjkfCsMPgcXian1qtq5xhCdOmlKKcp0+eFTlabUXGTcnGUlF25DyuDMyxvEscTm9WnTjg6GipyqTpVq8nCUlCjUVGtT5otRc+eCjFVKcZSjzqa810jVHHxTjvvgXourfCf4jSSzC68H6u+2wvBIgkS3h82NBGzqd4hnRIvnjMToUjDenhMLHG5FGniMR9cyynK3PDmhXw1rpP2c43cEpa05qXLCSlBOEIJvNcTPLs6nUxeGjh8zq0005ctXD4u7TlerRqOPPzRa9vQknOrCUK/wC8q1pR9t0r9uWw0rUD4H+NfgPVfCevRJDDfzWgE0du0scbCZ4WIliTa5fYPMcKABuJ48yn4eUc5pTr8N4+niOVJuD9ycea7ipR1cZNLTnUL67JHn47iXF5HKn/AG9gKmHhUcowqfHTnyNRm4VElGpGLfvOm5pab3PqWy8uD/RDbRW8gG/bEMI44BZeB7Z7j6YJ/Mj64kuryK0MPm7v38qwrgZ+Y5xn24oAnoAKACgAoAKAEIBBBGQeooA+a/jc0114/g8I3c0s+h3/AMNPHaXemzEtbXPlS6FGjSRn5XOyeYDI4EzgcMRXxfGyUcPh6sfiVWNn1Wjej9UvuR1YT4pLyH3HgP4f+HvC3xF8CfCf4S6dYX0WnaX4/trS0heysrrVoGdtPVFhUgFJtItmaKNSvzAshMx8zDhKrVzfJ62GxVRyd5Ru3dpSivm9W7X9OhWJSpVVKKONmvde8HfE/wCJ1tNqvhXwXpuj6zoXxG0a9mmi+0XWjKsUfiW+ljCSSSH7P9rtVeQHZvgAEIEUjfP5Jio0qmXYvV6zoystm23BX2+2nprZPdpo2qxupx+f+Z9RXmJlmW6j1K5DEJBJtMQUsNuCPlGcsRnBGCB61+tnmmPqkX23U0jgtZIFZ1gG5R67eTk5O4PznnHtQBx3x0NtffDbXNO1WDUxY+JZLTwu8tlIDLD/AGxdJZRyDLAoEa9iZmUnaqOwDHAPHmOIeEwdXER3jGT+5NlQjzSUe5xOu6Z4I8SeA/ij4r1jUbO90XVtY1Oac6xaW0sOjvp1uulXUi/aTLbkRtp0k43qFPmkOgO/Pg8O0quB4fhKEbz5ZSS11bvKPm9LL8jau1Os77He/Bmz0dfGfijRLKR/svw/lt/DGjWaO3l2Vq2nafdPvLOzTzuZkzLIchFVVVS0sk/PwXg6SwKzBq9Wq5OT9JNWXRLS+nXySSrFyfPydEeV/GG38H6B+3J8Ntfv9R1eXV9U09LJNPjsIjbbJVu7YSm5MwdGUyBtghb7ud4zgb472VHPKFVt8zVrWVteZXvfz2t8z924V/tDM/CzNMDCEFRhNyc3OXPeLpVOVQ5Gmny25vaLf4dLv6JuEexu4yqBLqAEb/7z5JLux5YOPfgb/wC7z9Wfz+a0PiSKT7PLJb+Tbz7gZXlX5WAyRtHJ54zx1HrQB5R8RvCOtWOr6rrvhye2AuUHirQ7+6ilum0vXbaJINioHeQ21zbkRvFbJE+xr0B2a6Gz5DOsvxWHzCjm2XxcpL3ZxVlzRs3fpd9NW9eSy0OmlOMoOlP5f1/XU2rX4jDXiusadLa3eh6tfS2dvqPh+7bU7dxDIYszSRR5hfcsqvhWjjMRVpM4B2wPFuCxFR4fFJ0Ki3U9Pxflbe176XFPDTiuaOq8jSt5beW3S6tXhkgniSaJ0+ZGO3crDLYIYcgnqG4U19Sc5m+IfGFppt5c6RY6ffa7rJtop5NG0xo5LxEZpBG8pmljSJW8mYK0zxI7QuE3sm2vPzHNcHlUFUxc1FPbq36Javz7dS4U5VHaKPBfi58fY11a6+H/AIb0ybxv4yaSaBvC3h6Z/sdnJD5MgNzcKsdxesskUge2tgkbRSTxTxlUMlfBYniXMc6k6OXL2cLetSXR23S0d9NVa6l0P2Dhbwpr4ujSzfiGf1bAycf3krL3Wm09WuSMrKKnPRynDljNN2+dPGuj+O/2hviH4Z8BR+J/Dp8Rwaa9vZeGtOCJpOjhHw1rbT27SxM3kRC4ck4VEEW9pI1irysPlLlVWFozvUlrJN31Tf2ldN2vLyu1dy0f9DcPwyHw5yivm8MHKOHg7KvJfvpwkou8oTjTnCHtOWlGKXvSSqckYNzPrn4B/spaZ8A7uTWrrUE1bxVqWnQW1w1vuSzt0OwvFGjsWl3TRKWkcDPy7UiGVb9DyXIoZVerJ3m1Z22W10u92t38kj+cfEzxVxPHqjgaMPZ4WnNyipWc5P3lGUmklHljKyhG9rvmnUdmvb4IWdCVsVAMf3vMJYLtxn7p6LMhOB/BmvfPyQ8L8V3MfgXxvqt9qUmo3kGn+M/DfiPT7ZY5Yra1fWw2gPaRgkRsiXH2i8cDILXBJQOVlf5DFQeE4loVoLStCUXtq4+9d9/sr0W/Q6Yvmw8k+jPb2tYkZYUDtCxfyHdsFlbaFOQQNuSM8dGccEV9ecw+FrBrpI5y0UbEZLrHnYwwGz2OPKOevMhoAZciztLgrb3CSBCwEnmk5H8IypBIwY+/BVuDQA+PTdVuJXZbKd0JY/vFEWPmJG3ceOueOmT17gHQabpuowQeTeah8ufuQjGB0wGPQewAx2IoA0YYIbdPLgjVF64Hc+p9T70ASUAFAGLqtlcT6pE1u5Pn2zxSJkKDGCM5YgkZLDoCfp1AA+Dw9CLV7e7uHlMuS5X5NzZJDE9WIJ43Ej2oAxntPs941tq1xuKDMYjBzKrcYVR3PII45OTuoAla2sSXxp2tqHyMLboAuc9OP9oj8fYUAaN3qNhcadJaXSXFoZEKR/aYmTJAyDkcdcd+1AGZqdydQKXEEsRJtVMgzu2t5cxIyOhxn8xQBznjHwD4I+I8Mmh/ELwV4T8Rxx3G2GDWdKivFDtMSWQSqwXnGTjoB6UAeXeJvh/8LNO0OXw74V0pfh34Z0q4t5tS1DwddXOgu1wwVIrW3GniM3U0kspQxfP+8lVQrylcfF8T5jh4VI4ShRjVxUvhTipcvW7vt3S26vTfrw8JNczdomnL4Z02AWHxC+INxqXhS20OWJdE0TT9Tu7Qaess0OIbqOylA1G6nnMQaBhNEWEcMSSM0ktxtl3DOEwmEnLNLVJzu5yl00eze1k372j630VpniJSkvZ6JbHMzan8SPFfjfUH0a88QW/jDVfAekaFceFbDVPtmk+DtWuZJZb2+vnjlSDz4I7i0ZMGOeeKF/IDhj5fx2TZTiMzoxwdJXw0arm5tW5kkoqyfdKV1rZvW1teqrVVN8z+K1rH1HYeHdC0rSrHQ9I0m00/T9Lt47SxtbOJYIrWCNQsccSIAERVVQqgALtXAGBX6+eYUL7TdY8uJYVtXWFtsaxgoFzxv2jowyecnGcjHJIBo2GmC2c3VzK89y5JZ2bIXPYDgcdM4HfoOKAL9ACEBgVYAg8EGgDGuLNbRpF84Ro2HSSQ/KdpyQ5zucqucAnGB04JoA8s8V/DvWPDK32s+DPCuq+INPvr5tWk0ixubWC/s9UYr/p+mi6kitodzF5JYXdVdnlkwXeVLj5XOcgrYnEwzHLZqFeNr32kvOyfp5rTojopVlGLhUV0YVp8TfiN4TE3izxhB4803TPDskM3iXS/FtloxC6PMHVtRtrrTCYS1u8ZklQzu6wRT5g3y2zHkpZvmeV46nhs4cHCqnyuCekl0763S23as9y3Sp1IOVK913Oj8X/EnUNctb7wpPZanpd7e6ZaXcfh/Qb5J/FMkU5AKSeSfK05DLHPam7WcxAkyLdWzIJBnV4lxmbv2GRUm9WnUkrRXmvvT11t9h3BUI0taz+X9f15mt4U+DZsdc0TWPEE9qbTwtK95oWnW8lxPNFfvbz2kl9eX8zmW+me2nkUb1XBnmMhuH8qWP1smyGWX1pY7FVXUrzVm+iWjsl20XyWiWxnVrc65IqyR6jX0ZgeNfEb4sWM0OpwWR+1eFbGQaNf3FuLd5NX1qS9ito9GtDcHyXLSeba3DSbESSdEWVHiuDB8XxPn8sO/wCzsFL960+Z6+5HlbbutU0veVk2ktrtHXh6PN789vzJfg/onja58aa/4m8bfYGi0+2i0rSo7a2ZI7OSXbNeQWshVftFoqrp8YmYLI9xBeFkjQRQxLgnA06ODli4L+I9G92o3V32bd9E2klHd3kzFzblyvofHH/BXz9l3UPH9nof7Q2j6x4e0q38J6S+la9Pqt/JA80LXcf2OOBMMjsHubpiPkYjjLnao+vxFZ0KbqKLlbot/wBP+GN8oy+Oa4yGEnWhRUr+/UbUFZN6tKT1tZWTvJpdT1T/AIJWfEj4ceMv2ZbX4WeGPEV/rl/8O7h7fVF1HTFtdhvJZbmNoQJJN8G9p40dykjeSxaOMFVq6cpSgpSVm+nbyOXF0qVDETpUZqpCLaUkmlJJ6SSaTSa1s0muqufYi6PbQ7PskksHl524YPtz1xvDbfwxVnOOSe4tDs1F0dCQFnVdoyezDJx7Hp9D1ALtABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P/AKW3FAH0nQAUAFAHF/Gn4c/8Lf8AhJ4v+Fv9s/2T/wAJXo11pH277P5/2bzoynmeXuTfjOdu5c+orKtS9tFRvbVP7mn+hdOfs3fya+9WPh7wt/wS1+PvgbQrXwt4K/4KJ/EDw/otlv8As2naXp17aWsG92d9kUeqKi7ndmOByzEnkmtSD17xd+wG3xI+AHhL4X/En47+Ktd8f+BtQutV0L4jOJf7Rt7mWZpF3LLPI7Iv7kYEytmCMq6YxRN3cJQ0lHr31v8AftZ9LdtAhpGdOesZdPlb7t7rZ376l34PfsUeLvCvxxi/aC+OP7R+v/FLxTpmmx6XpCnSo9HtLaFUkU+bDFK6z481mX7oDs7sHdgy3GfK5SS1l/Wi2/TV6X1FLmnGMG/dj0/z/DXd2SvbQ+Xv2jp/2Ybz9tvxAD8c/ip+zl4+gghi8Ua1aIkGma/aBIWCW08E/mwyzReS2+RDCfs4LR+b9/lwsuZS5Vyq8rp2Wt9/R/EurUr6GuJ5vdTd3ZWer0ts9tvh3srW1NL/AIJaeDvC19+0N8e/G/w5k1XV/h5H5WhaXq+qh5H1XfMztLI8igySOsYlcEKQJ1yqbgtbYWE44CdKul703ay05U52XyjKKf67mOJ5frsZ0n8MNddeZ8t385Rlrt2PSpv+CZPi7w1p/jD4efBj9rXxP4G+FnjW9W6vvCI0RL140+XzIor1rhJEVgNhKqGeNUSUzbckUVJRjV95K34a/nrta+ti5N3lKno3f0+7+tNLnon7Q37BOg/Gj9nXwV+zr4Z+I2o+GtN8F6nDqMOp6jaNrF1dlILiN/NLTRfO7XDSFgdoI2qirgLV28VDEv7Ktb5x/wDkfO+7FD3KTp9/+D/mbP7Yn7HP/DWU/gCb/hY3/CK/8INqU2o4/sj7d9s8ww/J/r4vLx5PX5vvdOOXRl7LEwxG/L076p/oaupfBzwlvicXftZSW3/b3foN/aj/AGMLb9oDxp4R+Lvgn4oar8NfiT4M/cWHiPT7UXW+03M3kyQmSPdhpJMYcKVmlV1kDALEOanU9pB6/evu29e60ZnLlnDkkv6/r7nqjjvh9/wT51Twj+1B4W/ao8T/ALQWseMPE2l2t1/bo1LRo0Oq3c9rcW3mQtHKEs4EjnjCQLG4URYDYYbTljF3grX3831f6LskkNycoKMt0392llby1d+rbKt9/wAE8vFWkfGjx58S/hD+1P4r+H2h/E+7e58V6Jp2lxTT3ImmaW4+z3byAW8m6SXyphC0kPmuAWVmUzTpxVN0aivDotttlddO60Ulo77jlPVSho/v+f8AWz1VtjqPgH+w/cfs/fB/4sfBjwv8XLmbSPiC9++jXZ0kw3mgNc2rWwdpFuP9JZEEBDKIctEx43Dbd28PGjJ3kr3fe6XTpt+JNO1PEe2tppp6Nv8AU9Y/Zs+DutfAb4P6P8MPEHxDvfHF9pct1JJrd5A8MtwJrh5QCryykbQ4QfOeF7dBc5qSirbK3rq3+plCm4Sk273d/TRL9L/M9PqDQKACgAoAKACgAoAKACgAoAKACgAoAKACgD5Z/aw/4J2/A/8AapupvFl+Lnwn44aDyh4g0qND9pKqVj+2QNhbgLwMhklKqq+YFUAZ+zs+aLt+X/D+f33NPaXXLLX+vy8vusem/sq/A65/Zu+A3hn4LXXiOPXn8ONfgajHam3Fwk99PcKfKLNsIWYAjc3IPJrepP2jTt0S+5JGdktjx79sH/gn9p37YPxR8JeL/EnxHufD2heHNONjdWVjYLLd3qtOZGCTO2yA7SAGMcnP8NZQhFVHUl1SX3X/AM9vxNqlVyw8aMd1Ju/k+VW/8lf39T3v4KfAn4W/s9eCbfwB8KPC1vo+mREyTOCZLm8mP3priZvnlc+rHCgBVCqqqNZ1HN9l26f1+L6nOopa9Tv6goKACgAoAKACgAoA+YP2pv8AkuXwK/7GBP8A0ss6APp+gAoAKACgAoA/GH9qj/lJT8W/+wPpX/pt0yv1rwa/5Htb/rzL/wBLpn534mf8imn/ANfF/wCkzP0C/YO+ILa98Lp/But+Kre81XRb6VbGwkuEa5h0zy4ChCj5mjWSR1BOduQmQAqiPFvJ/qmbrHYei406kVzSSfK6l5X12UnFJ2Vr6y1bbK8O8y+s5c8JWqJzg3aN1dQtG3nZNteW3RI+kjpWmFy50+2LN1zEv+FflB+hHKeEPhp4E+GZk0vwfocGlafql5Nf3ECszRvdMEVcbidowDtQYUY+UA16ua53mGdzhUzCq6koRUU3bZd7JXeurd2+rZ5+X5Xg8qjKGDpqCk+ZpX3frsuyVkuiO1ryj0AoAKACgAoAhu7eC5hKXHCqd4YNtKEfxA9jQNJt2R+e/wALfg74E/aD8Q/F7wz8K/FHiKx8O2Pieyv9M8VPP9vQ6raNcrNHvUjzBtvCyZl84DypGJ3lK+EoZRHHVK9KlzQg3GUZPW7XNqnpdPm0d77Ns/qzNfEOtwtg8qx2YOhiMTGnVoVqFNqLhGfsmoziuZQnF0kqkfZqF3KEYpJM9g+Ev7HfxB0b4m2PxH+Mvxp1XxbN4dz/AGJGLy4mfLKAxkknJMaDMg8uP7x2tvGCh9LAZBiKWKWJxlZzcfh1b++/z0XrfofGcWeLeT47IqmScOZbHDRr/wAV8sYrR6cqp2u9E+aW2seV3Ul1GjfskR6vpOmWXxr+Iur/ABCvNA8TPr2nXV+z+VJA8UAeznt5XkVoi8LHaDgZ4wGkRumnkSqQjHG1HUcZcyb7WXutNtW0/q7T8PGeK0sJiatbhnBwwUK1BUpxha6kpTaqwnGMJKaUkrta9buMJR9h8B/DnwX8MvD1n4W8EaFFp2nWAmFvHveV0EshkceZIWdgWOeWPQDoBXsYbCUcHTVKjG0Ve3Xd3er1PznPM/zHiPG1MwzOpz1anLzOySfLHlj7sUo6LTRd+7OlroPHCgDF8YeDvDnj3w/ceF/FmnfbtNumjeSETSRHdG6ujB42V1IZVOQR09K7stzLFZRiY4vBy5Zq9nZPdNPRpp3T6o5MdgcPmNB4bEx5oO2l2tndapprVdGeLfCWbQfGPxsuvEsZ0nw9N4W8OyeHdP8ABgJ+2WMQvCWu3iKIkDFBHGVh81Nrr+9IK7vu+Io4jLchjhXzVVWqqrKv9ib5LcileTmr3lefI7p+5dO3yeTOjjs3liFy03SpunGl9qK5/iaslHSytHmVmveta/0HX5sfbhQAUAFABQB5h8cvjR4J+APhJvEur2tvc6pdF49J0qJ1inv5hjfhsHZGoIMkhBCjHVmRW4cdjoYKF7Xk9le17b69Elq29Ej6nhXhbEcT4pwUvZ0IWdSpyuSgm7RSitZ1Jy92nTj705aLRNr5F1T4R/E3xNb3v7S/7TUF5/YyMZNX0xGms7rTNIjB8y5jijdJIo4U3lYkZLjlpwZZkS3uPnsRgMxxOGqVoS/ePVJ6Xa2snpGKeyknz689m04/Z8ScTZZlcKOXZBFQUE0pJxm4RmvevUUbzrT3nUhLlpK1Ki+VTlP1zxTrPiS6sNHstV8VxeL7C6hm1Hw/ZeFJ4lvvHlqqxTQNeygLBaWiArHcOji1uTPb/NAk5sZfLp8QZnn1P6jgqTp1dqknoodHbqm+ifvKzSTtzL8wdCnRfPN3XTzPRPCXwdstfjsk+KOi6PrNpp8e2y8OpbKdC0MI8ZghgtigWeWJYYQJplLqwkaJbZZWtx9Lk/D2Eyhe0j79V/FN6tvrbte/Tfq3a5hVryq6PbsYvgjWbKO18K3Gr2erQPoMN94LvZ7xDGy3sVzbQIZYgJGEd0bZJoJH+Vo5YSwBmTHgZHUhlme43CYiVpVZKUezT5pWv3tL700tjasnUowlHZHsHhee2Z5bMqrSR4ljYgZ2kDcBgkDB9/4ugr704xvxD8JWfjfwnd6Fc6hPYSb4bqzvbcK0tndwyLLbzorBkdklRHCurIxUBlYEqccRh6eKpSoVleMk015McZOLujwlPGXizSfGEdn9mitdauG+13vhCeZFg8VKsS7rrQ76RlCTxxxSlrWQqu9P3ot1mW+f4XC18Zwe1hsYnUwrek0vgu/tL1f4+7d+6dklHFe9HSXbudj4p+KXgnXdHu9M0/Udc8PJpljcar4iuDay6dqWn6bbHbJ8kii523BSWNLiFGV0huWhmWVInr0s84ihTwUf7Mkp1Kr5Y21s3b7mrqydtWm1ZNGdGg3N+00S1PR/CWp+MLvw/Y6h4v8ADi6fqF3CJ5rCGeOWSxLAEQSsrFJJEB2s0bFCwO3IwT9Jgqdelh4QxM+eaWrta79DCbTk3FaGx/aunq2yW5ELEbsTAxnGcfxYrqJHy6jYQgGW9gXcu9cyDLL6j1oAiM1xcJJKrm0hjyVeRMFsdyG+6vX0J9R3AK8N8dRvbeNYtogLymQHMb4UphDgbuX5OOMe9AGrQBx/xM+KXgn4W6KNR8X3433e6Oy02BRLeajJlR5UEOcyNl0B6KNw3EA5r2skyDHZ/WdLBx0jrKT0hBa6zlslZN93Z2TPMzTOMJk9JVMTLV/DFayk+0V1eq8ldXaPnf4w3Pxu+Knw81DUNU8HzeFfhrYQW1/q2lyXcM2t6jaxTRzTNG7gpB5aAsEY7w0BVgd2wfo3C0eH+H8zhRw1f2+Nk5RhO0lRhJqUYp2fNPmk7Nq8bSumrcz+Kz95xnGBnUrUvZYZKMpRunVlFNSbXSNlqk/evGzTvZYMfgbx3+yp4h0/xr8MfG+j698IvFNxYm90zxBq1lZzOGiZi8clyYIS5QM6FHVm+VXRlj31+OccZ/n2Pz2WZZhKKj7tN09IqHLe6XM9780vibbbXwpW/pvwxyDgnNuD/wDVqhh6scbTVSrGvCFSr7RzceVzVKMpKNuSDTp8sYx5oyU5tPqv20bWLwpB4B+L0Ubz6r4M8TwoIhIsf2uBwJ18yTaWyDaKgOCPnkJBr73gClDG4zFZW5qMsTQqQgm2lKorSjdpPZRk9npey6P8W4hxdbD4CliHByo4etTq1JJKTp0vepTcYuUU+Z1YK3NG8lC7Vrqh+1J+0Z8BfHXwd1HwtoniFtc1bVEt5LBLSzdXtmEiyCR3lQCMfu9rKP3hD4wMlh9JwLwLn+FzmhmNel7KFNtty5btNWcVHV3km1eysr6ppHyvFXF+Vf2biMvpVFVdRcvLFy5bp3UnKLSajJKSV2m0rpxuewfs+avqHiD4MfDnULm2vtMlOkfYWS4QebIsI8tZRvH3JBCsinHKuuOua/POJpqeeY1qNrVqqt6Tkvxtc+1y3CrB5dhKaqKd6NGV1rrOlCbj6xcuWX95M9MGjWzMWu5Z7v594E8m5QfZRhe/pXiHWX6ACgAoAKACgCOe5t7VPMuZkiXOMuwAJ9KAPBfiz/Yuo6p4gu0vbA+M9D8IXmm2UF3qEdtCW114I7aBtzYHm3WlxwozYyw2ruJYD4PjataeEoTvyOblKyu7Qtdr0i5P/hjswi0k1vb8zhvAJl8J+G9K+Gnhb4k/Ds+MPAPhPTtBv7GytrnxNeSJZtbRxzy6dbTW88UYDzqyBWObqJjIFiKSeRk+a4zC4mvXweFnVhXk5K65UtW97ST37rVeZrVpxlFKUkmtO5j6Z8NPhDdNojeDdN+J+pzaN4Si+EU08Xh0WobRYiBcNcSX9rEkoUx/OYiW3I6Qx+Yzo3hV8TjqDqUsUqcW5+1s5NtT1skoydnrtJaaczSVzZRg7ON9rfI9l8OeLdf8QWy3NpeW3iWK2VvtzM8ul6rCxRp7e3m02aMeVc/Z5rQt5rwAtI0nlRI6xj7PC8dUYcsMwpOEn9qNpQfRyTvtdP4ebbds5JYNvWDv+Z0Oi+INL1zSLLX9DeZrZrSJoWnt3gkSUvvZJIpQskTqQ6tG6q6spUgEEV93CcasVODunqmtmu6ONq2jM3xjaajfeIPC2n2umWjaZ/bhutVt7mBJlFrb2tw0LqxR8PHfRWTqcpgpuDZAVvluNMT9XyepFNpzairet2vRpNf8A6cJHmqryPMjHrHi/wCCWgfCbVdMm0zxR8TfDkp8RQRK1m+kJdRRtrF4fNBeN4pbgrHGweQzzQh/k86aPszTMKHDWWprXlSjBd2lZXtbtd7aba2RFODr1PzPRfhl4gutY0/V/H2ltHa6X4r1L7dpg+xrHPNZR2tvDFcSne4bzdhlQkI4hkhR0R1cBcL5bVyrLYUK3xu7a7X6fJWv531sPEVFUqNrY4f9sDwR4o8U+DdK+LnwvuLC38U/D+ZNZnl+yW/2g2sQaQ7JpIyxETB38neI3UyAq7bFqeIsHWqUo4vDWU6bvsr2Wu7XTe17PXRux+v+DnEWXYPH1eH87UpYbGR9mlzT5VOVo6xjJL31aPtOVzg1G0ox5mev/Djx94Q+OHgWw8a+F9StZ1c7JTbsXNpdpjzIWDoj8EggOilkKNgBhXrYHG0sfQVak7rr5Pquj+9LTXqfnfFXDOO4SzOplmPg4yWsW0lzQfwyVnKOuztKSUk43umNubdIZrpJL9/PiYABI/8AWYY5JwcZAAbk9c9+a7D501NDe7tLyKM26JA7NvkaMBxuwVUt1PLJ/wB9Y7cAHO6x8PfDGo6iddstZ1TRvEgdi9zojCJrh0Roh59uAYbkBNwUTo4TdvQI2GHnZjlODzWHJiqal59V6Pdfr10NIVZ03eLPCviN8Wvhd8H77xVY+LBbReJdWsooJdO8Kate293qE0qfvLl4IsR6Zdb3B84TG6aJIiJTsjUfLYZw4XdTDzxUqkbe5Cy5o389dlay0Tu3yn6Rwn4aZ/xvGGKwtFU8O271pu0LK92l8U9U17qaUtJSjq1y+heBf2jf2p/Mj8TJD8Mvhzd3Bu5dJ0+IQ3WoK8srMZSRvZ3DHc0gVGPlyCFuTXkZZwriMfV+tYxySfWTvNryvt6v5Jo/SsTnfBHhVH2eSRWPzFJr2j1p05WWqt7vxK6UOaaXNCVWJ7N428EeBv2T/gB408S/CPwnFZavDpgi/tAOHu2nk8uBJ2ll3ZVGKzeUMIWVsKCxz9licNQyPAVauEhaVt+t9Fe77b228tT4XJc6zXxT4twOC4hxDnSc78lrQ5VzTcVGNtZJOHO7ySavJqKLH7GfgLwToXwO8H+KtI07TbrW9S0t/tetLZBLuRXmLNbNKyiQpGyrGBnafJUjIwarh7C0aWBp1YJOTWsra77X3stu2hl4w57meO4oxuAxE5RowqLlpc14K0UlNRTcVKable3N77Ts7o9xvNPjvJI5TNJG0fQpt5+ZWGcg91Br3T8oKyeHtPTjNwVIIKmZgCMY7H04oA89+Jfg3xVqGppb+G7TTtR0zWNKvtK1aynvJ9OuZIpNrRyR6hbpJMhjLXCqoCnN2XEiNGA/z2f5JPNlRq0J8lSlLmi2rrp09Un8rdTejVVK6aumXPhL4c8YaPZXmh+NPBekadp1gIo9InXxVd+ILy7X5zI1zLd26SKwOzBMsxbJyV2jdrk+XY3AyqVMbiHVc7dLKNr3sr21utkthVakJ2UI2sehJY2UY2x2cCjrhYwK9wxJ6ACgAoAKACgAoAqXC3AvYJ4YPMURyRsdwAUkoQT3x8p6A0ARXz3aWktzcSrCkcbExwtksew3kcZ9hnPegCSw0y3skV/LV7ggeZM2WZmwATk84OOlAFygCGe3W4aPzDlEbcV7McYGfbnOPXHpQBzt7HC13c2dz9hjjjD5m8sJJkoCoJB5PzdOM7D7igDkfiro3iSDwTDdaBe6ibu81fQrW6W0V/tCWk+p2kd6yyRnfGRbvOTKuGjGXDKVDjz82qzoYCvVpu0owk0+zUW0XTSlNJ9zG8MWHhq78X37LfQyyeBr99OttOtoFistKmlgjuRIIlUj7QYLuBfMZW2q7CPy/Nm3/NcF5fS+qLMp+9VqXvJ6tWbTV33abb66J7HRipvm9n0RHZeE/iF4103UdVKaFaa9J4lupbNvEMQ1Wz0izt55ks7m3tYDAZJJ444pvmljkja7kzIRAltV5llWY51mMqNecoYRR05WlzNpXT1b3vurWjoru4qdSnShdK8j17wv4fi8LaFa6JHqN7qDQKWmvb1ka4u5mYtJNJsVUDO7MxWNEjXO1ERQqj6vDYalg6MaFCPLGKsl/X4vd7s5pScnzM1a3EFAFPWNZ0fw7pV3rviDVbPTNNsIWuLu8vJ1hgt4lGWeSRyFRQBkkkACgDnLv4q+D9OsbHVNSfV7Oy1AvtuLjRL2OO2VAS0l0TF/okYAJMlx5aYGc14tHiPKq9Z0KdePMtN7J+jej+TZq6FRK7RQvf2gfgNpuiaV4m1H42+AbXR9d8/+ytQn8S2SW1/5LhJvIlMm2XY5CttJ2k4ODXqRxFGc5UozTlG11dXV9rrpfpcz5Wlex0vhfxb4P8e6ND4i8GeJtH8R6TM7eTf6XeRXds7I5VtskZZSVZWU4PBUjqK2Ec7H8cPhXqfg+/8AG/hDx34f8WafYzJZqdC1izuvtF9KyJBZpJ5oiE8sksMaK7qC0qZIBzWVavTw9KVao7Rim2+yWrGk5OyPI7XQ59d8e6guqWWsf2itvMPHPi6wCNKY3t1kTw5psyvHfW0SLcW86SW0IGI2O43k80y/ltHNcFm2N+uZ1VtS/wCXdO0mtG0pStddGnffW9opJ+i6c6cOWkterPXofF/wd+HulaLFZaz4T8NWXiy+36VbCa3sP7Tu7p97GCI7TNPI7liqguzMeCx5/UVOjRo86aUEt9Ekkvutb5WPOs27dTqF8R6BJq0mgRa1ZS6pDDFcyWUc6vcRwytIscrRgllRmhlAYjaTG/PynE4fFUMZFzw81NLS8Wmr9tByjKOklY4r4m+ObibRNV8HfDjU55vFt4zaRHd6dbpdLoFxIsG+6uS/7hHt4bqO7FvMyvOqhUVt1efm+d4TKKMpVprntdRvq3rbTVpNq17WRdKjKq9Foed6LpPg7WPFfhDxYL3w1qHh7Tby80PwJPZTSs0bx2Ee6KKRJGjuARb6p5pfCj7Ja7UDxNI/5xjMDXwnDbxTUlVrTTqt72vK2+yb5X3be9tDuhNSr8vRLQ9h+Fsum3fw58OappSQCDVtOh1VpIbZYBcTXSieWdkVVAeWSV5GO0Es7E8k1+sYWlTo0IU6PwJJL0S038jzZNttvc439qH4JeCPjr8JtQ8P+Nfh43jhtD83XtG0IaxJpf23VIbaZIIjco6+WH81o9znYu/cR8tbiPgH/gnfpn7T2gftZa9osfhO60H4YaLNqela74cXxDZzWWhSSec8DLBE6pcXBns1iaeGEZBY/JGQo8zLamH9/D0akpyg/ecrt316tJdLaafefc8a4LN39VzbMcHSw1LEQTpRoqEYOKUXfljKUk3zpt1Ped7bKy/VmvTPhhGVXUo6hlYYIIyCKAKfk3dmMWeJ4QOIpHIdf91jnI9j+YHFAEsN7DLJ5DBoZwMmKTAbHqMHBHuCaALFAHkX7X3/ACal8Yv+xF1z/wBIZaAOJ/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFABQAUAFABQBy3jX4VfC/4lLCnxG+G/hbxUtsMQjW9Ht74Rc5+Xzkbb+FKyvfqO7tY2dB8PaB4V0m30Dwxoen6PplopS3srC2S3ghXOcJGgCqMkngd6pty1ZKSWxoUhhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9ABQAUAFABQB+R/xY+FfjD4wf8FUfiz4S8FWcM13/YWl3E8k8wiit4BYaSjSuTyVUyLkKGY54U1+geHOf4HhvMq2Nx8mo+ykkkrty5oNRXm7Pdpd2j47jXJ8VneCpYXCJOXtE3d2SXLJXfkrra77I5fWdPk8L+IrzTLTXLK+k0y6eFNQ0ydnglZGx5kMhCllyMhgBkcjjBr+pcLWWPwsas6bippPlmkpK/SSu7PuumzPwOvTeErypwmpOLtzRejt1T007M73wR+0N8dvhxZLJ4e8basNMnk2LFfqLy2LISzJH5wYJ/rMt5ZUncCe1fO5pwdw7ndRxxNCPtEt4+7LXRN8rV9tOa60dup7WA4lzrK4c1CtLkb+170dNWlzXtvra2+vQ+xP2a/2mfFv7QOo3vhPXfBdnp50+xa6vtWsZJPILGYLHEsThjGzKWIJkbPlMQOy/hfHvAeD4TowxOGxDlzysoSS5rKN5S5la6Tt9lW5kr9/1fhHi3E8RVZUK9FLljdyi3a99FZ3tdX+072fy+l6/MD7wKACgAoAKAPzl/br/wCCjn7PGo/DvXPgb8O/GeseIrzxKtxpGsaz4VdVj0u2CZZo5pV8q8WVisZSJtrxGf8AfRtsLVGMJSUanwvfS+nXS6v6XV+4KpUpe/S+JarW2q21Wq169D40/wCCbn7UfwX/AGWfilrfiD4oWHiEp4iso9Fg1WxjEsNhA1xE7ST24cMVGzezIJZAE2oh3NnGaqKu+Vp07ekr3+atbz0ffp3xlgXltpKSxXOuzp+z5dV0kpKXk1KL+y4+9+5Hgfxz4P8AiV4V07xx4B8SWGvaDqsXm2d/YzCSKUAlWGR0ZWDKynDKysrAEEDQ4DdoAKACgAoA5XxT8VPhv4I1iz0Dxh430bRr+/jaaCG9u0iJjGfnYscIpKsAWIDFSBkgivYwHD+aZrQnicFh51IRdm4xb17K271TaV7JpvQ83GZxgMvqxoYqtGEpapNpad/Jdm93ojzz9njw9a+LNOi+OPinUbrWPFuqy6jbGSS++0WOmRrdvG9vYIjvEsP7lcSBnLjJ3YcivpeM8ZPLqr4fwcVTw8FTdlHlnNuCalVbUZOXvO8WkovTlukzw+GcNHG01nGJk51pOa+K8YrmacaaTaUdNGm2976nttfBH1wUAFABQB518b/jj4Q+BnhRte8Qyrc6hcBl0zSo5VSa9kGM8n7kS5BeUghQRwzMiNy4rFww0ddW9le227b6RXV9PNtJ+9kOQV88rWjeNOLSlJRcneXwwhFazqzatTprWTu24wjOcfkX4c+ENf8AjX47X40fHHTZ/Euo38UFzoHhGNGjgktmdhBcXgfctjpYIkeNHzJdeVM4W4IKT/I4jF4WinmGZS9x/CvtVLdovamm1aPXSU3tf9Tz7Po8H4SOQZPaOJg5czi1JYdyVmvaJfvcW4rlq1vhopulh1HVr3EeI/E3xE8RRyQyP47trbUGuLdNPMdh4U0ee3cmIz3DM9xqU6s5QNCk8IubaNjBaSxh1xqrOuKaXIorD4eW99ZyXppptp7t095I/Gl7LDu/xS/A9M8BeEdM0G+v3a2i1HVdWihj1jVPsiwyv5CsIosr9yJGeXy4lJCF2YMzvI7fY5fl9DLKCw+HVor72+rb6t/8BaWRzTm6j5pHY27tpaiylgkeFf8AVSQwlsj0YKOG9+h6+ortIPM/ipoVmdTsNftfDFtqGl63ONB8ZWl3p00q3mlXEUkKMYwuyQpcPb7zNlEtnvDwTz8pxdlixmBeKpaVaXvRa0emr1321VtbpHThqnLPlezIvAfiywtwngXU9e1W78V+HLaG11UajGIb69toyqLfoFdvMhuCAwmVmUMxhOyWOWJPYyjM6WbYSGJptXa1S6S6r5firPZmVWm6UnFnUSRR7GljmkZBD5cmxiSCEX7wPIBkPTA6cV6ZmN8S+D7HWrG3i1/RheRC4gmRJdkpWeGQSQScINkiyIjpICWR1Ug5ANTOEasXCauno09U0+jGm07o8+8Nafr+maheQeDPHs2qyWWp+HtCNhrDG4msYoLhLm8U3LK01xI+n3YIMu9wUDPKdzMv5xjMrwdHiTC4bD0+Va1HZu19WtNkk4LRaa2sd0ak3QlKT8v6+897kUujIrlSwIDDqPev0k4DnXub7S0/sppoITIVZJS4IiVmPYjLdOePUk88AEcV/LpMrwfaLfKkrKpRUXOVG8KgBPDKforj0IALem3N1r8Sfa4ovs0bbpcZxK3VVx2A4Jz1IHagB3irxB4W8L6fDqPinxPpnh+384QwXd9dxW8fmlWIQNIQpJUMdvopPbI68Fl+LzGo6WDpSqSSvaMXJ22vZJu12tfM5sVjMPgoKpiakYRbteTSV+131PHtb/aG1fxBqNz4R+Dnh++8U6hNcGwg8RW0Q/sC0nKli0tyFcP5aGNzGgJcHarFmAr6/CcHwwNJY3iGtGhBLm9k/wCPON7WjT0a5neKk37tm2uVXPnMRxJLFVHhcmpOrJ6e0/5dRdr6z1vy6Npb7J3dhfBvwg8TW/iW3+Mnx21G38SeLbW1ewtbe1hjGn2EW7MZRSgYvzITIcEGUqF4DGc64pwzwUsmyGk6WFbUpczbqTlbXm95pR2Sir/CnfVpVlmQV1iVmeb1FUxCXKrJckVfS2ifNvrpu1bS56hqHiPQdD8LazrHjHUrO00q3VnuZrmZVh2SALtyeDuclQBnLHA3Hr8jgcJicdiIYfBwcqjeijq9Nenbdvolc+ixWIoYSjKtiZKMFu3t2/Hbz2PDvHng6x8U/sO6Pb+LrW+mj0Xwxp+rG2tJIYJ5BbwKyRrJJBN5RKYGVTd/CThmBPEqjQxOYZg1rFVJy0tq1J3s2n1vt+R9p4EZrjMozPLPq7jGdSEad5KTilNJXcYzp81tNJSst7cyTXZ/A3WNA+M37P8AoMnizSxLYarokGi39rfnaZ5rffDKw+YkAupZGzvHyng14uTYqVbB0a8G4ySVmm01KL3TTummrpp3Wj0Z2cf5bLhzizHYWm4ySnJrRSi4VVzqMotcsk4T5ZxacX70WnF6+dL+xL8BrPUpNSN14mvrWJ5HWyl1e3EEnUBD5aJPsGeCsm7AHJya/UMT4ncSYrA/UnWUXZpzikqjT81pF20UoRjJbp31PyTC8IZFg8weYQwsXqnGEnKVOLjbXllJ86bV3Go5wd2nHl0PobTU/s2XT7KRobeysI2WBFtTbxxxhQioF2qqgbsDHGABx3/Pz6Wc5VJOc3dvVt7tnUUEhQBx3xX+MHw0+B3g+fx58VvF9j4d0SCRYPtFyWZpZmBKxRRoDJLIQrEIis2FY4wpImUlHcqMXK9uh4no3/BQ74DXOv2GleMtH8ffD/TNdlSLQPEXjDwxPpuka0Hxte3uWyFjwVbfMI1CsCSKuKbfK9Jdnv5/132J15eeOse62Pp2kBVvrp7fy0jIVpN2HaMuOBnGByWPYex9MEApzT3skqzy2LBLcnKpufeGTqFKjPOB7ZOQBzQB4dq9x4cvvD2ufG/4wvpnhHwpY6ppWprfaj59tcy6Tpswu7A3EZfMUp1KWWWKIASGGW3iljExkhX46k1nGdrG0Xejh043tvN3vy90k1d+lrp3Opr2dLke8vyNDxv8P28K6Dotl4cPiS50W88WarrmqW2hQytPdx6h/aF08D7dxEb3V3H85aMKdrbo9oI4M4yzM8Jm9XF5eryrQ5b2+FpwTu9lorp+tloOhWpVaMebZa+u5x/irwPqNx4rtD440/Vtdm8P6po89t4Z0KyuJNEg0vUBLp0Vu9sziC++zXAku5rmSFTFFGpVECgHPA5R/YebYfDTSnGrGV5NJvnV23dq6VrK19d3qXOr7anKS0t+R3/i74Z+G0082nhXwxoWm63ogN9pd7BappvkXq7THh4Ar+WdqxyKNwlieSNldHKn7nG4KljcLPCTS5ZK223Z28t12aOOE3CSkjjNK1L4r6DqGp3vgDwz4A1jwlr96uv6XbeIfEd5oV/YLdRwTXMEtt/Zk7BnuvtMxMjbx9pMZRQgA+Ayji2jk+EjgMdTm6lNuLsotaN6X5ltt8jtqYZ1Zc8GrM868e/Fn4M/EC71uUfGb4Yw3a+BdY8MaaND8Z2mp3V9LqptxKIoUQzeZD/Z8TLGkEzTG5CqpaPa7zbNMXmv1dPB1IxhUU5XTStHzaSSd3q2krXegU6cafN7y1R1GpeEtQute2/EC807VtJ+Ims+V4u1S21F5LTNvI8Vn4VUfKYLVpJCpZ2KXE7XsLxI9+kbPK8yocQ53Gvivd5F+7g1vez5r7OXW1v5Wm+VsVSm6FK0eu7PpR9Dn1jy77faW6SRAosUZJGPu88cgH9BwcV+kHCec23xM8LXFxdaT4Xi1Xxbf6bcXNrf2mgotxEt1bk+bZyXTOlpBcKcKYppkceZ06Z8zMM5wGVr/a6qi+27+5XdvO1jSFKdT4UfKnhx5/2Rfj/YeIbLWYNP8C+Jb1bLX9BttT+3S6NBMjG2/tDCFY3VvOki2s7lLa5RZJAHZ/h8ux1HB4lY3B8yw05OL5lbZb9brW8X8Wklbe/9SYbNP+IzcJ4jK8dTi81wkVOnJWTnrq4pNNOSjyVFb2alKnPR8qh996vodxqV8s8c0UcXlhG3KWYndk4H0xznPGOlfpZ/K55n8WvjD8H/AIH2Mtx498RteaokayJo9pIhvJ1dm2ssO5SFyG+ZiFyOvavOx+aYXLY81eWvZfE/l+ux9jwnwHnvGldUssovku06kk1Ti0k2nOzV9V7qvLVaWPH7XxR+1h+0VbW1t4A8Pf8ACsfAWoaf9oj1WeYxX115igK6SDMgXf8AvE8tYw0Y/wBYQyg+J7fNc5/gx9lSavd7v9fNWtdddUfqTyngHw3T/tOp9fx8J2dOKvTjZ3aafu7e5Lmc3Gb0p3jK3r3wb/ZT+GfwjjsNUe2fxH4nszNIdd1NQ84kmCeb5a9EUlSVBLMvmSYb945b1cvyXDYBKSXNNX9576727eXVXeurv8Dxf4m51xbKpRlL2OGnyr2ULqNoX5eb+ZpNKT0UuWD5VyQUfZwAMkADPJ969c/OjI8ZeGbPxr4Q1zwbqE80Frr2m3OmTyw48xI54mjZlyCNwDEjIIzWOIorE0Z0ZbSTX3qx6OT5lUybMaGZUUnKjOE0ns3CSkk7Wdm1rY+cv+CfPjPUNV+FOrfDvVgHuPA2rS2UU0fzRPbys0igPuO9hJ5/IAAQx4zzXz3CuIlPCyw896bt8nr+d/lY/ZPHzJ6OFz+jnGH0ji6ak091KKUXpZWTjydW+bmvbQ+pa+nPwoKACgAoAKACgAoAKACgAoAKACgCjcO11eR2KA7ISk8zZGMDJVfXO4A/Qe9AF6gAoAr3Vp9qZBJNIsSg7o0YrvJ4GSOcDnj/AAoA5670y4toCQPKluZ47XG4FXTaF3E9eTk/0yAaAMLxT4N8QeJ/CPiLw7oWrT6Nqd3p1zY6fqIY/wDEvuZITsmGyQOdrsr4VlJIGGQ9M61KNenKlNXUk0/R6DTad0eUfDnxNrUHj3Utal8LX1taeNb4Q+I7KSeKS48I+KLa0toBBKYsl7e7tYbYxuAGBELZ23iCL47hqvLKqsshxdlON3B6+/F3b+a1/FfZbfVXXtF7aO3XyPcFtLnSpVktRJHdEriNVDKULbQJDnA3HpjJHOSTzX2pyGtZ647TfZr+BUPm/ZxNESY2kHVeeR1GPXn0oA0Td2y3H2Rp0ExUMEJwSDnp69DQBleK/GfhzwVaQXfiG/aJryX7PZ20EElxdXkwRnMcEEStLM4RHcqikqiO5wqsRjiMRSwtN1q8lGK3bdkOMXJ2R5JP4h8XfELxfbWN9Y2ejalbQR3Gk+GrpTqB01pMn+09YktQ8UUqrHOltbpMsbtFNi4kaRTafC4zGVuL6iweAUo4ZP36m17dEn0287tNpJa9kYrDLnn8XRHVxfAnR7Oe41bTfHvj6DXLmFo5NRl8S3N1H5jEFpVsJ2ewjbI4CWyooJCqor6GfC+UToLDuhGy6pWl85LV/NmCxFVO9yw3wq8RSahc3z/GvxnHFOIljs4bTRxBbhF2lkLWLSFnxvfc7DeTsWNcIPPXA2TqKjyO+uvM7v8ATTZWXrd6mn1yrfc5/wAc/B34heIb2wk0Pxb8PnhtLFLeWbxT4DfV7yaZSd0nmQ39rGisCDsWPAbcRwQq4x4FwEG/Z1akU+ikrf8ApI/rk3ukbdz8OviNdWNlp3/C3fsnmrM2rXtpoEAvPNZF2fYDK0kNtGkgY7biG7cowUvuHmkjwFlMZxk+dpdObR+tkn9zQPGVLW0HXHwF8CFHv9MGo6X4jkna7k8SWl46ancTkk5uJelzEPlAt5Ve3VY4kWNUiiVPfqZHl9XCrBypR5ErLTVeae99d97tt7mCrTUua+pydt8GvEdpqmuDT/G9n4Ij1WWO9Z/BPhWwsr2+vGG65muJL1byO5Z5d7hvLjZFlcOZDiUeRHgzLW17dzqJJJKUnZJK2lrNel7drLQ1+tVOll8hlz8IPiFaiNT42b4iTwC4KN4oNtpV7pUk0XkGSw1LSrOJ7eTyJblWKwmRvNXbLCARJnX4Qo0GquUVJUJrs201vZpvvbrbumNYpvSqro6O9+Ddnrdk1r45gtNW0RJ0nj8M2Eb2unLH5Z81J0Vh/aG6SSZmSZRA6+V+4EiGR+vKeF8Lls/rFZurWevPL9E72fndvzs7E1cRKouVaLsa/wAUradfD+haxoWgw6jd6Tr+lSWsZhybeGedbO5mT5GKGO0urlsgD5QyllUsw6uJcMsVlGIhJ7Rcv/Afe/SxOHly1Yss/CvVxe6BeaFL/YcN14Y1S60aSx0dWSGxt0bzLCFkYDZIbCWykZQNuZPk+QrV8PYpYzK6FVNv3Unfe8fdf4pirx5ajRyn7X+o6fpf7KXxiudTvre0hfwLrlusk8qxq0stlLHFGCxALPI6Io6szKBkkCvZMj8bv+CUunQ3v7b3gm5lZw1hZ6vcRhSMFjp88eG46bZG6Y5A+lAH73UAFABQBDdWkN3HslByOVdThkPqp6g0AQxz3VoojvkaYAcTRRk7vqg5B+mR9OlAHkv7XWp6fN+yt8YokvIhJ/wg+uJsZtrZ+xS8YPNAHJf8E4f+TJvhZ/2Dbn/0tuKAPpOgAoAKAPzc/bm/4Ki/Eb9nj443/wAF/hR4C8N3UnhyC3Or3/iGKedbia4t4biNbdIJotipHKAzOWLMxAVQmXAPLvhD/wAFdf2kvH/iy+0LWfBPw1ht7Xwv4l1tGttNv1cz6fot7fQqS14w2NLbRq4xkoWAKkhgAdv4m/4KffHzRf2VfAHxytfCPgB9e8VeKNT0S8t5LC9NpHBbKpRo1F2HDncckuw9AKAOI+N//BXb9pL4a/Gjx98OdC8E/DWfTfCvijVdEs5bvTb9p5ILa7khjaRlvFUuVQEkKoznAHSgDtf+HoHx9/4Yv/4aL/4RD4f/APCSf8LQ/wCEK+y/2fe/YfsP9k/bPM2fa/M87zON3mbdvG3PNAHt3hH9vSe3/bE8YfAr4wa98PPB/gfQ/CWn6xYavqN0dPuJ9QuLXTZzA09xceSwxeXJVFQPtjU5O1iQD678LeLPCvjnQrXxT4K8TaV4g0W93/ZtR0u9ju7WfY7I+yWMsjbXRlODwykHkGgDWoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv8AsYE/9LLOgD6foAKACgAoAKAPyK+MfxO8b/Cf/gqL8YfEfgLWRpt/P4e0yzlkNtFOHhaw0lym2VWAy0aHIAPHXk1+h+GmRYDiHNamFzGnzwVNySu1rzQV7xaezfkfGccZtjMmy+FfBT5ZOaT0T05ZO2qfVLzLXwD8K6R4z+Lfh3w/4i0DUdY0i5ugl/BYo5ZIiColcoQUiR2RnbIwoP0r+huMswxGV5JXxOEqxp1Yr3XK1rp3aV005OKairaysfjXDODo4/NaNDEU3ODeqjftZN2taKdnJ9rn6vTQWWmaaBBYRmGxjzFEqgBAoxxnpxn8M1/HDnJttvffzP6WUUkklsYtpbaNpniN7620GLS73UNsV3PFbopugN5QSOoG4h2cqcn7zdMkDWpia9WnGlUm3GN7JttK9r2WyvZXt2XYiFClTnKpCKUpbtJXdr2u+trv72dPWBqFABQBU1bV9K0DS7vXNd1O003TdPge5u7y7mWGC3hRSzySSMQqKoBJYkAAEmgD8lP2wf8AgpcnxK1P4u/A7wbrH9jeBU8PXegaXq+nQx3s+u6mt7bLK/mrKESymt0u4UZCxMcxlYMWWOPajSjVU3Kajyq+t/e1SsrJ6631srJ6mVSo6bilFu7tpbTRu7u1ppbS7u1ofPH7D37Gfg39rL4gy+HNV+NFppdtpdgNVv8ASrDTbt9VlgV7ZZAkksItIl3XDRiTzZH3R58hkO4FenTpzUaU+ZWi72a1cU2tf5W3G+ztdaNBRnOcW6keV3atdPRNpPTurO26vZ6nrn/BRn/gnh4d/Zt8G6b8WfgxfavL4Qa4tdL1vSrrzLqS0uSsvl37TgbRE52xEOF2ySIFJ80InPyxcua2v9f5I6lWqRpuipPkbTavo2rpNrZtKUkn0u7bs+W/g9+2B+0d8AfCtx4K+EPxLn8O6Ld6hJqk1tFp1nNvupI4o2kLzRO/KQRDGdo25ABJJoyPt/8AZ2/4Kxah8NfAMS/tI65rHxG8Ra5qEV1atpUdjEuk6Z5whlS4EcUWLgBJp1TdJvVoFJhyzVmpT9o48vu2Wvnrpby0+87JUMOsHGuqt6jk04WekUotS5tnzNyVlquW70aP06+JPjqLwJ4EvPGMC2tyI2tYYGmmK2+64njgjkkdFZhGrSqzFVY7QcAnAr28iyv+2cfDB3aupN2SbtGLm0k2ldqNldpXau0jwc2x/wDZmElibJ2stdFeUlFN2Tdk3d2TdtkczpOpftNadp083iXwl8ONauYy8iR6Trd7ZGRAoxGqz28i7ywPzM6ryAduC1eviKHClarGOFrYinF2V506c7O+7cakXa3RRb332POo1eIaVNvEUqM3/dnON/L3oSV/NtL03OPP7R3ia/1afxPpnh6007wFous6XoWtPrttPZ3sM10QlxIZSfKi+yyyxJIjKwOxiJAHQ17f+pWEo0VhKtVzxlSFSpT9m4zg1DWK5V70vaxi3GSaauk4XTPL/wBaMRUqvEU6ajhoThCfOpRknLSTv8K9m2lJNNaP3rNHP+GPjRfp8X7LxBrvgfUbHwx8XI7Cx0iXVrgTTGKMTpG0MUcbBIZDLAzRO/ytcPKCys5X0sfwxTeSTw2HxEZV8C5ymoKyu+Rvmk2m5R5ZJSjHVQUHaSinw4TPZrNI161Fxo4vlUXJ3dlzJWSTtF3i3FvRycldN2+nba1trOFbazt4oIUztjjQKoycnAHA5Jr8nnUnVlzzbb7vU/Q4QjTXLBWRLUFBQBFLdW0DKk9xFGz/AHQ7gE/TNAHiv7QH7Tfgv4HrZh0Gu6/dQO9npVrcKvyFgDLO43GKPK7VwrM7EhVO1ivnY7MaeCVt5WvvZJd29bK+ism29Enrb7LhTg3FcTT9prGjzcqajzSnO1+SnFuKlJR96blKMKcFz1JxTjzeMfCz9m74ieJtaf4vfGWysrPUpElfw9o+oRS3UFlPuWWzD2SOpFtGXm22vnRlNqhhuJK+e8NWrUpYmpBykrtRbtz21jf+VJ3tF31d5Xldnu5txHRyKP8AZ2SVItctuaN2qDlzRqxhN29rUqLlVTEKMbpOFHlotRXYeCdF/wCEi0jw7pnja8mXT/GNhOfEcN9ZPZT614jC7r2K+huo0uJoXgilMNuqRxi3tmjZXt2hjT57JqVPG53XlmcW68GpQTd0o9LJXV1ddXq7/Emz89qtwox9ns9/U9u3DS7VrWK2K+W5SB1AABA6HGPmQEgYyMk46Cv0I4jb0C7jgY6d5LIC5KhtpZWIJwdowQQpIPpx02kgG7QBS1rRdI8R6PfeH/EGl2upaXqVvJaXtndwrLBcQSKVeORGBVlZSQQRgg0AeEeOfhjqHgK3ivooNY8T+DdIu11OzWCWe48R+F5ukk+n3AYzXMAVnLWuGlMUlxCnnxNFZH47MuHKtCs8xySXs6ttY6KMvK2yb+69no/eOqnXTXJV1Rq+APGc2t3tv4e17WdP1CTUtJi1vRta0lfKtdc0l2RfMjhLuyyQ+ZbK4UujfaYJQ481oYfUyLO45xSkpx5KsHacez/ydn5pprzedal7Jq2qex6LfXyWemaedQkVUhia53ySKgUxruUk9MHDAexGea90xPC7L4jeHra+0L4g6SurWtnN47uH8QWdroMk2pyxL4duY7Tz7OCOS6aSS3TTLhV8sTrE0IkRAroPzqhj8NPiepiq00qap+5Ju0dGouz2tfnXZu9tWdzhJYdRS1vr/X3HvN5480zT7U395petC1ClxJDp0lw5AUsP3EIabJA6eXnJAIBOK+ip8W5NVn7ONdX81JL72kvxMHhqqV7GR4u8f+EYtL0u+0q9k8QXes20t1otloZju5NUhVA5eM71hEOTEPPlkSAPJCrODIob1sbmOFy6j7fFTUY/n6Jav5GcISm+WKMTTfG9vqWvR6B4mbwvbarZWkl5eaZpmtPqeoS26N5SyeUIIpiN2ASEOX+QAkgj5rCcXTx9aEcLhKjpuSTnbRXtduya0vffbU3lhlBPmkr9iX4g/EC58C6Na2ngfS7bV/FevRPZaDpUuYft1zHgtK3T93HEXkc5UHaE3qzKa/UeHsopZnXdTGydPDU7OrNK/Km7JLf3pytGOjerlytRZ89nGY1MDRUMLFTrz0pwbtd7t+kVq9V2um0ceP2Xr/x/qOieKfj148v/ABTqWnQIx0xBHFptvM0jPKscaou5CPJTJ5YREtuBVI/pXx4sqo1cHw5h44eE2/f1lUcUko3bbSl8T0ulzWjqnKXhrhL+0KlPE51WdacV8Oigndt2SSutlrZu2ujSj7ToOkeHdBT+zdA0+0sLW0hS3ihgjEaRoC3yqOmM8cccY7V8BXr1cTUlWrycpS1bbbbfdt6s+vpUqdCCp0oqMVskrJeiRqXaQPaypdY8kod+ey45rI0PPtd8FaZ4y8Iav4Z8T2El7oer2vkzNAyeYrj7rpuz8yNypwRlVIFd+V5liMnxlPHYV2nB3X6p2to1dNdU2jkx+Bo5lhp4TEK8Zqz/AM15p6rs0SReCkuPhk/w08L+IbuO0g8PHQob+UAum238mJsAL8wHzMQBnoCO3Nm+Iq5vWr4ipZTquUnbZOTbdtW7JvTX5np8OzpZBiMJPl54UHTdna8lBrR6W1S7W8j55/YF1OXUPgjexXFrJPJD4jvo0JQymd2gtpHJO4MD8y8jPf1xXyPCU3PAyv8AzP8AJM/b/pDYeOH4qpcmzoQaVrWtOpG34H0o08ccENm9yzsrIrxPIyBcEEgrIpUdOoI69McV9QfhBqeH4Ulvbm4EaBUjWIbFUIS3LDKEqcYXkfpQBvgBQFUAADAA7UALQB8gfErRIPip/wAFJ/h34I8WmG68P/Df4cXXj/TNPlhV45dWm1E2QkcNnJjCwyRnqjxAg8mihKMak5tO9uVeWzbWl9U7PW2ia1RNWTlGNNWSvd766Oy3to9dtnK/l9KfFT4YeDfjP8Ptb+GPj/Sxf6Fr1sba6izh15DJJG3O2RHVXVuzKDUzgp2v0d/u/r5rTY0jKUb8vVNfefNX7THxovvhDr3wz/ZV+Gvxj8N/C+TUdGa51Hxt4su7e4fR9Is1WGAIt66pc3Nw6Mm6Ri2I5GwSd6NSdevNSduVcz/vXdkk3u92+ttfJns/YUYyUdJNxXZWV3t2TVunTqc/8Ev2hvFnhL9o7wn8D9f/AGq/Bf7QXh74iWGoNYaro8enw3+gajZRGcx3CWUjoYJog2xn+YuuBtCHfdO0uaMlqldPv3T7WWt/lbtlPmi4yT02a7dmu+ujXz9YPhjffti/tN3/AMUJfDn7Ttt8PdI8B/EPxB4V00W/hGy1C5vfs8+6MTNIEVIYopII1Cgu581pGPy1lR55YSjWl8Uop+T31+burWSXKtNWaSi4V5wfw307rpb5WvffW3RHmvx7+JnxV/aD/wCCTMvxM8UeK7XTdZtL2ODxTFbaZE0euR22r/ZFQEn/AEYmQW9wzxgZaFkCqj4DqLm5KsXaLW3nda3+T08/IUbwnUoT+KEmm/le34pX8j0/4weJ/wBrv4beIv2cPgT4Q+PNjq/iX4hS+JLLXvEd74Us4Y5II4YZIrlbRMhZLOCaR41SRVlkhTzdysy1u4e0qOLdt233tr2e+3+W5zU6ipUYztdPlsu17L5pbt9jf+Jvin9pbwPf/Cr9k3wX8aLbWviP4+m1XUNX+ImqaBbIdJ0i0PmExafGpheVlZYlLkgtG2dvmB4sov203BK3JG7a63bS3+52tbR73Ohp0Y8715nZJ9LJX2+9X6u3Ydpfif8AaM/Zs+OPgH4ffG74wW/xX8AfFae40O21e50CDSrzQ9YSJpIIiLfKzR3H3Bu5BBOUCHzaotVq31ZfE4ykvSFnK/TRPTq3tsya79jRWIeylGL9Zu0bdd9+iXqeg6t4Rl8F+L77Q9JhiWy1PU38SeH5pJ44IIdcdXN1YXBWPd5V0jSSqfLnZd92Q0ZjtVr4PiClVyXHwz3DK8fhqLy7+X3pcyjvdnfQaqwdGXyKXin4neGfFM9jYapDPpdh4YE3irxtDcwbm0dNO2XEdvceRKXgna5a1uIiBJFcQafcbGljdWbfiTOIV8qjHAS5pYhqEbWvZ/Fo/wDwF9nJbE0KTVT3/s6nAfDjwn408Ol9B07TdO1ew1iwlbxP4Y1OSM6b4deZJJDor3McJge1gSQW8YVJJ4okhR45baaE2PzvFGXYHL40n7b98rXtrJvT30r6NvVptJv3k+bm5t8POc29NPw9DrbbxDY6l4S0qz03Wp/G3gRNUNvHdza1PbJp9vJGjJHq8i75NQtGikcxysrxzR3No8yzI5vx0UM6zDNVTyutV9hWejlb41qtNnGaatZNa8y0kuUTpQp3qJXX5f8AAO6PgnT9QsYYvGOv3mt2tpDH5ljat/ZGkrIluYnVbaJi8trIN5NtdyXEYLAHICbfqsv4VyzLnzxp88/5p+89737Jrukn5nNPEVJ6XsvI+ff2u/ih8MdK+B6fBDwn4TtXku2hu9FSw06O1stMtIZyxntoyAxiZYp0SSFPLMbsd4BCt5nEec4LEUJYOi+ed91qo2s3rte2mm2t7H754BcM5hVzpcQc/s8PS5oPde0co2UNNLJyhK0nq+XlTd3Fng/4nftV/Hb4Z+H/AAj8D9Ki0HRrSwh0PUvE2ozut40kMCpJcC4YbdsmcfuFknR42Ysm5czh8ZmmZ4WFLArlilyuT30W9/P+7eSavpofRZtw3wFwPnmJx/FE3WrSm6sKMEuS05NqHIne8LXftXClKMlFKVpW9j+En7Fnw88CaxH458d6jfePvGTuLmbUdZfzIEuctmWOJslmwy/NK0hDIGXYa9jAcO4fDT9vXbqVN7va/e3+d+6sfnXFfjJnGeYZ5XlUI4PBpcqhT0k46e65K1lo9IKCs3GXMj6Hr6A/HwoAKACgD5C1JtM/ZK/aRttQS+vbfwV8VLmOzTT1t4l0/TZPMy8nnPODEI552k2CIRiK6k2kmPFfJT5cizFSu1Tqu1rKy8730s3e1rWb7H9DYZV/FbgyVFwjLG5fFyc+aTqVFbSPIqbUuenBR5udzdSnG6tK59e19afzyFABQAUAFABQAUAFABQAUAFABQBU07c0c0rtlpLiXJxjhWKj9FFAFugAoAKAM/VraWbyZo4nlFuxkEaOFLOCCvXgjg59j+FAEGhORLe2zxMjJKsh3AAksuOwH93OcDOfxIB5J8afAd7d+LVv9GubCyfxfpyafDPPb3Fx9n8R6bIdQ0W88tP3SxxmO7aVpMeYYbOIkgBD8fxZCWEVDN6S96jJX2+CWjX32S7Xb811YZ816T6/mdf4G14+PfDGleKNNsbu0uNbt0uLsXsYFzpjlQJbSVQSEuImLRyID8jqyfwnH1tOpCrBVIO6aumtmn1OZpp2Zuao0ECpoMFu7W0S/vSq5cucBf8Ax5lJPfOPY2IzobiaPFz563UjbW2TqxBfbGBnkAkbyAT/AHc9xQByV5pGrXvi241qKXS9JtZIjBLe24a61q8h84TrB9qmXbbQpJJdKIdkylJQYnt2XFfJVuFVmGLliMyrSqR5rxhtFLt+W3Le13e50rEckeWmreZ3fgfT9E8MaVa+HNG0uK1jALSGOVpJZJcfvJbhpD5skzEZeWQuzsdzMSc19VTpQowVOmkorZLRL0Rzttu7OqqxBQAUAcd8T/EVxoXht5LP/hI4hJMsVxqHh+wivrnTUAL+abd0lZ1baseEhlYeaG2hQXXzM3qY6lhXPLoKVRNaPt19X5XXdO9k9KSg5Wm9DE+H3xZl1J9N8PeP20q01fVCV0bVNNkZ9J8SRiFZRPZuSwikdPMk+yPI8irFMyPcRRNOebJs+w2cxcYe7Uj8UHuuj9Vfr6XSehVWjKlvt3M2H4sz+MdQtNU8OQQN4VkuANMvLVZ7vUdfiZzbPPaQxqBDZrPPaOt6WlieItIRHCY7huHGcR1Pr39n5dRdWcX7zulGPdX2vutbWemr0KjQXJzzdl0PUNFlaXTohLYtZTIoEtuxUmJyAxBKllJ5BOGPXqetfVHOXqAPPPjB4m1jTdGutB0e5i0VbzSNRur7xTdOgtfD0EUOFuSrgiWYO6ukbbI9kM7vICixy/O8SZrSy7DKjOm6kqt4qK630fn1tprdpea3oU3OV07W1Lfwt0m2t4vEXii20e+08eLtVi1krex+TPKP7Ps7ZJHgZFkgYpbIDFL+8Xb82wny0OFsBisty2OHxdlJNtLeybvZ7q97vTTXvcMRONSpzROV/a+8O+KPFn7OHjXw94N+HOlePNXvLa3WDw/qt21taXii6haTzJFntyoSNXk4mQkxgc52n6FtJXZlCEqklCCu3oj4K/4JHeBfGWi/FXxBrWufArRdJ0pdG1Gzi8XWpuZpftiT6cW0/wAz7TJbhDG5lB8sO2G2yFVZa4Mur0sRScqNV1Ffd27LTRR/LqfWcZ5Xj8px8KOYYGGDm4JqEHJprmkub3qlR3bTXxJe6tOr/VevQPkQoAKACgAoA8V/a7xe/sv/ABimx+5tfA2uqhI+9J9hlDH6DkfXPoKAOW/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFAH5B/8ABRTwl4z8dftGeMNDjj/Z9i0iGfTLmCbWfEWgaN4gZxpsAIuZmuYb5ky77UlbaUERAIVCAD7N+Ef/AATu/ZE0Hw7o3ie2+Edsmt6l4bksb+6tvEupTwzR3+nvbXgjIumjKyRXEyh06BwyEEKwAOwv/wBgv9k/VPh3o3wovvhT5vhXw/qFxqum2H9u6kvkXU4Alk80XAkbIA4Zio7AUAZPiz/gnD+xj458Vaz418U/Bv7brXiDULjVNRuf+Ei1WPz7qeRpJZNiXKou53Y4UBRnAAHFAGr/AMMFfsnf8Ks/4Up/wqn/AIoz/hIP+Ep/sz+3dS/5Cn2b7N5/nfaPN/1Py7N+zvtzzQB+XP8AwWD8E+GPBn7VWly+GtM+xtrfgrS76/PnSSedPFLc2cbfOx24t7O3TC4B8vcQWZmIB+jn/BLj/kxP4Zf9xr/08XtAHrf7Qfxln+B/hHRvE8Hh9NYbVfE+keHjC9yYBGL25WEy7grZKbs7cDOMZHWiHv4ilQ/nko37aN3/AAFUfs6FWt/JFy9bNafien0DOf8ACOsa7d+GrO/8bx+H7DVZnmWWPSNTe9s/ld9nlzyxQs58tQzAxja28DcF3FJ6XZU0lJqO19L7/wDDlCL4w/CSbwneePIfil4Rk8M6dO1tea0ut2xsLaYMFMclxv8ALRgzAFSwOSB3pzfs0pT0T28+mnfXQnq49Vv5epf1vW9WfStL1TwUdA1KK+vLXdNfam0ED2UjDfJBJHHL5smw5jTCq5wC6jmm04zUZd9e/wB3f7gTUoOS7advm+3nqc34U+Nnhvxd8Y/HPwbsLd4tQ8C2mkT3NzJNHsupL5Ll/LiQEsfLW3G4nBy5GAFy2FKt7Wc4pfC7fgn92q+d/ntUoulCEm/iV/xa/R/Kx0afET4fyeMG+Hsfjnw+3ipYPtLaGNTgOoCH/nobfd5m3/a24raPvX5em5k042b6nQ0CPmT9iL9sq6/a40LXb7WPh03g6/0kW13bQfbjdRX1hO88STxuUQnE9ndRsNuAUHPOBpKCinZ3aaTVnpeMZLW1tU+nZktyjNRktGrrVa6tPTdWst+6NrS/2prvWf2sLn9nWw8ERPoltBewnxKNSVjJqNpa2VzcWywKp4RNRtlLFwd+8Y+WowvLiY1ZJ/Ar+vvKH/pXMuvwixMnh504W+L5W0lL56JPtaSPfEu7WWOSaO5ieOIsrsrghCv3gT2I7+lJ6K7LWrsiC51jSLO1+23mqWcFv5LXPnSTqqeUo3NJuJxtAIJboBSbUU5PZAtXZbsxrHxxonjDwXd+Lfhf4j8NeJoTbTtp93BqyPp006KdqyXMAkCJuADMquVGTtJGC5JxQUnCpJa6Xs7a+vz8h0/jLTPC/gi18XfE3XfDnhyKOzgm1O7fVVGm28zKN4S6mEW6PeSFdlQsMEqpOAXsG4t94mm1TwzZeI/h7eeHdagv5rVoLqfVTHZTWryqJJI54klDt5ZYooG12CqWUHcK5XGpGE1ZX1728l1fldeopP3JOO9tO1/N9F52foSeKvHvgXwLps2s+N/GmheHtPtpI4prvVdRhtIY3k/1as8rKoLdgTk9qlatRW70+ZSi3qjXF9ZNZDUheQG0MXnCcSDyzHjO/d0245z0xRL3fi0Eve2MHwb8Tfhv8RRen4ffEHw14nGnSeTef2Nq0F79mk/uSeU7bG9jg07O3N0FdX5epyf7NHxnn/aB+DekfFW58PR6JJql1qNsbJLk3Aj+y309sDvKrncId3QY3Y5xmog+eEZ91c1r0/Y1p0t+Vtfceo1RmFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9ABQAUAFABQB+Uvjv4Nap8b/APgqP8dPDOiapDZ6hp/g2x1W2WZMpcSJZaOghLZGzd5v3ucY6en2fAvE1LhXNPrdeDlCUeR2eqTlFuS01ty7aX7nzPFmRVOIMB9XpS5ZRfMr7NpNW8r3319D1/8AYh+EsUvxW8TXni4XOn634Lh+yx2BAV1luBLDK5OcHagZcYIPmgg8A1+qeK/EPPk2Ho4JqVHE3bl5Q5ZJbaXbT6Nctu5+f+HuTcuZ1quKTVShoo+cuZN/JJrqtb9j75kjSWNopFDI4KsD3B61/PB+zEV1Y2t5sNzCHMZJQ9CpIxkEf54B7UAU5lFld2kcV3IZJnIfz5iVde+AeN2SMBQPyzQBp0ANd0jQySMFVRksTgAetAH4E/txftWfE/8Aao+OGteAvDPiLUdW8C2XiB9M8J6BpJMsGoMjiCK4VIlDXMk7KZI9wdl88omATkA+5v2JP+CfWu6f8DvF3ww/bA+GHhW60rxBfWeo6VDHfPJrFhJGJNwaeBtkIUuCnkvuPm3KybkcLXVVxtetQp4apK8Kd+Vduazfnq1/luzCnhqVKrOvBWlO133tt91z7C+BH7M/wU/Zr0W90P4PeCbfRU1OYT3900r3F3dMvCLJPKWcogJ2pnapZiAC7E8puR/tU/Do/Fj9m/4lfD+Dw+ut32q+Gr8aXYlgpl1KOJpbLaSQAwuUhZckDKjPGaAPw08Bfsu/tN/E74c6f8YPh58IEvdK8LyXGiW8Vlpafa72S3825nmeIjzLmQNJIglO4740gj+aOOKuKrT5l7CpzSjUvrouXTb3bO3Z666Nu59VlmNVCazfBexo1cKqbUGpSdV83K5pVPaQc1dOUVyJL36cU4NrvPiF+0LrXwI/am0r426Hq/wr8d6zrumaFrfiO00PSrW6sdJvIkEc2nWd9I108coihAe5gl+Yzk4JUAbuLnZRk1ytfPy1Tun1a189zx4VY4ZyqV6UKntYu12/cbbXMlTnHllFp2jNONmm4NOLP1e8FeLtb/a98I6fr9hJoOk/DHUHSeZbLVY9R1PUjG0cscDmMeXZLk/vEJNwrR7CIySR93QxWU8Mr2+BqvEYq1lJRcaVPmTUmua06kl9m8Yws7tNqx8LVoZjnj9jiqao4e+qupVJ2aaWl4wT62blpZWvc+ha+KPqCC9srLUrSWw1G0huradCksM0YeORT1DKeCPY1pSq1KE1UpScZLZp2a9GRUpwqxcKiTT3T1RLHHHFGsUSKiIAqqowFA6ADsKhtyd3uUkkrIdSGFAFS4u5ftAs7SIPKV3O7Z2Rr6n1PoP1FAHgn7RH7SGg/CWCfwX4PjTxF8Rb5EhSzihNw9oZVyjyqoOWI2lIB8zAqSAh3V5OPzKOHTp03ea362vtp1k+kd3u7Ruz9A4R4Jr5zKnjMZHlw8r8t2oe05H73vP4KUdqlazjH4Y81Vxg874D/stXFh4ih+NvxsvJ9Z8cXjfbEsblxNFpk2cRszEt5syRhFU52RfdQHYr1OFy5SmsRXvfdJ669JSf2pW26RWkdrnRnvGcqWFnk+VNcjXLKpFOKcNG6VGLS9lQctZf8vK8l7Ss7ycF9L17B+bnztrC6VaeKJfEOuQGDVdA8fz6tqk2nfLbW6y6ZPY2F1OtwdxiNpJaRyPCGUXIkb5Y45XT4VVXh+LJPFOynTUYdn8Ol/VS38l1SOy3Nhvd6PU7bxX4y8K+B9PXxF428T6D4Z0+JVKXOqajFZwKSyoCXmZcZZ1Xkj5n3A8mvujjNzQtSsdS0+18R+H7/T9St5U329xZyrPDcR7ivmxtGQrZLFUHTAIGcCgDsbS5ju4Fnj6MOR6H/P5jBoAmoAKAPmrx1Y3vw18Yau2lWrTw6Jdp8TfDtlap5k0kS7rbxBY21nCUM8jQ3EskZk3BrrVlYnMYI+IzZLJs7oZmtIVf3c+1+jbe3RvbSD8zrp/vaLp9Vqj1D4x+CtA+LngW88DahNqo0/WzFDd3OkztBLNaiQPLbiZVJ8uVFeKQJz5byAMrYYfY4iFSpSlCjLlk1o2r287aXOWLSd2cT4V+Gmq3fk+LNV3+B7q7e5jv9E0K7t44Jo5X8z/TbhkE1xdLKZ5TNE0GGuJEzLhpZPlsv4OwlGko45utLTe6StpaKWytZb9FotjonipN+5obn/CsPDln4cvBZeOPFejy2v71NWfxbeXcsBhjkVZPKu5ZoXUCRiVkV0cqjOr7Ex69bh/Kq0HTlh4JPtFJ/JqzXyZmq9RO/Mzh/hrplx4t8SxTNplv4c1vXdFs9W8Y6jp6Gxl1C1TzBpzm1ZlubOS5M90X8zMkJ0+a3LSKI52+J4fyShj8ZUVSTq4ehJqCequ3quzSSTstHzJ2V3E661VwiraSe57nY2mjaTp9ronh8OLHT/JigsrC0iS3hjQgqihEVEUAAYBAFfpqSSsjzzxf9pf+zbLwt4Q+IjQwQTeDfHFlcxSnYD9nabMkfmfwocoTg7TsGc8Y/QPD51cRiMXl0LtV6FSNtWua14vl6tapdVd23Pj+MVTo0cNjZ2vSqwd/K+qv0T0b9Fc9i8bePPDvhHSm1HWfEFlptmpjWS6mkAAaRgkUSDI3ySOyIiKdzFlABJFfn0pRhFyk7JH2CV9jyfWvGGu+Jbe02NqXhDS9VuYk00Ppit4k1UBTiO0s2EhtY8GJnadGlSNZ/Nittnnj43G8U1MVWeCyOHtanWX2I+d+vrdLa3NsdcMOornrOy/E5258H6/8NtNuPEerWWu+DdIWGLytcufHmp+Jjpq+bGVOqWN0WgjhIGy4mimk8pHkZZ41Buo86uH4ky2KxarKu18UOVK6trZ2TbXSyTfZ/CxSoVHy2t5nofhnxnNf3w8L61aT6dqdrGbqG2ldZrbU7bADXlk6NiWIFjuRtrx5TzFVZojJ9FlGdYXOaPtKD95fFF7xfn5dns/W6MatKVJ2Z29hclL2C7hdcTMofdyzBjtIJ25ODjkbUBGBk16xkfMf7K9xc+HfjV8bfhteTS26WXiWXUtLsngcSeXLLMHnBjAJDRLa4BIGGBUdc/K5BJ0cZi8LLS0rpfN6/dy/ofvvi5RhmHDeQZ5SSk5UVTnJO6uowaja+6l7W+l7pqT0R9NR3dzHPcxzXLQq5SQI5wxJXBPKyH+Ed/8ACvqj8CNTwrCkWjxuud0ruz59c4/kBQBrkgDJOAKAPDvHHxfk8T2llaeEfEWo+HNB1a7Szsdes7SG4v8AxC7qxSLRYXEmU+RpJLuWFo/s6PJFmNvtcHyeZ8Q1HX/s/KIKrX6/ywtvd6Ly30ej10fTToK3PVdl+Zw37RXw2+KuteN/h3+1r+z14Wg1Hxz4GF1pOseF9TuRYy6/oszFZLXzi2xJInLyxBztzIX+ZkWN/pMDVrRj7WrHlc4qM43vs7q2rW+t1q0kntYxqKNSKp3+FtxfqrPpfVaeWve5yPxD+On7Tfx28I6z8H/gl+yz8RvAviXxPD/Z2u+J/Gsw07TNAWVEhmmspQ7NdARh9rW4Qq2JQjMSD0KLqJShK0b77PTpbfXa/wBz6qYT5NWve/D1vpt0/pEH7Rn7MXifw94i+EXxe0f4YW3x/XwD4ek8J+MNG8SGC81HWrLZvjv4/tgkV7hJvOkIBLsZQi/KWK1GUY151GvdlFR6Xjyu6a2V3s31+d0rTlh4UU9Yyclq0m5KzTu3ZaKy1t8knp/ALTJPGnxt0fxb8O/2CfCfwd8AaLbXUlz4g8U+DrPSvE9zdtF5aRWUEBD2oBdt0j71kj3AFCQC4xtzSk/RLd979l+P3u0zadlFet/079P6Wvov7Gfwz8c/DOx+McXjnQJdKfxL8XPEfiLShJLG/wBp0658jyZxsY4DbG4bDDHIFOLSwWFpfahTSfk+aTt9zRpUalVnJbNs8Kh/Zp+M8/8AwS98UfAabwdLb+Obm5vryDSXuIi8qrrf2xVVlYoWeFMqN3VgDg1zyk4YemkrtJJr56/ctfyuzRxjVx2Ine0ZTk0+/uq3pdq2u270O78n4i/Hz4wfss/G+y+D3i7wppfhF/Fcfiay8R2q2V3pTzadHbwlonYO8ckqMEZVyVwzKmcDrqqManNF3Tu/Pra61s/K73POpqXsVTa1i4rydrNtd1922x0v7U3gH4o6Z8Vfhr+098GfCX/CZ6t8PF1LS9b8Lx3SW9xqWmXsIBaCRztEsLDeI8EvvGOmG5oT9hUlJq8Zx5XbdWd1Zddd/kra3XbNOrTjBOzi7q/mrPr22+/pZ8lZv8Wv2vvj38MvG2qfBfxf8NvhV8L7268QFfGEMVjqmtax5QitlFkC0kKQuXcOTtdS3Q7ca4b9zWeKduZRlCO70mrSvslptu00rXTdsq9q9BYfW3NGT6awd4201132unbpr9SeMvD9nrWm3dvq4SewuRHDNbljGCmcBt6ncHVmDKwI2lQeoFZThGcXGSunui9j5gsRZXeo36+Lb3VPHmueM5tbt9I8Pva2uj/2lpOhyKkVpcySuGuo1uDOUPmeXONWkeSHyMmD8czahSweNnRwb9nSpSjeesnGU93FLZpWXRr2atJSevqU25wTnq309P6/E9f8MWOleJ9Ai1TVNQj8UWV9sltLC80b7DZae0TgpCthJmUTxtHFuS4LyRToxUQj92n6FkvDmW5bGOIoLnm1fnbve/VdFfo1rbqziq16lT3Xoux5V421Ww+GV9e+NvBDrNpOn2vlXn2jEGmlDNOv9nzXMuIpITKkyoIjJJp0r5YfZ5pYX+b4nwGX18SqVGa9rN6xWrTtfnVtpWteLt7RWS99JnbgaeJrp+zg5cqbdk3aK1bfkldt7RWr0OCl+Mur+ONUPgX9ljwfe+L5tEZ5bbX9eZVtNIW4Z/KW1t5PLjRogdkMkwMuyJ02yKGJ8Wk874hprD1JNwSSaWidv5pdW7Xs29dUfuuA8MMj4QowzLj3FKjzP3aMHzTlZrdx5nbdPkWiaftIvQ9i8EfszWer+OPDPxN+NOrS+JPGui2NrG32aVobNbu3nmdLnYAGYrtiXHyIWBZkJkwv3GWcK4XAunUmveilom7cyd+bV37dlptrZfHZl4n1sLl+LyDhyn7HBVpzfv2lU9nOKi6fZJ2k/tSSaip+7zS+lI444Y1iijVERQqqowFA6ADsK+oSSVkflEpObcpO7Y6mIKACgAoAKAPOv2gfhefi/wDCfXvBVqlgNTnt2l0ua8tYplhu1U7SDIreWWBaPzVG9FkYqc15+a4L6/hJ0Vbm6XSevz27X3V9D7HgLib/AFSz/D5nUcvZppTUZSi3BtXvytcyTSlyP3ZOKUlY4z9jr42H4u/CyDTNcmuB4r8ICPSdaS5ZmmlKqViuHLsWZpFQ7y2CZEl4Axni4fzH6/hVGfxw0lffyfz6+dz6Txd4M/1Tz2VfCpfVcTepScbcqu7ygkkklFtcqWnI4a3vb3ivdPyoKACgAoAKACgAoAKACgAoAgvboWds9wY2fbgBFGSzE4AH1JFACWMMsFsEm2+YzPIwXoCzFiB9M4oAsUAFABQBHPPDbRmSeQIoz174BPA7nAPAoAq6hdfYCbiKyeeWRQuQwVcA8Ak9OWOOOaAOA+IdjqHjfw5LppjtEnt5rbVNN+0bxbx39rPHcWrylGDvGJUh3KrLuBYHAzXNjMNHG4eeGntNNferFRk4SUl0I/hl4l8M6XqA8N6ci2dl4knu9V00KUMI1EyyPqdkDGgRJ1mEs7pJI0sjyXhUbbeTZ8twhjqnsp5Xi3arRdrdeXp6pbXWluXudGKgrqpHZnQ619qGp3FzapICpDBt4A+UA5xnnmJ/yHtX2RykF1ZpAN63ezy/NVBIFJ+XzF9ucRxjPqwPpQAyeSfU5VIVXL/MGCFWXMrADPPGZAcDtgZyKANTRdREEbRNYvNfFQyhMYEZUMPmPCL7dATwOwAN3T7v7daJdeWE3FhgNuHDEcHv0oAs0AQ3Fx5JiRV3PNIEVc49yfwUE/hjvQBjXN/FPqcj2sbxmzykk8aFiTnGHA6oMHPfuOhNAHgHxR8JeC49b1XwPFqEmk+Friz/ALf8ceH7W0e6gmgu7hha/YI1Vnjury7tboFbRC7kzsES6mguK+E4toUaNejWwtN/W5u0GtL2Vry6O11a/le8U0+zDNtNSfurc1fgtqeqy/EHV/Fuq6ZZ61qOtXX9iXiQWwsdR8E20C7rfS7i2lkJlgL+dK11GwaZ7iGSOJ7Vo3t64Zrwyms8lr0XCo7yUr3U9NXdLTTZapWs3zbrEJ1F7VO6/I9x1bVYdFne6lcNGYd0kfTaFDHzMnjGAQx7AA9BX3JyElrqF6ytLJFDcxlixNtJuMa4GAVIBJ7+vXjoKAPIvGCw/E74seNvhRq089ppEPw+0+IXNgpjvk/ti51OC5IZgwDImm27RfJwzyEhwyhfh+K5tZjl0OjqX+6UP8zrwy9yo/L/ADPYtMmt8vbwWxgwPMKyOPNYnqzDJPpy3NfcHIeY/tZ/CXVfjt+zz41+E/h7xENF1jWbGOawusOQJ7aeK4jRhGC4R3hWNmRWYK5IVjhSAnbVH5K/8E/v2jfhj+zt+0fb+GhevrGi+Kb9vD0/jPVZ57KK3s3d/LeOz3mOJZZ47BmlmJaNEkB2gsRzUI1Yyn7RRUb+6lfbu3pq+yWndns5rWy+rSofVJVZ1eX97Ko425m/hpxXM+WP80pXm3fkhaz/AHHrpPGCgAoAKAKl7LIxWytmxLMMswP+rTu317D39gaAPHv2upT/AMMs/F6w01FWC18Da4szkZUYsJcIPVuQSe31NAHMf8E4f+TJvhZ/2Dbn/wBLbigD6ToAKACgD8T/APgopqn7Mtt+2P8AEGH4heBvifqHiBf7J+2XOjeK9Ps7N/8AiVWhTy4ZdOmdMR7AcyNlgxGAQoAP2I+FjWL/AAx8INpcU8VmdB0826XEgklSL7Om0OyqoZgMZIUAnPA6UAdRQAUAFAHw1+3LYfHS6+LWkyfDH9k/4C/E/Sx4dgWbV/HtppsuoQXH2m53W0Zur2B/IVdjgBCu6WTDE5AAPon9k+Hxfb/ADwtD48+HHg7wHrq/bvtXh7whHBHpFl/ps+zyFt5ZYhvTZI+2Rv3kj5wcqADhP2+v+ST+EP8AspXhT/04x0Uf9/wv/Xxf+kyJxH+44n/r2/zR9L0FH5kfDLwrpvjf9jL9lHwnrQZ9N1P403kF/AD8t1a/aPEBmtpB/FFLGGidTwySMDwa0yynCpVpqavanN/OMbr8UtOos+qSp1arj1qJddpTUXs007PR9Hqe46D+y5+z7H+2h4x0FfhJ4WPhw/DzQ9XPhttKgbRjqEl/qEH2v7CUMAnWK3VFcKCBLN3kYmcLCM6GIqSV5RqQSfZSheSXq4pv0Mq1ebxFKitI8km0tLtSVm+7XNJK/R+SPM4tJsPDn7Jnw18K6PCYNL0H9omLS9Og3s/kWsPi26WOMMxLEAcZJJ965sDOVWll1abvKSi2+7tL/I6a6UJ5lSj8MW0l2V4afidlZeDNB8NftJ/tdePPAXw98P8A/CbeGvCWi6x4au4dGt2urbVbrS9TeaWFthZZLiQL5pHMpPz7s1xwrThHFe80k7b7Lkg/z19dT0KlOMvq11vHXz9+a/JJemh474e+C19rf7GmkeP0+Hn7O+hC98O22tL8VL7xffw+IbPVXZZv7Qmvk01plu1vD80QuGAcGEErxXpTSw7jy+5ZxtbbpZWeln+p5lOTqqTfvXvfr3v936H6X+GH1qTw3pL+JJbGXV2sYDfvYOXtmufLXzTCzBSYy+7aSASMcDpW+JUFWmqSajd2T3Svon59zKhzeyjzy5nZXa2em69T8yf2SPE2hfs+/Br9m39pLxFNBYeGdX0Txf4D8V38juxhjGpX+pacyRqDuPnWt1Hn/p4Ap4mbp1JrV3pxaV0lzQin1tq4uSXojblU4w1s1K2zektPkk1F7PdnsnwT8Lan4e+MH7OmteJLSODxL438M+O/GviALEY2GoapNp128bA85iWVIBn+GFR2pYaEqVXEUZNtwpU07739p73/AJM2zkrzVVUa0dpVJNWd1bkfL/5Lb56mv8G/EOk6T+yz+0rqN7dokWjeMviN9s+YZiYSzSbSCeGKupAPXcPWuPMIOvlNOlFq7pOPzbmkvxR6WWtUs0lUnolVTv5JRu/lZnn9h4I8N/EXVP2APCXjLSrbVtAuvh1q0uoaXdx+Za36w6Jpc0Uc8Z+WRBNHDJsYFSYlyDXq4aHNTryv8ME/J3nBa/fddmk+h5lacYzpRaveTS8vdk7r7rejfQ3vFngLwb8L/F37YPhb4deGdN8N6JqHwas9Ul0vS7VLazS5+xarEZI4YwEjyqLkKACcsckk15kZyqYbEKT0jJJeV4Xf36fcethko5hheXrq/O1RJfgY/wAGPCfhH4p/FT9nnwF8YNDsPEXhbSf2btF1rw5ous2aT6dNq8hhhvJhHIpSeZLZYPkbdsVt4UH5q1oxhL2slrNcvm1B31t9m8la+l7Wu9jinOpFwi/gd9e8lbTzsm3bW2+nXo/i34Y+APw88D+Ofh18D7n7Jcaf8V/A+p+I9AtkkSw0e9u9SsCqWylBEiukQdkiZgjfLhAFQaYacp4vA9Yqs0n52d1fyb26X82ZY2NsJi5N6ult0stE7d31e769DU8F/Cf4NfFD9vb9of8A4Wf4O8O+KtQ0zS/Cf9madrlrFeRRwyWH+kTJbTBkLBo7dTLtLIG2ggSENyUI3dSVvtb9vdj+f428jqqSa5I3+zt395/lf8WeVte/Dvw9+z940+FLeGdc8beDp/2hj4Q+H3he01VNPs7+Tfb3SaZJcyqyf2cLpbxCSHyVABBwymGlWrKk5xvJOTXbki5Wk7t8y5fW7tpppVZ06VWrGNuWUY3te/NJLmStbld7Polr897QvDGt/D39ur4CWk3wX+EnwobX9J8UC50jwJe+Zd3MC2HmAaj5drbRMiyRoYsK43JKQ3yit6TtOok/sr/0pf1svPoc9TVQbX2v/bWe2/8ABN7/AJNB8Jf9hTxD/wCnq9rOj/Ap/wCFHXjf98rf42fTVWc4UAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+mI/D+meAf2ik8SaX4ZgMXxEil0q9vVQb7W+trf7UhBLZCzxRzeYqrgvbxMSCTu+zjja2ccNzwlaq/9kanGPSUJtQaemrhJx5W3dRnJLS1vmJYWllmdxxFKmv9pTjJ9VKKcl12nFPmSWrjFvW9/Zq+MPpwoAZNDFcRNDPGHRuCpoA8d/ai+NN1+zf8DPG3xW0zTX1e90Owhk0+ym3zI11NMIUaT5lbyVZ0d8MCEVsHNAH5D/GX/gotr3xN+Mvwa+Oem6Fruma18OoopNY0r+3P+JXfTecTcJaxCMm3WeEtHJIxdyjohB8kPIAfbP8AwTZ+FXivwz4++I3xU+FvjnwXqvwG8c6tey6TaWizLqUckU262UwGGMWvlxzSRSI2MmNSiGNo5TtLEVZUY4dv3IttLs5KKb+ajH7jJUYRqOsl7zSTfkrtL5cz+8/QSsTUKACgBscccSlYo1QFixCjAyTkn6kkk+5oA/Ob9qH/AII++DPiHqmueP8A4C+Lf+EW1/VbqS/k0HUkDaO8shBdIGiTzLRSS74xKoJCqsaY2gHxp4Isfj1+wD4M+JniDxP4F1bwp488Tra+BtEnu4bgwi0uobie+u7K6t5BA88LQ2GyRXk2u+AOHAznz3jyWtfW/az2872+Vzqwzwqp1frClzcvuctrc/NH47r4eTn215uXW1z7q+Af/BQHxMnxW0H9mn4peG2nvvCehzRfEbxpqus2sMWk6pbM6SyO0caW5tjMbaBWLK2+4VSZGXMuNLFKUJVKq5UpNavezsn83sepj8inQxNLCYKft5zpQqNQi2481NVJRaV7uEW+Zq6STbs00vtn4ffEXwN8VvCdh46+HPinT/EGhalGJLe9spQ6HgEow+9HIucNG4DqcqwBBFdR4J0dABQBBd3kVoo3ENI/EcYI3OemB+Y+lAHy9+0D+0NqC6yvwJ+Dnh7/AISbxreXCfa7i2LmPT71JElQR4wHkQqGZmYRQhB5m4BkHj4vMpRqexw8eZ7N+f8AKtrvu7pQWrfQ/Rsg4Lo1sH/aOb1fZRa5oxuk1Db21RtS5YXt7OHJKpiJe5Tja811f7N37O9l8JYL7xh44hlvvHVxLPHe6rcXguoWRnLl7Zj+8w4K73lAlZ1fgIVB6MJgI0Gqkvi9bpX3tfVt/ak9ZPsrJeRxBxXWzWEsHQ0otpt8qjOpyq0XNRbjGMFpSow/d0o6LnlzVJey3fiOKCUxQWksu3hyVZdp4wMYJzz3Ar0D5AiutZupEKwqsCgZeY7gFH1dRg+mAxPPHegDgPGPhK+CSfEvS9SttJvtNtHS/kNjLeJqdihZ2tZ7aIebcFC0hhKEzJKzbS6SyxTeLnuUYTN8M44p8vLqpaJx7u76d1t6NJrWjVlSl7pxHws8BeMzrXiPwDolz4I8D+FtC0yxhgfwL4cgtrjTNYnIubqzV7h5YXRYzHKS1lHvS/jI2ujE/GZBw/hc+pPHY11JatLml8SWie11bb4nqt7aHXWrSovkhb7jX8SR+Ivh34isJtfvZrC0kNzJH4p06zLWNzIY2UprVqmBEViWKZL1WRA8Eil7ZHEFz6UstzHhiTrZXetRe9N7rzj+WivtdStdZ+0hiNKmj7nd/D/xJqNzaaRNd6DbTnXxLO19oWoxXel20A3PbyebKYpZBMjLjy4nAJwTtAkb0ct4vwGY4hYRqUKj0tJJa63V7vtbW13ZGdTDThHm3R6DX1RzhQB5n8cIbPRNP0b4pXP9oKngm9a7vjZMo36ZNE0N2JwfmkgjV0umiT5meziKq7KqnwuJctlmmW1KFNXno4+q107Nq6+fY2oVPZ1E3sW/g7FqVl4Pg8Fa5I0l/wCDrhtKimd5Xe5sI8ixuGkkAaaV7QxCaQDYbhblV+5xrkOaQzfAQxKfvWtLykt+/qvJoVam6U3E0fEHjbwF4e1+38Oax8RPD+jarrTiK3025v4Y7y7kwPlgjZw7MQU4VSeQQMmvUq1adCDqVZKMVu27JfMzSbdkcJ8RPF2l+JtK0fRr+FNL8IaiXnuLTUo5otV16OCBLmOxtNPGJnWQCTz4pV80pbyQm2dZzJH8RnHEv1+M8uyaMqlV6OUV7sU3Zu/4c2kVe6loddKhyWnV0RteBtG1ubSY7/xNa7dc8QN/aV3D9p+0LpzsqhbRG3MmyFFWMtHsjkkWeYJukOfp8myulk+DhhafTd95Pd/5dlZdDnq1HVk5M6bxClnB5VqH3JbxkunyZ3HpjkEHnJxkDC/LivUMz5a/aS+Ib+KtOsfhRoUngzX4r3xPYQ31rp3iNG1aOaSadUtZLIIqQKRCqGdrhcyCRfLQfMv0nhjxFHC5rmGJxNGpT+q4evVUrXjKNPlV1dK7s7pbba9D5LjjDPEYLDYaLT9rWpwf/b1+22256ldX3ifxFr+kLH4c/sDXtVbyfD+lapfW893pFkip/aeqzx27SQiRRL5MaxmdFea3UyxLezJH+L4rFR4xx8MFhZP6tD3puzXM+i1+5Xt9p2dkfdxj9Vg5y+J7HtWieG9J8PrKdPgdp7gsZ7qeVpribMkkgVpHJYorTSbEzsjDbUVVAUfdYPBYfAUlRw0FGK6L7rvu9NW9X1OOU5Td5M0nRJEKSKGVhggjII9K6iTxr4geA5fD1nLcQR6xd+GrctfQNpitPqfhy4SN/wDSLJcO8sewtH9mVJMq7ReXLFI8VfG5zkFeniFmuT+7WXxR2U+/ZXfXvvdS1fVSrJx9nV2/Ik8JeKNR1C7stL1PUtJvJtZhS/0bWNN3DT9Zi2I/mQjfJtkAZn8su4aPMqMQJBF6WRZ/SzqEoNclWPxQe66XW2nfqno+jedai6Tvun1PMdY1KDw1+3Rd22teL7y/m8VeHFg0jSmid7bTY44VllMjO21Axs5CixI+55n3BMAvjGoqOfOM5tucbJdFpd9dPhdrX1bvbr+1VMJLM/CeFXDYeMI4avzVKjaUqjcnCNkotyaVWKk5uNowjyuV7R99drm1cTwLN5c2I90IEKZXnIZcKw+9yy4wD6ZH0x+HlObxvbeFbgWJsNQ1O/vWkZNMsws10zqrtvYsUSNDsKeZKUi3FF3h2CnhzDMsLldL22Kmor8X6Ld/L56FwpyqO0UcVrl9rfxFvPEvhvXdCtPFGg/2qmlvpkjRwaNFaRRRzTSXblXluZD50kTRBXiZ4oEaOHc8x+SWIzniZRlhH9Xw7T97eT16bPonpyrV+89jp5aWH0l70ja8NeCG0i8fV/FOpXWu+I74FJb92cW6HchkgtLUvstYQVCkKxLCJDJJNIhlP1OV5RhMno+xwsbd31fm31/JdEjnqVZVXeR1VheT2N5I8AjEZVUCxIJVAJOCQrkjoT1PX6V6RmO1XxRdW1jPrFxdR6dYWMPnXNxI6LEE5w2ZAuBkEE/oe6lJRTlJ2SA8/fx94xt70T2Onyx39/CrWmjrZ/PZ2jSosmo36oSSRgmO3TbIwV1xuMhg/N8fxZLF4yNPCVvZUIuzqNX5nbZJp6fLs3bQ74YbljeSu+xzF38b/Eni+DXvB3gbxFew6naaZpWl2lzeWsdlrUd/fy+TJfTaZdQxuotYfKuwPLEU3myxgfuuPazLMKmMx2DwuBq3hJuUpR1TUdeXmWmtmmk+11sY04KEJSmtS18dvi7deFNN8ZXml+M9b0658PeFUFmba2ikSTUNXu2s9MlCNAd00dzZuApJULc5eNsqQ+JcTiVisHhMK9ZTcnZtNqCu1o9mm9OtgoRjyylLt+ZteJvFHxMuPGz6P/bmq+G9O1JtQ8OaNPZ2NreLLdzabBd2+quzxkQLbvbalAIpQ6SSOhYEFAPEeeY2vkNLM4VL1KVT3+iau1ytJJO6lH873NvYwVZ02tGtDNm+NPxA1yKPXvA9j/oWo23hRYNO1eKKK8srm61GZdatZ0BULd2ligkMXmMyOhyjDAk+jxuZ1J5pg8JhanuTUpStyu8bXjq07JtNXVr30d9sIU0qcpSWqPU2u9Y0y2hvXnDfa43l9wScZI6H7yHnnjHAHP0xzmml9c6nvlsZ5AkLKFd/ljwpBLueCdw42jgDrjNAGL488Wp4N8Ha54zbS7vXpdCsrnUFt4lbEphieTZHGoZiflwCFYkngk8VMpRhFyk7JBueL6Vd+EtKg1z4Z6l411bxjrvhbRtC8OxWVvEtxrNvqlpavenWSbs+REZFntH+0zMIWmgWN5HkKx1+e5HXwdPLMRmGZ2jHETk7dWtrK3vOzcvNb6LU76kKk6saVFXat954340+MtjDc6RZ6zJaavqPj7V7GwurjSdH1JvCk08IW1vBFbpMk2vyOZngwp8l0NspjkeArJ4GExmOhD+z8ulP2erUVKLqa3tqk/ZpaNre/M7pNW/SMp8PauKweMzPN6kaH1W3PTnenUbaUoxtNJR9orqEne8kkoSOt8Ifsm/Gv4h2Ojal8efFFva3Ph7xHcazbTJe3V7e3Fu77ZLZI4pIYrCNjb280LQsWVZNhhhZSK+nyvhBwlGvipcrVvdjZPTXWS3fR2u+vMenmHiTlvDuIxFHgfD8mHxFKMKirR5/eXMuaMZSkrcsrNSvGTbvDv29v8M9G+EWmadDpN0tt4e0aZrfw/rMOmpHd+F1mkhVLS6EGDd2Eko3SSSY27I3mdnX7TDrmuV4vKK8s1ylvXWpC+klq21f8lqvs9j8nqY6rmEv9tm5yskpSbbsklFXfRJJLokkj0/wN8SbDWfEWq6RqtiNJ13Q5fI1exklDOiyoHiuYgpJe1mKOYpOMkSxsFlhljT6fK8yo5thY4qg9HuuqfVPzX47rRnBUpunLlZ6Np+pW2pxvLbbwI3KEMMHPrXoEFugAoAKACgAoAKAPkP9on9nD4g+FvGOoftF/s5azqFr4geaG71fw/YL5a36oQ8rIsRXz97Ro8luysZWaRsliEb5LNsoxFCs8xy6TU9G4rr323vbVddeuh/Qvh94iZRmmXU+DuMqcZYdKUadaevJe6im5J8nKpNQqJr2aUVZRTkvY/2cvj94f+PXguPUrW5tl8QaZBbJr1lBHIiW9zImcoJBnYWVwOWwUYbmxuPsZRmlPNKPMn76tzLXRv16f1rufnPiHwHi+BczdGpF/V6jk6Um03KMXbXl05kmr6K6adlflXrNeqfABQAUAFABQAUAFABQAUAU9Rdh9liVAwluUDcdAMtkfiooAuUAFABQAUAYHjS5l03RZdYttBudXksxua3tZds3lEgSNGCQHIXJ2ZBYAgZJAPLjXiI4ebwiTqW92+1/6/4dblQ5eZc2xynw78bXeqXGpeAfE2qWup6jpcNpPFeKyx3OoaVdowtbyaFVCxyNLDdQuEwpa3MqpEsixJ5vD2bPOcEq81aabjJdLrt6pp+WxpXpeynZbGlMVtrsRyxESW0PktherAkZ7HBUjkc4Ix0Fe4Ynjvxa8IaV4chn1bWL65TwdrFxDd6w1q0bnwnfxujW3iC3klzHbqkgVrlSkkWdlw6oguzP8nxBlVVTjm2XK1enq1/PHqmlq3bRdWtN7W6aFRW9nU+F/gdp4D8X3uq3eo+D/Fam08UeGGjTVBZwzxW15DNI32e9tVlziKWPzPkDyiKRZoTJI0bO3sZPmtHOMJHFUtL7q92n1T/Ta6s7amVWm6UuVnU4LIHgeUtGqNITGGAG2M9h/ssfov1NeoZl+V72O1s/IntisFur4CkMOPMweSCcw+3054AHafYxyXtvFdlZfLfYyHBXIWRcHuR+5UgH1oA6aWWG0jRAnX5Y40Ay3sB/nAoAgOoTxBnutNniiU/fBV8D1IU5/IGgCd0tr6EZ2Sxt8yspzg+qkdD7igDntf1C18Kafd6trWpQ2dlbJJcyanc3aW8VtEiAuZpHO1VWOMku3HGTjGaAPJrXUH8c+LtN028bylhitfGeuadc209veQWzySjQrJond/JKSWks8zRyjE+nyZjC3RA+GydPPM5rZtJ/u6V4U9dH3elujvqvtLX3Trq/uaSp9Xqz2XSNNsxex6veWMD6q9u8KXjQgT/ZtyHazdQCQp299ozkrX3JyGT8UjrNn4aTxJo090T4euBql5Y21p9pfUbNEdZ4FjWN5Xk8t2kiWLa7TRQqSUZ1byc9wE8yy+rhqbtJrTW12tbPyez8maUZqnNSZP4X8NappGkJpWoRaVPFYN9n08eW7OLJQBCju7M29V+UsS+4rv8Al37E2yp4r6lT+uq1W1pa31Wl7rS73fmxVOXnfJscB8VvC5g8QWc8Wo6xoA8b6Zd+CbnUtDCtc2kzQzXFjePduoe3FuVvY4fvL59+g25YGvn+MqEo4almNJJzoTUtb7XV1ZefLfyT1N8K1zOD2aOr8B67e+JvB+lavqb2OlXMsKQ6xZaVdRzR2OoL8k8JnXO/y5lkhY8EMhB5Vq+qw9eGJpRr0/hkk16NXRzSTi2mdbb6S9vnZd+WzHLyRxgPIfVi27PfpjrWwj8X/wDgpP8AsK638Lfirc/Ez4ReEJ38BeJLeXVtRNvCUs9BvFkVZ1lkIEVvDI0sbxAsoy0qIqrGBWVatGhB1J3t5Jt66bK7O7Lsur5riY4TD8vM7v3pRhFKKcm3KbjFJJN6tdtz9Av+Cbv7QNt8aPgBY+GtY+II8XeM/AaRabr18Y5FMkcu6SzfdIiPLiEeQ8rKGea1mbMgKzSXCXPFSatfo90c+JpLD1p0ozU1Ftc0b8srO11dJ2e6uk7bpH1fVGIUAFAFLTT56y3+c/aXJQ88RjhRz64LfVjQB5J+1WAv7JHxjQdF8F+IlGeuBbTjk9z70Acp/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoA/GT/gpJ+yB+0b4t/aO+KXx58PfDSe88CQWFnqcmsDULNFW2s9It1uZPKaUS/IYJRjZk7eAcjIB7H8H/8AgoV8aB4I/Zg0YeGPBXk/EXxhP4H1RvsV3uhsbS40y1jkgP2nCzFLuQszB1LKhCgAggHB/G//AIK7ftJfDX40ePvhzoXgn4az6b4V8UarolnLd6bftPJBbXckMbSMt4qlyqAkhVGc4A6UAa17/wAFX/2ibb9mzR/jGngz4cnWtQ8cal4ZlgOnX32UWtvYWVwjqv2zf5he6kBJcrtC4UEEkA9g+MX7f/xj+Hvxy/aA+Gei+GvBk2l/CnwdD4h0aa6s7pri4uXl0pCtyy3Cq8eNQm4RUOVj54O4A/Mr46ftQar+1n8aPC3xG/aA0i007TdOt7HRNTi8HW7QztpUd3LNM0C3csqm523EwUswTITI6kgH7bfsHRfDGH9lDwKnwaj8Wp4NK6i+mDxWLcaptbUbkyGb7P8AusGUyFNv/LMpnnNAHpXxa+Dvw5+Ofg+TwF8UvDx1rQpbiG7a1W9uLU+dE26NhJA6SDB54bHrUShGTjJ7p3XrZr8mylJxTS2ej++/6Hm/gP8AYW/Zd+GXi/TPHngr4d3thrmjTGeyuX8TatcLG+0rkxzXTxtwx4ZSK2hUlTfNHs196s/wZlOnGorS7p/c7r8UdnoH7O3wc8L+FfCHgnQvB/2bRfAetyeIvD9t/aF0/wBi1BzcFpt7Sl5Mm8uflkLJ+8+78q4VGToNSp6NJx+TVmvuKxCWLblW1bak/VO6enmr9jq4PBHhe28cXnxHh0zb4iv9KttEuL3zpDvsoJZpoovLLeWNslxM24KGO/BJAACg3ThKEdpNSfm0rJ/cS6cZTVRrVJpejs3+SOam+APwkn8NWXhCXwnu0jT/ABN/wmNtb/b7kbNX+2Pefad3mbj/AKRI7+WSY+du3aAKinCNGNKENFTso+Vr29d3vc0k3OVWUt6usvO9vu2W1h1/8BfhLqXxg03493Xg+IePNKtGsbfV4rueJjC0ckZWSJHEUxCTSKGkRmAIwRtXBCMaTk4K3Nvbrste+iX3Lshyk5JKTvbby66fM4g/sL/smHxz/wALE/4Ulov9sfa/t+wS3AsftH/PX7D5n2XdnnPldeetVS/c25NLbeXp28rbdBVW61+fW+/n69/O+/U93oEeST/so/s/3XwQtf2crn4eQzfDyxl8+20eTULtjFJ9pa53LcGXzwfNdznzOjMv3SVpzftJxqT1cbW+SttttpqNScYuKej0f33/ADO61LwB4R1fxpofxD1DSPN8QeG7S8sdLvPPlX7PBd+V9oTy1YI27yIuWUkbflIyckG6cpSjvJJPzSd1+JEoRkopr4XdeTtb8jznWv2Nf2Y/EPxFuPivq/wh0mfxNdzG6uLkSzpDPcH/AJbvbLIIHmz83mmMvv8An3buaxnRhOEqclpJNP56O3b1Vn13NVUmpxqX1VrfLb1t2enTY6vTvgZ8LNJvfh/qGn+F/KuPhZpk+j+En+3XLf2dZzW8VvJHgyETZihiXdLvYbcggkk9UK06cZRi9JKz9Lp/mkYSpQm4yktYu69bNfk2T618Gvht4h1PxbrGseHPtF3460JPDOvyfbLhPtmmqsyrBhXAjwLmYb4wr/P97gYwUVGMoraTu/NpW/I3jUlGpCqn70NvLW/59znvGv7LHwC+IngHwx8MvGfw5tNS8P8Agu1hstAia6uI7nToIoVhVIrpJFuAPLjjDfvPn2KW3EAhuKbuJSaTj0ZPoX7MvwK8MfDu3+FPh74eWWneF7fVrbXRZW886PLqEE8c8VzNMH86aQSQxfNI7ZWNVOVAWrUmqkKq+KDvHyevTbq9NjOUVOE6ctpqz80eL3n7GXhH4tftK/Gjxp8dvhdbar4a15fC7eFdS/tEwXDPbWM0V4qPazLcRJuMQZH2pJtQ4baCMKUOVzb6yv8ALlivzTNKj5uVLa343f6M9u139m/4F+JPhRa/A7V/hjosngaxCG00eKIwR2zqxYSxPGVkjlLM5MqsHYu5LHe2dJpVGpS3Wq8vT+ttBQbgnGOzVvX17nO/D/8AYw/Zk+FviLRPF/gP4VWel694euLi5sNUF9dzXivPAbeQSTSys8yGJmURyFkUsSoDEmmnZ3X9Xt9+3y6Ct7vL6P7vy322fXY9E+G/w18FfCLwfaeAvh5ov9k6FYy3E1vafaZp9jzzvPKd8zM53SSu3LHGcDAAASSjFRWy0KnN1JyqS3bu/U6emSFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9ABQAUAFABQB+fPwk1Cwsv+Cy/xrtry+t4Jr/wLaW9rHLKqtcSi00SQpGCcuwjjkfAydqMegJoA+pv2kPDWt3vw/n8Q+HXjfWfC+pQ+K9KEgbAubNC7IyqD5gaFZlC8ZYoCcZI+v4IxtHDZssNiv4WIi6M7Wvapomm9rS5W30V9GfOcU4WrXy918P8AxKLVWPa8NWrLe8bpLq7HZ+G/ir4A8UHw9aWPirS4tV8UaLD4h03R576BdQmsJEDCYQByzIM4LrlQQRmvmcZhZYLE1MNNpuEnFtaptO10+3Y9zDV44qhCvFNKSTs91dX18+51tcxuFAGTeaNo/iWy1TT9f0q01PTtSil065tLyBJoZ7YgpLDJGwKujEuGVgQwOCMUAfkf/wAFAP2EPgj4LvbO1/ZcjFl4q0rTptT17wg2qtcGTT15W5hkupjJ9oydotlLtKpUoqsuJvRp5ZUqYCeYuUVCMlFJv3pSetopX2WrbsraJt6HFPHQhi44JRbk05aLRJaXbdt3okrvuktT79/4J/Q+Bh+yh4EvfAnw1ufBMV9p0NxqVncabJayXl/5SLLfb5Mm5jnCo8c+5t0RjXI27F4684VKsp048sW20ld2XRXd27bau/c6aUZQpxjOXM0ld6K776aa+Wh9E1kaBQAUAFABQBW1LTdO1nT7nSdY0+2vrG9iaC5tbmJZYpomGGR0YEMpBIIIwQaAPkP9q79hTTPGPwk+Jtv+zhouk+H/AB18RdUt9b8QTXl3dY1lopJZZIEcylLZpHmdvuGJm4ZVyssWdSlCrbnV7O/zW33fnrudeEx+IwHO8NPlc4uDa35ZfEr7rmWjta8W4v3W0/yr/ZQ+PP7QH7JXx2t/D/hnTbzz7nUl0rxD4L1m5+w2t4S4V1nMxCWkyYyty2PKwS+6IyI7qVYUYudSSSXVuyFgsDisyrxwuCpSqVJXtGMXKTsruySbdkm35K5+/HgX4heCPiboP/CUfD7xVpniDSftM9p9s065SeHzoXKSKHQkHDA8gkEEMCVIJs5Tfd1jUu7BVUEkk4AHrQB8mfEn9pHxH8UPGb/Br9mmSG81fUtsMviSOYeRa2q5MzxOAdq8jdOM4GEjVpShXxa2YfWZexwzvfqnv6dor7U/+3Y3k1b9My7hBZLQeZZ5Hlcd4yi2qe1udaKVaSd6WGbV9KtdwoKSn6n+z1+zR4S+AVhdXNrdtrXiPUlEd7rE0IjIhBysEEeW8qLIDMNzM7fMzHCBenA5dDBtzbvN6XtZJdIxWtl8229W2zxuKuM8TxJGOGhF08PFuXLzc0p1GrSq1Z2jz1HsnyxjCFoU4xje/ol9dXV/fG1izHBbTRrNnIY7mABH6kemM9cY9E+MGaRoVtdWNtcXmHBXcIwBjHuTz74BA9uuQCbVbTw9odjca7qV9b6RY6bBJdXd3LKkMUMKKWeSSRuERVDEtkADJJoA8rgsPHXiXVLd5fDNxqksd3Y6jNa6rrKWOk6JCeVs0itvP+13lvsjnJmRleWUSJPEohji/O08y4wVV0aypYW7irK7lbq9nZp6+8l0s9Wd37vC2uryPUvB+hTeEvC9ppeq60+pXkSvPf38pdRPcyOZJnVZJJDFGXZtkW9liTZGp2otfd4PCUsBh4YagrRirL/N+b3fdnHOTnJyZYXWNPvIpEljM8Zk2IqwO4fABxyuM9Tjtj8a6STjm+D/AMIbvxm3jex+FnhI+JgyzLrraJbteLKqqqTicpvDIiKqHduIAxhQCVZXuBz0/wAcdG8U+HNR1rwVfW9jplhdz6XL4q1+Wa00dLgI0aTWwLxtqMX2poEBSSKGVWk8u43ptPzWbcT4fLqv1WhF1az+zH9Wr2druyTfdJO50UsPKouZ6LuYFjqfjjxtqOk6to2ieMr63i1G1vzfa3qt54T0+3RUMFzHFYKDfTYkheQW2oReU5n4uNnlgcCwvEma3derHDwaatFc0t+99NOqktvhRfNQp/CuZkVv8CNXvNJHgxx8F2+H0uCPCn/CtHMbsz+aTzqPk7RckSljbcnJxnDlf6lw5vb/AFqr7b+fm17eu2nxfhoH1p7cqsYb/s2fDli0U3wb+BhjOFcH4fwElQTxjzOe/HvVLhTFrbH1fvf/AMkH1mP8iO6tvCHxDlj0u51n4qLYSWWoyPcDwt4ct7C11G38xdkVxHfi7dQAjKWhuImxK33SFK3h+Bspo39qpVP8UtvTl5d/O4pYuo9tDoPDfhLRNEuZ7vTbaeXUZyqyX17dyy3lxb+bLKkRnkdpDEjzzFYx8kYkfYVU4r6jCYLD4Gn7LDQUY9kreV33fm9TnlOU3eTudLZ6jYac7va2Je4mQFCAFUrnGBycD8SxbIIBwK6STzT4xfGCw8D/ANl2unaFf69rfiK7EVhodszBrqVWVZCsm1ggCMpztzlgOmSv1HDPDNTiKpUnOoqVCkr1Kj1Ub3tpdXu0+umvWyfg57nsMmhCMYOpVqO0ILeW19bO1r/1q1gt4H+IfxS8Y2GufFX4bad4U8J6RaXdpH4cm1SPVTrF1dwtblp0QGIxLDJJhWAbJYfMsjbfVxtfIskymvgMsrvE1q7ipT9m4KEItS5Vze9eUkr/AGWktnFX8/C0s2zTMaWLx1JUKdHmajzqTlKS5bu2lkm7dU77p6e1eGfCngzwTLqFz4f8G6doE2s3Bu9Su7ezt4Hv7kkky3DxcySEsx3vySTzk8/n9OnCjFQppJLZLRI+vbbd2dKjrIodGDKwyCDkEetWIdQAUAeO+PvhZaaNo+sy+GNFbVdK1W8XUrvwvHP9nf7UHaSS50uYOhtLxpdk4IdE8+MShreSSW4r5jOeHVjayx+Cn7PER2l0f+L5aX7aNNWR0Uq/IuSavE+Z/jP4o1zwV43+HP7RNrolv4v0nRmWw1fxPcxy2urupDxGzvdNbyY7efypnbcsKfvgyyRwBUSX5aefzjjqdTGUeTEU3aS11jZq8dbbN/g02r2/efC+kuKskzHgqpXlFVYc9Je4oc8ZRl7z5XP4owuk2uTnslJJn1T4I+K9p8UvCvh/XdB0LUbWx1q1jvbe8vZrOQo4lKCFhBNJtmG3LjlUyVJ3goPuMszzCZtUqUsPe8LXumlr2f8AnZ6aK2p+J5zkuNyDGVMBmEOSrB2af5+aas01o0007Mk8Q+GLnV1Wa88SahbabJDLbS6fp872jvOpBEr3UbC4BURRhFjeMYeUOJFZFjnEcPZdi8Y8dXp807Ja7aX1ts3bTW60Vkjz415xjyRehftdE0Lw9p0emeH7K00mxsY/s9raWCCGCCNDN5cccaAKqr5UQUAAAAcV7RkTSTRC1khkLPIS0aJn5mxs2ZwOnzu/uQMk0AKyyvOEluJLgLlkaePlf7xCE9OnLELw1AHlt7rN14y8RaFqWhSPdQz3bW/hnSo3kaHUFjaJJtduj8pltLVXkMQ3Lbylrd0d5Z7Jo/hc2xdXiHGPJcE7U1rVmtf+3V89O97rZSv2U4qhH2s9+iMv4mfAO08Px3/xL1vRvDfiF9MhjvNR8RpqV14Y8TW1nDBL9smGq28h87KH5LXdZ2ygsC6IFxpiuEZ06fLgMQ4x19yaU4d1o1ZecmpPrvuo4lN3nH5rRnN6rrF9qc8NrPrOgaje+GdZtr5NA+K+kpb3Gl6pLGstta2Wu2Ya1jnht23q0cd7cjzMvN8wI+Fw+HrYeH1ylCcYu69pRbs7btwdnq+jdOPaJ2SkpPlbV+z/AM/+HNjw74Y1eXWtMs7rQdd0W71S6hfxHoHiPX4taWTTra0uM3FjdPM9xNbJfXdpH5UjbUzv+yQG4Mr/AEvDVetmuaxrV6ntVShLlkk01dpJSVkk7Xd1dvbnko2XPiEqdOyVrvb+v68jW8TxfEC78E6h4rvIdY0vWo/B+k63a6HbaksmzV9OlkvprLzFdUcTMIbaZsoJoiV3AHcm/DODp1sLj8mnZqM5K9u94p9UrON1vZixEmpQq+RS8B/Dafwn448bRWNrBFZav8Sz4vhWOeEubS90GG3e5MaEFQ9+l0gyNzFGb5hlj5HDOMWIzPB8z1VOUOu8XNpX8oWfZbeRpiIctOXrf8v1PedRk0G2m+yW+mxTzAlTuk8uNSByCxOMjjj9c1+tnmmOuWeSKSSCP5siMSqYTx7ZHAJ5znsOaAPDvj78SbIeD9QltfEEvhrw5ZX8VrqXiC1s47qeW7ilSR9O06CWJobl3SGaGSSY+RA77T57pcRw/IcRcRYfBxlgKS56sk01eyinp7zWvXZa26q6b+p4S4SzLi/MI4DLYpyerbdkoppOT8ldXsm+yb0PGNW+Hvx0+OFxNZ6FpraD8OdfvE1OF11NWl1uFnRlutRnaWa4kmFrHGihlkVGhtoljjjVpIvmsBwrjMXKFSq0o2VpaWSavaMVa2r121bbbe/7DguKeFvDDBReBwzr5v70antdFQmlyySaVnHmvy8jcpQbUqkVyo+mvhP+zr4C+DPhnSZ9Kii1fU7GIpdatPHtmjuJNvnyW8ZdhbrIVQMqMWeOKAPJL5Ubr+h5dleGyyDhh46vdvd+r/TbfTVn4xxRxXmnGGPeY5rU5p2sklaMYptqMV2V3vdvdtvU9NOtasJFuZJdrRrgpuG0j/aXjJ9cY6dsNj0T5wvf2Np3ieC9XWNGtZtK1SzksrqyuYVkjvIpRiUSKRhkYZXBHzAnI5oA8B0/Q7zwb4r07QW1lSvgHVT4ff7VI8lzJ4V1YRmwZIYj85ivYLW2WebJMNjeMWMjMT8Ph4xyPiKVCKtSxKuu3Or3V389F1lFWtY65fvqF+sfyPeNA07VrBmuYoPlcENHKdgfHQjjIOfVcYPX0+4OQ6WGaO4iWaJsq3T29QfQj0oAfQAUAFABQAUAFAHyf+0B8MPGPwj8eW37RXwMTTdLgNxJc+Nontyyywk7pbiQRxSTGBwD5yx52sI5gm5XkX5XNMFWwFdZjgbJbz9Ordk3Z/at1tK17tfvvAXE2W8WZVPg7ilyqSso4Vp2altGEeacYKcXb2TnvFypOXK4wl9HfDzx74e+J/grSPHvhWeSTTNZtxPCJQBJGclXjcAkB0dWRgCRuU4JHNfRYTFU8bRjXpfDL+req2PxriDIsZw1mdbKcekqtJ2dtn1UldJ2kmpK6Ts1dJ6HRV0HjhQAUAFABQAUAFABQBTvXxeafHj70znP0jf/ABoAuUAZfiLxJpnhiwS+1J3JnmS1tYIgDLc3DnCRRgkAseTkkKqhmYqqswwxOJpYOjKvXlyxirt/1+C3eyHGLk7I462+LHiK4txM/wAD/HFs5cqYp7rRdwAYjdlNQYcgbhz0IBwcgfMT43yaLspt+ai/1SOhYSr2NzTfHtvcvaRaz4c1rQ3v7iO0txeRRTK0zxyOFd7aSVIh+6K75GRC7xorM8iKfWwGf5bmc/Z4WqnLtqm93opJX0Tva9upnOjUpq8kdTXsGR8qeIPhrZajd+APA62WharbaL451HRrCPXdJhuYodJi0y+nhsQsYTMcbW1ptLbiTawu++RA9fmn9me34hxWEw9SVLmip3i3dSbi29+vNJbqybS00O/2lqEZSV9bHTW3gX4m2ljfPeaB4v0LXNRbYNQ8MeOR4gS3gjeN1bGtqkaSsQ6FUtnAQD584CetLA8TYCalh8RGtHtNWd9umtlo/iWvTvnz4ea96NvT+v0C3i8Q6JJa+E7rXfi74u1SeOPUTZTaHptkptJN8EtsdQS0tbNSplMzxpci5xCpTcnyvm6/F2Jbo+yp07/b3t8uaW+3wv5Dtho63b8jnNJ8Lz/DvUPD3hzVvC194R0Tw/NbaZ4E13VZUuzEl2hjk0K7lhupX8rfHB5EkksaSTCyi2SS28RufNeCzfhuo81aVTmv7WMXo+vPays972i7au9pNRvnpV17Pbt/kel+CvE83iWyntmt4dN1WwvJNL1jTkuVuTa3a8FQ+xGZHinSWJnSN3hmikKJvVR+gYDG0sxw0MVQfuyV1+qfmno/M4pwcJOLOlUXDruSOCJSySsAhQYJHHGcjE+Mf3QPoesktC8tbcafe20SyTwoWmkI27mZcHOAWbHJJ7fU0AdLawRjF0ZBPLIvMo6Eei+i9OB+OTzQAl/BLcxpCnMbSATLu2lk7jP8x3GR3oAZ5E1ixaxiEkLHLQbtu055KZ4/Dge45yAeN/FnxEuveN5PDmjJc32reC9ItNat9Dvk+z6df6pqV1LbaTLNMMuVgmsbospRkUzRTKrywxFPjeLa1aq8PldKXKq8rSfkrXXzv+FtmzqwyS5qj6Ha/D3wJH4at5JL3VtT1S8ldLjUdQ1CXMl/dCGONpmjGI4ARCjeVCqRKSMKGDlvp8FgcPl1FYfCx5Yrp/m938znnOU3zSOysVMiteuQWuMMuBjEf8A/I5PuTXWSD6lZRyvE84Bj4dip2IfQtjaD9TQBaoA5T4qeHb/xR4A1jTdF03Tb/WoIk1LRbfUndLRtVtJFubEzGP5xGt1DAzbecA1z4vDRxmHnh57TTi/mrFRlyyUl0OD+EOoaVqVzr0PgnVLS507V4LLxpYQRi1+zpZasrkxqsTl2Etza3t00zqFd7xwjvtk2/NcG4qrVy94aunzUZOGt+m266bW3SSva6N8VFKfMuup6xpDX3kGK+gKeXhUZmyWHv7jpnv79a+tOY8o/a0/Z88H/ALSnwdvPAXjQa59lsbyDW4f7E8o37SW+4tFAJf3fmSRNLEpf5Q0gJzjFAH5bfsVftmwfsoat/ZXif4N6b4d+G+t3CzajrJ0uZ/EVz5sWbTZKXUTwLkzbWUhUeYxszMkb+dg6vs5vC1qynU32SstOi9eurv2PsuIsAsZhYZ9lmXSwuCbVNNzlNTqe+21Kdm9ItPlXLHls/eev2NZf8Ff/ANnHxFf6xoXhDwn41udSstL1fUNPk1KC0sbG/ksrOe5SETee8sZn8gRx/uSxeVAUycV3VKkKUeabSXd6HyuFweIx1VUMLTlObu0opydkm27K70SbfZJt6Hn97/wWh8IQaD4A8TR/CS6ZNYv7+18W6ZHqJkuNJihkiET2szRRx3TtFKZNjCMZAjLrneJVWLqOkt0k/k7pfkzWeX16eCp5hJfu5ynBd+aChKWnpUj669j790X4ieE/HfgXTPGvgjWodV0nxJp6Xul3UQZBNDIuVbDAMjAE5RwrBlKkBgQNTiNIPfCwto4G+zxStHBEuzdNs/vE5wp2gnGDj+QB51+12ix/sn/GCNAcL4E1wDJyf+PGXuetAHF/8E4f+TJvhZ/2Dbn/ANLbigD6ToAKACgDO8SeHdF8X+HdU8J+JNPjv9I1qyn06/tJM7Li2mQxyxtgg4ZGYHBzzQB5jpn7Iv7N+jQeE7bTPhNpFvH4F1STWvDqq82NOvZJIZHmjy/3i9vCecj92OOtAGN4k/YU/ZJ8X+ItU8WeJPgdoV/q+tXs+o393I8++4uZnMksjYkAyzsxOBjmgCeT9iL9lOXwhb+AZPgrojeH7XUptYhsC8/lpeSxRxSTD95nc0cMSnnGEHFAG/4g/Zf+AXinxL4p8YeIPhlpd7rPjbT10vX7uRpd+oWitAwifDgYBtbc8AH92PfIBxKf8E9f2MI3V1/Z+8OEqQRlpyPxBkwaAPfdJ0nStA0qy0LQtMtNO03TreO0s7O0hWGC2gjULHFHGoCoiqAoUAAAACgC3QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QAUAFABQAUAfi5+1N4n+Gvgb/gqJ4/8AHfxK8Nah4nXw5b6TqHh/w7aQGQazrq6TYLYW8zK6mOESsJmOH3CHZ5cm/aQD239o74ifti+Kf+CcHizxX8bvCt14I8QPrkH21bUxWj3OgX06bI3iDmWAKblLRoX/AHrRr++3FpcgH51eCPjFoOk+GvEur+PLPWfFfju20C28LeAZb+6Mmm+HrJ1mjuZwm4OJ4YnAtVX93HLNJMQXRDWlWrOtLmnvZLotEklt5Jeb3epFOnGkuWPdv73d/i/l00Psrx3/AMFDfj9+yX4V0r9nePxVpvj/AMfaXozN4p8Ra7cHUZtE1i4cMNOjeMhLhrOLcrySSXG6eQgsqwmE5lnH/DH9vD9t7wT8K/EP7Suv/E7RfFWg3XiCPwla6N4gtUkEmqPEbt5oobYRPDHFBuHDpGWlQbW2YUA+8tZ/4KFeBPC2qfEHwLofhLWfGOs/DLwBF4vv7i0tjpMN5cCREuYWivSJbdV+02k28GcmOWYAO8SrN147A18txEsLiElONr2aktUnvFtPR9H5bnPhMVSxtFV6Lbi9rpp6O2zSfTqj8vPjJ+1X+z/8c/jo/wAdvHf7LutXd7ftCusaN/wsJ49P1CKKz+zxkmKxSeKVdsLBklCfuQDGdzE403SSl7RNu2lnazutXo7q19FbWzvpZ6TVRtcjS11ur3XZaqzvbXXtbW6/av8AZT8SDxd+zh8OvEFv8OT4D0+60C2GleHzqx1P7HpiLssf9JIDSb7VYJPmG8eZtbLAmsjQ9XoAKACgAoAKACgAoA+Sv26f2OPD3x18F6h4l8JHRPCPimW5spvEviZdCkvtQu9CtY5hPbxJbq00sux1ZY4wGm+zxQlgu0plVoU63L7RX5XdeT7nfgc0xmWqqsHUcPawdOdnbmg2m4t9nZX7rR6Np/M//BMP4yXOj+Lrr4U/Bv4PakvwmtFvNQ8YeOtX1qeREu44AY7oCQRWkG8qsfkxxCZoTE8hb7O7FU5VIwc69l6O6S9Wl+St+JeKoYSpiI4fK+epeyTlFRlKT6KEXO2uiXPJy30vyr3f4mfFvxh+1P4ivPhz8BZ9f/4Qy30//ipb6O0RVuYS5IKJIUkwyqQsXmRvP8ylVjRnPlPFRzS9GnflavtunezlqmoPSyupT7cl2/vI5HX4DUcyxfK68J8ukk+SUXDmhSfLOMsRBOXNNwnRw+nvPEOEYfRnwJ+A3g74DeFf7D8PKbvU7xY31fVpU2S38yAgHaCRHGu5gka8Kp5LMWdu3A4CngYWWsna72vbbRaJLZJaJfefN8V8WYrinEKVRclCDl7OndyUFJ3k3KXvTqTfvVKkm5Tk23ZWS9KruPlT4h8T/wDBTX9n3VfiT4s+GfgjWzcajoul3DaRr07n+xta1O3RpPssLxuGcbl2xyfKszFxGxzC0oBtf8E6f2uPil+1F4K17U/if8P9I0O30zUlstI1bSpXitdQcxmSS1EE8skvmRKA+9WZGVyMIYyXSkm2k9jSVKcIxnKLSls++ttO+qa9T6v8V6XY654Z1XRNTmkitNRsprSd4riSBwkiFDtkjIdGw3DIQwOCCDilOEakXCaunujPY+eP+EO+KNi1zdQ/D7wr4k1XU4pLTxLcah4y1PTNN1qFLZLaO8udOg0+e2eeaEOkqtEQiRQIJZVRBF8RHhbMKKlhsLjXTo3vFJO6u7tXunZaddbvRa37PrEH70oXZJqH7LXwknvbp9I+DXwWtbNFRo0uPhrFcFBj5t8itH3BI/djrjsTWsOFcYo+/mFVvybX/tz/ADE8THpBF/wdrGvW+pyQ6Rok9vc6ZDDZeI/Bb3qSSaWjLL9nvdInmCJNZzbfLVWMULRREILae2urSXioZ5i+Ga7wOdNzpvWFRK7a7Pv56tp94tNU6McQuelo+qOi8a+KG13SrvwTfeFbW8bXrYwweFb6XM12DvLSanPAZorewcxskhZJEZcIfMeZbVnis8xXEddYDJW4w+1U2068uz6rtJvsrtkaMaC56ur7GhoPhBtN16/8W6xr0+sa1d7bRpJA0VrYW6qubaytC7rCXKs0jOzvK/HmbEiii+oyfJMLktH2dBXl1k7cz+fbsv1u3z1a0qruzX1C/trVLc397cyrPOlpApQ/v3dgIwEX52XbGzuQDtWNnYgKzD0MViqOCoyxGIlywjq2/wCvuS1b0REYub5Y7mTL43n08SXOs+DNd0HSdpF1e3Mto8FpG2AciCaZ1TO0vJs2qAWby0VmHgYXjDKcXXVCFSzeibTSfze1+l7febSwtWK5mjo7SaAQRTWt/cKskcbKyw70GfKOR8uCPmzkdcLzyM/TnOT6bbWBubiTVYfNPlC7NwdwHzAEjA4z8wwAM9fYUAQT3Nq8ksNnZCFrgEiVnZncZzklvuArknOcgjPHNAFZbCa9OyAsybV2yOnzO2GCoPb5SPQYOSQoNAHm3wyvbTWf2jPi5rSI7yeE9K03Q9KZzjyLd1kmnTAOGDXEe8M2Wx0IBIr9Czyi8v4Ry2hDRVpVKk+t5K0YvXa0Haysuur1PjsqqrGcRY6rPelGEI+UXeUlpveSvd6+mx69dm6khi+2XbtMzosUykNbtkjDAqBtZTk5PoRkg4r89PsTXs73ULqzim+wKJHVWJeQKhBGcjG4+nUUAWrSOWKIrMVyXZsKSQuTnGT15J7DjtQBNQAUAMMUbSrOV+dFZVOTwDjP8hQB578R/hD8OPGEs2reJ9Mu1+3W62GpnT9TutPfUbVSzxwTm2kjM6K7PhHJUCacY2yyBuPF5fhcfb6zTUrXtdbX3s+n+aT3SOzL8wxOV4mnjMJPlqU5KUXo7Si7p2d07NbNNdz5Gj0fx1+wP43ku5tL1Txd8JvE6r9tKxrvsJPNAG5x8q3CJtxzHHPkDgxgx/I8tfhWvdXnh5fg7/nb0Uvlp/Tzq5T495Uo1HTw2bUNFq/3keVuy6um5Xdl7SVHfVTfP9e6Jf8Ah3xv4ci8ceCvENlr2j38BnjuIzhsbDweMhwT86MFcMuGAbNfaYfEUsVTVWjK8X1/r8uh/M+bZRjsixk8BmVJ06sHZxf5ro091JNprVNrU17OSz3ySLoscolOIg6xhRmZh15xw6DgHt2wa2PNIUEV1fRqmnRQGc5AwmxQ0aFBuKMOdr8Yzkn3oA8w+IniTQr0at4b1PQLrVvDMNxFpV/aRoXHiPU3YbNKtRuhSZVKgXLy5tArSxyuFjvPI+R4gzTEVKscnyx/v57u/wAEe7fRtfNLVK7idNGnFL2tTZfidl4c0C68P6hda7evDJ4k14RjUbqKGRVWCJGEVtHneTBFvkdMHHmTTybQZWr2snyqjk2FjhaOvVvq293+i8kkZVajqy5mZPxV1ibX28P/AAwe7VoPEd29/rBM8Q2aHYMkt1uJBSaGaVrOzmhZVZor+U8hGxw8VZi8tyypOHxz92O+8vTZpXa80i8PD2lRX2WpnadJc+B/Aur+MJtNe71zxDeNqiWmr2cdsbjU750i0+wuXtBKimOI6fZNPmTCwCRsgEjpwVKlkOVRjV0VOF5W11teVr93e3qTNutUuurJ/g58HvCHhu2WHwfCllpnhi0fw3pbQ28CQu+6F9RmjWB/KUS3FvAskYihK3FrcNtPmsW8rhGlWrUKmZ4n460r/wDbqbstdertq/d5eiNMS0mqcdkRQaDp3h/xboGreIr/AFO711NR8QeHdMjnmEsLxXt0NRAkwCSFg06FYxuARW2EZA28uUv6txNjMNBLllFS+fu/m5O/X9aqe9h4yfoebap4Z8P+HPCOjHXNAh8P+FtDgbT5lsUkbVGi8Mah/aOgxo8zMjW32WzvWkMjBna4j2sm5ivzsm8u4jlFO7VVSV/hUatlN9Hf3o26aO9zf46F/L8tj6Y0K4gtZzBckGR1CxSntGMDb0GAW/Nsg4biv1o808N/aM+OOi6Dp2rDR7u4Wz0beNUm0++S1vtTuIpY0NlYFvm2o77Lq9hy1sN8UWZxI1p8jxDxBLCN4LA61mtWldQVndu32rapPTq9LX+w4O4Rr8VZhSwzmqVKc1B1JJ8qk7tRvs5ySahFuPPL3U1e65b4C/s+6r8Q9XT4vfEyJrXwjq+nRQaX8Pr7TUWDT4beaVLW2aF41jW3gjVHhMaI0gnYthWkE3Lw/wAPKCWKxDclNaqS1bvd8176XSkne70vZaP9D4s4uwfDGAp8L5NSprFYWcrYqlJ6qUEnKEk+ZVJxbhVi3KNNw5YXtB0vpDWdKGlI0sUW+1Pyhewyeh/Ic98dmAJ+4PwwxorgxlymEDMMZ5A6g5GMHhj2+noQCa2e1mvIZLmCQwqMFd2Q2D03HGAB656AEgcgA7pry2S0+2vKFg279zAjj6dfwoA8y+NFpo93ocPibW2sp/BzWd5pXi6K9uXjtv7HuYSJJmQfITHIsYZnKiOCS6bOeD8xxXllTH4NVsMv31J80bb6bpfn5tJHRhqihK0tmL4S8b+Jrj7N4G8ZTpbeKrO3LyyBDHDq1sk7QLfRSKoQF9scssEZ3WxnRGJDwySd2S53hs6w6q0X7ytzR6p/quz2frdEVaUqUrM7HR7+Gxt727muC9vuWQFV/jbPyg5OW27M5JwTyepr2TI0LDX7HUJzbIssMmNyrKoXcPbmgDSoAKACgAoAKAK+oafYatYXOl6pZQXlleQvb3NtcRrJFNE6lXR0YEMpBIIIwQSDUzhGcXGSunujWhXq4WrGvQk4zi04yTaaad001qmnqmtUz448AeKNF/Y0+P2u/BnxTfiw+HnjWRNb8O3khzFps0h8vZM7Essf7vymkZmwIoXbaruw+PwtaHD+Png6rtSn70X2b018ul/JN2u2f0bn2WYnxg4Sw/EmAhz5hhU6VaK3qRiua8UlZy151FJX5pxjzOMU/sXTNU0zWtPt9X0bUba/sbuNZre6tZllimjIyGR1JDAjoQcV9hCcakVODun1R/OmJw1fBVpYfEwcJxdnGSaaa3TT1T8mWqowCgAoAKACgAoAKAKdwgk1OzDZ/dpLKMeo2r+WHP6UAXKAPDtYvdc8Z+NdOtNI1WbT9Q17+1RpGpmGOT+x9AjFok89uYjNaXM9xNFbz25uCCsdyWaN/s8ttL+f14vi3NZ4Vz/2Wha6X2pa9dGvtK6urLT4kztX+zU1K3vM67Vv2d/gP4iuLe/8U/B7wf4h1C2tobRdS1vR4NSv3jiULH5l1cK80jAAfM7lieSSea+5oYWhhafsqMFGPZJJHHKTk7tnDaVp8ttrGq6npOn6qLq0+IF1f+JNEsL+K/e6EdoY7JWlvJYvsyNCNKvfLjY7DtiAMbMa/N8TisHknEkqmKhyU4Q/d8sbLXd2Vr6ua/4Y74xlWoWi7tvX+vuO9tfjV4Vt2li8bafq/giS20/+1LiXxDbrFYwW+5UO/UYnksVk3EjyftHm4Uvs2Yc/b4HP8tzGyw9ZNt2s9HfyTs36pWOSdGpT+JHlHiHxtpWheK5vEr7p9M8AeJz4w1y5X5I/+EfvtIvI11K1kfEd1DEZ2aQRuWC21wEEkgjjl+eqT/svif2+JVoV4qEXv73uaPqtVb5rpdrdL2mH5Y7rU+lq+5OMiaCN7iO5bJaJWVfT5sZP14/U0AU/EXh/RvFmgal4X8RadBf6Xq9pLZXtrOgeOaCRCjoynIIKkjBoA8GD634B1XTU1+XUdRm0q7tfCetXlzcQzSalp8ocaTq7xQo8rTfaZVgfIt49899I6GKKF1+EyxPh7Op5a3+5rXnDyl/L22TXW9odXY7Kn7+kqnVaM9UEUUUyieQRxiYq0GfNZAWxlQfvY2bDkHpkZyK+7OM2buWLRNPjk0mON0m/dm4LB+QPlycgEZ75wPTtQA3T7ea00yPUbG5MjlPOmgxiOTqSAB91uwI4O0cUAbcE8NzEs0EiujDIIoAyPEXi3RfDVjdXup6ja2qWqrvluJlSJGbG0OxIC9VwDjORjJqJ1IUo803Zd2NJvY8d8LXf/C0PGMGt6RBaXV1eNps3ibVLSaLbpdhZNNc2Gl+dE00Nxd/apXaRY2ISKadzJHutPN+NoYbF53nP1zEwcKOHbUE7pyl/N002fb4Ur6nU5RpUuWLu5bntupov2aPToQyi6k8ttn3gh5dvfPIJ/wBrNfanIH9mGM3U32uR5p4zGkjKodMjHBGO4HHHI9yaAM6C4s7aDdbSy2cigBwAHhkccEH+EZKkZypPX3oA2bKygs4yI4Y43fBk8sYUn2HYZzge9AElxEZ4JIBK8ZkQpvQ4ZcjGQexFAHhPhPxDceENf1PUr7w1bjWtEtLbRPFthYpcNcJpVs90+m6lZQnPnQOstw5ij8xiXmhWWSezeGT87yzEPhPHzy3Gt+xqvmhN2td2Tv8Agm76WTslK67qkfrMFOG63R7HprHXLcajcS+ZaT4e2SNh5bxnlZMjlgRjGeO+ORj9EOE16APjz9tz/gnfZftjeMPD3jVPi5d+ELzRNNfS5IG0VNQgnj80yI6gSwuj5dw2XdSAm1UIYvKhFScktX19Nvzf3m0sRVlSjQlJuEW2o3dk5JKTS2TajFN7tRV9keUfA7/gjj4Y+FnxT0Lx94x+MkfjXSNJed7jQpPDL2K3heCSNMzLeuVCu6PjaQ2zaeGNZ4jD0sVTdKsrxdtPR3/M7cnzjHZBjI5hl1T2dWPMlJJNrmi4u1018Lavut1Z2Z8c/t//ALP3wR/ZE8YWHwh8C+HNU1691zR4PEP9va5rDtd2QeW7tzbxw28cNuYi0SSBmVn3IRkKcEqwnPl5Jctmm9E7rtr37rUjL8VhsL7X6zQVXmhKMbylHkk7WqLlau4/yyvF31TPtv8A4I/ePtb8bfs8nwRqeiw2+neAtaubaxvo/MH2xJWN0UfcSvmRy3UhyhAKSRgrkFn2PPPvmbMup28YJ2wxvMwx0Y4VefoX/I0AeXftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAeN/tT/GTxP8AA/4faf4s8J2Gl3d3d6zFpzpqMUkkYjaCeQkCN0O7MS85xgnj0+44A4ZwnFWZTweMlKMY03L3Wk7qUV1Ula0n0PlOL88xHD+BhicNGLk5qPvJtWak+jWuh8sf8PDPjT/0LHgr/wAArv8A+Sa/Xv8AiDeRf8/a3/gUP/lZ+c/8RMzb/n3T+6X/AMmH/Dwz40/9Cx4K/wDAK7/+SaP+IN5F/wA/a3/gUP8A5WH/ABEzNv8An3T+6X/yYf8ADwz40/8AQseCv/AK7/8Akmj/AIg3kX/P2t/4FD/5WH/ETM2/590/ul/8mH/Dwz40/wDQseCv/AK7/wDkmj/iDeRf8/a3/gUP/lYf8RMzb/n3T+6X/wAmdD8Ov26vi34u+IPhjwnqXh3whFaa1rNlp1w8FpcrIsc06RsULXBAYBjgkEZ7GvMzrwnyXLstxGMpVarlTpzkryha8Ytq9oJ2utdUd2V+IWZ43HUcNUp01Gc4xdlK9m0nb3tz7qr+fD9jCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/AJLl8Cv+xgT/ANLLOgD6foAKACgAoAKAPyB/aUt/iXL+2z+1xdfCHUtYsPFGm/DTSdTt7jSL42d3Hb20nhue6aOVWVgRbxzHap3OAVAYsFIB4j/wT7/aC8S+DvjR4w0PxJ4XvPiPpXxR0LU18VaZeaoqPfpBFNe3V1I06st1N9mS+URyPGJGuCGkXJNAHnXw/wBK8H/D7VdG/aZ1v4C6l47+EiazdaZBol94gBS31SGOOSO21G7js1WT5ZY5lTyUSYfKdwSZCAfrz4Q/Y4/Zm/aS8P6H8f8A4v8A7Kum+FPGPiy3TWL7SYdavFWIyO0kbTpAbeJ5pEKSSh4Q292V9xBJAPYPF/7KH7N/jvwHpPwy8T/BnwxceGtAjmj0myhsxbf2cJjum+zyQ7ZITIwDOUYF2AZiWANAHlutf8E+fAer/Ej4sfE2X4leMZ9T+L3hzUPDurLfSwXC2cV1NA4NsRGrKkSW0UUcblsIoG44FAHDeDP+CPH7JXhPxPYeItQvPHPieCxcyHStZ1S3NlcttIXzRb28UjAMQ20OFJUBgyllIB9uWVlZaZZW+m6baQ2lpaRJBBBBGI44o1ACoijhVAAAA4AFAE9ABQAUAFABQAUAFAEV1dW1jbTXt7cRW9vbxtLLLK4RI0UZZmY8AAAkk9KAP5xv2n/Guu+Lvj94z8MeHdQWTw7pHinVNI8LaVo8EdvZW9iupXDW8dvBbqsfLSF9wUszOWJZmJOUqFKU/aSinK1r2V7dr9j0KWbY+hhvqVKvONLm5uRSko8ytaXKnbmVlra+i7H6U/tLft6fCX9iXw3/AMKG/Zo07S/EfjvSL1YdYe/t5JbK0kCKZ5buaJojc3bfIm2NtqEMrFPKWEzh8PDDQ5Ierb3b7vz/AOGWhpm+b4jOcR9YxFkkrRjHSEIq9oQWtoq70u223KTcm2/nnSf2gv2wvj14X+F/7Qvgrxt4217WfB3iiLwv4y8L+FZrlUvYoXF/a6hPptirqUnia6t5XMOw/ZEUI2StOXtmp2t/d+7r8/wFSeXxqYZy5mtPa3tb43pCzvbktdtp8zaSSSb0PF37LPxsutG/bY8aj4V+KHn8SeI4bLwxZx6BLLd61BJ4oju5Z7QKDK8SRQxOSilHDhs/uqtxk5qSelnp3elnfy19b+RzQq0FhZ0pU71HKLU7v3YpTUo8uz5m4u71jyWXxM0v2Kf+CT/jLVfEei/E39qLQNPs/Cn2WS6j8I3FzMuo3MrpiD7UsW0QIN3mFPMMm6MRyRqC4qzlMv8AbF/Z58ffs02Mvxe+If7UgkvvCupWtv8AA7w1YoqSxLDc2u+R7WOCO1tvJtIlMvkRCOWXymkcM6xSyrpvt/X9f1rrNxlTjr7yurWSVt073u223e60SVm9o/Nvhr9uL9s2b4hN46svi94s8RagJDdXGl3DNd6W6EqGX+zwPIiQ5C5jRCu75SpINRWr0sPHnrSUV3bS/M6suyvHZvW+r5fRnVna/LCLk7Ld2im7a7n7M/s+fGzw7+0J8J9F+J/hmzW0XVbVor7TndJ5dOvYmKTQMwjHIYZVtqlkaNsDcANE01dHFKMoScZKzR6eZ/swlClhHOyKVWJOXBPGFO1k5PpuIA6A4ZJy3jn4ZeH/AB1JplzeyR/2vol0moW2qC0juGgZZI5jHJHIMSwNJbws8TjIeGCaMxzxQyRceOwNHMaDoVtn1WjV01dPpo2uzTad02ioTcHzI8GisdUvvHFnoehX/iCXxjoKazbaz4g8CXPhq5mvUuLi2jHmjUZDOipFp9hAyyW0sy4tozdziOSWb5HD5LnGTVassulCUZcqSk5aJX6bJ3k3pJRu21FXSXU6tKqlz3NbVYPEltc2Gq/ET4g/Gfwxpnhzz40v9ffQxok0jtLmW+bQGjngRUiEiyyy20KbY9zbndJHjq/FOHoupywknv7O/NH05k0/uk9Xbo0QWHk7Xfz2OxtYDY+I9B0maKPwzq2vabd2EHiC61m51yZf9Gt5pbfSLi6Vo8Ha0uJfLeX+zS/2SaNDJD4uUUJcWVmsxxTnGm7qnblb1avJLTyfK5NXtzLS+tV/Vl+7ja/Xc2fHvw9+Hfwa+HPin4ufDvwdonh7xXoWi3Wu3txawiKfWnggeeSLUJB+8u2kw+ZJmeQO5k3b8k/dZtk2EzLB/VqkbKK922nLZaW8ultrHHTqypy5ka/gS7t7XUvF3h2Iy2ll4c1lLDSreDHlQQf2bY3OwAtwFeZwijCqqxqoCoFHFwhiquLyinOtK7V1d72Tdr/LQvFRUarSOrsvtVys4hmkMg/fGNWGMKUxwOQACeFx91cdBX0xznIfFnxdL8Mvhjr/AI3lt1c2Vrut4tgZLm6kkWOIEKR+6WR0JwQSoYr1497hjJv9YM3oZc3ZTer2fKk5Sto9eVO2lr2voeRn2Z/2Pl1XGpXcVp6tpRvqtLtX12PlD4P+Ov2vvi74im0zwl421CKC4g33uo3NtAltZ2ryMm+MtH8nzK4VYcNmNtoAQlf3viTJuB+G8NGvjMPHmi7RinLmlJJOztLXSzbnpqr/ABK/5HkmZ8VZ3XdLDVnaWspNRsk21dXWmt7KOujttp9Z/Bv4P6B8IdF1XT9K1nUNf1PWpxda9r06hpLiXB2gJkkplnYglz+8fLHPy/h/FPFVfietTcqapUqS5YQjtFfhrZJaJKyVkj9UyDIKWRUppTc6lR3nN7yf9NvVt3buzvXP2u8SCAxESlhKbd8xTbsAtjPDBC5PXnbycivlj3zpaACgAoAKACgDmtfuFub+OyugUtI5UUtuABc7Sc85wEY9O5oAoT6fpmp6PP4f1nSotQ0rUrcxXllMoeOeNo1ZtqnuA4BHcAHqDUVKcK0HTqK6e6OnBY3EZdiIYvCTcKkGnGSdmmtmj5Jn074kfsIePdS8T+HdIu/EXwP1u5ge5ijuVlksnlGAwBIaOVCNocjy5UMSO4kKmP4t08TwxXlUpxcsM2r+V/ya2vs1ZN3tb+mqeLyPxyymjgsZVjRzqnGXK2mlNR1a00lGS95xT56clOcYuCkp/TXgnx94O+JnhC18efD7VjPpV+GSOJwyyWtyP9ZbyIOY5AQjDkg7QVJVlY/W4PGUcfRVeg7p/en2fn/Wx/PXEnDeY8J5jPK80hy1I/NST2lF9Yv707ppSTS5j4oeK4V0rW/D1jqt9YW9np8k/iPxJaRyMdHtHVm8u0REbz9Qk+9HAqFoSySuG3W8Nz5Ge52ssUcPh1z4ippCPrpd7WXzV35JteRRo+096WkVuc3L4W+KqNB4ok8Ja3pMSWEel+FLDwfq1kuqeH7N5AsizW92os7ljHFbySs0k0SFUihiZoftV14WF4czbKUsRga0XVld1OdP3m9bX10X/brbu27NKOsq9Op7s1p0saOg/EPxJdXgvfD62Pi+Aw3Fy2kS2L+HvE1vBCRHGRYXvlLM8kuMyu1jGoKsocOoHTDiutgJKlnWHlSei5l70G9393ZOT32sJ4ZT1pSv+Zyl94k8SfFWDxLrNh4B1e9tNc1CLwm+kXDaVctJpGm6nJba2rxzXcYbznN1bORIcRLbOpZwEry83znA4rN8NUrTtRpJSvaWspJSjstvhavrumjSlSnGlJJas63xb43uYb6x1Cbw9rFtP4N02/8AFt54fstSi+3XN1Iktnp1m8EcjxSrdeZfCJfM+aa2h/iUiteJM9wmb4GGEwVS6qzUW2pJJK0m7tLb3W97LcVCjKlNymtkdV8IfEPgt9LsPhyvxF0fXNX0uzCX0VhfwCa4uCEa4uLiGLDo80kvmkuAWabJ5b5vvMKqMaMY4dpwSSVtVZaHHK97y3MrxNb+G/A/jDxImlaTrF5qmqa34f1uRLdEdIZNREWhoYwCpCLFZu8u/O1WZwScKvyGZXocUYSq2lGUHH7ub821Y6aeuHkvMz/GGgjwtrWsQ2HhnSFk8TXa3lle6jNMdOn8QLZJZfZr1t7GNLm0WGGMpCyBklLAyPDHLx8a5XUlUhmUb8iSjPl3ST5lJej31XTo21WEqKzpvfoeW6n8UvFvgj4OPp3ifw94u0a28NbtEvLu+1OKXW9eMSB7a0t7uFnVpXs1L6hexsRCyXSW++VfOtdsbxevqcIYNqVaUfeaT5Ya2crPXV/An3Tlo0pfQ8I8Kz4pzqjlSqxpKbtzSa230V1zStoop6vS6V5Lnv2XPh9rfiH4waH8SfG1sv8AaGkw+VHpFmx0/T/CFibZvsNmsYB/esjoY7FAFitpFmncNNBFPxcJZdWr4n69Je4r+827zk92u/Xmk7pvRJ62/VfEjjTKsmyyXAvC9OPsI2VWp7sueSs30s3dRvPR3VoKMYxb+85ZY4IzLK21V6nr9AB3PtX6Ufz4Y15PDNbvdX8L+YhHlwE7DECTh89NxHOTxkbRznIBztzMt5O04GEClWbkLLhsk7e3UEgHjOaADTtLXUTcIbqGKSJSQNww55/DHB5HHIoA0LXTr3UNJmiluQILYkwSM3BI6+20YIyDwc4JGaAKWg2UWom4sZb94DIoKxr/AMtCM9fXHXH49qAPPPEfwztvC+qaV4asNT03StM1G3K+E7OfLW+g69axStF9iRZYpjE9oZle0ikSL7PaSxgIks2/4TibDLJalPO8DG0lK00tFKMt+bdK7Vr23knukdmHl7VOjP5HaadrmjeLfD2j+ItHDiDVbGDUAdoyVnj84bgpI+UOBxnA3DPAz90mpK6OMvy/axGkLR7ZI3A8yLO6Rhnav+yw6AEBgOMdiwOi0rVph5FrqaqrTIDBMG3LJ/sk/wB7+f5ZANmgAoAKACgAoA5P4k+AvAfxO8PN4O+IHh2HXLCWWO4W1JdZEkU/LIjoVeIjJUuGX5WZScMQebF4OhjqfssRHmj/AF1WqPayDiLNOF8aswyis6VVJq6s00904tOMls7NNJpNapNfOR/YE8M6Z4jOpeEPHmt+GLe+059HuLeGOG7E9tJYtb3COzquHl+aRm2lQzNsCHYV+efCtGFTnoVHFNWa0d042f37/lbS37FDx7zHE4RYfNcFTryjNVIyvKPLONX2kGkukLcqV7tJczkuZSW8/Z0/a3+HmnabpHwZ/aLjvtJ0dJvslprUKxvDGVCx26hop1lVUUBd5VFP3VTk1MspzbCRjDB4i8Y3spfgtnf52S6JG9HxC8PuIK1XEcSZM4VarjzSpNtN3blN2lTcW27vlTlJfFKRBZfEH/go1aeZpA+Dvg/VZNKdbOXUJ3ih+2sqK3nj/TIlYOGBJjRVDFlwpVlWY4riKPuexi7aX0V/P4lv5K3TTY0rZB4N1bYj+0a1NVLyUEpPkTbXK/3E2uVqyUpOTVpXkmpO7q/7SP7WWj+HdNmm/ZduJtX+23thqKWsd1cIWg8rbKiRqdkT+YwVvMkV/LYq2BV1M3zWnTi3hfeu07Xe1tdOjvpq720ZzYTw64AxeMqxjnqVLlpzg5OnF2nzXi3Jq8o8qclyQceZKUbsrP8Atr/E/wCHwtrn46/s2a/oOkzPEJNVsnbYvnQGWGNY5VCPJxh1MysnzBlDKVMviLE4Wzx2GcY915q6Vnpfvrp6qxvHwZyPP+aHCudU61VJ2pySu+WXLJuUW5KP8r9m1LRpuMlJfRnwz+LHw/8Ai/oC+I/h/wCI7bU7YYE8SnZPauSRsmibDxnKtjIwwGVJUgn6LB47D4+n7TDyuvxXqun9WPxviThXN+EsW8Hm9F05dHvGS01jJaSWqvZ6PR2d0dfXWfPBQBWu7P7SUkSeSCWLO10x0PUEHgjgfkKAI2h1SRWie8gRSMCSOIhvfALED68/hQByvh7wRpvhjxFf6yJbi/1zVJojd6neMDLNZI9wbe3UKAixwG6kRVVQTvLuXkd3blwuCw+BjKOHioqTcnbu93/Wi2WhUpyn8R29dRJxPjb4HfBX4lapHrnxF+EPgrxTqUVuLWO81rQLS9nSEMzCNZJY2YKGdyFBwCxPc0AZNn8AvCXhuWxPw41fW/BFpZSwn+ytFuE/st7dHZ3thYzpJbwJIzEu9ukUxPSVcnPi5jw9luaXeIpLmf2lpLa17re3S915GtOvUp/CzidT+E3jAeKdR8Q6F4P+F9xrni+1bSr/AMUvpkul6pBYMipuZ40uG1AhY42EDzWq/uUXeOGT56rwPF8lKlipqlF35Ze8vlqknq9bPf791i924q/c+gq+7OMKACgDyX456RLpyQ+PhpV9qujRWNzo3i3TbLJlm0aZSTdRxxr5s81s4ysaMG8m4vPLSSYxRt85xLlFXNMNGeF0rU3zQfn2v0vo15pXsjfD1VTlaWz3MjwL4mmuGbwdreptqGq6dax3VtqYSJIvEGlO2be9jkj/AHMjlQqTKgTbMu5YI4ZbdpOnIs4hnGFVR6VI6Tj2l6PWz3W/a90ya1J0pW6dDvNOtWu5xElwFjZTJFFztMwVTgnoD3ypyAD93pXtGRptdwwjy9Mtj5oQPCEXBgHO9JOvAIJxySTgYIBoASTTrRtOvtQ1TVZLCFopDdTwTrbRLFtJMgkHIAUk78gjk5GKAPGfA9n4n8W61ocSeO/EWjaxeeGbPU9fjS0t4ZZzbTlLaW5tJrVxaC+ElyWVxFOBZrHGI2gmC/nHD2BoZhDGZY05YZTvGSbtfte/vWtFrRq+r3V+6vNwcan2rGjoGreIfD3xA1i3udFv4dSvYY73WNDNwsMepNHbiJdT0iffslLeVBbzW80iGNUgkIgLBrzbB4rEcHNYPHrmwzb5JpX5bvaS+96Xe9ubZKUY4r3ofF1R6fo/iDw9q+n2/iXw/wCImubS9aWJJJIzIEeJ2jlgdSBJHJHIro8bbXSRHVgGBFffU6kKsVOm7p6prVNd0cbTTsy6+q7pol1K1MBs2EszL+8VSUIUccjqTntgdc5qxDvJeTUE1GTTIZhKgeMKw8xAO5zgMeV78Y4zjJANKOW9eQbrNI4jjlpfnHH90Ajr/tUAWKAPLPiWumWfxR8D61BNZrrEGna3FdJOjFxoRS2e6liKj/WLeR6SuC2NksvysQGT4rj2MJZXFSvfnjy+tnv5Wv8AOx14NtVPkdX8LG8RN8MPCDeL9M/s7XToOnnVLPAH2a7+zp50WAzgbX3LwzDj7x619pG9lzbnIdRTAKACgD43/wCChv7Bi/tbaFpvjDwLqVppfxE8NQNa2rXny2uqWRcubWZwC0bIzO8TjKgvIrLiQSRgHw/+zn4h+M3wz+OOmfDTwv8AtS+D/BXwu+FviKeJ7zxV4ntbKz8QWQunFxJb2MkzPPHcpJPLGqHyUc7vOSVVevPwEo1faVo1edSk9norbJfKzfd69T7Di2hWwH1XLq+BeFlTpRvzRtUqOWspybSuubmUF9mK5N4tL9hPBHjLwt8Q9OHjLwRr1nrehX0YjtdRs5PMt5zFLKjmNxw6hgRuGQexNegfHnDftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFAHy/wD8FDP+SLaL/wBjRbf+kl3X614Nf8j2r/15l/6XTPzvxM/5FNP/AK+L/wBJmfnpX9LH4cFABQAUAdr8EP8AktPgD/saNK/9K468Hir/AJEWN/681P8A0iR62Qf8jbC/9fIf+lI/Xav4tP6fPlf/AIKVppMv7L1zHr5gGmP4o8PLem4YLF5B1KHzN5PAXbnJ9M0qSg8ZhlV+HnV77WtK9wquawld0/i5Ha297qx87/tK6J+wzoXw6Wf9j668JH43yanZJ4G/4V5qTXOq/b/PTfxayMBGbfzw3nfuyCR97bVQ9q6kVR3v8rdb9LW7/PS5SVFqSr35bPbe9vdt/wBvW217an1V40+Pvxmf416p8B/hF8I9D1vVtA0DT/El/q+ta/JZWLQTSSo1qojt5GWdjFiInKEeYzbQmHwozqV6lVxSVOnJRbvq24qSsrdm7vpyr+ZWVRRoxpQm/fnFy20Vm4u79bWW7v5M5jUf2mPCXwi8MfGr4g6H8GSJPC3xKs/D+rwadqANxrlzciwia+G6MBZQlwgERJDeSo3ruyulBucKUIL46k4pea3fzt5eoYj93OpKT1jCMr+TWi67fP0Oh8PftA/GzQPjb4I+E/xz+EnhvQofifb6jN4eu/D3iKXUmsJ7K2+0TWl95lvErOY92JIiVyMAMCWWrwUnTctbXXnqlp10vdtgoVZU/bKPu3Sfk2m163t0MS6/aZ/aG8ZWPiz4ifAn4CaF4n+H/hDUL/T4Hv8AxBNBq3io2bGO4k0yGG2ljCCaOZE3uzS7BtAY7BNKpCpRhiN4zV1azdr6O3prbR29UEouNWVB6Si7O6aV9Hb8bXWl+ujH+IPj/wCD9D+Nes+IV+CVxD4o034DSePzqmp3BstTFgl27jRJ7cxt5DCRdzNuba+RsOMlVpexhUkteX8dGVTi6vInpdv5bf18jGH7Yfxx0b4ZaB+0r45+AOjaN8IdWh0+4vvI8Svd6/pdldPGq6k8C24ilt/3iHykbztrBmC4ZV2ShCSjWly3dk9LK+iTvbd2Wm19myHecb0E3ZX9bfFbfbWzdk0r6XPTvH/xO/aHvviPqnw4+Bvwd0e4t9DsLS8vfFfjO/u9O0qeafeRaWYhtpGunVFDPIjbIydj4bGck2+ZvRL8dN15LbXW/S2pUklGLTve79P1u/Ta2up55dftPaD8Sf2bdb8b/EP4QWV9eeFviBZeDNd8O/2ws9mNUh1i0hS5guljPmxo00FwuY1JZCmcASHShapicNyaOclyvrF+9r5vTppruYYhqnh6/NqoRd10ls7eV7mh4j/af+MGufFLx34B+CPw68Da4/w1mWHUdK13xXJp+ua232WO4YWFotu+xP3gRZpT5bt0OAa56dXnpPEJe6nJWs2/ddm/vvZK7fq7HVOly1FSvq0nfS2uqV/S172Sfpc+jfCmr6jr/hfR9d1fQLrQr7UbC3u7rS7p1eaxlkjVngdkJUsjEqSOCVOK6JpRk0mYQbcU5Kz/AK9PyPEfD/x9+MXxA+NHiv4e+AfhFog8N/DzxJa6N4h1vVdflie7gnhhkLWMSW7AzRLKzvHIwUqIwHzJ8nNhZ1cRTdeSShzTitbtuEmnpbRWs/Pma+y29cQqdGr9Xi258sZPTRKSTWt9X8S8rJvdI4bwR+0/ouhfCTwTpPwY+CNrD4s+InivxDo3hrwjFq4isxJZX1wdQ1C4umjBSFVR7h1WNnLSKig5LjSm3OlRUdFKF1faKV9PPXba46seStW5teWdm+rb6+X6HceEf2ifiN4a8fX3wo/aO+HWk6DrY8OXfifRdV8L6lLqGna7b2pH2u3gSaKKdbqJXibytrl1LN8oC7pqVo06VWrZ/u480tOmuq6b6Wbvdro0KMOapSp3V6j5Vr13s+q01vto9bo5n4NftQfHn4r2/hD4g6b8IPBOteAPGN7a2ki+GPGX2/WPDMcyFvP1GNoEhITA8yJHEiFsYZhtO1nBqM+vVLTa9nf89u120nm7yTcOnd262v19bflqz6qpDCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/wCS5fAr/sYE/wDSyzoA+n6ACgAoAKACgD8+/hI1wn/BY745vboHK+AbMsmQCy/Z9C4BPGeh/DHFAHqOj/8ABN/9mjR/iRN8UfAnhbWfB95JpOo6ctpaak5tVkvYZ4Jp1hk3sjeVcSIEDiIALtjGNxAOx/Z2/Yl+EP7NvgHUfAfhiTVNfh1m/t9T1CXxDJHdxzXMGxomFsEWBAjorqQnmBgpLkohUA90W+uYCsd/Zybj/wAtLdTIn5D5h+X40APTVdPfrceXyBmVWjyfbcBmgC3QAUAFABQAUAFABQAUAFAHK+MPH1t4cuo/D2kaZPr3ii9tZLqx0a1YK8iIQplmlb5LeEMwBkc88hA7YQgHEw/DTxX4otdWsfi5dXerWaJPPpmk6fdq9kRLI8iRyNKFa5uICFRWnUQ7RE4TeHKgH4a3vgrRtS+Oduf2QPBHxCv7rwRZP4j1PTPHsdhLqEV7ps0k1zugjCxyRqkcQ8hlaR38xNrZVaTkk0n1NIUp1FKUFdRV35K6V382l6tHtH7Mn7DSft165P8AGew1vS/BHhN/E1zD4s0bTbCWFrVikdx5ek5RoAj+dsCO+bcbWKSKUjPNhViI8yxDT1dmtNPNeW3mvPf2s9qZNW9hUyiE4N04qpGT5kqi0bhLdqaSm017sm0tLKP7EfBX4K/Dz9n/AOHmm/DP4Z6ImnaTp6gyOQpuL24Kqr3Nw6geZM+0bmwBgBVCqqqOo8E7qgAoAxPF/gfwV8QdIPh/x74P0TxLpZkWY2OsafFeW/mKCA/lyqy7gCcHGeTQB4H+15+zpJ47/ZxHwi+GPjXwz8IPBdlere+I5F0uCLT7fRIvMuLnZGiqIisoSYlXiDBZA7gOaidONRcs1dafg7r8TowuLr4Kp7XDzcZWkrp2dpRcZL0cW0/Js+AP2UP2zvgf+y/4k1X4e+G9A8SXnwmlkjtbnxtOl19ovtXJYteSWYcx20EsSBFhjXzjFboz73JEahK3uSacvu/DUrEU1K9ehTlGk3ZXd9bXtzWim+rsla60PoeH/gqb8FvF99qPhz4c+H/EN1r8vh3Vb3TpdVt47Wylvre1aWC1crNvAcQsM42glFA5yqr1oYenKrU2Wr9DXK8txGcYyngMIk6lRqMU2leT2V3ZK70V3a58Q69+0F+2L8dtM0j42XGqeLNS0jR/GkFrLpvh+2K6XHLHLb3llAtlEmJ2jkU7WnMp/wCPdSc4Lc9WpXlOdGmre7eMunM7qz9NHsevl+EyuhhcLmeNmpr27jVop2m6UVTleKTTtK9SN7rVKz3ayP2vv2Tf2kPCXjjx58cvEvwh1ez8GeIPE+p6tbajDJHdLDa3N08sUlwkZ862UrIgzPHEQxCsFf5R2K9tdz5yooKbUHddLqzt5q7t6Xfqz9C/+CY/xP8AiZ8SfgXcSfE/4r2vi6WyuTNYQzQST3+lW7SSxL9vujxM8klvM0aMryKhBZyssaxZxr051JUov3o2uu172/I7K2WYvD4OlmFWDVKq5qEtPecOXmst9OZa2s3dJtp2+q9J+H1paf2pbaFZyWttd6vF4ie2lAijW782OZjBGBtjSSaEyuP4pJZnLbnJHmVMkwtTMo5pqqkVbS1no1d6Xbs7Xv0XY5VVkqbp9DYv/BVr4w8N3Wm6w1td2eqQXMF1azQFkmSZfLkjfJ5BGQeO56167V1ZmQll4RtIbq/lsrqG31G+m+0XaPCqvNJ5McW9iGOf3UcagjOAo6VhhcLRwVNUcPFRiuiKlJzd5DtWfRvC+lzX3ibX10mBAUlubiVYbYBgMkyMAMYX7pIJIx7n0MLha+NqqhhoOc3skm2/kjnr4ilhabq15KMVu27L72fL/wC0/wDHHwF8S/DemfCv4beLNR17xBNrVukH9m2pjtPOjk8tELMoMgYtuQxF1LIpBAIB/aOAOEMzyHFzzjNqUadGNOV+Zrms1dtJP3WkrS57NJtWvt+YcX8SYDN8PHLcuqOdVzVuVaXTtZt73buuW+qTv3+wtQlae6+wAI0ezLxeZsabIYFQfUDBxx1GSB1/Dz9VM65u2t1jt7ctJIqhYTjZPDjHyuuMMvIwON3GMnBoA1tM08WcfmS5adh8xLFtoznAPueSe5z7AAF6gAoAKACgAoA5GW4tZru4juZQQbhy2fmyvmR8f98xt+lAEdvHOkKQ20k7GQLljIqqGZo8hRtJBBdPmyOQfSgBiPb31pc6H4i0iC60bUofJureZFeApIo3ZU8bGDjcvQbu2cGZwjOLjJXT3RrQr1cLVjXoScZxacZJtNNO6aa1TT1TWqZ8W/E2y+Jn7C3jTVdY+GFzp+reB/GUN5cWukX4kmgsZhsVXkiR1bbHJNAodWXejLGzKxRq+GxTr8L4j2tH3qM2/dvaz8v0eum5/UOT18l8ccqp5Zms/Y5pQikqlk3Uit3a65lvKULrll70WouSPoH4V+V4507TPHcE00vhL7PHdaVZXjwXN1qt8Cqm/vpo3bfIpQRJAhHklWMoZvLitezhvK5TbzfGyU61TZ3uorsuz6Pt8Omt/wCdeIcsxWQZhWyjFR5Z0pOL31ttJXSbjJWlF21i09mj2C6F558lxbXYlleAG4ZWGFVmGyOI4JBIyB65B6kV9eeGfOPxo8b6V458MX+vzaFpPinQItHvb3wdoGpqI9O1a5tYpWm1O+MxWIaTAv2cpcyskDPKsq72bT5G+IznMcRmmKeWZfJqNPWrOO6S+zFq75t1ZK7emykddKmqcfaT67Im0nwxeeHfFOl6XqerLBfWsNxodppOu+TaxaxHcTCSe/0/ULWNXN5cC1lvLiCTzpvMQY+zI32iT5HKc3wuFc6mIw6q05WbaV3C2iTjJtWjzKMWuVNdW/dXTVpSlZRlZ/mQeFbddY8ZXHh+4kltNT1rxcNY1DRPEbSTahp+kaRY2bw28V280sUix6jc6fdr5crRgX8ygeZ5zD6DB4LA53m7lh6aWGjT05fdTcm4ttKzTa5ovZvkT2sYylOjStJ+9f8AL+vxPU7rwp4kvPB83hc+NLbXnvI5rO5XxTocGovfxOSHjmhtDZwhCjNGQUwVAyCQ2/0K3AmWyqe1w8p02rNWeia6q6b899yFjKlrSszw69s7/wCF+meM7j4d+G/BejDRLC7tdM1PwRp8+kf2h4wlQ29rY/YI/Ot7wW5Y+bPN50KSOqlFNpcFfnszw8cszGhR+syqyjKMnz2koRT5m7t3TdlZKzaVteZI2py9pBvlt6dSr8Uvj/p+leDNVsfE2rXGuaTcWMmgahYa54ZNlquoXxhUTxGbIs7kbWXzJILY2qeYWVpd0cEnrYnifM8EpYXFUoqt0aldJd3HX1SbTemlrs+t4H4BxvHeYrCYFtU42dSbWkI/heT1UYrWTT1UVKUfCPhh478e3/7QXhrxJ4m1a98LP4vEeh2V3pxEsWnRXPlkL9nlZwWZbhJ1W4JxJd297IlwhEdx8xl+AoYrEUqeLT9nKVr2+JrSyeml7J22Xuqz1X9H8U8PZXT4Ex2Q8NVKVSWCk51eZXmnG85O6/5e8q5Oe1mozppRs4w/Tjw34J8K+EtBg8NaBo8VtYQIy4ZmlllZ3LySyzSFpJpZJGaSSWRmkkkZndmZix/ZYQjSioQVktElokl0R/GbbbuyprZ1C0vLcG6BjjJe2LAMCQeQ4PJIB4PJ4GOSTVCJbS5Hid0EqiGOBD5qrJhpCTyvH8HAz749M0AUtSSC1uJNIiYrbKyzMGKErgZbYxOQcbQAck+w6gFTULaOKVJGjh8sx5WSNSqykHGSvGD0DKORknnpQBoaHDcavAbW7lZbOAqTCMguSOAT/d4zjPf0xQBK/hWdJitpdIkIwVZs7859scryQevbpQBl/FmWPR/Ddl4p/wCEc/tqbw7qEF6u2ya6ubWIhobm4gRFMjTLbTXACxAyPuMaqxfY3jcQYCpmeWVcLS+JrTzaaaXztY1oTVOopM5Hwp4lgglh0i71HRxY6kj3XhXUdPjQafqNg+ZEjhCu482OExhhkrNErSxjaHih4OFs5pZhhI4WV41qSUZRe+ml/nbXqno+jd4ik4S5ujOsKxFRZybCqghXIAcDnhgSNuMjuozn73f6g5yGW7uGGyVQYt5JjY8tJjGX77sHBOBnB780Add4dvJLzTVM0jSSQu0Tu38WOQfXoR15oA06ACgBskkcKGSV1RF5LMcAUAVBcXN8oayHlQtz5zryw/2FP82/I0AT2tpDaR7IgSTyzscs59WPc0APmhjnjMUq5U4PBIOQcggjkHNAFOaW/sI3mkaK5giUsxPySADk9PlY4/3aAHNqaQki6tbm3AGd7R7lA9yhIH40AWopop4xLBIsiN0ZTkH8aAH0AfJHxw/Y2j0jUU+MP7NVw3hLxborzai1jas5guiFkdhbxhW2SNny/JA8llO3aozu+TzLh/2cvrmWvkqRu7LZ77LXV7W2a0sf0FwV4vvF0Xw7xqvrOEq8sOaVuaOsUueV43jH4udv2kZLm5m7W9N/ZM+Ner/G74Vw634qbT18RafO1pqEdq4VnXAMU7w5zF5inp91irMuFIVfTyLMZ5lhVOrbnWjt+Dt0v/wx8R4q8GYfgvPpYXAc31ea5oOS2e0oKW0uV/NJpSu1d+1V7J+aBQAUAZ+rwx7Ib184tZVkbGfuZGenYYDf8BoA0KACgCG4S7fb9mnjjxndviL5/JhigAtLf7LbpBv3FckttAyScnAHQZPSgCagAoAKAOR8a/ETS/BGn6nqetWrGz02HzHKbnkmYgbIoolUtJK7MsaRqCzuyqBk1M5xpRc5uyWrfZAlfRHj/g7wd4Tbxf4d8OaesdoPhrLf6jLJFK8kmlnU0nMGgx7QyNBHbzo7wbwsaW2mkQ7JITF8NwxTqY7MMTnFNOFGbtGNrKVvtPz03/mlJX0d+zENQhGk9Wj1DduzbwuzSQHEMzZVjhh0BOR3wMnqMLnJr7s4zpLOxupisoi+xweYs6oxLSq+MNz2B75yTk5xngA5/wCLup3vh/whHqVtbTnR7O7im1wWUJluV01AzyeTCFbzsssayRgF2haYRBpTGp8rO6OKxGXVqWDV6klZX030fzte3nY0ouMZpy2MX4T+JNEttS8SaJqN276/d3M3iL7SZBLDqmkyvstLi0dPkaKGBYYJEUKySJvcMLiOe48vhCvhamWQpYdWlDSa6qXVv13Xlp0stMUpKo3LrsT/ALQekyeIvgj4w1Pw9ZXVz4h0jQdU1Dw5PYo/2231NLOZYXtinziUsSgC8sHK8hiD9HXw9LFU3RrRUovdPVGCbi7oseD7XR7rVtd1LRtXsNW/tUWetfZoZ0IaGWBIYZMqSGSQWhKyEYbywo4UmvkeA5zeVypzunCclZ9NE9umrenc6sYl7S66o6GCyScYt4/tKmMeYxQRshxgrG/U4BA2nIAGCR0r7Q5B2k6dDLPOD9ot5o92SB5TBWOE6AA8IT3Hzd+wBqGe8sQz3xWa3Uf66NSHX3ZR1HuPyx0AJ3vrOOJZ2uY9jjKkNnd9MdfwoAxdY8JaJ4wDz69pK5l0270tJUZobyK2ufLM6pcRlZYd/kxE+WykGNGzuVdvNisHQxsFTxEFJJp2fdbP+t1dPRsqMnB3izoa6SQoAKACgDnviD4gvfDHg3V9X0gWcmrx2kqaTb3cqxx3V+yEW0BLMo+eXYvUdeo61hiakqVKUqdua2iel30XzZ6uSYOhjswpUsW5KjzJ1JRTbjTTXPOyTfuxu9ntsfhh8Zfj78KY/jRe6D8Wv2O/BeqN4TS48O6xaabrd/o8p1KC6uBLJDJaTGJFBdYyjJOpMBZGRZNg5croSoYZKpCMJO7ajtf8elj3OPM1oZtndSeDxVXE0IKMKc6rbm4pXd21F253K10nZ663P2T/AGSdB0vw5+zn4HsNF+FZ+G1hPYPqFv4WbVp9TfTUup5LgI9xcKsrSN5u9ldQY2do+iA16J8cH7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/AOltxQB9J0AFABQB8v8A/BQz/ki2i/8AY0W3/pJd1+teDX/I9q/9eZf+l0z878TP+RTT/wCvi/8ASZn56V/Sx+HBQAUAFAHa/BD/AJLT4A/7GjSv/SuOvB4q/wCRFjf+vNT/ANIketkH/I2wv/XyH/pSP12r+LT+nzwv9sz4a+Nviv8ABmLwp4A0X+1NVXxLoWoG3+0wwf6Pb38Msz7pWVflRGOM5OMAE8VeHahjMPVl8MZpvyVn/WhFdOeEr0o7yg0vXQ90qCzyLwh4D8V6X+0/8R/iJfaV5Xh7XvDHhzTtPvPPjbzri1l1Bp08sMXXaJ4uWUA7uCcHHNhKcqUq7kvimmvNKEV+aaKxL9pUoSjtGEk/Jud1+B4d4s/Z7+L+p+EfjPpdj4R8y58WfGTRfFejp9vtR9p0qCfS2luMmQBNq2052OVc7OFOVz04P908O5/Zqyk/KL2f/A3Ixv75V+TXmpRivVLVf1oex/F34d+MfFH7Q/wF8daFo/2nQ/BeoeIp9cuvtESfZEutIlt4DsZg8m6VlX5FYjOTgc1xzpTeMp1UvdUZp+rcGvyZ2wqwWEnSb95yg16JTT/NHk3gLQv2pP2avhx4m+Afwv8AgPD4qsdO1HUH+H/ieHxHYwWkFpf3c1wv9pxXMiz+batOzN5UTrKFCjby7aUaco0I0b2kly8ztr2lorX7q1rrqia1f2+Jnia2vM3KyVtXq477N31vszL+L/wq+MNn4w8ffFvx1Z2N7ZJ+zBqPhbVtbsZY47afX1lmuJ44oC3nLGV3OrFAuCBndxV43k9lW5PhadvSz83b736szw9+akpO7T1+9f1/kYXhzT/2kvj3+xv4B/Z6i+DMWhaX4p8MaFp1947bxBZS6fBoQtreU3EVqG+1vdPCvleS0SIsrE+aUAata9GFeajU0imm+94yTstHva93stPMwo1Z0YOUNW00u1mmrvVbXtpu9dtDU+NfwD8c+Kf2jfGfjb4k/sz33x28H/2Vp5+HlpH4ttLKz0GdLfZeW01pdXEKgzzqJGmCTEKseATlVSsoydvfb3b0a023ta2ttHvZa3cU4qFK9qa6LdW+5PR9Xpt6ZPgD9mP436N+y/8AET4c33wr0nQNc134xWnizTND0jUrRrGLSRqWmXDfZ33KqxxR28yqjhHIiGEyyg60ZcmJwlSb0pzTk/L3tbdtVZbmGIpuWHxEILWcdF52St66avY6T9oX4K638TPGPjG3+KX7D2hfFu2wtx4R8U+GtcstAvpIvKASx1GWe7juVaKRT++jMsbK4IhUhkrmhGUbzWk77p29H6pW3W67JM7JSV0lrGy0ffr8m77dO59Hfs7+DviH8P8A4I+DfBvxX8Wt4m8W6VpkcOq6mzs5llySEMjfNL5alY/Mb5n2byAWIrsxNSFSpzQVlZL1aSTfzd38zkoRlCFpvW7fybbS+S0Mj4F+BfFXg7xd8Y9U8SaV9jtvFXjt9Z0h/Pjk+02Z0ywgEmEYlP3kEq7XCt8ucYIJ87A05UcPyTVnz1H8pTbX3r/gnRiX7TGOrH4eSmvnGLTXy/4Y+b/C/wCzd8ePh/4K+EXxN0HwHaar46+E3i3xjeyeE7jWLe3Op6VrV1cKxhugzQpMI2glVZGC43BiGGxumhKVKlRg27KHLJLve69bb26lVnGpXrSj9qfMn5W7ef4Hp9p4K+OXxk+PumfGbxb8OrX4b6f8N/D+r6f4LtdZvrfU7y81fUYoklvLuOxnaJbWNI9ghWYSMdzb1DALpTq1KNOtyNNzio2e2klK9+l2kuulzFxjOpT5rqMXd266Wt17t301S8zxC6/Zm+IvizW/BviLw9+yBYfBz4tafrdodV+InhnxNZW+iW1tFLm7uoLC2uPNuhdRq6C3mt1OLjbJKQrFjCTVCrCdvc05o33S15dLqz/RdBYjnqUpwXxO9n2b6330+f3n6GVBYUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoARlVgVYAgjBB7igCjHnTZUtzk2kh2xsf8Alk3ZT/snt6HjuKAL9ABQAUAFABQAUAYuu+MvC3hgXD+I9bttMjtbX7bJLdt5Ufk7xGWV2wrEOyKVUkgyRggeYm4A5T+1/iB8SFH/AAiiz+DfDzPg6rqFnnVLxVlAP2a1lG23RkDbZpwz8g+RghyAdJ4N8B+HPAtnLBoltK91dlXv9Ru5Wnvb+RRjzJ5my0jcnGThckKFHFAHQ0AYWkeBPBXh/wASaz4w0HwppOna34jEA1fULW0SKfUPJ3CIzuoBkZQ7AM2Tg4zgCgC7oPh/QfCujWnh3wvolho+lWEYhtLGwtkt7e3jHRI40AVF9gAKANCgAoAKACgAoA+P/wBrn/gn9pHxy+Eeh/Cv4Naj4X+G2l+HtUvNej06DQT5F5fSoVUF4pEFunzybiIpTgxhQoTawB478Dv+CM/hT4f+NtA8c/Eb4zXfiRtEubXUl0nT9FjtIGuoXWTy5JZZJTNDuXaR5cbMvdc4ClFTTjJXTNKVWpQqRq0pOMotNNOzTWqaa1TT2Z96eAPhH8M/hW+sy/DzwTpWhTeIr+bU9WntIAJr65klklZ5ZDl3AaWTapO1A21Aq4FMzPkv/gqn8Z/iD8N/hhofgnwv8Mk8VeHPHMWp2nieaS2uXWzt40gSJRJCQIneS53o7kjdABtYEiscRLlpSfOoafE7aPo9dN/vPTyai6+YUY/V5Yhcybpx5rzitZRTinJXinqldLXofAH7GvxW/Y1+AHxR0/42eMdS8eX2pWWlXFva6CNDiljsL+b929wt0t0gmTyDKgjeH/ltuJ3IDRh1WjSSrtOXVrZ/1+Ys4nl9XHVJ5VCUKDd4xm05RuldNrdJ3Se7jZvW5+0Pwm+LXwk+Kng7SfH3wy8QxXmheI7iWx0u6mtp7I380Hm+YkUdykckhUQzkkKeIpDyEONjzTvIYYrePyoUCJktgepJJ/UmgBLi3juY/LkzwcqynDKfUHsaAPlPxT8MX/ai+PvjXw7468Q6ppfh/wCHcVjZ2NhYMqvK90jSGctIGUE7DkhMlfLGflyf2HLc+/1D4bwmKy+lGdbFublKWyUGo8tk07a6e9o+bvp+bY3Kf9bc7xGHxlSUaWHUUlHq5q99brp21Vu2vtPwt/Z7+FXwhgibwp4ahk1GNnf+1r5VnvssCCBKVGxdp27UCgjqCSSfhs/4yzjiOT+uVWoO3uRuoaf3b6u+t5Xd9rKyX1eUcM5bkiX1aneWvvSs5a+fTTSysvnc7rUdJt9QRslopGxl043Y6bh3A/P0Ir5c94h06wvFuHuNRdXaNsRYYnjbjOTycZYDPPLZznNAGpQAUAFABQAUAIzKil3YKqjJJOABQBxBkj+zxxzlJZ2j2qu0lyXWQg9PWVfxX2oAmMrMkkSI8Lo5EYjLPk5lbjHuqDJ5+UGgDJ1zX9M8L6ZJrOravDDZ2pA5DNKzuxjSGNArPJJIUijSJVLu0kaKpL4MVKkKMHUqO0Urt9kt2NJt2R5lpHgJfin4s8Q+CPF159qgZY7zxu2nXUkSZdGOmeH4pgTKsNvC/wBqmCPCWeSKTyxHfzRV8PlmHXFOLnmmLh+5V404u/zlpbX77PS94pnr4PMcTkWIpYnAz5a1OSkpK2jTutHdNd01ZrRqzaPKPh7ql5+yR8Xb74M+PfFTWvw61SV9S8O6lqEDqEuWeNGTzY0YIccOWwi7UlxGJCG6cHUfDuLlg8RL9zLWLa66aXX49Nnpc/e+JMFHxj4dpcR5RQvmdC0K9OElrFKTUlGTTd94WvJ3lTvNwTX0X8Sl1aCDw6329bfQb7XrS31cxyMGurW5SWKIQvH8u5rp7JW+7uhabuefX4irYijldarg3aaV7+XVr5X1+Z/OdKny1uSqra2a8/8AhyppniPSdBna68d6VqsPijUJ7q5nkGiXs9jbafBLMI4UvIVmt4Y2igWT95JG0hkEjwwPIsEficL47JMHgoUqFaKm7c3NaMnLtra9r2VrrzerNMRCrKbclocvrHhL9nTxloijwvrnh7StD1BpbWAeH9UtI9Oupp7jb81hN5mn30zSKAvm28jB3IXD817+JwuWYyv7Oqo+18naa07pqS089vIxjKpBXW34Fi58H66+i2F54evtH8daXqEUkcNzLq11Zatb2V20LyyWmrQvKzbjGCkUSwo+IyssSRqK8KpwjLCy9tlWIlSn8uV22TSS+9qXo2arE82lSN0O0Pxd4vn0K+8J/D7W/JuCoTUrzxFMseveFY5JiPMkSWOSO+VYo7tLe4bejy20ZZ7xJJp0458U47J6c8PmtH98l7kkvdm9vLybt0drRdk7WHhValTenXyPCviB8QNG8M2WjfEW21TxJo+h+GZpbXwlpSXTCa9gmsponlnMyySPe3DTfaHmnZpLaJIGkC3FxcW0/jY3D0cDhJUsYlPGVmpTd/hV00nbT/t1bvryqLPtOB+Dcw46zRYHAe5SjrObWkI7Xt1k9oxvdveyUpLz/wCC/wAG/EXx48ZQeIL/AEew0HRNKgtreytobZTpvh+yUI0MSQPuRpWSVZIrd1ZP34u7lWikhgv98jyKpm9R4jEt+zvq3vN9Un26OXT4Y63cf2DjnjfAeHGV/wCpfCEv3quqtVWvFv4ldLWq9nL/AJdpKK95Lk2f2rfHXwO0nTPDvhD4XS3x8Y+DNQjv7bV4FkljjLlpJ1kuZZN1zLJN5Vz9pImMjMzGQtI7H0+IsfgrU6WF0qU2rNKyS7Lbyasmu256fgjwVxBgaWJq5rCKwOKg4ypyl70ntzcqTto5wlFyhLXWL5Yn3L8JfGd34i+HfhXxRqsKxw6/pNpfB1jZFglliVniw3O0MWCseCMEEg8fbYKu8Vhqdd7ySf3o/mfibKY5DnWLyyDvGjUnBNtNtRk0m7aXatddHpZPQ6l7qLWrttOjmC20Y3SEHDT8kYX/AGeCCR16dOa6TwytYaXLHqpijuP3VkylpPLCu+V+WPI6gDGemc9OhABtyW1oZWupYYy+0KXYfwg5HX35/CgDmbjR31e+lv0mAtDIFVn+QnPdOMEZP4nPfmgDS02TVTJLBDcQzw27+XukUAnGPlBXpx6g9vUgAF59Q+zc30DwLkAy5DR5OO45AyepAFAEt1bC5EamRlEcqyEDo205AP44P4CgDxj4lfD7RPBem67rMOu3eheDtWl+36rHbNDFFoGoCfzzrUDy/u4U80edcRyLJAzr5zIN119o+Q4hyeqqsc3y2P7+nq1/OrWtbq7aaWbWl78p1UKqt7Kp8L/A0vCHiPUdQuJ/D/ix9Pg8S6AyNe29nOI0ngd3W3vYoyd0EM4iYhTu2SLNH5k3kNIfbybNqOc4SOKo6dGuz6rz8n1RjVpulLlZ2VlpM13GGtI0jhUZErptMh4xgH1AGSQRl3wCDXqGZcsrcSRRWFjGojgRkujIp2eacAggH5zw3GcYI56ZAJ7eQW1xNBHGkc1uA7xQLhJo8feC8kMPQegGSMYANZWV1DowZWGQQeCKAKYgW8uZnulLxwSBYkP3D8oJbHRjkkc9Mcc5oAu0AFABQBHcQrcW8tux4lQofxGKAM/Trx4Y4rW9n8xsmLzSOkg6ox9fQ/xD36gGmAFGFAHOeKAFoAKAPh7UvF+pfsdftMePfEWueGdd1HwL8RnjvYbm1tlKtqMkhl2+c+1AUaS8Hlhs7WRiDjj4meIlw/mVWpOLdKrrdL7T138ry07WP6fw2U0fF3gnL8Hha9OGOwScXGUnf2UUo35Vd+8o0nzNW5lJJ6nuHwp/bK+BXxYubPSLDxI+ia3ev5cWl6xF9nkd9wVVSUEwuzMwCoH3t/d7V7eB4gwOOahGXLJ9Hp+O2vRXv5H5hxT4P8U8LQniKtH2tGCu503zJKzbbjpNKKXvSceVfzHuNe2fl5j6zrf2L5LS4tmkUMzqxDHIKjbjIwcEn/gNAEtpqkOoWrQzCEXDKVa3Ziu7I6YYZwc+h/GgCW1nuoJIrG/8tnZDslRj+8K4zkEcHBz1PQ0AXqACgAoAp32pwWBw8U0hC72EaZ2r6n0HX8jQAxdasRsE7NB5n3DIBtbjs4yv60AQeKtbsvDnh6+1zUtRt9Ps7KEzXF3cSLHFbxLy8ruxCqqrlizEAY5OKAPEb3xJ4g1nW9F1XSLfULfUdQtbo+GvDl2ot7jUZXZEfVr+J2SaK1t4pEZo5Cjr9o2vCbs20dfn2a46XFNdZTl2tJNOpPpZdE+uq0/maVvdTZ204fV17We/RHb+F/CR8L6Bb6dLqOo3FpHM8txfXu3zbq5kdnmnljjCKHkld3YRqiKSQihQFH3eHw9LCUo0KKtGKsl5I45ScndnVaLAUvzcw2qfZRF/r2TYO/Khizc/UcdR0rYRdttTvLmR2t4PtKIDG2wqE3joysT0IPI5I469wCK2KPqUkd9Msu/HEefI87GChz1YADH54BoA8O8e+Etd8E+JLXSdG1CWzsL2+Oo+B9Tm3yW+k62RI8ulXzZZjY3YL+WGXCZljjkilXTwPh87wlTI8Ys8wUbxelWPdO3vJd+/nZtW5jrpSVaPsZ/I9F0f4p2mq+CVu7C1u9L16S5bSItJ1O18y6sb/C5juIYn+eOLertJHIUkhAljkaOSN2+ixOdYShl7zGElKFrx1tzPpH1b0ta61utGYxpSc/Z21OP+AmkWll4p13WvDc0t3odroek+AtPnucRvdXGg3WpRXkvT7izXJg6BjJbzME8oxySeVwfhq9HAyr10k6snNJdpJW+/f0tfXQ0xUk52XTQ9SeKZbqaO5nS3VFCSSxt8+0DcEjUDIXHoOoOSwBB+rOY37e3toEzbRoocAlgOW9yepPPU+tAE1AFRtK01yT9iiViwbci7WBHcEcg/SgCKb+0bGGWcXcU8cQL7ZY9rbRyRuBx/47QAuk6smqwl/KMUi8lCc8HOCD3HB/EGgC/QAUAFAHy78XtK8S/tS+NbH4TabYeR8LUtLTxDL4ttZDIt5KrHMVu8bGJ9yybAkgUoQ8uGCRhvmsfTq5zVWES/cWUuddfJW0faz231sr/t3CWMwPhpl9TP6lS+ZqU6P1aStyJpNSmpJTjZrm5oNqSap3i5Tcfzv/bu/Yt0D4L/ABf8X/FNfjr4H8I6Trl/L4h8P6A1xcPrhkk/eukNrbwfu0FyJo4XyI1VYw8iHOPpIxUEoxVkj8VrVqmIqSrVpOUpNttu7berbb1bb1be5+r37K/xJ1r4v/s5fDr4j+JLfUo9Y1vw/ay6k+oWS2ktzdonlzXKxIAoimkRpYioCtFJGwABADMyt+19/wAmpfGL/sRdc/8ASGWgDif+CcP/ACZN8LP+wbc/+ltxQB9J0AFABQBwfxk+Dfhj44eGLXwn4sv9UtLS0v01FH06WOOQyLHJGATIjjbiVuMZyBz6/RcM8TYvhXFyxmDjGUpRcfeTas2n0cXe8V1PFzzI8PxBh44bEykoqSl7rSd0muqemp43/wAO8/gt/wBDP41/8DbT/wCRq+4/4jJnv/Pqj/4DP/5YfK/8Qzyn/n5U++P/AMgH/DvP4Lf9DP41/wDA20/+RqP+IyZ7/wA+qP8A4DP/AOWB/wAQzyn/AJ+VPvj/APIB/wAO8/gt/wBDP41/8DbT/wCRqP8AiMme/wDPqj/4DP8A+WB/xDPKf+flT74//IB/w7z+C3/Qz+Nf/A20/wDkaj/iMme/8+qP/gM//lgf8Qzyn/n5U++P/wAganhX9hX4SeEfE+j+LNN8ReL5bvRb+31G3Se7tmjaSGRZFDhbcEqSoyAQcdxXJmHixnWY4Srg6tKko1Iyi7Rne0k07Xm1ez00Z0YPw9yzBYiniadSo5QkpK7ja6d1f3dj6Mr8wPvAoAKACgAoAKACgDN8S+HdG8YeHNV8JeIrP7XpOt2U+nX9v5jx+dbzRmORNyEMuVZhlSCM8EGpnFTi4y2Y4ycWpLdDPCnhfQvBHhfR/Bnhex+xaNoFhb6Zp1t5ryeTbQRrHEm9yXbaiqMsSTjJJPNW25O7JSUVZGrSGFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/ACXL4Ff9jAn/AKWWdAH0/QAUAFABQAUAfAPwb/5TIfHb/sn9n/6I0KgD7+oAKACgAoARlV1KOoZWGCCMgj0oAZbxPDEI3lMhUnDN1xngH1wMDPfGaAJKACgAoAKAOJ8SeM/Fp1yXwj4D8EXN/fQqpudW1TfaaTab42ZSJNpe6cER5jhUj58NJGQcAGbp/wAGbe61uy8Y+P8AxNfeKfEenXf2qxnlXyLHT/mPy21kGaNMptUu5kkyocOGAIAPSKACgAoAKACgAoAKACgAoAKACgAoAKAPn79rL9lzV/2qvh3F4In+J9/4JubO+jvLe80yKSeKQDIeKeDzY/NQjDDLAq6IwOAytE6cKseSaTXZ6nRhcXiMDVVfCzcJraUW01dWdmrNXTt6HxP/AMONZvtPlf8ADTR8oJnzT4LGN2RwB/aGfXn2qznPkfT9D+GPwh/aNn+GPxQ+O/iG18L/AAo150stZ8HaK4u7rUbe53M6idx9nkik3r5uJlDRfuxIreYefDYSlDFua0dVrmk9o2VldK7t/hjJ3fbb6LGZzic1yijga9WEaeDjP2ULS5pupNznZqLjzXt8coQUYq3vczf7G+Df27P2e/GJgJ1vVdEju0ie3l1XTnjSQSYxkpv8vGQSZNgHc8HH6TX8MuIIYaOLw8I1YyXN7rs7Wuvdmou7XRJyvpa5+WUuOsnlXlh60nTknb3lpe9t4uS+d7dbnqFt8a/g9d2Yv4fil4U8ggtubWLdOBnOQzAjoa+bnwvndOfs5YOrf/BJ/kj2459lU4c6xMLf4o/5nln7Oeqj4gfFz4rfF/SbK9i8O67Np1lpFzPbSRpfJbRPC80ZZRlcxA46ruwwBGK+v41w/wDY+S5bklaSdakqkppNNxc2pKLs3rrvs7XV0fOcL1v7SzTHZpSTVKbgotprm5U4tq9u3qr2dmfQ9fmZ9yZfifxR4b8FaDe+KfGGv6fomjadH5t3f6hcpBbwJkAF5HIVckgDJ5JA6mlKSirsqMXN2ij550f/AIKU/sQ67rlv4esvjzp8d1czeQkl5pWoWlsG9WuJrdYUXj7zOF96cPftb/L8yJSUFdn0tBPDcwx3NtMksUqh45EYMrqRkEEcEEd6bTi7PcIyU0pRd0ySkMKAKup6npuiabd6zrOoW1hp9hA9zd3d1MsUNvCilnkkdiFRVUEliQAASaTaWrGk27IZous6T4j0ex8Q6DqNvqGmanbRXlld20gkiuIJFDxyIw4ZWUggjggina24k77C6xKItKu3JA/csAScckYH86AOeE32WWNblAqIBwh3co8anrjtAfzoAteHmb7WsfnqwET/AChcH5REuTyfQ4+h9cAAzfiNba5faj4QtNI1DTo7OLWmvNatLpEeS7sY7O5CCEMjEOl69hJuBQgIfm52P8lxviVh8nnB3vNxirevM7+VotHThI81VPsZfwzl01fBnh7VLKxttKfVII9auzbW/lxNJeRi6uWI6ZkuLh2PJO58nk5P0eBw/wBUwtPD/wAsUvuSRhOXNJy7lf4teCPBfxItU0vxj4b0rW9LYkx7ydwk24LCRCHRgD1VgcHryRRi8HQx0PZ4iPMv62e6PayDiXNuF8T9cyiu6U9nazTXaSd4yXk0z51tPgV+1D8DXlsvgZ8SrXxD4VFvNfReH9cAZRCzMywqrgpk7hudDAGckkKua+ajlOaZY7ZfV5oa+7L8u3zTjr2P2+r4hcCccxT4uy90cS+VOtS1vok5u1pWXSMo1Wo2ScmcdD8WPix8Kknt9Z/Z98W+G7i1FvG134b1u8uNE0/T4RCfIg0+4+1WMSCOAqRC0JVGZVaJiXPjY+hCspLMcv8Aebu5w033d0mm93q7PrsTS8MeEc8tU4cz+K5laNOqo87leyVm6cknov4TfVXukdl4O/al8AeKdVbUrj9oTX9EvL6HyI9H1jQbK30q1mwrGVWjgkl28FQJL08luPuGubD5XwviKSptyjK+7bu/W14W+Sen3/O5z4N8b5TJyp0I14JXvTkn1tbllyzb66Ras997fR/grWvDvjLT2vPCnivSvEzWqmO6k029glw7g4LqjFUDKG/iJByB92v0TL3hoUIUMNNSjBKOjT0Ssr29D8rzTJ8yymoo5lh50ZSu0pwlC/e3Mlfc4T9pjVvA3hH4cXHib4habHjyn0i336alxc3CzkNLbRNKCiPN5ZdidyqsRJVyFFcef4mOEwUq2nOmuXmV/e6NeaV2j2OC+E8dxpm0MrwOjablJ7RgrXk/m0kuraXmfKvwx+F3i/8AaW8WxeLPE+NN06wb93aC1WSw0a1ZUmj2pIzedcyrIJVidGG11u7nzIpILe++FyLIqmbVHiMQ37O+sm9Zvqk+3eXT4Y63cf3LjTjXAeGmBlwfwhb21rVayfvRlrzLRW57dU/3a91JSV4/YPiT4O6Z4l+EOp/CvQby68N6TfQSWkVxDM8k4uJGMpluJXLST+bI3793bfN5jlm3MWH6LjMDHE4OWEp+6mrK2lrbL06NdtD8F4T4gfDOeYbOJU1V9lLmcZa3Wqdu0kneL1tJJ2drHjX7F+meEdQ8J+I/h/42+EugXXjX4ca8tpcXtxp9vcymV5JViLTksWeKSGWMFSFCrFtPU187w3So1ac8PiKS9pSlq2k31trrqrNdrWsftfjZjsywOMw2cZTj6iwmOpXjCM5xja0XL3PdtGanGTT95yc+boj6sMeuT2clvcRSyorK9yjgKz/MSVjI4Ixj+npX2B/OIt1PDaQWsauTDOPMsZS217duPlY8/Lz78ZBzQBJYXmp6fqj2moIXF1MWLBMKM4AZW7joCD0478UAJqN+dbuf7KsJiIR8skiHO9iDgAf3Qep6fpkA0NNt7yexey1i3iMSgRKB1YDufTtjHpn0oA0Le3htYUt7eMJGgwqjtQA50WRSjqGVhggjII9KAKVtcNa3X9l3MqsWUtbuW+Zk/usPUevce4NAF10WRSjqGVgQQRkEelAHyZ4nEfwr8SeDtM0PwnrPiCPQPGup+GtJ0zT79YZ006fRptWWCMPPBbvHE1tDFHFMwhSCNV2l0Rh+dVsPjIZ7isPlVRU5zhGeqVm00nf3Zb8ze179bNncpRdGMqiuk7HQ3fg7xhrGsv478OaN4b8O68spvrPUPGQuPFGo2RuolF1bQxrexR6cgK7DHZ3MsDjkBf4u/wD1Yx+ZwtnOLlL+7CyjvpfRJ/ON13I+sQpv91H7yOx+H3iLQ7iS51Hwx4Qv9S1i8kufEGt+DZbvwffTxAiSMBIZ5mvZDMXLebdxqRI3BywYfDGPwOuVYyUUlpGfvLt6JW29x7fcfWIT/iQ+7+v1LmkeOvGttpsOgRa54r0vxRj7ex8a+Bm1NrVPNAeJ7jR2hsnZ42Xaq3DuBgnJRlUnmfEmBnbEYWNWPeD309W184r9QVOhNe7K3qdbYfGuJ9T/AOET8M6n4P8AGut6PYPc69pek6/DbapFIkaEi3sHLhQ7ugVbi5iWPzUDSEEGphxnKhJf2jhJ0YtpXabX4qP4Xdr2B4W/8OSZ03h74laTrDWqaZo+rs1xcz22pWrwxiTQrqOMSSxXp37VYmSHYI2k80TJLFvgPmj3cZxDgMFg44+UuaE/hsr3/KzWt72tZrfQxjQnOThbVHR6D4r8PeJ9IsNf0LVIbvTtUtI7+yukz5VzbyIHSWNjw6lCGBGRgg169Kp7WnGpZq6Ts91fo/PuZtWdjQiu7WdtkFzFIwGcI4Jx68VoImoAKAKctj5k8m7Y9vcj99E46sBgMD9Aox7Z69QBIpJ7Flt7pmliZgkU3JbngK/v23d++D1ALtABQAUAfNX7Z3wm+D198HPEviXVNL8MeH/En/H1p+rtaxw3V3eqTIbcMm15nmRZV2ndgsXwdmR83xDgcHLBzqyUYz3TtZt727u6v379D9q8HuKuI6HEeFwVCdWth/hnT5nKEYO0eezvGChJxd1y7cl/es+g/ZJ+KHiX4p/s/wCna54ytrq3v7JpNFGoOJJG1JYgqJdgsSzsS2xzk5ljkPGdo6uH8ZVx2BjOstVpfvbr+j80zwvF3hrA8L8U1sLl806c0qnKrfu3NtunZWSS+KKsrQlFa7vvUtmupRYxPHGELKXZdvyICCzDGU+6M5IOWbru49o/MiNlIRku1O4IJTH5PCqxB+UKQBwEyRt+8wPNAFnRpJjf2wilcwrON6gjYHKtyOnXD8ADAx1oA66O5t5pHhinR3j4dVYEr9aAJaACgDE1N7tNTZbbGGih6tj5w7so+jYK/wDAhQBDZJb29z5s6eRLJO0E8D4ZMSDKhQvHZRn2OaALE+k2mtaL/ZEl5cxrDNHIj2t5JDIpilEkW5omVmQ7E3Rk7JELI4ZGZTlXowxNKVGqrxkmmvJqzGm4u6MzRvDGm6LqGrXtpNPBqOtXSXt8Z3knYmNVjjERJCpEsaIoRAFyWZgZJJWfDA4DDZbRWHwseWK6avfzd2/n6bDnOVR80i6NLOr3S3jPKtoH3wlm2tg8naOwJ53HBx26GuwkW0hh8iG/vpPOt5WbyoZcv5YJJXAOSzdR0J5GOByALPcXQnkuFLojKGuLeFR5iIAcOzZwHIwMdcdOmaAH2strHNawXAA6CKKPBhgJGVy38Tnsffgc5IBwXxutbjWdI03wTJbW0t14s1GDT455rRLmOzjjJup5kR1ZUeOG2cxTOjKLlrYEYPHjcQZhHLMuq4hpN2sk9U29EraXXVrsma0Ye0monDSeC/ij8QtS1j4q+Ate8GWnh3X9Cs49FaFbyXUNYs186Xct7HIsenrcCSACaGCeXywjZ3xxBPlct4LU8BTji6klLWSjZcsZSStdNa2srq6V7ra9+ipirTfKjo/hDq3hvTktvAXh5ITolzZz6n4W0lbs+dp8dvIlvqNgyAMsKWdxLCqjzT5bXTQJHGlsmfZ4WzDFYinUwWOv7ai7Nvqnez89nr1Vnd3MsRCMWpw2Z67BZtcnzrwyMmcokuNxHHLYAwOB8vTueeB9Uc5oUAVo7plumtLkBWYloWAwJF9P94dx+P0ALNAEVzbQXcLW9xGHjfG5c4zg5oAq3dvHZuuo21uoePIlEaDLxkjPTuOD+BHegC7HJHMgkikV0bkMpyDQA6gD5S/a0+IvjTxx4osP2S/hPYXsOu+KIkm1jU5S1vbw6awYugcrl4yqkyOhI2qYgJHdkX5bPcXWxNWOVYVPmnu9ly/qu7X+HVtpfvXhTw/lmS4Gpx/n84uhh21Tpq0pOqrWbV/dkm1yRlZ3aqtwjGMpe6/Bj4TeHvgp8PNM8A+HkjcWieZe3iwiN767YDzbhxknLEAAFm2qqICQor3MvwNPLsPGhT6bvu+r/rZaH5ZxjxVjOMs4q5tjLrmdoxvdQgvhgtFourSXNJuVrtng3/BRb4UfDjXPg1P8efEvhfwvqviX4TRNq2ir4kuZY9Muw8kavZ3UcbL9pDkIYoWIDzrFGT5csqSdp8weW/8ABNnWvjz418ba78Ufip+1b4M+Idl4y8Nw3j+FtP11bvUtJukljKNJZiJEsFjSWZHjhwjPMhYEhTQB9Tftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QAUAFABQAUAfAPwb/wCUyHx2/wCyf2f/AKI0KgD7+oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgCOeZLeCS4kztiQu2OuAM0ANhuYpmMfKSqMtG/DAevuPcZFAE1ABQAUAFAH5T/8ABbX4XX8UngD40Wdm01ncGXw1qV05gH2Z0D3FlCgwJiHDag7H5lBjXO0lQ2cYONSU+Z2aWmlla+q0vrfXXXS+yt6FTHxngIYJUYJxlKXOk+eXMorlk72cVy3iraNyf2nf2D/glN8arn9oT9njXPhR8VLUeJbjwBewWYl1WAXUd1ps4aS1SUyu5kkikhnXlVVY1twMkMR14bFV8FVVfDTcJrZxbTXo1qeTXw9LFU3SrxUovdNJp/J6H03qv7JX7O2s6pLrF58MbGOeZlZktLq5tYAQABthhkWNRwMgKATknJJr63D+InE2GoqhDFtpfzKMn85Si5P5vTofO1uDMir1HVlh1d9nKK+5NJfJHqWk6RpWg6bb6PomnW1hYWiCOC2toljiiUdlVQABXyOIxNbF1ZV8RJynLVtu7b82z6OjRp4emqVGKjFbJKyXyLdYmp8e/tc2cHxZ/al/Z6/Zw8TK9x4K1W51fxX4g00hTDqb6fbb7OKUEEvEJA++M/K4fkZVSJo8ssRJyV+SN1fa8m439Vuvmno2iq140Ek7c8rab2ilJr0f37NWep9R+JPAPgrxf4Nuvh54l8LaZqHhq8tBYy6VNbKbYwAAKgQDChcLt24KlQVwQKdRe1vz63/q/r5k0/3VuRWt933bW8j4H+Dv7SOs/st/snfFDw39oh8S3/ws+Kmp/C/wR/bV0LeOdPOQWv2uYkAJFvuJG5RRHEsYZAAy03UlCiqjXNJ8l9UrpXu7t2VtEl0S82KHs1UqtJ8sVzWVm7N2SWivd7t92+yLmp/tYfFX4AjSvif4/wD2uvgx8ZPDl3fW0Hinwn4cNjbXuiQTOsZm0t4ZWmvVidwzLMm5kU8JkuhFL2qp9G7XelvN9kKXM4Oasmum9/JefrZeux6J+01+0B8QLP8AaJsf2d/Df7QPgz4FaYfCcfieXxb4h0+3vJ76Z7qSFbS2S7kS2AxGSdx3n5towpqKSdaU9bcrSStrK6vdeS26O/dXtdR8iglG/Ndt30VtLer36q3VOydD4vTfti3v7G/xp0D4oHwdb6toWnaosfiS2hU2vibw0NPlM0kdvHKzW12w3D5lRASuFIBYqfvQjLZ31Xps0/N/Oy1tew6dlKUfiVlZ+b3uvJfK70vY5Xw9+0B8Yf2dv2MPg1pureIvh5ceMPiEujaR4MvNTkew0rRdEbTrdxcaizsC7QICHZSAWljIDYKNpUkpYhUL2k1Jv0jvbz1SS6/gRSSjRlWabirJJd3tfy0bb7LputDwt+0547+E/wATvBHhb4i/tR/C74+eFviLrcHh2VvD8VhaatoGo3JIt2Fvayus9lvAR5Hw6l1OcgK5DWfs7d7P06PzfS339Gp83L7RNK268vJ+X5dOq+3pPD2kOzOLXy2YYzG7LjjHABx+lIox4LS/t7uaXTrsofMeMCSB2UqrnILbSOcdQfagDz/4jXPivxDBqD+D/Gvh/R/FFp4V1Oz01bp03Wl/fvEmnXcyMr4h8+0lUZRg5R1CuVIHxHGHLiK2CwM1eNSorryVk9tdpPY68L7qnNdEegTR6z5iPAtvdNJ50qtBO4G0su7kEdDtAANfbnIVLK7k+1mRraa5ldQJkC+aTGSNyEFiQBxgnnIwevABS1DX9GSbUrbSBPO2m3EekTxRpKkkEsqRTbSH27lCTrIxBI2t3KFT5VXOKFHMaeWST55rmT0tb3t9b/ZfTsaKk3B1OiIl+0CLyFlXy2w5i835SSMg4z1xj8sdq9UzK+p/Bj4XfFBb3U/HfgHQ9Rmuwbd5GsUSUDYEyk6/vQcAAMrAgrweBjjxGX4XF39tTTb621+/c+jyfi/P8g5FluMqU4xd1FTfJe99YX5Wr3ummnd3Tuz5R/aN/Zt+AHw4trrxb4M13XPBcuiyRv8AaorqS5gS8C7ora1ST97Pcu0ZPyzqsPzO5AULXwHEeGy/KZxhhVL2z1ST0Sv8Tbu+mlnufvXh74qcZcU4+GTYihTxcJ39o5R5HyPSTk4+4oq6TvTfMrRScpK/mvw+0v4x/tIeNfCd/wDErXLvU7DR7WCCznvbZLpLO3kaWOG7mtgyvKJZ7eRPOdXR3gYzFo4mQ8Kp5jntN1bOoqdo2va7dtL/AIye9ra7Ne14gcUZD4a4bEZBwpBUsViG5VJQ/wCXafS+tnZtQhGyppuSUW1f7d+H3hG7+H+iiLVdD0vw9dalqMqWumW2pvIs00ZfNxJI7nz7mcia6acRiVhIqzK7oWP2uCf+ruUutj5t21fW17JQSTaSWiST5U+qR/KE/wDaKto9f6uavhjxPYeKdLknhinsrm1maDVdPu1US6XehELwSojFd+2WNspuVkeOWJnieNm9zCYujjqMcRh5c0JbP+vxRjKLg+WW54J8GdTtvBn7eXjrwvHqCQWni7SBdrZw3kbiW/8ALguWeaNWysuPtbgMN+2bdjY+4/N4WLwef1ab0jUjdLu9Hf71LzP6Az6rHiXwlwOMjadXB1fZzlZ3hH3oqKbWzjKhez5dEr3Vj7Qr6w/n0rX9hb6lbNa3AO0nIIxlT6igDDlU7ltJGkXSI5ShkZfmjPK7N2c7CeN3o20nrQBs2ml2tlM80AcbgQqFiVjBwSFHbJAJoAuUAQ3V1DZwmaZsAcADksewA7k0AUW1K9gthqU0MclqwLbYh+8RD90kk4b3AxjPegDGu/LF00gJN3cS/wCp+YNCSRtCsThXOOvTHTjAYA3rC9uHnfTrxA1xCgZ5Y+YyDjHup68H0z0oA8VW4f4zyeHPHLKNI/4Qjx94jRrVT532xLA6xoh+fC+WXV/P+620jZz98fCYD/kr8V/17X5Uzsn/ALtH1/zPQ7238oHbGI4WkkXaJDlCrYZVPAOTtwSufwXNfdnGRw2tzfBpLdp5znLOykqSAM7mHJ6ZA6gAYB3HABNpwkjkWSIlpoTFLEkaoqyqW2MNwHP3sZJ4yfegCbxh4X8LfErSbfRvEOnzT2dw4K/fglhkCmSOeGVcPHNG8auksTB42CurBgpqKlONWDpzV09Gns0+jGm07o8V8V+EbnQxDpvxH0TXvFFnaRS/2xrOhWgu4dWs0iXaus6QgL3UjqkMe61glYsjlfssUhjr4WrkOZZRUdTKuWpSvzKnP7L7xbta13rzRdtHzPU61Wp1Vapo+6PR/BfxC/4SPUbbwbrs+majJqej2+saPqunzq1prdoY4lubm2Vmd4vKllQmNnmXyri2ZZ5GaSOP6DIs9hnUJ3g4VIO0ovddui7PommmrbN41qLpNa3TPQYPItrom6gWO4k/drcYGJh2GR0bpkYHtkCvdMTQoAKACgAoAKACgAoA/Pz9r3StY8VftI2GjfEa4ig8HyafIPC2oXd61rplvc/Z1ZkvHJCmM3MLCURNHOY5E2yZWJR+c8VzrUsZCpWhzUk01a+sdOZaaJ6PXfVX0tb+o/DTGSwHAmLxHCs1/aN7VIOCnNtSlyyhGKc37k48vMp0lKEvcXNUk/sbwbr8Ws+A9NgsND/seS08vTr/AEfbGJNKdEB+yEITGGVSiqyFkYMrIzKysfvcHXoYmhCrhmnBrS21v0ttbpsfzFiKlatVlUxEnKcm223dtvVtt6tve/UWITzSAuZcMnlfxICGbO1P9nawOME88KRXSYkeRPEZZ2DrK6hct5eW3EsMfdB+bqeAD054ALB8uKXbHeyKd0e6VBnyl2tngYz97j+JQGHbJANX7da6Yi2GhxGdnOHnA35PU9PvEA+oAyM0AXdD1qPULeOOeQC55UgjG/HcdunJA6fSgDWoAw7h1u53dpSkEzqN6nB8qEFi4Pb5yBn8vWgCvLBIYDbrbNJPKJZIIC3zjcTiWQk8FRgDJz+PQALKeG2eO/u3hMyR+WUjnBdQSAEEZUbdvoDnr1zQB0SOsih0YMrDIIOQR60AOoA5cTLYQQ/aftC3CqIfm5kTgKVhTp/CPn6c9zxQBHb65cqvlQ23kxZBBjkUuxKk/MWU7mbBxwDnigCzp94l1HFbxhEs5GxKgTBicnIXr8qseQecE4BHGADxvxdri/E6+V/DPivFnrNp/wAIX4Y1jSb1ra5u3vsXGo39pNJ+5n8iytY54JIt3z212Bv+Va+GzqrDOc2w+VU7SjB89TqtOj/FNP8AmR10k6VOVR9dEe269r3hn4f+GZNX1eeHTNI0yOKFFhgZsZZY4YIYY1LSSO7JHHDGrO7siIrMyqft5zjSi5zdktW3okl1ZyJX0R5N8I76K18Uf8I+nhS3t9Yt5fEOq6kmnXzTw6O+oail7FZXjF2C3U8V1HcOqlkUxP5f7t4d/wATw/KWY5xic2pJqjNKMbq3M1ZXXkuV/er66Lrr/u6Uab3R7nX3ByBQBm388Qd7bUf3MDYeC4VsYYds9mB5HqPoaAJ9Pu2uEMUwImiA3ZUrvGOHAIBAPP0IIoAsSzRQRtNNIERerE8UAVNl1qHMvmW1sQCEB2yv/vEfdHsOfUjpQAv2E2uW06VIF6tEy5jPHXHBU+4/EGgDw74m/tM2iW1ppXwk8Oar48TVdTk0C61LwxMsradL5Cyu8J2Ok0ixSb15EeUbcw2kV4mMzeKjFYWLqKUuVuHTS+mjTdnddNNWrH6fw74e1qlepLPa0MHKlTVaMMQmlUXO4JSXNGUYuUeWT+O0lyxldM6r9n34JS/BbwpLo+q+L7zxTqU97d3R1C8jAaNZpAzJHu3OiuUSR1LsDLubqa3yvLnl9H2c5ubu3d+b6dVfd676nlcecY0+MMwWKw+Gjh4KMI8serhGycrWjJxu4wfKmoWjsj1SvUPhT4n/AOCvXjtPCv7Heo+GxbiV/GWvaXo5O3PkqkjX2/ORjJsdvfO48dSAD5j/AOCH3gzTb7x98VPiHLPcDUNE0fTdFgiUr5TQ3s0s0rNxu3BtPhC4IGGfIPBAB+i/7X3/ACal8Yv+xF1z/wBIZaAOJ/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFABQAUAFABQAUAFABQB5z8fvjPYfAT4bXPxG1Dw5f66kF9Y6fHYWMkcc0011cxwRgNKVQfPIuSSBikuadWFGCu5uy9bN/oEnGFOVWTsoq79DmNN+NPx6vNRtbS+/Y48Y2FtPOkc13J4r8PulujMA0jKl6WYKCSQoJOOATVwSlJKTsu/YmbcYtxV327+R7bUlBQAUAFABQBU1W6vLHS7y90/TJdRure3klgs4pEje5kVSViVnIRSxAUFiFGeSBSk2ldK5UEpSSbsu5F4ev9S1XQdO1PWdCn0S/u7WKe602eaKaSzlZQWhd4maN2QkqWRipIyCRzVPQhamhSGFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoArPqWnRsUe/tlZSQQZVBB9OtADDcQ30iRW0yyxqQ8jody8HhcjvnB+gPqKAJri1iuQBJuDLnayOVZc+hFAEEtxeWULS3EcUsUSlnkEm1yB/s4xnGO45oAto6SoskbBlcBlI6EHvQBR8Qa/o3hXQdS8UeItRhsNK0ezmv767mOI7e3iQvJIx/uqqsT7CgDi/hh+0T8C/jRd3GnfCv4r+GfEt9axtNNZWN+jXSRKUDSmAkSeWGkjXzNu3cwGc8UAfnN/wVT+Kd34y8TQaBpUngLx/wCA/CMj2mqaNpviN21rRtZjO6e5nggdHjXyt1vuKTxR7bgP5UrIF9DLMO6+I53SlVhTXtJxi7Nwi1zXdm1HVKUkrpO6aeqyxNb6vQlJuMee9OMpJuKnOMuRq0o3mrOUIt2lKNpRlG8X9g/sB/s8/D34K/Bmz8W+EPB/iPw5q3xEsdO1fWbHXbtZZ7d0twEiREYiOLLyyqrkzDzysm0qI48cbOhVxNSeFjy03JuMW7tRb0V9b2Wl7k4WFWnQhDES5ppLme13bV26Xep9OVym4UAFAHzh+1v8I/iRrmufDr9oP4IabBq/j74S6jdXFvolxcJBHrWmXkQhvbQSOMJKY1HluThcvwWK1nzyoVPaqPMmrSXW3R/9uvW3z1tyunFVabp3s90/Pt89r/ir3XMXv7dHj3VdKfSvA/7EP7QEnjC5jENjb634WFhpK3ZGP3180u1IQcnzCoyAPu5yLleS/dfK+n3r9PlcmLULOqvW2v3O34287Hnniz9h34jQ/sdHQrsaV4z+LH/CeL8XNesbsI2n67rBc/aLLaVCbHt2Me0gI8gP3Ff5VU5cO6SprmjBq+95LVN6vzWujaW3M7OqUpVo1eZ8kqidtvd1TS0Xl3aTe/LqZNtqvwv8dXOg+Ffhh/wSbv7DxLql/axX1x43+HFno+h6da7gbqVr3YxYqgYIPLBbIIVmAifWMVKolf3b6vrbyXfyM5twi9Lvt5+vbzPR/wBrXUZZfiNJ4U+Ov7Hmp/Gb4U6lowfQNW8G+Gn1HXNI1Asq3VvMyzrJCrhVkWaIw8FU/eEOyYRUXKXPHXSzXbqnpvfpfZdb6bTvyxcHprdPv0a8reW/VdfOfgL8FfG9p8BP2ptM8A/C/wAdeDfh/wCONCubP4ceCPFTSpqtvc/2XcxXZ+zyu7RCe4kiCbnLMEGScBm0qOUaK53ezbstXa67L8Onzu84KMqz5Va6Sv0vbzf3v/KyzNd8B+Lvi7+zL8C/H1v+zb4p1TWv2e5LPRte8B+NfD4tZfEWnrpttDfvZW0wcXAyiGEsoYtFJhC6qjE5KOJjXesGpJq17c1rStvpbS2l3ronaqblLDSwqdneMr7X5W9L+abv5edjs/h/efDbxz8WPBekfCL/AIJl3XhCO21NdQ13xL46+H1r4fj0q0hG5ZbORFcyXQl2FFGDlewLSRVTT5r3sl99+ljObSja17/07/L+uh93VJRyVx5y20hbTp4pFi3ySfZNvz45O9HGRk9wfegDxzx74h8PSy6lZaLqlt4d8STazpekWGo3fh651j7ZJYCLVlt3it2jd12T3W2MSghfOkU8OB+d5/VxD4gozw9N1fZQ5uRO2rctf/SbtJ7JM7qKj7FqTtd7nXt8QWvfFuteCvBV14G1bVrC0Q6jZN4m8rULNZjvDTWkVvI0assibWZhkbTjBFdb41+qxUswwlSnfbTf/wAC5SfqnN8Ek/6+ZeXxJr/9uaraX/gjWLTTtHthNDqbTRz21/lN7rbxQ3D3ZdT8pV4U3Mvy7hgnuw/GuTV4pyqOLfRxd16tJrz39SJYSquhyui6j4b8c+JvDWv+FPF19bNd+K9Q1e/s3s7iL+0lsIH0a5tpVlCugina2ciUMS8I2rjDJwZfUWP4nrYhWlCFOPK+3MotW9U5bdH563NcmHUerZ726JIuyRFZT2IyK+6OM8P/AGhfjxo/wx0W70+z1ZdJjsXg/tjVlRWWz8xldLC2QkCfUrlCRHGPlgjf7TOVXyI7n57P88jldP2NFc1aSfKuy19+X91fjay6tfQ8McNY3irMKeX4JK8mrybtGKbSvJ9FdpLq5NRinKUYv5K+Hngrxf8AtXfES21vXtGs7Pw7p9s7aNoDu5tdOsZmnVbyfYyPIpnikO4lZLuaJkBWKOaS3+DybKMRnNV803yp3nN6tt2ulf7TVvKKs30T/onijiPK/B7KXw1w1Lmx87OrVsrxeju01Jaq6hD7Cbk3zPml9Ha38Kx8O7mz0iXVzYyy6hLN4X8cPaRmS3vrpokOl6nFF5STQTlIoo0XyomSK3hT7PcW9nM/v5nl+K4brLNMrvKkklOnf7K6rd923q025O6ckv5leIeOlJ4mTc5Nvmerbe7b6t/iegeFPGOlfELTdT+HfxK0GOz1fT0ij1TSZZfNMO4t5FzBKAplgkMTNBcqqNujZWEU8MsUX1uX5hhc5wqr0HzQlo0+neMl3/BrVXTTOKcJUpWe55t4+tJ7fxLNaeIPB15IdJtxaad8S7a2Qa5aagpuZrZIbOKMTXcUUajzTAWjla68swPE155H51iMG8nzRUckrOU739lrZLS6cm2rvfVKyWsuZR5u6Mva071lp3PC9e8PXX7NPx58H/tE694OmtvC+qXAtJtOe5mmvdFZ7M2zNdXHmSC7u2Tz55CWkDyidfMkKrdP6ssNichxVHH42XtHK6m+zd9tNlHZW2TVkrW/obgTELxC4GxfBFCfssRQ9+mulSPPz2lr/wA/HaT0UeanK0mmn+h2n6hYatYW2qaXewXlleQpcW1zbyLJFNE6hkdHUkMpBBBBwQQRX38JxnFSi7p7M/m2vQq4WrKhXi4zi2pRaaaadmmnqmno09Uyh4lvDa2CxlXEdw4ikdQCVU9QM9yM4/pxVGRFBJZ6Ja2oeSIJcJGJo94PzFQPMHPI9e3fscgFv7DfWe4aXcxeUeRDOpKof9kg5A6cc0AJc6hqlty2j+YMgbopiwGfUbd3HsDQA6wg+1bNRuriK5c/6ryv9XGOh2jufUnntxQBN/Z0H2hZwXCqS4iDfu95/ix68fTPPXmgCG6gjn1GFY4kL7G89mXIMR6Kfqw4/wB1vxAI4EGiM0TY+xSPuEpwDGxPRz3B4w3bofWgDyj4k+Hr7S/GUhtZbazsPFhSfTdRexmnXSvEsKFEeYo4/c3VuEiK74FJgki8xpL1QPieJ8PWy7EUs9wiu6ek13i/+Hts7XT+ydeHkpxdGXXb1Ou8BeKdI8b6FLK0iR3dhevBq2kbXlutMvlKtJbTEjJ4dSr7QssUkcsbPHIjt9dhMVSxtCOIoO8ZK6/rutmuj0OaUXB8rOil1W2srt/s9k6qsYSQlRGvG3b16Y3gdP4h2FdBJk2LRyzX9zbhU3W0sqxbWIxnqp46Mqkcd/agDppY0E1pbRfL5W6Rc84VV2fj98frQBy5Ih8y6vCNzu7vG8ilt3JwOm08KOACNwOcZWgDy6w0fSvCXjfWrGLSYLi/tNek8eeHNPhVYmeJrZLLU2tIpGIE5kmvJJPLWOPzNTj3OHmkevg86qf2Nn1DM5q1KceSTS66/F/5K+rtF2Wh2Ul7Wi6a3Wp7pp2o6R4n0iO/0+dbqyuQwB2lSGVirIykBkdHUqysAyMpBAIIH3UJxqxU4O6eqa2a7o42raMsWsrsZLeZsyQsBuIxvUjKt/MemQaoCxQAUAFABQAUAY+uaq1uGtbU7pAN021iGVPQHsxGT7AE+lAHk3x2+EWmfG74d3ngi6lktriGb7Xp10A3+j3cY2KWTPzAq+wqc4V1A+f5h5ua5dDM8M6EnZ7p9n/Wh9pwDxjX4GzuGaUo80bOM46e9B2uk3s00pJq2qSejafhn7LPj/xTZeMtS8I+PodRi8VaVBFpfivS7zfi9so/ktNTRY43El3EZILeWRigktmglaZ/s4FfE8OZhVyjGf2XilaMnpf7M309JdLdXpo7n3ni7wfgcPKnxVw/KMsDiduV6Rnq2kuidm7fZkpwajyxT+qLiBbUz2wDxQnEYmaF1G7g4J+9yoBIIIznA6Gv0g/DyFWjcsskjgfKPMBzsVVYbeTznjAzggY+gBYmu1tPKntnA27GjhByvBBJb15XGBg/KC2GHIBGYGv7iRLC3ZZpXZmt0HyInoScYIP4cjGOlAFjSp47fVIZryVY1Ezb1cAMjEMOcds+3HPTPIB2pAIIIyDQBzd3Gtg0unbTJ5hi2yyvhEgz/q2Y/dGVbpnOfWgC0rpK0pj1G7lmnzkW0IVWAIX5WYEYGf73Uk96AK6RmIxrBcPFPbSkgTeUVRcZZpGQZGQDwTkkZ7ZABsaY1v8AZViguIJdmd3ktlVJOcDk4HPA9KALdAGXeeH7W8ne5a5uo3kUq2yTgr6cg8e1AGZd6Bd2GyW1vUeJCMecuBFhtwYkdeSeccZ9MkAHnXxK0ObxWum+Btyx2Gt6nC2pFJTLBLYW8qXMtrIqnEq3CQ/ZnjfarRSSFg4Bjf5zirMpZXlk6lO/NL3U07WbT1v5f1Y3w9P2lRJnPaF8QWsINU+Ki6SNTuBF5s2ueKLt/DmhaHpfmB/scHnJJKJkheOSecQGOeZWRp41hit7f5/LswynhnDww1FOpiJpcyhaTcmtrrSyeiSbfWzvd7ThVxEnJ6R8y5HP4u+I/iO31nw/MviLU9OimOl6tfaVPYeGNGWZmEd9bQsxl1O5ktZwglhmeJ0gnRJrBbpxLrWwmccU2hi4/V8Po3G95y8n6NaXStdO0mtEpUsPrH3pfgeveG/C+n+CNMt7SC9vr5laWS91HULjzbm5mlbc80rYC8twERVjiXakaRxIqL9vhcLRwVGOHw8eWEdEl/X3vdvV6nLKTm+aW5mn4o6RrMDN8PYV8VMLl7Rrq1nCadE6LNuZ7sgrIqywGBxbiaSOV1V0UB2Txs34my/J7wrTvUX2Vq/n0W99WtNrmlLDzq6paEHgfx7qHj+MvoUCXGkx2kMo8SpA0VlqEsihl+wxuzNPF5ZWTzwxhIliEbzHzfK2yTGZhjqUquPoqnd+6ru/L/eT2a76X/lWl1VjCDtB3Oo+xRQzMl6Xm844juXbLIxPC+iHPQjA6DqBn2jIlLXAIjutNe5dMhZUEeGHHPzMCD0yPagCk91BeyPawaLKzxE5YNGpR1PZs4yMjv3oA4T4j/tBeF/hd4a8S6lqaNrOqeForeW90rT7iF7qJZ2CwtMA2IgSyknGdpDBSDivOxuZUcFSqTfvOFrpWur7X7f5an2PDHBOZcS47CYWK9lDEuShUmpKEuRNz5Wk+ZqzSWzl7t0728UsfC37Y37Rut22v+LtbHwj8H2t5FqOn6XHAst45jdmi82I4eQjgOJyiEhWEJ6Dw4Us4zWoqlWXsaaaaVtdNrrd+d7Lryn6risf4b8AYSeCwNL+0sXKMoTm5NU7SSUuWSvGP9101KaV4uqt39EfDH4QfDn4Pf2npXw58LQ6Na6i0V1c7ZpZmlkG9eXlZm2gAYUHaNzEAFjn6HB5fhsBFxw0eVPV7v8AO5+OcScXZ1xdVp1s5rurKC5Y6Rikr3ekUld9Xa7sk3ZK3XtqtghIkmMYDmPe6MqbgcEbiMHkHv2Ndh84Nk1KOQBNOKXcrED5HBVB/eYjoP1PagDzP9oz4Var8T/hBq3hXw94U8CeJvEEt9p+o2Np42huG0eSe3vIpczrbnzSojRwEBKvnZIGjeRWAPjT9gHT/FmhftWeJNS8YftLfCXxfL4t8HTSp4a8Aau81rC1tdWqwuYIbeO0jMUbTKAX84+dI+H3SvQB9j/tff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAZmv2/iS5slj8Latpun3YkBaXUNPkvIzHg5UIk0JDZ2nduIwCMHOQAc//AGV8Y/8Aoe/Bv/hJ3X/yxoA53wHo/wAak0O5W48X+HbZzrWsEJfeF7t5Cp1G4KupN+v7tlIaMY4jZBubG4gHRf2V8Y/+h78G/wDhJ3X/AMsaAD+yvjH/AND34N/8JO6/+WNAGd4W0v4txa/rMmq+LPDz2X9rQtIn/CN3MZuIRa2277O7XxEYOGXO2QB1c8/dAB6JQB8x/wDBRgX5/ZjvBpT26Xp8T+G/szXCs0Ql/ta22FwpBK7sZAIOM4NOhzfXsNyb+0Vr7Xs9zPEcv1Svz7cjvbe2h2/gWz/bGi8V6fJ8TPEnwaufDIZ/t8WhaJqsF8y7G2eU8108anfszuU/LuA5waFbqVLmt7v9d/wPjeHwnr37RvxP+K/jP4gfs5eOPi5N4Y+Ius+G/Dmo6R8RbfRLXw5b2DLBFDb2rX8LRz4VZnkKYdpFYDklqcHGjTnqnJN3dr7tdNFa1tVe927qwRqc85JrRNK2tnom3rbe+tm100d0uzuvC3xV+JHwx/Za+GPxt8Xa/a6nrXinVtN8Rz2HiBTcaxpVtZag8cNxd2M7rL9otIIkmZZSxMkhLb8mvNp1KeJxtFztKMqVR+6/db/d2fmr69mnZWTOnETdDC1nTTVqtOK5rNpNyTTtbVbbLVXaL/in9mT4Wa3+1R4L/Zjmg1+H4VaL8KL/AFP/AIReHxJqKW9066ygjSaQT+bIiPPvUM5wYowPlULXWmqteftFe0YW8mm9fWyt+O5jV92lTUdE5Sv5rlT/AD1NHwL+z/8ADb9pz4jfFDTPjfDr2t6L8KvEdn4N8H+GZfEN/bwaPZWlhA6XmYJkkmnuGmZzPMzuQigNgADSE/b03iZfHKc7taLR2slsrdUtL9FqKrFU6vsV8KjF2/xK/wDw34nF6X4o+Inhr4KeIfH9l4v1/wAQj9l740alpiXMk7XN/q3hGBoo761uXZts8kdvdyHzGHH2RcAEVMayc6VWrpvCTd+r5VKyWrvyu9trvq21KnJQq0qb7SS0WqV+W76b3V3e9uyXpXiTxLq3xS+JP7QXxJ0HxHff8Il8L/h9deDdFNnfb7K71maza+vrtNjYWaBGtIN3J5bBFTVi6eHrTlpJ3j1TShvdNfzbNP7J0YSSq4zDwjrFWk9Fq5tWs09VyrVNbv7/ADHV5/AnxP8Ahn+zl8OviJpvxV+KOoX3wts9XvPh/wCFrtoIL9ZYLWNNU1S6Nzb4RZFkRS8jfO5OBlt+skpVXG1/djvsn3T7vXTXTs9+dW9lF3s+Z+rXa3RLR301dtenefsBRar4U+Lfx3+EcfgXWvAHhvwofC8+keDNS8SLrf8AYzXdrctM0c6yyhRN5UUpj8xtpPbOBEKtSd4Td7PTTZWWl+vfsr23u3cqUYcs4q3Mu++r1t07d3a76W+1qokKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gAoAKACgAoA+Afg3/ymQ+O3/ZP7P/0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQBHcO8cEjxhS6qSobpntn2oAWGIQxJCrMwRQuWOSfcn1oAr3SmCVb5M4UbZhngp/ex6r1+mfagC0rK6h1YFSMgg8EUAUyyak2yKTNvGUfzE5EjBsgA9MDaM49cZGDQBPFAYXYpK3ltk+WcEAk5JB69zxz7YoA/FT9vj42/HvxB8f/Gnhbw5+0DoPiXwbqED6Fa+HPDfiMR2Mdo4KrBPa+aBNeB3JdwZGLjb8qBYU7cuyutm9RwpU1LlaSUqkYOUpJ8vLHnhKbi2mkk4uVk1LVHXiMx/1cwsMVGtFOvGpGSVP2soQTSlzuVOcaXOrpSTjUUOZpwTTfzTY/shftIa4kVz4S+EfiLxNZS3cdiL3RbKS7t1nZVYo7oP3e3cNzPtVcgk4IJ9TiHhjH8MVo0Mfy3kk1yyT09L8ys7q7STafK2lc+bybPcJntJ1cJzWTs7xa/HZ6a2TbSaulsfWP7OH/BOD9qjxX8WNF+LHxLgn+Fthp9/b3N/d2+sPDrWpEs6zG2+zOZLeSTaFklkeMgTmVFl5SvAXJThGNJNWVm7ttu7u9e97W27WWh9BjMTUx2IliKqir2soxjCKSikrKKSvpdvdttu71P2UQMFAdgWxyQMAn6dqkwHUAFABQAUAFABQAUAFABQBDd3cFjAbi4cKoIH1J6AUAePeI/jFr1x4nstF8GNpken+LNM1HT/D2tXrtLDB4ks/OZrO4tFRJMFIp5CRN0sZ0Iiba0nwedcVVMLXlDDRa9jJe0i0vei2leLvotltrzprRM7KWGUleXXb+v62PU/C3iC18WeG9L8T2Vtc20Gq2cV4kFyqrPCHUN5cqqSFkXO1gCcMCMnFfc06kasFUg7pq69GcjVnZmpViPBdR8baEfibovgbx/qXgiPxTdXOua1ptpBcxR3Zhhne0tJoknO8zNpzzpIyjlo59h8tGFfEZOni+IsZjEnyxShd7XXKml/4Df0etrnXV92hGPzPQI9Otb23uU1bTYbi3azk3w3dqGguIwmzYQSQ4YOMjocd819uchkL8OPAsGjX/hqw8NJocWqQD7XNoVxc6RceWp+VvOtCHU8sQ6uMZYcZOePEZdg8VLmr0oyfdxT/ADRcZyj8Lscx8MbjwzJD4Vt2k8vXvD/hrSr27hDTzbYNTZ2eVp5t8kzvNpspLNI0jFWaQszhj8fwbSUsTjcVCNoOfLHbRJt2t0STj5bJbHTin7sIvexr/HX486L8NfD99Ja6vDbvaeUL6/G1xa79ri2twwKTahJCxeKFhsVcSzbY9ol93PM+pZRDkj71WS92Pb+9LVWjf5vZdbevwtwtmPF+Ywy3LYc0nu/sxjdXlJ2doq+rt2STk0n8g/Dzwfr37YWpWnikTJ4f0rwvrT6TYWw1CWaKWVojdtLcbX883iqY5Z5tyNd+ZGqvbspmH5vgcNUzTM40cVN81W8nJrWyve3S2jS6Rts9j+g8/wA3wPgtl1TIMjcK+KrQXtJSScqcmkveVmpRknzU6Utaes5upGokfdHga08J+AEtvh9ZRvDqc4e7mPlM7XTYCm4kdECIG2hEU7EVUWKNVSNUX9YjLB5XClhU1BP3Yq+7+e7ffdt92fy/VqVsVUlWqNyk3dt6tt7ts6zVdK0zXdMutG1mwt72xvYXt7m2uIxJHLGwIZWU8EEEgg13mJ8++MfB934ev9P0jUPEFxaDTbk2fg3xncA3EmnzSiJRpGp7nDXcM7eWgZ2XzykSl47yK2uZfz/MstxHDWIebZSr03/Ep9Ld12S8vg6Xg2l206ka8fZ1N+jN/wAHa/oviPWrlvGGgyaT438P2psb6ATfaJraCcgh7aV9oksrgwZWVFTzfICSrHNbSQw/W5XjcHmlL67hLe9ZPRc2nSXmr+mt1o7nNUhKm+SR1niH4faV8QfDV/4O16zifSNZszDdJsRpEjkyyyIWDDcGVWRjlkdEI+7k9mJw9PF0ZUKqvGSt/Xmt15noZLnGL4fzCjmeBly1aUlJb9N07NNxkrxkr6xbT0Z8+fA34t3/AOzv8SG/ZR+KyJZ6JZIo8L69c7kN2ZXLZZsbPKld5CmD+5ZTCzykb1+Xy3HvKcT/AGVitIr4ZPrf8LN3t/L8Lb3P3fjXhSn4gZL/AK/ZE+evJv29KNny8qtor83PCKip/wDP1NVYxpp8r+wpI1lRo3HDAj8xivrz+dClpui2mnJgASuMgO6jIUknA/Pn1+mAAC7FEkMSQxjCRqFUZzgDpQA+gCnPbTQFrnTgu8nc8JOFl9ef4W9+/f1ABLZ3kN7D50O4YJVlYYZGHVSOxoArWDRW891bzTHz3mMx3nqrEBdvqAAF9iPpQBPfNZGAw3k8UaScDeyjn8eDQBi6t4a0Hx34RufC+vwm5sJ8wloZnhljaN8xTwyoQ8UyMqSJKjB0dVZSrKCJnCNWLhNXT0aeqafRjTad0eEJD8UvBHiiPxAvg3xH401HTtWbwv4mGny2Nn/bukJZyXWn6tFHcXFtALqKSeC3maNokdnvB5LJFa+R8BhsZQ4RzKpga0+XDzjzx0cmm3a10m7aPe+iWt279koyxNNTS95aHpGma/feIdKbV7n4ZeKPDkq3SWz6Zqt3YSXDoWj/ANKVrW6uItih3yC/mYjfbGSU3ejLjnJ41lTUpNP7XK7L1vaX3Re/qQsHVtcu+E9f07xNpFhreiTi7sNZtIrq1cK0W6GZomVm3rvG4HvkjIyBzX16aaujlO7gNy93K80DRoEREyVOWy24jBzj7vXH0pgYuo6fPBqE1zDZs4mG8OiBgGBB5UDrlQfQ85PzEqAZerfDq38WW1lFravYnTbiO90+ezmK3dncozFZI5Odv33RkO5JIpHilV0d1bDE4WjjKToV4qUX0ev9eQ4ycXdHnsPi/wAQfDnXrlfENlBFrcCx3Wu2loBBYa5pSPHHca9YozM6vbiZDPCWeZAghYTh7K4l+KwdTF8MY5YPFyc8NUfuzd24ye0W+l+q2+0re8jrko4iHNFWkt0e6rCVupLjjDxonv8AKWP/ALNX3hxktABQAUAFAFPVb/8As6184BSzMEUucKpIJy3fAwenJ6DrQBzUPmEeZJdRmKRi/mPGCVJOckDhh/7MndUzQA6aQPKt4sexI/k2xkj7RhT8xJ6ZUscnlV5zyuQD5N/ba+G+k6VpukfH7SrO3trqyv4tM8RXLRRyW15YTxmFEuIGZRcByUtDEAQyXSxyYjVmT5DinKvbUvr2HVpx1dtG0tnp1jvfdL0R+6eEvF8KsanBWdL2uFxS5acZNWhUk9EnL4VJ6qyk41FGUI80pN+s/DP9pb4H+L/DPhy/8XfFjQ7XX9S0m2fUoLy4FoIrxYQLlXZ1RE+eNsEbdwxtyrLn1sNn2AxEIydRRbWzdrd027f8E+Uzrwm4synE16VPB1KtOnJpThHm51f3ZRjFyeqabSvy6p2advYtWs9Otljitbs/aFbakQlJfJwAPUDbgdRkBeeK9k/NirpWizXk/wBpj2xJu+aRVxtI4PlnJBOc89BgEc8AA6iysbawhEFrGFXue7H1J70AVtT0aDUAZEPlTYwWXgOOmGx1GPyoAgtNVa1drC/ikEw5hjClmZf7ue+Om7PI64INAFa91K6t5YdQmRHgdSk1sikskZPV+2cgjBxzkc8kAFfz7KeeLTzvubeONhEpRtyowBDbSAWKgHpztIIyQaALTxyW+z7XYWcCoqIJJArx7wTkxovOW4Pbpz0FAEcEU8jJLFFDDPdyM8coRozG0eB5bAZyCF5BOPvdcDIBTtfi98LLrxfH8O0+JHhYeL5FU/8ACO/2zbf2mCYRPg22/wA3PlHzPu/c+bpzSutgOmM18xcR2SDa2FMk2Nw9RtB/WmBkalNFpEMuoa3qEWl6VFHLNcTG92RW6rGXaVnYKERVRicnaOuBzQB5v4y0y78T+INDsfs+q2Wn+HtXtr6wksYkk1DVblFUMwZgxs7BVleKUkxvLuZCyRHF18BnGcSz6csnymManMvek/hir7r0dmpa9OVNnbSpexXtamnZG34Q+Fy6DcJ478Xx6dP4naWabFmmbLRYpmkMkNirqMMTKzz3RRZbp9zOEjENvB9Pk2S4fJqCp0/elpzSe7t+SWyjsl3d2+erVlVd2Urn4hapa6houo6Xqtje6PHf6pY6pBo+gX+vyTus5hRVurMiOzeOZW80SwuMrIg2eWz142Jz3OalWph8FgXeLspSdouz315U07O1pdn5GsaNJJSnMqal4f8AFvia2ube7g1DVrZbErbaj4oaFtPmF0FZQNLtPL+1vDLbQNtuUgdRKwimG+QDmWXcRZw19fqqhTvrGG7+ab385NdeXQrnoUvgV35nVQ+AdGghsdd+IVz/AMJFqWnSvdW32lFW1tp2eKRTBaj93vjeBTFLJ5k8QaQLLh33e/lXD2X5P72Gh738z1f+S87JX6mNSvOr8TOq0xpIne3nQo8w+1KCOm7765xztY/ky17ZiXpoYriJ4JkDI42sD3FAHnnxC+Mvwu+FF7p+j+OviVDpV9qTRpa2kjo8212KrI4CExxZVh5shVPlOW4NcWKzLCYKUYV6ii3sv1fZeb08z6fIuC8/4loVcTlWFnVp003JpaaK7jG9uadmvcjeburR1PJdaH7Rfxth8S+DPCej3XwhttJ8RQaf/bkkkyyalpaeYs0kB2I7yb1ikR08tXVyhlGGL+TWnmOZqpQpr2PLK3NrrHW9tE97NWsmtL9/v8tw/BnBFTCZrjaizF1aDl7FKNqdZ8rgqnvSilyuUJKXPKMlz8julH2HwT8EPAXgjxVqfj60043virWUjjvdXusNKwSJEKxqMJErbNxCAZJ5JAUD1aGX0KFaWItepLdv0tp0XyPg814wzPNcuo5M5cmEpNuFON7K8nK8m7yk1eycm7LZJtt9/XcfKmbqonFxam0cCWUvAeOVRsFnH+7tH5j6EAvQQRW0KQQoFRBhQKAJKAMHx94M0f4j+BfEXw98QtcLpfifSbvRr1rdwkoguYWikKMQQG2ucEg4OODQB+Nf7Y/7K3iT9iD4L6X4S8B6Ze67Z+K71J/GHxH+yi3eRopvMsdIhiSd2tIFeJbiQsD50yW5En7kRIAdb8F/+Cj0vxF/ZU+J37O/7QGtM/iweA9btfDXiW6fJ1kCwl22l25/5ewBhJT/AK/G1j52GuAD7y/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQBw/wAY/hF4b+N/go+BPFd9qdpYHUbDU/M06WOObzbS5juIxmRHXaXiUMNuSCcEHkOnL2danXW8JKS7XV9/LUmpFVaU6L2kmn6M7ikUfP3xC/Yt+HvjfxrqfjzQPiL8UPh3qHiCeO68QQ+CPFc2lWutTIgQPcwgMhYoApZAhbJJJYlioxjFctrx106a7v59e5Tk2+brpr102+7odjpf7OPww8OW/wAM9O8J6bcaDpnwnurm70DT7GRRAXntJ7aTzzIrPJlbmWQtuDtIdzM2WBiVNSxEcS91GUV2tLl/LlVvne5DV6Dw/RyjLzvFtr5a6/hY2ZfhH4bm+Ndt8d2vdSGv2vhebwmlsJI/sZtJLqO5Zymzf5u+NQDv27SRtzyKUFGbn1aS+67/AFKk+aMYvo2/vVjhfit+yN4E+J3jSb4i6X45+Ifw78SahHbwazqPgTxHJo8mtRQAiBb0KrLMY1ZlV8CQKQu7aqgEY8reuj1t0/r0/NsTs+mvfqdt4L+CHwy8A/Cxvgx4c8Mxx+E5rS5s7qznkeZrtLnd9oaaRyXkeTe5ZmOTuxwAAHWXt4eylpG1klpZdl2/z13Cj+5n7SPxXu2+r7vvt92mxzfwq/Zd+G/wd+A17+z34TuNXfQdSt9RgvdQupIW1G5a93iSaSRIlRpFR1RWMfCxxgg7earN148k3pa333/VtlUJvD1VVjumnr5Wt8tOhyviX9h74aa5ongmx0Tx58SPB2s+BPDdt4RsfEnhbxCdN1W60mELttrmWOPZIhdA5AjX5iSMdKlq9T2ndJeWm2n9eeysr/u/Z+bafVX3/p/q79V8Ff2Xfh38BfFfijxj4I1bxTc6h4ztrGPXW1rWH1Jr64tTNsu3lmDTGZhO4b95sxjCLjJpWimkt3f09PV6u93fqQ03JSb2VvXzfpay8j1+kUFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QAUAFABQAUAfAPwb/wCUyHx2/wCyf2f/AKI0KgD7+oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAr6g5isLmVQMpE7jPTIBNAFigBkyytGVhkCMcDcRnAzz+OOlAGD4i1HRvCWkaj4k8S6mbHw9o1hPqWpXNxJ+5hiiUu7tjnaqK5K424A4NAH5na9/wWnu1+NcWn+EvhrpEvwugkktZLi/lnj1S9IEgjuUkXMdtGWMJMbQysFV/mBcBLp05VpqnGUYttK8m1FebcYyaS8os2oPDRnzYxzVNJ39nGM57OyjGU6cW27LWcbLXW1nR/Z3/wCCn3xE0v4n+L/E/wC0P41OofCSW8lstEddEt1u4JmvFWFYfska+aqW7ySS+YzfJAdhaTbHL24nK8VhqP1pwvR53BTWsXJa2T63SbTtb56HFhsVQxPNCM0qijGTg3afLLS9vJ2Ule6una12vhb453Gg+D/jX4yvPDNpcXkl14mfxBoOqXjFR/ZdxL9ssZliG3c01vLBITIBhX2+Wrch5bmLy2brQpp1IyhKEm37jg3J6J2lze6vevZKWl5JxMfg6OOw8aTlJX9opr3eWUZRioW05ouP7xuz1vDblfN/R5osWjRaTaL4djsk0xolktBZBBAYmG5THs+XaQcgjg5zXFWrVMRN1a0nKT3bd2/Vs0p04UYqFNJJdFoh8u2a+hhxnyFM5I7E5Vc/UF/++azLLVABQAUAFABQAUAFABQAUAFAHH6hf3N7etI+xbfaFjVn6BgA3QEg7XOfx6lRQB4l8cIPE/hfSta8R+BdOluG1m2W8FsmwLH4gsgJ9MuZzlG8iV7WGyuSzEsv2Qjyk86Wvj+J8kp4twxyWsdJ+dN6Sa0esU21897JHVh6rjeH3ep6l8Gv7QsNT+IXh+62RadY+LJJdGh+bcLa5sLK8uGJbl919dXzZBIXcEGAoVevhDEPEZNRcpXauvSzaS+UbfIjFR5arseg6pqmmaJpl3rWtajbWGn2EEl1d3d1MsUNvCilnkkdiFRFUEliQAASa+lMDxvwJovjjRfEa6LqHjGLU9G0vw9pOmR2kFtGSuqKJBdyybYw6qYltXjXeT+8lyuDGR+WZHw/hOJqFXHYrmjKVSWqdrp2fVNbvolsejWrSw7UI9ippfgfxFpPhW7t7bwl4Ku9T1O0ksdW1LRTN4XV4pCfLVHhNxNGQrD5hNuR1DKVJAX23wnjcPK+Bx04xWqTu9fOzSev93bR3MfrMJL34Ir+LNG8bx+Hh4c8N6p4j8IXLeHorSx8SC+0/VNMt9Uc+RBFLNdl726l814yXZAJRjLCVttTVlxNlMXiK84VqcbuSSSlypXb2itl3bv3Q19Xqe6k0zP+JHxZvvC0fi631DXIbKx0K4W9knjEZmsrA2ttt8tG4kup7hrmK3En7otFMWJ8kxSeXkuaQyDIYTguarWlJxXTT3bvyVle2+ndte3k+QYzinN6WV4FXnNpdbJbtuybSSu27OyT0ex8o+CPBXjn9rPx9Y+bppsPC9hmWw01pZGgtrRpXD312+4SSmWSOUPLkT3lwkkcbRpFc3FlxZVleJzzFSq1ZNq95zf/AKTHpe2y2itX0Uv6M4g4gyjwTyj/AFf4ftUzGok5zaT5Lr4pfnSpO6SfPO9/3n1Z8MfA958LPFvxH8OeFILSWeHxZY61ptpfX6b3sJvD1hYi6ZIwTFF9ptr1UjVI0xbPHEqIqqO/Oq1Hh3OsPiFTfs402kl1fv6XfW7Tk9Xrd3ufzBKrWzBVKteblUnJylJu7berbb1bbvq9ya8udF1HRbvxH4g1yYeErmSKW51Ix/6X4vuCyLAESNSXsGZliigjUveMyxxqYD/pnoZXldXG1XnmeOzWsYvaCXVp7W3S6bvXbnqVFBexo/8ADno3g7x94p1d7jRvEVjY2WvzQf2nFpUGZJtNsW2pFHdSKzwyXLMszkIyoMOiGZYTPJ7mTZ9HOqlX2NNqnF2U3tL5bp9eum9nZPGrR9kld69jZ1KxtNS0m/05bdbrTru0lg1KHUE8yOcuu0xsjcFiCQR905AOelfQGJ5P4j8HXXhm10rUde1OS40zSDPDovi2OaWW/wBCiljVWg1Dfn7XZ7ox5ksrf8s7Z5lMkBvF+DzHJsVkVaeZ5L8L1nTto1u7f5LVa8rs+U7IVY1kqdX5M9O8DeN5764l8K+JrZbHW7FVLRhi0csRZljkjcgb1bacHqDlWAYc/T5Rm+HznDqvQfquqfZ/o+pz1aUqUuWRxP7WvwU0z43fDVtGjRY/EmmM99odz5IYrIq/vIXc8rFINobBADCNyG2bTGd5XHNMM4L446xfn29H/k+h+h+GHHdXgTOo4md3h6lo1Y3fw30mkt5Q1a0u05RTXNdc1+xz+0dc/FPQ5vhl45tLqx8deD7ZILwXRfzL+GIiIzv5hMgnVtqzBv42DA/MUTkyDNnjYPC101Vgtb9UtL663/mv1162X0Hi74e0+GcTHPcqlGeBxUm48trQlL3lFcvuuDV3ScfspxfwqUvpOvoz8YCgAoAhuruCyi8+5cpHkAttJAz64HH1oApSPFBrVtJFyL+J1baRtJQBlb3OCR+VAGi8UcmPMjVsdMjNAEcVnaQSGWG1hjduCyoAT+IoAzvF3iFfCPhTWvFZ0bUtW/sewuL/APs/S4BNe3hijZxBbxkjzJn27EXI3MyjIzUVKkKUHUm7JK7b2SXVjSbdkeU+A/D+peK9d1HTde1mC9TT55JfG8cVvE8Gqaxd2cRTTwThhaW1nLB8rQh5Eax3TuyXSy/DZFhP7fxc88xsbxvalF7JJvW2ut9tfi5nbY660vYxVGD9SP4i/Arwla3elfEn4QfC3wvZ+NPBd22oW0lno9pay6rbi3uIJ9M88eWy+ZHcSeWS4iSdYnfcqMjfS5rlNLMsDUwUfd5traarVX8rpX8jCnVdOamQ/BbxPbaUNI0RGvtT8HX6i28Lam0KK+iPCHibQtQjQbopYGWWKOaQZ3IbacrcpG955XD+eSrSeV49cmIp6W/mS6rztrZbr3lpdLSvRt+8hrFnu9fWHMFABQB438Y/Cmna78R/DE+oatJp76l4T8UeFbN3iiMMs962nT7AzSq7TiLT5pViSNw0cNwzPH5QD/F8dOcMvp1oxuoVIt+lpb9tWlfu0deDs5tPqj0jwX408PfEDw5beKfDF49xY3LSRkSwSQTQzRu0csM0UirJFLHIro8bqrKysrAEEV9jTqQqwVSm7p6prVNd0crTTszcqxBQAUAFAGPHr6u224sysZHJVt235cncCB6MDjPKt6UAVtcsoLB0vYSkMUz7JgRlQcfeCj2BBHcHHc0AZlon2SeK4UNIN4Pks/3sngA+oYDPGCwPGEJoANW0Sy8WaTLo2t2Fhd6VqUMi3Nhe24ktjCykO7Rt8pUg4CsMNyxHJwmk1ZlQnKnJTg7NaprdM+V/2xvgX8IfA/wOPir4b/C+zttR0wWGkNqFtdS28lnZbiPPkjLYunkJETSOGkbzdzMQnHyHEeWYXD4N16FJKSsrrSyvvbZ327636H9G+DPHOe5zxLTyvNcwlKlLnmoTXPzzUbqKm05U1FJ1FZqKcOVK8z6I+AGm6FqPwh8G32m63BrViujWUKXUWP37RQrG3mfM20h1YNHnIYYfkED6LLJQngqThLmXKlf0Vv8Ah+x+M8cUMRh+JcfHFUnSm61SXK+ilJyVtFdNNOLWklZrRo9TVVVQqqAAMAAcAV3HywhdA4jLjewLBc8kDGTj8R+YoAqXmpJbl44V82RMF+DtjB7sQDj6de/TmgDK8SahpHhzSn1fV5ried2WK3+zoXuJpmHyRwIvc88dMAs52hmGOIxFLCUpV68lGMdW3/X/AA44xcnZHF+EfEt9qWtaj4W1pZr/AFFg91qKWAVrHTrgrEYbF5ztZp2ifzyoBIUhm8pJLdZPAyHOsTnVSpW9ly0F8DfxN9dNn8rW297VratSjSSV9ep2OoSedbW11MYZDDC8NwHUkGYYGwlTwc7sHoD9RX0pgZPiPxf4Z8HWkdzq2pQ2pu5DbWsWn2k19fXcuwyNDbiMNJNII0dyqIxVEdyAqFhjXxFLC03WryUYrdt2Q4xcnZHn/ivxLrWtTQi90fxPaeGNMu44tP0u1t1i1PxdfuweGCMLMrxW6FX84TrCsgDtM6WUc5uPic14inmk45ZkUuac956pRXWzt+K22V5NW66dBU17Stsuh0Fj8NPiHb+HxNe61odxcSW0yz+EIrSKPQQjIwS0juPI+0YUlFa4dWWTEjC3iDrFHU+BMJ7FezqyVZWfPvr3tdfLW601fUWMlfVadjB0DxVrHw+juPB+j/8ACYteQTXt7aaX40gvtQFzBEkirb2usL5xYyy7LkCWW7nSEyoIY1QJBnPOc9yHl/tSnGpSulzxaTu9fLZXWsY3dterPZUa38N2fYz9ak8a/EHxlp1vf6VFca1dRpc23hW9uRJpfhTTTIskWp6qkLNHe3/2i2XyY1cL5iNHbOFgu795ni8VxhXlh8HJ08LHRz6zfbpo106LWW6iPljhVzS1l27Htfh3wXovhrUNU1m1E9zqmsvGb2/un3zSRxAiGAHAEcMYZ9sSBUDSSyEGSWV3+xy3LcNlOHWGw0bRX3t92+r/AOGWiSOWpUlUlzSN0gEEEZB6iu8gjuIEuY/KkJ2EgsB/EAeh9j3oAhW3lnu/tV1lVhJEEQbgdRvPqSOg7D3JwAVMLeQC5mG57qQwxqwOBCWwQMeqKWz+uAKAMPxb438PfD3w9J4o8a6nHp2l6E7C7vJMsdhXamFXLOXLxjABJbHFY4jEU8LSdas7RW7PSyfKMbn+Op5bl1NzrVHaMVbXS71eiSSbbeiSbZ4ufij8Xv2gvEXibwP8M9OvfCngybw9avY+Mo4f9Jjvrq2huowS7qEUwzeWywq8sbMj713Db4TxmKzSpUoYZONPlVp9btKXfs7OybTs7o/Vo8NZBwLgsJmudTjXxirTU8M37rp05zpOyUXd88eeLqSjTnFShyys76fwi/Yr+HvgDWpPGvjvVLr4h+LpLn7V/ausoTGkmW+dYWd90h3Al5GkIZFZdhq8Bw7h8LP21d+0qb3f+V3r5u/lY5+K/GTN89wyyzKqawWES5fZ092tNHNRjaOj92Cgmm1LmR9D19Afj4UAFACYGc45HegBaACgAoA57xx4T8NeONCuPCnjTR7bV9A1aN7O/sbmMNDMki4G4fXgYwQzAgggGgD8Z/Gnwr/aK/Y60n4+af4Q/Zw8Ow/DvxHNrGgDxr4hPm6hb+HppZLe3itp/taKm9HjYDyi8sjRhg+2NVAP0m/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgCK6iM9tLAMZkRk56cjFAEtABQB8r/wDBTC5+HY/ZM8R6N8SvHOqeGbDWLiC1sXsbRrgXuoxh7i2tpgsMpWEyW4ckGLmJV8xd2GyryqQpuVKPNJbK9r+V+h6GVUMJicbTo4+r7KlJ2lOzlyr+blWsrb2Wr2utz4V/4JFfs0/Bv4u+K/H/AI3+Jel6R4wl8G/Y7HTdIvbYz2JF0LgNdzQTw7JciHbEGJKnzWeMN5LDpqwjTqShGSkk2rq9n5q6Ts+l0n3SPMpyc4KUouLa2drrydm1deTa7M+tP2h/+CWvwK+JutweN/CcOseGV0yC8ubjwv4eithaapOUlkQQxzPFHbyvMYQ/7xI2jiCAwk+cnpf2tVxFWh9eXtKVJr3NIpq6cruK+KdrSm7yel27I4f7OhRhV+qvknUXxayadmo2Te0b3UVaK1sldnwr8Fv+CX37SHxb+L7w/GPwrL4D8MG8kvNb1EXNvM7AyFmt7RUeQGViSAz5RBljvIVH8m0I6U48seiV2kuiu227ebb7ts9jE4mtja88TiJc05tyk7JXbd27RSirvpFJLZJLQ/baz0eO3tYrFhDFa28KwQW1mhghijUYChVPTGAB0AHAoMC3bWVpab/stukXmEFtoxnAx/n8fWgCegAoAKACgAoAKACgAoAKAK2okfYpU37TKvlKfRm+UfqaAPMvFPiG7jkt/DvhfUdNi8Ra3FM1lFe7pQqJxPdSRp+8kihW4RTzGHkeGIyxGRXXys6zalkuEliqqv0S7t7L9X5J77GlKm6suVHlPxh8bP8AFr4Y+LtL8AeJtI1jTvG/hPXPD+iSRu9reya8LCeWCJA2FmiuLfE8bhUxFbpMjTxXCPF5GQZ5jcbiJ4LMqXs6qipK10nF26NvXVde6aTTvrWowhFTpu6PWtKurUeMtf0fzrd74PaaiYEuAs8VvLEIYZiBnYrS206gsOTE2Oma5OAJJ5U0uk3+SKxv8T5Evj3UIb3wPqXhnxZYxalo3iOKHQbpZJWiLRX7xWrAlOTxdDpgnP3h1H0+bYiWEwFavB2cYyafnZ2/E56ceaaRD4YFpcXniTV9J1q31WDUNSmnSbT3WUWzQW0dlLbOVyNyT2827upYqcFTjx+DaHsMmpXjZy5m7+cnZ/NWt5amuKlzVWdna6Xei0jjtLuCeLzfNEqvsJATYFxtYcYH/wCvmvqDnPJPjZ8UPCngLSrmy8QXbx2GmX9nqWrTEI4S4ilS6gtLcjAlu5jDERD8vlws00jIvlCT5Pi7M6eHwUsFF/vaiskuie7fZWuvN6d7fTcJ8MZjxZmcMvy2HNJ6t/ZiuspPW0VdXfoknJpP5AsLP4q/taeKtusXl0PCMms3t7on262SOG0t3nJMlwITE88UUTJEApMsjqkSPEpuLmD4jI8leZYj2cPdjFLna+7S9/elq+y1dtk/6JzrNso8FMsjgsnSqZnWglUk3flum+Zq2iUnaELK6s6nM0m/ufwD4S8P/DO2bQvBaNLHJJEZLqbb517OY0QvIVVUAwiIsaKkcUccUUKxxRJGv65hcLSwdKNChG0V/Xzb3bererP5WxOJrYytLEYiblOTblJu7berbb1bb3ZseNvBPhbxBftqHiTRNN1F4YZoojd26Oy286LHPHGzDpIIwJI8gSJtBq6tGnXSjVipJO+qvqtn6oyTa2PKbG91rxh4oOr6RDZahcy65qdl4Ve4ui40fT7ayW2vNSNm2zz3W/SS2Dg7hDeR7ZFSZ0f4fNYVOI82llUajjQpxTnZbyvtf0atfZp6Ox102qFL2ltXsejaLoll4ctlsLdplna6e5urmWQm5uLhxzM5ZRljgYAwoRUQBQqgfa4bDUsHSjQoR5Yx0SX9f8P1OSUnJ3Zzvw/8d+MPF99aawI9Mn8O2V74stL6Vw0bfb9P1n7HapGrNnLxRXrM20jfGhGwHafFwOcVMRm2Iy2skuRJxsndppXu7tdV23Np0lGlGoup6Xe2Ed2Lm1W3iubudTJMjEmCMFcAFfuliOm4c8k4HFfQmB5B4j8NxeD7a1kjuLm38KRO7aPq3khpfDE7AqUZVG1tOx8pDECHHaEK1t8Nm+UYjKsQ84yha/bh0kt20vzS9Vrv2UqsakfZVfkzuvDHjy71V5PD+tae0WuaLeRQ3SLvGVbBjliZ1USxypnEikruEkbHzIpVX6fKczo5vhY4qjs911T6p/1qrPqc9Sm6UuVnhH7VPwW8QQ61D+0T8Gbn+zfF3hYm6uPsyFZL6COMcEDgvHGJFaNx+8iOzkKqHys9yyo5LMcHpVhq7dUvzaWluq07I/cPCnjjBwoz4M4kXtMFiXyx5tqcpP70pStJSTXs5rnVrykvav2c/jxoHx8+H1r4jsri1i1yzRINd02MkNZ3ODyFYk+VJtZo2yQRlclkcD08ozOnmmHVSPxL4l2f+T6f5pnw3iHwNi+BM3ng6qboSbdKb2nD1VlzRulNWWtnbllFv1SvUPhAoARlV1KOoZWGCCMgj0oA5xrRLG5kF9bzLp0RK28m7JhJKnIIOQMjg/n1NAGpBeyQ3CWd6ysJRmCcfdkHofRv0Pb0oAt3F1bWkfm3M6RL6scZ9h6mgDzb4n63ba9eeFvBdnYTXkGpa3Bf6hNFefZXsrWwDXiXIU/NLH9st7GB1AAK3XJxwfmeL8Z9TyirZ2lO0V5826/8BudGGhzVV5F34O2ksPw/0PUBHqNrc+KIZvE9+mo3CXVzbXF7ILlrUyr8jJD5/wBnj+8FhgiQEhQa9nLsGsvwlPCxt7kUtNLu2r+b1+ZlUlzycu53NtNZC13W9zG8MI2lxIGAwO5rtIPHfi3oWmaHJeePvDXhy21O11AqnjHTLewEj6xZAKhuY/LQzTXdsi7kRN5kQPFs3tC8fznEWSPNaKrUHy16esGtNe3z6Po9drp70Kvs3aWz3Nn4OfEJdTEfgTWvEkGtX8NgmqaDrUbb08SaEwjEV+ki5illQyxxzmJsFmim2Qx3UMY3yDOFm+G5qi5asHaceqa8nqk/wd1dtMVal7KWmz2PUa9wxCgDE8Y+DtB8eaFJ4d8RwTyWjzQXKtb3UttNFNDKssUkcsTK6MsiKwII6YOQSDnWo08RTdKqrxejT6jTcXdHi/iXw74q+Gl1L4w1fxG9lPZWQhk8d2lkjxXUKyhYk8QaegVHijDRu13CY1jUXzq+mxOwf4ieVZlw3OVfKP3lFu7pPddfdfV7rvtdTZ1qpTrq1XR9/wCv69D1Twb8QI/EF7J4f1zTxo+uIkk9vbPdQypqdpH5Qe9syjFngDTRo28JIjOm9AskTyfS5PnOGzqh7Wg9V8UXvF+fl2ez16ppc9WlKk7M6+vWMwoAZMsjxOkUvluQQr7c7T64PWgDk9QjkWaY3MZLCQx5VRiRtoPbBBY7DgdmfHegC9axya7bPmQolopigHmnJlA4kYr+GMZ/ioArabp0WrESBpY4/L2zFWBB4+VVPOCAT06LgHkmgCy08Ruks4cXBnciQsVDTlcgA4+7GuDnjnGADk5AH3Oj6L4s0XVtD1to9TtdXtZLDUIVZlR4JEZWjG0hgpVmG4HPJIPpFWnGtB05q6as/RnVgcbXy3FU8ZhZctSnJSi9HaUWmnZ3WjV9VY+RtU+DXjv9jj4gz/Fb4Q22teIvht/Z11LrehpcJNcRPsbYrIQD5Ik8g/aEDyRxrKXDKpEnx08vr8P4h4rCJyo2fNHd/wDDXtqrtK97rf8Ao7DcYZV4vZRHIOIZQoZjzwVKq04xaurtNXXO4869nJxhObgoOMmuT6W+EPxp8K/F/wABad420s/YZbu0+1XOmTTI9zagTSwliqnJjMkEoR8AMEPAIKj6bL8fTzGhGtDS626rVr7rp2fU/EeMeEsZwbmlXLcV7yhKymk1GXuxnpf7SjODlG75W1q003yHxFtvF1zcza95s+paczF7Z9LaGHXvDFwVEBnsQ6GO8j2t5rxyAsNsy7bpZEtk8TOMox0sUszy2rapFWcZfDJdvK/3X1vF6nztKrDl9nUWncq+C/jtc6ot/p/hi10/xRbWV2ky69Z3qxaMulzQfaY7me6y4inVD5b28YkdmME5S3guA0fPHi+jQwUauMg419V7NJ3bTtpfaLfV9mlzNFfVnKdoPTuOspPGnjtbDUPDtxZTxNsuV8X6nZMulwS+TKkkukad5m+QOolKzyShPLvFZJ7tA0dcUcqzXiVqpmsnSo7qmt325vw31unaMbl+0p4fSnq+52HiC58PfDD4fR2Gk2Uen2UU8FhpyXkspa91K7uVhg8+XZLL++up4zLcuruWld33EsT9nWqUctwsqijaFOLdkukVeyWi6aHIk6krdWcFff8ACfXDP4UuNN+JXjZXmSx1u/8AD6aRodhZyPHFI0cJnntrvyFWYMjwSXDp8yec0sbAfE4TG8SZ9SeKwjp0qbuo31drtX2lqttle10jrlChRfLK7Zv+FPgfqtqs017LpnhaW9tjb302iTS6lrN0RICpm1m9UTSqU3ghofNUlSky7efQp8JLEVFWzWvKs1rbaP3a/hb0IeJ5VamrHe+Gfhh4F8Jz2F/pegrPqmmWUmnW2sancS6lqq2skvmvA1/dNJcvGXwdrSEcL2UAfU4fCYfBxccPTjBP+VJfkc8pSl8TudTXQScr4u8JWvxD0xbO5vbvTvsk63emajYyBbq2ukyEuImKlRgF1KMGSVHdJFaN2RsMTh6eLoyoVleMlZ+jHGTi7oq+AfCth4Fs5/P0WwsdV1dxea3c6Vam3sb2+ChZLoRb28tnABJcl8Kqs77FNcOT5Z/ZGG+rKpKau2ubouiXkl+N3peyurU9rLmtY7JHSRBJGwZWGQQcgj1r1TMdQAUAMmKrC7PL5ShSS+QNox154496G7asaTk7Lc+XPGf7WGseKfFifDj9lbwT/wAJ3rGnl4rnVZQw0izIIUPvDKJFKrMocvGhJQoZdwFfM4jPp16v1bK4e0kt39lflfrrdLtc/b8n8JsLleAWc8d4r6nQkrxpq3tpXXZqXK1eLcVGcrX51Ttc7Xwb+znZ3XjPXfih8SL2+1SfxjpNjBe+FdRuPttjp8i+VNLEXcsJws6NswEjVJJFClW47KGVKVeeKxDb9oleDd0tm152e2yV2rWZ81mnH1ShlmGyLKIxj9VqVHHERj7OpUT5oRkkkvZt02ue7lOTjGTkpR19p0/T7DSbC20vS7KCzsrOFLe2treNY4oYkUKiIigBVAAAAGAAAK9iEIwioxVktkfnNevVxVWVevJynJtyk22227ttvVtvVt6tliqMgoAKACgAoAKACgAoAbJGkqGOVFdGGCrDII+lAHxx/wAFG/2ePDXxI+Det/ELxTr/AIvltfh/4e1bUtP0awv2OnT3gtJRbT3NvjloXYsJVIOwsr5WgDtv+CcP/Jk3ws/7Btz/AOltxQB9J0AFABQB5r8U/E3xl0Pxh4D074aeDtP1nQtV1Iw+Kbu5Ul9PtPNgHmR4lTB2POeVflBx2Pm42tjKValHDQUot+830V1tqul+59pwxlvDeNy7MK2d4mVKvThehGO1SfLN2l7ktOZQW8d3r1XkWtfEH/goPBrF/DonwL8D3GnR3Mq2k0l5EHkgDkIzD+0ByVwTwPoK8mpiuIFNqFCDV9NVt/4GfoWDyDwinhqcsVmteNRxXMlGVlK2qX+zPRPzfqzodQ8aftpx/DfRNT074ReEpfGU+o3Eeq6c9zGLeCzAHkyIftoBZjnP7xvoK6J4jOVhoyjRj7S7ur6JdPtfqeRh8m8NJZzXoVswrLBqEXTnyvmlN/EmvYPRdPcXqzWHiz9rHz/iMp+GPhrytNeQeCG+0JnU1Fwyobj/AEv5cw7WOfK5Pb7ta+3zW9b91HT4PPXr73b0OB5VwByZY/r1W9RL61o/3b5E3yfudbTutPaafecrr/jz9vO30/w/J4e+Cngy6u7jTDLrMct3GFtr37VOojj/ANPXK+Qtu+QW+Z2+b+FeWris9UYOnRi2172q0d3p8faz67nu4DIvCmpWxEcZmdeMIztTajL3qfJB8z/2d6+0c47R0S06vlta/aj/AGo/gxar4o/aG+B+g23hy6kFhavo2oRJMb1gXQMftM/yeXFNn5ByF+YdDy1M6zPLl7XMKCUHorNXvv8AzS6J9D3cH4ZcC8Y1HgeEM0qSxEVzy9pCTj7NaO37qlrzSj9p6X06r670+7F/YW18qFBcwpKFJzjcoOP1r62EuaKl3P56r0vYVZUm78ra+52LFUZBQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+Jvjx/wVI+FPws+JOhfDrwLoq+NlutZ/sfXdYgvxDa6VKk0STJGgR5LqREk3EqFiJKhJJGEiRkbzl7OmnKXZK7d9kktW29Elu9Ebww85KM5WjCT5eaTUYXVr3k7RSipJybdoppuyMT9qz9t79hz4lfC3xx8NNe8RaJ4m1fSbNdQ0jTta0TVDZ3Wqope3QSW6xyxkPtjkIeMhZHUkr5i05QnTk4VIuMlo1KLjJNdHGSTT8mhYij9XqOnzxltrCcKkXdXvGcJShJecZNdNzxr/gln41+Nfh34p33hD/hTmjD4d+JrNpL7xPotq0dpbXNojtDtnDmKZnM4QwqDKRLHIMIrE+vjczr4vLcFg6tGMI0oe61HlnNO0eeprdylyc12ouSfPZqSZnj8rWDzrG4xyi5Vp8zjTkpUYXvLkouLceSDm4WjKSg4une8JJfqUIptSctdRvDbAEJGJGVpM92AxgY/h9zmvHJLqIkSCONFRVGAqjAAoAdQAUAFABQAUAFABQAUAFABQBDdXUdrHvcjJztBOBnGeT2HvQBwPi7xbYWRs21W8dl1CdbKwtoIGkkmunBKpHGoLOdiO5Y4CRpJK+yNGYc+KxVHBUpV8RJRit2/637JavZFRi5u0TzPSNF1rxydQ0fxBe2ENzfy2p8bWkLed/Z0PkpLb+H4XClJFMcm64kkZpGS4do4lW6j+zfGZfRlxPmH9qYhNUKTtSXSTvrJ38123srvld+qb+rw9nHd7jfFunanoPjOSLQ49PtZ/GV1FrGi31zbq6W3iS1tVjMErMsvlQ3ljAYd8caFEjuNreZcxAdHEdKeWYulntFNqHuzXeD69NU3168r2TJoNVIui+u3qc9o0up2VrYR/DnStRl1Hw5pepXng/TbkwRWl1ZB0jvfC148btFbz29xFHFHIAhiWKAo06Q3iycGKrLh/HRzbDWeFxFufybu+ZaX2u7a395WXu2uK9vD2UvijsdnL8R7Px/L4R8KeH7mwtrzWi2p6jY3stpLqFnpYtJJLbULRQ0kFwI7yPTQtxGJoNzEDJXK93E2cYWvklSpQle7Ubap35k3GS0a0TunbT1Jw9KSqpMp6anipvCUPjq6gttIl1ixXxHb654W02WVZmlVJNl7puXlnYRJaQmSNmmkHmiM2uRXJRyjOshjCpl03VhZc1OTWmmvK9t77W6XUinVpVrqas+5teLfjJrvhTwVPea7eabp+mxovm+NLK4jntTCVIc29sQ7i9MgZY4nEsChkdppnBt2upxxS+rSUaTjiFpyS6Pu3pouq0lfSyXvL0sm4bx2f4+nl+Xw55z2t+LfRJLVt6JHyh4V8F63+1N4y0/xhqPhyex+HljdyaboukzTXLC+mO6W6u7qaLJQ/flnunKmWURW4kaWQMvyeX4TE53jnSlO9SXvTk+i9PuUUtuySP6OznOMF4I5H/YeUyU8yq2lOpyq0O17p30T5IO+8qkrcyjP7W8OeEPC/hK3tNE0m2t5p3kt4J72MKnmbEjVdsSjy0jC/IkajYirgDrX67gcDQy6gsPh1aK+9vq2+rZ/LOKxVbG1pYjEScpybbbd229W23q23q29zV8UXGieC9viHX9bsdK0e2kge5vb+4htoIiZVQbmO1VyWQD1LV1NpK7MDj5fiT4mutb8GXel6jcNpHiXxZqFmlvcaY1tLNYRaPqMqRsk0SyqDcWiSKwGWXaQxRsH5HAZ1Uxmf1sHTqKVGMLq3K1ze7ezWr3fXv2OqdJQoqTWtzVtfCtjB4pTxO8ctxeR2psNPOxMafbO0e+KERhNiySRQO3zHJEXJVUVfoMHleEy+dSphocrm7y/rotXotNTCdSU0lJ7HTafqkUyC31spPbFSFmkAITGBuLcYznqcEZX1zXeQeX6VcXGr63oFroVrNpdo9xeeL75Yp5IZI4btpo7KG5t5gkqvP50s77gVjntHU9UYfD5F/woZ7i8zp/w1+7T6Sa5btPb7N/SSOut7lGNN77npTXl2bI6WFigbl5n3tunGSGY9xjB3A84B6AEV9wchBPapfWVxoN9sutNn/dT2UipIsiEgNEw77vmU8YJIPRs0AeeWUN1oXiC90X7Pdavqnh5LOzE0d7NLLeaBeTn7NNO0zxwvNbyQz5Z3uZhBHM6nzLxoz8FSguGs9VGGlDE7LpGfZJX6tLorSV/hOxv6xRu/ij+R6ZokN7cm9czC5yEEqMTucYJVlY9GGOM/jjivvTjPkD4o+Brr9k/4k2v7Rvwpi8zwpcSLZ6/oFk6W4RJMDAyjqkDyKmcD5XwEIDDy/i8xwcsixKzLCL929JRWm/yejdvR7b6f0zwbxJT8VsllwTxBP8A2pJyo1pXk249/ei3UjFys72lC7mm4vn+0/BPi/RvH/hHSPGvh95G07WrOO8t/NUK6q652uASAwOQQCcEHk19dhsRDFUo1qe0lc/nnOcpxOQ5hWyzFpe0pScXba6e620e60Wht1seYFACEAjBGQaAM6+0mK5klkjjXfcxeS5bG1VB+9jHLdMfSgCrLZWcV2ttaR/aL9lBaa4YyGJRj5znjPTAH8qAOB+LlyfA9rB8RpbK1m0/wpFO/iG6feZo9FuMG7nUb1AaFreCdyRITFBKkaBnXHzvFGUzzjL5UqXxxfNFd2k9Pmm7ba26G+HqKlO72NfQvEEHhX4b3958RdZvPs3gSwlh1rXL+Eqt/awWaSy3uV+9mMhpCo4lSVV4GT0ZLnEM2wCxlrPXmS1s1076qzS31QToONT2a699Pxeh8++Gv2mv2pfiVoEHxB+E/wCwvHqHgjVQ13o82ueNbLTtR1SzLExXH2d428nzFO5QzMCGDKzKysfbacPi/DUw0baiz174F/FrQP2ifh23j7wppuqaHf2l7caLrugaogF3peq25VZrWbkZK/KRu/gdSVViUA0nGM4u8ZK6fl+gk3dxkrNaP1/X1K1x8LPGfhHVtV0v4fhZ9PubXU9c0DULxonfw7rUmFNsivkyW9wZ5pdpGIis67tkkMcHyGZ5NiaOY080yz4m0qkbpKUer169OutmtU2dVOrFwdOp8j25GLIrMjISASrYyPY44r645jymH47+b+1Ncfs0f8IrjyPAi+Nf7a+3dc34tPs32fy/ff5nme23vV0Ie2o1au3s5xj680XK/la1rFTjycvmr/jY9YqCQoA8f1f4Ranb67Do+kSXjeGb7UJtW0+9trmOPUfB+qYeQzWzyAiW0lLTL5TBzGZ2gaOaynaK0+Ux3D1VZjDM8sqKnNv307uMl10XXutE3Z3jJXfTCuvZunUV10N34XfGzw58WfEnj7w74f0zUbZ/h7r7eHL+W7VFE92iB3aIKzZjAZQGbBJz8owCfrILmpKr0bkv/Abf5nLJ8tV0uyi//Ar/AOR6HSGFAGHqzpHHq277phiPTPztlc/otAEmmWyQanOti6G1RNp2uThic7PT5SGP/A6AM/7RqK3DWtu0sFs91JEZCAE5dj8vGexyQfbjg0AS6faW8SPp81vHPaMSzXBRo8oo3Bt3RhnA4I9eQc0AXBJb6heQoqy28MassbAtE0rD7yDGCAuORxkj0BoA4fxH8WI10TWW8KazosEWgS3tnqvibWt40jTHtoz5kjHdELspL+7lSOWNI2iuUeaOSLy2+bznibDZVL6vTTqVna0I7693Z28lZt3Wlnc3pUJVPeei7nw/oOknwh+0xqPhPULTxBc6h4+nF7p9xqeuar4Ggke5aaVNyWYaSUOWWJEkiXbKXTYjKa+Pw8c0hj3RlP6vOrq9FJa6pa+d1o3rp3t/SvEuGq8b+G2BzXCKMvqK5ZxUnKfLCMYSk7RXLLT2ji7/ALtqfN0Pr+w0Tx94Ys9J8FHwF41uoNUDJqF7pfjZdRstPDyMo33uo3EOpAKgVz9mhyobaocrivdnlvFMb06eLg492rPz2g/zfyP5uVTD7uL/AK+Zs+FfglbPYeH5fHlpo7f2JHH9j8L6REY/D2mFJDJEI4CF+0yw4gVZ5UVQ1ussMFqzutevlHDmFypus26lV6uctXe2tuyevd62behlVryqabLsel6naG7tgFQM8UiyoC23JB5Ge3GRXvtXVjE+f7P4c+MNP8TnWLbStLht7W8zbatq/j7WfEVzBAWKme3sLuPyI7l4TLEJFmPlebJ/rU3xSfGVOGswx37rMcY50t3FRUb9te19dn99mupV4Q1hCzPXfANrZ6Fa/wBh2puGR9915txJvkmmdi8rs3HzMWDYAA6kBVIA+uw+HpYSlGhRjaMVZI5pScndnX1sIKAGuiyKUdQysCCCMgj0oAUAAYAwBQAtAFLYdPn3ocWszYZf+echPBHsSeR2OD3NAF2gDM1/xJonhiyF/rl+lvG5ZIUCl5biQI0hjhjUF5ZNiOwRAWIU4BxWdSrCkrzf+b62S3bstlqdWEwVfHTcKEb2td6KMU2o80pO0YxvJJyk1FXV2j5s8J/En4hftYa/4f8AEHw21K68H+CPDGs3Efiax1GzYy6pGOIohIoMcokhZg8IYeQ7b283EDH53D43EZ1UhVwrcKcJPmTW/ZX2d1uvsvV391n7Nm/DWUeGOExWBz2msTi8RSg6E4TVqbfxNxdpw5JpOE7P2sVyrk/exX0F4A+HXgr4XeHU8K+AvD1tpGmpI07RQglpJWxukkdiWdiAo3MScKoHCgD3cLhKOCp+yoR5UflGfcQ5nxPjHj82rOrUaSu+iWySVklu7JJXbe7bfSV0njBQAUAFABQAUAFABQAUAFABQB5F+19/yal8Yv8AsRdc/wDSGWgDif8AgnD/AMmTfCz/ALBtz/6W3FAH0nQAUAFAHzx+0t/wqf8A4Wx8Ef8AhYP/AAlv9s/8JEf+Eb/sb7N9l+1fabL/AI/PN+fy9/k/6v5tvmd9tfP5x9U+t4X6xzc3N7vLa17x+K/TbbzP1/w4/t/+wM9/sj2PsfY/vvac/Pyclb+Fy6c1uf49L8vS59D19AfkAUAFABQB4f8AteeKvFnhD4babqXg74o6T4DvZdchgk1LU45XiliNvOxgAjgnO4lVb7gGIz8w4B8TPq9XD4aMqNVU3zLV37PTRS/LofqHhNleX5tnVWjmOBnjIKlJqEHFNPngub3qlNWSbXxN3ktOq9l0qSSXS7OWadZ5Ht42aVejkqMsOBwevQV7FNtxTZ+bYqKhXnGKsk3p21267Hzv/wAFBtc8R6B+zlcXHhfxjrnha8uvEehWD6pouoyWN3BDNfwxy7JkIK5RmHp6gjiqpwVXF4elJ2UppP0aZhUm6WGrVYq7jFteqaPGfjV8KdO/Zz+Ffij4veEP+Cg/xkHiPw/pk93o1r4o8c2ms2F/eIhaO0azlhAmaUr5a45UuGwQpUzOp7Kz7tLa+7tsvz6bvRFRpud/Lzt/Xp12Wp9Dan+09beCvBHwxk8b+APFN/8AET4i6Ra3kXgvw5pv2jUI5/Ihe8DCZ444YoHmCu80iY98NjerFKtKnBbX6p2V7ataeV1o3sY05fuo1Jve3Rq78lq/N32W7NjwT+038PfFeieN9U1zT/EHgm8+G0TXPivSPEtiIL3S7YRySpcMIXljlikjikdHidwwU454rGVSEYe0vp+N+1t7vS3e+hsoSclC1r6rs13vtZa37W1PP2/b28BWNh4Y1rxL8Hfi14d0bxvfWmn+GtU1TQbeO21Sa4uEiiVWS5ZodyO1wonWIvDFIyByAp3VGp7VUJrlndJp9L9+l09Gt0+m5jVqRpwlUWsUm7rrbtez13V7L8Cxc/GD4b+D/iL+0lfan8VPGuhT+C9K8Pza1ca/NFdaBobXFhKbSTSLUEvvkODNEwBmm8tUDZrkm/dlZtPb5tK1jv8AZy/dtxTVr6X1Scr3+56rokeSfAjxvqXg74kfDq3+KnxB/ac07Q/EKzQeH4/iXp2n29hqevXIYCGe5tJHnUujSG3srsAKyZRiURa60kpyglZ2sk3qlo99nLSzb13W8jgnN2U+a6b1aWjeyt1jHW9lps9ke/8AjL9q3w/4f8a+IfAXg74UfEn4jaj4RtxNr8nhHSILi306Ro1kW2aW4nhEtwY3VhDD5j4OMbgQMU7x51te1/Pqvl17dTSXuyUHva9vLo369O+pLrH7W3wttvAvgjxx4StPEHjVviNdtY+GdG8PWCy6lfTR7zcjypniWIW4jk85pXQR+W2TxSlOKqRpJptptarZW1/FK293axShJwlNprldndap66fg/LQ3fg78e9A+MWp+KvDcPg7xb4S8ReDLqC31jRvEunx29zEs6M8EyPDJLDLHIEfaySMflyQAylnGUZq8Xfo/J9nf713TTJknF2kul15ry/Xszjv2q/ib8QNCvfh58E/g9rVvovjn4sa3Lp1pq89ulx/ZOm2kJuNQvUif5JZY4goVGwCX6ggVFnOrGleys236fZ8nJu1/X1Wi9ylKra9rJervr5pWf/DaPnPHH7Jfjrw54Fl1f4IftHfGQfEPQbeW80mfxB4zn1Sy1e6VG2297aXJNqY5M7crGgRir8hdp0u4NSir26PZ9/O9vNakQjGT5Zvfr27dNl6O60PR/iH8f7D4P6P4K0zxx4Z1bW/HvjMLbWPhbwjbfbrm8u440a8MBlaJBbwb9zSyugCYJ54qp8rquFK7WvyXRt6L+vUiF1TTqNJ6fN+S1835LqN8EftNeAPFdr4z/wCEi0vxD4D1P4e2v9oeJNJ8VWSW1zZWJSR1vAYXlhmgZYZSHikf7hBwSM5SqRjTdW+i30d0+1rXu+i69LmijJzVNqze17Wa732sno+3U47SP24fAd7H4c13X/hZ8T/CvgzxZepZaT4y13Q4bfR5TK+y2llZZ2nt4ZyyeVLPDGrCRWJVckaRi5TjSatOWiXVv+VWv73l1s7Gc5KEXP7K3fRLu7208/M6Tx9+1J4c8H+PtU+Gfhj4a/ED4ieIPD2mpquvW3hDTILldIikXdAk7zzwr50qbmjhjLyOqMQvTOammpPpHf8A4C3fnZfjcuUeVK/U5e1/b4+CF78G7z48W+n+LX8J2fi8eDmkGlr9pe4LoouFg8zzDBiRWwQJsZHlbvlq66dH2Kiud1fhUdb/ABd7b8rt6r5RQkqzqp+77L4r9Ph9duZX9H85fF/7W8Fpotj4Zi+EvxT0P4geL5r3TvDnh2XQrK51M+VbRSNqXlreC1+yxG4jBMlxGS6SLgbWIc6Up3pxetm2017qu1d9tVpvfTe5UJqNpva63vq97fdv262OS8IftNaV4A/Z+8faDo3g/wCM+p+Nvg1p9vbarpvimy/tLxDdTXYYw6i7C4lSe3Z/NlZkmIjihfaoQRg5YuonSlOi0t1r9lpaOWnzvazNMPSlGcY1ru9n/iTe0fyt02bvc4L9lXxn/afw++F/xA+Inj79pN9f8e+NrC3EXiS7ig0++1E6NPcu1tD8obQpRJIyBMlpIISFAQ1vh0vrEaUFryVHrqrKKvfzW8OzbuclaTVJzm9OaC003k7L0e0/LY9t8Wftt+AvDl94zbR/hl8S/FugfDy8bT/FHiXw/osM+maZcxti5jZpJ45pTbj5pjDFII1+YnFZQlzrmeivu/z7287a20urN9ElyvlWr8vy7X8v+CdB4t/at+H+iat4Q8N+DPD3in4ja5430ZfEml6Z4TsopphozKCmoTvcywRQW7EqitI4LOwUAmqknGo6fVb7W3tvs/lfv2CK5qSq9Houj2u9OltL3s/xtl237aHwwHgHxp4113wz410HUfAWpwaNq/hTUdHA1z7dcsi2cEMEUjpMbkyJ5TJIUYMGLKuWD3UHHXnbivVbrXrbX/g3JTXNJS05Um30s+unTp6nQfDz9o7S/HHxEl+Feu/C/wAfeBPEf9k/23Z2/ifT7ZIr+0EgjkaCa1uJ4i0bMm5GZXAcEAjOCNpcyT1juvJ7P0e339gk+XlbWktn5rp69f6R67SGFABQAUAFABQAUAFABQB8wftTf8ly+BX/AGMCf+llnQB9P0AFABQAUAFAHwD8G/8AlMh8dv8Asn9n/wCiNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDA+IGha34p8B+JPDPhrxJN4e1fV9IvLDT9XhUtJp1zLC6RXKgFSWjdlcAEcr1HWgD+cmzsfhl4c8dWPhv/hbT634TXWWcavY6VPZmArvSK9ZZYnlWMEwyNGiO7IJF2h1Td7GTQyzC4jD5jjKklJN80YRTnFRlJxalJqD5vca3avJSScY87z3F5hLBVcky+calBqM05+7F1J04qaaSc04XnB2fLLljKN1J8n1bq3/AASO+I/jcReOPgV8cfBXjTwZrFsNRs9Z1U3djPMHLE5VIplcju28Etu3Ih4rzcRV9vWnVu3zNvV3er6uyu+7srvWxlRp+ypxp6aJLRWWnZa2XZX0P0X/AGHP2PrD9kD4d6n4eXxrf+JNT8R3iahfTyI1vaxFU2xrDbb3WNtp+d87nIUE4RAuJofSNABQAUAFABQAUAFABQAUAFABQBS1DVIrDCnaXOD8xIABzjkAnJIOAATwewNAHK6lqE95dNdu/FuuAnQIcjp6E9P72Rn5cYAB5Hf6gLvxN8QdavdVuvDd14Zk0rw1b3d7bG4Ftpc32e6ur+2VFD2yztcPbyTbniSTSllfd9ndB8FxEo47O8Jl+K0otOW9lKWtk+nRLv7zS3R2ULwoynHc9K0Xw3pOh2MHhrw/apZ2kIdiPNZ2lkkkaWRnkYkyyySNJK0jMTI7l2O9mJ+6p04UoqFNWS0SWiS7I5G23dlTxZ4Y0rxl4bu/DGpyS24fZLFcwohmt542EkFxEzhgksUqxyqSDteMMclVWlVpQr05UqivGSaa7p7gm07o8M1y51y8+NvwJvorO01Max4i1qXxTqOkFF0+11jS9D1LT5GWL55VaZpXibzJX8r7DDEfnLF/yStOWCyzMcok3y0pRcL78rmvlbZ7K92+p6SXPUhV77/cdZ4Wh1xftY8B+P8AT7zXvDHhe70eTwzrpVE0fV5DCbJpI44lultZXglG5nZXjSNrfch3G8zy7Lq1PArDLXESjzNO+1lLduzvLtve4qc6ic+b7J6N8bPjdonwn0qSwtbmwGrJbR3UxuVLWuk2LOyC6uVQhm3skkcFuhElxKjKuyOO4ng/Rc5zejlFD2k9ZvSMerf6JdX0Xm0ntkOQ4/iTH08uy6m51Jvbt3bfRLdtnwnp/wAMfEn7UXjiz1u2sZPD3hPxBfSMl6bjTzrOp21pK0U+o3e12ZpETEaZjkjt2urSFIkgZmT8nxeY1as5YjFz52tftcqbWkFtbz1V1GTTbSv/AEa81yzwcymeV5dDmzap7tSTWkFupReqlBpp01u7p1YxknTX3N4GsraTRIdRsdBs9MsLqMWXh6LaqNbaIgTY52u6xidozMMbSYVtxIiyxsq/o/CmTPKMFzVv4tTWXl2Xyvr5t6tWP5qzPH1syxM8TiJOUpNtt6tt6tv1Z0Ut9PARMj7I7iSO42AAEncxJGc4wy4z3AFfUHnnnFh8MNWfxfeajpZ0rR7hpTaxeKtQe68Ra/PaT/NPBBLd7U01UnZHijDXNsCcm3TO0/Ff6r4rMZKec4pzX8kfdjo9L+q3sk9fiOv6xGGlKNvMz/BQ1qL4j+NINc8Ya3rkvg3xZbaDps99OkYMDeGdMu3eSC3SO3aV7ieV9/lAr5jKmxCVrip4DDZVxLQwuEgowcHLu72mt3d7dL2Kc5VKDlJ63/yPWriK0kZ7q1RmWdg3lE7XCHuvGCAdy4/vKnXOK/QjiON8f2sOsabF4KurrT4rfX1ePVUuGhJ/saLa1+SjspeKSN0tWdNxjF/E3O3I8DibM3lWXTqQ+OXuxt/M76/JXa81Y2w9P2k0nsSeB7uTVtPfxVqS3MT+IZ/7SigmW4V4LZlC2qGC5w1u/wBnWIywgKonaY4y5atuH8sWU5fTwzXvWvL/ABPfbe2yfZIVap7WbkdMJWaMvDFIshOV+YjsvAPXGCR1yQyDkivZMiRriEQ+TboyRnD/AChvkzg8nvgun1MSnuKAOR8d6DD4pkTQT4pvtG0/xJoms+HWsraJlM1zcpHLFcrMuHSSCGO5C4YZ8zOQQlfE8br2GGoY6CvOlUTT7Lf8WonXhNZOD2aPOvAfxO8Q/Eey0rxp4f8AHfiS11rXNFsdel8JDSbUaLM0tnbXkunRXj28cpnMF4hSRrrG9y+ySOCeBMMRxNmeCrfWMRRj9V9pKHMr81k3G+77P7OtraXTHHD05rli/etc9fvL3wJ4j8EN4hTUrC58K+I9KguIZrxSkV1p95D8oYy4KBoyeCA2GA6nA+6qU41YOE1dPR+hnhMVWwOIhisNJxqQalFrdSTumvNPU+Wv2dPjAP2efifJ8EfEHi6HXvhxrt0//CK6vazrfx208jjbHvhztDM22SPb8srLJhUkZ2+HwOYxyHFTwWIqXo6uL3t16d9bq2+qVnd/0fxfleD8VeGVxllNNRx9FJYiC05lFauz3cVaUJXbdO8G3KCivvOvuz+aAoAKAIboXJt5BZlBMRhC/wB0H16GgCPT9Pi0+JlVjJLId0srfedvU/0FAHA/F1dNuNGXwx4ksydH8U6vY6dd3Ehi+ymAkMbW6Ekib4rl4xZ+UBJ5jXqoY2Rnx4nElevhsqr1cPfmS6bq7Sb8rK7v0te63NqEVKolI53TL7wj4k+EfxJg+NnjDS4dCv8AU9c0HxAt3c/2bbaTprO1pBbs8ojkh8yzNvcF3Y73vDLC3kyQAcvDGEw2GyimsPL40m3dP3mkn5XT0St0s7u5daUpVm2r26eSPEPC/wANP2/v2c/Dlp4f+Cfjz4c/Gn4eaVZ2seh6d4kjfT9YhsUJCWttPCRbuoiMYWaaUjCjaiqAp+nlKStGpHVaO2j7arp+fe5yWi1ek9Hrrru/x/LsrHO/tAftDw/HH/gn/wDG7UG8GXfgPxl4Tv4tE8T6BLcpJNYagl/akss0YXzUdSMSbRkq4GQu451eWdGNak7xbtfzTXMvVX1/GzuldOTjVlRnpJK/yadn6Ozt/lZv0P8A4KL3Vza/8E+/GFzbXEsMy2egYkjcqwzqVkDyOe5rpqf75H/H+rMsF/u8f8P6GR8UPAmkftcftj6r8EPiZfatcfDP4X+EbLU7vw3bajNZQ6trV9IxjnnaFleSOOAAKNwKuDg7XkVuajTVVVas/sSjFL1i5c3k+ltbrXTrpV5qKoU1/wAvIynfrpLl5X5X16fPpy/wo+GNh+zp/wAFCvEugaVrmqan4X074HS6noNje3D3M2k6eNYj/wBASaQl5Y1ljnaMuSypIqZIQGumM5UcsxVWetpwfm7Qqb6dLWXkkaRpxq1adOGl9PJNvp+b82z50+H2o/DT46+Dh8XP2gv2Zv2rPiN8R/Est1eweMPCui3badpCmZxbx6MY7pIxDCApUSRviQPnK4Uc0aLoQUIt81l7z3btv281ZJW2SVkolP27bmly7cq2ttb/AD/G7u37R4hu/wBpv4ofsGfDhfH/AMOvifrGq6D4wt7P4i6DDa3Om6/4g8OQPOuAjbZpvMje0Em3LSMrtuGGYXiIRU6VStZwkm2o7qV7Rv5JrmastGmuVarOlOVq1LDtpxdoyl9qNk382ny3UnZpp8+qd/8AYr1L9khPj8+n/s5eK/G3wwvLnRryx134SeKYLtTfzRvvW8Vp55UW4jXJ2K7v5e8hUHmkij7WE0/ei1912rv56L9drOT5JQa0d/8AwLTRfLVnP/s7fDjwH+zzD+1r8aPAei3C6/8ACvUPEGneHvtWo3M8KW0Nis6RyozkS/vEXLtl8ZG6s5VJQwXMnZylKN+3vRS020bv+YcsXi+WSuoxjK3qpX+9fceSaVo/gDxZ8K7b4gXP7Nn7Yms/HTVtJj1a3+J9lol64OrPEJIprZo7oRNZBiqptiDGADBDYIqUHRXJR91rv1fW9777Pr6vUtctaV8Rqm9bLb0Stqt1sr9kfp9+zj4s8deOPgV4I8UfE/QtS0fxdeaRCNbtNRsGsrhb1Mxyu8DKpj3shcLtAAcY4xW9eEYTtF3Vk/vSbXybsYUJynD3laza18m1f52udL4y1/QPDOmTal4gvrPTtPhK3d/d3jCKCK3j+aSSSRsKAoUZJPArmnUhTSc2lfTXu9l8zdJvYuXX2qdYoyEtbN5Ej8onbJIp6jj7o9hzjOcdKsRDexWqaXEsjRRRxXkhVWTKEB34wO2MnHtigCV5b64tHD3NqSrrFIiwtjzDt+QnccrkgNx0zQBxHjHxZ5Mknw58Na6mnanJayaneak8imXRdM3sn2wbo5IhM0qOIFuAscginkxMLeWI+BxFncckwvtIrmqSdorz7tbtLrbq0tL3NqFH20rdCv8ADrwLY+IvsPjLxLoN5Fp+miKPwhomr2+w6XbxKFS+kgky6XsuCVaUiWGAxx7LeV7tZOfhrI3ltJ4nFK+IqXcnu1fW1/xdt31aSKr1vaPlj8KJfj1+zt4D/aC0O307xX9rtNR01Zf7L1K0kIktHk2FsofkkUmNMqwzgHaVJzXp5plNDNYKNXRrZrpf89v8rH2XAviDmvAWKlWwFpU6lvaQktJKN7a7xa5nZp2v8SktDwj4I/Ev4n/An45v+zf8c/FV7r2lauFTwtrd2CQ8jEmIebId5WTPlbC0myVERflJavDy3GYrLMd/Z2Om5Rl8Mn+Gr11262dktNT9V404cyPjjhZcZ8LUI0atO7r0o9EtJe7H3U4W57pQ5qblOXvJRPsmvsD+cBCARgjINAHE65pzWN6LeIytHMd0aKCcrx8ue5yAMdgF69gCC1XUIp44rVS9yJFdMAnC89G6bck57HjkjNAGl4t8X3ujaZBc2MNrbK8clxe6hqDBbPTIIlLSSS5ZCxGCuARjlmIC8/P59n9LJaailzVZfDHXXp/S3e3mb0aLqvyPJtZ+KfjnXJkbSvE3ivS9G1W9lsraLTPhFrC6zBMghZF+0XiSW0EZMgBmubURMrSBXQxM6eVHNuJJw9ksElU095yXLZ+V/LpJtaXWqvp7KgtefQsp8GLrxxY2N3r3hnwxrEAtjHdy+O9Kh1fWL65S4naOaSWGTyIYgJriSOGNSqi8KIlqsZhbKnw5muNcsVjsXKFRvRQb5Yxe63XVLZ20u3Ju6HXpw92EbrzJrmH4h6A9hHPYeMvCtnoCi1eHwdDYano+oWcTAw7LeaCS6hCxLgxQwxODI8atcCOGSlDCcS5RKcMLNYiD1Tm3zLy1kvza0vpdjcqFWzkuV+RFafHHVNRij0LUrjxvBdX1ubP7dpfwd8R2jQ3UkhEc6PdW88ESImARKrruG9mCnZWk8z4onf2WDivWSen/AIGuvX5W6iVPDreX9fcT6Tp/i7TNPmhn+HPxY1eS5kkM1xeeLLNpbk9GlMf9opFAJDlhFEkaKCAI4/uLksDxbO81iIRvraydvL4JberHz4Zacr/r5mF4y/a10T4KeHtQs/H/AIM8Vadr8GP7G0bV73T5rnUo8AeYJbO4uPLhB6yXGHY7tokKsB2080xuS0ZRzicalR6xUd2ut/dSS7Prr2PuuCPDjM+PsTbAx9nh4u06sl7setkrpzl/dW11zOKaZm+APhH47/aCi8N/Ez9pnw7JomseH9RkvNIsrK9uLR3tZAzKksCsGtmSTyWVg/msIgsgGMt1YXBV81VPE5lHllBtqza0fddLO2t76WZ9RnvE2VcBVMXk3BVf21CvCMKjnCE0pR0bjNq1RSjzpxcfZpzcoN3svpyzsLHToWt9Ps4LWJpZZ2SGMIpkkdpJHIHG5nZmY9SzEnkmvo4xjBWirf8AB1f4n4tWr1cRJTrScmkldu+kUoxWvRRSSXRJJaInqjIKACgAoAKACgAoAKACgAoAKACgDyT9rtgn7KnxiJUN/wAUHrowfexmGaAOH/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFAHzp+038TfGvgb4t/A7w94X1aK0sPFXiM2WrRPZQTGeH7TYptDSIzR/LNIMoVPOc5Ax89nOMrYbF4WnSdlOVnondXj3Wm72P2Lw24byzO+H8+xmOpuVTD0eam+aceWXJWd7RklLWMdJJrTbV3+i6+hPx0KACgAoA+cf27fEPg/w38I9IvvGvw/g8YWMniO3ijsZtSuLJY5Ta3JEu+AhiQqsu08fPnqBXzvE1WjRwkZVqfOuZaXa6PXQ/ZfA/AZjmPENallmLeGmqMm5KEKl1z01y2mmldtO++ltmz6B0R45NGsJIYRFG1rEyRhi2wFBgZPJx6179NpwVux+R41Sjiaik7vmevfXc+c/+Ch2jSeIP2dP7JTSW1JLjxX4dWa1EHnCSL+0oN4ZMHK7c5yMYzmtcPGMsdhlNXXtFe+1rPc5MRKUcHiHF68jt3vdbHHftP/sg+DfBmn6X+0T+zV8GvB1j47+Gs0erDQbPw7a/ZNesYZBLNbi3WMql2AC8VxEvngrsU5ZCkRqvDVFW3jqmr20fVPo1v2te6lsaqmq8HR0T6N97bO26fn1trHVnAftFadZ/Ff4jfC/9q+38W/Gzwx8Mdf8ABVxoeo6r8O5JrbV/D1yLj7SP7Qihjmm+zsRJFKERlWW3iy33N5KiqNebnJWaVn31b+WjVtL30dugqrrUIQjHVSd+6ukvnZp+VtU2cnB+zRffG7wB8brv4PeLvj14mj1bwdZ6Hofib4m6ySPEUsN6mofZLK2uLaCdYAYTCLiWRIw15MBGQGetJVJRwsqcoJ3nCVnrdQu7+W94qzu0vejZk0eWOKjP+5NXWlnJWtqrNO3vO90londM7z9pr9qTwp8ZPAvgHwn4Z+Hfjm31o/EHwpNr9vrfhe9sB4ZcahERFcTTRrEZ3ciNUiZ9ymRsgLzMYr69h+V+6p77X0enrrdrVKz12vlVk1ga6kvecHotezvttpbo9VoZfxc+F3j74i/Ef9tPRfBfhhtU1K4Hwz1TTrG5Uxw6sNPi+1TWyMRh2ZYWQAfxlQSM5rGnVeHrRxFm+SSlZbtW/p6Xemib0O7FU1iMPChdLmg1d9Lyl/w2tlrq0tTv/i38ZLD9q228C/Cf4S/Dvxy2tp4z0LWfEkuueF7vTF8IW9lcpczG5muI1iFziNo1jid9+XAY8btoU3KvTqxa5YSbb6Oya5V1u2100623OWcrYepS6zXKvK7Tu+llbv2seM+JfhVpnwr+M/xYi+NHxn/aa+H1t4s8YXnifwzdfDi7vf7D1uC+CusAW1tZit/GytE6y7MqkJGVwxzpL3OX7V3p31vp+vn+GlS0qnMvhtHXtpZ33+XddF16F/hj4d8Gfs4/D9PiZ8GvjX/YOqeL9W8RzeJJPEEt3408CTTpI1vqsqWNqr5lIaWaNg3kvIDJ5smEG1Sd3Rj8LhBq62V22433fNe17JaWdt3lTi/3nKr80k7dZaJc1ulu12+qvfT2D9jDW/iFe+MvGGl6V8QviD4++DVpp9g/hrxF4/0yW11STUGaT7TFDNNDFNeQBQu6WRF2sFRBgMzUlek3VVpXSjtrG2rfztZ9dflEnaoow2s29976a/fprbTbr0/7WPgrxhbeKfhV+0f4C8KXvirVPhBq99cX2g2CeZe32jaha/Zr/wCyRkgTXKIEeOLcu/awBzhTz3casZO7jZppd3s/k1+PbVb/ABUpQjZO6d/Jbr5p/h3sfP8A+0f8WP2B/wBo6PUtQGneMvG3xYtdAm0rQtD0bTPEkGppOBK8MZtYhFFuSWVmYyDoCMsABUYiNWpRmsM/fs7PonbRvyW+23QvDzpQrQeJ+G6uurV9V6vbffqZ3xV+B/jjwjpv7O3jT4ieI/jV4Y0PQfhtD4L8XXnw1v3Go+HL2OG3mM9z9mjnkmtZGikjmKKVDQQNvJ2q1RpRjialWbsnGKXrFvRu+2ujSeu+juojOc8NToRhdqc5edpJWsrdOXXbRvtZ0PDv7Oz/ABf0H41an8HvEfx48YR6z8O28MaL4t+JmtkLrdwbgXQsrK3urSCcQBovLNxJIsYa4mXyzy9XWU5YaUGvtRaXVqOt/LfS+7tqrMVKUYYhP+7JX7c2ltte7ttbzR3/AMcvjDH+1N+zy/7M3w7+Fvjez+JHitNJ07UtG1Xwte2Vp4VEdxDNLc311NEsKQRrC2xo2d3YxhUycDWo44mtzwfuuV2+2t7Naa9Levzxo82Gpck17yVrLrpbTy/Tex3Nh8UbL9mX4/fFy0+K3hTxSuj/ABC1bTfEHhfxDpfh+81WPU5Dp8No+mE2kUhjnjktWKRvjKybvlBG7Ki3Gh7DVyUpyt3UrPT0tZ3a6aWLnStV+sacrjGPo4Xu311vfa2+t7peD/Da41Xxn8D7vXZvB+p6VNqv7WVvqdxpF3bn7RYI+s2zyQzKBgGLLI/YFW9KMWpQ/s5aJrtsv4uz7dmctJqVPMWrtPa+792l07nvP7XfxA8b+Fvin4P0XxB438c/D74Q32i38mqeKfBehHUb9tXEkaw2c0gtrhrOMxnerrGTI25ei7kzjzSqyUvh5Vbzbbuvkrdu929F3y5Y0oyjrK7v5Kys/m7336dzyT9nvTLjTvFn7S1rCnxUu7HWPh7p8mg6j8RmuX1vW7aCHUYZborOqusfnNtRNifIY22LvxWGKpT+pYmfLa/S2vwNa927PX5dLGtGrB4vDxTvZb3uvjT07b7d7vqehabpepj4L/sQwHTroSWGp+Gzdp5LbrcL4ZvFYyDHyYYgHOOTiu/CaZhFv/n1V/8ASUeZiFfBtf8ATyn/AOllD4TfGyP9k/4ceIfgL8Q/h5491XxroGua/J4agsvDV7fr42hubya9t57a4t4XhBYXKpIruDGwIPIKjkwtGVLB0qFNXnGKTWyTWmmr93rd676aa+liKsa+Mq152jGcnJW10dnbZe8npbVba66J8efib8T0+IvgvS/jHr3xE+DPgDV/AUN5fXfgLSv7VuYvFM1xiTSpNRhtLlkEcKgqIo1Ejbm3nauOpqClNczb0sujWt3fvfRLW61916PlcqqjGTS5dbu+ulradFa7v30XNq4+N/DTw5bTad+03p3ib4FfGL4leGLzUfBbS6X4umvYvFmp6fFE8balGzKJppleHzkhUxkIFjPlgbKHVjKjSjKylGbvZ3cLqNm7a3tuvN/Z1IhSnGvUnF3UoK19OazldK/Ts+tl1PWf2Z/EHimL4/aF4e+Bfxc+L/xK+E91pupzeMpviRpt4Rol7GsYtIrO+u7aGRpSzKptxuVU3v8AMTlNaal7OXttbJcr0Tk767avS99ElovIqe8eTTXVdlbT019X+Z9z1gWFABQAUAFABQAUAFABQB8wftTf8ly+BX/YwJ/6WWdAH0/QAUAFABQAUAfAPwb/AOUyHx2/7J/Z/wDojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPyy/4KcfsA+ArGzu/2jfhlr2h+E7+9vpZPEWl6vqcFjY30rxyS+faNKy4umaNgYF3eaX3KEZG8yKk/Zwc2r2101fyXU6MLh3i68MPGSi5tRvJqMVd2vKT0SXVvRLVnR/8Eebbxvpnwx+JWg3/AI20HU/D0N1ZXen6dY6xb3c+nzyRXAuHeNMvEs3l24G8hW8qQoNwc11V8O6Cg3JPnipaNOykr2l/LJdYuzT0aOeXu1qtF705Sg3um4uzcZLSUXvGUW4yWqbR+lIIIyDkGsAFoAKACgAoAKACgAoAKACgAoAKAOe8TRykr5eSWC4UnKnB6kYxw3lgZ/vn3NAGPILeFUeIid49i8gqoXeSD/sggKevJkbng0Acn4v8NaxLqFv4q8F30Ufie0hMbx30rR2Wq2hZmNlegBig3MzRzhS9vI7sBLFJNDP42d5Jh88w/saukl8MusX+qfVdfJpNa0a0qMro5Xwp4q0bQLG4m00TWPhaymOn6npt4ipe+DLoLGPsdzCpYC0+ZZFkUssSOksby2jxPB4eSZ1icLX/ALIzjSqvhl0mumvV9n12dpLXarSjJe1pbfkeuIRcxR2MNubd4SHll2/MrAnCoOTwTgAcknt8xr7U5Dwb4R+GdF8X+C7vw94htUmtrjxP8QXAR2QxSx+L5pEmiljIeKSJkEqSxMro8aujBgDXw+Bpwq8TY2E1dOEU09U1yw0Z1zbWHg13/wAx/jDVrXwjoXjHxHP8QdA0vXdP13R9Kg1fxOhH+j2cqaqtq6Q7GvZIobu88qKIJLKqqjSb989fPZnSw2Q53TWGpylTgnU5U9pSXKteiuoXbbt+B62UZbjuIK0MBgoOdao+WKW76+iSV227JJNtpJs+Y9P1nxb8b/EOpakmr3EfgaO9mvL61169a0PiFY4i9zqGp3UcZt4Y4o4rfz7jiOxje1itYy32W3k56OGxvEmNcpu83vL7MF0SX38sd5O7b+Jr+jszWReCWSww0IKrmtWLkprlbhLRXu7SjDV8q5bVeWUZpJ2X038N/h7AlzH4d1KGCa+8SWltfeLFnCKun+FbZphp+l/YZInW3iuHaWL7I6f6ptSU3M9xD9om9XJcvoZpmSnh1/s2Hfut/bn1k31d0n6KCsk7H8z4/G4jEznicVNyq1G3JvV66u/9dz3q4nt7+4D3btDKSwfGVbnOYwSATtRduO7t3BNfph5BQ1NJYmkjlKsVMKsRgYIVhtGODj7uRj7vSgDdtrnRJJ4o7TTZ2kDkoscy43KQScCTGPlHJ4OBQB4t45uDf/FHwRqm2Sy+y/E27MibvmfZ4S1GHkq69eG4bsBzXxWF14sr/wDXpfnA65f7svX/ADPZI4YNRtI7vznl8q5Masu5WUsFwQSSc79pyT3NfanIeB/GTxp478JaV408ZeDLG0lh8M6ABGt5o13qgvNS3LcJaW9lA8TTl45EV51kVImEYIk2TeT+f8WywWOx2GwNWbum+azUeVStq5O6TVrqNrtdVdX7cMpQhKaLsX7Nfgu9u5te134WfBTWL6WeOfWb0eAYIJr6eRg0soYzTkM7s5y7yEE8ljnPX/qliYR5KOPqxS0Su9F0WjW3lb5E/WYvVwRo6l8OvHvg9LzwP8Lvgd8MtK8K3r/bd+l+Mr3w7cPKqxF5GjsdKPltuRE3LMSyou7A+QRDhfNaUueOZTv5ptfc5tfgN4im9PZr+vkYWk/DPxDrxli+IfhbVNI06GAzwSeHPjH4muLqW4GAkRUpaKEK7uTKRuVPl/iW6mT8RUVzYbHKUu0opK33S1+XzEqtB/FD+vwN2O2N7r2iXLfDH4lRTaffRSwPceI7WWK2ztheaSNtRYsixSHcArEoGCqzYU/O8Q0M9w+C/wCFTEQlTbta2t7Nq1qa7aPRX3aN6Doyn+7i7/15nK/AjwjNB+zl8Cbbw/cxaMsvhqy1C01EStK6aobKKQxPbFNk0M8b6iZSZIygCiLbIySQ/WcX1qGFytqtS54SaVk+Vq93zJ2et/LW+ulzmwqcqmjsz034S+B9On0/TPH/AINm8CwaB4riXXpJdF8JyWM+prcxCSK4MjTAq7BwzGSJmIODtNcH+otCsksXiKk0kktVou2qlp2tYv6418MUil8XP2X9G+K/gjVtD8QeKNU1nWRPcX/h6/1KOzjOkTso2W6G3tkLWuVUOsgkdl5LFlRl7nwbltKhKGHi1O2knJ7+a29bLvax9lwJx9mHBWaxxdKTdGTSqw6ShfW2q9+KbcJXVno/dck+W/ZD+KniSKbWP2bvivKU8beASYbZzE4F7pibFjcOfvld6YYhS8UkTAMRI1b5Djai5stxX8Snt5x6fd8rprzPrvFnhfBSjR4zyBXwWM1lqvcqu7astk7O6Tko1IzTaTgj6Yr6U/EwoAKACgClrejaX4i0e90HW9Ptr/T9Qge2ubW5jEkU0TqVZHU8MpBIIPUUNX0YHjbaDoviLw743/Zl+Kmo3mu213p0UbTxRyTXLaRqs1zb2ZdwjHzomt5ozK28gWyTyNl3K/GU6dDI8c8vvy0sR70LW92orJpXT391xvon7tmdsKkpJVVq46PzT9LdLp21PLvCnwy/4KOfB3QbX4a/Dr4h/Avxn4V0GGKy0XVfGNlqtrq32NI1WOGZLPMX7sDYG3OzABmOTgfaSqTq+9U+Lq+/m79f61er4I04Uvdp/D0XZdlbp/SsrI1dF/Yrsn/Z9+Jfwf8AiL8UJNd8cfGG6uNZ8R+J/saR41FijQCG3BH+jwvHHtiLDcPM2+WrBUVWKdGNClpGLv6ttNt9E5WSdvz1LpyftXXmk5NcvXZJpLptd2/E82+Kf7L37fvx4+A1z8Dvid8Tvg7aWNjbaetpNosOpfaNcmtnTH9ozSxlYV+Xzj5EJLSoi4RM1pzKdZV6l+ZO9ltvq/uukttb9EZwTpR9lD4bW89Nkvwu99LdWz1743/BP4r3Pxy0z49/sxeOfBmnfEPT/Do8O+INB8VpNJpup6RLNJJbyy/Zv38UiTRybHA+fy9u5VjdX54XhOfI9JNXXmlo/W1lrstt3e2lKMObeN0n5Pp5K+unXcyfgp+zZ8ftG/af1T9o346+OPCWvzeIvAT+G77T9H+0pb6fdG/jlW3s4pk5sxDECXeTzGmllJXBzXXF0YYSrhrNucoyd7a2jJP0+JJb3Su3d2Ep1XNSlZW0Vui831bd30tey0VznPC/7P8A+3L+z14fv/hJ+zV8Q/hHqfw7huLubw7L42ttQXWNFiuHeQ26tbK0Myxu7MskoYszHKhcIOaPPKKhUe2nN1a7vS1193y0VyVNS5qatfW3RO/TVu34/PV934l/Z0+Pun/BbwFoPw7/AGkNZu/iX4AvxqLa54hurv7D4mDM5lstShikJa3O5FVmEsiLEMHc7PVSco1Izp7JWs7Wadrt6bro91fe9pITVSEo1tHJ3vHSzWyW3u9GlZPezXuvN8B/Af8AaY8cfHvwb8c/2o/Enw0t5fhnaalD4a0zwDb3xS6m1CAwXEl5Le/MFSMLsRM5Y5yu0iRxUI80nq2relne/d9VZ6dVqS3JpJbL+v6/qzPB/wCzH8WtD+Kvxp0PX9Q8E6p8FfjNc6hfalEJLyPxFDLd2awvGmE+zrGCZFzuZiArDacoJjFPDuhN9ZNNd5Nd77JaefkEnauq0F0imn2in6bt/ccto3wN/wCCiHgfwDB8A/BPxj+Eb+CdPtF0PTfFt5ZalD4nstMwEVkii/0bzoYvkj+bnYhLhjuBLmrRSqOz6uPX/h1v1876jVqLvT17J/l10T/4a2h9ceB/Dt94S8HaJ4Y1PxJqHiG80qwgtLjVtQbdc38qIA88h/vOwLEds4rSpJTk2lZf16fkRBOKs3cxPiV4Y1m/jsvGHhW61Jta8O+ZPHplvOBBrEBX97ZyRySRxGRwP3MruvlShGLGPzY38TPcop51gpYaWkt4vtLp8uj8n3N6NV0p8xwngH4g+HPDPhyGZ3vb3wnPc7NB1aDS5NumWqEl9OvY1UPZG1eG4iLTRQxxJFHFMwnVy/j5DxDHl/s/NJezxFPR8ztzbWab0bd11974lo9Na1DXnp6xZ6bpVzb6rpun6hpt3DqVnqdqlxZXcLxzQSRzDe04YHDKVb5SMqQRyd1fYHKea6H8TdW8TWenxR6Xr2l3Wt3c954bstI0WSaaLSIZt0Et9PPG1vaNfQwtIBdC2kVbh7df9IheQ/C4nPc2zDF1cPklOMqcNHN92t1dpOzv0ktL6po7I0acIqVV6voWfBPh3xJ4w1w6t4g8AeKPBNhHqMeraha61e6bPPqt3BFBHZhPsVzdJFbR+SsjKrQMZYYj86POsnTleQ454/8AtDOKkak4q0LbLfVrlitL6b733SJqVocnJSVl1PZq+xOUKAPDP2p/CPxa1HSPDvxB+D2taguq+CL46nc6JBcTLFrNqCjtE0MRU3DAwqBEWXcskqg7ioPiZ3Qxc4QxGDk+am7uOvvLe1lvtt1TfU/UfDDNuH8PiMTlHEVKPssXDkjVai3SnrFSUpJqmmpNudnyyjBtcqbWp+zf+0Z4f/aD8KS6lDZxaNr2nzyW+oaM94ksqBdpE6DhzCwdRuZFw4dedu5tMozanmtLmS5ZLdX/AB72+W90cPiJ4e4vgHHqhKTq0JpOFTlaTve8W9Y86s3ZSd4uMtL2Xr9eufnhT1XT11K0aAkK4IeNiMgMPUenb8aADTtMt9NiKRZeRzuklblnPuf6UAc/rXgDwxrfimz13X7C41ExKgt7a4vZ3sY5opkuI5Gsy/2d5UkiSSOZozJG6ZRlzWMsPRnVjWlBOcdnZXV97PdD5mla+h1lbCK02n20rtMFMUzDBliO1/xI69BwcigCvPJqNiEd54prcNiR2jw6LnqcEAj1OBjrzzQBo0AePfFH49W3h7xzpnwT8ElJfiB4itnuNMe90+a50y327yPtRhdZQreTKu6MN5eC7japB8nG5mqVeOCo/wAWSurpuPXezT1s1dXtu9D9B4a4IqZhldXibMP9woSUaijOMKrvy39nzRlBuPPGXLPl5/hg7tNY3wm+Amr3h0j4mftAadoerfFHTSRb6talnEcGN0SzRALA00bPIu9E4CptckbqywOWzly4nMIqVZbNdul1orrXVLtqd/FXGuHoKvknB1WpSy2o7unK2stpcsnzVFTmlF8spatu8UnY9tkurqyUy3wjkgAy0kSkFPquTke4P4d69s/Ly4rK6h0YMrDIIOQRQAtABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P/AKW3FAH0nQAUAFAHh/7Q3wV8R/Evx18JPGvh25hJ8C+J4by+tZcLvs3nt5JZVcn7yfZQAgBLeZ1G3nxM1y6pjK+HrU3/AA5JteV02/lbbrc/UOAOMsHw3lWcZZjIv/a6EowktbTUZxjFq20vaX5rrl5dnfTCtLD9tAeE/iSl3reknXZbuyPglwllthgF05uRJiPaSYNgHmAnPTB5rCMc59lXvJc11yfDtfW+nbuepVr+Gv8AaGWOnSn7BRqfWv4t3L2a5Le9f+Je/LbTfQw/Hmm/t8SXejHwJ4g0WKBdC09dSEqWBLamIF+1sN8R+Uy7iMfL6ACscVDPm4+wkrcqv8PxW16dz1MixPhPGnX/ALVozcva1OSzq/wuZ+zTtJaqNr317u5d8Ead+3LH4V8Zp4217R5dck0+IeGnjSx2x3Xm/OX2RhcbP74Iq8NDPFSq+2kua3u/Dvf07dzmzrEeF0sfgnllKaoKb9vd1buHLpa8m/i/lszV8H2H7Y6eLvAD+L9a0qTQYrBx4uSNLPdLdfaLraU2xhseSbQfIQMg992dMPHOFWo+2a5be/tveXl25djgzev4cSy/MVl1Karua+rt+0soclK97yt8ftPiu9V0seZ/E/4H/tb/ABr+Ht94P8f6hod1cR+LbK+0l3mt4YrexWC8jlZzBHuODLBxhmwDgHnPm43Lc2zHDujiGm+dNbKytJPZeaPteGuNfD7gzN6eY5TCpGLw9SFRJTk5VHKlKKXPKyvyz1ule1z7L060+wafa2HmeZ9mhSHfjG7aoGcdulfYwjyRUex/OGIq+3rTq2tzNv73csVRiFABQBh+OfBuhfEXwXrvgHxPA82keItOuNLvUjfY5hmjKPtYcqwDEhhyCAR0rOrSjWjyy8n6NO6fyaTRcJunLmX9LqvmtGeJ6N+yPqdxrPhh/in+0L4/+Ifh3wNqdtq3h3Q9XWyhVbq2Ui2mvri3gSe+kjba4MjgF1y4bJB2Um5e0nZy77Wv1S7+fTWxjyJQ9nHSPb9PTy9D6IqSwoAKACgAoAKACgAoAKACgDwrxr+zLrN/8S9d+Kvwm+OXiv4b6x4visoPE0NhZ2OoWupLaxmKGVYryGTyJxGQnmIQMIvy5yS05W5G3y3vbzdr27Xtr/wAlrZrdK3/AA/f/I9C+EXwo8MfBbwNZ+A/Ck2o3VvBLPd3N/ql0bm+1G8nkaW4urmYgeZNJI7MzYAycAKAAC9oxgtFFWS7IVvecnu3d+bOzpDCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gAoAKACgAoA+Afg3/ymQ+O3/ZP7P/0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB4f8S/20/2bfhL8TbL4OeNPiPa23jC9kgjOnJBIy2zToWh+0T7RBBv+QASSKQJo3YLG28TOcYRcpOyW7NaFCriqsaFCLlOTSjFJttt2SSWrbeiS1bPgn4m/8Ft/F32u+074U/ArTdN+z3U0UF34nv5bmSSJZAEaW1gEXkyFA25BM4VmGGYL8xGSmlKLumKtRqYapKjWi4zi2mmrNNaNNPVNPRp7HjP7QX7bXx9/aP8Ah74P1XxF4P0+78Mas1x4e1jQ7HRTLps2ri5MkMttJMkk1veNbNGibZnYeXKU2+ZIlb1svqvD08TUnanKTS5JLn9zlcrqUGop8yUX717O6VrP0MBmmXZfWThSWIqqD54VYSVKLk5Rg4yp1oyqStFyldQUbpJyfvR+9P8Agmp+y9f/ALM/w38ReK/EMOtW+ufECDTNUk0fUY44ZtMtIbd3jglQfMJ1lubpXJwSqwkxwvvjBXdJ1JOgmo9E2m0uzaSTt3sr72Wx49JVFBKq05dWlZP0Tbt6Xdu73PtKzVls4FZSCI1BBHIOKyNCagAoAKACgAoAKACgAoAKACgAoAxfES7lU+UZFWPDKDjOZosDPbOD+RpNpK7A5HS9VsNa0e31bTNUtL/T7o4huLadZ4JPLYxkJIjFCysrKwJBXYVyORRGSklKLumBz3iPxPuS/wBI0PWLXSRp0LXOv61ceWLbQ7fymkWRvMIRp9o3KD8iKxklHl+XHP8APZ/n9PJqahBc9afwx79Lu3Tst5PRdWt6NF1Xd6JGTpXgP4gazp+keL/B11qHhbRdBumv9H8K3KMk/iGGVWFxNq3n7ZopZVmmeCBnVo5fLnu98hNtbeDR4PqYzC1a2ZSviaq36Qa1S006JOy0Xux01ezxShJKmvdX4lPw/wCNdK8K+HYr6K3ubHwnpEs2masl3biG/wDB84fi2vIosoLOKKaJRKg2RW8UM++a3la5TsyfPq+GxH9kZwuWqrKMr6TXTV9X0fV6NKSs5q0VKPtaW35GP4q8QaN8KtRn0p9Rg8JWMN7qfiTT9Wi0t7+wijunaa9ttRjEplzNdy3MkTp5UTSC3hVmfENxy50sRkGaPOKFpxqJRlBytJ2srx8lZX3a1bVtV6GTZZiuIMRTyvA03OrN2il1669EkrtttJJNtpJny74Zn8cftV+PNDGvaAkmkStLdf8ACPQayI5NTv47dBPdTzJD5i24kMKSXbxKscPl28JadoYZfn44XF8Q4+c6bu29ZNWUY300va6W0U229Xpdn9K4vF5X4HZBLL8LOM83qxTclFySu+7taMUny31lLllKHK7L7Bg8PeB/gnoGo6LZwNcz2EFvqPiW90vTZraW5ZZnksNIsIrdlGGnkcR2SvKzCYidZpdRaeb6LPOTKsHTyTLV+9rad3Z6SlL12vaySdrKKS/mbE5hi84xc8xzGpKpN6uUm23ZWSu+ySSXRWR6N4Z0HVtE0JbPV5IZvEGqzJqut7ZRcQG8fYoton8uLdBEkaxoxjR2igV5AZGkZvrMry6llWEhhaO0Vq+76v5v7tlojy6k3Uk5MsSS4idJAQzhQm99yyZGVJbjGWdpDu9FFegQeXfGfxP4m8O6rb+HtPsNNu9EbwprPiDULvVNaudEgt2sLvTQhfU7dJGto2iu9QlI8ti4hQFlXea+Z4ox9XBYanCjNxlUmo3iuaWzfuxvG7bST12fex0YeCnJtrZHOw6ToXh3xHo1v4m0X4hL4y1HRtW1w6boXxM1u90yGCyntLaYIbu6tY2crfwOB5C8rLzuVS/zWby4hyOEZyxkXTclFNxV7tN6rkl2a3fT5b0vYVtOXX+vMm1yx8IaZq+gf2x8LPicL7Udfuk09z4vBK6ithd+a5b+1eM20NynfO4DGSK4qFDPp5vVo08RT+sKPvPl+z7ul/Z+aLboqkm4vl/rzL9r490nS9QuIfDfhHx54h1i1keKTR4PHWm3k7Oow4NvcavsJQDnPI28cjFepiIcTYX+PjqUfXlX50zKP1eW0H/XzOd8H3ttf3dj4svvDd9pXgm2WXw14b8N6iBe6nqeqzGRLy4iEM7q8xYXKyX0ss4kiFxOrW9v593e/LZmo01LDuaqYlvnqVE/diltG9tVs7JL3uWK5mkl009bO1o7JHvNhM13YRPJe/PPawSO5mU7nxnJzMQTwOuceg7/ALaeSDajezpKs95I8qI8Zw+QVOM9FIxwO/agCRIY7mC2jRESVmjjG5QN2SB1EX82P49wDjfFvi678CfFLRdD1hLODQbnwf4j8QX90EaSWI6fcaWq7QgHy7L2csNjMSqYIwQ3w3H0JVcvpU4bupFL/wABkdeCdptvsZWt/D+XwN8EfBOnaLcie7+FI8Ozafd3Sjd9htkSzvnVMFPOmsHv41yODOMMnDr9Fn2FjjMsr0Zfyt/Nar8UjGjLkqJnonwk1dLiy8SeFn1W/wBSuvCniG60+e4u4Y4wFuEi1C3giCMd0UFtf29uGYKx8k5H8Rw4YxLxeUUKklb3bf8AgLcb/O1x4iPLVaO8r3jE+SP24PD+q+AdZ8EftQ+CbRhq/hHUY7PVTEJALizcnyxMynCR5MkDHGWF2FzwBXyfElKeFnSzOiveg7PzXS/luvPmP6C8FcfQz3DY/gbM5fusTByp3t7s18XKmruVuWotbR9k3bVs+sbS7tb+1hvrG5iuba5jWWGaJw6SIwyrKw4IIIII4INfVxkpJSi7pn4DVpVKFSVKrFxlFtNNWaa0aaezXVE1MzCgAoAKAPHvjJosd38Q/Bk2pwzXukeIdJ8QeDLjTzF5trNNdwwXiS3SN8piWHS7uLJDHdcqoUh2I+N44ozeXRxNPR0pxlfqlto+92n027pHVhGudxfVHonhrWk8beD9M15befTpNStYriS2kI86ynwC8Eg7SRyBkdTghkYEAivr6VWFaCqU3eLV0+6exzNNOzEWytdLtYTqs5aYNshEUzRgDAHHzADOCSTjk/SrEQWdyt7o2p2t0jyyrC0b+c/mrIGUhQAcg56YHXjk5oA8r1DxPqnwr8TanrV/p3jv7Nq91a2ltGPA41+EWFp5gKWzaHC1zCrmberX7NtLt5aD94o+Nrf25l1apDLsLTdJttWdtbJXldxV3bVJfPq+pexmk5ydyXTvGfjvR9Wt9HitvE76/pUDXN5oHieXT1m8U2AMMUuoWVxbM1vHcRsgIhRoYVefZPDbrd21zHxf21m+RYmMs7SlRqdYrSD7aK7st07trWLbTTv2VKtH91uu/U9g8M+JtE8Y6FaeJPD141zYXqkxs8LwyIysVeKWKQLJDKjqyPFIqvG6MjqrKQPvYTjVipwd09U1qmn1RxtNOzNEzRiYW5J3spcDB5AIB56dx+dUIfQAUAVL/TbbUWtTcvcL9lm89FiuHjV22MmHVSBIuHJ2uCoYK2NyqRx4/A0cyw7w1f4Xa9tNmnb8NfIqE3TfNHczNHtvD/hnR9P8HeErSGzsrC3Sw0+1tU/cWkMabUjUfdVEVQAg6AAAYxXVCKhFRXTu7/i9X6sl6mzbSmaEOwG4MyNjoSpKkj2yKoDltX+HGgat4gTxdp99qWmauimJ5bG/lS3nBK7vPtd32eZ9qhRI8ZkVeFZa8vM8lwWbwccVTTdrKW0l6Pfztt3TNKdWdJ3iz5n0G7ttTtL3xJqr6Po3grVZ7my8Rz6TFNJpXjxndJYZdM0ySFnklvW80SGBphcxSiFW1BnhuLT4CrisblM6mS5dWddy0it5U9bO8r2VlZdk/e9y1pdqjColVqK36/1/Vz6f8A6Frej6beXvia987VdYvGvp4I2YwWKFFSK1iDM3CRxpvYELJMZpQsYk8tfvckyqnk2ChhYaveT7ye7/AEXkkcVWo6s3JnT16xmFABQAUAfFf7ZvwrvPhEmgftFfBa9Phm/8NSDTLi0sbeFLeOG4mnl88KeDma4ZHjKuGEq8KEbPxnEOCeA5MxwT5XHSytazbd/vdmtb38j+lvB7iinxY8RwdxLH29OuudSm5OTlCMI8l1qrQgpRleLi4vVuSt9f+Fdfs/FfhfR/FGnTLNaaxYW9/BIqMgeOWNXVgrfMMhgcHkd6+uoVY16UasdpJP79T+d80wFTK8dWwNZWlSnKDV07OLaautHqt1p2NWtThCgCC8tlu7doS21uqP3RxyrD6GgCpoevaZr8E76df2tzJZXD2d4kE6SfZ7mM4kifaTtYHqDzyKiNSEpOCaut11V9r+o7Pc0qsRV1O+03TNNutS1m8trSwtYXmup7qRY4YolUl3dmwqqACSTwB1qZzjTi5zdkt2bYfD1sXVjQw8HOcmlGKTbbeiSS1bb0SWrPlrTv2g/EP7S3xCHwu+Ddlr+neAltbqHXPGUUUsF3BKIZfs7W8oceUDIIiA481wTlVCtn5iGbVM4xP1bCJqlZ809U07O1n01tvq+yP3PE+H2E8N8mWd8Q1Kc8fzQdLDNxnCUeaPOqkWnze7zJuL5Iu1pSbVvYfgl+z/4K+CfhnS9J02M6tq9hbvDJrV6u64bzHMkqQ5J+zws5LCJDjuxdyzt6+XZXRy6lGEdZJfE99dXbsr9F87vU/POM+PMy4yx1bEVv3dKck/ZR0j7q5YuVre0mopLnkr9I8sUor06vSPiQoAop/wASthG8hNo7YQsP9SSeFz/dOcD04HORgAvUAFABQAUAFABQAUAFABQAUAFABQB5F+19/wAmpfGL/sRdc/8ASGWgDif+CcP/ACZN8LP+wbc/+ltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/wBjAn/pZZ0AfT9ABQAUAFABQB8A/Bv/AJTIfHb/ALJ/Z/8AojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAOT+Ll/4z0r4U+NNU+HNo114ss/D2o3GgwLEJTLqKW0jWyBDw2ZQg2ng5xQB/O78RvjD8Tfjt8QNM+Nfxn8W3ckguLLS31WytbeSa2W2jQlrey8yJCfvTFAY4jLI/KbjXXhfbYRrMMPNKUJxt70eaMt4vkvzJaO0uXlbTV7poWLg8RRjh8RTfs5qST5GoyStzx50kpSXPG65nKKlHRJo+lfhJ8Rv+CdHxM/aS1Cz8T/sveK7bR/F+q2kej3V/wCLi1ppsgZVeee2V4PJhcFp5zLc3e0oQi7SRWOYY+WIq1MbjJrmk3KUnZK8nq+iV2zbKcprYiVPLcsoynK1owipTlaK2SV5OyV+uiuz9Mf2TPjT+yF4w8H3+kfsyJpGg6Jpupx2t1aQ6JJpEc19MGSE7pY0W5mljtg3DPLsEYcKcLWRke4+I1uVtDc20YYJHJHLzzscYPGPXB/4D9aANSKVJokmjOVkUMp9iM0APoAKACgAoAKACgAoAQMrDKkEZI49aAFoAKAGTSrBG0r5wOgHUnsB7k8CgDntV0zU/EWm6hY2+pT6XLcK0H2y2SN5YHKlQ8Xmq8bGInePMR0aRTlSuQc6tKNenKlPaSafTR+a1XyGnZ3R8+eKvAHijw94yv8AStO1nRJfEeq2Av7jxrphl0XUbOKCMW1vd67BCGstV8uM3DxLP5cUkqGNLSOFZ5oPz3M6L4ScKmCxMlBv+E/evq3aN9leybtfVu7dk+2m/rN1OOvc9G8B/Dy28Q3NvqGp6b9m8G6Fei58N6Q8jTDUrkOs/wDbF40mXkkFwXaCNiQHX7W5kleD7L63DeR1KUv7VzG8sTPXX7K2tbu1vorL3UlrfOvWT/d0/hR7DX2ByniP7Q+s2Xw1gHxWtJTY6raxw2e9oXFlqMWZCYdQlRT5UMYZ3SXDSLIQkKTSTfZbj5zijA4PGZfOWMT93ZpXkm7LTybsmm0u7Vk162R5djs3x9PL8tpupVqOyiuvX0SSu23okm20kz4Ssre+/aH8cSeGvDtj4h0XwTLI8/hfwxGXa0MluIIpIvteww2VvFHMG3OJVtUlWC3jk3wwN8FgsHjs9qxi5c0opKU29o620vdvS1lbmera1kf0/isTlfgLlUaWHjDEZlXTu9uWP4v2akopRvB1GnK65bR+vdF+Dr/CbwhY6FH4g1TUvC+l3txqFvqCWcUV94T1S4dmaewZtzJZxm4ul8uf7Q4imMUkk0O9D9PmXDlXLnHMMmbVWCSa6TSWt0rXbsrpWT3XvH8x47OcVneLqYvNKjqVKju5Pe/6JLRJaJJJWSsdP8Irey8YeF/AnjnxkP7M1nWdLHjbUPDsk03nTarPFA7O4lk8ySO0M/kxwyBkjJtNojNvBtw4bccwzvGYuu1KcHyxellG8lp8ktV0bu3dnHX9ylCK2Z6Hcbru6MkvmJNh/M2gAyE5VkU85/gjGM9zjrn9AOIYokI/0uKN4leQCWNA6+YxO9yB1AUE4H91DwDyAeJeOdJuvjDf33w58T+HvF/gbSfFPhLXrW4uIp9Ke4+xi6sopolKvcovnQycNtLBGY5jkAx+ZcT5/hq86VXCWlKhPVSUkuZp27P3XHXbW2jVzvw9GSTUtmjz61+J3hyTxXq/xI/trVNft/hd8L/E76zd3GsaFeXuoR3E1hexPHb6TOYIiEs7iPdJFbl8RAeYEZk87M8VmOa06dDGSV6lWHKlGUYx0lF6yim7tp6OVtdtE9KcYU23Honc9i0n4b674lstMj8efDz4c6XqPhnUhe6DdTveeMHhmLtI8sUtzbWkkEu9UdZFdzhegwMfRR4MrzrTq4jGzfOrSsuVyW1n7zTVtLNGH1qKSUYLQ7i68J694ggsdfi+JPiDRW0di93YaZaWAtNTKhHKTC5t5pkQ4IxFLGwVyN24Bx2YfgbJ6CanGU7/AM0np/4Dy/jcmWMqvbQ8e+IGp2/w4+MnhbxBqut6xeXunfDnxDb3OpPpl1rEjpBquiK8zafYKn7xzNuka3iQbVVmAjiVU83inJcJhMBToYOmo81SN/eUb2hJK8paffu2/tSbd4erKU25Pp+pr6d4p+Kmjap4Ws7/AOJvhuSz8ZT/ANmaHaz+ANWsr2Nlsri+QXRl1VWRhb2swZWhjbfgbUORXRV4rzLCU51MTgXFQWrcmk9VHR8jT1elm9NbsSw1OTSjPf8Arua2rfFzwj4LjudI+Jnxz+GMGoTySLp9tc6iNIY3CMfMRhPeTFjudFJCblychiRUU+MMfUiqiy+bi9brmende5Z6eYPCwWnOv6+ZyVl8ZPiDf3Ntp+nWnwIu7yeZIreCH4qyu80hICoFGjksxJAA5znoa65caYeCcpYeqkv7q/8Akifqkn9pG9488B+O/F3hG/8AFl54a0q38fJ4P1/wzYaVpusifTCdRe3IaSeS1t3bBs7Zi2xdqtMoWQ7DXy+ecV4POJ4enTUowjNSk2lfTa1m+je/W2u50UcNKkpN72NvxFrfizW7uy0vUIf7FtbeHd/YNh9n1DVNcgRI28u581TbWNu0kc8JO5vM8yDFxbuTE3uYniLE5/GWCyak2pK0py0UU1Z7devfR2izGNCNH36r+R6F8LdL1bTfDtxJ4mh0pfEN9qFzd6u+nRARGdmxHH5uxGuPJt1t7YTuiPIlujFEzsX67KsvhlWDp4ODuorfu27t9bXbel9DmqTdSTkzsa9Ag8N/bbs7u+/Ze8bwWVtLPIsdjMyRoWIjjvrd3bA7KisxPYAk9K8TiOLlllVRXb8JI/UfBetTocc4CVWSSvUWumrpTSXq20l3bSOp/Zr1S21f9n/4eXdrdQTpH4csbUvA4dQ8MKxOpIJ+ZWRlYdmUg4IxXVk81UwFFp391L7lY8LxGw08JxbmVOpFputUlqrO0pOSfo000+qaZ6TXonxYUAFABQBzvj/wpJ408JX+gWuqS6XfSKs2n6hEoZ7K8jYSQThT8r7JFRijAowBVgVJBwxOGpYyjKhWV4yVmv6/pDjJxd0eNPo/xZur/RvGEFn8StLvBeLFrPhrw/qnh+50oXEUqxzSiS//ANIS2mUbsQPDIYxvMMVw7hviMDl/EmWU5ZfhXT9mr8s5b2euiV9U9feT1druJ1znQqPnle/Y2fDPie/0vxdLp2o6zqltpVpfQaTeaV4ivEN1ZfacJZXlncxs008Nxc5gK3Dvuk3bHh+zPDJ25bmeZYLMI5Xm9pOabjOKsm1dtPRLZdEmtN73JqU6c4OpS6dD1q506yi8uCK3wLydFlJYncFDPznOc4PvzX2JympQBznjzwNpfj3RF0y9nnsb2zmF7pOq2m0XelXqoypdQMwIDhXdWVg0ckckkUqyRSSRthisLRxtGWHxEeaEtGn/AF9z3T1Wo4ycHzR3OEsfEnifwl4ku7bVoJbi+SH7RqGlwFpBf2keY11DTUaQ7DkoJrYkspKj5i0clx8Fg8VX4PxCwONfNhpP3J/y31s/1+9dUdsorFR54fF1R6MJbfxFbWes6Dq0M1s6SGG4t3V0bcMZDcggEYI/XIr9CjJTSlF3TOHYuLPe2yhr+NZFIyz26HCHuCuSx9iPxAxVARaZeWl5NPLHLulc556eWPu7fbnnuCTnHSgDE1G4utZ1TyrFysaCSJBvwJCuC2e2Dx144GaAI7BRYg608DN9yO3BOwOSCN205PC9hkcnHA4AMbxR4oTStCu7y/ae5+yBFs7dPKD3t7K6pBbI0jLEGlkYRpvdVDSDL7eaxxFenhaUq9Z2jFNt+SHFOTsjidK8La/42v8AUdM157vxXZ3U8S6pZG9gs/DOnkD7PeaeohjFzqJNvPK7Q3STW7yxYJtCdqfD0a2ecTRdSjNYeg27Oz52uj+/qnHd72Otqjh9GuaX4HpnhL4cQaNrT+NPEt7Frfiua1+w/wBoLA8MFpbEqXhs7d5JPsySMiPKQ7PKyR72ZYoVj+oyfJcLklD2OHWr3b3fr6dF09W2+erVlVd5HZ16xmFABQAUAFAHyn/wUiuL2L4D6Vb2t3JCl14otYrhVl2LLGLa6cI3IDDeiNg91U9QK+W4uclgYpPeS/Jn719HanTlxXWnUjdxoTa0u0+emrrs7Nq66NrqfS3hm2stK8K6TZ2dvDaWdnp8EUUUSBI4o0jUKqqOFUAAADgAV9JRioUoxirJJH4nmlapiMdWrVZOUpTk227tttttt6tt7tjxAJYl1C6nNrcyD5XyAY1OSIyCMH3z3z6DGpwlLXPFMnhuCFbvR7/VLy6nENvBplsX8zKu252ciOFQsbZaR1XcUQMXkjRuTH46jluHlisQ7RjvpfrZfe3/AJlQg6kuWJ57LrHiT4iSXthbva6lc6fZeVeaDY3UkeiJfOh3Q3epmHfchJ7WaFkijBWO6BuLRt0bL8a8dnXEqSwMfYUG/jb95ryW+qd9NLq3OdXJSofG+Z9v6/ryOq8IeG4/Bs+laUL+zMNnpq6ZDZaXbfYdOs0jYbBHaq7BBsEUahmfaExHsDuG+iyfIsPk3PKnKUpz1lKTu2/y3u76vV6swq1pVbX2Ru+NPGXhz4feFtS8ZeLdSjsNJ0qEz3EzntnCqo/idmIVVHLMwA5NeriMRTwtKVaq7RW53ZPlGMz/AB9LLcvg51ajtFL7232SV23skm3oj5/n0fx7+1a+oQ6nqelH4G+J9OtJdOn064+z6qk0UzSN8rRyq0qzQiOVX2oEMZjO8SqfCcK+dc0W08NNKzWkrp37PW6s76WtbW5+qU8TlXhj7GrTpzjnmGnUU4zXNRcZRUU7qUHyunPnpuN5c3MqicHTa90+G/w38I/CfwjZeCvBWmJZ6fZqMnAMtxLgBppWAG+RsDLewAAAAHtYPB0cDRVGirJfj5vzPzTiLiLMOKswnmeZz5qkvuiukYrW0VfRfN3bbfT10nhhQAUANkjSWNopFDI4KsD3B6igCmZZ9OQtcMZrVB/rOTIg/wBofxAeo59R1NAF1WV1DowZWGQQcgigBaACgAoAKACgAoAKACgAoAKAPIv2vv8Ak1L4xf8AYi65/wCkMtAHE/8ABOH/AJMm+Fn/AGDbn/0tuKAPpOgAoAKACgAoAKACgAoAKACgDhfjR8ZPCPwH8CT/ABD8bxanLpkF1bWflabaG5uJJp5ViiVIwQWJdgMDnmlf3400ruTsrd7N/oO1oym3ZRV36f0zzWH9t74U2N1aJ4+8G/FD4e2F9dw2EOreL/A+oaZp32iVwkcb3LxmOLJP3pCqgZJYAE1S1aj1e3m+yJb5U5PZb+R9CUhmH4c8Vw+JLzXLKLQ9c086FqLac8mpafJbR3bCNH862ZuJoTv2iReNysOoNEfejzebXnp+j6dwl7suXyT8tf1XXsblABQBz48YwHx8fh//AMI/4g84aQNY/tX+zZP7KKmbyvs/2vHl/aM/P5Od2z5ulC1G1ZXOgoEFAGL4u8Tw+D9FOtz6LrOqotxbW32bSLB7y5JmmSIP5SfNsQvvduiorMeAaI+9OMP5na/RebfReYpPlhKfZX836Lq/I2qBhQAUAY954lis/FWm+FG0bWJZNTtLm7W+hsXexgEJjBjmnHyxyP5oKIeXCSEfdNEPfcltypPXrd2su76tdFqKT5OXzdvTS932XS/fQ2KBhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/wAly+BX/YwJ/wCllnQB9P0AFAFW81XTNOsLrVdQ1G1trKxjklurmaZUigRAS7O5OFCgEkkgAA5pNpK7DyKfhjxd4U8baUuu+DPE+k6/pru0a3ml3sd1AXX7yiSNiuR3GeKpxcbXW4k09jWpDPgH4N/8pkPjt/2T+z/9EaFQB9/UAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfNn7XX7C/w+/bAm8OXfjHxj4j0ObwtFPBYppa2vklZihkLh4jIx/dRgfvAq4OFyxJVle5TnJxUb6Lp67/AJL7jxfw5/wR1+BnhK11C90f4heL7jxBcaNqGn2N9qEkbQafeToUhvY4bcQuzRKzfu3lKuSCcYxW1GvWw1RVsPOUJrZxk4tejTT12euquno2JctnGcIzTTTUoxnFppr4ZJq6vdO14ytKLUkmvkX9qb/gn58VP2ePgz4f0vw7Br3ifQotQvNZ8d63p+2bTrfypZY7C4WzUi4TyrFpXmZ0eONpGCzEMa8+WHca8sVFty5bWvZaNv79d+h79LOI18so5HXhGFJVXUdRRcqi5lGL6q8Uo35VbmaV3dXPbv2OP+Colx43+INt8DPiL4R0Dw/oOrwW+jeEbnTIpoxb3IxFHbTozyAiUFVQrtCOFUhg5dNqCqqmvbtOXW2i+V7/ANdtjz81lgJ4yo8rjONC/uKo1Kdv7zioq73slpe15W5n+n2mxvDp9tFICGSFAwPY4HFannlmgAoAKACgAoAKAK08rSymygYhsAyuP+WanPT/AGjjj06+gIAjadBhViLwgcMI2wHXGMEd+ABnrxwaAD7NLbH/AEEoqHrE+doPquOn06H25NAD7e6WVjBKnlTqMtGTnj1B/iHuPxweKAKS3FzqMy+R+6VBnPB8vIPJ/wBvHRegzk54FAHMeNPGlzoVzbeAvAVla3fii7t1uES4V2s9Jsy5Q3t4UIJUlXEUAZZLmRHVWSOO4uLfxs7zvD5Hh/bVtZP4Y9ZP9Eur6ebaT1pUpVpWRyfws8MaV4wsDrFvc32peGJbhbsarerGJvGF35aq+pStHtWW1YBFiAjjjdYVMKfZVti/h5FktfEVv7YzdXrS1jF7QXTR7NdF06+9e21aqor2VLb8z2evtTkMjxb4t8N+BPDd/wCLvF2rwaXpGlxGa6upidqLkAAAZLMSQqqoLMzBVBJArKvXp4am61Z2it2ehlWVY3PMbTy/L6bqVqjtGK3b/JJK7bbSSTbaSbPzr+IvxI8X/tg+MvtV3Y3ulfDXQjdT6dpxu1tDN5MeZby6uCkkcSxK6NPOytHbRSLGgkmmjS5/OsTiMXxVi/q+H92nHXXaK/ml3k9eVX9NOaR/UM/7I8BMmtTca+b10ruzaSurrdONJW0ekqs0m1aKjT+nPgf4U1bw2dC8P6t4P02xn8IeCNNtopLSQkRanfSSS6va7BNIHVfstk43BmIlYiWQlq9HgmjTcMTi6TfLOdl/hjs9lr72v5I/mfPczxmb4uWMx9Rzqz1lJ7tv8kkkklolZLRI9jYQ2jK+zzBgYh3b1cAEqCP40xna3Veh6cfcnjHiXju70fwrq8ug29nql9pemac/i6V9FWOe/wDBriTyobq2WRixVoxeMsCxSeakF1GscysYH+A4kwccux1HH5fNQxFSVuXpP1t3dk76NtNtWudtCfPBwmrxX4HVfD7xi1xYw6b4h16x1Wwlki/sXxbp08Zs9YA3hlZYyRb3UTrMrRglJC8bRnJlgt/cyXiGlmb+r14+zrreDun6q/Ty3Xpq8atB0/eWq7nS+JTeyeH7u2sLu5E81rLFA1j9nF5FK0anMRuP3AkHyopl/d5XDDbmvdxHtVRn7C3PZ8t9r20v5X3MY2uubY8n8feG7Hw3qtz8cPA/wGttU8fTfZ9JuI7nxL/ZlzeW7Mvlxebb+fbyBpfJTZKyqfmLEKvzfnGPyXN3hJyzXEwVJPmfLBN3bs3pGPdttNvybO+FWlzL2cXf1Lfh/wAO6B428eeKvD+gWfgyDw3Ytp8Hiyx0vQ/Ku9WuYoyItOe7S4U3NvGYnjlL2phaPzLQbmS6WHbhXKamYuGZ4+cpuD9zmlzfNxabj0a967dpbWvOJqKF6cElfc94h1JGsbF57a5LQuMGOI7SeUAyepwfz/Kv0c4SSIpDoFxJNbrE0zyLKisAFJcocZOOBjuBx+NAHhHxo8bfA638XW/9tfGbwd4N8Rad4P1/RLWPWNbshDbx6hPZAzzW5nSSTbNYoAPMj3fvVzkhl+D4wqVcZSw8cHB1Yqd5cmrvFbXSaV03un3OzCpRcuZ206mdL8VtD+JfxU+H9h4f8W+APFOh2OvT32m6ho/i9NRvzKujajFIz2ccOyJB50iGQXEh4XgGQhPM4gz+rmOVVKFXDzpS0b5lp8S0u0m3s9kaUKKp1FJSTM74F/FL9oT4h/B3wXfadpHw68TWkmh6bFqWoy+Prj7Ybn7PE8hnij0mQR3GGVniMpIZiCxHJ9mtxjgMql9TqUZx5NFZK1lomryWmmhksLOouZNan0NcTzjUAD5NydzI3mQRsSwl2AHAB+7tP/1uK+2OQ8O+LvwV+H3iXxVrHifV/gT4e8U3viWy8LaVJcr4fgkuIjc6hLa3N2JTDIxa2tpopnJHEdugZlUBl+OzmvVq55gsFSqOC1lKzeqWtmk1o+VrXu9O/VSilRnNq57B4X+F3hf4c6Q3h3wF4L8O6BY3Vx5rw6RZpZRvJwzSvFGoUuwjQFuThVHQDH2JynVxD+z9XlR+Ib/Dxt2EoHK/UjkfTAoA1KAMXxt4fj8WeDde8LSxCVNZ0y609kM3khhNEyEeZsfZnd97Y+Ou1uhxxNL29GdJ/aTXbdW7P8n6HpZNj3lWZYfHxdnSnCd7c1uWSltzRvttzRvtzLc+dP8Agnf41GufAePwteTWkc/h/Vr61tIUOJZbYmK4aRgT82JbtlJAAA2A8nJ+d4TxPtcD7J2vBtfJ63fzb+4/X/pBZXDAcXfWoX/f0oTd9uaN6dl5csIvW+rfovqWvqD8OCgAoAKACgCrOPs0v2xSBGeJx2I7P+Hf2+goA8M+Pdj4gsdY1mfwF4Nt77W9T8C6lqdjdiOEFtZ0m5hn0mImSMqSLi9lkTzH2KUJ2sCxX43izlw1XBY+UuVQqpP0lrL8I9up1Yb3lOHdHt93IlzaW+o2jrNHGy3KFDuEibTnbjrlWOPfFfZHKXVZXUOjAqwyCO4oAWgDF8WeFbHxbpq2V1NNbXNrL9qsL23bbNZXQVlSaM9CQHYFWBR1ZkdWRmU8uNwVDMKEsPiI3i/6uuzXcqE3B80Rmk6Hp3hePUV0KEyT3902o3cD3chUzNGis0UbFlhDbFYogVWdnkOXkdmwynL1lWDp4NScuVbvrrf5LXRdEOpP2knIpy6vqdy8c8Mge3VtrmNSqbjnC5+8SRjuBllyBzXokFLUDBdOs8dzHuZXcsQpLALuAbMjH+ELyB15zQBBBI/mx/ZpGXLeWuyZFYEgA/wgjgDnHr70AYN546Oo+KIfBek263lno0e/WtTeQQW2mhkzbxnIPm3Uu5G8kYCQnzJHQvBHcfO5/wAR0Migk1z1JbRTtp1bdnZdtNXt1a3o0HWfZHGR+I/F0Nza3erXGiarLZtdHwzZ6ZJf2d54l1LLWp32jI32eziMoDS7ruJlkS8Z4Y4lz8pjc6xHFdKGV4Wi4uTXtHpKMY7rXT1d+Vprl1bOmFKOGbqSfoe6+DvDFl4N8NWPhyxYSC1RmnuPKSN7u5kYyT3MgQBfNlleSVyB8zyMepr9Jo0oYenGlTVoxSSXZLRHA25O7NmtBBQAUAFABQAUAfMH7emnTa78CIr288K3+qWemautzOdP1OO2ksptstvDK6vBL58QebayKUbLLggbivzXFNPnwV3FySd9Hazs0ns7q71WjP2zwGxawvFDhCvGnOpTcVzwc1OKlGc4pqpDknywvGTUo6O6vZP6D8L6vZ6h4U0PUJ7K50v7dp9tcpY6kojurfdGreXKh6SJkBh2YGvoKEuelGVmrpaPdabPz7n5Hm1D6rmFeg5xnyzkuaDvGVpNc0X1i90+qsWmitRG12863dwwMaNkHDEfdQdF/njqe9annjJNFtorCONIt724VyqjCysoGcr0JbGMnpmpnCNSLhNXT0aezQJ21RU1vUYYIo5NPn2StEchRgGIHoR1BBBA7jLdOooC5e2UEVjELlY5dkqhiy4BEjgN9PvZ69QDyRmgD508Oa/fftO/FH7R4V8fM3w48GLdaPr+jy2gkGtSSM4U72WS3nhkSNRvB8xE3bdhn3j52FZ5xir0Kn7mF4yjb4r+eqadvVLtzXP2TFZdDw5yBU81wf8Awo4hwq0KvNZ0VFJ6xThUhUg5PRrklK1+b2TifSumaZpui2EGlaPp9tYWVqgjgtraJYookHRVRQAo9gK+ghCNOKhBWS6I/IsTia+MqyxGJm5zk7uUm22+7b1b9S1VGAUAFABQAUAFAFAmbTpJDHatLav84EQy0bHqNvcHrxyCT26AEkeqWM0iRRz7mkOBhSRnBO0sOAcA8ZzQBboAKACgAoAKACgAoAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP/BOH/kyb4Wf9g25/9LbigD6ToAKACgDm/Hviy28HaNFql1BrkqSXKwBdH0O71WbJVjkw2sMrhfl5cqFBwCQSAfJzitmNGgpZZTU531UtrWd38UetuvyNKSg5fvHZHA/8L40f/oG/E3/w2mu//K+vmvr/ABf/ANAtP71/8sOjkw38z/r5B/wvjR/+gb8Tf/Daa7/8r6Pr/F//AEC0/vX/AMsDkw38z/r5B/wvjR/+gb8Tf/Daa7/8r6Pr/F//AEC0/vX/AMsDkw38z/r5B/wvjR/+gb8Tf/Daa7/8r6Pr/F//AEC0/vX/AMsDkw38z/r5FzRfjTpWq6xY6XHp/wAQ1e8uYoFa6+HutW0ILsFBklksVSNOeXZlVRkkgAmt8LjuKZ14RxGGgoNrmaaulfVr949l5P0FKGHSfLJ3/ryPVq+0OQ+Wv+CktxfWn7Mk13penfb7yHxV4dkt7TzhF9olGpQFY97cJuIA3HgZzRRlKONw0oK7VRWW13aWl+lxVoxlhMQpuy5Hd721Wtjjfi1bftlfta/DjV/gL4t/ZW0f4U6L4mezTUPE2pePrPWRaW8V1FM5itLWLfJLiLChmVckZYdQckZtKbsrpvvp2/4JcasqN5QSbaaV9tU1d+nS2qdn0Oh8XeCfiL8Zv2r/ABj8NU+PXj7wX4J8LeEvD+qJZeFdRSyuZdSmmvFjkM7Ruwi2Qy+ZEMLI3lFs+WBWGDmpzxFSerjUikulnTi2vv8AzY8Ral7ClFfFCTb66Ta/Lr5HEfEb4v8Axisfhx8fb7wv47vrbWvD/wAZtG0Dw/PM3mJZWssulKbfbkZhYzybkyMh2GRmtMPGVaNCKdnKrON/LW33CxTjRlWdtI0oS+b3+87AeFPiF8AP2rPhBolh8e/iJ400P4qS+IrTxHp/i7U4r2FJbTTmu7eWyjjhjS1+dGDLGAu0gAAZqViI0qiwtr80W03q1yuK33d+b83vY0WG9pRliXLWLirLZ8yk/lbl7X8++H8OfAnxZ/a08Baj+0hpn7TfxH8Havq2qa2PBGiaReR2Wi6XbWl7PbWcd/Z+U/21i9vvmZzllk2ADbkxhakp0IVl8Ukm07NK+tl0slpe3nr1rG0XQxFTDytaDcdOttLv1fS7/RXPi38S/i14X+OnjvwjP47nH9jfsz3/AIlZNPVre0GvRXcsZv4YWZzG3y/KC7FVwCTjJrEytSqyhpbby0ZFGGtKMtdbPz2OJ1XQPjh8Nf2WNA/bTj/aa+IGu+NdO8NaN4m1DQNT1GIeGb+zkWBpbI2Qiwrm3kKeeGMrSjfkM/y6V68MG1Jq8XKKe1/eajo+mr0300s2RRpSxUXG9nytp+ibV11ulrqtXfyOt/aV8T6T4g+NWseDdf8A2ivi/aRaRomn3Wm+B/g1p19/bEE0plaaXUbi2glBLqITEjyRAIc7OfMkajrPq0/ktNvV7+hPtIThTlHS6bs93ruvRaNW369DkfAnx5+KupfsdeMNYm8X+L49W8HfF2y8HafqGvRLb68NLGsacgh1DaADc+TcyRSnAJ5B5yTtSftMXg4yS96aUrbP4t+nRbduu75q7dPDYqUW/di2r7rSL0+/r+Wg39pHXPH+ifGfxne/Gjx7+0f4E8LwPFL4P8TfDuLzvCem6d9lTMupxQI0zyrcC4eVZckoAE2psJ5aLmoN1GlO8rbuNr+5p1bVrrRX6nbO3tFyq8LL1v8Aa+V9vyPtj4Q+IrXxZ8LfCfiK08d2vjVL7R7WRvEVraC1j1WTy1D3IgBIgLsGYxfwEleNuK6q0JU5uMlZ/wCeqt5Nap9V1ZzUZKcLxlfpf0dnfzT0fn0R81Xv7RHir9n7wr+0noXj7XLzW9c+G1yfEPhG51GEsl5pusLjSrUfP5kwhvvMtXbjhUwecDn5albDKFL+InyaJyd21yyeiTbUr2V7crT7voUoU8RzVfga59WloviS3sk1ZPrfZHVeCZvib4C+K3wH+FnjDxxq2rXdx8PNeu/E7XV4Zxf6tC+mlpWY/eCSTziP+6jADgV0UeSeJxUVrGNODW+/tOVy12clv+hxVHUjSwzlo5Tlf05G+XS10nt6a6nyv8SPiTqE/grxf8XvD37Tv7Q/jjxxocmtXVvefDvR7uLwFYvaSzfZ1lSW2Fq9okccYmfzpwQJGDycM3PG9OEZRd3pq+vfT8vy6HZVdOpUlFbdlo1ptdX+/qux6D8bvGvxb8U6p4D8ceO7j9oDT/hnr3gPSNWtbv4MGNGttZmUy3rakqBrgR7Hg2ZzHgHaGbzMdFenGjiK1Oo7KM7RetuVaPm877abeZz4arKvh6VWP2opy73eqt5W38z6g/ZP8Y6T43+B+iaro3xtPxXt4JbmzHiabTDp91OI5mCRXMDYZZo4yiMzKrSALIR8+SpxlFRc92r3Wz812Xld2d15FxlFykovZ7dV1s/PXstLHsFZlhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/yXL4Ff9jAn/pZZ0AfT9AHhH7c/irxV4L/AGU/H/iDwbq15pWpR2trbNf2f+vs7We8ggupo2HKMlvLMwcYKY3AgqCJapylCFaXLFyim+ybS+d9rdblw5vecVdpSaWvRN307b/LXQ8X+OHwy+GU2v8A7P37Cfwz0qCy8A61qlx4u8RadYXkkyy6Lp6GdI7k7y7w3dy4HmM3LxAg7hxrFTniLzXu04ttW2d7R30sne6ad79GZPlhh26b1qNK/dby1WqdkrNNfdodt4B8H/D/AOFX7dd/4J+D2jaf4b0nWfhg2reJdC0WzjttOS9h1OGKwuTFGoSOZopbxcDG5V3EE81hSnOUqkY6wXLfraTT0v0vFJ20vvbqaVYxUacvtPm8rxVtbdbS0vrbVXPqmtCT4B+Df/KZD47f9k/s/wD0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHyd+3f+25o/7MfgG/0/wLq+h6j8SZDCbTTZ5Y7hLJPOtzI13AsqSpvgmYx4HzH5vuqai8nPkjG+jbelkk0urTbbkkkrvd2sm16FHARqYKrjZ1Yx5HGKi7805S5naKSatGMZSlKTil7qvzSin+Nfhv4ZeJ/i98ZPCmn/AS3vDr3i+WLWEh07TZbODw1cveuj7GhlnkjtLZlVhcMVYIAxVSOeuvh/YQpz54y5482ju4+842lorS929tfdcXfU8qnWVWUoqLXK7O6snoneLu7qzSvp7yatpd/wBItc5qFABQAUAFABQAUAU7thZN9uVsIzosynoQSF3exGfxAx6EAFmOWOZBLDIro3RlOQfxoAfQByuoa7LcXkn2GUxRRjyfMB/1hzk89uAdpAJ64BzwAbMT26aTNPpMkcZKPIHdTLtkIzl1DBmIOMjcCemRQB5NovwdvNXu7zw74khuW8KNOl5rdxfTQy3/AI51B4Yt0t48OEWyVVSJoNqCbyhbiOGxhWG7+Py3h6vUx0s0zeSnVT9xL4YpbNX/APJV0+J3k7rqqV0oezpaLqe1V9gcpmeJfE2geDtBvvFHijVbfTdK02Iz3V1O2EjQfqSSQABkkkAAkgVlWrU8PTdWq7RW7O3Lctxeb4ungcDTdSrN2jFbt/5Ldt6JXbaSPgzxr448Tfts+MYfCNv4S1/TPDWn3Ed74dsyxtH1LKRGSS9Eit5KCCeOX7QqsLdJljEdxLdW8cn59jMRi+KMWsJhklTg3d726czs7N6NRS3u9bJtf0ZhJ5f4JZTTzDDVYYjMMVBpxaa5Em9Y6KUYwmuWpColKpOOns3TlE+kr74N+GPhf8ENW8F2ehyazN4pjsfDOsz2Nu0DvaXlytm4iVZA8Frbx3k8qqshdQZpWeWeSaaX6mvSo8P5VVeH93li3d7uVtG+7bstrbJKySP55zDM8Zn2PnjswqOpVqO8m+v+SS0SWiWisit4L8Z6BpieN/E/iiPUvDYvfEN1NcHxA81nbRJarDZRPBLdJFH5UsFtDcr5Rba88gyxD48/hTEYLD5bRwsa0HPdpNXu3e1r3bV7fL5HLiYzlUcmnY9Etda0aTwnc+O5tWa48Ox6e2pSXVlG9z9otVj81mgWEM0iso3fuwWkPCjnn6uUowi5Sdkjm3OP8I6Dqvi3xJB/wk1hZxlbxfE/iO0SIyRx36+V/ZNjmRZYma3ihilke3licT2dtNsVLoq3w2Rr+3c1q51NP2cPcp39LN9Hs27NNe81e8TsrfuaapLd6sofFXRPDuh+JEmbTI08MeNL+Hw94sEV+1mIrq5iEVhfqyEP9o89LayQwFZczwuXC2se3q4vy6U8Os0w2lajZppbxT1T6WXxa32atqThqlpezlszqfBNxrGv+FrbWdaXGqW4+w3tykT2dv8A2hBIYrt0Sf8AeeUbhJDGw3BlKlWdSrH6fA4uGPw0MTT2kk/v6eq2fmc84uEnF9DoNU0LTddE+m67DBcaYNtzcxNLJIUdR8hDnDBshWUjlSgIIOM9E4RqRcJq6ejT2aJTtqjyDxt4b17wxbWN3BfXl1beHdV0+8s/El9rVvFqOkaSt7BLqVtcyyLEWtBZ27CSR5JZZcESbnjWVvhcw4crZbVqY3KJckXCfNBN6vllZxt1u00tLNab2OyFdVEoVddVqVZdO1bxJ428e3Xh/Rte1LUPDOtRWccWq+PtU0vSbnztNs7mULb25miIWOZypa3P71SMqSZK4clwueZtgKU4Yzkpu99LzupPro9dPtbfcXVlRpTacbv8DovC/wAF9RsfEH/CYeF/C3wr8DaxqD+Z4jWw8LG/u70vdM1xjUUktGYy+UpzJbviRQ7CQYWvcwPC9bD1oYjE4ypUlB3V9trbScu7V1Z2elnqYzxCknGMUrnK/EPw5qtj8OfiTpnibxrqvi06R4W1DU7OfWbWxW4s7iazvIGRGtLa3QKsayKMozfvny5GAPAznKcJk+aYJ4OLj7SaUldtO04Prfq+9tFobUqkqtOfN0X6M9W1fWvDY8faN4NvYFvdWmsr7XtPuVhHl2/2Z44J8Nk4cjVEUY6qXzjHPqeIE5RyuCT3mr/+Ayf5ojBL94/Q5j4Q+Bb/AMIfAPwN4O8U29jdXdh4R0zSbxY2EsbzQ28EMv3gNykq/OOQ3IGa+52OM7gQwWsdve30MRS5/eIFHHDx7gQBxxu46c0AcT4Qv2n8Wxz6H46bV7DxB4kvbmSwSMq9hZ6fpi6fd28fJcomp26u0iBf3l0E53Zf4eKeN4slLl0oU0r+bV/lpNqz7N+nX8GG9X/X5Hrv2iEz2VvALkATNkyxyDI8t/4nHPPvX3ByFu8tIb6BrecHaeQQcFSOhB7EUAVbC8lim/srUHzcou5H7TJ/eHofUfjQA7Wr6DSNPuNbvL6O1tNOhkubl5XVYhCi7nZ2bAUBQTuyAO/FAHxT/wAE/dDbTPiZ8R9J1mNtGv8Awy721noP2uKYWwu5l+1/OAXm8s2FnGHDbRknGXBr4fhCdCVbEyo/C+W3+G8rb69T+hvGx1M0ybIs9rRvVq0Wqk0nbm5ackrfCvelUaStfXdJW+5a+4P55CgAoAKACgAoA4jx/wCBH8RrpN/p8wg1Dw9ePqOkTtG8q2Vy1vLAzNCssYnjMU00ZiLDAkJQq6xsnBmeXUs1ws8JW+GXbdWd1+JdObpyUkecabffE/w1Nc6V4e0d9FvYY5Jo/DV9FNqGi31rbRIFTTdQHk/2c8nnW8PlzLsQ28zRWjoGuJfkli824XShjF7fDqy59pRT7rVtLXfulzdF08tPEax0l2PXvA2vv4g8LWWoXeh6holykYiutP1HyftFrIoGUcwySRNxghkkdSCCCa+zweMoY+hHEYeXNCWz1Wzt1s90csouD5ZbmtLeSeY8FpbNNJGQHJOxFOM4JPfBHQH3xXSSc3rN214UuftMj2k6KwiztEZHOGHGc4JBzzggdjQBmeZFZWsl/d3EdlbQxu5lZwgCKoY734wCHAJOBgknAUCgCnYftAfAK40iGC4+NfgCPfAEdf8AhJLJccYPHmcUALqXjvwtqei6dqPgEt4pj1HU0t7STTLsXFrNiRlmkNwGaOOOMCV3cnJ8sooeVkjbzs1zKjlOFliq70Wy7vol5v8ABXb0TLp03Vlyo8zt5Nb8fRvJpOo+LfEOm3+kmxNzpzReG9AublJCTPbXKZ1cK4C/v7Z54GjI2s+WB+bp1eJs0ipwUMPB6q/vTt6O6+9Rd+x0NYeno7yf4HT6T8M5vtGkXPj3SfD8UGnSzalpXh3S1M9pYahI7F9QmuJ445Lm4JkmkWQxwhXnlLrI4Wcelk3DsMrqyxVao6taWjk+3krvy6vbSy0M6td1FypWRydpNZ6DYagmk61c6dpvw6eHTvDHiiPTzOUuJpTDJoYtoQG1G3jItbZkQBnYxRh/t1q08fyuYuWS57FZO+epV1nS6d9+l7uS/l1fwux0w/e0f3uiWzPo7QLzVNQ0LTr/AFzR/wCydSubSGa80/7Qs/2SdkBkh81flk2MSu4cHGR1r9NPPL9ABQAUAYOseJp7DWIdEsNHku5Xtmupp2mSKC3XcFRGPLl5D5hXajKBDJvZCYw/hZ1xFgsiUVibuUtlGzdu7u0kum+vTZ22pUJ1vhOe1rxnqmtWc2gaZpuueG7m4sriW51aeG2K6ZEJTGjI2+SKSeRVeWNQJFjVQ06qTHDL5GN41wSwylgL1KstIxs7pvv6X2V+Z6J7tawwk+b39EN+E1le6V4PVdJ8I2fh/Qmu5ZNF0SPTBp09vYsQQ8se4hZZZfOuNrLE6rOiSokqSZ9rIMJi8FgY08dNyqO7d3e1+l+tvV63s7WMa0ozneC0ML9p3w5fa/8As4+PtMs2ihl/subUSLiTCqkLi4kXcAQCVjYDtkjJAya1zuk62X1Yx7X+7X9D7jwux1PLuMcvrVU2nUUdNXeacF8ryV+tr2Tehzn7Duq2Wtfs6eGRDNDK1pDcWFzEGDGKRLqc7G44ykkb4PZ/TFc/DdSNTLadul0/vf6anseNWCq4PjbGOonafJKLta6cIq67pSTjfume329nbWOpnbEAJo/3TE52kdUGenGCB6BuwFe6flRpUAc/Fpvmvdl44VD3Eg3zPvVWLHG2McZwRyTnn0oA8Y/aw8SfElPhveeA/hXaHW/EeoSWtldiwKveWlnc+ZH5zRHcUDmN4xJxjczqymMkeLntTEfVZUsGrzdk7bpO+vzta/TV9D9O8KsFk39vUsw4jmqeGpqc4ud1CdSnyvkvs3FSU+XW9oxafOk/RvgN8I9L+CXwv0bwHYLC91bxefqd1Go/0u9fmWQsFUsoPyIWG4RpGpJ2115ZgY5dhY0I7rd931f6Lysj5/jniuvxnntfNqt1GTtCLv7tNaRja7SdvelZ2c3Jrc9BrvPkQoAKACgAoAKACgAoAZNDHPGYpVyp98fQg9j70AVFuJrBhFfv5kTNhLjGMDsJOwPYHofbuATfbA8hjt4XmKsVZlGFUj3PX04zz1oAb9tl4DaddAk7cfIecZ67unv07deKAFgupHl8i4tmhkZS6gsGDKCM8joRkZHvxmgCzQAUAFABQAUAeH/tp+IdP0r9l/4q6U0iT6lqHgfXDa2CXEKXE0S2xSaZEkdd8cIlR5CuSFIwGZlVgDnP+CcP/Jk3ws/7Btz/AOltxQB9J0AFABQAUAFABQAUAFABQAUAeY/tEfBb/hffw7TwF/wkv9hbNZ0zVvtf2P7Vn7JdRz+Xs3p9/wAvbu3cZzg4xVUpeyxFKv8AySUrd7J6eW5NWPtaFSj/ADxcb9r21PTqko4HQPhX/Yfxq8X/ABg/t7z/APhK9F0jR/7O+y7fs32F7pvM83ed+/7XjbsXbs6tnjGhS9i6rvfnkpeloqNvPa46z9tKlLbki4+t5c1/Lseaa7+yL/begfETQ/8AhYPk/wDCffEPTvHnm/2Tu+xfZJbJ/smPOHmbvsWPMyuPM+4dvzbYf9w6T35Jyn636eXr+BOJX1hVOnPBQ9Ldf+B+J6F4++D/APwnHxa+F3xS/wCEi+xf8K1u9XuvsP2TzP7Q+3WD2m3zN48rZv352vuxj5etc8qHNiI17/DGSt/icX+HL+J0xr8uHlQtu4u/+FSX4834Hk/iX9jXxhJf+INB+Gf7THirwN8OPGl7qF94j8I22kWN4WkvgftS6feToZbBJCzvsUOqu7sgXNa06cY01RnrBKyW1l29PKy03MnNqo60NJt3b7vRX9dN/wDIzvjB+zJ4e+H2hePvip4T1cWOi6F8ANT+G+n+HRas/k28CzXCXBuWkLMdoCFShJOWLknFRjJOVGq31T/BPQrDJRlTguj++7Rx/wABv2SfE3xI+Avwp034iftD+KvEHwnuPC/h7Wm8A3Om2ISW4WKG6FtNfiPz5rFZCAtsw+VERd+EXHXN8tVylq09Pl3XX5/O5yxblBcrsmnfvr2fTz/Bo9X8ZfsueP5PjH4g+L/wX/aO1r4cT+ODp58W2CeHtP1ePUPsUHkW7W7XSn7KwiyCQHDHBI4ArGPMlyN3jdtLs3a9vWy8/Pa2k0nrBJS2b769fvfl5b3xPDP7DqeFvg34v+Dtv8XdW1SDxR4/tPHY1fV7Fbm+SSK6sriSGd1kQXDyPZNmbCYMxOw7fmqjJ0qtCq/+XUuZJaXWtl1111fXsiK9NVadWmtPaRt31sk307aLp3ZueMP2Y/iofiBrnjH4MftT+KPh7pvi+/j1HxFor6JZazDLOsKQs9nJdAtZsyRjON4zjAAVVEQhFRdOXw6vfW7397f0XTpY3lNykqj3SS8rLbTb179bnq/wg+FXhH4IfDTw98KPAtvNFonhy0FrbGdw8spLF5JZGAAMkkjPIxAA3OcADAG9atOvLmm9bJfJJJfckkYwgoKy8/xd/wCu2yPOfjJ+yhoHxf8Ajh8OPjVP4nu9Im8ESqdUsLaJyNfhguEu7CCZxKoVLe7QzAFH3FyOOCMIQjGr7Vq/k+jtJKS6JpSetrvRXsjWcnKk6S676LVNptPS7T5V1su2p3HiD4Urr3xn8J/F1tc8pfC+h6xop077Lu+0i+ktG8zzd42bPsmNu1t2/qu3l0kqcqzeqqQjD0tLmv532tp3Mq0fa+y6cknL1vHlt+p882/7Avjyz+HGsfASw/a78ZWnwkudPubDS/DdvoOnJd2iSu0hjn1HZ51xD5jtujAj3RkxlgpxSq89eH7x3kkkm/JWjdaXtZaXW3Q0go053grRu20vN3dn0vrunv169VqH7JHxN0ay8L6j8Hv2otf8C+KNI8I6R4Q1e8GgWmp6bq0FhGVS4/s+5YrbzMWY7kkICnbg5Zm2nUdStOrL7b5n6+Td7LV6bddzOFONOnCnFaQXKvTz7+r1+R6v8DvgroXwN8J33h7StWv9Z1DXdZvfEevaxfiNbjVNVu333Fy6xKsaZwqhEUBVRRycsSdRyjGHSKsuul29+urY1Gzcnu3f/hl0PRKzKCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gDkfi1q66H8NvEGoSeALnxxELNoJvDtvHFI+pxSkRyQ7JiI2Uo7bg3BUEGom9Nr/8E0pRU5Wbto39ybt89jx39kH4LfCTwJbax428D/sq6p8INW1tVWZtXvYLy5uYHO8pCyXU7wRBlUmIiFSQhCnaCNpQtSUOb5ddlrf/AIN073S64/FNza1/rp0+7sel/Bv9n74V/Aa11eL4ceH5ra88Q3KXmtanfX099f6lOq7Vee4nd5GxliFyEUu5VQXbM30UVokU7yfM9z0WkB8A/Bv/AJTIfHb/ALJ/Z/8AojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPxe/4KiWHwh+Hn7aVp4p8T+ANe8WT6/ZaZr+vWFxrgsbG8tY1W0S1gMMHnRZSyffL5jHdINqrsO7nr0605R9lPlSeul7q60Tvp1Wz3PYyvF5ZhqVdY/DOrOUWqbVTkUJOMlzSiotzs3GSXNFXjZ3TaP06/Y7k+HGo/s2fD/xL8MfDHhvQdM13QrO+urLQI9trDfmJVuo8kszPHOskbF2Z9yNuJbJroPHPZ6ACgAoAKAGu6RIZJHVFHVmOAKAGG4j8hrmI+agUsPL+YtjsMdTQBCs90qJcERzRONx8rkqDzkf3xjHTB6kA5AoAso6SIskbBlYAqQcgj1oAr2GD9ofhS0zExjqhwBg+5xu/wCBd+pAOM+JXjgaGkOgW0iWlxq26yttSu0JsIb9wBbW07KQymZ22qcBWK+XuEkkSP5ecZlPKsK8VGk6lmrpdF1b8l/VldrSlTVSXLexU8G+JNJ8WafI8Vpc291C4srjTnZVvdMvREDJbzqjFNyhg24MY3V0lQtFMjt24XFUcbRjiKEuaMldP+vxW6ejJlFwfK9zd0h7rS79bVkzvdbeaMZJzg7XHXjGDnjjOBxmtyTof+PA9ha/THk//Y/y+n3QCLXdf0PwvpNzr3iTV7PS9Ns1D3F3eTrDDECQBudiAMkgD1JA6ms6tWFGDqVGkl1eiOvA4DFZniI4TBU5VKktoxTcn10S121flqfEWrXPjD/goH8QYLDw+NY8N/B7ww6C9uJyiSXl3gk7EGVaYqwVVJdYk+dsGQRt8TWdfiqty0W4UI9X1fp1flfRa3u7H9N4anl/gLlEquK5K+b178qV2oQ21ejULpttKLqS9xaQc4/V/wAHfg74Z+DnhpNG0WGGW9lijS9vUhMQl2bikUSFmMUCGSQpGXY5kkkkeWaWaaX63L8BRy2gqFBadX1b6t+b/BWSskkfzXm+b43PsbUzHMajqVajvKT/AKsklokrJJJJJI89/a0Twvf2XhRfEuv3ulReCbyf4nzPbI/72y0RUFxGzLDJgH7dGcLtlODs6MR4/FWKnRwSw9JJyrSVNX/vJ67rrbfTucmHinPmfTUteBND1h59a8M2PjDxdY2PgrW7izFzf3CXp1OK5t7e9XzJru3kldIPtogjKTZxD87OQVXwMk4eyvPcshiq1NKpK6bi2tU7fCvdTaSb93rc3rV6lGo4p6HNeO5brRtf8TeKF8FeBPF+tafpNkunu9u2i3xu5bmNbPTRqR+0pLJPMpMaq0TQyLahkIuElTzszyGGV4ihl2BxNRe2bTjpJcvVtJx073Vmk/5bO6dZ1Iuc4rQ6rwjruoeB5rPQ9EsooBqV/Klxo/ibUn/tO4kCA+da6gWm+3n7PbPIsUhZ/LdA01ukIhHqYXNcfwvD6rmdC9KO06aVtX1WiWr/ALr8ne5nKnDEPmpvXsyxP45srxdK0vxb4K8QaZqWqFWitr3R3u4bN0naSOWe8tRPZwNuhZwWnXb5kX3WIWvoqXEOTZhCVP20WndNS9266q0rX3MHQqwd7HovhLXfA/jrw3a6t4L8U6X4k0fcfJ1DSdSS6gdxndiWJyCfmOQD3r26NGnh4KnSioxWySsl8kZNuTuyne3VusZt7GIx2UJZ4yjZ82RTyWIOSPocgYbBGMaCOG+MHhm/8X/Cnxt4U0i8SDU9a8N6lp9s938q+fc28kSs/GSFMqknGSN+DwKUlzJpgGjazoDfEbxDoWlaKALix0jxTd6gl08i373Xn2iKIyNqBIdMj5BAbf0BBL/E8AznLK5Qn9mckvLSL/Ns68al7S67Ha27NaRrqjWyrEJ4V+U5MQADNt68NuYYyMZwfb7c5Dyb4+eFX13xBqep6XpnhvW9V1Pwpb6hpmi+J9IGrWd1/Zb3jOIbbzovJnZtWt0M+W+VlXae3w3GNqdXC1KkpQg3KLlF8tuZx1ejurRbt1tvodmF1UktWcJJ4F8LfDT4maVqfhfwl4P0PXr/AOGPjG4vdU8LaDFpUd48NzojROVRnf5PNG3dI+MsQRnFePxDk1XLcLBVcRKrCVWFlK75dJ36ta36JGlCqqknaNnZ/od/ZeGvHi3unSn4O/D6GeEW4WS28YXkWDlGVtqaaoJBdT+HXgV7tLhnM6U1J5jNq97NP7tZmTxFNr4F/XyO+1zXtM8NaJd+IdXNvZ6fpVpJf3tyWwsUEMUckkjcZwqozHr1Y9sn7Q5Dmvg34W1D/hIEu/FOn+H7vxD4T8MwaddX+lXtw0UetalJ9s1m38t8bY3eDTJ4948wJKMBFb5/jOEP9qeKzPVKrUdrq3ux2/Np+a3OrE+7y0+yPWhYx215YASSyTbneRmdsEBCC23OByy8D1r7M5TVoAq6hp8WoRBHJSRDuilX7yN6igDyPxJ4e8d6re397/whPh3xJ4is9VzpKeJNUbT7BLPyADLZPFb3jxMA7Rybo1eVhKSVj8tB8fm+Q5nm2Lk1ifZ0bWSV7vTXmV0tddbvTSx1Uq1OlH4bs8c+HVnr/wAPP2/Nc07xnroluPHnhtruxVF3I+FjZbcFY0G2FbS4jWRlUskQLHe5B5cnwEcjzZYNPRw031ejb62u1J76bH7xmLjnfg/h6tCN3hK7U3p7vNKe2uqftaa2vd3tZXPsmvuj+fQoAKACgAoAKACgDn/H/hC28d+D9T8LT3BtZLyE/ZbxEVpLK6Uh4LmLcCBJFKqSI2MqyKRyKzrUoV6cqVRXjJNP0ejGm07o878A6/rEniS9tdYC26a0ZNM1vTlvJbsaR4gghQARkIwjtrm1CyKZDAuEtmEXm3rivi+Ga1TKsVUyHFP4bypvbmi9Wl3fXr9pXtE6sQlUiq0fmd9JJozrYSJawvO65khtgFZTt3E5BG0hlHU9Ca+4OQuSaX/aeh2axMsc6wR7JDngFQGGR2IJoA+ePHrSnxL8RV+KOleHLa38Ivb+J/C1zrum2+o6c+kW1jE13d29sbmJhdQTy3cLysY/LSa15KyEt+dcTYupic1o4CNSSpNxhNQbi1Kb0u7NNOLTS1vaW1md2Hio03O2u6v5HreqeFvFNvNJJL8UYrTcC6wJo8RAHou5yx6dya7lwDlSVm5v5r/5Ej67U8jB8SfC/wAW+JJbqaN9DMeo6Q2kvrLWctnr1pZvOpubeKaFwf30Y+R0eHyJUjk2z8KhT4HwlGvTnTqy9nGXM4OzUmtuy9bp3V1dA8XJxaa17nYTWdxo+m2WlR6ZFaWdsiQ2lpa4jhQDkIqpgKq/KoUDgL8pHU/bnIef+L/ETavc3fhzR9b+zJpwjfxRrgnYLptsVSQ26uDuN3NEwCqpDQxuszbG8hLj5niLPlldNYfDrmr1NIpa76X+/ZdXpte3RQo+0fNL4UW/BXgW716/tfFOq6bbw2mkIyeHtCETwxaTbkFN7B1VjfSxEh2ZQIEcwpyZ5bh8O5EsqpuvXfNXqazk9XrrZP13fV69klXre0do7LY9g02ZrixhlbcWK4JbqSOCep64z1r6UwLNABQBneINf0rwvpFxrmtXDQ2lttDFInldmZgiIkaAvI7OyqqKCzMwABJArOtWp4em6tVpRWrb2Q0nJ2R4T4C+HD+LNfn8J/FXwwdUtYNMs/FmvtexGWw1zX9QkuY3ikQ5SWOwjs0SKGR51RJrM8NawSH4vhhRzjF186q6vmcIL+WKSel+rT7Lr/M0dWI/dRVJerPTvDPws8GeC/7PtNH8P3Qs9GM40yD7fPNaWMcjq4SK2klKRiIIEhCJiGPMcPlo7IfqKOVYKhiZYunSSqS3dtfP0v1tv1uc7qTceVvQ6u51WKBCyW9w5zjmF0VfdmIwAO55r0CDyr9qP4la98J/gb4h8TaWBdapcRpp9jJFZO6W7zEq0z43KAibmBfCFginO4A+TnmMlgcDOrTXvbLTa/X5eel9D9C8LuG6HFPFGGwWKklSTc5K6Tko68q1Tbk7JqPvKN5K1m1h/sdfCfWvgz8FbG31mJ5b/X5Trl9bBjvtDLGgSMKQPmEaR716h9wBYYrLh7AywGBjGfxS95+V7afclfzud/jBxVR4s4oq18Nb2VFKlF/zKDk3LdqzlKXK1a8eXS9z3GW+0e6i2S31syPgjMoBz2xzkH9a9s/LiOOe6gvLW0WdLiCYM285L7AvBJHGM4G7vu6d6AK+t2kdnBc6wkly0sY81Y0ZNpcAAYV8LngYyRzjnOKTdlcqEVKSi3a/V3svPS7+5NnhH7NfhG38feK9U/ac8TfDu+8J+JdU+0aMlnd3huPMiiZIzclXRWim/dGBhhc+UzbRv58HKqMcXVlmdWk4VHeNm76Kyva109LdNr21P1jj7MqmQYClwPgcdHFYSny1eeMOW0pqUuTmUnGcPe9on71nNR5vcsvpGvfPyUKACgAoAKACgAoAKACgAoAKAGpGkShI0VFHQKMAUAOoArXqSgR3MCF5IXztGMsh4YckdufqBQBJBdW9yCYJVcrjcoPK+xHUH2NAEtABQAUAZfijxNoXgvw1qvjDxRqMen6PodlPqOoXcgJWC2hQvJIQoJICqTgAnjgGgD+dv9q/9prxL+0j8e/EXxVs77V9P0ucS6boVi9w6vZaT5Rh8oqrssZljLvMisULTSjlTQB+0v8AwTh/5Mm+Fn/YNuf/AEtuKAPpOgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv+xgT/0ss6APp+gDzv8AaGPwtj+CXjK6+NemjUPBFnpct7rVpvZWuIIcSiNdrIS7MihVDDLEDPNY15xhC81fVWXd3Vl83Y2w/tOe1N2dnr5WfN8rXv5HgXwO0H46eA/CiS/An9i/4afC7Q9SlFxLp+v+MZ01a+gGfJkuBb2cvlSlG+5LJI0ZJU8iuqfPFKEnor6Lo9nrt0W26tqcsbNuUevV9V9+n6M9c+DPx+1zx5428QfCX4ofC+6+Hnj7w9aRaqdLfVYtStdR0uWV4o720uo1TzE3xlXDRoyMyqRnIVRg50nVS0TSfk2m163Se3VMqUuSoqb6pteaTSfpa60etmmey1BR8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPzn/4K4/soePvjLD4C+J/wk8EXfiLXdMlk8PatbafHJNdyWszh7WTYPlEUUv2gO/BH2lSx2qSqbsrlQg6klBbvTVpL5t2S9XojR/4Jg/G3wv4J8M6X+x14v+I+i6v42sUv9X0600qW2ubGyt3k899PS/gkaK9uR5k103lFwiO6F8wsiKLcldq39eXff87PQutTjSkoxmpKyd1e12k2tUneLfK9LXT5XKNm/wBB6oyCgAoAKAKOqieWIW9t/rMNMOO6cqMnjO/Z17A0APURxXEc8Ue1LkYfA/ixkE9hxuBPclaADeLO48uRlWG4b92TgYkPJX8eo98+oFAA8E1vI09mAwbJkhPAY+qnoD+h9utAGVcas9xcPJZ280Cwo/nTMMFkGQFCnvu+7noQePvCgCzP4csdQ8PT6BqKO0V5C0dy0crI5ZxhmV1IYHnhgcjAqZwjUi4TV09Gn1BO2qPJfhpc62fG2leItb1RrUXX/CQeC7mC5tALnWLjT9Sl/s66JRSsYFra6lKSpjRzdg7eI1T4fhxRyzOMXlNO/IrTiui2uu+0or5b9Tsr/vKUar32Pb/Kj8zzvLXzCu3djnHpn0r7o4zA8d+P/Bfw18PTeKPHniGz0fS4j5ZmuCf3jkEiNEALSOQrEIoLEA4HBrnxOKo4On7WvJRj/X3vyR6+SZDmXEmLWAyqi6tV62XRaK7bsoq7V5SaSutdT441Dw78Vf24PGdnbzG98LfBXSit1Z3EUgdtQU7cAHO2W4xvQ8MlsVkQl3BEnyNSli+Ja6v7mHWqff8Azl06qOq1e/8ARODx2QeCWVzdO2Izid4yi017O1/nGltJaxnXTjNcsGnD7R8JeEvDfgTw3YeEfCOkQaZpGlxeTa2sIO1FySSSSSzFiWZmJZmJYkkk19hQoU8NTVGirRWyP5vzXNcbnmNqZhmFR1K1R3lJ7t7dNEkrJJJJJJJJJI162PPPnn4r20ni/wAd+JNKgu/D+q2k8Xh7wbJpl+8chjWa6e812AI0i8y6RLbStEDudbdGZJFVAfjs4tjM9wWD1tDmqO22nwt+jja/nZas6qXu0Zy76GN4N8eXui21v4h8e69pmp3us+FdPtHsNPa2WW58RWF3JY6mEmaFI98l1eafbRo0ifvF27IyGrLhipTyuONwlS8Y0ZuWt9INad3tG/z63HiE6nJJdV+Jt+GdJup/Eg0vUJLm4k0G5Gra3LBdmWK81+8UyLb78rJLHZ27RiJJ4iAktgyHzLUbZ4ZoyzXF1s9rx+J8tNPpFaNr8rrrzdwxD9nFUV8zvvEOgeH/ABpoc+i+LdAsPEOjXeVurC9t0mt7gJlyrRyJtK/LnBHAMeOVr7g5DPm8HWEeord6Tq3iPSr+WwbTES1v5WhgRdpUxWk/mWiv5isok8ksE+XIX5a8TE8N5Ti1apQj30XK/vjZmsa9SO0jh/HHwJ1DXtWt08Rz+AfHiWWktatP4y8BW9/qAn8mUBzdQzQQiL52kaBbZd+ZUDoJQYvKlwbh6V/qVepSTd7Rlpum/O9lZNt2snrbXX61J/GkzN1rQPG3hdtRvdH8K+LLK41C+W3L+CvE1tdBIkiZFl/s3W9thZISGLx24kfdtyZA8hpRy3iHAWWGxUasV0qLXe+6u38391kHtKE/ijb0L8HxPv8AS7u4sZvEvhi/jv8AXdN0bT7HxRp+oeE5J2dcyxxzzpJFqU0hyY44LeNDh03ZUkSuI8ywKvmmCkkk25Qakvu2StveXmHsKc/4c/vD4TaZrNhqw1L4geEL7RvF0nhTTNA1CRbq3bT7w6Wbk/aLMRzO/kPPqMjIJdk2xV8yNDgNhwTWpyWJp0J80OZSW91zX0ldLVKKva8b3s2Vi0/dbWp615sNo262uOfMSSONnkG759ylgUAPy4/i7E85r7o4zI+JHiC5g8IXfifcItQ8KY1xIIYGlN5bxxkz2ypvUtJJC0iJydshjba+zafJzvLI5tgamFlu1deUlt+Oj8m0aUqns5qR4l49+fwN8YvBukadaaVrfgzwVrVp4VvoBFB5WkX+nxS2z26xKDbwpPbNbDaCG/s1GyWBVfzypmMszoZXGq7/ALzlknrdxlFK/d8rvr3fc7lD2cqlux9A3CTPMl5Cq2iPLC8MceCF3g89uhj6Y61+snmnA/Euaa9tdH8HReIIdP8A7euVl1GQtBui0K3XzNSkeOR1fyJEjjsHlTLQtqEMnBAz4PEuYf2dltSa1lL3Yru5adnsru3W1jbDw56iXQ6z4P6RFc+EZfGlx4aittX8b3h8S6ja3sMttcwCaGKK0iuIJDJ5NzHY29lDMi4XzYXYD5iT1ZLl/wDZeApYRu7itfV6u22l27eRNWftJuR3NnbWtvqZ22sVvM1uCEi6EbvmPQd9teoZmnQAUAQXdnb30Pk3CblyGBBwVI6EHsaAPmj9q34L+MtVm0j4xfDG5v5PHvhNFh0p7aZElmj84PtdZAUm2IbjCfKZPNYNvwsbfPZ5ltWvy4zCX9tDa3VX89HbXTrfrsfsXhZxpgcr9rw1xByvLsS3z8yb5XytaOLUo8zUPe15OVNct3Ndr8Bv2ofBfxjjHhvUT/wjvjqy3Qaj4evwYZhcRhvNEAfmQLsclfvoFO8DqejLM6o5h+7l7tVbxejv1t3/ADXU8fjjw0zLhF/XaP7/AAMtYVoe9HlduXma0i3dWfwyb91vZe017J+ahQAUAFABQAUAcV4++JGleGPO8OabBqeteKbi08+10bRFhkvhGwkCTsZ2WC3jJil2yXDpG7xlFLuQh87MsxwmXUXPF1ORPRPr8lZttejt10LpwlN2irnlHgv4farqGp+P7i6h+Geu6peXtnNN4Wt4XsYrVluZLq3uNSHmXim8ZJEk85IELtChJZVjMXweA4fo5zQhicuxFSmoNpOaTlsl7ri4vl6et7Ja37J1nSbjOKd+xsv8OfH62rQRfB34auEQrCj+LrwBRkttB/sslFLEk7fXODXqf6rZpv8A2lP7n/8AJmf1in/z7X9fIqp8KvidY6zJ4i8NXvwp8Hai9qmnjHgW91ieO1UhvKbUEvLMyoXy+3ykCk4+YjeblwXHFQUcfiqtRrz0+SfNb7wWL5X7kUjI0T4B3HhnRPE9noWi/DXVdb8TpeqG0eyuPDkUUVzbWtvcqpZ7/wAppEsbMlY0VGaAOys2WMYng2daVNwxUrQtpJKV7NtbOO3NLe71snYccUle8dyDxFrHiXwGi2Gr3/irwhZ6NC0Nrd+Jt3iHw5eLvN1cXM+phmvrbZErxedezQQxlxsjnVAKv2/EuUvmrxjiafXl0kvRJK+vaMtt0K1Cpt7rO38L/GibRbSzufiDpMFj4ZvNPS/tfFum6lFqOgJGys6iS6AjliQxKZPPlhS2ClF84s6qfTy3inL8xqewu6dS9uWas77WXS99LX5tNjOph501zbryOq+IPjy20l5PDGmz2Y1JrOS+vbm7H+iaRYpjddXLkbF6ny43KmUxyEHZFK6dGe51TyXDqduapLSMerf52XW3dLqKjSdWVuh574S8Cza3faasWlT6b4Z8PXDXGgaLO032i5uCWMmq6k037ySR3kd4o5Cx3M1xP5lxJEtp5nDmRVMPJ5nmL5sTU3v9ldl5237L3VZXvdespfu4fCj1SxkuLW2k0c258y5lCFk58vcPmDAnIwoODyCBnmvrjmOkg8gRiK3ZSkX7vCnO3HGPwoAkoA8s+KXjGz/tmXwlb6he3L6NYW+qatoenBUu9TF9LNa6baCSULF5c88NyDiaIh4IRI6wPIH+U4ozDF0FRwGBdqldtKXZK1/R6rWzaV7a2OnDwi7znsjlYtO1eDxfpltqXg7xFYeJrOJ5LDR9JvtQn8KSuI3RLl7sW8dvCUW7ulZHSOSQkuYLh4bNk+fxeQ8QTvljre0w83FucrXVnqrOTluk7JtOys1eSNo1qK/eWtJdD1/wX4N0jwPokej6WpllYJJf38yILrUroRJG11cuiqJJnWNNz4H3QAAAAP0ahQpYWnGjRioxWiSOFtyd2b1aiKuqappmh6Zea3reo2un6dp8El1d3d1MsUNvDGpZ5JHYhURVBJYkAAEmgDwT4++E/E37QfwL1Tw14B0jUdJnuCupWlpqpjtpb8QzAxwmJwxgWVB5qh2hkRvKSVY8yovxeNzXDcRe1yzANucVzX05ZWaTSfq9HZJtb23/AEnww4gwnB3ElDNMzj+6tKLercOZW50k9bbNWl7rdouSiVv2QfjY3iLwZpnwq8b+HJvCviTwvbpodlDeny11dLNDFIbcPhmlhMLCZFDBDtOfmKp6WQ5j7WjHC148k4e6r/a5dHbzVveXT8vc8WuDFl+ZVc+yyssRhsQ3Vk46+ydV80VNq6UZ8ydKTtzaq2icvowRxq7SKihnxuYDk46Zr6I/GiOO0tYZDLDbRI7DBZUAJHpmgD5Y+OXjTSP2g/irbfspeFdb1SzNi8esan4h0W4LpY3dszs1pcR4UMqjYd6yHZcNCpG6NhXy+Y4inmuK/suk2re85RezXRr7tb6StpdM/dODsnxXAORPj3HU6clUvShRqxs5wqJJVKcruzfve64e/RVRqXLJM+m7SG60m0hsLexiltreNYYVgbYURVAUbXPQYxncT7V9OkoqyPw6pUlVm5zd23d+rL8Xm+Unn7PM2jfszt3Y5xntTIH0AFABQAUAFABQAUAFABQAUAFABQAUAVri3YOLq2VROMA9t65GQfw6HsfbOQB8N3BOxiVwJVGWiYjev1H9eh4x1oA+Yv2tP29/AX7M3hFdf8P6FH8Rr1Nafw/e2+kazbpb6TfopZra+mXzXtpmRZSkZiJbyZAdu3NAHwd8Kf8Agq5+078Sf2iPh1oev33hDQvCus+JLLSNV06z0oLA9rdyxwNI807vKjRBmlVlkVd/31dPkoAp2fj/APah+Nfhn42/C7xX8T9W8RfGf4MeMNL8Y+G7bTISxvJdKuJrC/S2QKkAjiL286RLGsksg4SRmYAA+lL79l/wZb/srePf2nPiB+z5pHw4+MV18NfEsWp2WmXB+yLJLYPF9qW0VjDaTSQoxKR4ZRczCTc5O0A95/4Jw/8AJk3ws/7Btz/6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8ly+BX/AGMCf+llnQB9P0AcF8efDvw18X/Bzxb4W+L+t2mjeD9W02Sz1TUbq7itUs0kwqzCWXMcbo5RkZgQHC8Hoc6ijJe87ary1TTX429djahKpGd6Su7P7rPm+XLe/kfPfgHxV+2J4l07+zPgv+0j+zR8XdN0YRW9zrtzbXn24krlTcx6ddPAjnBwBtzgn1rZ88vfkrJ9V1a3/NadLnOlGK5YdO/9f8Oer/Bv4HeM/DvxA1v43/Grxzpviz4g63pcGg276VpI0/TtF0mOVp/sVshZ5ZQ07tI8srlm2xqAoT5qjNwpypRekmm/VJpelrvbvfcHeTTl0vpd21td+rsumnoe1VmUfAPwb/5TIfHb/sn9n/6I0KgD7+oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDM8T+GtD8Z+GtW8H+JrBb7R9dsZ9N1C1ZmVZ7aaNo5YyVIYBkZhkEHngigD8cvjf8As8/Fv9inXL/4b/s46b4pt21/SZtZ1v4pR3DxXMeiRXcZNnE0KqtgkDfZjcuHeSY+SwMETNCeXFVVhYSxDTlZbLX7kur0328tT38iwM88xNLJ6cqdL2krupNqKSSespSdlGK5naNuZuz5moW+5f2L/wDgoP8AC79pe7PwxudQvdP8caXbhIW1VYID4kSJD5l3bpGQqylYzNJbAfu1c7DIqOy9EW5RTas+x4teEKVWUKcuaKbSkk0mr6Ozs1fezV+59d1RkFABQBn3c5sL2O6lcGC42wNnrGw3EN6YPOfwOaAIVkkXzNIeFl8lS0UoXcAAQYvlHJx69Mp70AaDJ9rtCrq0Rlj5B+8hI/mKAOH0z4teF/FGoWfh7RtQddQvl1hhG8bxkrpd2lnfMrEYCx3Esa7zjKuGTdnI4cPmFDE4irhabfPTtzad1dWe3dd7p6Ws3coOMVJ7M6fT4YJ40htkYwRSCR5tu1ZnByAo67Qcf98gc813EGvQB8/RajC3xE8RalrFxZ6P4f8Ahf8AECa9mvDefZoYrOfwslxcT3jyvtZVn1WZjjaqqsZwNhavh5KOE4tjyv8Ai09fVX27aQT18zr1lhvR/wBfmU/Hv7bnw/8ABNtfw/8ACOazPrYMCaPodzGba+1HzFVhJJDtaSyjw6ALcrHcE7/3AUIz+jieLMvpKaovnce3wt63tLbS2r21Vr62/QeC/CvO+MOXE8vscJrzVp7JK9+VNpy1TWmiaalJHzzpPhP47ftieM73UPiHNBZReG3NvcaFqWlXsOlaNciFZFgktRcW1zLdSQ3EMyGOTaY5cvcDy1tZPHw2CxvE7WJxc+Wi9uVrX0XvJa3u5a6Wts193m/HPD/hzh6mScC0+bEJ+9ipck29m1FuLUo293RRgtZRUm+c9/8AC2sar8Mr6XStHtIbLUNHR9Q1Xwvp6zPpniDSgsCS6hoduDI9pLCSjNZxDHnSyRTK5urbUKdLFY3hXFwwuMlz4SekZu14PtJ/nforxtZxPwvE1p5pOeJqycqrd5NttyfVtvVt9/vPonSdW0rX9Ks9d0LU7TUdN1G3ju7O8tJlmguYJFDRyxyKSroykMGBIIIIr748wt0AfOGnwX/ib4g6NruqeBo7dX17xP4nn1CCaWSLzrErounOygyRhrjS5t+GIUNGW8oNkx/HZXbGcQ4zEqV1TjGCVu+r+6UX633sdVT3aEY99TjJryx0TxS7eHPhxL4hvfCXxD1HR7NLe4cfZf7Z02PWGnvGhjk2W7alPAhaREiiAimKs0EbHxc7y+vic6q4KhU5frEIN+fLJJrptFOWl27NbNmtGajSU2vhbPcfD2gDwj4ftNGvngmvITLPeSrZrb/bLuVzJcXHlocRmWZ5JWAO0GRypJ6fouGw8MLRhQpK0YpJeiVjhlJybbNvySx+0RsqsCfMEiEKdp3OsgHOBwW65LKqjArYQ6y1M+bLcxRObkQrFEABIQWYlnzwM7j09WAoAitQj3bRvBPMNoO3eFY7pMDcwI3ZWTrz970oAYYSEjEwt0+0OoMjEkqGXJPPtMvfqvtQAsM8izQz+St6jPlojF8pJGepz0aUj8B6igDyD4R2cS+IdJvPBvg228O+GrCx+IGkPpmhWgs9LjeLXrSC3lljX5DcNFaytn7xL3BUYJA+MySfs89x9CUm78r1d3bV2Xkuay7KyOqtrRg7HsU++3muIxp8iO6yfKJt3EhXbnHXGfxJHpX2Zyk+jxMLq1MlpMIGJG94mVMMhAGSxHJYduc0AfOPhfw/baloHxB8G2F7qes+N9N+Gdl4NvJ9RWSKbUPsN1r1hDdFpif+Pm4gupNxkbKsjFiCGb8dxOFqYLOaNBpKH1htW/vOk7fKLiepGSnScuvL/me6+L9a8O+GNC1LxNrF3FptjpMMt7dXMqmNILaIzPLI3HRUVST2xjtiv2I8s8W0rStT8deNdT0bxLLqcN94105NRvtONvEkvhXwexZLfSn8wSot3ezrObh9vKR3aLKDZ2jt8Nh5f605msRb/ZsO7R1+OffTotHq+38zS65f7PT5ftS/BH0xbXUk0tzcW8kSNK6gsAJiFAIXKRkgdepY5x0A4H3JyEjbYLuC6ke6nmGVkf7M+BGVPACrj72OuT70AakUnmxiQI6A5wHGDj1x2/nQA+gAoAZJFHMhimjV0bqrDIP4UAfPfx7/AGPfCXxNEnjDwDInhLx/ayJdWOq20jwxyTxsWUzeX8wbOMTKPMUqp+YLtPg5rkVLHfvqPuVVqpLTVd7fnutO1j9a4B8Vsfws1l2Z3xGXyTjOlJKTUWrPk5un/TtvkknJWTlzLzWw/bU+J/wRl03wX+1L8J9Sj1OaKWSPWNPmt918kZjDFIVxA7J5qbzHKAN8YKruBPnRz7GZdFQzOi/8Str93u39H8j7Gv4T8N8Z1p4jgbM6euqoz5k4paN3l+9UbtJOVN7/ABPQ+g/hZ+0n8GPjELe28GeNbNtUuEB/si8P2a+D+X5josT480oobc0RdBtPzEc17mCzjB5hZUZrmfR6Pa+3W3W115n5XxP4dcScI808yw0vZR/5eR96nbm5U3JfDzO3Kp8snde6noenV6Z8QFAGfr/iHQPCmkXHiDxRrmn6PpdmA1xfX9ylvbwgsFBeRyFUFiByepAobtqwPJfFPxovb6aws9LbUPC1jqk5j0+S401pdf1oLECw0/THRpIkRpIzJNcxkxrDNvgSNkul+PxvEs8RUeDySHtanWX2I+r2e2mtvN7HVCgormquy/EueCvhReXumIniTTZPDmizm2u30CG+Nxf3sygFn1e/DM1zKQlukkaSOreTKss93FNsV5bwso1vruaz9tW8/hXouttbaJLpFNXFUxGnJTVkehaL4R8PeDdHt9H8D6Bpmg2FhEY7bT9Os0trVEyTsEUYCqMkkbQMEnrkg/XnMbFvMtzBHcICFlQOAeuCM0AULxbv7WXgtbw46PHcKFOR/cY44+lAGYt0ZpLmWW5XG9YAt9bZX5RnDMmFU5Z6AILx/IuII4owkUS5Aedp7Y5AAznoMMB0H3genJAPOPFPgLRr3WrTWvBEdx4a1jWtS+z6zJp9pDNa3mnmMyTrewuPLlLeV5cUzDzIZJhtzG80UvyHGkcHDLniMRBOaaUHqnzPzWtrXbWzt0dmunC8zqcsXp1POPA15pc1houmaH4Gk/4RQXZ8R/2HazImo6vpn293068tT5UUE1hbQ/2bL9lhkMixTRwOQ0H2W++ewsZZTmNLGZ9zO8Uozd3GL5VvrJ81+Za2s05Wd7w3l+8g40fmv6/r9fpnQ7/Q/EmlW/iTwPq9le27tNCLhH81JGSRo5YpDncHjkjZGU/MjoyMAQQP06E41IqcHdPVNdTz2raMtz29zG7axfGLzraMrBHFkqWIxySASSTgDtn3qgHnQ7craqYo90ZzNIMrJJ8pGdw55JBNAHOeKPE+o2eqxeB/Dmp2r6xLAt5O8uZJbCxZigmdQhXezK4h8wgSGKXhxFJXg8Q55TyLC+1avOWkV5931sutu6Wl7m1Ci60rdDhdH026+J8/h/VtLvNc1PRr/UbPWNQ1/UdOGmTmzsGjurGzh2x28rrJdPHKGMUsTQ/bkZ0LxKfEy3L81zLNIZjm9NQVONopNWbd9bXltd31Tuo9mbVJ06dNwpO9z3GvuTjCgAoA8k+Np1zzm1qw0w69aeDtDvtfi8NxxyNLqusLhtOK7M7tnkXQWMq+ZZoZAokhjYfF8Y4ipKOHy2nJx9vNRbX8t0mvm5J7ra2zOrCxXvVH0Qzw94Th0G9utWkv7jU9e1IQNqGrXIUTzCJT5SIBhYoY9zMkUYCK0kshzJLLLL9Nl2XYbK6Cw+FjaK+9vu31f/DLRJGE5yqPmkcP8av2TLT426nB8QtD8SN4R8dabcIyavbQN/pflIPJ8zYyFJEcJtmX51ClWDbUCedm2QwzCaxFKXJVXVdbbX21XR7+ulv1rw+8WMVwfhp5RmFH6zgppr2cmvd5n79rqScZJvmptcrbunG8ubgNO/aL/aI/ZhjtvDv7S/gK68SeHxcrY2PiiwuI3lkRd4BZvuzOyR7lWUwzEBmcsSceXDNswya1PMqfNC9lJf1re2l7PufdYjw94P8AEtyxnBOLVDEcvPKhNNJN22W8FFytJw9pTTsoJLf6L+LXxr8EeAvAlvrjeO9F0268UW7Q+GLy4LTW09xJFuhmPlhs26lo2eQ4QKy5Yblz9FjcxoYagpuooufwt6q7Wj06bXex+NcMcHZrneZzw8cHUqRw7TrwjaM1GMrTiuZr947SUY/E2nZOztyf7PHwAf4D+CSNX1H+1PFutagdS1/UYpWlWd0WUpEjMquUAZuWBYvLIehCjDJcr/syi1N3qSd5P8v6fVs9bxM48/14zKEsND2eEox5aULJNKy5m0m0m2krLRRjFbpt+6I6yKHRgysMgg5BHrXsH5uOoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDx/wDad+MknwB+Hc/xLl+HXijxlpscn2PVbfw8jfatOsfKmkmvi6kGKKMRjc+RtJQ7lAyAD82/2J/2ffAfx38afE3wh8MrzVdT/Z78baOo8RWesBrLW/D+pRTyPpapKqvHPcxsJZUaN5IfJkIuF3lI3AP0W+EX7Ff7L/wRsYbfwN8HfD5vIpFmOq6pbDUdQaUIilxcXG94wdgbZGUjDFiqgk0Ae3UAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAFABQAUAFABQAUAFAHgP7cPxC+Inwz+A0/iH4W+K/+Eb8Q3GvaNpcGpfYYLzyEur2KFz5U6sjfK54I+hHWlCEq2Jo0E7c81F/NP8AUKk1Sw9Ws1fki39zRwHizw7+3H+z/wCDtc+L19+09onxfsfCtnJq2oeFtU8B2mifbLGBTJciC7s5GeOfylcx7kdS4UFSOCp1FSXNJXXXW1l1eumm78r7uyKjTdV8sXZ9NL3fRaa69PP717Nq37UXwV8OeAvA3xD8S+KZNNs/iPa2dz4asBYz3ep6h9pijkSOKztUlnkcCWMNsRgpdQSNwzrOPLNwi7+avr56pO3qkY06ilTVSXu7aNrRvS2javfTRvUueHv2j/gx4p8GeLvH2j+MSdK8BJdP4mS5027tb3SRbRtJMLiymiW5jIRHIUxZbadoYis3JKKn0fX8DS+vL1OVf9uH9lqG4hiuvirDawXkN7caff3Wk38Fhqi2h23Asbx4Bb3rK3yhbeSRmYhVDE4q5RcW091/nZW736WvfoKnJVUnDW/4WV3ftZb3tbrY7j4ZfHL4ZfF++8QaT4F1y8n1PwpNBb63p2o6Re6Xe2DzIXh823vIopVDqrFW24O04PBqLrl576a/huPrY4R/25f2Xo7aDVJfiPcpol1q39iQeIX8OaquhyXu8p5a6obb7GRlW+cTbMKW3bQTV04yquMYrWW3S/pcH9p78u9tbfd6r7zqx490nRviz43tdf8Ai5bJpnhzwvp+sXvh+50sW0OiQF7oyag9+QBIsqxEGMtiMW5bjfUUmnTqyevLKKv/AC3jflt1vvfpsE0/a04LS8W7fze8le/S21uu5l+Cf2uPgH8QfEXh3wr4c8Xakmo+MLaW88PLqnhrVNLh1eGJQ7vaTXdtFFPhSG/dsxwcirjBybit0r2629CZTjCzb0vbyv67eXrp1O6+JPxL8EfCDwZffEL4ja4uj+HtMaBLu+aCWZYTNMkMZZYlZsGSRAWxhc5JABIlayUe7t2Xzey9XZFWdm0m7JvRXdlq9Fq9Ownjj4l+CfhxL4dg8ZaybCXxZrdv4d0dFtZp2utQnDtHEBEjFARG5LthFAyzCnFOUuVb6v5LV/cD0XM9v8zy7QP2hfhx8PfAPjv4hfFT9oKz1nQ9D8catozX9xoTacdLljlwujRwohkvZIAGUSorNKAzchSQo6wppauSVv7zu+n6eVyo05TlU5VpHV/3Vpu/VrXu0tzrvAX7Rnwk+JPjW8+HHhnXNVh8U2GnLq8+j6z4d1LR7v7EzhBOsd9BCzpuIG5QQCRnrQveTa6b/wBf107mcpKLUZddvP8Ar9H2Zzenftp/s16rqGmWVj8Qbl7fWtbbw7purN4f1NNHvdSDMv2eHUmtxZyMSjYKzENjgkU6cXVkoQV29l1fp3/zHVaoqTm7cu/l69vPt1Pb6QwoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv+xgT/0ss6APp+gDxb9szxzJ8Nf2Y/HnjiLwt4d8RvpNjDKNL8Q2P2zTrnNzEm2aHcu9RuyBuHIB7VLs3FPrKK++SX/DHXg6ftajTbXuzenlCT+52s/K5yHxA8UeKPCPxT8P/svfso+FfAHhHXNS0GfxfrepX+klbHTtJjmW1j8i1tvLE1xJO4A3OFRI2yG3grcVOo5Xuoxtr5u9kvO0Xe9tFo7nNyONKNW3xNr7km3+K+Zv/Bb4t/FNfi14g/Z4+PaeF5/F2maJb+KdG1bw5HNDa6to8kzWzvJbys7QTxzoAwDlWEq7QAuWuEHUpSqqNuVqL1WvMm011to007O6e6aM2pU5RT1Uk2nZ6Wsmm9r6pq3R90z3msyj4B+Df/KZD47f9k/s/wD0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQBynxW+Gvh74xfDjxD8L/Fk+pwaR4lsZLC7k029e1uURu6SJ7gZVgyOMo6ujMhAPwt8e/sEftS/Bj9pHS/hZ8P8AStX1fVriVtU8K+KNGL2kU1tC6ZvDOG/0N4WeISBnBjd0wzLJE8kzk4xbSv5GtCkq1WNOUlFN6yd7Jd3ZN2W+ibfRN6H6z/shftg+FP2ib3xN8OdPvNS1vWvh1HZ2WpeKV05YdM8QSFPKkvYDEWSASTxTMkTEFoyroDiRIqMj6WoAKAKFnbW07S3kkO6Zy8EjN0dQxBGOmOMdM4HegDKjKWtzJe29wZIopjCJZHLdFBKs3Xac4HXDKp5yaANRI5dTcS3Mc0NqACsD4BkPOS4HbkfLnscigDwrwna6Z4X+JOn+GZY/EEQsfFuveGbOO5leOxuYtQtBr6yQxxxbHS3SJbSJnMYjCXCB2baH+NwPNhOJcRQhZxqwjN23i1Za69bt6XfvRe1zqn72Hi30dj6Fr7I5Snq2r6ZoOmXOs61fwWNhZxma4uZ3CRwxjku7HhVA5JPAHJqZzjTi5Sdkt2a0KFXFVY0KEXKcmkopNtt6JJLVtvRJbnwl8UfjzJ4pvPGumfs02N1q114m8VWV3qOrXCxzSJdRafBHEumWewyzkxaS0wZVkkTyJ3CAGN0/MM++r59mEfqUZSqJcunwuKvfpf7Vm20ulnc/fuH+AMq4KVLM/EKqoQnFyp0UpScpK11NxWjjdPk2babnaM4mD4Y/ZR1HQLnTbD4gWFm+s+NNOivtJkvtTYLfX8U/2u+0e+mi3NbSTWkaor2jXBAj1J5GnhKW8muJyufDSo5hXiqkIy99L7KatFq+js76ytrypJP3j57jbxZzTjPny7Cf7Ng7JRpx0cklb35K10/5FaCVrqTjzP1XwT4o1/Ubu18eHUdQvvFekabcrc28geG+8R+H4LqWKeKS0th5A1nTrnfFKtussRkYxq8C6gpt+mhVhwri4Ok74LEWafSEnbW76Nd2nbu4u/5U08TF3+OP4nt2teHx8VfBml+J/APiLSrTUNPRNY8K6lbKt3amd4SEkk5xNbTwyOjbGDGKdmjkSQJKv22Y5fQzTDSwuIXuy7brs15p/LumtDlhN05c0TlvhL8SF0XVo9O1K0u9J8P+I72SKSwvoAJvCnieWQSXGlXUi4/d3Ulx51vI4KPI5Ec0kd5YRD5zhzHVsJUlkmYaVKfwP+eHS3eyXrbdXjI3rwUl7WGz38mev+PfE03grwL4j8ZW+kT6rLoOk3eppYQBjLdtBC0ghTYrNucrtG1WOSMKTxX105xhFyk7Jbs5dzxHwnpun+Cr/wASr4OuNS1e98L6NoPgKGzvdx+0zWcDXMBlkSHcBIuqQebMqyRqqsxCbJBXxHC+KWEymtmmM0U5zm/ysrvdyTST1bt5HZiI81VU49EkcDf22h+FX8SW+qeM9XvtN0vRrLX9Qv081oLi98O6wdR1q4jQ+cB50t/HAY4FbYbaW3Xy/s8ajw8NWxUsVhM5xMUnVqyS78s4xhHtokna/wDi1uzWSjyypR6L8tT6StobvMTtI0m3iJeRtVujJz8rNg7RjtuOAM1+qnnF/T4rIq2p/K0SAJ5asV8yQANgg8bVHOT1IZjxQBxmr+K/EOlyrEPh3f6za6hczSyappUts506NERi88cskcxLOXCpbpOxCHOC4UfPYnijLcHi54LEycJRtq07O6vo1fa+t7G8cPUnFTirmJf/ABv+GGg2v2zxb4t/4ROCS5ktYn8WJNoUcro2AsLXywibHlclCw2+WwOCpr1MLmODx3+7VYz66NNr1W6+ZnKEofErHclZWsy0FrbpEVOGHzMyklsgj08lvzPY8dpBeaz1cos0LNJGoBXy4AOAflI45GIYz9CPfIB47dW3izwBF4ZsvAPhPXI4rTxzrl/f2NrKyNqMNzY6reyM3nlY9kt7cRhBK6wiUxfMmF2/m9XMMPk/E+IxOJdouEV9/s187Wu7a2TsmdyhKrh4xj3/AMzrbX4oWD2Il8X+G9d8I3sdtNe6g2t6VB9ksYY0aR3nv4fMsowEXcW+0kDocNkD6/CcQ5XjbKjXjd6JN8rb7JSs39xzSoVIbo6LQHste0aw8SeH7mK+s72KG7sru0DyxXMLhWSaKVCUZGUhgwOCOR2r2TI8F8dX+o6Z4u8b6tp00mn3c+veBrA3FoPs7yWreMLuJ4iyY3I6yzq46N5su7O9s/neKjGpxPShJXSnJ281SptP10T+SO6OmHb8v1Z7nrWgWfjTUNLi1GySVLHUP7Rgt5iphlliLzQeaCGz5crJKpAyskUTDla+zzbATzPBzwkKjg5aXWul9Va60a0evX5HLSmqc1Jq5j+CPC1xoumzW+o30F/rWq3E+q6vfw2whW8vTGuWWMMzKipHHBEHeR0hiijLuY9x2wGCpZdhoYWgvdirf5t+ber8yZzdSTkz2BEWNQiKFVRgADAA9K6yR1ABQAUAFADJporeMyzyLGgwCzHAGTigDK8U694T8NaRPrfjLXtM0bTbJDPNfaheJaxQKvV2ldlCAdzkUAeGXPj3wN44/aU+Hs3gHXrbXNOj+H3i6dNS07dcafcK+qaFH+5vFBgndXgkWRY5GZCBvC7lz+f8f16VTL404STlGpG6TV1eM7XXS/S+524KLU7vt/kbfxF/Yy/Z++IloYW8FW/hq5PlAXfh2OKxkCxlyF2BDFz5jbiY9xATJ+RcfS4vh7L8WrcnK+8bL9Lde35I/QuHvGHi7h+pzLEuvHX3azlUV3bW/Mp6cqsuay1svelfzCH4GftjfA6GK9+Efxoi+INhBHAkmg6+pj3rGdiQwCeV1jQK5Ztk8BIQD5iFFeYstzjLVzYSt7Rae7Ly6K7dl6Sj+R9zPjbw542k6XEOWvBTblarR1s5auU3CMXKTasualVs23pds831D9qHx98H9V0dfHf7J3h/w74hvLh43124uJtItSGI8yWJo7K6kcIkq+YI2kbk8ZISvi5UMzw9XkxuLrU09n70vX7cdvK/oaYvwey/O6VTF8E4+lioxV3Tk4qa0dk5RVrzlF8vPGnG32rJs9T+B58K/EPXIJPAXiXw9q6Wk8GopLrnxC1/xRqViYnaK4lttN1ZEa1doJ3hW7R1wbk7llVfKk+hoZdX4giqeLzDnhreEIqLa89na9t4tdtWfk3EPCOecIu+aYKVJOyUn70Lu7SU4uUOayeilfTVH0d4O+HfhnwS11e6ZbS3OraiB/aOsX0nn398Q7uBJKeRGrSSFIU2wxBysSRphR9xhMHQwFJUMNBRiui/Xu+7er6nx0pObvJnTV0kiMwVSzHAAyaAK2lf8guz/wCveP8A9BFAD5rGyuH8yezgkfGNzxgn8zQBQi006bYNMLySCWONnkMXzRk9SQh4/ID8KAMVrTyl331lPDdSbiJIgArM3RCudrDkjC/MQDkdyAeE+C10fxtqOn6f4ensNTg8YaLJ4q8QaheNPc3UujahNjTdNhS5Ie1huY0nLKq7IxZzrsSWdZ0+AwODqcQZtWxOLnJ0qE7QjpyuSf6aeburuysds5KjTUYrVrU9uv8AwbovjGKLT/EDTG90qe31GwvIZGhvLC5TcoaNxyBtJVlwUkjmkjkEkcjK33GKwtHG0ZYfER5oS0af9fc909VqckZOD5o7nCap8HPHNzLcPBbfC4a9d3DmTxRqHhWS9mvoTEYdtxZpLAVk+y7YWlF0yuI8CJEkEcXxy4Kipey+tVFQWqgns999Vvd/Cn531fT9be/Kr9yeb4R+IfD2ip4c8IeAPAgh1O1C67qGjandeFZ5Lgq7PLbJaQTSRlpWLKftIdPMY72Iy6/1WzPDy5sLmE7J+6pXat0T96z0/u2fZD+sU5fFBGTp2r+KvhReQ6LLrHiGygjCWlnp3i++l1PSL+4mjYxJDrzLLdwySXs8MAa9eRiI2jhtcPCwqWYZ9kl54+Cr0lq5Q0klfdqy2V3tbvJai5KNbSDs/Ms6fp9z8TPEMXgj4mQwaZNqKXmu+IPD63Mvl6vbwzra21vEWLpdWARY3uVjeFn32v2m3iS+ltm5ckVHiPNqua1bONNRUIt3tpe7Wjunfo1zN8rfKmVWvQpqmuu57zX6CcQUAQ3NytsqkxvI0jbERAMscE98AcAnk9qAPJPixr/xL03ULix0fUBoVlqtnAPD+sT28UsFnriTjbZ3wBYm1ugYUBVQ4H2tBNHJJbY+cz/FZhlyp4zCLmpxf7yNlfl7rrp1100drcxvRjCd4y36HGpqafETxnqfh+TVtL1e50bVrG78RaiLKO3Y21rcXUthp1oUYTSrb3kGZLiXdC0v22GMrJ50Vn4+UU8RxBj45ziVy04K0IXu02lrLTqnfo2uV25bN61XGhD2Ud3uz00NhvMgZkAwBl/m45yOmO355x6fdnGdJ4Ztr2P94JGS3J+aInKg4PAzznJGcHtg5OQoBB8UR4Hb4fa6PiRfW9l4c+yN9uupnCeQMjZIjdVlV9hjK/MJAhX5sVy432H1ef1l2hbV9v8Ag3263tbU93hl5os3w/8AY0HPEc3uxSvzd011i43U0/dcObm9258P/sa/ATw14++I2s/EYeD9UX4c6VcQ3Xht9eYG9ur6Gb91KJYo0V0TZMZYlby97xI3nGPcPiuH8sp4rESxHI/Yppx5t209HdJaLW62vZO9rn9PeMHHONyLJ6GTvEw/tGpGUaypX9nGnOPvR5ZSk1KV4ezm1z8qnKPs1Plf6ATPtvLdHzscPt9N4H/xO78j14x9+fyIQ6bIIS+lSMd9qAIy3V4v4T+H3T7j3oAv0AFABQAUAFABQAUAFAHl3xl/af8AgB+z7Gn/AAt/4p6L4euZUjmj093a4v5IpGZVlW0gV52jLRuPMCbAVIJGKAOn+GPxS+H/AMZvBlj8Qfhj4ntdf0DUd32e8gDp8ynDI6OFeNwRyjqGHcUAdVQAUAFABQAUAfBH/BVf9pb4+fs36f8ADTUvgn48h8ORa3Nq0GqqbKwu5Lho1tWgKpcxSMFUNPlkAALqGOSlAF7/AIJH/FS/+KfwU8bal4mGhf8ACSQ+MZpL1tJ8O2OkrLDLaW7RySrZwRRyyNItzl2DScfMcbaAPumgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AfEv7HP7UH7UHgb9mnwN4U8DfsM6/430LTrOeOz1+38Ux2sd8puZWLrEbVymGZlxuP3c96AP0Z8B654g8S+CtD8Q+K/CcvhfWdS0+C6v9FluluX0+d0DPAZVADlCSNwAzjoOlAG9QAUARzQJOoR2kABz8kjIfzUg0AQ/wBm2/8Az0uv/AqX/wCKoAP7Nt/+el1/4FS//FUAH9m2/wDz0uv/AAKl/wDiqAD+zbf/AJ6XX/gVL/8AFUAKlhAjBxJc5U5GbmQj8i2DQBZoA+X/APgo3d2th+zd9uvrmK3trfxd4blmmlcIkaLqcBZmY8AAAkk8ACqw8owx+FlJ2SqL8mRiIueCxEYq7cH+aGftIftefAuX4OeKfCnwz+Jfh3x/408V6Vd6D4d8P+FNRg1i9vNQuomhiHlQO22NWcO7NgbEbG5sKcK1F4mDw6+0mvk93fZWWuuhvSqqhNVpbRaf+S+e2h88/Fb4d/E74C+P/wBniLU/2gLL4Q6Xo/wwj8CjxjJ4bg1zS7TX4zE9zA7XeyK1FxGmUnbaxW2dcBS+N3zTxNSSenLFJ7cyi3pbo7Wer19bXyShHDU48uvNJ+nMl+t1pf7tRmk2+t69oX7U/jo/tNXXxot4fhBqWg6vr2neCrHR9EmvI7W5ltoo7q2nK3txDFJNuKRMqx3MYM3CJScoLCTXKk3UhZ9Xa6dtNtUndp6Kya1U1L+3gm27RfXRJtOzV93q1ZNL3rtPR++ePfCnh2Pw1+yBox0i2ks9J8XaNFZxOgYQiDw9fvFtz0KvDEwPYop7V6FenCOf1FGKSUcQkuiVtl9yPMo16ryii3J3k6Lfm7rf1u797s07m/OhftLftA6xa+EW8TS23wv8P3h0OJAzau8Z1ci1wVYM0oURjKt94DB6V4WHtGjjJWv+8j/6aR7da7xWESdvclr2/ebnw58cPjJf+N/2NzI37WPgKSHxDoFv9m+DXgLwZZwDT3WSKZ4JXLzXFtDaRxyyMxWEHyuHII3dGKXMlFu7vFpvbRp9PLRK+9rrdBg58rckuXSS031jJde71k1fS9ujPsLx9H4b1D46ftFw+KNA1rxBoF38HtFj1DT9BVZL+7tHfVRKtsGZQ0pQsVG4ZOMZOBRTXJhsUpp6Vafy9zfXtu/TZ7GTftcVhnDW9OdvP31pp32XqeSfD74uXHhfxz8GPD/ww/ae8N/tC+E/EWv2WlaT4Q1fQrSTxN4U07yHDai11b4mWW3h3ea9xEhKBgAMsR2KVSdeXtfedpNyV1p01210vfWT6XemChFUEqS5UuX3Xr+C2tra2i723+9fiV4C0T4pfD7xJ8N/EiudL8T6Xc6VdNGBvRJo2Qum4EB13blOOGAPauOrD2kHFb9Otn0dvJ6nXSmqc1Jq66ra66r5rQ+L/wBlTX/Ffx9+MngrTfH0P+k/sxeF7vQtcxPKTL4umuJ9NMrlgFnX7DYvMCB8j3h5z06L+2csY01zJJXWt2k6lmvO0Wm3pZ26nO0qSjg4/Zbbta3KtKas1ddWmt2vLXi0+I3/AArv4feI5Gs/BlkNX/ai8RWKeK/F2lDULDwnN5srJqSxll23C7SI5CyKpZtzKuTWEJS5cPFPlvB3f3+6vOX5J6dVs46YipyuVpR0XS/KuZ6bL5atb7PU+H/xJm179uP4TaLf/tTW/wAbr6z0vxWpvdH0Ox03SbbzbK3kWztpLYstzNiBpJN08gQCHhDu3duCly0cXL2bf7tJPT3mqsG4q9tUtddPe33t5+NpSniMLyyVvaNta+6vZ1FzStfRuyXW621RwkPxO0T4S/Cuwl/Z3/aEs/F3hy01e2sLH9nn4heHbW88QpftqC77JDGftiSxTcxApIiMgO9yATnCrU9rScn7T4bNOzSS/m2Tirq7SSS0ukr9LoQ5KkIx5Piumrq930vs3rZO7bbdm3b9ThyASMe1Ymid1di0DCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/8ASyzoA+n6AOV+KXwz8JfGPwBrXwz8dWc11oWvwC3vIoZ2hcqHV1KupBUhlU/hzkcVMo81vJp/c7o0pVZUZc0N7NfJpp/g2eTfD79kaT4cfFTw/wDE6x/aA+JviRtFtdT0+Wy8WX9vqjXFleBGNsLowpcCNJoYJVV5JADHgBd7ltISUea61aS+53/zXzfo8pLmSSbVne19NrWt933d9TsPht8DF8F/Ezxp8YPFHjbUvF3irxc4s4Lm9t4oYtG0aKeWS10y0jQfJGnm/OxJMrqJGAYmiE5Qoqje+zk+7SsnbZW1sl3euukyjz1FUla6TS0tZN3d31bfV9El0PLP2jv2QfjN8bPiL/wmvgb9tP4i/DbTDYQWg0DSPNW0ikQtukT7Pc2+d+4EmQSPuz8+zZHHJZ80/sSfDjxN8JP+Cnfxa+H3jH4mav8AEHV9K+H6faPEerGQ3d75raNKm/zJZW+RJFjGXb5Yx0HAAP08oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAMDx/wCBvDvxM8E658PvFttLcaL4isZtOvoop2hd4ZVKsA6EFTg9qAPyP/bc/ZY1T9n1PDXhPT/Amq2H7MWlX2mX3iHW/DjxX2uX98V8iW51Bp9gjuPnkjh4FohuAVAeRoRmqcYSc4rV2v8AL/LVpbXb6ts7ZY2riKNPC1ptU6fNypJby1baVruTUYym7y5YxWsYRivr39hD9vTQ/wBp3xx4v+E1h4W1DSbHwrYwXfha41LUJLzUb7SIfKtnk1CWRmLXXmNHIzB3z9o2lmaMyy6HEfZOo3H2a0kkEmxiCqsBkg/3sd8DLEegNADXldQtppyIWVBhmB8tF4wMjqccgenpxkAS302KKZrqZzPOx3b3AwpwAdo/h4A9/UmgC2zBFLkEhRnAGTQB89fC6fTra88HeLvHWrzEXOmzarBqT3i3Wi3Gt63deZIkEr5eIwEm1tWk2Zg1BYI2lYuq/A8OY6his4xdau1GrJ8sYtWlyx069WkrpO94ttWsdteDjSilsd/8avj58O/gPoC61411JmubghLLS7Ta95dnuUjJGEXB3OxCjgZ3Mqt9bmOaYfLKfPWer2S3f9d9vnY+l4N4EzjjnFvDZZD3Y/FUldQj6uz1fSKTk97cqbXxreSftJftqeJbdJT/AGB4TstRhnj0yF5IrewibbMst3MBl5/s/luiHMpNxbSLDDbzm4T46Mcx4qneXuULv007fzPpf4Vrs9H+1YnO+D/B6jUy/JaaxmZcvJOpL4Yu2q6pK71pw1a92pU5onufwb0FfhVr/hL4VeIPDtjJqcl94z8YNqGmWg0+BPI1JLWIraKZD5RtNWjVN0rGOOCJSWOCr4ZwdKnneLlQfu0oxp66t7Xbemt4NvTds/As8zvMc8cMRmdaVWpZJOTbaitkr9Nb+bbbu22eu+PfDek+NdKuvCTzb7K9ZJI54djPYXocPBcRO6MsckcqpNGwBKyRhhggV97VpQr05UqivGSaa7p7ngptO6PnTxTceJ4r/VfGU0mm+H/E/gy5iuvErQwW4s5HMbW9n4ojjaRybN7dLi2uoZZUmFrHcLFceZYwPN+dwwkMqqvh7MG5YetrTl1i77dr3s9NLtNq0nbucnUXt4fEtzuvhn48vPC+rHV7u0bStA17VDp+p6DdyRC78LeJJnaSa0meNdjw3UkySRSSPuMtzEIzJFeQJB63D2PrYGv/AGFj/jgvcl0lHp80u3RNPWLvlXgpr20Nnubvxd8KWGk3i+PdRimk0XxfdaX4a8R6XZ3TRhjdXK21hqEDoVeO7iubiBWmieNhDl8u9vbhd+K8udbDf2jQfLWo+8peS1affur3102bFhp2lyS2ZZl8Uan4q+Gfh3QP+EnbVNTvvFx8O3Wr2tik8U66VfzSXgvFMUccTTwabPbyGOMRpcT7Yvl2PUZlm8pcOSxtWPLKpC1ttZaXWr6PmXW2/UKdL9/yLozz7w34ot9Z0rSvFNlqem69dXfiLUoPB+ph47q3vL/Uri5liuCSR5sWn6U3ll4LvlE1CAIrxRrXg16M8dTwnDlD3UoxlV6W0Tt63bdmt3F9zaLUHKu/l/X9dTrvEfhjwx4Qu/AOir4Zt9T0WV7vwZfXOpFbhYrC+tHlZriWVGac3V1Y2kTeYy+dJOrMXeQK3u8ZYRvKOah7rpOMlbpb3dLbWTv5WMcLL97Z9Tr/AIaz+IrvwJoeq+KtsmsLplt/wkBiyii+CKt1sBWPDGQS4wiYTAwCcD6nD144mjCtDaSTXo1c55JxdmbepzLdiW9t4kjjEIj2AL9zIC5x0I4799uPlathHNaj8PfBd/r8XirUvBOgXetaepMOqrp8T3MO3aoIlYbl+e3ibII5VepCgZVqNLER5KsVJdmrr8RpuLujItPh9Jodpb6Z4X+KHjfSLa2kkmkjuNRXV5JmxL8kk+px3M2CTGQEkUAquMDcD4OK4SyfFtylRUX/AHbx/BafgbRxNWPUtfDv4Nabpl7qep6/ovgprya9W9S/8J6HJoc8sUUkXkxTulxI9wM2o35dY5EEcbRldwM4Dh6WWYiNaniakqcU/clK6u769FbXa2+t+g5VlUXK4pPufOn7Pnh74t/tx/D+P9oDxb+1l458Hi81G8XRvCngG7t9Ot9AMNw8awX52O96zKkUmybb8sn91xj6aEZUqcZXvNq92vd+S8u979N1d4uac5QtZJ7ddO76fl18l634E0z4gaP4C+Itj+3PonhnxbofgWK8lsfHEuk2rrrvh1ohcXBlsIfMeF4/s6iSMIBJtjCrIU8x5SjJKo42nqn6LZ389Xbp+UWcZcqd1p9/VfLv1Ohu/D/watPhrN8fvB/xC8VeD/CcvhY61HqOj3dy9lb6S1uLgXEOk3Uc1tG+wLJlbQSk7gQd7q3i4/IcqxUb4mhHdapWd3otY2fXvb7jalWqP4H3/Dfc5PxV8bvEQ/aC/ZW8M/D7xxcX3gT4naP4h1DUHmsYUfWYINJiubKd90KvC2XEhVBFyxDKANo9rDYFZfz4K1lSikl2afLv1/pkVJc0FU6t/wCRr+ONPtPiPF4t8N+FvC9roF94W+InhqG9nmRIjqEVrqGma1NIDECfnW7uNob70kjM23zGI/N5YWeG4wpc8rqfNNLteEo/f7v5djtUlLCu3TT8T2bTktm1W0ktYZIYsyOiO4Y8xR898DDDjJ/DpX6ScAzQ7yxWKC1luFZ2uUYLggZ8oAZHfDcfUA0AdRQAUAZXiHxPo3hazF7rFxMoY4jhtraW6uJjkA+XBCrSyYyCdqnAyxwASMMRiaGEh7TETUY920l97HGLk7RVx2h+JdC8SRzPoupw3L2piS6g5Se0kkhSZI54mxJDIYpY38uRVYB1JAzVUK9LEwVWjJSi+qaa+9A04uzNOtRCMqupR1DKwwQRkEelAHjeh2VknxG1q2sLW2hv4fEp0m5v3sJr+4ntW0iO8SIyRrGliiF4lBbfG3lAEtPc7h+aZzl0c24kWCxNSTpyhdJNR5bJ7Jp817N6K/vX2izvpT9nQ54rW5znxJ026+HXxO03x3DqWueMdS0nwH40vbPRDFAby8UT6JILW1W3iTd80CouUdy1wAWOFWs+JchwWV4KjhsMuVVKkVKcm3spJN30VuZvS2w6FadSblLWy2PoGyvbPUrODUdOu4bq0uolngngkDxyxsAVdWHDKQQQRwQa/TzzyegCpquk6Vr2nXGj65plpqNhdoY7i1u4VmhmQ9VdGBVh7EVE6cKsXCaun0eqOjC4uvga0cThZuFSLupRbUk+6as0/Q+fvHn7B3wJ8VPc6n4a02+8HaxJ5s0F1pFy4hiuWYOknkOSiqjDiOLyxtZgNpClfAxXDGBr3lSThLXVPS/e3l2Vvyt+t5H45cVZWoUMbOOJoqycakVdxSaa50k25J6ynzu6Td/eUvEfCuu/tq/Dl/Cul+D/ABjY/EGHWE1K3k07WZoHFpqllL5d1p7XU0qPNNGyT7FinbctrcsF2Rbj8zgMZnFHGV8FRlzypv4ZvVp7SV3dK1tFLTmW92fTSzrwt4vTnm2DlgKklfnpXcdLaJQi43bb1dGzUbuSbseleHf2/vDul6ufDHxz+GXiT4f6qrhWaSB7iFE2bt8iMiTICcYCxyZDA5xzXvUuKacJ+yx1KVOX3r57P7kzjzDwHxmJw/17hbHUsZS8motu9rJpyg7dW5Q2atc+gfCnxT+HfxI0q6vPAHjbRdb8mAyypbXatJADuCmWPO+MEqfvAZwa+gw2Nw+MV6E1L0evzW6+Z+P53wznPDk1DNsNOld2TlFpNrfll8MrXV7NlHRvi/4b1JNP1G5dbDRNVhto7LUrpmiX7fJOYGsZ0dVNtOJTDEqSEO0rtFtWRdrebg8/wuMxtXAaxqQbVpac1use/fvbX08idGUYKfRne17hiUNZnhS0NtLdJB9pzHuY9F/j/wDHcge5HrQB5n42Fjq0X/CAfbYLeXxXFOLqzkyxGmoyfbXEayxMFCSrbCUbxHNPEzIw+U+FxHmv9j5fOvH437sf8T+T2V3rva3U2oU/azS6E3ga6muzdeJ/EenXdudbnMtvaXVuI57eyKqtshVoYpoyUUXDwygywy3MqbiFCqcN5V/Y+XQw8l7796X+J/NrRWWmjtfqFep7Wbl0Ozgv7a0vxez6ik1q1uUik6uMMp2sBySAR1GfWvdMS8LqTVGiudHu4tkJdZBJG2CSOPQ/r3/CgDRiMxB85EU542sTx+IFAFXVbD+0rOSzdLeaGVGjlt7mESwzKwwVdT2/TrkGgDxXUvCE/gLxRoWg2erW8Nk11/angy1llaGK01KGOf7ZpglZm3QTWc0vkxeVIYEju3QBYYEi+Ez2j/q/joZ3hYe7J8tVLqnbW3Rt7u6Tly33d+yi/bQdGT9D2vSNVsNd0qz1vS5jNZ38EdzbyFGQtG6hlJVgGU4I4IBHQgGvuYyjOKlF3TOPYmSctdSWxTHlojhs9dxYdP8AgP61QCQu0txOGYFYmVFXbyDtBJz7hh+VAGNILXXnuNE1b7Pf6dOJInhkiV0nBzmKUZIZdvbGGAOehFAHjnjHw5q/g7xbZ6Pp0Md9dSHUdY8Gahf3siBp93m3egXLnJMUiOZLcKsqrHbMxg/0CMzfBZph3wxjY5thNKM2o1IJaLtJJaffb3na9pO3ZTl9Yh7OW62Z3Oiavo3inSdM13w9cDULPWLeK8sZ7dgUngkQNHKhHBVgchsnrk9CX+9OM7eFrLwzoMl1q9/b2lrYwyXN3czyLHDCigu7szYCqoySTgADJxUznGEXKTsluzWhQq4qrGhQi5Tk0oxSbbbdkklq23oktWz5z1XXdb/ao8YxeFNJ0rStd+BWp6c63+qWl8Y7tL+MLKm8YEkE6SGErCVIKOXYnJRPnalSec1fZQSlhpLVp6p7+qadtO2r7L9mwmCw3hpl7zDEVKlDPKVT3KcoJwlTd4u28alOUee9RNNTXLFKylL6K8LeFvD/AIJ8O6f4T8LaXDp2k6XAtva20QO1EHqTyzE5JYkliSSSSTXv0KNPDU1SpK0Vsj8jzTM8XnOMqY/H1HOrUd5SfV/kktklZJWSSSRZ1ItFHFeBdy2snmuO+zaysR9AxP4VqcAuoq6xJdQxNLJbOJAi9WHRh7/KSQPUCgCxFLHPGs0Lh0cZVh0IoAfQAUAFABQAUAFAHk/x8+Omk/Cmws/COjazoC/Ezxpa30HgPRtameC11fVIYgY4HmACR7neJFV5IvMd0jVwzAgA/CfSvhD+0x+19+0N4y0G9sLzVviWq6jquvpq0wtmgktRtNu2/wCWL5xDaxR/LGhaJcxxqWUA+1v2bv2yvH3gLw3B8adc8H2PgL9mTwFo8Pg7SvDFlDE9/rmqySR+ZcWZkeE3N35olnmdmKJCJwFkm8yYgH6deAfHvg/4o+DdI+IPgDXrbWvD+u2y3dhe25O2RDwQQQGR1YMrIwDIysrAMpAAHeOPHfg34aeFr/xv4/8AEun6BoWlx+Zd399OIoowSAoyerMxCqoyzMQqgkgUActp37RfwU1T4sv8DLT4gWA8dLZRaimjzRywyTwSQiZTC7oI5m8o+YUjZmChmIAViADV134xfDLw18R/Dvwh1rxlYW/jPxXFPcaTooLSXM8MMUsrysqA+VHtgmw8m1WMbKpLDFAH5lfGP/gr3rmueGPiZ4X+Gk6eGdf0zxCn/CC+IbGyS6g1LR47hY3FxFdI4Sd0DTAlAmxih2PGDMAcdr/iu8/bk/aV8O/Bj9rD4hWWiQaz4Csb/wABz+HtPWztNL13VtP07UI1lWeR3n3JugKPKfMcIIvJaQbQD9F/2H/2W3/ZH+Csnw1vfEa63qmoa3ea1qF3ENsDSSBIYhCpUMi/Z7eAsrFj5hkwxXaAAfQNABQB5F+19/yal8Yv+xF1z/0hloA4n/gnD/yZN8LP+wbc/wDpbcUAfSdABQAUAFABQAUAFABQAUAFAFTVNJ0rW7NtP1rTLS/tXILQXUKyxsQcglWBBwaVkwuVNH8J+FfDssk/h/wzpWmSSrtkezso4WdeuCUAyKq7tYDQvLKz1G1lsdQtIbm2nUpLDNGHR1PUMp4I9jUtJ7jTad0QxaNo8OlHQ4dKs49NMTQGzWBRAY2BDJ5eNu0gnIxjk03ruIfJp2nyi1EthbuLJxJbBolPkMFKhk4+U7WYZHYkd6fM3Lm69/Xf7xcqty20HJY2Ud5LqEdnAt1OixyzrGBI6LkqrN1IG5sA9Mn1pLS9uo3ra/QqL4a8OK1+6+H9NDaqCt+RaR5uwQQRLx+8BBP3s9aOlhWV79SxbaVpdlObmz021gmMKW5kihVWMSZ2JkDO1cnA6DJxTbbu++r82FkreRHZ6FomnXt1qWn6NY2t3endc3ENuiSTn1dgMt+NF3a3QfmXqQFa003TrCS4msbC2t5LuTzrh4olQzSYxvcgfM2ABk88ULRWQPV3e5ENC0MWd3p40axFrfvJLdQfZ08u4d/vtIuMOW7k5z3o6WDbUbb+HtAtBYi10PT4Rpm8WPl2yL9l3jDeVgfJkEg7cZFFtbgtFZDhoWiLqx19dGsRqbJ5RvRbp55Tpt8zG7Htmmm0rINy9SAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/8ASyzoA+n6AMPxv4O0j4geE9T8Ga9LfxWGrQGCaSwvZbO4QZBDRzRMrowIBBB7YOQSDM488bXt/X9eXcunN05cy/Hz0/rqt1qfJ/wY/YVs7PTtd8OfGXV/iLf3uj6xPDpHiK3+IepImuaY+JIJngiuR5E6BjDIhRVLRB0LBuHC7pxc7c2qdr9Ovkn2u2nfXYzelSSj8O6+fTzt3srq2m57x8Kv2bfh38HPEFz4l8I6h4vnu7qzaxkXWPFOoanCI2dHJWK5ldFbMa/MBkDIzgmtFNqDh0bT+6/Xfr89L7Idup6pUAfAPwb/AOUyHx2/7J/Z/wDojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAON+M3w1svjH8JvF/wr1C9+xQ+KtGu9KF35Rl+yySxlUm2Bl37HKvt3ANtwSAaAPxy/Yf8Ahl8UP2UvjlpXx6+PszfCTwPpBvtJ1M+JbiWwuNcMkXlC1tbNQZr1Vmlt5ywQwqIPMLgxip5ve5bf1/XY2dG1L2rktXor3fW7stkrdbN3Vk1dr9sxL9o1A7SrQ2yMGJHSQ46HPYZB44yRnqBRiM0JGXTIXdNhlzJjduwpPyjJ/wBnaPwoAv0Acx40+IWh+CRBaXMN5qes38U0unaLpsay318Itu8orMqRxq0kSNNK8cKNNEHkTeueTG4/DZdSdfFTUY+f5Jbt+S1KhCVR8sVdnyJ8bfjj4I03xdreifs4R+brYtLrUPE9/DcQDww0IhfzJrpJFeFpAXQNcDyRuECSzPGpiHwvEuNyzHTjGlT56ulpR73ulprL06X01P27hbwvlgsHDP8AjCr9WwfMl7OXu1J69nZxVrysr1JRjJxivdkHwe/ZC8W/ETXn+KHxt1bVReX8iXU11crJb6jdggELbRtsfTo1j2x75I1ukLMII7FraG4m9DLuG6uJq/XM2d5N35d//Auj/wAK0srO6dljxj4szxODXD/CdP6rl8Y8tkrTmt3dptpN72fNO8nOT5ml9lWWneFvAHhp7bStMstF0TSoZrkwWdsI4oUy0srhEHUku5wMsxJ5Jr7SMVFKMVZI/FTwr4F3Gs+AZfDHwt13RrjUtQ8GfDTw7b3FxYQL9kM0vnwT+RPLsLfPp0ZKNtO0xsBndj814ezrL8DPFY7Ez5FWqOys27LXVJO3x/fc769Kc1GEVsv6/I9Ui8VWVgZNJ1Tw1rUVhclmhZbQXIQYBYP5Bdl5PykjPvxX08OL8lntXXzUl+aOd4aquh5jqnjPRviJPYax8NbPUovHXhKK5m0y28ReHL7Qn1rTtqfarNRfwwTNG/8Ao+54g6RTJayyLIEWJ9Mxw+A4mwk8JTqxk1qnFqXK+j0+afdXs1uKEp4eSk0eVm50vRbqzv8Aw14fl1r4c+KtNn0VrCPTGimn0uJ7g3Gim0ytzHe6av26SCKGIFrWK6tGgM8EUx+TjCtneH+pV7wx+G1i76ytazu9NdNb72lezaOm6pS51rCR203xN8UeHfDsDX2tHX7LwWlx4yXU7bUbe1Xxb4Wj0+fdcwhAYLiWNri3MkYNvEZ0hmBginhSvXliJ8W5NUw0GoVlZTi09Gnf1SbXnbVatGfKsNVUnqugmkaT458YWXiPQfCFp41TWI0vrK4m8QeJNNsl8K/2gyySwQHS1nEt+sX2e8hM6SMsdygNzF5jxnycBl+Y8RYejGrUgsNSataMrTcEle0uVtauL1SunZdTSc4UG7J8z/A9H8E+DbnwZcHUXstHt9MtrA6boehaTp5srHSomCvcL5asVmkeRMlyiBI0VEVGMzTfW5LkNPKOepKbqVZ/FOW78utvPV3fyS5qtZ1bK1kuht+NPCQ8ZeAh4Z025t4NS+0W2radPtdkgu7S4juLeadEkRpEE0Ue+LzE3ruTcBkj1sXhoYyhPD1Phmmn81bTz7GcZODUl0OA+G3iPbqMhun03RvDmsam0euaW1x5baD4neSNXtQzBBJBeySLNG3lxNJLMswE329PK+R4dx1XLcQ8hx1+aP8ADl/NHV+ey21a3jo469NeCqL20Pmej6ibOPUnfT4x9mV9w3HMTugyQB3HOMZ78cEV9scgfLA8beUAUI8x4JCu9V7Y4wcxFs+pz1wAAM80xQmLzl/2oZE2hgDk8j/rlGODyT160AXo9U/4R3QNZ8SDSLzVH0qwmvFs9LjV7m+WND+7iRmUNKxhIUFgC0nJGc1nVqKjCVSWyTemr08lqy6VP2s1BNK7S1217+R8weGf2Wv2Tf2s9H039qP4L6h41+G+r+K2a+m1jwbrJ0i/FwCUminiXzYI5Q6t5hjUF2y+5t25tZUXQtGLsrJ6barb5XtZbNWM41fa3clrd7777/PfXozjdI8YfF3SvhZ+19+zH8TfiO3xIj+FvgCZtK8UTWvlXs0V7olzJ9muiGbzJE2DDMWkY+YWY5VVFUVaEqjsrO3k/Nemz7P723BqrGnFN8yv57rT5308vuO/1fXtG03/AIJPR6hfanbQ2z/BK2sFlaQbTcS6SlvHFn+8ZnWMD+8cda58fGUYJNPeHT+9F39La37a7DwTUr2f8/8A7ccD4a/5Kr/wTs/7J7rP/qMWlexjv+RljPV/+lsl/wACHr/ke7aK8958XPiDp9iZ9v8AwtKyS5jRyvnRp4R0ydl6jI+UEA/xKPQGvzrFJPiyg30pP85nZH/dn6/5HqEUsMciSRzzlgkYBSRsqcRBunIH3x+AHavtDkEsJpkvIrbMbJO0KOyE8AoU4PYgOc+hFAHeUAcv4z8cW3hl7XRdOhi1LxJqqu2m6UJ1jeREKrLcyE5MdtEZI/Mlwcb0RQ8kkcb+bmuaUMnwzxOIemyXVvokaU6cqsuWJ5zomn69r63Wp2ni6ZvPn8vUtajiK3Gp3CqVFvZQyLItvZpuJTaWy245Znkml+Ry/Ja3EdX+085XuP4Kd2rLo3az/V7vSyOmdVUF7Olv1Zg6rqXiL4e6xaN4h1sT3cryL4d8TX4S2iaR5U/4kmqzRw+XEksrpHDL5e5/kXabhI5Lp4vLK/C2J/tDLIt0Hb2kFd2XWSu/ne+j/ut2I1FiI8lTfozuk+Nd5pEcUHjHwXKl/qTGPRU8PXo1O21WYJIxtkuJEt1hnAickTiOLaVKysRIsfr4TjDLMVTnUlJw5NWpLW2mqSvfV27+VjKWFqRaW9zg/EvxP+Ies+INQ8Mad4juLDxFoNsuuNofh2yhmt9Us18tL3To768UfaL2Hcsisgs1Q31kJQyFmPg4zi3HzpRxNCmqdCbcVOV279JPdKL6pKTspWd7G0cNBPlbu10Ou+H3hy/8QzS+MNN0CTw1pPi+7tPE97eL4iW61O9u4VtoYQVgM1qLeW1srdS0VyweL5SgaR2X08u4fzH+0KeZ5pXU5wTSUVpZqS3tH+Zv4X6mdSvDkdOmrJnafELwRH4utdN1C0keDW/Dl6NU0idJAmJgjRvE+VYGKWKSSJwVJCvuUq6o6/SZpl9LNcJPCVdpLfs+j+T+/YwpzdOSkjgvgr490xZbbQ4nurTRPEc922g22p3ifaNM1GAuNQ0IpyN0Lw3EsSxzTL5aXSRiO3tYS/hcM4+sufKcc/31Hr/NHo13tpuk7NX1ubYiC0qQ2f5ntNfWHMFABQB5p43+FfiTVJtVvPAni7SNMfXJrea+sPEWgtrWmmSNSrSxwJcW0iSOFtwf3xiHk7hEJJJJG+dzbhjBZvWWJqOUZpWvFpN+t0/v3tptY3p4idJcq1R5Z4z+B/hzQNJj1T4leDPB051BHh13xj4U0JNJexcBktZrq0me4FxZxq43GeSeKORIpXh8oPJB89j+H8flFJ18vrSqwjq6c1zJrd6bN37KLtezvv6WX5tiMDXVfC1JUqnScJOMl800/wATk2/Yu8AeJJx4m+F2szeDfFvhzUvs97Dpl6L2CyvECSoTHMQy74JIJRGXGFnXIcGu7A5Rgs2w1PMcC3Sb1XWzTs9/NPqvTofq+A8cM9oUamXZ9ShjKE04yUlySaa25oq3XW8G+zT1OLi+Fn7a3wY1S71zwxcQfEfRNQSUalbXEouVvwFZd9xDIyyvKQ5Akid2dI4kdmSONB5ObcOZhVs6i9rbaSdprW+71flfmt0sfQLHeFvGsZSqRlluIdtl+7bs0tIp0+VaOWlJt9d2dx8DP2oLDxZc2vhz4bJFpeo26yJJ4B8Q3LhZURjI76VqADeXsSRwLWRCm21RI47SJHmaMs4pxWWRUMw/e09PeXxx6WkvtK63vfXVttRX57xv4Y5vwhVc6kOfDt+7VirwktLXtfkk7r3ZaN3UXJLmPftC+IWl/EGwbVLC2u9OntzFaX2latCI7nTbghZHguolLbHGUYEFo5EUPG8kbozfo+GxVHGUlWw8lKL2a/r710PzKUXF2kcM0sPjtLfTVebVfDfjlvNb7JhrVfDcEeTmVvPtZ/tskgTK+RJLa3ZMeHtS4+Lt/rDxBqr0cL+M39z0a817n946/wCBQ85fl/X5nqniCfThP9qt3SWWQESIeqsMBcjrxt5VsjjoCc192cZk2y3DBrvykcJgeY/Tco3/AInCEfjQB03h6TNxcFny0yBh77WYE/qvOWzzycEAA3aAOe8WeO9B8Fm1XWoNalN5vMY03Qr7Usbdud/2WGTy/vDG/G7BxnacceIzHB4OShiKsYN62lJJ2+bKjCUtYq5w+vfEPQfiR4Vu4PCNh4rtdVt7rbplzqXhDVLH7Nfx7THK0dylq0kGW2vtkVJIzLGXALV4ma51keIoTwmJrxcZLXlfN81ZNXTV1520NqdKtGSlFHJeBPiY3wwguNY+Kfh1fAOg+JSNUns7i7tZbTw1qrBmvFvL2KUW8EF1IDLFIVCvN9oaWRZrqK3Hl8IZzRcXlk6vO4fBJ3XNHsk9bx7dForqN3piqT/iWtfc98e2gvVjuwXikKDbJG+GAPIBIJDD2ORX3ZxkU9rdxR7re/uWdpU3fLGcgsoJ+72XP5UAcvqvjPwnY67c+G3vdYvtXs4RNL9j0i4uooGGx0hkmhi8hJcPG4id1k2SB8bG3V5uOzfA5a0sVVUW+nXrrZa20etrGkKU6nwo85+J3ju+1TTfD2r+J9J8OeAbLRtZsNUl1LxX4jtbe909luTDKIo4lmgYXFtJLbKxuUbF26kAjDfH5xxPgM3w1XL8LTnVlJWVo6X3T/m0tf4eh00sPOlJTk0jm/BNp8QW8O3Wh/2n478W3Nxd38U5g0C48G2ttaX8jyQ+Zc37fbEhtSHiRtPlaWKFogYZGRHO+HfEeIpQwtCnGhCCS5pNSk7adrba/CtV8S2E1QTcm+ZvscJ4L1/4j/HLWdP8K/CLxXEnhbwtrL6b400bX9Sn8QW9zCz3IN0l/cM0uoWdxErokJeMqwjbYqnzI4y2eb42tKi8RGrCErN23i073SSTi9Ukm7vXRWZ+14XIMq4AyieL4pwtSGLxFNTw0oTtKE4yi1G2kqNWLcJym1JKF4K01KE/r/wT4I8K/DnwzZ+DvBejx6Zo9gHFvbI7vt3OXYlnJZiWYkkknmvt8NhqWEpKjRVorofjmdZ1j+IcbPMczqOpWna8mkr2SS0SSVkkrJI3a3PLIbuD7Vay228p5qFNwGcZFAEdndtKTb3Mfl3MY+ZcfK/TLJ6rk/Ud6AG24Nvez2x+5N+/i68dA4/Mg/8AAzQBcoAKACgAoAKACgD8ev26/wBqX4V/FD4p+Ofhl8Rf2SL7xQfCWrPoWheL9N1VtF1m2itgEnQyC3uknj+1fa2jDjy/LlQmJZNzEA/Tn9nz4cX/AIB+GfhxvGbtqfj670HTLfxTr14kEmo6hcwQ4WO5uo4o2uRAZJI43kBcqMuzOzuwB8gf8FPPgHrscng39pTwd4Ju/Htn4AeaPUPBdyDPo1tbsss3242MSBnTzxGbo7/njjjDAIrOoB+fvwO/4KNftV/AjT9K8OaF47j1/wANaREtvbaJ4gtVu4I4FAVIkl+W4REUBURZQqgAAYAFAHvut+IfGn/BQX4t+M/2avit4+0hPEUaN4o+GF14e1OLU9C0mc2wnm0i6uLKLy7tDBIkb3Zy0M9o/llxK0UoB4T+1X+zh+2z4A8Rx/GH46aBq+o3JsdOuJvE2klJ7bTjFDDHFFLLaqEtpISI4t2FVpFLI8m4OwBzvwk1v9oj9of41Wd74P8AH3ibWfjDqGnvoml3kwhYf2d/Z8lrcvNezSqYGW0Mv7wI8jOxcMJmDEAu/sofBvXV/bK8GfBr4p/B2TVJry7kstd8M66jWjxWNxYyNLcOGwyPDbyG6jxhi0ce0hipAB+8ng/4I/B/wDfWGr+D/hn4b0zVdM0q20S21WLTojqAsIIY4IYGu2BndFiiiQbnPCLnOKAO3oAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP/BOH/kyb4Wf9g25/wDS24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gDjPjN/wscfCrxV/wAKi8r/AITH+y5/7H3iMn7Rt48vzf3Xm4z5fm/u/M2b/k3VE76W2ur9+W65refLe3mXT5bvm7O3rZ2v5XtfyucX+z9P44uNX8VSX58dHwUY9O/sD/hNokj1T7Ztm+37QQJvs+fs2zzhnzPP8v8Ac+VWqVqdnb4na178to2vfz5rfat8XQxSalbXZXvbfW9rabW20vsez1JYUAfAPwb/AOUyHx2/7J/Z/wDojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD8oP+Cg/wW+EXwg/aUX4+fFTRfEvxJtviDaiPw34Hgvp1W41iDyop1urvLSw2aRyQyRQQ7meWRowIoY8PMpRgnKTskaUaNTEVI0aMXKUnZJK7bfRJatn3d+yx8Q/FXxF+GOlaf8AGnwn4U8KfEfT7VZ9T8J6ZJEJNMsZWJsXezMsktoHgCDy5DuDKwwv3Fad1cmcXCTi+nZpr71o/VaHuNMk8o+Ovxlb4W6Wr/atP0WJgrTa5q8RmtYRziGC1jdZry5fAxGpRETzJHkzGIpfnM94ipZPalGPPVauo7K2qu30Wj83+J9Dw3wvmnFeMWCyuk5z622S7yb0S820r2S1aR8Mvq/xL/aY1nUPB3wl0bW7bSLiFLrXtd1S4Av9dxM6RSaleKoiii3TTMlpHiGNUuBbxP5SQr8Rh8BmXEmI+sVnfXd/BC/SK+XTV6czV7n9CUMJwv4IUPrGPccXmkl7sI2tT0TvrrHXabSnJO0IJc59DeEv2QdM8HWa3+geJYZ9S022aOPTtW0a2v8ARA5Y7ri4tx5VxNciJiizNcBlLyiJYYpJLevsI8JYNYf2TlJSe8k7N+S3tHy9G7tJn4NxPxrm/F+OeNzOpzb8sNeSCfSMbu2yu93u22ev/Cvw94m0maGHTrCw8OaVpl9cWV5pNnKs2l31oYA1vd2EQw9k24RboSERXN4pSc+VdNrk+BzbLsRKjiKyq0Ojk3zp/jfs7vs1bVHzdWdOcU4qz/A9RvbaG9s7izuII54p4nieKRQyurAgqQeCCDgg19Kc5+UP7Rf/AAUV+D/h/wCJmrW/hDT/ABP8Q77T4/7GOraH4tvPDOjvHDNPJG0X2OR5b5lM/ltK7CNjEzwBFlJbwcLw1luG5/3SkpScrSUWo36RVrJf1c2liKkranW6F+0p4C+z/AHwl4y0X4h6t4m+M+mi+1CPw98RPEVudKMt01vZtGkuogPHJLHKHJnQxpEzHeCFrp/sXK72+rU//AI/5FXq8impbtq19dLdN7O+j6622Z9OXviTwv8AEXVfE+g/DPxppPxJh8N350/xD4VGq/Z9T0u8g3Hz9OuSVlhuQwYIzyiNpYgY7m2eOTf4WM4RoR5cRlEvY1Y7NNtPyd7/AKq17pmnt5xbp11fuuqPP5vEy6LHq+saR8d/h14ZXWrv7deaT8U/BM+k3QjRMR3DwzXVm8kyr+4a5MRSRLaFBh4JGk+ZzaGNxtSm8wwVT2sY2c6T+LXraEkurtfrtZq21PkgnyTVuz/4cg0b4i/BNbm2+F2ifHz4Y21rqWoRXHgtNA1+yvLTSL0IRJpktosqs1hcAMFhzg+dPEktu/2IDzY08yw/Ni1RqJ8rjU5oyTlF6c17P3l1flGTUvfLvTl7t15eX9f10Pqr4P6NPp/hy/1jUp7e41bxDrN9qeoTQQvEN3mmCCFlbGWgtYLa2ZwFDm3L4y5r9S4fw9PC5XQp0tuVP5y95/i2efWk5VG2beu6Uyk6nZlhsO+aJf4uQSw7Z4GexwPTn2DIqTzQ6bZtaWyQyNKAbqVcqhBHABHVcFcnqQ395s0AeffEDwWuoCbxVpel22p39xaLZavp0scLQa/pTOwlsp45Pkkwssxi3MqpLKy7hE9wsniZ7k0M4w/Kny1Y6wls4y9VrZ2V/vWqRrRqulLye5T+H+s2VtDbaXY6tc63pM8b/wBj6jqAErjyHZZ9PuZixdryCSGSNvNVZWUFXMksU71ycPZzVxvNgccuXEU/iXdae921v00ejWjSLr0lC04fCzup4xbsjrbQsYyAzRSkZ2/KSMYwT5TnOD9/1xX0xzkcbeX5bNKhaIGQRvGArNyGByBwVQeuSfUmgC7pV6+lTZS1WaPD73iOcrkcn3xE7AZ/iOKAPnzxL+x1+zXc+NtX1TwN8SPHfwe1vUZba81+y8E+LJNGjvjdzyQ24ngIeNVlmimjQRBAzq4XJrgq43DYGdPCVJWdRvljZu9t9k7Wv10S2skaSUq8nVau1u+v9fm9Xqen+Afhr+z/APs2fDfV/DvhTw1Z6P4TaG41HxBeX8pmFypi3TTXc9wxMg8vcW3thV4AA4HoVH7Wn7Ka93XTprv9/wCVl0RlTTpz9pF+9366bf1316njzfsQfseeDtH1a313xH4u1PwL/advotv4OvvEl3e6LoGqam0VrCYLNMvFcu2oQlZJi5jM6SgpgOPOw+bYPE4R42nLmpxT95p6JK7smvyW/mXKjJT5bWbtdLq/P+vwPbtK+DXwXOpfDTxLpun3LXPwdsrzQfCkn2mfFpC0EdhMjqT++OxETc+eRuB712UMxhmaeOpS5lV1va19b7O1tfIUoOK9nLocl4ukXVPHfgnxF4WgbS4rv4i3k+tCLEEuoLB4b1G1Jl8s/vT+4t8bz92GPoUVR8XhG6nFtfn15aatfp8G3bd7d33OqWmGVu/+Z2fie1nbRmtIL/XNJnZUia+0e1imuYHE5YsFljkjYbUYMSjYUNj5gCPqMzqYylhpSwEFKppZPbfXqunmtTnpqDl770ODv/EPjnw3MbHS/C3xk8c2czKyX0S+HLJIZI1SFohFeSWcwOUEm4xFT53yOY9qr8xGPFuLXtG6dLpy7/Paf59Njo/2aOmr/r5GtYePvHUtulzqHwa+K7T3jGRUuNb0FHVTkD5LfUwgX5SeBxnkjpUywPF0npiaa+X+dManhv5X/XzHWXwW1rU/E3im31GxtNI8M67O02sNaa/eXOr+IJHhMYjuruVA9lbxRbdsNtI7ZdNksCRyRz9WC4aq1q8cZnNX21SPwraMdb7aX+5LunpaJ10ly0lZHqF1ZXWnr9vmSCKFAsStaY3QQ4wFjBAA6gbuoHTivrzmKPj2+tvBXw68V+ObvRLXUJNG0O91H7BJgRSLBA0ghY4Od2wAtg9emOKAPM9C+FOn6ZrWo+FdL8aa/p+i3McGo6/La30n9oajfs3zESg7LGNooj5iWkcLYkiMLW6xYf8AOOHMDDiZyzXNPfkpNKNko2ST1/mSvZJ+d7307q83h/3dPQ9K8b/D+3u/C+kweC9J0+z1LwfLHe+G7ZNtraxPHE8P2b5UdYopLeSWAlY28tZNyKWRa+2zTLqeaYOeDqaKS0fZrVPps+nXY5KdR05KSOK8A+PrbwqBaWhvtS8KXfiCTT3WaCeLUfC13ctEYbS5tpv3ptnmmYqwC+RFcWm2M2uZ4fmeH86rYapDJc1g4VUrRb2ktbeWysnd3aabUtH0VqSknVpu6Pba+3OQ8o8aeAdT0rxNeeKvDGlT6np3iG6srnWtMtXiingv7ZkEGp25baskgEVus0crbWjtoig3I8Vx8tn+SV8XVp5hl8uXEU9r7SXbt1dujvZ910UaqinCfwsd8KPifqPi3VRYXOt2Ou6fqI1WfS7yDSLjTLi2XT7uKzuLW8hndi9ws7sGkVIVJRh5SYBbXJM7xGPr1MFjaXs6tNK9ndO/5fe/UVWlGCU4O6Z6rX0hgFABQBkeL/DGl+NvCeteDNcSVtO17TrnTLtYZmikMM8bRvtdSGRtrHDAgg8jmk0mrMDzv4Va/Ya8L3W9GtLKPTfG2l6L4kWVFlW5u7i6gMEsjK5IVVtbayCgAFSGLZyK+K4InOlhq2AqfFSm100T6fepfedeLSclNdUetAAAAAADgAV9sch85/tGfsY+DvjNPc+NPC923hvxx/r0vlZjbXk6Kgi89RkoR5agSR4K7ixWQgCvnc24eo5g3WpPlqd+j9f819zP1/gPxaxnDFFZPmtNYnAP3XTkk3GLbcuW+kr3u4TunZJOCuz5xl+OXjTw14m0z4ZftQQaj4P8RWN5CIfHVpbMs7acbtTNbTrBsEtncRxzQC4j3JFJ5M7JvgM8f5/h6WOyDETWHvCbT9xu0ZaNKSbum0/eX81nFNX5X9Jxl4cZdmeAXEnBUvb4Vv34RvKdNuzsopc1kmuaMvfhpJ3i24fTfg7xZo0/iq91/V1+zX3jF4YvDuptcWtxp2sabGJWs4LG6gA8zdFJLdeTNmQvcXOzzoI98f1/BdXBUcH9SpStWTbnFpqXNs9H0WkdO2qTZ+DYpTcud7dD0ybS9VitUmhtfOjdM5jJJyeM7TzgjAxjoBnHIr7M5R+hWUN7NFbPu8uM/aGU5KuQAMHOB94n+HpkbjQB1kFtb2qlLeFYwTk7R1oAloA8/wDFfhjxPqeuTapD4q8Q2lk9sbVdPs0tDBkNkTBjA06yHJB/eBNoGF3ZY+RmGQ5fmlVVsXT5pJWWslpvsml1NYVp01aLOZ8RfDnwJp93F4w+I3iPU7TS7S3+xXTa14wvbXS7mA7vluLP7QtpKzhm3eZGdw+X7qhanD5BlWDV6dCKs73au183dr7wlWqS3bMDUvBfh/TbrTdW/Z7S38D2z33lXkkVstp4fu1aR0/5BxVRcyyzNCVng8hpV2lbplxFL8pxPjslxq+qxh7avtH2e68uZJprfRKVn0T1OnDwqw969l5klj8XNO8KalbaPJ4wi8H+F47COS0tfFng6+0lImaQGGGC/na3tgghRgLPY1wgySVEZjXXC5pn+U4WFLF4N1bWScZXdrfaUVJ389Pm9WpU6NWTcZW/r5GneeKPGniHXW8NNJ4r12OV2gmXwxo/9iacmm3ixtDdPqN5MGuHgCMDJp1z5g+0cwbghXb65xLm8f8AZqUcPHvP4u1rNNr5wW1762Fy4el8T5vT+v1LPhv4S33iaOM+N9Yn0i0s5XuB4e8K6neaciX8h3TT3WoQyx3F+d5lwSIY3MsjyxSyCN4/Qy/hTC4ecq+Nft6st3NXXyTv97vZKysrmc8TKS5YaLyO08C/Dv4f+CYmHhP4e+H9Aknc3DyaZpUFt5pKKgdmjRcv5YRCTyQvoK+ojFQVoqyOc+fPi94r8W/tWMvw5/Z58e2UXha3v5NK8a30QIkjiZiI2RsgzwSCOUARHD8BmCM235fHVauef7Pl9RcibU32XT1Ts9t+rsfvHCuAwHhbfOOMMFJ4lwjUwsXa0paXTVn7OcHKLbqK8NXGLmkn9A/CT4S+Dvgv4MtPBXgyxEVvCN9zcOAZ7ycgBppW/iY4+gACgAAAe7gcDRy6iqNFafi33Z+V8VcVZlxjmU8zzOd5PRJfDCPSMV0S+9u7bbbZ2ddh82FABQBT1PZDB9uLhHtTvB45HdOf7w4+uPQUARiUX19BNaxvtty4kkdWUYIIKAEcncAT6bffFAGhQAUAFABQAUAQXt9ZabbPe6jeQWtvFgvNNIERcnAyx4HJAoA/AD9qD9pz9o/Qf2lPivofh/8AaD+JWm6ZpvjXW7Ozs7PxXfwwW0Md9KiRRxrKFRFChQoAAAAFAH9A1ABQB4z8b/2PP2cf2iGguvil8MtPv9Rtj+61O0d7K9ClixRpoGVpEyznY5ZQXZgAxzQBt/A79nD4L/s4aFdeH/g74HtdBg1CRZr6fzZbi5u3VcKZJ5maRgOSEzsUsxVRuOQD0ugDn9B+HvgHwtrep+JfDHgfw/pGr63s/tPULDTIbe5vtn3POlRQ0u3JxuJxnigBtr8Ofh7Y+NLz4kWXgPw7b+LdRgFtea9FpcCajcwhUURyXIXzXULFGNpYjEaD+EYAOioAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gDzf9pD4max8GvgP46+KPh7SBqeqeG9Eub6zt3jLxmZVwjyqGUmJCQ7gMDsVsEGsa9T2UU+7iv/ApJX2e1+xtQpKrPlbtZSf3Ju3ztb5nzxq3w4/a+vdY8DfD2f8Aak17xZ4I+JUc83iTxfoHh2006+0JILYzx/2fd2wKQxXZZYw8iuU8tdjZcg7umozdOrfTbo7rS0t+rvpbZxMIzk6SqQau2l30d22vkrXezadjr/gDP4v+FP7Rviz9mS7+J3i34k+G7TwlY+LrLV/E14l9qOi3M11LBJYXN0qhpfNCrPEHAKorAAjDHanBVMPOo2rwlGPn7yk9dd1bttJdkTVk41opaKSk7a9GldabO9vibuttWfUFYFnwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAOE+Ong3xp8QPhH4r8I/DfxjL4U8Wajpk8Oi61EwR7O6KEKfM2O0QYExtLGvmosjNGQ4UgA/CX4yfsH/tqeCvFNxeeMPhH4s8X3utXNxdSaxoccuvfbZiwaWaaSDfKjO0mczqjOdxGcE0Aey+Cv2rv2rv2YdX+GX7NenTJaWXgiyi8ReJNPuNL23N1Y3saavdWd21zGzwG3gkljZ0WNo280EkIpHnVa+Jw1KrVnHms/dUbtvZK/q99NF1Z9jgMqybOsbgMvwtZ0uaP7+pVcYxi05Slypu1owVotzXtJW92Ddjyv4x/tUfGT4hJpHxw1nUNGvr7WLue3jilt5LtdKa2dDFE0EsC2mx1JKx/vlcLKZFyTnyYZNRxWOlVxdV1JRt7rVkr7Ls11svne7v8AoOI8ScyyHhejgeHsDHBUK/Ovaxnz1JuLSnJPSUZaqPNJNpfw3Hljy/Rf7Fn/AAU+n0+51rw3+1N4psLTTxbpd6Zr9ropibzkaONbJ7awtzGcoC6SFF2iJwS5ZNv0ySjaKPxGcqlVyqzbbb1b11eur7vX1PYv2wP+CnXgf4d/Dmy0L9nzX9O8UeLPFdot9b6nFKLi20q1kDKZLkYG+54IW3YAxld0wG1Y5WZnyP8Asz/tY/tZfGv9oDTtI8Q/FzxPrTTeHvEsUOnxMIbKW5bRr37L59vAqxOBdNBtMikKxXGKzrc/s5ez+Kzt69Dsy76ssZS+u/wuaPPv8N1zba7X21OMs/2fv2z9W+FvhDw7cfCT4k/2U/jDUpVgl8P33nQtNb6akk86iIyLbssaBWYbCY5tvKvWTVd1Z2fu8qt/ivK/6HfTllccDQc4t1lVn7TfWlanypapXv7Taz2u9j6E/Zy/4JFeM9Y+MfiB/wBovSZNH+H2hXcy6amm6vG0uustwPKCFQ0kdq0SsXZ/Km+dAoDbmTSgqkaUVWd5WV2tr21/E481ng6uPrzy6LjQc5OnGXxKDk+VPV6qNk9Xr1e59neMP+Cd/wAM7jxVoXjv4XeLPEHgvWPCfg/UvB2g2wuDfWFlbXNjqFukirKftHmRy6gZs+cQfL27QW3iqkFUg4S2ehhhMTUwVeGJo/FBqSuk1dO6undPXo1Z9T8yPiXqHx4/4Jv6yfgr4G8fSWWva9DpviHxPqlpo2y3u54Zmktre0ubiIPdWqDIkIAR2lnhdSBIpxhTqqvKcpe5ZJRtt3be78rWVul9T0sRjcvqZXSw1Kg1ieeUqlVyb5k0lGMYq0Yrdy5lOTlZqaTcF+tv7Jn7RGnftH/A3wn8VJptJs7yRJY9ctLacbNO1CHfHLEyl3MSvkTRI7bhDJHnOdx6TxS347+Iej6/q3hTxFDcfYvC+gapcahFqc8LvL4guTY3ttHZ6Xapme5ZhJJMJFQ+ascQt0uBMZIfguKM2pZlRlk+Xp1asmr8u0bNPV7b2T6LW7VrPsw9N037WeiPSvhtpmsaT4E0S18R232bWZLRbrU4BceesN7NmW4jWT+NVld1U91Ar7XCYdYTD08OndQSX3KxyylzSbOlroJOZ1bSl0+c3dqhWCQ8hMDyX7EE9AeRnopOewoAy/N+zICq/u3bGWJVVyOSAPm28n3CtnguMAHI6x8FB4uttYktrrw9e6Z4j1K2vtT0jxP4fGrWE8luqIJYY/Nj8mRkhgHJkjUwIyxhzI0nz+acO0MyrrFxqSp1UrKUHb7/AEv0autL7W3p13TXLa68zmNe+Gvj/wANTanqeheC9dsxDYW1hpcngHxcjyRJFAF/daPqqx6XaKpUouzzWPyE9WK+dDKs/wAByrC4tVIp7VE//SlzSf3ot1KM/ijb0G33jbxZ4evNRg1HxPa20Gl6Msnl+M/Dd3pAmnURRRvPrsZOngyOWykNuzAS/LH+7YEWd53g7fXcE5K+rpu/3R957d2lfrqg9lSn8E/vOi8L+K/EOtWNld33gA3S3MImfUfDevWeo6ZJ8wXEM8zQTSEqGJJgVQxIBIXLaU+Ncpk3Gs5U2ukou/8A5LcTwlTpqYMpj8Xa0G8jSNctNX8UJG80ktpHd2un6RHvyc/PMLfXVZSpy8TXbH5ME1w4fEQzniSNai26dGndaOzcutmla6lfzUV8rlF0qFnu2dZ44t5NW0az8OxiJI9Y1e0tb97mGOeGSyL+bcQTKy4ZbiKOS2wWPzTj5TX0HEeKlgsqr1ob8tlbdc3u3+V7/IwoRUqiTKtj4c/tX4TeNddg07T/ABRrd34l1DX47W0mmtobrUNJvhHp8BaQqUkjXS7KKQ/6tpYpGGUbnkyjL6c+H4YSk7KpT385q7+5v9C6s2qzk+j/ACLHgvxJBf6hr9naT297a3RtfEvh25sVmFtLpN1DGSzysfLmlN1DdyERZ2xS2pbBYZw4LxPtMsWGnpOk3GSe61bWm/W2vVPsPFRtU5lszH8FJD42tfD+u2UYgtPDnjrxOmoLL8skotZNX02QptzuzJgjJGUGeDha4cB/yV2K/wCva/KmXP8A3aPr/mejTyu0l6uZQsMkkYCqCTkTn5uvqeR2z6Zr7s4yuUjgCIqTrtlPHzkf8svUDnr2/u+1AEyJDLBFCRcjfCincxVM7ZTnLELgEKfwbHegDWtU+2aKn2aJpi06tdRs25nK4JGWx1wufQZ60AXLPQ4ooIRK8isAHljR8Ru+QclenBA6Y6enFAFX4geDtN+IngPxJ8P9ZknjsPE+kXmjXTwPskWG5heJyjYO1trnBwcHsaAPKvAnibWNV8QxWviDTYNKn8V+HbLV2s94kmt7+3k+z6hEDgfJA89imSfmMz7eASPiuDEsLTxOXN3dKo/mnon/AOSs68V7zjPuj1Owj1G1LTxpJJHGzq/zbzIEJG1QTn1K9O4OeCftTkPPPjl4V0H+19M8R69ao2g+J7aTwF4qT7SbVZ7TUG8uyd2jXzZZFu3FrEqumz+1Z3BGK+T4wwU62BWMoaVKDU07Xdlvv20k9/htY6cLNKfK9nod98NtZ1TW/BmnTeIJHk1m0VtP1WRrVrYS3tuximlSNgpEUjoZIzgBo5I2XKsCfocBjKeYYWniqW0kn3t3Xqno/NGE4OEnF9Dpq6yTw7xh8OLaPxr/AMIrp1+umaZr0Oq+KNOuLC3WHUNA1tXgilu7KbJjXzft1xI6NE2957kSGSOd4q/P+LqTyetTzvCSaqOSjJfZkrN6rf7KT12StZq524Z+1Toy2PWPBmty+JvB+heJJjAZNV0y1vn8j/V7pYlc7Mk/L83HJ47mv0A4jZoAKAOA+Ofj68+HvwR+IXxB8OXNk+q+GPC+ratYifEkX2q3tJJY1dQQWG9FyuQSOKAHeB/BOj+D/wCyNP0WS9Ww0/TrXR7K0kn3w2sFpHIqFARu3usqh2LHcIIuPlJPwvAb9vhK+Kn8c6ju++if5yf3nZjNJKK2SO9r7o4woA8++LvwI+GXxu0j+zPHnh6Oe4jQpa6lb4ivbThseXKBnALE7GDITglTgV5+PyzDZlDlrx16PqvR/pt5H13CfHGd8F4j2+VVmot+9B605bfFHvpbmVpJXSkrnwn4w8P/ABl/Yy1W58J67ZyfEL4P68hgu7G7SZrH7NNK4dI2YbLO7xvfy1JikLkn5iJIvzjNclqZdVUptq2sakVqn0Uu+217pW5XZOL/AHLEZXkPjLhJ5lknLhs0v+8pSklGppHmkkru2uk1FXldVI6qa+mfhJ8eYLzwx4cXwl4v0fxTY6yySafpXibXPsPiezsxJKs0D7/O/tCeJ08mJnMQkWMM9zMSbiT28JxXicJSTzOi5R/5+QV4vW12tOXZtrR/3Vey/n3OeHsZkeNngMdB06sd4y3+TV00+jTafRs9U0/4peHxc2s3irwr4h8I3l3HeyFtTsVkt4YLcbpJbq9s3ns7dCihk8+ZGYcAbgyj6DB8S5VjtKVdX00fuu76Lmtf5XPHlQqQ3R1mj+KfDfiLSV17w7r+n6vpznCXVhcpcROeOFeMkE8jgHuK9tNNXRiaaksoYqVJGcHqKYHjXj740tPbTx+C9bs9N8PLF5Vx402pextdNIirZaXbLk312wZ1VgrxrN5cYS5kEsCfMZvxHHCVVgsDH2uIf2VsvOT/ADV1pq2lqdFKhzLnnpEraZ8JfFOv60/iaM2/gp5RcQNqstpDqniu4iaXzQReXBlgtIvMMoFsY7lRC0YT7MwEcfBh+F8RmC9tnlaU5PXkTtBaW6ddvhtr/Nu7liFDSirefU9F8N/DjQvD9/Br11cX2ua/FZpZtrGqyiW4KhVWRo40VYLYy7EaUW0USSMillO1cfTZflWCyuDhhKajffq36t3b8tdDnnUnUd5M6qvQIMt721OqxPHdecBBKpSIeZsO5OygkZx39KAHTWxsojexXKo8ZkdiycSKzlth79TgEc9eOcUAfOHxG+J2vfHPVtf+B37O3jG58M+JvCd7Bdalc3Vm8UN7Es6rNHFP8xWOORwXBQeaF2pvjLbvm8ZjamYzngsvm41INNtrRpPWz8nvprsrq5+1cN8MYPgvC4fibi/Cxr4PFQlGEYzTlCUotxcoaJucU1Fqf7tu8uWajb6A8NeE9K8H6Stppek6dbz3Vwl3qUmnWK2qXd4yokty0YJwzbQSSzHA5LYzXvUKMaEbRSTersrXfV28z8nzTM6+aVlOrOUowXLBTk5uFNNuMFJ20jfSyS7JbHQ1seaFABQAUAUL9Jb2Rba1lVWt3WZ8/dJByqH69fbA45FADIJpLeSSZreSK0kbc3mnBjc/eOBn5M9T6knpzQBpUAFABQB478Rv2sfgt8LfjL4L+A3ivxBMni3xzMkNlBBB5kdqZSy25uHyNgmlXykADEswLBUy4APA/BX/AAVk+AF1478WfD/4v2F/8Pbzw3q17p1tfP5mp2V9Hby+WG3wRCSKRirnY0ZQAD94SdtAHy9+3n8cb/8AbL+NHgj9nf4f+N9K8H+BbqxttY0nV/Exu7Gw8R3l2gMEu5IpCIwp8qPzI0Ky/aVchtqqAdf4B/4JdfFf4vfGvSvjT+0ta+GfDWjTJY3Gv+HLC++26hq9/aQwwXEtw8MawIL+WGW7lkSV5P8ASSp/eMzqAfqhQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9AHJ/Fnxb4M8B/DHxV4x+IsEVx4Y0jSLq51a3lhSZbm2WNt8PlvhZDIPkCHhiwXvWVecIQftFdPS3e7sl827GtGM5TXJo1rftbVvTXRanzH8GvjzH+z94e8N+DviL+ydrHwM+F+sXZj0HWpfEMWrWdlcXk7yqmpKP3umiR5RgyFkjZwhMaodu9nFqlVdprSy1Ta3Sl18u6uzni1NOrTXuvW7Vnrs2unn20R33wwj+FHwY/ae139nf4ZfCSx0ObxT4Tk+JWr+IIr55Jru4OpG1+zOkis2xS7umJQibmVY13EmsPho08I+TSNNwil/iUuvlyed7+R01Yua+szd5Tcr/AC5X+PNt/mfRVZmJ8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAOX8d/DLwL8S9H1DRPGnh6G+g1TS7nRLmWOSS2um0+5MZuLZbiFlmSKUxRb0VwH2LuBwKAPIdC/YH/Ze0b4VTfBe8+Hiax4Xl1mbW1hvrhxcRzv5yxqLmHy5nWGO5mjiLszqrcszfNUKnCMnNJXe7722udFTF4irRhh6k24QvyxbbUeb4uVbK9le29tT448Sf8EVb+28Py6H4K+Ntvdtf+J7G5kn1TTWtxY6Okdys+EjMn2q6BlhKDdBG2yTJXcNucqEXWVfqk15atP8ARHXTzWtTy2pldl7OdSFRvXmvCM4pJ3ta1SV9G721Wt6Pwd/4Jj6x4M/a6vvDniX4bx+MPghbaWdPvda1iSCF57qXR13yQR5E+RdTPteHAjIBEvmQ4EfV5ydOc5u8b3tondPRrsul7tW3erfU82w9KGKw+Gw8VTrKKjzJTnT5ZRakptXU5JNT5eSMuZ2jFKMY/pf8LfhX4D+C/gfTPh18N/D8GkaHpMXlQQR5ZmJJZnkc/M7sxZmZiSSTXUeEdEmm2kUnmW6GA5BIiYqpx6r0P5UAWqACgD5h/b1/Yz039r74aWlrp2oLpnjXwobm68O3kjMYJDKi+baTKDgRymKH95gtGY1IypdHAPziMHxS/ZO0n4UfssNpjfDXxJ408T2niXx34t1LSYdShSZLzy7K2gJT7Pe21nAIbxlWSYC5mwrwujrXNi6VOvQnTr/A1rZtaddtduz12OrDUJ4irTo0NakpWS0td2UdW+rfWyWjvrp9Wfs4/wDBRb4FeIfEvjzxn8TNN1rR9O8NReXp3jzWIzOtxb3FzCjWENpAjGzdn8l1ggErSQ2we4kd7cyvx5TleDymiqWGhytpXvbmdu762v00V9C8VSqOdRxlzwg7c0VLlers1dJpStdcyUmt0mmffmgeJvDfivQrbxR4W8Q6brGjXsZmttR0+7juLWdASCySoSjDIIyDjg16pxGa+t6jHdy3IwbcctCR80Kg7QXGAQWOD1OAScHigC/JrAuLeIW9oZWuPlxgOpHfHI3d+DjH8W3pQBhXVo9tp6ShkEE6v3yVYZIRjgZIOfTnr90AgF+y1W50y3SzNjGyxfKWMnl/NzuABzuw24fLnpwOQKAJ08VRb28+xkijCs6kuoZgGI6HHPB4BJ+tAEdrqkbSedBqKPJcgBVu4mTIGcLvX5eMnoOT9aAOf8TeGvhzoqaz8RfE/hDSNMng0aa31HxJZMlpdRaau6WVWvEMc0cSndJ98BWBfII3Vz4nCYfFx5MRBTS1tJJr8SoylHWLsfPNrpfi/wAN6hpHw9WO0k1CztNP1fQ9XmvUMS+NfstxJqel3YjMkkUV7a75nldHOb66kEjSvAB+WYGvVwr/ANYaMbU1OUJRX/PttcrW3w35Vay92KskmehNKX7h72v8z2S3Gm/F3wOPsUl3bpq0QEU4hga/027jl+8I5RJHFdWtxEchlPlzwsGJK4r9Tao46hZ+9Ca+TTX6pnn6wl5o669v/Dnw+0bU9X8Xa1DARaT63exxyScx20MazzxxZL7VUR5CjCll6u25vHwlPCcMYWnhZ1W1KVo82usuistFe712vvsaScsRJySPNPEaXnw+1HVpNRkub+bw9DdeJLKZoGlnn0WcH+0LGKX99cTNDMomWKKOJSP7PgHyhmHh423D2fQxcValiPdl2Ur/ABdEuju7vWZtD9/RcXvH8v6/Q5rwFcz2XxJ8O6Jp2oXMek3cfxMu7iG3k/czzjxLaKk8ij5WbNxL85Gf30mD85z1ZJFSz3Hzkrtciv1tZ6emi+5diav8GC9T2b7RKzyBp5N87IXbapGCjKxPHX5/1PtX2RyjrZZrs8Mofeu2JHWIsSFxtTb6heenHPrQB09t4ftQ3n6gWu5id371tyryTgcDI5+h64HSgDUREjQRxoFVRgKBgAUAVG1ew3iOGdZ3ZWYLCwc4HXgHr7deD6UAeJHxv8VbzxK6+DPCH9qaz4in1eaODXvEsmnaTYaXpd4lpCYntra5IluRcR3Kq0RdhJKJJVEMMI+AxMMfxDmtehhcU6VOjZe7e7b3vZxvZpq9+istWzti4UKcXKN2zb8B/D/xnYXUGteKfD3h/wANQ2zaoZtD0DVJNUt72TULuG7nupJpbO2lSUTpM2xAVbz2ZskKB7WR8P1Mqr1cViKzqzmkrtNOy76u99PPTzMa1ZVEoxVkj1Gyv9KISzspolwxRYlGDkDJ46+vNfSmBR8c+ErLx54O1nwbqF9fWMOs2Utp9ssJRFd2jMpCXEEmD5c0bbZI3wSrorDpUzhGpFwmrp6NPZoE7ao5L4VaT46sb2XXfF1vBaSeI9H0651PTbIRva2GuQoYrx1nL+bIJUNtGi7dqrYliVaQhvmeGMDjMrpVcFiIvkjJ8jbTvF+S2769ZeR0YicajU479T0mvqDnPNvjkPDNnoui+Kdb8Q6dpF94X1eLV9LkvbuOETyCN7ee3QSzRRvJNbXNxCm9tqvKj4ygry86wEczy+rhpO11o3ok1qm32ulfyuaUp+zmpHM/Br4px614f0CESLouj63rOq+H/DNre28L3c32bz5EjX7GotYYoo7K+SFQZRJbw20rTmRmjbweHc1xM8U8pxTjKVOEdY31tZfE2+bRxu7L3rq1ld7V6cVH2kerPZD/AGpCRtMFyoX+LMb5+vIOeewr7I5Q/tGMZSSGaOXOFjZeXPopztP4HjvigDh/jT4au/E/we8faRp+iJNd6r4Z1W0hs1hR5buWS1kVY26qwZiBt5B4ycEioqc3I+Te2nqNb6mR4J8WLc+JNQ0vUPEUd1Y+LLuHXPA2pW7iWxubMadbiS2imT5Wl3xXNyIyT5kExkiMginEHxPAuJw8cHLA/DWhJuUXo97Xt5aRfZrVaq/XjIy5+fo9j1CwvReRsHQxzxHZNGeqN/UHse9fcnGWqACgCpq+k6Zr2lXmh61Yw3lhqED211bzLuSWJ1KsjDuCCRUVKcasHCauno0dGExdfA14YrDScakGpRa0aad015pnx/8AFH9lrxH8MNQ1Dxz+y8ftdrMVbXfAl+gvNN1CFQVeMJKdsuVZ/wByx8wCSTynU7Er5bFZPiMtm8VlLt3h0fp/lvvytaI/e8i8SMo41w8cg8Qaalo1TxKSU4N7czS02S5kuV2j7WElzSLfwZ+JvwW8aTQfD61g1v4HeO7G2n0mDQLe4a0sIJ55GJa1spgbJp97hws1v5m9yAJACTz03k2eS9jj6ChW6prlbey95WfbR69LOx8zxf4V5vw1ReaZXUWLwLXMqlO0rRtduUVdWWvvxco2V24t2O81bQ/EHhPxsurSWU2sa14WfQpf+Eml0mGCTVNFuZnt762uXhwJ3t1S5vNsSRIrtaYjba3m8cMLHhfOKFLDzfsK/MuVtu0tLW+bik3d2vc/L3L6xSbktUd98RvCN741uLZJbTWtX0aWKS1uNBlu4bTSrjckgaS8UKJ7iN0Z4WgcyQtvQtCcean0ud4PMMdThSwNZUk37z1vb+61/wAC/ddeelKEG3NXIPB/ww07TdVs/GHjOCbxf4y08N9m1W706GCLSS8bJLHpkJYi0icPIrFXkmkQok00wSPbvlWTYTJqbp4WNr7t6t+r/RWXZCqVZVXeR6Rb3EV1EJoSSpJHKkEEHBBB5BzXqGZLQAUAY/iK/wBI0mO11LV9at9NUXEVtE9xcCFZZJJEAiGfvu+NqoMlicAE4qJ1IU7ObtfTXu9l8zow+Er4tyjh4ObinJ8qbtGKvKTtsktW9ktWfIfxI+Jnxq/a11vxD8FfgdoDeHPCmmXL2HiDXtVdreSUoGDQOFBaNHdSpjRWkYBd/lo0iV8hjMZjc8qTwWCjywTtKT0+Xkn2V2+tldH9G8OcOcMeFuEw3E/E9ZV8TUip0aVNKSV7NTV7KUoppqcnGEXfk55qEj6r+HngKz8BeH7TTpL5tX1dbO3tdR1y5t4kvNSMKlUad0UFyoJClyzY6szFmb6nC4ZYaCi3zSsk5O13bv3+d/V7n4Ln2dyzrFTqQgqVHmlKFKLk6dPmd2oJtqN3q+VJX2jFJRXU10niBQAUAFAFa9u/ssahF3zSt5cSYzlvf2HUn0oAqaTLdRxRR3NrGplLB5Ul3FpRnO4Y6/KehPQCgDSkjSVGikUMjgqwPQg0AQWFtLaRPBJN5kaufJz1WPAwp+hz+GKALNAHg37TX7ZHwX/Zhl0Tw/8AEHxW2n674p3JYJDp7ah9gizs+33cEciP9mRyMhWEkm1xGGKOVAPzz8OeCPjP+0l8ZR+zT+0xJqknxCsfM8X+AfjL4WtFuUs7Fna6WRp4vKS50iV2zayK0b29wyQrtDGOIAt/tLf8EfPi5J46vvFfwL8Y23i7StZn+1XMPiO9jt9WiupNzTu8iRJbzIXBYMojYeaF8shDIwB9l/Bf9gb4faT+z/8ADX4SftEaB4a8e6l8Ob241PTrmG3mjhhluLhrmW3Ys4N1D5sjBlkRY5VSPfFkUAfV9ABQAUAFABQAUAFABQAUAFABQAUAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUAea/tJ/ErXfg58BvHPxR8MWlhdar4Z0ebULSG/jd7d5EAwJFRkYr9GB96xr1HTipLul97S/U2oU1Vk4vtJ/cm/wBDmfDMP7WviPw3pPiEfFL4RW41SxgvRD/wr7U28vzI1fbu/toZxuxnArrr0vYVZUr35W19zsclCr7alGra10n96Oi+Kf7Qvwl/Z58N6VqXx5+Jmh6Fc3saxrthl33syhRK9vaIZZzGGYZxvCBlDNyCcZThz8q/z77/AHb6G0Yy5bv+vT/I6LTvi18LtW8Aj4q6f8RPDk3g0xGc6/8A2nCLBEDbGLzlgibX+QhiCrAqcEYpz/d/Fp/w9vzJg/afDr/w1/yMD4Q/tKfAn49tfxfCL4naN4juNMZ1u7WCRo7mJVIUyeTKFkMWWAEoUoScBjVunJQ5+n9b9tnuF1fl6mBpv7af7KmsfEWH4VaV8dvCt34kuWSKCGG7L200rEBYY7sD7O8pYhRGshcsdoGeKiH7zWP9enf5BN+zdpaf117fM1LP4s65J+0zr/wcu7fTIvD+keBtP8UJdFHW5+0T3t1BIruX2eUEt0IGwEEsSxGACk1OlWqT05JRXycXJ3/rYmo3GtTpx+1GT+akkrfeZnhf9s/9lfxr4/Pww8LfHPwtqPiIyJBDbxXREN3K5ASO3uSBBcOSwASJ3YnjHBpwTqfD/Vv6+7XYqclT+L+r/wBffpuezu6Ro0kjhVUEsxOAAO5qW1FXexSTbsj5O8F+O/2oP2ttDvviH8H/AIieHPhF8OpdSlg8K38/hn+3NX162glkikupo55oorWCR0+RNhl4YkgbWdRU3FTno3rbsul/Prppru9wco8zjDVLS/d9beXTXXfRHpvwd+Ifxhh1Xx34N/aH8O6Xp0ngowXdl4y022mtNE13TJUkbz189nEE0IiIuIzIwQsCpKEMa54Om5vRxdpX22umm7XVt+ia1d9EuWUZqC1urrv2adtnfZaNrVKxreGP2oP2d/Gmr+ItE8K/Gjwjqdx4T05dX1iSDVIjb21iVDG58/PlPEgK73R2WMsocqWAMqScXLona/n/AF+N1umFm5ciWu5R+GX7XX7Nfxj8XXXgP4a/GHQNb1+1d0FjHI8UlwUDM5t/MVRcqFRmLQlwFG7OOauKc48y2FJqMuV7nl3gr9rHUfir8X/idofgz40fBLS/BPga2JsJ7qU6jfagg00TTak0kd/DEtlb3EsQdlVgypKhkiYBxFLmnSdR6ayVuyTSTf8Ai6bfMuThGoqa10Xzb1svNdd/8vYPGn7Q3wi+Dvw90Txt8YPi34VsbXVLGKe31C2ciHViUjLzWNujzSyxHzEcCMylUdSWI+YuUkpcvUlJuPN0New+OPwc1L4c/wDC3rP4oeGG8FBVZ9ebU4UsoyWChHlZgEfeypsbDBiFI3cVVROj8f8Aw/p32ewQXtHaP/DevYxPg5+1H+z/APtAXV9p/wAIfijpHiG+04M9zZR+ZBdLGpVTKIZlSRogzoPMVSmWAzk0crtzC5lfl6nfeH/FfhfxZHezeFvEml6zHpt7Npl62n3kdwLa8iIEtvIUJ2SoSAyNhlyMgUlqk1sx9WuqNWgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD5g/am/5Ll8Cv+xgT/0ss6APp+gDivjT8LtI+Nnwn8VfCjXbhra08T6ZNYG5VN7W0jDMcwXI3GOQI4GQCVxWdWm6kbJ2aaa9U018rrXutDWjVdGfMuzT9Gmn+DZjeAvBHxE8RfCO8+H/AO1DL4Q8WXt/HcaZfPo1pNFaajp5UIjzRyHKzuuXcR7UVmwmAoNa4iNLERV42vq10Tu2uV72Wlm9bq5jQc6EtHton1atbVba63W1jxz4BfsmfFH4M/tIt461v4j2vizwJpHgCXwT4Ze9LjWbS2/tGK6gt7nCeXMsS+ZGswYEqqAouK61inPD1KdXWcpQd+6ip3cv73vrbR2u9WyvdjBU6ekU20u1+VWX91cul9Ve2yVvrGuMR8A/Bv8A5TIfHb/sn9n/AOiNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA57x38PfA3xP8OXHhH4ieEtK8RaPc5MlnqNqk8YbaVDqGHyOAzYdcMueCKAPzV/4KQ/sI6L8Pv2dtM8VfA+4h0bwb8NZ57y+8L+RJLLdPqF0iS332nLSTSxg20eJs7beH/WDywjzyRcue2u39fhf0XY3WJrRoPDKTUG1Jro2k0m11aTko325pWtzO/N/8EWJviu2v+OLeQ6o3wrEMRm8xs2S68Xj8oRBufMMO7zDH2FuJc5ixRgfq/q1tNJ5V3boJHtyxMbZw4I74BJwQpx3x64oAyzaCWRZ7C4bzWzEskbFftL53OzYP3ByMjn06LQBn3cjpC2nvC0ZuHErrAB5RiTOWQE5yducnrgUAKrS20vnPA8bMrPKyPkbWywIKbAQccfOfYZGKAIMvaK00Es0axhIJMCQMnyjdjgY+cscbhkjpigCPymxgx25McO05ZNpLYQHcBgnknqehJPFAHL/ABSt5b7wsvhmOwglXxRf2OjXFvcBkS80+W4jGowYVBu3WQu26HhCcpzIvk57i/qOW166dmouz83ovxaNKMeeokc1o/hDSfiT4M8Vale6dc6FN4x1y81SG9ikSRy9jOlppuq2rPHsy0OnWN1ESrL/AKsgyqRu5chy+NHJqWErK6lHVNW+O7afpe3+RVad6rku/wCRf8E+PdV8OXa6rrkENomr6lBoXie3Nwgt9I8QYjiiuYlZi32a7V7cKm/fmWzYxI81yy+Nw/iKmTY2eQ4p6aypPvF3dvzfqpK+xtXiqsFWj8zrPEeianquoaB4s03yo76wvBmabYpu7SUeXdWzyCN3VNpSdI02eZNZW4Yqpavd4jyv+18unQivfWsf8S26pa6rXa9zGhU9lNPoZ2t+FtXXw9oMWjvpp8ceElu28PR3F1OgvdOVlQWFzK0kk5823NqsksnmAXKRXBjcxpGefEYCOc5cstx817ZRTdmm1Jfatpv123aT6jjP2U/aQWhwXh248Ir8Qvg3rvhqyt/Beia14F1xNM8PPY/Y5ka7uNFvBCsUKmBJY0gmZ41k35DlA6rKyfPcDxq0sXjaOIlzVIuKbve/K5pu71a2/A3xdnGDitP+GPYow13cRrbCdnk2Z2pklsR72598nk46etfopwnSaJbSwSyQ3IkDQRxiMO5PykEbsb2UHII47DtmgDYoAjnjSWGSKRiFdSrEHBAI9aAMrz765gLRWjzXFuxENygVUk6HOGIO1uhxkdwTgUAeKfCnx74c8P8AgzwxceFH1gaDY+DNMubDwla6Yk11cpqbAaVDFJuwjQR2lzE2WWJUcyzSJHEZB8Dw9XpYTE5ljq7UafPv6OV/m7qyW7dkdldOUacFvY67xB8eNM8HarBcePJfCfg3w2+n/aJrrxH4rt7LUIbkzmNYzbBHgMLAKRMLrO5tnl5Ga3qcbUq3u5fh6lWV+istrvbmd12sJYRr45JHPy+PPiTdagt9pX/CW+LZfDsjz3Nl4S8PWWl6Vq1tdxqbUfatZm2XZhXc5ksbxQGBWZQSkZPrvEuaRvhqMaEXs56yVt9LdbW1hs7ruHJh6fxO/p/X6knw++LGorrGrjVvFUus2pj/ALS1HQ9Yk0+01/wmz3XlGKSKARwy6ekYmdZyzPttWaKW/W4jMV4LiLFYGusDnlPlk2oxqJe5LTdvz01Ssr6qNmE6EZx56L+XU9ugnhuYY7m2mSWGVQ8ciMGV1IyCCOCCOc19ochJQB4z40s7678c+JfEGjeML22meDQvBscQtlc6NNJdGS5vLUy74jLLDqVp96IpvsofMEqqEHwPEdKGa55hMrrJ8lnJ6776eXwWve+uljsoN06Mqi3/AK/zN/RPhZ4p0s2Nnq/xX1XxPYQXq39zJrGmWS30jxtE8EUUtnHbwxxK8TMwaCSRvMI8xQBXs4ThPLsBjIY3DJxcb6XutU1d3u9n3t5GUsTUnBwkej19KYBQAUAeG+Ovh5beHtfOn6JZWp0TxbfJqljYXLS/YtM8VWcx1GC6EUSqwhuJYPNnUTIpmt1IjaS7nc/DcVYX+z61LPcMrTptKdl8UXpr8vdva9nvojrw8udOjLZ7HoXhjxrpnjDT016wgu7DUrJAmr6JebY72xbc6tHNGCRwySFJELRyqN8TyRujn7DCYyhj6Sr4aSlF9V+vZ909V1OaUXB2kdcjpIiyRsGVgCpByCPWukkdQAyWWOCNppnCIgyzHoBQBBpcTQ6baxOpVliXcCMEHHIoA8u+O/7Mvw2+PenM/iHTxY+IYLcwWGu2oIuLb5gwDqCFnQEEbHzgO+wozFq8nM8mw2aR/eK0+kluv816+drH6BwP4k51wLWSwc+fDt3nSl8MtGtHZuEtd42u1HmUkrHzJ4p1Tx58DNct/C/7WujXXxC8C6lper+GLXxPaXDtcPp1+IDcW9wCwZy32eLiRhIoaby5JQoWvlsbHEYOVKnm1504TUozi9brZO/Ty0faTSsfp8+FOGPFOjUx3B0lhcaopzw8laD1s3Cysu14Jx+HmhTcnI+1fCvxB8EeN9BtPE3hTxTp2paZfRiSGeGcdCM7WU4ZHGcMjAMpyCAQRX3OHxFLFU1VoyvF9f6/LdH4Fm+T47IcZPAZlSdOrB2cX+aezT6STcWtU2tTX/tPTm+VNRtdx4H71Tz9M1seaV7KZbGE29xb3CyhiXdYnkEpJ+/lQcZ647dKAOO8efH74S/DKOdvHPjPTtLmt0jkaza4SS9KuQFItY2acjJ67MAAk4AJrixWZYTBX9vUSa6X1+5a/gfUZFwVxDxM4/2Vg51IyulLltC6V2nUlaC26yWtktWkeb6f+1F4s+Ivi3wlpHwd+FGr6j4c8SQtPceJL23YQ2CLdXVuzyICqAA2rSANMruCFCh8A+ZDOauKrU4YSk3Cau5PprJa9Ps33u1pa59viPDTA5Bl+Nr8Q4+FPE4eSjGjFq9RunSqWi9ZNtVVBtU3GMk5OXJdqvpv7M3jb4q6Gtp+1b40HiS40nxAb/SP7HxZlrRV2NFMURVCTbVbbGqyJjPnZYqkwyivjqfLms+ZxldW002s7dH5JNfza6bYnxFyvhbGuvwFhnQjVo8lRVLzSm3zKUFJt80LtXlKUJbeytG8vpGNFjRY1zhQACzFicepPJPua+iSsfjcpOTuwdgiM7ZwoJOASfyHJoERw3dpcsVt7qKUr1COGI/KgCagAoAZNNFbxNNPIsaLyWY4AoAp2Aa9k/tSZAAwK2y8/LHn7x924/AD3oAiuV+zNLCc7HkW7TqfusrSKB68Fh6lj6E0AXre7trtA9tOkgKhuDyAemR1HQ/lQBNQAUAfgp/wVd8S65rv7bnjLS9Wv2uLXw7Y6RpumRlVAtrZrCG6aMEAEgz3U75OTmQjOAAAD9dP2Gfhz4d+GP7KHw10Twxqup6jZ6noNrr7T31w8haa/jW6kESHAhiDynbGoGBy252d2APd6ACgAoAKACgAoAKACgAoAKACgAoAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP8AwTh/5Mm+Fn/YNuf/AEtuKAPpOgAoAKAPCP27P+TPfi3/ANixdfyFc2L/AIa/xR/9KR04T+I/8Mv/AElnKeAPAf7OEvgTw3LefG/xVDO+kWbSxp8a9ehVHMKZURrqQCAHI2gADoAK9LGJLE1FF3XM/Pr36nnYNt4em5b8q8unbocv+0Db6JH+074a8ffCX9pTQPAPxTl+H/k2ll4n02O60DxLoi3rTxW4u2KtGzzbjI1vI83lKGVVA3PzxnKSmo2drNrbe9n3tv5XstzqUI2ipJpa+8rvtdauza0ffV9Dx7WfG9n43+A2haj428F+BNJ8M+Fv2jLGD4k3ng2czeGNbtsCWbUpvN/1lq11dWiyLKZBmJScYCIsNWpzqUZNOMfejC6atJKSjJOyUYtp2k2o6rV31yr06lJVVTd37spNa3TcW11bklZNJXXTbT6J+M3xh/ZctPG03iOLRYPHnjiw+HXiW/a58MXgmeDQba3E01veT28uYo52YLA0isolZthVm+aKjl7GsopNctpJ6JpvRN2fXV2Tajq1ZG9N/vKTb2leNtWnbVpd7K3nsfHXxz+JWuXv7AFnp1z8S/gL4X8NalDo03hnwH4Ttbi+1U2oureVYJbq4vMpcQg77hxbMS8bguC5J2rKX1mnGpK8lJbbaaLXX3bLR6J6LZ64qzpVHTVlZ/e9elldu/fufS/x78VaD4K+PPxx8UeKPCNx4p0ex+AVm19osLOhv4G1DUkeFnQFo42ViHkAOxN74O3FYU5qGFxDa/5e01p5xt3Wmuvl32G4e1xVFJr+HUevk0/vdtPOx4j8bviJ4mi+Bvwo0jxT8Y/gLZWkPjDww/hnwj4DsZr+4S0guIzAVvp7tjEkMAy8q26gkCPzMSYbtoU+fMqF/eaqK9rJdVdLt0SVt/I4sROSy6v7O6Tg7LVt6Xs/O+r1d2u5+lXinR38Q+GdX0CK6a2fU7C4s1mXrEZI2QMPpnP4VwYim61GdNbtNfej06FRUqsaj6NP7j5i/Yp8Ux6z+zLa/s/Q+LYvBfxU+HtldeF9b0/ZBNqGi3kLusd2tvKGjniZWjlSUB4X3EbiVYDfEVljIqrSdlaP/kqSad9r2afVX7nPh6X1S9Oor6yf/gTbTXpe6727Hn9r8R/iVdePvjZ8IdQ/aOHxe8K6H8HtR1O6vItJ0uBNN1l5J4jaySWMSgSCGMtsZs/M3Hy8TU9lVyvE1ftJpJ904Tbt0avbXW23cqn7WnmFGH2Wm36qUEr9t3p1L2v3/wAP/hz/AMEufATeIPhrpHiTR9R8KeFoZNM1C4ks9OF1eyWshuryeIbooVuXE7kY3MoXIL7hpUqynVpxvq3Fa7Ky0cvKy8zKhBKjOXRczst3eWy89TkfiV8SfGepfHT9mWw+JHxy+Dut+KB41R/7A8BaRKfssEttJFJM9/PdTOImLpEsflw+aXLAMYfk3y6mp47netqdXtp7jfrrb0016HNmEqiwijDb2lLRXd0qkde2ib9E9zuteijTwH+3cVjUYTUgMDoP+EUtq86h/uK/6+VP/Skeo/8AkYf9uU/yZh/APWvBXhH47fCTW/idqFjpX2/9nbw5a+EL/VZkgtWmjbdqEMEkhC/adkluxVfn8rP8Oa7Kc4pV4N++5Rt35LSv/wCTa2+ZyTU26L+wlL/wJ8tn/wCA3V/OxW+NPxa/Z4h8L3L/AAO+HXhPWLvUvjZZW6a/4hvri18Kx+Lza+e2qySwOTMke1UdV2I7MzfOBlsqMX+7UNL87V/Le1++0PuVrWN6ztKftFfSKdrWs9vu+1176j9L8fa3r3/BQP4M6d4o+NPw38YeIYvC/iG31Kw8E6O1rBpqPbrLHHLcyXVw9w0hiZljJQxrFu8sCbLdOBhH/apr/n3HXTpUjp30v1b306nDi5VXLDJ7c7bWrX8Oev3pK/yPu3TdT0jVFuH0jULO7W3uHtpzbTLII51+/G+0nDjjIPI71zdLnb1sXKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gAoAKACgAoA+Afg3/AMpkPjt/2T+z/wDRGhUAff1ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAFTVtJ0vXtLvND1zTbXUdN1G3ktLyzu4VmguYJFKyRSRsCroykqVIIIJBoA/D341/Cix/Ys/as1TXvix4Z8TXXgmw1KXxf8LtP0PUFtLDUbmPUYJobR5jHJ5CQRyyrMqIsuUjKlVlR25pUo06ntacFzysm9E7eb3e2i72Wi1Xs0cwr4rCrAY3FTWHpKUoU7ylHnd7KEL8kW3JuUna0eeS5pWhP9OP2Nf2qNW+OPgXQNP+Luk6V4V+I+qWE+rwaHDcHzb7SI2iWPURA7NLbRyeemxJW3SKPNQtG2R0KSbaT2PKnSqU1GU4tKSurrdXauu6umrrqmuh79qFtb2f7wIyxXL+XNIFLeXGcsVUDkbm4+rfSmZhptpIl+Hnb54LRYwvHCs7lQcYGQFA44oAzNY0i1snZpUP2aZhslAP+jnflhgdVIJx1wfzoAzpBveFmKTSTMZJNpXcTyWB8vcwBz7fTjNAEzuolN3IjmQSFjKNzYVFwQCrlvvE8lgCFHPagDzf4n65ZaX4m0/Xbzwjc65D4Q0XX/GVu9ooa7TULaCO2jjhjWQea0sF9foqsXUnBLIwUt8dxo51sLRwNKVnWqRj8vudrS5X3/E6sLZSc30R6V4X8G2ng/wX4f8ADN7qBSw8OaXbaXbqJGZmjhhWMb5HLSOWVBn5iT6k8n7BJRVlscpwnxd0bS7bTNR8c/8ACOPdeHG05tH8W2kcSmS80Nw3mSkKyy5thLJMoUsxilulSN5ZYynznEuTyzPDqrh9K9J80H5rW3ztp5pdLm9CqqcrS2e5R8A+Mde8PxN4K8Rx6lrHiTTBGkF3pohlOt6dIJfs+oGTbHDAXSB1lVtipOjBMrJb+dlhOLcBUy9YzES5WtHHrzJK6S3a10fbe2tnLDTU+WKuc3beFZdVkje9u767itfEN3qN14d8GXdsbTT72aGRLky6k8dvcFzP9ouHELQz+ZezLIs0JNfNw/tHOcwnmWU0fY80Uuebevmo6p3VltNaJpqWpu+SlBU6rv5L+v8AIseOPCviXTo9O+IfxC1ezs9Fk0saHJcaPvvLrwaXJb+1odVnQSyK0qW63BmthGFSGSXZHbztP14jhvF5Th3jsHVc8RGXPLoprqnG+trt73d5Ldq0xrxqy5JK0bW9DtfBnizUNQiufD3iWG3i8TeGpPL1KOKIwpOjbUgvoYpDJi3nWIumHkCMJYTI0kEtfYZRmlHN8JHFUeu63s+qfp6K6s+pzVabpS5WdjeeJJJLuSWMeXiNVjMbNgsGBDH7u4AFsAjGTyDXpGZ1dpeQX0Pn275XJUgjBUjqCOxoAili+23JjaQ+RBw8Y4EjkZw3qoBHHQ556UAed/tDXMV54CbwJd6za6Np/jidvDGqarLOYpNPs7uCVHkhONouJG2W8LSMqLNcxNiVlW3m8nO8fWy3A1MTh6fPJdO3m+tlu7fgrtaUoKpNRk7Hnsckmq+P7i/GjXHh3xZ4g8RyeBl1OdEnNjYWtjd38Fzbg5jmMsJdk3DEMl46sr+XLHL+V5DgFmdallk6n7m3tGl1eiafVWtbrorr4j0a0/Zp1Etdj3fwz4c8PeB9Ls/C3hrS1srFPNkUBmdpJWbfLLLI5LyzSO7yPK7M8js7uzMST+0QhGlFQgrJaJLRJLojy223dlHXtTNxdtZci2jDxsSSAz7TyR3CnB+o75FUI8t+NurT+F/h9r3xK077UuseBdKv9cscTsqzG3t3kktplH37eRI9jRuTtO2VcSRxuvnZpllDN8LLC11o9n1T6Nef6XWzLp1HSlzRO3+Gskuka14j8NRQTR6XBqUf9nxlmMdur2kUjJGG+7GX83Cr8qkEAAV4nBeKq4vKoutLmcW18layNsXFRqaHolfWHMeWnwn4oufiFqOtQR39rBp/iq01e2aG+jjttQt5NJjsp4p0DF3EYaWUIyqDIlswLbWx8vUyrER4ghmNOKdNx5ZN7xdnrH7kr66N9NuhVI+xcHvc9Sr6g5woAjuJlt4XmYEhBnA6k9gPc9KAK39pxRMsd/G1o7dC5BRj7MOO3fB9qAKXifRfC3ivR5NG8T21rd2LSRT7ZWwY5opFkhlRgQY5I5ER0dSGV1VlIIBqZwjVi4TV09Gns12YJ21R4f4rsfFHgfxA9pZWl14r1XRdHv8AX/B+oKrtqM+npJAL7QriYNHG7Sh4Et5ZHYOVhlmikmsRPP8AAY3Bw4TzCnj8K+WhVkozjfRXvZrfRavurNJ2lZdsJPEwcJbrVHv2kahpeq6Zb3+i3kF1YyoPIlgcMjKOOCPTGPYjFffxlGcVKLumcWxLb3a3MsyRo2yFvL3nGGbuB344qgHT28dzsEuSqOH254Yjpn1GefqBQBLQAUAc5450Xw94k0b+wPE2hWmtWN6xV7C6hWWObCkrlD6NtOf4SARggGs6tGnXg6dWKcX0Z2ZfmOLynEwxmBqSp1YbSi2mtLPVd02n3TaejPjP4k/A6X9ltJPiz8EfjDpGirPtmn8K69dReRqEcYZmihMrZnZQwVUYeYAzlZQxAPx2Oy2WRN4vAVlC/wBiT0duivv5Lfezuf0hwvxrS8VIQyDi3LpYhxVliKUHzU3KyUpKK9xNq8pJqDaipU3FNrrfhl+23p3xQ0t/Dem/DnUp/GMeg316dOSMzWE17AmY0gdS0qLJjhWC7WKorOxUt2YHiWOOi6cab9qot23TaWy66+fpds+d4r8EavC9eOMrYuH1GVanDmbUakYTlZyknaLcL/ZburzcYxTS7HRfCn7RHxJtPCeq+N/FEfw7i0LUpp/EOkabMblb9I7mFre3ykhCRmJZVkbzGHzAlWDMqdsKWY4xUp1peycW3JLXms1ZaPa176v01svl8RmHBfDlTMMNltJ46NWnGFGpUXL7JyhNVJ2lFXkpODh+7TVnaScVKWr4U/Yy+AWna7qfi698JtrVxqeo3N7GmqzG4iiSR8+WsZAQoCCVLBnw2C7VVLh/AUqkqrhzNtv3tbX6W2t6pvzMsw8X+Lcdg6WBp4n2NOnCMP3S5HLlVuZy1lzNbqMox0Voo93tLW2sbWGxsreOC3t41iiijUKsaKMKoA4AAAAFeykoqy2PzWpUnVm6lRtybu29W29233JaZAUAFAFHVBaukccsBkmkJWHYF8xWxncpJGMYznPpQA2HV7ZUWG5kH2pTskijUu28Lk4C5OPegCTz9QuMG3tlt0ODvn5b8EU/TqQfagBn9lJKu7UZpL1gAQr4VAQc8KMDP1zxx0oAsw3cE1ulyG2pIP4iAQe4PuOQR7UAYupXlnqN4sJnVra3USb1QyKZsjCso6jbu/M+lAGhaa3Y3PyPMkMygbo3cdxn5T0Ye4+vegC+xKqWCliBkAdT7c0AeHftVftYeAv2U/hzN4+8VmW/vLvfZ6LoUeYrnUr4LuVQWXMcQB3SzMCqKBgM7JHIAfid40/aJ+Knxa+N+s/HDV/Etx8MNJ+LM8fhTXtU0eK5eyj0+G3s4LpCilpJ/LtzbyyRqdzFxtA3AUAfuh+zJ8R/B/xO+CnhnWfBXxFvPHtnptjBo1z4lu9MurCTVry1iSOe5MdyofLuGYnLDcWG5ipoA9ToAKACgAoAKACgAoAKACgAoAKACgAoAKAPIv2vv+TUvjF/2Iuuf+kMtAHE/wDBOH/kyb4Wf9g25/8AS24oA+k6ACgAoAoa7oGheKdHu/DvifRbDV9K1CIwXdjf2yXFvcRnqkkbgq6n0IIpOKlo0NScdUzzn/hk79lj/o2n4Vf+Ebp3/wAZpiOk8RfBr4QeL9B0nwt4s+FPg7WtF0CJYNJ07UdCtbm10+NUCKkEUiFIlCKqgIAAFA6Clyq9+o+Z25b6GvZ+CfBmneEz4C0/wjott4ZNpJYHRYbCJLD7M4IeH7OF8vy2DMCm3BBORzVSbn8Woofu3eGnXTvuZ/gb4UfC34YLer8Nfhr4V8JDUvLN6ND0a3sBc+Xu2eZ5KLv273xuzjc2OppeQdbmFY/s2fs66WmoR6Z8A/hzaLq1ubO/WDwrYxi7gLq5il2xDzELxoxVsjcinGQKSSS5Vt/kPmfNzX1/z0f3pnZ/8Ir4X/tm48Rf8I3pf9q3dimmT332OP7RLZozMtu8mNzRBnchCdoLsccmhpOMovaWr89La99NPQS92Skt1t5enY47Tf2b/wBnfRra+stH+Avw6sbfU444r2K28LWMSXSJKkqLKqxAOFljjkAbIDIrDkA1SbW3qHkei0gPO/iX+zv8CvjHexan8UfhN4X8S38ESQR31/psb3SRKzMsQmx5mwM7nZu25YnHNLlSd0NttWZu+F/hf8NfBHha48D+Dvh/4d0Tw7diQXOlWGmQwWlx5iBJDJEihZC6AKxYEsBg5pzftFyz1W3y7EwSpvmho9/n39dEaB8H+Em8Kf8ACCN4X0g+GhYDS/7GNlF9h+xBPLFt5G3y/K2fL5e3bt4xik0pbjj7nw6f8Hc5nRPgD8CPDMcUXhv4KeA9KSDULbV4lsfDdnAI763Di3ulCRjE0fmSbJB8y72wRk0wudFJ4I8FzW+vWsvhDRHg8Vbv7djbT4imq7ohC32obcT5iVYzv3ZQBegxSSSjyrbV283u/n1Hd83P10166bfd0KHiL4U/C7xf4Z0/wV4s+G3hbWvD2k+V/Z+kajo9vc2Vp5UZjj8qCRCkeyNii7QMKSBgHFN6u7EtFZDp/hb8MbrwQvwzufhz4Xl8Hoqovh+TSLdtNCrJ5igWpTysCQBwNvDDPXmiXvO8tQXuqyKfhz4KfBrwdPpl14R+EngvQ5tFnubrTZNN0G1tmspriJYriSExxgxvLEiI7LgsqqrZAAoHdpW6G/4f8KeF/Ccd7D4W8N6Xo0epXs2p3q6fZx24ubyUgy3EgQDfK5ALO2WbAyTQtEktkLq31Zq0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDl/iJ8L/AId/FvRLfw38TPBmk+JdLtL+31SC11K2WaOO6gfdHIAe/wB5WHR0d0YMjspAP5+fiL4s+Nv7NX7Zmv8AjLXvENxqPxD8HeLZ7yXU7+3eBdV+ckSvEjKVt7m3cHy0YDyZtqkDFTGEYK0UbV8RVxM/aVpXdkvkkkl6JJJLZJJLQ/oG+F3jq3+J3w18K/EW2sHsI/E2jWWri0kfc9t9ogSXymJC5K79pOBnGcCqMTdRlGqzIT8zW8RA9gz5/mKALLosilHUMrAggjII9KAOX1DTI9OlRCsKQLuaKaRFIOeSjEq2SMHbkfrQBJpugC7tpDdzuGKoB+6TIyoc8kEjlyOCOn4AA8w+JnwysrXxNqHiSfVzp48Z6fp2gSan5kYGmahZXU1xpNwiTNh2N1eSgoxcSSfZk8sq0mfj+LqFelCjmmG1lQd2u8Xa/wCWvZNvodWGabdOXU1R8SdDv9D0X/hIP7QtPFGrSSW40V4JJblZY50gnCRqmWhilePdcqvk7GEpcROr177zfBRwax8qiVNq6b/LvfyWt9LXMfZT5uRLUydQ1zXb+W/0vW1kEU0kNouieHZ2k1JVkj3tJeXIZI7L5Xk4VsgxIYp2aRIh8tPP8yzuXssjpOMetSa00fTdfm9fhVrnQqNOjrWevZHO2enR+HNP/wCEW1q2Ol2PhmD+1LGwsdQmvEudEaNBeWYLAz3EdtcYfZHEqmAWUKg+Y8deRVyqORZtQrZharCrpKTVkp9JNbdVdtu/vStdK2iqe2ptQ0a/I9V03RrfT9HTTtF0qCy0fTIktYYLOJI4reGMKqxxxphVUBVwq/KFVQCAK/UTzze0NLXWLLUtH1K3jubSbiWKXLq6yAh1IbtweDzyevWgDgvD3gLR/Bxtxbtc6tqWifatMstU1JRc6hHpz3PnLam7I81o02Qr8zFmEMbOzOCx87A5VhctnUnho8vtHdrpoui2XV/PtZK51JVElLodvZeFrbULOG8Fy8RlG4oFyByeBnnpjrn8a9Eg6iGGOCMRRLhRnvnrySSepz3oAgW3u4TJ9nuYyjMzqkkZJDE5I3AjjOe3egCJhdNHJHd6XBMrgFxFIDvbgchgPQd+woA8F0axg+Ed83h6DT7ey8P+F/GdqmjadZRGRpLLWSsUMLbmjjh8rUb2QgqrhLaBEAZ2Y18N7OOW8VR9mtK8Hfya10/8AWnm+yR139ph9ej/AK/M9vk1GLzraa+dYJkuDugJJZB5bLwP4skjkDHOO2T9ychT1mK1klSe0ju0knkAcLA6ZYAlXG4AFge2ecnoeaAOC+Jvg5vH/gLxR4D+2DTx4k0S+0dLuOIzrCLm3eLeqbl3BTKDtyCehIOTQBmWfjvxRJ4osjqXhjUvCus2nnQppWoXGn3Nj4gWMBpjaXMEjyLJGglaIS/Z2dWkaSAhC0H5zlOEzfhWtHDOCq0akvs7p7X1ta6SvduOm63fdUlSxC5r2aPaIw+oPIZxLAsTbBEsu1s4ByxQ+hGBn37jH6McJbjjSJBHGoVRnj3PU/WgB9ACMSFJUZIHA9aAMlLoXKWOnu0yzB0aYSRlWIQbs88csq9CetAGvQBDdXAtYGmK7iOFX+8x4C/icCgDhvix4LufEXw81D+yi8PiXSYpNW0O6tRJvttTiibySixsrOh/1bRE7ZY2aNwyOynlxmDo4+hLD14pxkuv5+q6PoVCTg+ZHP8Awq8R6Zf3+veHfBWo2l7pl/aaf40snspbdYYbXVTcFhD5aAuJp7S6ufOcsXe7l+YKiivnOC8RWq5a6Nde9Sk4a76Wdn6XtbokjfFRSqXj11PSbaO1vrlXsXP2OJUZwpwryAAoMY/hAGf+Ag9DX1pzGoHRmZFdSyY3AHkZ6ZoAUkKCzEADkk0AeEeK/wBsP4W6b42tPhn4Qu28S+JNSKQ2f2TH2F7iWLfbxG5ztPmFolDpuRTKCxAVseLVz7CQrrDU3zTe1trtXSv56K6ulfXqfp2X+E3EOJyqed4uCoYendy5/jUYy5akvZ7+4lKTjLlk1FqKbcb8dp8/7WnxX0DSvFmn3Fp8MbttXnW80rU9PknuLnSx5flM2IzLGwdZ12gW/mBtw2gKx54TzbMKUKsLUfed4tXvHS3nff8Alv5aHrYjD+H/AAjjq+AxPNmKdKPLVpy5VGt7/MlZ8ji04O/77kataTckrUH7HXwduPH2q+OvEfhvVPEl1q15NfvZ6hqDx2sEzTeZlVVIywH3djl1IzxjAGn+rmBnXliKqcnJt2b01d+lvxujk/4jRxRh8poZPgJwo06UIwUoRXO0o8urlzJPreKjJPrfV+sad4b8NaRC+j+EvAen6BCjM8lvpNtHBGZGCjzGWJACcKBnHIxzwK9mjQpYePLRiorskl+R+a5jmuPzeqq+Y151ZpW5pylJ2u3a8m3a7bt3bNvStVutHmkj1a3mWK4ct5hjI+fPLc9eMdPQcVqcB1D3EEZjEkyKZThMsBuPt60ASUAFAEE17a27eXJMN56RrlnP0Ucn8qAIVuru6RXs7YIjgMJZmGCp7hVOTx2O2gCle2VxPdQW97NLLHI2FYYRUfa7ZUKc5GFxnPegCrY26WyadeT30khlZDHDGoWNNwwSVHoWwTxyfegDpKAK9xd+U4hhiM07DIQEDA9WPYf5ANAEGnSSr5lu9qysszmQhwyqW+frwT94Dp3/ABoAqzpL9tm1S0MyrG0cLrGmfOw/zkjHOAcAjnIPpQBJfpY3emuLUovnTJhkUAiUsBkj19c84zQBdsWAgEGwI1viJlUkhSAMYJ6jBB/H1oA/JX/gqJ4C+N3x/wD2hF0PwzZeFB4b8BafDp9mt58QdGtJZLm5iiuZ5ntLm7jktmIkhjwyZdIY3yVZAAD0n9lj/gmn8C7/APZy8F+L/jj8LZdV8ZeIoTqd0JNcv4DHDO5e1h8q3kjVf9H8sspG8SO4LcBVAP0G+HXgHwh8LvBWleAfAfh630PQdHhMNlYQKAsKli5ycksxZmZnJLMzMzEsSSAdHQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/sRdc/9IZaAOJ/4Jw/8mTfCz/sG3P8A6W3FAH0nQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPy/8A+CsPwF8VeKfFGm+NPAXwf8F2NjLp5l8Q+OtW8R6Vpkl3dsUhitSt5NES0UNtHtfLbhcbVCsmTy1qSnVpzlNq19E7KT8+9rXt9+h7uWY2ph8Bi6FLCwqOajepKHNKlFNpuL2jzcyi5NaactpWa+wv2KfE8+vfstfC7Xrlceb4Y063uM3EMx3QxC388tE7rlnhYspbeuQJFjdXjXqPCPcRIs2r7Izn7NAwk56FypA/JCfxFAF2gCO4t4bqF7e4jDxuMMp70AYkOonQC2mXkMkzEmSF4vmaUMSeQed2fz/mAeS/ETxNrXjPw9qWkeILPVdE0rVLeWHTrPSgJPEFzKJWSK6t9uUs9jfZpY55W+Qyo0wtGjOficbxFXzGtLL8ooe0e03NNQS2aadn3WttmkpHXCgqa56rt2tuXbLQNburqW+vpk0ODWAlzqljpUSQXupXTWn2ZpLm5VmMZEawBRbsHR7WMi4kj+Rs8q4Iw9BRnmD9pJbRu+WKve3Ry1vvZO793qOpi5PSGh0On6TZ6VYtbaZY21hZ+ZLNNFbRrGu+SQyu2AOWkd2csclnZmOTk19xCEacVCCsloktkjjbvqzM+IFu9ro1p4s8KaXcap4l8PS/bVi8wo93asVF3ar8yBjLF80ayER+els7n92MeVnuVRzjAzwr33i+0lt/k/Js1o1HSmpFHwH4hje4Hh3TtQgutJ1KwTWfDl2l1Gov9JcKQQgVCRbmWONvkceVLas8jSytt4OF82lmOFdHEaVqXuzT300T+dtfNPpYvEUuSV47PY7jSb+HS7lZrlZJJBE8bqoBZVGCAe+Rg9egIGflIH0xzlO7WS/f7Vsih8wuwBkQE5djzkgnrjOO1AHc2LpLZQSxxLEskauEXouRnFAE9ABQAUAeGeNtQjHjnxLp8lx5+m/2l4XexS1KTzv4jW4ErWjRgs6IIIdLlclFVIZZZd4Cu0fw2cr/AIyPBywutVJ8y6KGuvrZz67paaq/ZS/gT5tv1/qx6rcS2iag7atKhHmbRDOhZQmDtdOo5xySOPbv9ycZjXCPJ+5Ds1vGEBJmYwCQ4yMgEADcRjI4XrnqAVVJjKXEcWzG0ghtxVsNtxgZHTIB54B560AJo3jjwnB4itdI1HV0XUrzVT4esLcWspLXo09r8xbtmBi0iZ9xIX5Sud7YPGsww0sW8CpfvEua1ntte9rfjcvkly8/Q7Jrh7S/mhFuX+0bZY9pALsBtYDcQMgKDjPSuwgtW13DdBvLJDocOjDDIfQj/IoAmoAKAIrm1t7uIw3MSyIecHsfUeh96AIDBd22XtZ/MjA/1U7E/k/Ufjn8KAI4bj+0bmB1t5FhiUy72HG88KAQcN8pY8ZHTmgDQoA+Z47DWfgjpdp4M0/xHZ6FqHhPSYdL8O6g+k3mrxa/osULrBbS6dBLHLcX0JjJKQMznb5kexLmW3j+Bq4fMuH80nWwNN1qVdtuO3LJu+srOy10bsrOz1VzsUqdemlN2aOi8OfHW71rUYdJ8OyJLqYcXcmia94S1XwxfarahgLmSxbUQgmkiDK52o0YYxRyPbiZZk0lxJmmWVIyzjDclKWnNF3s/Oza+WjerV7WBUKdRP2Urs7Lxp8YPhj4R8F6d8TfFPiZ9K0O92JE81vcwXMrOpKwm32CdZVIO6N0DR4kDhSrY+uxGYYbDUViKs1yO1nve+1rXv306anocP8ADWa8U4z+z8ooupVs5WulZLdtyaSV2lq1q0t2keCa9oX7Qf7WPiLUvDc16/gD4RabqU1k1xA7tdeIII8qJI2ODNE4wQxxDiTP75o8D5+vTx+eVZUr+zoJtXW813XdP7tftWP2XK8Zwl4W4CjjeT65m1SnGfLJLkw8nZ8slryzi9Gtal4/8ulPX3/4Y/CXwv8ABjwhY+AfBlzqLQB3zdX1x59yULPIQDgKihnbCoqqDIzY3OzN7+AwFLLqKo0r2XfV9/6S0+bZ+ScWcWY/jLMZZlmCipStpBcsVZJXtdttpJOUm5WSV+WMUu6trW3tEKW8e0MxZjkksT1JJ5J+tdp8yTUAQXNsJwro5jmj5jkA6eoI7g9x/XBoAqy6fe3sLLe3uxiMBIVHl59Tu5b6cD+dAGINPmkuf7PuJJYpoo2+zI+Hhfg8LuHPXOTkgcdRmgDp40S0gw0rFEyxaR8kDOeSew9+woAr77m/GIC1vbn/AJaH/WOP9kdh7nn270AWYbeC3z5MKIW5YgcsfUnufrQBRt4L1Vjs2h8lEkMryo42t827ao6jJPQjgcZNADb2G6u79raG5SBVijlVim5gwduVGQOnB68EDvQBnPAul3MttFAzRxBbj7wLLEJFfIyemVcHA7L9aAOjd0jRpHYBVBYnsBQBi3cZvdNt54wuLi7jkfoflLbQGHcj5QR7UARvbPb3V0EnljgieANHnaDGx5O5cEBcnHP8ODkDAAN2FIUiRbcKIwo2BemPagCi0Ii1gXE0QxNhIWTGNwViS/fONwB5GM9KAJrfA1C7CN8pEbMvP38EE/TaFHpwfegD8TL/AEa8/wCCinjfwj4ii/ZT8R+BNY1nV4ofEPjzwpZz3Gh6nayXixzS3EUkQjhlijEzfaBNIzyRMkiEMr24B+19ho1loeh2ugeHrWHT7PTraO0soIkAjgijULHGq9lCqBj0oAuQyiaFJgpUOobBGCM9j70APoAKACgAoAKACgAoAKACgAoAKACgAoAKAPIv2vv+TUvjF/2Iuuf+kMtAHE/8E4f+TJvhZ/2Dbn/0tuKAPpOgAoAKAPLf2l/EfjHw18IL9/h/rcWjeIta1XRPDen6nLbC4FhJqeqWtgbkRkgO0S3TSKp4LIM8VnNwcoU6jaUmk7b23dn0uk0n0ZUZOCc0rtJ2T2v0vo9L26HB/DPwh4q+A3x80n4YyfGbxv468L+OPCuraxFF4xv/AO0r6w1LTrqwRmiutqkQyxahzEVwrQAqfnIHRDnlGUbKys0+u9mvP7Nrt9TCpKnTlDV80m15bXWnR6Svbe67H0dWZqFAHz3+3Fqfxu0j4B+LtU+D/ifS/C8Gm+HdV1LWdbcytqcEUFuZI4LBEAVJJSHQ3DSBoR8yKzlSmGIm6cea/WK9byS36JX877abm1GKnK3Wzf3Jv5/1vse+aezPYWzuxZmhQkk5JO0V11klUkl3Zy0W3Ti32RYrM0CgD4z+NfgX4nfCbVdF+Imk/tPePPEPxa8T+K7S10DwgLpYPDmpwSX0YuLRNHAk2W9vp7SNJcGQunl+c0qsRRh0lUpwm9373p9p9bd+tthYh3hOcFa17LT/ALdXS72XS+9kfZlAwoAKACgAoAKAPMPjP491/TX0r4W/Da5Efj7xr5sWnXJtxPHotlHt+1atOh+UxwB1CI3EtxJBEcK7MsxtUqezTSsry7qPl5yekfnLVRY5Jxhz28l6/wCS3flpu0Uf2Q9c1rxN+y98LfEHiPV73VdU1Hwtp9xeXt7cPPcXErQqWeSRyWdieSSSTXRXSjJJdo/+ko5sNJyg2/5pf+lM9drE6AoAKACgAoA8f/a3+KGsfB39nvxX488PaxbaPqcC2en2mqXMImi02S9vIbQXjRkMHEP2jzdpUhvLwQQSCJKU4xk7JtJuzdlfV2Wrdr2XcG+WMpWu0m0r2u7aK+ttbXPFv2UPEEenfGO68LzfEn9oiJtV0S6u4/D/AMZNM41popoM3+lXBwYBCJCsttgFluY28tfKyNKcXKErLa3qtbXb1unt6211s4qSSnG7WrtfZS0vZLTXrtt06n2TWZYUAFABQAUAFAH5uftD/Ffx7Y/FT4p6lH8RvjXaa54P1a3s/CV34MngPgLSo2trdoF112BiR1uHla6F1ysZXYNu0VjQk9JNr47XlfltzLT7nbT7XmaYmMU7K/wp2XxXt/nqr/ZsfpChJRSxUkgElen4Vu9GYU25QTdr26bfLyHUiwoAKACgAoAKACgAoA+YP2pv+S5fAr/sYE/9LLOgD6foAKACgAoAKAPgH4N/8pkPjt/2T+z/APRGhUAff1ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfJX7an7Cc/7X3jHwDqtx8VdX0DQNCnaDX9JSSSWK4tMO6z2kRbyYrzcTCZGQ5jlDHd5CxSgH5f6d+2B8bf2X/j/e23hXSNW8N+DvD8s2j2/w51VZLa3j0R55J4oZI33MLgi4aVbpi8haUsXkR2V86UJQjaTu7t/e729FsvI7cwxNLFV/aUKahFKMUl/dio8z/vTa5pdOZu2h+yX7KH7Q/wAP/wBpj4Q2PxD8AxzWhWQ2Wr6bcYM1hfqqtJEzAYcYdWVxwyMvCnKLocR7HQAUAZf2KDVzcyTwYt50jSNs/M23cfMHp94Y9QPSgDlbzSItGvZVjsbeCW7l+0STQwKi3LrGkfmPj7z7I0UlucIoJ2hdqSS2AltZYrSOZbuyWeW4TCJIuwxnrn/ZXnORjoMd9rArwxmGRVIjMwdiSwPyheckEHgYyRjOMj1AAJr+4nlnFzeNGGkwWXYFPAPA4J9iTxk45CmgDmtH8O2eipdR2N3dfZ5dWn1e2t8iNLaaZf3qoIwu5HkeaciUyHzZnIIAjCeRHJcPTzJ5nBtTas0no/NrvouttL2uaurJ0/ZvY6cW0ltfSW8dtMgZLj5duD5RUhT8xGR9fSvXMinA0PkeVLGnmmeNlLDBKEHOT6fd/OgDu9K/5Bdn/wBe8f8A6CKALVABQAUAeK+CtIuLfxJd6X4okub+48M+Ldauxd6m7Bo4dRae8tZrN924pFBefYQcDAS5jHCfN8Xh6FXDcU1akoNxqQVmlotEvefS7g/w+XVJqWHSvs/6/M9KvRpNwimy1EvcKR5YM0kpPIyAASw4/u4PvjNfaHKY93st7gi3sXgaEK+wksyEqrbcjJ42s2cqeT0xigCSxaGy8tzYQzeYflablygAO0L0DEEEdfvgcnNAHmuueI4/hf448d6voGk2Fpp+oSaB4w1HVb+/8q2kgZo9P1VgpXEQtNP0+3mZidu6dN/lgl2+KzinHC8Q4LGzeklKG2zs0tfNz28up10nzUJwXr/X3Hu00MWoWq7wy7gHRgcMjY4IPYivtTkEt7mQyfZbmPZMF3ZH3ZADglfzGQeme/WgCzQAUAQJLJ9qkgkC4Kh4iDyR0YEex7/7QoAi1Nt9s9milpbpHjUDHGRyx/2RkZPuPWgC2AAMCgBaAI3gtzKtzJFGZIwcSFRlR9e3egD5x+M/xq0j4heHh4f+CGlSePdY0zxLp9vcXOk3Dx/2PcK7TJcrJ8qSriCRCS/kMGZZC8ZeNvnMzxtPMcNPDYWPtbvlkl0v16Lpo7pJ630P1DJvD54OsqvF1R4GlKlKrTckr1HGy5bLmlF3km48jm43SjrdbHhf9mLQPCmq+LPEni7U7bxdY+IdWfWU0PVbCJrHT5Gnln/ch9x8zzLm4+YbQ/mncpb5jrl+QUcFF06k3UjpZSs0lHb5rXay8jjzHxDxNXBYPC5TQjhJ0IOEqlJyjOpzJKbk48qtPljJpqUuZXUtbHsk0+px3DXcOlv+8jWJg7KdmC2G+UksPmORjPFe8fnZNby28c5kneZ7iXbGZGtnRQM8KCRgDJ7nJJ6nigDQoAKACgAoAqanbS3FtutwDPC4mhyeCy9uo6jI/GgCKymTVibzeTbo/wC6jxjJGDubnk56Dt169ADQoAKACgCC6hkkCy27Ks0WSm77rZHKnvg8fiAecYoAoJcWupOi2sAEplV53AB8vYeQSOueVGD0YnpmgDMW4vIIxpV7Jv2ERrGQY12fdBZgpJXIX0B3ck8gAF660k2tjHbQygRSNBFOAMEncq719GPGf8eoA4yzmHz2Z1nEIt7sMmCpwcSemAxbkcYJPagDWiiSCJIYl2pGoVRnOABgUARX0DT2zCNQZEIkjz/fU5X8MjB9iaAPmL9v/wDask/Zd+B8fjDwjc2v/CZ+JphpXh+G4RJQpK75rp4iwLLCg4OGUSyRKylXNAH5zf8ABPXxB+17+0J+0d4ZtD8cfiteeDfCl/Brnii4m8S3txYpbxM0sdrOkk4R/tUkfk+X8zFWlfYyxyYAP2+oApSONNlMrti1mbLf9M3Pf/dJ6+5z3OACW2uLi4O9rNoYioKmRhvJ/wB0ZwPqc+1AFigAoAKACgAoAKACgAoAKACgAoAKACgDyL9r7/k1L4xf9iLrn/pDLQBxP/BOH/kyb4Wf9g25/wDS24oA+k6ACgAoA4T44fDW9+Lfwy1XwRpPiabw7qs0tnqGlatFAsxsdQsruK7tJjG/yuqz28RZT95dwyM5qW5RanBJuLTSezt0fZNaNrWzGlCV41NmmnbfVWuttVvucJ8Jfhx+0Fe/E/8A4W5+0Z4j8EPqGk6Dc+HNB0bwdDc/YYYrq4t57q7lluh5pmkNpbIEHyIsZxksTWiUEnLXmdvRJX+9tvV2Wy+UO8motKyu79W3p8kltbfmdz3apKCgDgvj34C1j4p/BHx58NfD9zZ2+p+KfDuoaRZy3jskEc08Dxo0jIrMEBYZIVjjoDWGIpurBRj3i/ukn+hrRqKnJt9mvvTX6nb2kTW9pDA5BaONUJHTIGK6qklObkurOenFwgovoiaoLCgD4s8H/B79vjwv8SvEPxXv7b9n3xB4n8QztCNV1XVdbln0zSt+6LTbJVtljt7dOpCqGlf55WkbBDo2ikp+XM1u/v7dFsvW7HiHztunsr8qb2/4Pd219LI+06QgoAKACgAoAKAPMfH/AOzf8JfiZ4u/4TvxVpmvjXTp8OlNd6V4q1bSi9rFJLJHG62VzErhXnlILAn5zz0oWiaXUHrZvoU/2V/gi37PvwQ8NfDa8uTcatZWUB1eaPVLu9t5L0QpHI1ubk7oof3Y2xosaDkhFJOdKk1Nq21l+Wv4mVKEoJ82939121+G561WZqFABQAUAFAHn/x8+EVn8dvhF4i+Fl3rU+jtrMMTWuowxCVrO7gmS4tpthIDhJoo2KZXcoIDKTuFQnOlUjVpu0otNeqdxOMZxcJq6aaa8mrHm3w9+Ev7Qvij4veHfix+0trHw6L+A9P1Gz8N2HguG+CzXF8sUc93cyXRBXEUJRYVVhmVmL5Vc3TqKmp23kreSV1J/NuMdey21FJN2V9E7+ujWv39OvkfRNZFBQAUAFABQAUAfJXi/wDZw/aUsT8SPht8J/Gnw5i+G/xZ1LVNT1a78Q2F5LrejPqibL9LZISILpRl3i81kKl9jblUGleVWmqNZ3jG6VtHyuTlb75PXV9Rq1Oo61Je87N+qiop/dFaaL8b/T/hLw1pvgvwro3g7RzMbDQtPt9MtTPJvkMMEaxpub+JtqjJ7mt8TXliq06895Nt22u3cyo01Rpxpp3skunT0svuSRrViaBQAUAFABQAUAFABQB8wftTf8ly+BX/AGMCf+llnQB9P0AFABQAUAFAHwD8G/8AlMh8dv8Asn9n/wCiNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPkX9vT9lz4K/FL4b6z8UfG/gTUtT174faVda5ayaFfxabe6hYwRtJNYS3DwyhoRguuY2dCSIiu9w8yTkmk7ef/DmlGcaVSM5xUkmm4u9nbo+Vp2ezs0+zT1PhD/gn3+3lZfA7Vbzw78WPEltofwvjtng0rSrDTFnlt795Y3aZ2jU3Lp5Yl3M24GR0wBl64cPz4accPJym3duT2X5LXWyV7dran1mb/Vs7w1XOaUaOFjBwp06EL80lZ3e8pNxXK51KnKptu0uZcr/aAalY7FlNyqoxwGfKjPpk9+DXoHx5C8kWpyrbwyJJbJh5mUhlc9kz+p9sDvQBfoA5zxNexPNDaLIitA4ldioYrx6EjPbg9c+1AFa/uDqmnx+fbsl/HGJS4GFEPB3+456DkEH0oAyL17YbTZyyPlBv39UYcYB6HpkY7fTgAhjbbKJNqDDK5LL09fl9Dn0oAnmhltjLEp3RquUwx27WHLZ4z0A6c8D2IBZsJbvUrtALwI0Nu8YZxksmCcYzyfmP4DPOKAGxafcy3EMEUAneSDcAV24VolwxJ7An8196AOrh020ZUutOmltllAkHknCNkDBKEFentQAu3XRwJbFgOhKOCfwzQAY13+/Yf98v/jQACLWZcJLc20I3ZLwoSxHHADcDvzzQBNb2ENvM1yXeadxtMkhBbHoMYAH0FAFhmVFLuwVVGSScACgDln0u9nuDPbWhdbhzLG0snyhSQcvzzkFvlxnDnk9gDWbw/ZNbuiKkc7j/AF0abSrdioHQduOo6nvQB498TxpreJfh/oOq6Jp2pReJNU1bwbqkF3CrxSaddaVeXtwgQHad0ulQIQwKmNmG35gV+S41o82VPERbUqUoyi13uo/rf1SOnCP95y9zovhZe63J4T0S61HxHYarra26WmsCzxtm1KDdFdphCyJtnSZWAYhGVlydtfS4XERxdCGIjtJJ/ermEo8rcWeg3TXTfZLsKLaViYHRsMyhyMlcHBI2g/TJI7V0EklvqH+hGWb95LFJ9ncJjLSBtvGcYycHnpmgBzSaq+0xWtsg4JEkpLYxyOFwD75P40AZt1qe++RGuEsJrYMszOUZSGwQF7nkA9AcelAFiC+iTE0EF5ezTgAyLAyKQOR97AC89vxz1oAiOp6292bY2VtabYjMTPIXG0HBOV479KAPO/iL8fPDvg6x1220bX7HxN4p0Oxe/fw1o8itdyImNwLfPsCq3mNlSwRSQprzcZmlHDRmoNTnFX5U9f66vrbWx9tw7wLmWd1cNUxMZYfC1pqCrzi+RN3tba92uWOqi5NLmR5d4y8CfHv9qjRtDeDxDqHwu8KXtnMmvaRqNtcf2ot386lCm2FZrd0ZQAGRSGbepYBF8rE0MfndOHLJ0abXvJp819dOl4tea81fQ+9yTNOE/C/FYh16UcxxcJp0akZQdLk91qSd58lWMk7vlk00lGSi3J+wfBz4L+A/gbptz4b8A6DMy3Yie9vLmZZLi5cNIgeVzjjA+4ihAdxCgsc+xl+W4fLKfs6C33b3fr/VvI/N+LuNM443xixmbVL8t1GMVaEE3dqK/NtuTSScmkrd5BAsujG3kfasLsoJG/AjkO3I7j5Rn1rvPlClG+wwzzxaagMUUnmNAY/v5yofJweP16UAKtkupM9rbu0UCjc08N48qs3OFwcDPQnr0HrmgDatZjNF8+BIh2SKM/K46jnt3HsRQBNQAUAFABQBmyLLpUxkt42mtp2JaFTl0fBJKA9QcHK9uo7igC7bXVveRCa2mWRD3U9PY+h9qAI5r+KFzGI5pWUgP5cZbZkZyfwxwOeRxQBLNPFBA1xM+yNF3MSOgoAx73xBaSW04tLlXDQPtIIRlfB/vEE9sYGeKANCK/0yKJIlvrUBFCgCVcDA+tAELvZ3WoQlZY5klgmhO1twPKHHHtmgCxcx3ssnlwPFFGqq29495ZsnjGRjGAc+/tQBXs4vMuLyK5uGumKJFKxjCp1Y7Bj0Dc8nrQBma5438MeAPCupeJviB4m0/RdL0FR9v1HULhIYY0JAjZmY9X3IAOrM20AkgUAflp+2N+3N8Ov2tvhbrnw7+FnxU8T+CdY0fWprez0UWF09p8QbFy8USI9vC08bsFVlguBFGWnCuHIDwgHE/Cz4EfHLXPjzpninxj+w/wCIfGXgfxH4M0XwrrGla/bRabPDFbaRZQTXVpcTSxrbXCy2RaJmeN2RjHmJ5MoAfqh+zf8As1fDP9lnwFP8Pfhhb3/2K71GfVLu61C48+5uZ5MAF2ACgJGkcahVUYjBILs7MAerUAZc0VxrKtEzRpYMwHQl5QrA5BBwFJHB9Oe9AGpQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/ALEXXP8A0hloA4n/AIJw/wDJk3ws/wCwbc/+ltxQB9J0AFABQAUAfNcX7cGj6r4j8WeHvBn7Onxq8XJ4M8RX3hfUtQ0PQ7Ge0+32jhZVR2vVYjDKwyqna6kgZxQBb/4bA1v/AKM5/aJ/8JrT/wD5PoAP+GwNb/6M5/aJ/wDCa0//AOT6AMbxh+3jZeAPDV/4x8afsq/H7RtE0uMS3l9d+HtPSKBCwUFj9v4GWA/GgDyL/h9X+yx/0IPxV/8ABVp3/wAnUAH/AA+r/ZY/6EH4q/8Agq07/wCTqAD/AIfV/ssf9CD8Vf8AwVad/wDJ1AHR+AP+CsvwS+KniSHwf8Ovgx8ZPEGtzxyTRWNjo2nSSuiLudgPt3QDk0Aep/8ADYGt/wDRnP7RP/hNaf8A/J9AB/w2Brf/AEZz+0T/AOE1p/8A8n0AH/DYGt/9Gc/tE/8AhNaf/wDJ9AB/w2Brf/RnP7RP/hNaf/8AJ9AB/wANga3/ANGc/tE/+E1p/wD8n0AH/DYGt/8ARnP7RP8A4TWn/wDyfQBQ1/8AbdfwtoWo+JvEX7JX7Qen6VpFpNf313P4c09Y7e3iQvJI5+38KqqST6CgDxj/AIfV/ssf9CD8Vf8AwVad/wDJ1AB/w+r/AGWP+hB+Kv8A4KtO/wDk6gA/4fV/ssf9CD8Vf/BVp3/ydQAf8Pq/2WP+hB+Kv/gq07/5OoAP+H1f7LH/AEIPxV/8FWnf/J1AB/w+r/ZY/wChB+Kv/gq07/5OoAP+H1f7LH/Qg/FX/wAFWnf/ACdQB6T8Nf8Agox4R+MWhT+Jvhd+zZ8dvE2lWt21hNd6foGnSRpcKiO0ZP2/7wWSM49GFAHW/wDDYGt/9Gc/tE/+E1p//wAn0AH/AA2Brf8A0Zz+0T/4TWn/APyfQAf8Nga3/wBGc/tE/wDhNaf/APJ9AB/w2Brf/RnP7RP/AITWn/8AyfQAf8Nga3/0Zz+0T/4TWn//ACfQAf8ADYGt/wDRnP7RP/hNaf8A/J9AGl4P/awg8TeP/DXw/wBc/Z9+MHgufxXdXFlp+o+JdGs7axM8NnPdtGzx3cj7jFbS4AQ8gZwMkAHu9ABQAUAFABQAUAFABQAUAfMH7U3/ACXL4Ff9jAn/AKWWdAH0/QAUAFABQAUAfAPwb/5TIfHb/sn9n/6I0KgD7+oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAq35MUH2sMR9mPmkD+JQDuHvxnHuBQB+Gt18QfgV8Rf25Ne8IfEH9lzwxrmm+LviTcaK15Br+t6fej7RqUkIu32XrwmUmSOR0WNUJRlQR7srk41OdOMly9Vb7rO6t53T+R3U6uDWFnTq0pOrpyyU7Ja688XGXNppHllTs7t8+iX7qVqcIiqq52qBk5OB1PrQAtAHFXRuL24Ei+U4mbzY5CWzGgJ5YjkKC5GfVPQUAU9UtLx7O5i066u7S7dJEbULdVEtvIVOWQOGTeuScMrL8oOCM5APLRDrOmeLLu4git7fxnBbmXW9Etiq2/i6zt0SL+0LJHcLDdxs0MTEscAxW87NGbK6j/AD2GJxXCGM9li5OphajbUnq4ybu7/m/5tZR15ona4xxMbx0kj0Dw9qlh4htU1HStQjltrlXVnf8AdsroxEkLhxmKVXRkZGAZWVlYAhlr9AhONWKnB3T1TWzXdHE1bRlyUySMsUkbl4AwcHhsZyR35BLc+n0qgGwNNtZ44mJRcB0yCvU5yOvG7r2HoKALiaheRxp5U0gV1QJm5KqAq7WGAwxk46+nvQB12iXVvcadBHDMjtDEiSAHlSBj+h570AX6ACgAoAKAKeqPFHAhuW225kAmPbbzwfYnAPsaALlABQB538bfEF74R8Jw6r4a0yO/8Q3msaba2WnxSQR3epL9qje6t7czEI0v2KO6cBmVQsbMzxorSJ4XE1OlVymtCtLljZau9k7q17a722u+yez2w7aqJo5H4O6hFoO/w9oWhP4j8EeI9Z1jW7XXYDDHJpF5cXlzc31nqVvM6Sh0vJJYozFG0iZMFxFC1uZ5/m8i4twmEwscHmX7udNJLRtSjbRqyfS3k7pxdnZb1sNKUuanqmeu2HiTR77TTqcDXMNskjQf6VZzWzllODhJUViPQgYI5BIr7fCYyhj6SrYaXNF9UckouDtI+StZ+Ovxy/aJ8Y3Phj9mHws3h7SNNuJ47rxjrEDpC0kIX5cPGyROd6jyykkxV1YiNVbHzlTNsdmlV0crjyxV7zktNPVO3pZvW9lqf0FhPD/hTgTAQzPjqv7WtUUXHDUpLmtK+rcZRckrX5lKFNOLipVG439ZtfhB8TJPHfgvxx4v+MWqzQ6L4egs9Z0Cy82W01bU/KmWe425SJV8yWJl3QnOwA7MDHrwwWKeIpYirWfuxSlFXtKVnd9Fu01p06H57iuJ8gp5Pj8owOXRvWrSnSqy5faUqXNBwgm1OT92MlL94l7z1ld35vT/ANmD4oH4TjwVF+0z4qttZbxCdXHiBYpvtP2X7OYRZ5F1v2Fv3vEpTJ+6T81cv9j4v6p9X+tS5ubm5tb2tbl+Lbrv8j3f+Ij5B/b/APa/9hUfY+x9n7H3OTn5+b2v8G3Nb3Pgvb7VtBnxl/Zw+PnjLx5feLfh7+0rrug6fqAjA0g3V1BDabIY0/d+TJsbeyO5wiYLfxEk1nmOU4/E4h1sPiXFP7N2ktEtLPrvsjr4N8QuFMlymnl2cZLCvUhf97y05SneUpe9zQuuVNRXvSul0skUdJ/ZN+Les/DnxF4H+Jv7Q+q6rLrdxamG4/0i6NtbxsTNGDLKCRKDtK4AG0E787RNPJcZPC1MPicQ3zNa6uyW61fX+rmuN8TeHMPnuEzjJcnhT9hGd4+5BSlJe7K0YPWDV073d2ly2u/X/An7Pnwm+Huo2XiDRfCdtPr9lp1lpi6xeDzrox2tsttG6k/JE5iUK7RKm7vmvVw2VYXCyVSEbzSSu9XorL003ta58DnfHuf59QqYPEV3HDznUqezj7sL1KjqSTtrJKbvFTcuXoej16J8cYs1tBNqG9ZLdrgzMjpND5gUlAVIGRj5Y+vufpQBY0pdovLGR0lWKYj5U2phwGK4ye5agCe1b/TL1WY7hIhAOeE2LjHtnd+OaACwVo3uoGJws7MmVx8rANx6/MWoAbdE2c329VYxMNtwFGSAOj/hyD7H2oAto6SIJI3VlYZDKcgigB1ABQAUAUbhG1CdrZSY4rdgXkU4ffjICntwwyffHc4AMy4tbu1v5rm0vQHh8t5WZRh0bI+cKAOMMc4zg+1AGhpUkivPa3bBbnzDKUAwNpxyuSSVznntnGBxQBau4HnjXymUPG4ddwypI7Ef5wQD2oAbstdRixNCGKkqysPmjbuM9j05HsR2NAFC5if7XZWN3G8iCVjHcBsHGxjtJHIYfLznnGfUUATyaNYJvuS8kcgIk89pNzIF56tnjrnNAB9vmW0mVmRrhELRsgJWRTjEgA6jkZAzjHfjIBYZoNNswdrmKIDJAycZ5Y/nkn6mgD80v26vG2kftya9rH7L3wG+IiW3jH4Yaje3V94Z1axFpH4vuraJlli067Yl2ntGSdDA6xpL5rSqzJAJAAW/+Caf/BOi6+HcukftH/HXT7q18Vrvm8O+GbmExtpSMrJ9pvEYZ+0sGJSLjyQQzZlIWEA/SugAoAp3Gbq7FiwPkrH5kw/vZOFX6cMT9AOhIoAtqqqAqgAAYAHYUALQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/ALEXXP8A0hloA4n/AIJw/wDJk3ws/wCwbc/+ltxQB9J0AFABQAUAfl58Kv8AgoR8Ff2TviD8ffh18R/DvjO/1PUfjL4n1qKTRrK1mgEEkkUKqzS3EbB91u5ICkYK89QAD0j/AIfTfsq/9CT8Uf8AwU2H/wAm0AH/AA+m/ZV/6En4o/8AgpsP/k2gDyb9q7/gqf8As8fHL9nnxr8J/CPhT4gWuseI7FLa0m1DTrOO3RhNG5Lsl07AYQ9FPOKAPgD/AIZO/an/AOjafir/AOEbqP8A8ZoAP+GTv2p/+jafir/4Ruo//GaAD/hk79qf/o2n4q/+EbqP/wAZoA9V/ZT8S+If2GP2hPDvxX/aI+EnxD8P6RLYala20M+gSWtxdu0IRjCt2YVkCGSPdhvlDr6igD9A/wDh9N+yr/0JPxR/8FNh/wDJtAB/w+m/ZV/6En4o/wDgpsP/AJNoAP8Ah9N+yr/0JPxR/wDBTYf/ACbQAf8AD6b9lX/oSfij/wCCmw/+TaAD/h9N+yr/ANCT8Uf/AAU2H/ybQAf8Ppv2Vf8AoSfij/4KbD/5NoA4n44f8Fcv2aviT8FvH/w60Lwh8R4dT8U+F9V0Wzku9MskgSe5tJIY2kZbtmCBnBJCkgZwD0oA/NXT/wBl79pjVrC21XSv2dvibeWV5ClxbXNv4R1CSKaJ1DI6OsRDKQQQQcEEEUAWP+GTv2p/+jafir/4Ruo//GaAD/hk79qf/o2n4q/+EbqP/wAZoAP+GTv2p/8Ao2n4q/8AhG6j/wDGaAD/AIZO/an/AOjafir/AOEbqP8A8ZoAP+GTv2p/+jafir/4Ruo//GaAD/hk79qf/o2n4q/+EbqP/wAZoA+yv2F/24Ph1+w38MvEnwT+PHw7+I1j4rfxVPrEtnBo0Mb28MtlZoiSpczwyI58ktgpjayEE54APo7/AIfTfsq/9CT8Uf8AwU2H/wAm0AH/AA+m/ZV/6En4o/8AgpsP/k2gA/4fTfsq/wDQk/FH/wAFNh/8m0AH/D6b9lX/AKEn4o/+Cmw/+TaAD/h9N+yr/wBCT8Uf/BTYf/JtAB/w+m/ZV/6En4o/+Cmw/wDk2gB/gj/goF8F/wBrL9o74KeAfhv4e8ZWGo6X4k1TWJpNasraGFoV8O6rEVUxXEhLbplOCoGAeexAPqD47eOvE/gvxB8IrPw9qosrfxP4/g0XV1MMbi4sW0zUJmjy6kp+8t4m3Ltb5cZwSDthowlVaqbKM390G0/k1cyxEpQpOUN+aC++cU/vTaMu/wD2uPhdp2i6X4sm0/xNJ4b1/wAUL4T0PWbbSzcQaxdNbTTLNZxxs088DNbvCkiRHzZCnlh42ElZJO0XJWcrtJ726N9r7q/TV2Ro2ve5dVG12ttWk0u9m9fuV3oZUH7aPw/uLHVVT4c/E8694bnVfEfhxPC8kmpaFbsgkS6uwjNCsTxESpsleSRQ/lo7RyKmU6tOmuaT91bvZR73v262v321NXSnzcqV29v72ttPnprbXTc9u8MeJNE8ZeG9J8X+Gb9L7R9csYNS0+6RWVZ7aaMSRSAMAwDIynBAPPIFbTg6cnCW6Mk1JXRp1IwoAKACgAoA+YP2pv8AkuXwK/7GBP8A0ss6APp+gAoAKACgAoA+Afg3/wApkPjt/wBk/s//AERoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAjKrqUdQysMEEZBFAH4H/t3fs+6z8Iv2svE1/qnjnS7S08V30njGzvxK8VzB9ru52KeVneZUlilIZCU2+UxZGcRry4zETw1Pnp03N9l+vZff6HvcOZRhc6xqw+MxcMNTtdznd6XSaiktZWd0m4ppO8kfuR8LvG3hP4i+AdG8XeB/GQ8WaNdwtFBrYCA3zQu0MsjBEjQMZI33BUVdwO1QMCuo8E6mgCnq11JZadNcxKWZAOB15IBI4PTOelAHLgAI0QWIJEFSaWN/uhTt+U4ADHa7AngiTB7UANMaFCICws/KwxwQsuHzwTggbv4jkqWwSVoAwvFvhPT/GekQ213e6lb3tg66jpmpWrrHd2F0seBcW8jqQduZUZGDRyRu6OkkTyo3PisLRxtGWHxEeaMtGn/AFv2e6eqKjJwfNHc47TdW8V6TcLqOoaXbT6tBbQv4p0rTNOMQu5GCKdXsAcyTrsUB7dmlfy1jRWMkYS5+EwGLqcI4p5djbvDzbdObd7eT/XRWd3azOucViY88PiW6Ojl8T6XeaOms+FUl8RreWIv9Oj0uSKf+0IGid4WikZhCqy+UyxySSLESQDIOtfa4/MsLldL2+Lnyxvbq9fRJs5YU5VHaKOavvGuuR6zBoqanovh/UY7Ox1y70AwTa74hk0lyUu0bT7BwYCspSFbpHu4d28bG+XPzE+La2MvHKMLOruuZq0brb1XWzcXbtfToWGUP4skj17QrTTta0fT9U0/Tr60gvbaK4D3sU9pcBXQNhreTbJE/wDeSRUZTwVyCB9dhZVp0ISxCtNpcyWydtUtXs/N+pzSsm+XY6G0s7exi8m3j2jOSTyWPqT3Nbkk9ABQAUAFAEc8MdxC8EoykilWHsaAJKACgDwW/e/8Y+NtO8S+ENfOox+MdRis9J12xmjmGj+GYLQXNxPbwzRtE8d1eQLCZ0VvMW8spDJIsNui/n2KUOJc+eBqtuhRTbSuk5bPVNO6b/8AJWktWztjfD0edbs3/idofwS+H3hq98VeJtD03THttKTTptcjaWDU4rNUaGNRfQkXm4CZ1QpJ5m6QlTuOa+yxtLAww98XCPs4rqk0lpolb0sl5G2T5Xj88x1PL8tg51qjtGK3el3q2kkldttpJXbdjxn4d/BXx78ZLQaB8VLbxh4L0XwJ4ka40eSLxJe3k+s27hplDXN1NJNI0UnkOt1kjazxxCIoSni4fAVcevY1qfsYUp3ioaJrfZdU7PmS1u7JO7P1qtnOR+HdKNThyrDGzxeH5antYKTpSvaT20jUi5xdCTurRlOU4uKf1zZWFhpsLW2nWUFrC0ss7RwRqimSR2kkcgDG5ndmY9SzEnJJr6eMYwVoq3/B1f3s/F61eriJKdaTk0krttu0UoxWvSMUklskklohl2TDc211/CGMD8dA5GD/AN9BR+NUZCWx+yTPZyHCMxeBiOCDklPTIOcD0x6GgC5QAUAFABQBnarBagLeTozCJZMhGKkjbk8jBz8gHXoTQBRiig069W2uWVTMgnlEOUiXbu42j+HBHXg7cmgDXitrfzVuoWyCvyAEFcHuPQHjpwcZ60AQ6W6zJPeKyMtxMzIw7qMKP/QaAL1AGZcubC8CWakyXiPsjJOzzQRzjtwWJ9dvr1ANOgAoAQ5wdoBOOATigDOt7GWSK9tbpfLSaUupicggkAnB9j37nPAFAEVkiW15IlzMryzsY5iejNktHgHnBQsOuPlxzQA6S1uHtEEThr3T3xG5H+swOASf7ykZ56/SgC/Z3cV7bpcw52uOhGCp7g+4oAqu8kGpyrBAGeeKMjJ2qSpfcScHoNg6E8r2oAlLR3yyWN1CY3IPykg5H99T7HHuDjgcUAZdlaRW7FZobe8MDFJv9HAmQ5+VwMZII57k8kE9KAJptIkFjG2mXjSNCN9vv2kYIPAIA4Oe5IPcUAfFP7b37UHg211ZP2PLX4xXfw08R+KdPV5/FFvdgweHrnzYns7O+liYSQQXSB1klDBoUeF3V4JWIAOK/Zf/AGQfG3xy8Q6N8T/2vvh3rHhX4g/CHW7XTLbXLa8e2uvGENnCht5b1lz58ls6wCPUYJczpmNyxhD0AfpJQAUAFAFWwRWR7zzC7XJ3hj2T+ED2A/Uk96ALVABQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8mpfGL/ALEXXP8A0hloA4n/AIJw/wDJk3ws/wCwbc/+ltxQB9J0AFABQAUAfz9ftQ/sy/tI+IP2lfivrug/s+/ErUtM1Hxrrd3Z3tn4Tv5oLmCS9lZJI5FiKujKQQwJBBBFAHmP/DJ37U//AEbT8Vf/AAjdR/8AjNAB/wAMnftT/wDRtPxV/wDCN1H/AOM0AH/DJ37U/wD0bT8Vf/CN1H/4zQB+/f8Aw0z8PP8AoV/iv/4afxT/APK+gA/4aZ+Hn/Qr/Ff/AMNP4p/+V9AB/wANM/Dz/oV/iv8A+Gn8U/8AyvoA+Af+CrNn4y/aV/4Vd/wpX4QfFbxD/wAI5/bf9p5+HWu2nkfaPsPk/wDHxaR7t3kS/dzjbzjIyAfAX/DJ37U//RtPxV/8I3Uf/jNAB/wyd+1P/wBG0/FX/wAI3Uf/AIzQAf8ADJ37U/8A0bT8Vf8AwjdR/wDjNAB/wyd+1P8A9G0/FX/wjdR/+M0AH/DJ37U//RtPxV/8I3Uf/jNAB/wyd+1P/wBG0/FX/wAI3Uf/AIzQAf8ADJ37U/8A0bT8Vf8AwjdR/wDjNAH7nfCH49+EfC3wn8FeGdd8G/Fe21LSPDum2F5D/wAKq8TP5U8VtGki7lsCrYZSMgkHHBNAHW/8NM/Dz/oV/iv/AOGn8U//ACvoAP8Ahpn4ef8AQr/Ff/w0/in/AOV9AB/w0z8PP+hX+K//AIafxT/8r6AD/hpn4ef9Cv8AFf8A8NP4p/8AlfQAf8NM/Dz/AKFf4r/+Gn8U/wDyvoAP+Gmfh5/0K/xX/wDDT+Kf/lfQB+O37b/wZ+OXxg/am8efEj4d/AL4rar4d1q5tJLG8PgTV4PNVLOCNj5cturrh0YfMo6Z6YNAHhv/AAyd+1P/ANG0/FX/AMI3Uf8A4zQAf8MnftT/APRtPxV/8I3Uf/jNAB/wyd+1P/0bT8Vf/CN1H/4zQAf8MnftT/8ARtPxV/8ACN1H/wCM0AH/AAyd+1P/ANG0/FX/AMI3Uf8A4zQAf8MnftT/APRtPxV/8I3Uf/jNAH0p/wAE4f2evj74G/bO+Hninxr8D/iB4f0Wy/tb7TqOqeGb20tYN+lXaJvlkjVF3O6qMnlmAHJFAH6kftZ/A2X4/wCnfDfwfeaHcap4cs/HEGoeJooNRNkw0oadfwy5dZEkKs88UbLGd5WQ8YyRrQ5FVvU2tL7+V2287eXfQiq5+yap73j93NFv7ld/lqcPovwj+NNnY/C/4Y6/4bm1jSPhD8Sba40zxJNf2bf2l4Zj0u/jtJ5IwySC5tzPb20qmMF3QSpvDMV3pVk6ka9R+84zUt9W4tJ/9vX1V3Z32VjKtBuE6VNe7eDW2iU4ya3+zZ2el1brc7jwt8MvGum/Gz9oDxfeaGI9K8b6doFvoVz9ohJvHttPnimXaG3R7ZJFH7wKDnIyMmvKxVOVTLq1CK96UptLydOnFeWri18u1j0aNRQxlGrJ+7GMU/JqpUk/waZ2v7PHhXXvAvwC+G3gnxVYfYda0DwjpGmajbeakvkXMFnFHKm9CyNh1YZUkHGQSK7aslKbaOSCaVmehVmUFABQAUAFAHzB+1N/yXL4Ff8AYwJ/6WWdAH0/QAUAFABQAUAfAPwb/wCUyHx2/wCyf2f/AKI0KgD7+oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPzs/4Kw/swaz8Qbbwv8AHvwP4ZGtar4RH2TXbFpURLnSdzSCZjJKqgQynaVRGZxdEt8sWK5MdKMMNUcp8is/eW68z3+FaNXEZ3hadHDrESdSNqUmlGdnflbdkk9tdO6a0fJ/8ElP2j7lNU8c/BD4r/ELQ9KkhuNMHg7w4xtbSFJSJ47yGzWMBWYlLVmjUlnkaWUhpJJ3a8JOE6EJU23Gys3e7Xd3s9fxOfP8PicLmuIpYynGnVU5c0IcvJCV23GPI3FKL0STfLa26P0/roPII7iFbiCS3ckLKhQkdQCMcUAUItK0bTEjDxIxLLGjTfOxYngDPQ5J6AetAFDWdJ+yZvbVP9HX52hReI3HSQL07cjH+IAMO4l/dRl3TySzx4QMoGxy2BuBIPzjB7A4PegDC8ZeFbjxJbWeoWOqPpOvaJcSXeiXqQeYlvceWyOk8O9RNburOs0BZCAweNo5FjlThzHLqGaYaWFxCvF/en0a81/wHdXRcJunLmieKWPh20lfUvEV94YtnHjHxfNfeKPD9lK15LaaXoaSrcLaQxyo13CdbQ3DiKDfONTdJrffM+PzfBzo4DNqeEzOpenh04xk9YuU3eN9HyrkdrN2XJo7I7pJzpOVNay/T/g/mfTfg3/hCI7a3k8CWmkW2ls9zbGPS44o4Y7uJxHNE6xjAlRoTGynlTEykZGB+pUpU504ypNOLWltrdLW6Hnu6ep1daCCgAoAKACgAoAKAOZ8c+OIvBNjbyQ+HNZ8RalfS+VZ6To8cTXM+BukfdPJFBFGi5LSTSxpkpGC0kkUb8WPzDDZZR+sYqfLHb5voktX8ul3si4QlUfLFHlfj3xdqXjvS9P8OeKfBet2VtrrXDL4FtzbXOs65DbmFmW7kjnNnb2W4uk6PM0Mqz2sck6ee1rL8hiuIMVns5YHIY6WXNUd0op9tLp9nvo7R05jpjRjRXPW+4k1rxx4O/Z10C78ffGjxHoz+NvEMWJRplrmQxQoPLsLNX/fSQRly2ZGCtNcSyEQLLsj9rC0sBwpgVRcu7/vSfXT7l2Std9T6DhnhLOeOMb9WyqlzWtzSekIJ7OUumzdleUrPli2rHlnwth+I/7W91rs3xi+HVnZfCzUSkulQSxPb3qXAJkgu4Jiu+QmKTY7gpG28bFA8xTy4P61n3O8bTSoP4VqnfdNPd6OzeifTqj9V4j/ALC8JXh4cMYyU80hdVZJqVNxa5Z06kb8sbTjzQhaUotXm78kj7B0/T7DSbC20vS7KCzsrOFLe2treNY4oYkUKiIigBVAAAAGAAAK+qhCMIqMVZLZH4HXr1cVVlXrycpybcpNtttu7bb1bb1berZYqjIq3paXbZBMrMD5rHosf8X4nOB9Se1ADYY1v9MgFwXPmRI5bOGDYBDZHQ55oAks5JWV4Zm3yQN5bPjG/gEHH0Iz75oAsUAFABQBz91NLfNHMzn7JKyrKVQllRhuEY28/N8u73ZQM4oAuKltcXEtqgceeBdo5HMcgIXOD0wQvB/2geOKALGnloU/s6QDfaoihh0dMEK3t905HtQA99N06Ri8lhbMx6kxKT/KgCpLHc2dzDa2sq+RdFhtlTzBGQudoG4YUgHjnGPegB0Oh2scEsTpGGlfzN8KeWUPbbySMHnr39OKAH6bJflpbe+kR2hON2zazDPDcEggj6YII560AX6ACgAoAqFIk1L7p3Tw5PAx+7YYP1+f9BQASBYtRikyw86NojwcMR8y/TA3/nQBSmuU0rU2Aw0M482ZFUDyskLvPPOSeeM/kKANigCOaCG4jMU0YdT2PY+o9D70AUL62KIZLkCWNPuzhcTQc/eyOoHXtwOd3NAHH/Er4h2nwo8C+JPiJqMuntbaNaS3IS51K3021uLnbtht5J7lxHbPJM0cYdjgmRcjNAH4deBf2c/jv8fv2zdG8B/H/wAPeItL13xnrMms+I7vVbSSzabTkeSS6mgdYjGEZIJooGRfJ3+WqkL0AP3z8KeF9C8EeF9H8F+F7AWOjaBYW+l6daiR5PItYI1jij3OSzbUVRliScckmgDVoAKAKeptmKKBm2pcSrE/B5U5yOOmcYz70AWwAAABgCgBaACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/wCxgT/0ss6APp+gAoAKACgAoA+Afg3/AMpkPjt/2T+z/wDRGhUAff1ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB5j+0d8Kbr41/CLxF8M7bxpqfhMeIrI6fJq+nZM1sjSRucqGXfE/l+VLHuXfFJIuQCTSaTVmVCcqclODs1qmuh+Cv7Qf7K/xE/Zu+IF/wCGBrNn4o/sURXcmqeHkuCtkryAQGcPGpikIeBsqXjDTRqJGbgc0MbQniJYWMvfirteX9Nfee5iOGM1wuT0c/q0rYWrJxjO61krpq17rWMrXSvZn9CPwvXxonwz8JJ8R5o5vFq6FYDXpI1VUfUfs6faWUKAADLvIAAHoBXUeCaHiTxAfD9k08Gmz6ldNHK9vY28sSTXLIhby4zKypubAA3Mq5PzMoya4sfmGGyyi8RipcsVp832S1f9MuEJVHyxOBt/iVpHifXpfD8wvdP1aKzN2NM1G1ntJHiAjMpiLqFukiaWJJHgZ0V5I9zAFRRgcywmZQ58JUUkt7br1W626oJwlTdpKx09trGqXNstmJYJFcFGJUyTbcqCdo68N3HO1q7SCGaCe0ieeawdNrCYSyKxdty4KscEEZxkNjOTwM4AA10+zB2Yq1w43q6n5Y0HCsHY8L93ac88q3agDx/wlZeGPi1feHrzUfB1/wCFNd0bwhYeLImgupNPls7jWZ3nuIF2FGMqzacxnWWIrIJCrgh5VPwXD+X0czrY+tiLVKc6jir66Rvqn6SXK07q2ljsrTdNQUdGkdnoXgi6fxVDrmrwwTzw6ZiTWrW8ms77UmiyscV3awottdFFaR1kc4ieRxHDHuY162VcOzyfFOWGry9g7v2bV9fX/JJ6JNszqV1VjaS17nqWgyRHTo4I5/NaD92537ufY/3fT246g19Oc5o0AFABQAUAFABQB81w32peMbvwi/iHU9VtNe+IU8miahcxX8W3w39njkvL7RbRrYB45pPs1xC1wpV8W/mNceZBaRP+cp1uIM/lhMfZU6Dk1Cz99Xsm+j6PXRrRKzkzu0o0eaG769jY8a+Lfh/+y5pf2rwf4P8AEnizWte1ez0a526rc61qIdsvFBLPdzS3G1UmleG2B2b5jtEfnM5+txFfDZJSSw9HRyStBJau26XW22mui0vc+p4Q4Mr8YYmpRniIUFClKrzVbpOMbq6drcqmrTlf3VzSSk4uIz4efs6+IJdN1Y/tB6/pnxMnOuPrGiLf2W5bcFZFO9ZBtAkWTm32mKIqu3JClYweV1HGSzGSq+83G623XXvf4dl0Pa4i47wcKtKfB1CWAbpRp1eSdnJpxdk46+64/wAW6nUTfNZNp+9280dxCk0X3WHA7j2PoR0xXuH5cSUARXLTJbSvboGlVGKKe7Y4H50AZ1hpskyPPetKonO8xlirt/vkYPQ8KMADjBPNAE9hcJFcTaUxJaDLxnnBjPIH/AcgfTH4AEtqx+03iMCCJVYAjqpRRn8wfyoAtUAFABQBVgt57eQRxPClqpJWNY8MM9s5xjJPQelAEkFpBbvI8KsDKdzZckZyTwCcDkk8etAEUgUarbnABaCUZ7nDJgfzoAt0AVNQ/dpDdZ4t5VduM/Kcqx9sBic+1AFugCncrJb3aX0cTyIU8qZU5OM5Vsd8fN05+agCRb63lCG3bz94BHl8gDOMk9B3/I+lAFigAoAq36vGn26EKZbdWIDPtVlOCwJPToDn1A7ZoArRPf6ilreiCGFFIlVGcszZUjkgYHDE988dKAFvbfyNMui7GW4njKM+3lmIwAAOg9B+fc0AWtPnNzY29wWBaSNS2Dxuxz+uaALFABQB8Qf8Fcj4QH7OnhqHxxr3iLSNGvfG1nbXk+hWcd3cSILG+kSNoJZ4EkTzI0bDSDBVWAJGKALf/BMXSPAWj+AfFFl8MP2oNe+LPhq1ns7S2sNT0a605PD0irK7xwx3Dt8sokQkR4QGPuSaAPtSgA6UAUlvro/vFsGlhckRtFIpYjOASDgYPUEE8UAKqXtxNBJcQxQpCxkwshdiSpXB4AH3j3PSgC5QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB5F+19/yal8Yv+xF1z/0hloA4n/gnD/yZN8LP+wbc/wDpbcUAfSdABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHzB+1N/wAly+BX/YwJ/wCllnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgCrqpUaZdlsY8h+v+6aAPxj/aA/4J2/FT9ky8uvjl8LXh8R6fo+unUdGuPPgjPhmBJ7ZrKa5W4kU3U/nSvGFRHjAgEkn+s8tOHHVPYRjWnU5IRfvWV2+iXWyvvZNvo0fVcKYNZrXq5bh8GsRiqsWqXNU5I02k5Tm0+VSkoJ8nNUjGL1cZ6Jfd//AATQ+OXif4vfs3WMPxF8WHX/ABP4c1O50S6v5bgXE06KFlh86cMwlk8qVRvzlgoyWcOT2QkpxUl1+X4PVfM+ar0ZYarKjNpuLadmpK6dtJRbjJdnFtNaptH0f8QPAuh/EDR00bxH4f0rWrKOZbg2ep2qTwtIh3RttdWXcrBSDjggEEEZptKSszI8i8a+DbP4W2UUnifxJLf/AA8F0jXM3ii9a/n0G5YgwX9tezrJL8ly2W+0swiV1kSWFLcxS/EZ5w5HDL+0snjyVoWdo6KSW65fTovi1TTbOujX5v3dXVM7b4ZfEDXNcXUPC/iPS7G38Y6LIltqkcURt4rjPmeVfxIzMxtrhY2kTDybGE0DSNLBIB9PlWZ0c3wscVRe+67Pqn6firPZmFSm6UuVl7xl8RrXwN4E8UeM/EkN1fWei6TeapLbWcH75o4IWkeNB3YgEDJ6r15r0G0ldmZ47481zxVrHhfxV8IR8UvDur+NrnTrpLnTPCWnz2urKtxaSeWsm2a7fTkdzFm9kRkCyKFWOTy5D8JgeKMzzZJYTCNJv427xUbpPflUmtdOZa/M7J4enS+KXyOv8F6lNceNvEMbalDqNn4nMHiLw3JHeJKr6ZFZ2lvNDHFkvtiuh5rgIYh9uiIbfJIF34HrU1gZYPl5atOT501bVt2fnora66drE4tPn5uj2PS7CYW23U79zxkRKrBmnkwVzxknHQZJHzDGBxX2pymtFLE4XU7KBS5+W5jQ8kHknj7zDqPUE4zkUAWvtsfl+b5M4TPUxEHrjODyB9aAJ0cSIrgMAwBwwwR9QelADqACgAoAQkAZJ4oA+G/jR8b9e8I+NPCHh7Q/DEV98UprqLxCthLAphh1bUYntIUNxbtCt19nsnnswJIxvSe1mMm6EhvzNYzE0M1xGMlD99JqnTi76RT3t/esrWdm3J7Wv+yeHnhzT4ooVMzzebpZdRjKU6isnKSW0W09I/FJ8stlG13dfRXwG+AuhfCFdY8RW51OPWvFskd/qlvcapJdxWszIGeBWOBMElaXbM6+YVbBY85+3yzLKeA5qivzT1au3by87O+r1PmeN+OMXxWqGCqOLo4ZOEJKnGDmk2lNpXcOaKjenF8ia0W1vW69U+BKVv5cOpXNsgCiREuNoH8RLBj/AOOr+tAF2gAoAhuLqO32qQXkkyI41GWY/wCHueB3oAzrVmSSTUZfn2IwLL0mkYqNqew2Ko9SfzAL9lbG2gCvtMrkvKyj7znkn6entigCxQAUAFABQAUAQXVv9ojAV9kiMHjf+6w/mDyCPQmgB1vP56Esux0JV0znaR/Q9R6gg0ASMqupR1DKwwQRkEUAUbRhZXJ0x5TsKB7bd12jgoD324B9cH2oAv0AZsC2umXt15gSFZ8TCVuAeuVJPGQcn6N7UAfNv7ZP7cWmfsvfDTwl8SPCXhXTviDYeMbtrexubXXFhtfLEXmLOksccqzKw6bSB3zQB8reMv2w/wBsr4b6h+1PpfjPxvpy6t8MP7Kl8Oww6FZx2tvDd6xbRRzImHkZJbO4VlWaWRlEgydwzQB6R+w7/wAFM9I/aR8axfCb4z+F7Dw7401RWbSL7TXl/s3U2Te7WnluWe3cRgMoeSRJSrjKN5aOAfe6pcWDrFDC01qTwARuh9uT8y+3Ue/AABUlOtX9gYPItwbjcjMysm1eR908+4Pv0oAsQ3AsLp7K5wkcrl7eRjw245K+xBJx6jFAGjQAUAfj7/wUl/bt8dab+0jb+A/gr44lg0TwBazabrdoYo7jTtU1SYkXUNzbuXhu44kEUQWWPdFMs5XBCvQB91/8E9vAfiTwl+znpnifxjpHh3Sta+IE48Vz2Gg6JbaXa2sM1vDHbIIbcLGGMEMUjYVSHlYEFgWYA+l6AKVyn2u+S0lXdBHH5si54ZicKGHccMcdDj2oAuKqooRFCqowABgAelAC0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHkX7X3/JqXxi/7EXXP/SGWgDif+CcP/Jk3ws/7Btz/wCltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/2MCf+llnQB9P0AFABQAUAFAHwD8G/+UyHx2/7J/Z/+iNCoA+/qACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgBGVXUo6hlYYIIyCPSgD5W/wCChn7P+sfH/wDZj8SeFfC7ahL4h8JSJ4k0WxtpJduoeSj77VokR2ndoWnWJAATMsPzKpfIB+U//BPv9sC7/ZM+KN9carpUmpeDfFqWtn4hiiJD2aRTZS/XbG7OYEluP3QwHEhGQQpEylGPxOxrSo1K1/Zxbsm3ZXslu35Lqz94vAPxA8F/FLwjpvjz4e+JbHXtA1aITWl9Zyb0cdCpHVHUgqyMAyMCrAEECjIw9T8Y6TqfiG88C6tpepSRvZvPPnSLk2aw7/L/AOPsx/ZmkLbswiQuFwxUryfOqZvgKFSVGrWjGUdGnJJ6pPrbo1qi1Sm1dI8H8QXcei+BrC+8F6rb/EfSLLUU0jT73QtW1qDVLC1SBmW3ubzRYbu5uNuR/rI41KvEZGklAeX5HFZXhsBip1sBj1h3PVxbi1vuk2rLto7apO2h1RqSnFKcOaxmfCX4hfFPV7Bf+Ef+C0elS6xBaGbT/iB468UwzLO8c0gtYVv9HlWSZFE5lFuzLxksw2muGjgcXnk54d5k3a6suX3ls3aFS7X+JLfbctzjRSl7P8/1R7X+z94mh8Y6La+OhY3OkzeOfDui+Kp9JnvnuPss9zbnzFUuAflRYIztVFygO1Sxz63Bkq1FYnL5y5o0Z8sdLdZX+9q+7MsVZ8s0t0ZXwtutG8O6zp2lwwWWt6trHizxrFHeQ2iW8+n2T6te3Vy20LuMSTx2dtI/CvK8DsSzIGnLJTlxVjHC/JyK+9r2ha/n8VvnYKlvq0e9/wDM9B1q1t7e+ZNKXEjf6wrwsWevPTkAn2AbPHT7k5CfQyltqotrAySQy/M7FTym0lScgY5wPrnrkAAHUEBgVYAg8EHvQA2KNIY0hjGERQqjOcAdKAH0AFACEgDJOAKAPGvjf8YvEHhjQZ4fhr4Pl8Za5ay2z32iWU+b6OzlYjzzDGry7DgKPlyd+77qtnzcxx08LTbw8PaTVrxT1SfWyu/w89kfb8HcK4XPsXCOc4j6nh5qfJWnH3JTil7ilJxhdXu7y6cukpIpfA39mXQfhp4z174tajqWs6n4k8TFpUGquhn02GbbJJA5jd0ll3ghpAzDCqF43M/Nl2T08JXni5Nuc++6T1to2m79fu8/Y4z8R8XxDleG4epQhDD4dJP2aajUlG8VNKUYyjHl1UWk7tuWtlH3OvbPzAKAKKITrUsmRgWsYx35d/8ACgC9QAUAUmeCznYBZZ7mbLABdzBc9M8BVHbJHfqaAG2WneSwmmOMOzxwq2Y4i3XHAyeT9MnAFAF+gAoAKACgAoAKAK93qFhYeT9uvbe2+0zLbw+dIqeZK33UXJ5Y4OAOTigCK5dbK6W9kYrDInlSnHCkHKk+g5YE+4oAu0AV762NzEuzaJYnWWMtnG4HvjsRkfjQB5V8eP2p/hB+zTo2l618YNU1LSotXuHtbYW+l3F2DKq7iGeJCi8cruYFsNtB2PtAPFv2pf2/vgZ4S+FfxC8KeFfF3iS48dp4RFxbW2jaRepPpn9pWkX2G9kuyiQwIrX1oxcS7laRVUGTCEA/JT9if4DWH7TX7Qnh34P+JpPEcfhi7iv77V7jRSolso4rSRo5izxyRxq04t4izpgmVVBDMtAH7nt+yD+zZdeB7b4faz8I9D1vTLS0gsYp9YRr/UBBDvECfbpi1yBEsjpGBIBGjbE2phaAPyT/AGlP2Ern9kvxf4n+IupfFefwr4W067jvfhvfWCC51XUr/eZIrNVSeOW3kttqs92RswqsvzsIqAP0I/ZA/bks/Gvgj4f+DP2mL+z8G/FHxfapNoNveRSW6+JtPZjFa6imU8qB7iSOVEiZwZWj3xLsmiWgD67uNW0qzlsILvU7SCTVJjb2KSTKrXUoieUxxAn528qKWTC5O2N26KSAD4sf/gpt4Nvfhh8XPG198GPEFpefCbxFZaLqOg6ldxw3M0N1dPbxysdrCGZXim8yHDBCqgSNuyoB6h4X/bR/Zr8RWHhS+f4tW/gWTxT4e/4SSzs/F839msLD7Q9sMtcHyC5lilCqkrbljZ03JhyAeK/tjf8ABQX4pfAHXPB0fwm8CaF4y8H+KWV7bxi0n9oWepOsjRz2dmlnMoMqEL8zyZLF1EeF3kA+J/2bvhP+yn8bP2lPC/gDxH4H+KPhHxQ3iDVp9Z8HSwQ3mlRxWbTTRWEtxPIt7GVig2XDPG7PIXVRACCoB+4janEo/d2t0+Bk/uSgA9y+0frQBWhn1jUVLRpFYwkfLIf3rsc9V6DGO/PtnrQBKLK8tZXube6a4dkCsk+BuAJIwyjjqexoAvRuXQOUZCeqt1HtxQA6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA8i/a+/wCTUvjF/wBiLrn/AKQy0AcT/wAE4f8Akyb4Wf8AYNuf/S24oA+k6ACgAoA8E/bs+J/jn4NfspeO/iV8Ntc/sfxHo0entY3v2aG48oyahbRP+7mR42zHI4+ZTjORggGom2lp3X5nRhacak2pfyyf3RbX4oyl+An7W7KD/wAN663yM/8AJOtB/wDjVaPRnLF80Uy+vxP8dWn7ckHwZvPEwfwfH8H28TzWj2sCBtTXWFtzcmXZ5i/uiRs3+Xznbnms6lSKnKT0ilf036nUqNsNTlvJuSv3so209WxLf9u39nG5NhqH/CRa/B4a1S/XS7PxZc+F9Sg8PzXTSGMINRkgEAG8MC5YRjaSWwCa0gnNqKWr2XV32stznate2tt7apdN/UxvEn7Udt8Nf2o/F/w38b61qt9pf/CNaHdeG/DmjaFJqWo3F5K98buSKG1ie4kURwRl2Y+WmF5UuA3PhajqOvGWrjNJeUeSLf4vqViEqbpNaKUXfzfM0vwXQ9N0f9pf4G658ItR+Otn8Q9Pj8FaP5yalqNykkD2U0TBHt5oJFEyT7iqrCU8xy8exW3pu3l7iUns9vP0Ii+dtLdb+RnfDz9qz4OfEnxhZ+AdK1DXdH8Q6rZPqWk2HiLw9faPJq1mgBae0N1EguF2ndhCW2qzbdqsQdWuq3XVeoXH6H+1T8GPFHxEn+GHhXWtX1rW7DU7nRtTaw8P38tppd9BJNG0F3ciHyoGLW0+3ewDKm4HaVY04NWbsk79V0u2vWyvbe1u6vPtFdpXdrX0fXZ+l3a+179nbmZ/27v2b7dBqsniTXv+EXN//ZY8Xr4X1I+HjdeYYtg1IQfZyN4x5gYx992OaVNOo4pac219L/f+pUnyqT35d7a/kdt8Uv2ifhh8I9e0nwh4jvdX1HxPrlvLd6foGgaNd6vqU9vGcPN9ntY3ZIxz877Qdr7SSrASmnJxW61fkimrJSez0OC+JP7T3wx8T/s6XPxP+Gvx/tPCNtqWqQaFZa9/YMmpXttqH2hRJZJpciea920ayBYmjJAYS7SgyXOEk42+1tbW68vut963HTlH3m1eyd79PW3a9/u3Wjofss/GfWfFfjLXfh18RPjHqHiHxXY6RaXdroWrfDi68I38dmksqyahJHPnz2laeCNvKKxoIEKxqXctaSnByVrpq+vl236Xb13S00vi3yTUW3re2nn3tbTotG1d62dtu5/bn/Zstb64D+MdSfQ7PUxot14pi0C/fw9b3xcIIX1MQm2HzEfPv8vBBLY5rKEo1Emno9ul/T+ttTSd6bcZbrddV6nrepeP/Cmk+MtE8AX2oTJrniK3ubrToEsp5I5YrcKZS0yoYo8BlwHdS2flBwaqPvSlFbxSb9G7fn8xSaioye0nZeqV/wAjiP2sPHPin4afs3fETx94J1T+zde0LQri90+78iObyZlA2tskVkbHoyke1c2LqSpU1KD15or5OST/AAZ04aEak2pdpP7otr8T0LwjfXWqeE9F1O+l825u9Otp5n2hdztErMcDAGSTwOK9LG040sTUpwVkpNL0TPOwVSVXDU6k3duKb9Wjx+7+LvhHwD4w+LXxP+IXxwmfwj4Lm0nQ7nRI9EuFt/Dk0sUDF5XRGe7kme6hbzEGyJCFPR2HNRcZ0lJfam4ptpJNWjy62tru5OzbVmlv01E41HC2sY8zSTbad3fS99Fsum6ueoeNviV4K+Hc/h218YaybGbxZrUHh7R41tpp2ur+ZXaOICJGKDbG5Mj4RQMswpxTnLkjvZv5JXf3CbUY8z20/HY838PfHj4d+D/CXxS8f+Ovjp/bfh/wb4zvtL1C6udDazHh+RfIUaSixRB7zy2lTbKokaQzYDNjAyU0kle7bsu7fZLy/JX2N6sHGW1rJPfpZO9/Pe3nY0fAX7VHwk8f+Obb4Z28/iLw/wCKNQs5NQ0zS/FHhy+0WbU7aPl5LUXcSCbAyxVSW2q7Y2oxGyi5Xt038umvz0MeifR9TqPi14Q+IvjTw5baX8Mvi5dfDvVIr1LibVLfRbTU2mtxHIrW5iulZFBZkfeBuHlgDhjUgfJvw0sP2w/Hvxw+MHwjuP21tUs4PhhPocUF8ngLQne9F/ZG5YshhATYRtGCc9eK2VJPCRxN95Tjb/By6/PmE37/AC+S/E9ltP24/wBnOCSGC68dancaRHqC6DJ4yPh69Tw7JqIcRmM6mIfsgy/O8P5QBzv281k5Rm7rRPbz9O//AABJOC5ZO7W//B7HYfEj9pj4Q/C3xdB8PPEmsapdeL7ywi1Oz8P6Nod7qeoXdtI06iSGK2icuoNrPuI+4FBbaHTcKLkm103/AK7ef+YOcU1Fvfb+u/l/kziX/b+/Zd/4R228YWnjXVL/AEF0ik1DVLHw5qNxa6N5kzQxrqEiQFbN2kRlEcpVyNrBdrqzL7SV9+vT+vy62G7q907rp1+7+vI7740/HTwR8IdJsLTV9elj8ReKVuLXwxp1jpF1q95qF0kW/Mdnaq0ssaZVnI2qAVBZSy5ifM26cfis+l7eb/rXUuDirTlrG667/wDDnHfAD48/Dif4S6vHr3xd1jXdY+GNnv8AHeoeKNFm0fUbOUxvM8s9lJEjQxlVk8tVDfKm3c7qxOlWUIQ9rtHbfrord7tv5vREU4ylP2f2t/k/PayX4avc2PAP7WvwY+Ini/SPAulX3iLTNY8SW0t94fh17wzqOlJrVtEnmSS2clzCiTKE+fAOdvOMA4OVpuMtGt09102E5LRrVPZ9P6/rqeyUigoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/SyzoA+n6ACgAoAKACgD4B+Df/KZD47f9k/s/wD0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAFK8H2a4i1AD5R+6mPohPDfgf0LUAflx/wWH+DDeCdB0H4ofDXwpp2k6L4k1S6h8aTadpdlC9xqEgWS2nmmSIXLmXF1v3SGIuqHaskjNJjUw9KrONScbuO3lc9PB5zj8vwtfBYWq4U66SqJbSUXdJ+l397T0Z5Z/wTD/a8+Ef7MOkeL9L+MHxH1q1sfEt/p7afpFrYzXNpZyAyR3F5IAp2MU8jd5eSyRrkSMqKhGpOVWUHBqKtZ3Wr66b6ab+fzK+DwtLAUsVDERlVm5KVNRneCVuVuTSi+bXSLdly6ttqP2nrH7X/wCyvP4X1X4t6D+0nrem6Jc6+ulX91YQXl0YL2axl8iP7HeWlwbdCkLzo8USRvLbOXLkzRv5eP4dyzM6ntsTSTl3u0/nZq/zOGFepTVos9B+FVtZ/FfwtpvxQ8KftNeO/FnhLU4pJre0eDSrM3UUcrQySHydNtruPZIjdHQEphgyk544cHZLTd1Rv6yk/wBbFvFVX1Oe+Ht34n8a/APwFq3iHxlHqn9u/D/S9U1/SdZ8PprsWtpdW6S3x+yo8U8kmMoio7RL9o2tDJmIL8blmR4LG4nGucnS9jN8sou3Kk5fgrJ30em+51VK04RhbW6NSzbx/wCHfA3ivwroUvg7TfGcPhufQPCsXhi1SztNM1Ga1Elpp3ktPKxNmPKnknaOCMw3MDLEMMtepwzisPl2ExGaVZSVKTSvN3lJq95LRfFdK15Wad5PczxEZTlGmt/I9Y+HWheDLzxFdeIfD8Vyn/CJwSeELGBbdYbG0j/cTXItSqDzQzLbRSHc6I9kY1EbrPv7eBsHOngp42unz1ZXu3q0tn9/M/PfsRi5JzUI7I7XU9He9lSa2nELH5ZDtBBGOuPXhfyB/hFfbnIXolht1jtYyBsQBFzztXA/woAloAKACgAoA8Y+Nfxdlsp734UfDrVtNk+IuoaXJd6Rp93KI1uWDYKCRsIH2iQqhPzsmDtXc1eXj8d7K+Gw7XtmrxT/AKtfey627H3fCXCv172ed5vTmsshUUKs4K7Wl9l73KnyqckrxUtLyshfgR8FLPwy6fFvxt4UsdP+J/iGx2eIJbS4LQiR2DOVRcRLJJsjeQoCPM34YgktnluXRpP63WglWkvett9213a7t1vqdfGvGNTMIvh7LMRKpllGd6Kmvesr2Tk/fcY3caak1aHLeKaSXs1ewfnJUN3PLIyWVukqxkq0jy7V3egwCSfXjH40AMTVI45Vt7+M2szMVXcco/8AuvjH4HB56UATsAbyJw5/1UmADweU5/z60AT0AFABQAUAFABQAUAFAHx//wAFJPj5+0Z8EfhrpUf7PHgbVry51Zb671zxTZaTJfp4csbNYnZnURvFEZRIx82Y7USCXCliHjAPz5+Cv/BVn9qzw79s8IeKvE2heKTrkYsdL1TxFFHa/wBiXUjFVunmhQeZCpk3OsqtxGu1kAYMAZ/xH+FX7Wd/J4p/ZJ8dfDXXPEXjDWfibo3izUvG8bXFzpjfa4LiwtZZJRBtS3leZisrMu3yJIjGrRsqgHvVz/wV1k+C/im6+Etl4Si+LPhPwjp9n4ftvFU+svbX+uXdtCIrvUJZG+0pNFPKrNHg52kOXbftUA+nPhF/wUY+FXiH9n5f2gvinpkvgDw7L4obwrbwG5l1aX7SIkl3DyoVcpsZ2ICEqIm65VaAPlP/AIKoftK/FfxdoPg66+DXiqb/AIUZ4ks5F/4SDw/eMses6nmRZrK7dGDIscWMW8ipvYzMwkMYEIB5N+yV8Ev2nf2lPhl4g+B+u+DNYX4Y+LdSs/EcXjTVrME6LqaDYt9a+fLE2oJNDbtaypG7eWHjkBXaUlAPvf4TfsM+KfBP7Wfi34u+Mb/wn4j8AeIPANj4MGnTrLJc3RhsdNtpDc2skRg8l/sM3y+a/DoCOuAD2H9nT9jn4C/suJfz/Cvwm0eq6n5qXWs6hN9pv2t3l8wWyykDZCuIxsQDd5UbSb3XeQD22gDkPij8Mvh58VvDUXh34leCtH8Uada31tqNrZ6pbLNCl5E+Ynw3uSrDo6PIjBkdlIB8G/Hz9hH4zw+Pviz+0L4fiX4p+PPEMkFn8OYIZrfTh4Xhl8xJbqVJZYU8+zt1ijtJopWfzyLhot43oAfnv8Lv2Nv209b8Y20vgL4HeP8AQ9c0Zo9Str+/s5ND+yyRyJseO5u/JQSqzKyqrb8AsBhSQAfpM3/BLKXx/wCA/EmsfEj4xa74f+JnxDRLnxePD0kM/hu8v0uRMtw1m8EUjuzKJX2yxgTSzeXsjIjoA+bf2kf+CaP7cXijUtHvI7zwn4/sPCeg2XhTQI9KvlsJ4tIsVEVsJIrrYqyupeZwJZAHeQBiNgoA9R/Yb/4JvfG3Qo7lP2ndYu9K8BG6+2r8O4daaeK/v1MbR3s/2aUxQMjQwFXifzn8vY5WPckgB91eBf2bPgh8Ovib4m+JXgLwLDpnijxXPc3fiDVhc3Ms9xNcS+e6q8sjeSryMXaOLahKplflXAB6cNLgLfvZriZAQVjklLKCOf8AgXPPzZ9qALlABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB5F+19/yal8Yv+xF1z/0hloA4n/gnD/yZN8LP+wbc/8ApbcUAfSdABQAUAfLf/BTv/kxn4nf9ctK/wDTraVnU+H5r80deC/iP/DP/wBIkcHH8Iv+CRACt/a/wLDDB/5He36/+BVbxnKlNTg7NO6OKN3FcxZ+KWv2yftteJ/FGi+F7bx3bj9l7U7+00WJBdQ68n9r70tVVVcSpOMIAFfcH4DZweaqpRlNbOy36b7q626q56EZ/wCzUpwf25NW9IbP8j5T+O3xjbxn+xwwuf2v9I1yfxDpNhPafCjwb4OsrK00FIbm2aSznZEkuba2s1DKjStEJPKjwWBwdakYxmoX5tU79N1r09Et72ut0ccJOpGU0uXRq3W9n3fldtaWvbdH19e/F34T/Cj9v3xtffE3VLLQI9a8A6DZad4j1ECGwtmE17K9pLdt+7gadYw6B2UObUgEsFB58JNQ+tQk9faR09Kcf6sPELmdCS25JfjP+tTxjxTZ6v498MeOf2nPBena1P8ACSX44eF/GzaTDpUhfV9C0uGGDU9SitcZkimmCzncoLLaF3Axx0wdOhRoJwUWqk5c17cqkrRk7a73drXXNcj+JVrXfMnCMbb8zi7uOumi6rdrlPX/AIu/F74cftLfFf4BeFP2ePGVn4q13QvGdl431XU9FUzxaR4fitrhbqK7nUf6M1x5kcXkPhi2FdVO3OuGlCniXKpG9oTV+zlGyS73dnpta5liOadFRjreUXa7W0tX8td9722Zb/ZAvrnSfBP7VOq2Hhp/EN1afGnx1cRaQm3dqMiRwFbYbgRmQgJyCPmqsyhGnhKcoRTbhJtd3zzWu/RJbP0OjDLmrJOXLqte22vy3PkX4s/F6bxn+xvflP2qdCnl8Q6IGtvg14D8FWVjFonlOj3NrOAkt3Bb2wSaRpZDEriMlWIZS3FirVYxTfN8Du9tHG23baKvvbTdF4R+znK65Lc6slrdqXfu9ZO2l3bofYS/ELwV8Ef2y/FPiz40eJbLR9F+JXgvQovBfiXVAsOm2yWX2lr3TTeyEJE7vJHchCQr7l53bA1U1OjVxFKejc1K13raKjfayaaastVe/wBpmc5Uq9PD1KcdoNOWm7lzetmmtdvd8jz/AOJf7RvgfxH4J8PeLvhX4D8NfDnwt4r+KV5bL8UvE3g6K4sLS9WzJi8QW8DCNmmuJD5KXkwCp5MolYAcEeajWpfY5ozd1vvbl7L2i1ldqVr+7Jal1qMZUqknafK4ab9/e6/w2rK2jbjZ9DnPAfibUPH37YFl4f0P9py++KuqSfDfxNo1r4rj0m0sNKg1B2tnFvZPaRrHO0QMcsjK8uN8YyCCKPZOrSxPJHV00vV81vktdH66vpm6ip1MM5Pao2+1uX8+68ka3hj9of4H+Hf2BLj4CarKunfEWw8I3PgC5+HsllINbuPEElo8PlpZKplkE8ziTzlUpmXlgwIFYmaxUE6V5cySWmqaS3T1XLbrZ3Vt9BYeP1WbVbSzbfVNOT2ezv8Adr2Prj4Itb/Dn4WfCv4Q+Otf0uy8bW/g/TrJtKl1CE3VzNZ2cKXRiQNmZY2HzOmQMgk8it603VrTfNzPdvV7v4nfXV9X18zOmuSnHm0vovzt6pdjmP26/wDkz74t/wDYsXf8hXnY/wDhL/FD/wBLid2D/iP/AAz/APSWXPh7+07+zXN4X8NaLD+0L8M31B7CztVtF8W2BmaYxoojCebuL7uNuM54r2Mb++xlT2et5O1tb3elu9zycF+5wdP2mloq99LWWt+1jh/h34F0P4n+NP2s/hz4lR20vxN4httKuzHjesc3h6yQshIIDru3KcHBAPavMw8PaZaorf2lW3k+ZWfyep6FSp7LMXNq6UKV13VndfNaHl37Jes+Lvjp8Y/CGm/EC3H2n9l/wxd+GtWKyzfvvFk1xPpzTF2wtwp0+xMucHa16ec4x183tnLFtP3kkuZa30lUaa87Jq+1tDCUY0VHBw2i29NuVaU1Z3e12mt2vkcJe/EG4+Gvwz+NniKDw/oUwf8Aamv7STxJrWhnVrbwWskdsP7e+z7TmW3ICxnKjzJk5Odjc1KLnyxul70tX87WfRt9Xpve256GMqOlNys2uSF0r6rli9UtWvJa9tS74d8aDxJ+3F+z5YTftWN8crjR7jxNHealYaJY2Wj6ZJNochiiims4/Kmll2uWXzpCgjUEKTy0lOreK2T/AE29Or9PM5ubkovme7X6rXs77L18j9Ia0Mz4w8F6B4h8WfH79uPwt4R1L+ztd1nTPDmn6Xebyn2a7l8PSpDLuHI2uytkdMVrXjz5JGPLzXnX02vpDS/S+xpQmoYuMm7W5dbXtrvbr6HCv+0L8ET/AME+j8APNP8Awsr/AIQn/hXf/Cv/AOzW/t3/AISX7D9nx/Z+3zcfaf3vn7duec+Z8tY4mSruLXvptW30tbe+3Jbr2suhnSXsrr4bX+d79t+b9ddz034MeFNX8MftiaFo/jCYXviXQf2dPD2lX94X8xpLhdUnW4bd/FukhDE98A16FaVOt9ZqwWjqK3o1J/5Hn0lOnOjTenuO6vfVOH3211OPttM06z/Ym/a0+y2MEXn+LPiVLLsjA3utxOisfUhY0UH0RfQV42MSWW4ReVP/ANOnp5fOU8XXcnf4/wD00i/8d/ibqPhr4ifB3wXH478G/ByPVPBUt3b/ABS8QeH7bUJVmTyBJpFrNcssNrvjBlkaU7WzEq/NgN6GJnbH143t6by956PyWj0u79Fu+XDRSy+hO19l1933V73q9V00ueZ/CjXfCPjnVv2r7rxl4h8afGrw1qfg7w6LjVLHSLWx1DXrOJL+K4udNt9tvC0cEgcIy53NAxUyEqG82VGE6VWc17rlB6XunaNm+zTV9UkopXW9/RVZxq0qcJe8oSVns9XdRtumnbvzN2ex0fws+K+qaN8YPhPonwi/artP2i/Dvie7ktU8O6xpVtLr/hjSDATNqk+poFlikiAjR47lI3lEjKqFzlPSoQjVqy9ta6jJ8yT00uo6aWlJRT7O2hw1arp0l7O+rjo/WzfyTdum+tj9BqxNAoAKACgAoAKACgAoAKACgAoAKAPmD9qb/kuXwK/7GBP/AEss6APp+gAoAKACgAoA+Afg3/ymQ+O3/ZP7P/0RoVAH39QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFACMqupR1DKwwQRkEelAFK3F1CGtDCs0MJ2bjJ87LjIG0jBwCBknnGaAPzJ/a4/4JG+IviH8U734ifs6ax4e0Ww8RTTXuq6JrU0sENndsVLPavEkpMcrGRzEQgiPCEowSMA81sP+CM37RkfhPUNIvfHXw4N/capYXMDxatfeSttHFdLOGBsd3mFpYNmOMLJn+GsXRjKtGt1Sa+9p/oj0aeZ1qWX1MtSXJUnCo3re9ONSKtrazVSV9G7pWa1vy37R/7GX7V3wuufGXxlvtY1ey8IfCmKDRfBWpJ4lR71dDV5Ircw7ZfMgjjRlEkfyNvucIrIHZeWFPEYVQhF895PmbeqTu9Ft2Vlt23PdxWNynPamIxNeKwvJSiqNOnD3ZSjyxtKWsm2uablO7k7pzVop+a/sOftQ6v8LviFpXw68fvqHiX4ba3MkMmiSyJKul3yuZbXULMzSxpaSx3G12mV02qWkOXjjZfH4jyOnj6Lr0HGnWX2nomno4ydndNdGn22bv4GX0sRiqyw2HhKpKW0Ypybtrolrpa+nTU+ur7/AIKP/Bmw8Jp4x8DaRrOleHNX8ZwaT4h0z7M0Hie7t3ghmuNW/tHzJoSNkZtPs+fPKKjR3FttjMfhZbwlh1XjTzSp7SrFXUFfkUb2XRddbK2u6d236c8ux7yv+2adO2GdT2XPdfxOXnty35vh1va3S99DzH4//wDBV/42aB4j1j4bfADSfDPg/wAIaU1tb6Def2GDqVvaJFGRGUkmltQp5UYi4TbjB5r7vBYmnjKEa1JWi+/k7foYcR5DiuGMzq5VjGnUp2u4tuL5oqSabSbVmtbemh7r+zH/AMFUNf8AHHgzxl44/aN8L6D4f8O+FJbBDqnhyG6WS4kuriOFLZLeZpFdkDSzuROJfKjYxwybXZNvbQ9p7G/vWv8ALb09O9nbZnD/AGdiVgv7Qcf3TlyJ3WskuZpK/M7JpyaTUeaKk05Rv+hXhy/0XU9OttT0dZo01G1hvwl1by290IpVLIZoZgs0TH5spIqsCGBAIIGhxGtQAUAFAHkvxw+Mj+C7K58IeBpdN1L4gXuny3ekaNPc7JLnYyqQoHLPhiyxgq0mxgh4Zk87H49YaLp0bOq03GLe/wDXRbuzt1a+z4R4Ued14YvMFOnl8ZxjVrRjdQum1d7JaJSm040+aLktYqXnf7JX7PGt+HJbr44fGmB7/wCIviEmaBr075dMtnQDbsKgRTMMqQP9XGFjXYDIp8vI8qqU28djta0u/Rfo+nktFbVH3Xilx5g8ZThwpwu1Ty6ho1HRVJJt3vduUE/eTfxzbnLmfLJfSl1dyRSJBawCeZuSm/aEX+8Tg4/rzjpX0p+JDBa3l0oGozoE5zFBkK3szHkj2GPfNAFtESNBHGgVVGAoGABQBDeTqiiDyDO8wYLFxhhjnOeAORn69+lAFBNPkWWA2WnQ2bRv5jyNhwTsI2qAckfN329KALEslxYSxyz3Es8D/I5IjARiyhT0Bxye5oAV5TqDmG0uJEiQZeeLHzHsqkgg+5HTgeuAC4isqKruXYAAsQASfXigB1ABQAUAFAHjXx6/a/8A2ef2abmz0z4v/EKHStV1K0kvrLS7e0nvLueJCVDFIUbyldgyo0pRGZXAY7H2gH5m/trftnfse/tlWfg2LUJPjP4UuPCN3dMJLbw9pd1HdWlyIvPiKHUEKyZt4Skm4qo8wGN9wKgHsH7E/wCyJ/wTs/aQ8Ca7rvhT4Z/EnXYNH1f7JLfeNtVNpc7mhjbyYhpk6QtGo+bLL5mZDkldoAB+lumabbaRpdpo9q9zJb2VvHbRtd3Ut1MyIoUGSaVmklcgcu7MzHJYkkmgD45+Kf8AwSU/ZL+Jvi6fxdZ2vibwSbpAJtN8K3dtbWDSbmJlWGa3lERIIXZGUjAQYQEsWAMK3/4JH/B631rwVp0/xL8X6t8P/CN3d6lJ4V1VbaQ3t5cGLzHN1CkRSN1t4EdPLYssY2uhJJAMv9mP9gv4reCfiN8VNM+K8vhOT4HeNtQ1CRPAa5uYrkm5WSzuEiVViszGgQBkYv8Au1XaNsbqAfd2k6faaLJ/ZGmWMNnp8NtClrb28QjhhRF2BEUABQFVAFHAAGKANOgAoAKAKN7PDO0dlDIsk3nRuUU5KqrhiW9OB374FAC3Fy1xE0dizORIsTyRkHZlhu/EKfTigCzDbw26lIYwoZizerMepJ7n3oAkoAKAIrp5Y7WaSEAyLGxQHuwHFACW1vHbxbYySWO5mY5Z2Pc+9AE1ABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAeRftff8AJqXxi/7EXXP/AEhloA4n/gnD/wAmTfCz/sG3P/pbcUAfSdABQAUANdEdSjqGU9QRkUAR/ZLX/n2i/wC+BQBII0DBgihgNoOOcelACeTF837pPn+98o5+tAA0UTZ3RocgA5A5FAD+lADUjjjz5aKuTk4GMmgAVETOxQuTk4GMn1oATyossfKTL/e+UfN9fWgDy79oj4R+KvjT4FXwP4Z8Z6V4YRrmK8a9u9Jur2aC4gkSW0nt/IvbULJDPGkqiXzY2ZF3RkAgw4t1IzvotfO/TW/a/R9HpbWrx9nKDW+nlbr09NbrqtbnpWn2c1rplrYXt9JqEsECRS3M6Ir3DKoBkZUVUBYjJCqFBPAA4rapKM5uUVZN7dvLXXTzMqUZQhGMpczSV3pr56aa+WhOIoxtxGo2fd46fSoLF8tN/mbF34xuxzj60ABRSwcqCw6HHIoAVlV1KsoIPUEcGgCMWtqDkW0QI/2BQBIFVSSFALckgdaAEVFUkqoBY5OB1NAAI0AYBFAY5YY6n3oAQRRKFAjUBTlQB0PtQA+gBoRFYsFALdSByaADy49/mbF34xuxzj60ALtXdv2jdjGcc4oATy49pXYuGzkY4OetAA0cbrsdFZfQjIoAAiBt4QbsYzjnHpQALHGhLIiqW6kDGaAHUAFABQAUAFABQAUAFABQAUAFABQB8wftTf8AJcvgV/2MCf8ApZZ0AfT9ABQAUAFABQB8A/Bv/lMh8dv+yf2f/ojQqAPv6gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAIrqb7NbTXG3d5UbPjOM4GcUARadGUtVlkbdLOBLK3qxA/QDAHsBQBaoAKACgD5T8Qf8Ey/2Ute+K0vxai8LalpV1cQ3C3GjadfGDSpriVZA1yYlXzI3/eDCxSRxfIvyctuwxGHp4qm6VZXi7d+jv08z08nznG5BjI4/Lp8lWKkk7J25ouL0kmtYtrbrocJoP8AwSQ/Z9j+Ekvwq8ReKPFF648TNr6a7ZG2tdQC/ZhALQl4pUMPWTG0NvI5AGDXsKftfb2961r+V72+8j+1cZ9Q/sv2j9hz+05OnPy8vN68unoew3n7Av7IFxM+r618F9P1S6TSdP0lp768u7hxaWFnHaWyqGlIRlghjUsgDNtBYlua1PPPAvjZ/wAEoPC/ibwtY+HvgT4tPgnRotdGvX3he+eW7s7yYx29v/x9szzKYoY7lo/NW4Aku5wnlpIwEOnF3ezatfr1tr5Xdjqp4yrTUIN80ISclF3cbvl5ny7e8oxUurSSeyPhOHxF+0F/wTu/ae0v4h+NdLsBrt7aLJqPh0XrNHe6RcKhe2M6LsOxlCrKnmIJ7bOJQjBuDL4wwq+o04NKmlq9nfXTXXW9+zPq+L6+Jz2a4pxuIpyqYuU/3cW3OmoNRSmuVKKUeVQu25Rs9Xc+w/ib/wAFkfDuo+AfEWsfALwIY9a0iXSxBH4yXaZ4Z3uFu5Vt7SRgVhMdmoJuFLG7J24ixJ2zrwpzjTlvK9tH0V99lp336HzWFyvE4zDV8ZRScKKi5+9FNKUlBNRbUpLmaTcU1Ftc1ro+cPHf/BTX9pHxt478PXo8Xado2i3/AIdl0+bQfD8F1bj7Xd2TwvcStFI1yJlnlDweXNlFihYIGLGTmqYmrUpTeGheUXbXS9mr21XS6Wq1Wp7WEyXL8JjsNSzrEKNGtT526bcnTcoy9mprlk0+ZRlJKMn7OScdWjjv2Sf29P2ifAXxj0mz8V/F7V/EuieJpU0S8Txlr89zZWLTt5cV6ZZ3byFhkZZHIKho1dSRkMvXU5lB8lr20vtfz8j57BqjLE01iVJ0+Zcyjbmcb68t7rmttdWvufoNafG74F/CD4WTftN/GH4mfD/4k/E7wut3p+nz+F/EFtLPdh5Xjhto1hm8uV2WZnaXyVeOGRiyExszeVgMvneOJx1pV43XMtNNbbaPR72Wjtvq/vuK+LsLy18l4Uc6WWVeSbpStK87Rbd5JzilKKTj7SScouSfK1FfDHjv/grx+1t4i8eHxJ4M1fRvCXh6KdWg8ORaXbXsTwrIWC3FxNGZndlIR2jaEEDKLGTmvYPzg25ZNH1z4+Xf/BQTwL8MfHGteEodKHjL+y7LQr+WC28WKrxTWs9+DsW3huImv5JwSnlskPljeQoB2P7An/BQ34v+HviV4b+Fvx58U3njjw/8R72K00e+m1GG91HR72a6khTzAhaYRyTFVMMxVkj8uSICMgSAH6oeLfiN4L8GWF1qvjf4leG/CtnZGH7TJqV9BbC384t5IlaZwFL7H2jAyVYDODQBveHb/Rtb0i08RaBrEGr2Gq28d3a6hbzrNFdQON0ckbp8rIVbKleCDnvmgDToAbJHHKhjlRXRuCrDIP4UAKAAAAAAOABQAtABQAUAV9Q1Cw0mwudV1W+t7KysoXuLm5uJVjihiRSzu7sQFVVBJJOAASaAPjP4m/8ABXH9kf4eeI38OaVeeJ/G5gaSO5vvDWnxSWkUiSMhRZbmWETfd3B4t8bKVKuc0Afnf+0J8V/2cv2t/i54puhoWpeFvGuq+K1sPD/jDT5rm50vXtL86O2gk1GzuT51rKlrDGYjbhQzylZUjCh1APvX9k7/AIJbWn7MnxaX4mX/AMZovGNsdLvNLm0mbwuLWOaO4QK25zdSgjGcqUIIOKAPrX4O/BL4afATwvdeDfhX4bh0XSb3VLrV5oI2J3XE7ZY5PZVWONR2SNF7ZoA7qgAoAKACgAoAKACgCC6ufswQCPe8reWi5ABbaSMk9BxQBSXTZ5QttKkFtZqATDAd3mHOfmJUccDp15oA0YoooUEUMaxovRVGAPwFAD6ACgCCa9s7ZglxdwxMRkB5Apx680AVWlbVZjBbzEWceDJLG3Mrf3FYdvUg57etAF9ESNFjjUKqgAADAA9KAHUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB5F+19/wAmpfGL/sRdc/8ASGWgDif+CcP/ACZN8LP+wbc/+ltxQB9J0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAfMH7U3/JcvgV/wBjAn/pZZ0AfT9ABQAUAFABQB80fFf9gn4ZfFD4w6l8ddO+JXxS8AeLdasIdP1S78GeI10/7bHEqIpcvFIw+SGBSqMqHyUbbv3MwBif8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAf8ADvTT/wDo8b9qL/w4S/8AyNQAf8O9NP8A+jxv2ov/AA4S/wDyNQAf8O9NP/6PG/ai/wDDhL/8jUAH/DvTT/8Ao8b9qL/w4S//ACNQAjf8E8tNdSj/ALYn7ULKwwQfiCpBH/gNQAD/AIJ56coCr+2L+1CAOAB8Ql4/8lqAF/4d6af/ANHjftRf+HCX/wCRqAD/AId6af8A9HjftRf+HCX/AORqAD/h3pp//R437UX/AIcJf/kagA/4d6af/wBHjftRf+HCX/5GoAhk/wCCdGiTP5kv7Xn7TjuP4m8foT+f2agCR/8AgnjpkqlJP2w/2oHU9Q3xBUj/ANJqAHf8O9NP/wCjxv2ov/DhL/8AI1AHH+MP+CSHwR+Iepxa14/+OPxy8TahBAtrFd6x4mtL2ZIQzMI1eWzZggZ3IUHGWY9zQBhf8OVP2WP+h++Kv/g107/5BoAsab/wRm/Zp0bUbXV9I+Jvxesb+xmS5tbq21rT4poJkYMkiOtiGVlYAhgQQQCKAI7f/gi5+zBaTx3Vp8RfizDPC4eOSPV9PVkYHIIIscgg96BptbF3S/8Agjz+z3odxp93onxd+M+nz6Tdm/sJbXX7GJ7S5OzM0RWyBjkPlR/OuD+7Tn5RgEZ3/DlT9lj/AKH74q/+DXTv/kGgDvJf+CaPg6fw8/hGb9qj9pGTQpLI6a+lt44iNo1oU8swGE2uwxbPl2Y27eMYoA4/Rv8Agjd+zj4c1W013w98VPjFpepWEqz2t5Za5YQTwSKcq8ciWQZWB6EEGgCXVP8Agjv+z1rlxqF3rXxc+M2oT6tdi/v5brX7GV7u5G/E0payJkk/eyfO2T87c/McgHY6V/wTf8N6Dpdpomh/taftMadp1hAltaWlp48jhgt4UUKkcaLahUVQAAoAAAAFAFv/AId6af8A9HjftRf+HCX/AORqAD/h3pp//R437UX/AIcJf/kagA/4d6af/wBHjftRf+HCX/5GoAP+Hemn/wDR437UX/hwl/8AkagA/wCHemn/APR437UX/hwl/wDkagA/4d6af/0eN+1F/wCHCX/5GoAy/Ev/AATM8FeM9EufDXjD9qT9o/XdIvNn2nT9T8bw3VtNscOm+KS1Kth1VhkcFQeoFAHFW/8AwRc/ZgtLiK7tPiL8WYZ4XWSKWPV9PV0cHIZSLHIIIyCKAOs8H/8ABLj4b/DzTJdF8AftJ/tC+GdPnna6ltNH8YwWULzFVUyMkVoqlyqICxGcKo7CgDd/4d6af/0eN+1F/wCHCX/5GoAP+Hemn/8AR437UX/hwl/+RqAD/h3pp/8A0eN+1F/4cJf/AJGoAP8Ah3pp/wD0eN+1F/4cJf8A5GoAP+Hemn/9HjftRf8Ahwl/+RqAD/h3pp//AEeN+1F/4cJf/kagA/4d6af/ANHjftRf+HCX/wCRqAD/AId6af8A9HjftRf+HCX/AORqAD/h3pp//R437UX/AIcJf/kagBjf8E8NLd1kb9sL9qAumdrH4grlc9cH7NxQA/8A4d6af/0eN+1F/wCHCX/5GoAP+Hemn/8AR437UX/hwl/+RqAD/h3pp/8A0eN+1F/4cJf/AJGoAP8Ah3pp/wD0eN+1F/4cJf8A5GoAT/h3np24t/w2L+1DkjBP/Cwlzj/wG96AF/4d6af/ANHjftRf+HCX/wCRqAD/AId6af8A9HjftRf+HCX/AORqAD/h3pp//R437UX/AIcJf/kagA/4d6af/wBHjftRf+HCX/5GoAP+Hemn/wDR437UX/hwl/8AkagA/wCHemn/APR437UX/hwl/wDkagA/4d6af/0eN+1F/wCHCX/5GoAP+Hemn/8AR437UX/hwl/+RqAD/h3pp/8A0eN+1F/4cJf/AJGoAP8Ah3pp/wD0eN+1F/4cJf8A5GoAP+Hemn/9HjftRf8Ahwl/+RqAD/h3pp//AEeN+1F/4cJf/kagA/4d6af/ANHjftRf+HCX/wCRqAD/AId6af8A9HjftRf+HCX/AORqAD/h3pp//R437UX/AIcJf/kagA/4d6af/wBHjftRf+HCX/5GoAP+Hemn/wDR437UX/hwl/8AkagA/wCHemn/APR437UX/hwl/wDkagA/4d6af/0eN+1F/wCHCX/5GoAP+Hemn/8AR437UX/hwl/+RqAD/h3pp/8A0eN+1F/4cJf/AJGoAP8Ah3pp/wD0eN+1F/4cJf8A5GoAP+Hemn/9HjftRf8Ahwl/+RqAKmrf8E3vDevaXd6Jrn7Wf7TGo6dfwPbXdnd+PI5oLiF1KvHIjWpV1YEgqQQQcGgB+gf8E4Phr4b0Sx0DTvHfiM22nwJbxGRIGcqoxljt6nqcAD2FAGh/w768Bf8AQ96//wB+4f8A4mgA/wCHfXgL/oe9f/79w/8AxNAB/wAO+vAX/Q96/wD9+4f/AImgA/4d9eAv+h71/wD79w//ABNAB/w768Bf9D3r/wD37h/+JoAP+HfXgL/oe9f/AO/cP/xNAB/w768Bf9D3r/8A37h/+JoAP+HfXgL/AKHvX/8Av3D/APE0AH/DvrwF/wBD3r//AH7h/wDiaAD/AId9eAv+h71//v3D/wDE0AH/AA768Bf9D3r/AP37h/8AiaAD/h314C/6HvX/APv3D/8AE0AH/DvrwF/0Pev/APfuH/4mgA/4d9eAv+h71/8A79w//E0AH/DvrwF/0Pev/wDfuH/4mgA/4d9eAv8Aoe9f/wC/cP8A8TQAf8O+vAX/AEPev/8AfuH/AOJoAP8Ah314C/6HvX/+/cP/AMTQAf8ADvrwF/0Pev8A/fuH/wCJoAP+HfXgL/oe9f8A+/cP/wATQAf8O+vAX/Q96/8A9+4f/iaAD/h314C/6HvX/wDv3D/8TQAf8O+vAX/Q96//AN+4f/iaAD/h314C/wCh71//AL9w/wDxNAB/w768Bf8AQ96//wB+4f8A4mgA/wCHfXgL/oe9f/79w/8AxNAB/wAO+vAX/Q96/wD9+4f/AImgA/4d9eAv+h71/wD79w//ABNAB/w768Bf9D3r/wD37h/+JoAP+HfXgL/oe9f/AO/cP/xNAB/w768Bf9D3r/8A37h/+JoAP+HfXgL/AKHvX/8Av3D/APE0AH/DvrwF/wBD3r//AH7h/wDiaAD/AId9eAv+h71//v3D/wDE0AH/AA768Bf9D3r/AP37h/8AiaAD/h314C/6HvX/APv3D/8AE0AH/DvrwF/0Pev/APfuH/4mgA/4d9eAv+h71/8A79w//E0AH/DvrwF/0Pev/wDfuH/4mgA/4d9eAv8Aoe9f/wC/cP8A8TQAf8O+vAX/AEPev/8AfuH/AOJoAP8Ah314C/6HvX/+/cP/AMTQAf8ADvrwF/0Pev8A/fuH/wCJoA3vAn7EXw88FeLdL8Wv4l1zUpdIuY722glMccZnjYNGzFVyQGAOARkgZyMggH0ZQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAH//Z)\n\n# + [markdown] id=\"7xkWDWd3C3qq\"\n# ### Beneficios da abordagem de redes\n\n# + [markdown] id=\"40O7E5qy02X-\"\n# Através de uma abordagem de redes é possível obter medidas que indiquem quais ruas, quando interditadas, geram um maior impacto na eficiência geral da mobilidade na cidade. Este tipo de análise é especialmente útil em casos de eventos intensos como o do dia 09 de Março, onde diversas vias públicas tornam-se inacessíveis, e devido a escassez de recursos é necessário priorizar a restauração do acesso às vias mais importantes, algo que nem sempre é algo evidente. Deste modo, métricas como o índice de vulnerabilidade podem ser úteis como suporte à tomada de decisões sobre quais vias devem ser priorizadas. \n#\n\n# + [markdown] id=\"cG83B0pINKXn\"\n# ### Cálculo do Índice de Vulnerabilidade\n\n# + [markdown] id=\"AWX2hE72MXUr\"\n# A eficiência $e_{ij}$ na comunicação entre os nós $i$ e $j$ é inversamente proporcional ao comprimento do seu menor caminho, i.e., $e_{ij} \\sim {1}/{d_{ij}}$. Vamos considerar um grafo $G = (V, L)$, onde $V$ é o conjunto de $|V|= N$ nós e $L$ é o conjunto de $|L| = M$ arestas. Dado que $E$ é a eficiencia global, e dado que $V_{k}$ é a vulnerabilidade associada ao vértice (ou aresta) $k$ do grafo $G$, a vulnerabilidade $V_{k}$ associada a um elemento $k$ será dada por\n# $V_{k}$ = $\\frac{E - E_{k}^{\\star}}{E}$, onde $E_{k}^{\\star}$ é a eficiência do grafo após a desconexeão do elemento $k$ .\n\n# + [markdown] id=\"b25qUJQ4DCfy\"\n# ### Mapa de Vulnerabilidade\n\n# + [markdown] id=\"HeSUMXjiHnqG\"\n# A Figura abaixo mostra a aplicação do índice de vulnerabilidade topológico na região Oeste do município, indicando que é possível haver uma perda de eficiência de até 3% na mobilidade da rede em caso da interdição de determinadas vias. Dentre as mais vulneráveis, além da Rodovia Presidente Dutra, estão inclusas tanto avenidas, como a Av. São João e Av. Possidônio José de Freitas, quanto ruas, como as R. Emílio Marelo e R. Carlos Marcondes.\n\n# + [markdown] id=\"GQmX07U3u3qa\"\n# ![WhatsApp Image 2021-03-11 at 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1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nmpl.rcParams['figure.dpi'] = 100\n\nt_min, t_max = 0.0, 2.0\nintervals = 25\ndt = (t_max - t_min) / intervals\nNt = intervals+1\ntime=np.linspace(t_min, t_max, Nt)\n\ntheta = np.pi/6\nv0 = 8.0\ngx, gy = 0.0, -10.0\nax, ay = np.ones(Nt)*gx, np.ones(Nt)*gy\nvx, vy = np.zeros(Nt), np.zeros(Nt)\nvx[0], vy[0] = v0*np.cos(theta), v0*np.sin(theta)\n\nfor i in range(Nt-1):\n vx[i+1] = vx[i] + ax[i]*dt\n vy[i+1] = vy[i] + ay[i]*dt\n\n# +\npx, py = np.zeros(Nt), np.zeros(Nt)\nvx_avg = 0.5*(vx[1:] + vx[:Nt-1])\nvy_avg = 0.5*(vy[1:] + vy[:Nt-1])\n\nfor i in range(Nt-1):\n px[i+1] = px[i] + vx_avg[i]*dt\n py[i+1] = py[i] + vy_avg[i]*dt\n# -\n\npx_real = v0*np.cos(theta)*time\npy_real = 0.5*ay*time**2 + v0*np.sin(theta)*time\n\nplt.plot(px_real, py_real, 'o', label='real trajectory')\nplt.plot(px, py, label='centered trajectory')\nplt.legend()\nplt.xlabel('x')\nplt.ylabel('y')\nplt.gca().set_aspect('equal')\n\n# - 如果速度的numerical是正好符合的,那么位移的numerical就会偏差;\n# - 如果位移的numerical是正好符合的, 那么速度的numerical就会偏差;\n\n\n"},"script_size":{"kind":"number","value":1319,"string":"1,319"}}},{"rowIdx":965,"cells":{"path":{"kind":"string","value":"/Google Search Analyser.ipynb"},"content_id":{"kind":"string","value":"b6fe2421acd0b48bba9e99bcf6e2f06c9a0881d4"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"aath0/Google-Searches-Analyzer"},"repo_url":{"kind":"string","value":"https://github.com/aath0/Google-Searches-Analyzer"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1817936,"string":"1,817,936"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Importing & analyzing google search history data!\n#\n#\n# In this notebook you can analyze your google search history data. First, you have to request your data from google, with instructions that you can find [here](https://goo-searches-analyzer.herokuapp.com/). \n#\n# The goal is to get your data in a more usable format that what google provides when you're requesting it and be able to do some quantitative analyses yourself. And of course to get some nice plots!\n#\n# All of the plots that are generated from this notebook will be saved in your Open Humans home folder, so check that out once you're done.\n#\n#\n\n# ### Step 0: Import your data from Open Humans!\n# Unless you know what you're doing I'd recommend not changing this part:\n\n# +\nimport zipfile, requests, os, json, tempfile\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get(\"https://www.openhumans.org/api/direct-sharing/project/exchange-member/?access_token={}\".format(os.environ.get('OH_ACCESS_TOKEN')))\nuser = json.loads(response.content)\nfor entry in user['data']:\n if entry['source'] == \"direct-sharing-151\": # unique project id (you can find that in your OH website)\n google_data_url = entry['download_url']\n break\ngoogle_data_url\n\ntf = tempfile.NamedTemporaryFile()\nprint('downloading file')\ntf.write(requests.get(google_data_url).content)# write a temporary file with the download request\ntf.flush()\nzf = zipfile.ZipFile(tf.name)\nprint('reading index')\nwith zf.open('Takeout/My Activity/Search/MyActivity.html', 'r') as f:\n soup = BeautifulSoup(f)\n# -\n\n# ### 1. Retrieve your google searches from the messy .html file that you got from Google\n# Here we're simply retrieving the search queries that you made to google, and the dates when you made them\n\n# +\ndivs = soup.find_all('div', class_=\"content-cell mdl-cell mdl-cell--6-col mdl-typography--body-1\")\n\nsearch_queries = []\ndates = []\n\nfor element in divs:\n if str(element.contents[0]).startswith('Searched'):\n search_queries.append(element.contents[1].text.split())\n dates.append(element.contents[-1])\n# -\n\n# ### 2. Keep only the essential search terms\n#\n# Here we'll be removing stop words (common words, such as \"the\" / \"they\", etc). The goal is to keep only the most infomrative search terms, in the form of a list ('filtered_words').\n\n# +\nimport nltk \nnltk.download('stopwords')\n\nfrom nltk.tokenize import word_tokenize \nfrom nltk.corpus import stopwords\n\nstop_words = set(stopwords.words('english'))\nfiltered_words = [[word for word in single_search if word not in stopwords.words('english')] for single_search in search_queries]\n\n\n# -\n\n# ### 3. Do some more formatting\n#\n# Here we are extracting information about the date and time when you did your searches. Also, we're breaking down each search into unique search terms ('single_words')\n\n# +\nfrom datetime import datetime, time\n\nformated_dates = [datetime.strptime(sdate, \"%b %d, %Y, %I:%M:%S %p\") for sdate in dates]\nformated_days = [sdate.isoweekday() for sdate in formated_dates]\n\nsingle_words = []\nsingle_dates = []\nsingle_days = []\nfor search,timepoint,day in zip(filtered_words, formated_dates, formated_days):\n for word in search:\n single_words.append(word.replace(\"\\\"\", \"\").replace(\"“\", \"\").replace('\\'', '').replace('‘', ''))\n single_dates.append(timepoint)\n single_days.append(day)\n# -\n\n# ### 4. Time to plot something!\n#\n# Now we're ready to combine everything into a dataframe and do our very first plot! We'll be plotting the frequency of searches for the top 10 most searched terms. You can change that by modifying 'items2plot'\n#\n# Not very surprisingly, my efforts to learn python clearly show here! Also, it wouldn't be too hard to guess that I'm into neuroscience, programming experiments with [psychopy](http://www.psychopy.org/) and mainly working with electroencephalography data and [mne](https://www.martinos.org/mne/stable/index.html).\n\n# +\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pylab, os\n\n# combine everything in a dataframe:\nsearches_df = pd.DataFrame({'Searches' : single_words,\n 'Dates' : single_dates, 'Days': single_days}, columns=['Searches','Dates','Days'])\n\nsearches_df_week = searches_df.loc[searches_df['Days'] <= 5]\nsearches_df_weekend = searches_df.loc[searches_df['Days'] > 5]\n\nitems2plot = 10 # number of top terms we want to plot.\ncpalette = sns.color_palette(\"Set3\", items2plot)\n\nsearches_counted = searches_df.groupby('Searches').count().sort_values(by=['Dates'],ascending=False).reset_index()\ndictPalette = {}\nfor counter,search_term in enumerate(list(searches_counted[:items2plot]['Searches'])):\n dictPalette[search_term] = cpalette[counter]\n\n# and the actual plot:\n# %matplotlib inline \nfig=plt.figure(figsize=(14, 3), dpi= 150)\nsns.set(font_scale = 2)\nsns.barplot(y='Dates',\n x='Searches',\n data=searches_counted[:items2plot], palette=cpalette)\n\nplt.xticks(rotation=90)\nplt.ylabel('# searches')\nplt.xlabel('Search terms')\n\n# save figure:\nout_f = os.getcwd()\npylab.savefig(out_f + '/TopSearches.png', bbox_inches='tight')\n\n# -\n\n# ### 5. How do my searches evolve over the past few months?\n#\n# Now it's time to use all the date & time information that we've been extracting. We're still keeping the top 10 search terms that we extracted in step 4, but now we're looking how each of them changes over time.\n#\n# In my case I unfortunately only had google search data from November 2017. My python queries peak in January 2018, and after that they keep a rather low but steady pace. Maybe I did learn something after all!\n\n# +\ndef export_year_month(x):\n return datetime.strptime(datetime.strftime(x, '%Y-%m'),'%Y-%m')\n\nsearches_df['year_month'] = searches_df['Dates'].apply(export_year_month)\n\nsearches_by_month = searches_df.groupby(['Searches','year_month']).count().reset_index()\ntop_queries = searches_by_month[searches_by_month['Searches'].isin(list(searches_counted[:items2plot]['Searches']))]\n\n# %matplotlib inline \n\nplt.style.use('seaborn-whitegrid')\nfig = plt.figure(figsize=(14, 3), dpi=150)\n\ncounter = 0\nfor name, group in top_queries[top_queries['year_month']>datetime.strptime('2017-10-01', '%Y-%m-%d')].groupby('Searches'):\n plt.plot(group.year_month, group.Dates, marker = 'o', ms=12, label = name, color = dictPalette[name])\n counter +=1\n \nplt.xlabel('Year-Month')\nplt.ylabel('# searches')\nplt.legend(loc='upper left', bbox_to_anchor=(1., 1.1),\n ncol=1, fancybox=True, shadow=True)\n\npylab.savefig(out_f + '/TopSearches_By_Month.png')\n\n# -\n\n# ### 6. How do my searches change throughout the day?\n#\n# Going back to the time information that we extracted, you can now group the top search terms by the hour of the day when you searched for them.\n#\n# I'd say that I'm an python evening kind of person...\n\n# +\ndef export_hour(x):\n return int(datetime.strftime(x, '%H'))\n\nsearches_df['Hour'] = searches_df['Dates'].apply(export_hour)\n\nsearches_by_hour = searches_df.groupby(['Searches','Hour']).count().reset_index()\ntop_queries_hour = searches_by_hour[searches_by_hour['Searches'].isin(list(searches_counted[:items2plot]['Searches']))]\n\n# %matplotlib inline \nplt.style.use('seaborn-whitegrid')\nfig = plt.figure(figsize=(14, 3), dpi = 300)\ncounter = 0\nfor name, group in top_queries_hour.groupby('Searches'):\n plt.plot(group.Hour, group.Dates, marker = 'o', ms=12, label = name, color = dictPalette[name])\n counter +=1\nplt.legend(loc='upper left', bbox_to_anchor=(1., 1.1),\n ncol=1, fancybox=True, shadow=True)\nplt.ylim(0,75)\nplt.xlim(0,23)\n\nplt.xlabel('Hour')\nplt.ylabel('# searches')\n\npylab.savefig(out_f + '/TopSearches_ByHour.png', bbox_inches='tight')\n# -\n\n# ### 7. Zooming into specific search terms\n#\n# Here you can choose specific terms from your searches and see how these evolve over time of the day. You can add your own search terms in the first line of this cell ('my_favourite_searches'). For this we normalize each time course by the total number of searches for a specific term.\n#\n# In my case, after figuring out that I'm doing most of my python searches in the evening, I wanted to see whether my R or matlab schedules are any different. It looks like I'm mostly searching for help with R earlier in the day compared to python or matlab...\n\n# +\n# focus on these search terms only:\nmy_favourite_searches = ['python', 'matlab','R']\n\nsearches_by_programming = searches_df.groupby(['Searches','Hour']).count().reset_index()\ntop_queries_programming = searches_by_hour[searches_by_programming['Searches'].isin(my_favourite_searches)]\n\n# %matplotlib inline \n\nplt.style.use('seaborn-whitegrid')\n\nfig = plt.figure(figsize=(14, 3), dpi = 300)\ncounter = 0\nfor name, group in top_queries_programming.groupby('Searches'):\n plt.plot(group.Hour, group.Dates/pd.Series.max(group.Dates), marker = 'o', ms=12, label = name, color = dictPalette[name])\n counter +=1\n \nplt.legend(loc='upper left', bbox_to_anchor=(1., 1.1),\n ncol=1, fancybox=True, shadow=True)\nplt.ylim(0,1.1)\n\nplt.xlabel('Hour')\nplt.ylabel('search ratio')\n\npylab.savefig(out_f + '/TopSearches_ByProgramming.png', bbox_inches='tight')\n# -\n\n# ### 8. Google searches as a network\n#\n# Now you can visualize your searches within a network. Here you can see whether some of your search terms tend to co-occur with others, and in which frequency. The first step for this analysis is to create a graph object containing all search terms.\n\n# +\nimport networkx as nx\nimport itertools\n\nG = nx.MultiGraph()\nL=2\n\n# for each unique search term add a node:\nG.add_nodes_from(list(searches_counted['Searches']))\n\n# Prepare queries by stripping them from unwanted characters:\nfiltered_words_stripped = []\nfor search in filtered_words:\n filtered_words_stripped.append([word.replace(\"\\\"\", \"\").replace(\"“\", \"\").replace('\\'', '').replace('‘', '') for word in search])\n\n# for each stripped query add an edge:\nfor queries in filtered_words_stripped:\n for subset in itertools.combinations(queries, L):\n G.add_edges_from([subset])\n \n# Transofrm to a simple graph, and compute the rate of occurence as weights:\nGf = nx.Graph()\nfor u,v,data in G.edges(data=True):\n w = 1.0\n if Gf.has_edge(u,v):\n Gf[u][v]['weight'] += w\n else:\n Gf.add_edge(u, v, weight=w)\n \n\n# -\n\n# ### 9. How are my search terms connected?\n#\n# Time to visualize the connections among the top search terms! For display we're only keeping searches that appear more than 40 times, and are connected with at least 5 other search terms.\n\n# +\ncutoff = 40 # minimum number of times a search term has to appear\ncutoff_edge = 5 # minimum number of connections\n\ntop_terms = list(searches_counted[searches_counted['Dates']>cutoff]['Searches'])\n\n# filter edges by top occurences & top weights:\ntop = [edge for edge in Gf.edges(data=True) if (((edge[0] in top_terms) or (edge[1] in top_terms)) and edge[2]['weight'] > cutoff_edge)]\n\nGf_plot = nx.Graph(top)\n\n# compute size of nodes, proportional to word occurence:\nnodesize = [int(searches_counted[searches_counted['Searches'] == node]['Dates'])*10 for node in Gf_plot.nodes]\n\nfsize = 18 # font size\nedges,weights = zip(*nx.get_edge_attributes(Gf_plot,'weight').items())\nweights=np.array(list(weights))*100 # edge weights (for color)\nfig, ax = plt.subplots(figsize=(14, 14))\n\nnx.draw_networkx(Gf_plot, nx.spring_layout(Gf_plot, 0.5), with_labels=True, edge_color=weights, edge_cmap = plt.get_cmap('pink'),font_size = fsize,node_size = nodesize, alpha = 0.7) # OR: nx.draw(G, pos)\nplt.axis('off')\npylab.savefig(out_f + '/Network.png', bbox_inches='tight')\n# -\n\n# ### 10. How is my top search term connected to other searches?\n#\n# Finally, you can see how some of your most searched terms are connected to other searches. For this we're filtering our graph object for a very high cutoff (i.e. rate of search term occurence) and a rather low number of co-occuring connections (cutoff_edge). \n#\n# For my queries, it shows the terms that I've been searching together with Python!\n\n# +\ncutoff = 400\ncutoff_edge = 1\n\ntop_terms = list(searches_counted[searches_counted['Dates']>cutoff]['Searches'])\n\n# filter edges by top occurences & top weights:\ntop = [edge for edge in Gf.edges(data=True) if (((edge[0] in top_terms) or (edge[1] in top_terms)) and edge[2]['weight'] > cutoff_edge)]\n\nGf_plot = nx.Graph(top)\n\n# compute size of nodes, proportional to word occurence:\nnodesize = [int(searches_counted[searches_counted['Searches'] == node]['Dates'])*10 for node in Gf_plot.nodes]\nfsize = 18 # font size\nedges,weights = zip(*nx.get_edge_attributes(Gf_plot,'weight').items())\nweights=np.array(list(weights))*100 # edge weights (for color)\nfig, ax = plt.subplots(figsize=(14, 14))\n\nnx.draw_networkx(Gf_plot, nx.spring_layout(Gf_plot, 0.5), with_labels=True, edge_color=weights, edge_cmap = plt.get_cmap('pink'),font_size = fsize,node_size = nodesize, alpha = 0.7) # OR: nx.draw(G, pos)\nplt.axis('off')\npylab.savefig(out_f + '/Network_OneNode.png', bbox_inches='tight')\n# -\n\n\ntim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)\n#scheduler = StepLR(optimizer, step_size=70, gamma=0.1)\nscheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50,70,75,80], gamma=0.1)\ncriterion = nn.CrossEntropyLoss()\n\n\n# -\n\n# Implement validation\ndef train(epoch):\n model.train()\n #writer = SummaryWriter()\n for batch_idx, (data, target) in enumerate(train_loader):\n if use_cuda:\n data, target = data.cuda(), target.cuda()\n data, target = Variable(data), Variable(target)\n optimizer.zero_grad()\n output = model(data)\n correct = 0\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n correct += pred.eq(target.data.view_as(pred)).sum()\n \n loss = criterion(output, target)\n loss.backward()\n accuracy = 100. * (correct.cpu().numpy()/ len(output))\n optimizer.step()\n if batch_idx % 5*show_step == 0:\n# if batch_idx % 2*show_step == 0:\n# print(model.layers[1].conv1D.weight.shape)\n# print(model.layers[1].conv1D.weight[0:2][0:2])\n \n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n epoch, batch_idx * len(data), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader), loss.item(), accuracy))\n# f1=open(\"Cifar10_INFO.txt\",\"a+\")\n# f1.write(\"\\n\"+'Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n# epoch, batch_idx * len(data), len(train_loader.dataset),\n# 100. * batch_idx / len(train_loader), loss.item(), accuracy))\n# f1.close()\n \n #writer.add_scalar('Loss/Loss', loss.item(), epoch)\n #writer.add_scalar('Accuracy/Accuracy', accuracy, epoch)\n scheduler.step()\n\n\n# +\ndef validate(epoch):\n model.eval()\n #writer = SummaryWriter()\n valid_loss = 0\n correct = 0\n for data, target in valid_loader:\n if use_cuda:\n data, target = data.cuda(), target.cuda()\n data, target = Variable(data), Variable(target)\n output = model(data)\n valid_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n correct += pred.eq(target.data.view_as(pred)).sum()\n\n valid_loss /= len(valid_idx)\n accuracy = 100. * correct.cpu().numpy() / len(valid_idx)\n print('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n valid_loss, correct, len(valid_idx),\n 100. * correct / len(valid_idx)))\n \n# f1=open(\"Cifar10_INFO.txt\",\"a+\")\n# f1.write('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n# valid_loss, correct, len(valid_idx),\n# 100. * correct / len(valid_idx)))\n# f1.close()\n #writer.add_scalar('Loss/Validation_Loss', valid_loss, epoch)\n #writer.add_scalar('Accuracy/Validation_Accuracy', accuracy, epoch)\n return valid_loss, accuracy\n\n\n# +\n# Fix best model\n\ndef test(epoch):\n model.eval()\n test_loss = 0\n correct = 0\n for data, target in test_loader:\n if use_cuda:\n data, target = data.cuda(), target.cuda()\n data, target = Variable(data), Variable(target)\n output = model(data)\n test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n\n test_loss /= len(test_loader.dataset)\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n test_loss, correct, len(test_loader.dataset),\n 100. * correct.cpu().numpy() / len(test_loader.dataset)))\n \n# f1=open(\"Cifar10_INFO.txt\",\"a+\")\n# f1.write('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n# test_loss, correct, len(test_loader.dataset),\n# 100. * correct.cpu().numpy() / len(test_loader.dataset)))\n# f1.close()\n\n\n# -\n\ndef save_best(loss, accuracy, best_loss, best_acc):\n if best_loss == None:\n best_loss = loss\n best_acc = accuracy\n file = 'saved_models/best_save_model.p'\n torch.save(model.state_dict(), file)\n \n elif loss < best_loss and accuracy > best_acc:\n best_loss = loss\n best_acc = accuracy\n file = 'saved_models/best_save_model.p'\n torch.save(model.state_dict(), file)\n return best_loss, best_acc\n\n\n# +\n# Fantastic logger for tensorboard and pytorch, \n# run tensorboard by opening a new terminal and run \"tensorboard --logdir runs\"\n# open tensorboard at http://localhost:6006/\nfrom tensorboardX import SummaryWriter\nbest_loss = None\nbest_acc = None\n\nimport time \nSINCE=time.time()\n\nfor epoch in range(max_epoch):\n train(epoch)\n \n loss, accuracy = validate(epoch)\n best_loss, best_acc = save_best(loss, accuracy, best_loss, best_acc)\n \n NOW=time.time() \n DURINGS=NOW-SINCE\n SINCE=NOW\n print(\"the time of this epoch:[{} s]\".format(DURINGS))\n \n if epoch>=10 and (epoch-10)%2==0:\n test(epoch)\n \n# writer = SummaryWriter() \n# writer.export_scalars_to_json(\"./all_scalars.json\")\n\n# writer.close()\n\n#---------------------------- Test ------------------------------\ntest(epoch)\n# -\n\n# # Step 3: Test\n\ntest(epoch)\n\n# ## 第一次 scale 位于[0,1]\n\n# ![](http://op4a94iq8.bkt.clouddn.com/18-7-14/70206949.jpg)\n\n# +\n# 查看训练过程的信息\nimport matplotlib.pyplot as plt\n\ndef parse(in_file,flag):\n num=-1\n ys=list()\n xs=list()\n losses=list()\n with open(in_file,\"r\") as reader:\n for aLine in reader:\n #print(aLine)\n\n res=[e for e in aLine.strip('\\n').split(\" \")]\n if res[0]==\"Train\" and flag==\"Train\":\n num=num+1\n ys.append(float(res[-1]))\n xs.append(int(num))\n losses.append(float(res[-3].split(',')[0]))\n if res[0]==\"Validation\" and flag==\"Validation\":\n num=num+1\n xs.append(int(num))\n tmp=[float(e) for e in res[-2].split('/')]\n ys.append(100*float(tmp[0]/tmp[1]))\n losses.append(float(res[-4].split(',')[0]))\n\n plt.figure(1)\n plt.plot(xs,ys,'ro')\n\n\n plt.figure(2)\n plt.plot(xs, losses, 'ro')\n plt.show()\n\ndef main():\n in_file=\"D://INFO.txt\"\n # 显示训练阶段的正确率和Loss信息\n parse(in_file,\"Train\") # \"Validation\"\n # 显示验证阶段的正确率和Loss信息\n #parse(in_file,\"Validation\") # \"Validation\"\n\n\nif __name__==\"__main__\":\n main()\n\n# +\n# 查看训练过程的信息\nimport matplotlib.pyplot as plt\n\ndef parse(in_file,flag):\n num=-1\n ys=list()\n xs=list()\n losses=list()\n with open(in_file,\"r\") as reader:\n for aLine in reader:\n #print(aLine)\n\n res=[e for e in aLine.strip('\\n').split(\" \")]\n if res[0]==\"Train\" and flag==\"Train\":\n num=num+1\n ys.append(float(res[-1]))\n xs.append(int(num))\n losses.append(float(res[-3].split(',')[0]))\n if res[0]==\"Validation\" and flag==\"Validation\":\n num=num+1\n xs.append(int(num))\n tmp=[float(e) for e in res[-2].split('/')]\n ys.append(100*float(tmp[0]/tmp[1]))\n losses.append(float(res[-4].split(',')[0]))\n\n plt.figure(1)\n plt.plot(xs,ys,'r-')\n\n\n plt.figure(2)\n plt.plot(xs, losses, 'r-')\n plt.show()\n\ndef main():\n in_file=\"D://INFO.txt\"\n # 显示训练阶段的正确率和Loss信息\n parse(in_file,\"Train\") # \"Validation\"\n # 显示验证阶段的正确率和Loss信息\n parse(in_file,\"Validation\") # \"Validation\"\n\n\nif __name__==\"__main__\":\n main()\n# -\n\n\n\n\n"},"script_size":{"kind":"number","value":21145,"string":"21,145"}}},{"rowIdx":966,"cells":{"path":{"kind":"string","value":"/R for beginer 4 Graphics with R.ipynb"},"content_id":{"kind":"string","value":"679d5c21f9a370769e168477de5faa003fb21efe"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"zjmwqx/RNotebookCode"},"repo_url":{"kind":"string","value":"https://github.com/zjmwqx/RNotebookCode"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".r"},"size":{"kind":"number","value":22418,"string":"22,418"},"script":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .r\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: R\n# language: R\n# name: ir\n# ---\n\n# ## Basic\n\n# +\n# demo(graphics)\n\n# +\n# barplot(rnorm(1000, 0.3, 10))\n\n# +\n# qqplot(rnorm(1000, 0, 10), 1:1000)\n# -\n\ndnorm(20, 0.3, 10)\npnorm(20, 0.3, 10)\n\nqnorm(0.6, 0.3, 10)\n\nquantile(rnorm(1000, 0.3, 10), 0.6)\n\n# +\n# demo(persp)\n\n# +\n# ?device\n# -\n\ndev.list(); dev.cur(); dev.set(3)\n\ndev.off(3)\n\ndev.off()\n\n# +\n# x11()\n\n# +\n# split.screen(c(1,2))\n\n# +\n# screen(2)\n# -\n\nlayout(matrix(1:4, 2, 2))\n\n# +\n# layout.show(4)\n\n# +\n# m <- matrix(c(1:3, 3), 2, 2)\n\n# +\n# layout(m)\n\n# +\n# m <- matrix(1:4, 2, 2)\n# layout(m, widths=c(1,3),\n# height=c(3,1))\n# layout.show(4)\n# -\n\nx <- rep(1,100); y = rexp(100)\n\n# +\n# plot(x, y)\n\n# +\n# iris[,4:5]\n\n# +\n# sunflowerplot(iris[,4:5],col=\"gold\",seg.col=\"gold\")\n\n# +\n# ?sunflowerplot\n\n# +\n# x <- c(1,1,1,3,2,2,2,4,4,4); y<- c(3,3,3,2,2,2,2,3,3,3) \n# sunflowerplot(x, y)\n\n# +\n# ?coplot\n\n# +\n# quakes\n\n# +\n# coplot(lat ~ long | depth, data = quakes)\n# given.depth <- co.intervals(quakes$depth, number = 4, overlap = .1)\n# coplot(lat ~ long | depth, data = quakes, given.v = given.depth, rows = 1)\n\n# +\n# require(graphics)\n# with(ToothGrowth, {\n# interaction.plot(dose, supp, len, fixed = TRUE)\n# dose <- ordered(dose)\n# interaction.plot(dose, supp, len, fixed = TRUE, col = 2:3, leg.bty = \"o\")\n# interaction.plot(dose, supp, len, fixed = TRUE, col = 2:3, type = \"p\")\n# })\n\n# +\n# (-4:5)^2\n\n# +\n# matplot((-4:5)^2, main = \"Quadratic\") # almost identical to plot(*)\n\n# +\n# sines <- outer(1:20, 1:4, function(x, y) sin(x / 20 * pi * y))\n# matplot(sines, pch = 1:4, type = \"o\", col = rainbow(ncol(sines)))\n\n# +\n# x <- 0:50/50\n# matplot(x, outer(x, 1:8, function(x, k) sin(k*pi * x)),\n# ylim = c(-2,2), type = \"plobcsSh\",\n# main= \"matplot(,type = \\\"plobcsSh\\\" )\")\n\n# +\n\n# lends <- c(\"round\",\"butt\",\"square\")\n# matplot(matrix(1:12, 4), type=\"c\", lty=1:3, lwd=10, lend=lends)\n# text(cbind(2.5, 2*c(1,3,5)-.4), lends, col= 1:3, cex = 1.5)\n\n\n# +\n# table(iris$Species) # is data.frame with 'Species' factor\n# iS <- iris$Species == \"setosa\"\n# iV <- iris$Species == \"versicolor\"\n# op <- par(bg = \"bisque\")\n# matplot(c(1, 8), c(0, 4.5), type = \"n\", xlab = \"Length\", ylab = \"Width\",\n# main = \"Petal and Sepal Dimensions in Iris Blossoms\")\n# matpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = c(1,2), col = c(2,4))\n# matpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = \"vV\", col = c(2,4))\n# legend(1, 4, c(\" Setosa Petals\", \" Setosa Sepals\",\n# \"Versicolor Petals\", \"Versicolor Sepals\"),\n# pch = \"sSvV\", col = rep(c(2,4), 2))\n\n# +\n# dotchart(VADeaths, main = \"Death Rates in Virginia - 1940\")\n# op <- par(xaxs = \"i\") # 0 -- 100%\n# dotchart(t(VADeaths), xlim = c(0,100),\n# main = \"Death Rates in Virginia - 1940\")\n# par(op)\n\n# +\n# ## Use the Berkeley admission data as in Friendly (1995).\n# x <- aperm(UCBAdmissions, c(2, 1, 3))\n# dimnames(x)[[2]] <- c(\"Yes\", \"No\")\n# names(dimnames(x)) <- c(\"Sex\", \"Admit?\", \"Department\")\n# stats::ftable(x)\n\n# +\n# pairs(iris)\n\n# +\n# x <- -6:16\n# op <- par(mfrow = c(2, 2))\n# contour(outer(x, x), method = \"edge\", vfont = c(\"sans serif\", \"plain\"))\n# z <- outer(x, sqrt(abs(x)), FUN = \"/\")\n# image(x, x, z)\n# contour(x, x, z, col = \"pink\", add = TRUE, method = \"edge\",\n# vfont = c(\"sans serif\", \"plain\"))\n# contour(x, x, z, ylim = c(1, 6), method = \"simple\", labcex = 1,\n# xlab = quote(x[1]), ylab = quote(x[2]))\n# contour(x, x, z, ylim = c(-6, 6), nlev = 20, lty = 2, method = \"simple\",\n# main = \"20 levels; \\\"simple\\\" labelling method\")\n# par(op)\n\n# +\n# z[1:6, 7]=-10; z[8:23, 7]=10\n\n# +\n# persp(x,x,z)\n# star(z)\n\n# +\n# require(stats) # for rnorm\n# plot(-4:4, -4:4, type = \"o\") # setting up coord. system\n# points(rnorm(200), rnorm(200), col = \"red\")\n# points(rnorm(100)/2, rnorm(100)/2, col = \"blue\", cex = 1.5)\n\n# op <- par(bg = \"light blue\")\n# x <- seq(0, 2*pi, len = 51)\n# # something \"between type='b' and type='o'\":\n# plot(x, sin(x), type = \"o\", pch = 21, bg = par(\"bg\"), col = \"blue\", cex = .6,\n# main = 'plot(..., type=\"o\", pch=21, bg=par(\"bg\"))')\n# par(op)\n\n# +\n# ?ar\n\n# +\n# plot(-1:1, -1:1, type = \"n\", xlab = \"Re\", ylab = \"Im\")\n# K <- 16; text(exp(1i * 2 * pi * (1:K) / K), col = 2) #complex number represents coordinats\n# text(x, y, expression(p == over(1, 1+e^-(beta*x+alpha))))\n\n## The following two examples use latin1 characters: these may not\n## appear correctly (or be omitted entirely).\n# plot(1:10, 1:10, main = \"text(...) examples\\n~~~~~~~~~~~~~~\",\n# sub = \"R is GNU ©, but not ® ...\")\n# mtext(\"«Latin-1 accented chars»: éè øØ å<Å æ<Æ\", side = 3)\n# points(c(6,2), c(2,1), pch = 3, cex = 4, col = \"red\")\n# text(6, 2, \"the text is CENTERED around (x,y) = (6,2) by default\",\n# cex = .8)\n# Rsquared = 0.911 ^ 2\n# par(adj=0.2)\n# text(3,8,as.expression(substitute(italic(R)^2==r, list(r=round(Rsquared, 3)))))\n# -\n\n# ## A practical example\n\n# x <- rnorm(10); y<-rnorm(10)\n# plot(x, y, xlab=\"Ten random values\", ylab=\"Ten other values\", \n# xlim=c(-2, 2), ylim=c(-2, 2), pch=22, col=\"red\",\n# bg=\"yellow\", bty=\"l\", tcl=0.4, #tcl stands for - symble of tick; bty stands for cordinator shape :\"l\", \"7\", \"c\", \"u\", or \"]\" \n# main=\"How to customize a plot with R\", las=1, cex=1.5)\n\n\n# +\n# opar <- par()\n# par(bg=\"lightyellow\", col.axis = \"blue\", mar=c(4,4,2.5,0.25))#mar stands for margin\n# plot(x,y, xlab=\"Ten random values\", ylab=\"Ten other values\", \n# xlim=c(-2, 2), ylim=c(-2, 2), pch = 22, col=\"red\", bg=\"yellow\", cex=1.5, bty=\"u\", tcl=-.25, las=1) #las stands for axis lable direction\n# title(\"How to customize a plot with R (bis)\", font.main=3, adj=0.5)\n# par(opar)\n\n# +\n# ?mtext\n\n# +\n# opar<-par()\n# par(bg=\"lightgray\", mar=c(2.5,1.5,2.5,0.25))\n# plot(x, y, type=\"n\", xlab =\"\", ylab=\"\", xlim=c(-2, 2), ylim=c(-2,2), xaxt=\"n\", yaxt=\"n\")\n# rect(-3, -3, 3, 3, col=\"cornsilk\")\n# points(x,y, pch=10, col=\"red\", cex=2)\n# axis(side=1, c(-2, 0, 2), tcl=-0.2, labels=FALSE)#labels=c(\"A\", \"B\", \"C\").\n# axis(side=2, -1:1, tcl=-0.2, labels=FALSE)\n# title(\"How to customize a plot with R (ter)\", \n# font.main=4, adj=1, cex.main=1 )\n# mtext(\"Ten random values\", side=1, line=1, at=1, cex=0.9, font=3)\n# mtext(\"Ten other values\", side = 3, line=1, at=-1.8, cex=0.9, font=3) #default 3\n# mtext(c(-2, 0, 2), side=1, las=1, at=c(-2, 0, 2), line=0.3, col=\"blue\", cex=0.9)\n# mtext(-1:1, side=2, las=1, at=-1:1, line=0.2, col=\"blue\", cex=0.9)\n# par(opar)\n# -\n\n# ## lattice and gride\n\nlibrary(lattice)\n\nn<-seq(5, 45, 5)\nx<-rnorm(sum(n))\n\n# factor convert val to category value. only need to define a distinct values list of the same size of distinct number of org vec\ny <- factor(rep(n, n), labels=paste(\"n=\", n)) #rep n$i n$i times\n\n# +\n# ?densityplot\n\n# +\n# densityplot(~ x| y, panel = function(x, ...) {\n# panel.densityplot(x, col=\"DarkOliveGreen\", ...)\n# panel.mathdensity(dmath=dnorm, args=list(mean=mean(x), sd=sd(x)),\n# col =\"darkblue\")\n# })\n\n# +\n# data(quakes)\n# mini <- min(quakes$depth)\n# maxi <- max(quakes$depth)\n# int <- ceiling((maxi - mini)/9)\n# inf <- seq(mini, maxi, int)\n# quakes$depth.cat <- factor(floor(((quakes$depth- mini) / int)), labels=paste(inf, inf + int, sep = \"-\"))\n# xyplot(lat ~ long | depth.cat, data = quakes)\n# -\n\ndata(iris)\n\n# +\n# xyplot(Petal.Length ~ Petal.Width, data = iris, groups = Species, panel = panel.superpose, type= c(\"p\", \"smooth\"), span=.75,\n# auto.key = list(x=0.15, y=0.85)) #superpose get all plot displayed in the same plot; span:smoothness; auto.key: add label\n\n# +\n# coplot(Sepal.Length~Sepal.Width | Species, data=iris)\n\n# +\n# splom(~iris[1:4], group=Species,data=iris, xlab=\"\", panel=panel.superpose, auto.key=list(columns=3))\n\n# +\n# pairs(iris)\n\n# +\n# splom(~iris[1:3] ,groups = Species, data = iris,panel = panel.superpose)\n\n# +\n# splom(~iris[1:3] | Species, data = iris, pscales = 0, #pscales=0 remove all ticks marks\n# varnames = c(\"Sepal\\nLength\", \"Sepal\\nWidth\", \"Petal\\nLength\"))\n\n# +\n# parallel(~iris[,1:4]| Species, data=iris, layout=c(3,1))\n"},"script_size":{"kind":"number","value":8181,"string":"8,181"}}},{"rowIdx":967,"cells":{"path":{"kind":"string","value":"/3weeks(Data analysis and visualization)/1. Visualization and Graph/.ipynb_checkpoints/bar graph-checkpoint.ipynb"},"content_id":{"kind":"string","value":"26dd160c2e972ddf9bf6c2c27182b960d61871c7"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"rlagywns0213/codeit_DataScience_study"},"repo_url":{"kind":"string","value":"https://github.com/rlagywns0213/codeit_DataScience_study"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":46016,"string":"46,016"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n# # Example: Compare MIPS of RISC-V Instruction Set Simulators\n\n# Typically MLonMCU would be used to benchmark TinyML workloads on real wardware or simulators. However it's flexibility also allows some interesting experiments not directly related to Embedded ML. In the following it the performance of some RISC-V ISA Simulators is compared using the MLonMCU command line or Python API.\n\n# ## Supported components\n\n# **Models:** Any (`sine_model` used below)\n#\n# **Frontends:** Any (`tflite` used below)\n#\n# **Frameworks/Backends:** Any (`tvmaotplus` used below)\n#\n# **Platforms/Targets:** `etiss_pulpino`, `spike`, `ovpsim` (`etiss_pulpino` and `spike` used below)\n\n# ## Prerequisites\n\n# Set up MLonmCU as usual, i.e. initialize an environment and install all required dependencies. Feel free to use the following minimal `environment.yml.j2` template:\n\n# ```yaml\n# ---\n# home: \"{{ home_dir }}\"\n# logging:\n# level: DEBUG\n# to_file: false\n# rotate: false\n# cleanup:\n# auto: true\n# keep: 10\n# paths:\n# deps: deps\n# logs: logs\n# results: results\n# plugins: plugins\n# temp: temp\n# models:\n# - \"{{ home_dir }}/models\"\n# - \"{{ config_dir }}/models\"\n# repos:\n# tvm:\n# url: \"https://github.com/apache/tvm.git\"\n# ref: de6d8067754d746d88262c530b5241b5577b9aae\n# etiss:\n# url: \"https://github.com/tum-ei-eda/etiss.git\"\n# ref: 4d2d26fb1fdb17e1da3a397c35d6f8877bf3ceab\n# spike:\n# url: \"https://github.com/riscv-software-src/riscv-isa-sim.git\"\n# ref: 0bc176b3fca43560b9e8586cdbc41cfde073e17a\n# spikepk:\n# url: \"https://github.com/riscv-software-src/riscv-pk.git\"\n# ref: 7e9b671c0415dfd7b562ac934feb9380075d4aa2\n# mlif:\n# url: \"https://github.com/tum-ei-eda/mlonmcu-sw.git\"\n# ref: 4b9a32659f7c5340e8de26a0b8c4135ca67d64ac\n# frameworks:\n# default: tvm\n# tvm:\n# enabled: true\n# backends:\n# default: tvmaot\n# tvmaot:\n# enabled: true\n# features: []\n# features: []\n# frontends:\n# tflite:\n# enabled: true\n# features: []\n# toolchains:\n# gcc: true\n# platforms:\n# mlif:\n# enabled: true\n# features: []\n# targets:\n# default: spike\n# spike:\n# enabled: true\n# features: []\n# etiss_pulpino:\n# enabled: true\n# features: []\n# ```\n\n# Do not forget to set your `MLONMCU_HOME` environment variable first if not using the default location!\n\n# ## Usage\n\n# If supported by the defined target, the measured MIPS (of the Simulation) is part of the report printed/returned my MLonMCU. The following shows you how to get rid of unwanted further information and how to increase the accuracy of the MIPS value.\n\n# ### A) Command Line Interface\n\n# Let's start with an example benchmark of two models using 2 different RISC-V simulators:\n\n# !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike\n\n# The MIPS value can be found in the column next to the Cycles (which are in this case actually counting instructions). However there is a lot of further information we want to filter out next. This can be achieved using the `filter_cols` subprocess.\n\n# !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike --postprocess filter_cols --config filter_cols.keep=\"Model,Target,MIPS\"\n\n# That looks much more clean! However the numbers seem quite low, especially for the smaller `toycar` (MLPerfTiny Anomaly Detection) model. Let's see if the MIPS will increase when running more than a single inference. We are using the `benchmark` feature for this.\n#\n# *Hint*: Since we are now running our benchmarks 60 times more often, the following cell will likely need a few minutes to execute.\n\n# !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike --postprocess config2cols --postprocess filter_cols --config filter_cols.keep=\"Model,Target,MIPS,config_benchmark.num_runs\" --feature benchmark --config-gen benchmark.num_runs=1 --config-gen benchmark.num_runs=10 --config-gen benchmark.num_runs=50\n\n# This look more promising. This experiment shows MIPS measurements might not be accurate for short-running simulations. Also spike seems to be more than twice as fast compared to ETISS.\n\n# ### B) Python Scripting\n\n# Some imports\n\n# +\nfrom tempfile import TemporaryDirectory\nfrom pathlib import Path\nimport pandas as pd\n\nfrom mlonmcu.context.context import MlonMcuContext\nfrom mlonmcu.session.run import RunStage\n# -\n\n# Benchmark Configuration\n\nFRONTEND = \"tflite\"\nMODELS = [\"resnet\", \"toycar\"]\nBACKEND = \"tvmaot\"\nPLATFORM = \"mlif\"\nTARGETS = [\"etiss_pulpino\", \"spike\"]\nPOSTPROCESSES = [\"config2cols\", \"filter_cols\"]\nFEATURES = [\"benchmark\"]\nCONFIG = {\n \"filter_cols.keep\": [\"Model\", \"Target\", \"MIPS\", \"config_benchmark.num_runs\"]\n}\n\n# Initialize and run a single benchmark\n\nwith MlonMcuContext() as context:\n session = context.create_session()\n for model in MODELS:\n for target in TARGETS:\n def helper(session, num=0):\n cfg = CONFIG.copy()\n cfg[\"benchmark.num_runs\"] = num\n run = session.create_run(config=cfg)\n run.add_frontend_by_name(FRONTEND, context=context)\n run.add_features_by_name(FEATURES, context=context)\n run.add_model_by_name(model, context=context)\n run.add_backend_by_name(BACKEND, context=context)\n run.add_platform_by_name(PLATFORM, context=context)\n run.add_target_by_name(target, context=context)\n run.add_postprocesses_by_name(POSTPROCESSES)\n for num in [1, 10]: # Removed 50 to cut down runtime\n helper(session, num)\n session.process_runs(context=context)\n report = session.get_reports()\nreport.df\nentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms.\n#\n# ***The architecture of a ConvNet is analogous to that of the con\n# conectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex***\n#\n# 2. ***How Convutional Neural Networl work?***\n#\n# https://e2eml.school/how_convolutional_neural_networks_work.html\n# https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/\n#\n# Please go trough the above links to get better understanding at the working of the CNN,\n#\n# ![image.png](attachment:image.png)\n\ndef build_model():\n model = Sequential()\n\n model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n activation ='relu', input_shape = (28,28,1)))\n model.add(BatchNormalization())\n model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n activation ='relu'))\n model.add(MaxPool2D(pool_size=(2,2)))\n model.add(Dropout(0.25))\n\n\n model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n activation ='relu'))\n model.add(BatchNormalization())\n model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n activation ='relu'))\n model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n model.add(Dropout(0.25))\n \n model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', \n activation ='relu'))\n model.add(BatchNormalization())\n model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', \n activation ='relu'))\n model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n model.add(Dropout(0.25))\n \n model.add(Flatten())\n model.add(Dense(256, activation = \"relu\"))\n model.add(Dropout(0.5))\n model.add(Dense(10, activation = \"softmax\"))\n\n return model\n\n# # How I built my model?\n#\n# Here I've created a function build_model,\n# **Defining Cnn's Architecture**\n# Most simply, we can compare an architecture with a building. It consists of walls, windows, doors, et cetera – and together these form the building. Explaining what a neural network architecture is benefits from this analogy. Put simply, it is a collection of components that is put in a particular order. The components themselves may be repeated and also may form blocks of components. Together, these components form a neural network: in this case, a CNN to be precise.\n#\n# So the first step was to decide the model type as Seuential,**A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor**.\n# So basically,In a sequential layer there is one input and one output, and then the output is fed into another layer(can be seen, in the picture later)\n#\n# The next step is to define the layers of single Network or Architechture,\n# 1. Convulutional layer:- \n# What a Convutional layer does, it basically perform a element-wise operation with filters(used for eedge detetction) as shown here:\n# ![image.png](attachment:image.png)\n#\n\n# 2. Batch Normalisation:-\n# To increase the stability of a neural network, batch normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation.\n# batch normalization allows each layer of a network to learn by itself a little bit more independently of other layers.\n# 3. Another Convutional layer:-\n# It perform the same action as the previous layer\n# 4. Max Pooling layer:-\n# Sometimes when the images are too large, we would need to reduce the number of trainable parameters. It is then desired to periodically introduce pooling layers between subsequent convolution layers. Pooling is done for the sole purpose of reducing the spatial size of the image.\n# ![image.png](attachment:image.png)\n#\n#\n\n# 5. Dropout Layer:- \n# Dropout is a technique used to improve over-fit on neural networks, Basically during training half of neurons on a particular layer will be deactivated. This improve generalization because force your layer to learn with different neurons the same \"concept\". During the prediction phase the dropout is deactivated. \n#\n# Then we'll repeat the same layer three times \n# *All the layers other than output layer will have ReLu activation In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node or output for that input. The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero*. \n#\n# **Output layer**\n#\n# 6. Flatten layer:- \n# In this lyaer we are literally going to flatten our pooled feature map into a column like in the image below.\n# ![image.png](attachment:image.png)\n#\n# 7. Dense layer:- \n# The dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer.A densely connected layer provides learning features from all the combinations of the features of the previous layer\n#\n# **For the fully connected layer the activation function is Softmax: which is used for multiclass classifiaction**\n\nmodel= build_model()\n\n# # Compiling the model\n# model.compile is used to compile the model the loss is Categoriacal crossentopy since we are doing multiclass classification, one can use Binary crossentropy for binary classification,\n# The opitimizer is Adam,they basically optimize loss and make trainning better and fast to read more\n# https://algorithmia.com/blog/introduction-to-optimizers,\n# https://towardsdatascience.com/how-to-train-neural-network-faster-with-optimizers-d297730b3713\n# please go through above links\n\nmodel.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])\n\nmodel.summary()\n\n# # plotting model\n# Everything that I've explained earlier can be understood in a better way with help of this\n\nfrom keras.utils.vis_utils import plot_model\nplot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n\n\n# # Data Augmentation:\n# Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks.\n# ![image.png](attachment:image.png)\n\ndatagen = ImageDataGenerator(\n featurewise_center=False, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=False, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n zca_whitening=False, # apply ZCA whitening\n rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180)\n zoom_range = 0.1, # Randomly zoom image \n width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n horizontal_flip=False, # randomly flip images\n vertical_flip=False)# randomly flip images\ndatagen.fit(X_train)\n\nbatch_size=64\n\n# # Fitting the model:-\n# We are using model.fit_generator that takes the augmented data, I've defined epochs(number of times we will go through the model) as 20 (the larger the number of epochs the better the accuracy).\n# For the sake of notebook purpose I've taken it as 20, if you\"ll fork it change it to 50/60\n\nhist = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=64),\n steps_per_epoch=len(X_train)//batch_size,\n epochs=20, #Increase this when not on Kaggle kernel\n verbose=1, #1 for ETA, 0 for silent\n validation_data=(X_val[:400,:], Y_val[:400,:]))\n\nfinal_loss, final_acc = model.evaluate(X_val, Y_val, verbose=0)\nprint(\"Final loss: {0:.4f}, final accuracy: {1:.4f}\".format(final_loss, final_acc))\n\n\n# # Evaluating Model:-\n# This is the plot of model training and we can clearly see the decreasing loss and increasing accuracy\n\nplt.plot(hist.history['loss'], color='b')\nplt.plot(hist.history['val_loss'], color='r')\nplt.show()\nplt.plot(hist.history['accuracy'], color='b')\nplt.plot(hist.history['val_accuracy'], color='r')\nplt.show()\n\n\n# Confusion Matrix for better understanding of True positive and Negative\n\ny_hat_val = model.predict(X_val)\n\ny_pred = np.argmax(y_hat_val, axis=1)\ny_true = np.argmax(Y_val, axis=1)\ncm = confusion_matrix(y_true, y_pred)\nprint(cm)\n\n# # Creating Prediction\n\ny_hat = model.predict(test, batch_size=64)\n\ny_pred = np.argmax(y_hat,axis=1)\n\n# **This is the notebook I've created for learning purpose, I've missed out following thing (for keeping it short and not giving too much information in one notebook)**\n# 1. Hyperparameter tunning :- \n# Can done with the help of GridSearchCV, it helps you select the best parameters for your model,\n# 2. Callbacks:-\n# Callback is a technique that prevents overfitting, Earlystopping and model checkpoint are few examples of callbacks\n#\n\n# ***If you liked this notebook and it helped you in learnig something please upvote,\n# It has taken a large amount of time, and an upvote will motivate me to make more such content, and give back to the community. \n# Thanks for reading.\n# Feedbacks and Suggestions are welcomed.****\n\n\n"},"script_size":{"kind":"number","value":15670,"string":"15,670"}}},{"rowIdx":968,"cells":{"path":{"kind":"string","value":"/proj_3_test.ipynb"},"content_id":{"kind":"string","value":"ac0fa0c054cb7e4cc5690975886083338bda544f"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"braiyen/MagnetizationStatistics"},"repo_url":{"kind":"string","value":"https://github.com/braiyen/MagnetizationStatistics"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":245998,"string":"245,998"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n# %matplotlib inline\n# -\n\ndef weighted_die(num_steps):\n probabilities = np.array([3,3,1,1,1,1])\n states = np.zeros(6)\n current = np.random.randint(1,7)\n for n in range(num_steps):\n proposed = np.random.randint(1,7)\n r = np.random.random_sample()\n if current != proposed:\n p_accept = np.minimum(1, (probabilities[proposed-1]/probabilities[current-1]))\n if r < p_accept:\n current = proposed\n states[current-1] += 1\n earnings = np.sum(states[:2]) - np.sum(states[2:])\n return states, earnings \n\n\ndef random_S(L):\n S = np.random.rand(L,L)\n for i in range(L):\n for j in range(L):\n if S[i,j] > .5:\n S[i,j] = 1\n else:\n S[i,j] = -1\n return S\n\n\ndef two_dim_ising(L, temp, num_steps):\n N = L**2\n U = np.zeros(num_steps+1)\n M = np.zeros(num_steps+1)\n X = np.zeros(num_steps+1)\n C = np.zeros(num_steps+1)\n \n S = random_S(L)\n \n E = energy(S)\n s = net_spin(S)\n \n E_ave = E\n E_square_ave = E**2\n s_ave = s\n s_square_ave = s**2\n \n U[0] = E_ave/N\n M[0] = s_ave/N\n X[0] = (1/(N*temp))*(s_square_ave - s_ave**2)\n C[0] = (1/(N*temp**2))*(E_square_ave - E_ave**2)\n \n for n in range(num_steps):\n i = np.random.randint(0, L)\n j = np.random.randint(0, L)\n delta = delta_E(S, i, j)\n if delta <= 0:\n S[i,j] *= -1\n E += delta\n if S[i,j] < 0:\n s -= 2\n else:\n s += 2\n else:\n r = np.random.random_sample()\n if r < np.exp(-1*delta/temp):\n S[i,j] *= -1\n E += delta\n if S[i,j] < 0:\n s -= 2\n else:\n s += 2\n \n E_ave = E_ave + (1/(1+n))*(E - E_ave)\n s_ave = s_ave + (1/(1+n))*(s - s_ave) \n E_square_ave = E_square_ave + (1/(1+n))*(E**2 - E_square_ave)\n s_square_ave = s_square_ave + (1/(1+n))*(s**2 - s_square_ave)\n \n U[n+1] = E_ave/N\n M[n+1] = s_ave/N\n X[n+1] = (1/(N*temp))*(s_square_ave - s_ave**2)\n C[n+1] = (1/(N*temp**2))*(E_square_ave - E_ave**2)\n \n return S, U, M, X, C\n\n\ndef two_dim_ising_mag(S, L, temp, num_steps, sample_steps):\n N = L**2\n E = energy(S)\n E_ave = E\n \n #Run algorithm until close to equillibrium\n for n in range(num_steps):\n i = np.random.randint(0, L)\n j = np.random.randint(0, L)\n delta = delta_E(S, i, j)\n if delta <= 0:\n S[i,j] *= -1\n E += delta\n else:\n r = np.random.random_sample()\n if r < np.exp(-1*delta/temp):\n S[i,j] *= -1\n E += delta\n \n E_ave = E_ave + (1/(1+n))*(E - E_ave) \n \n E = energy(S)\n s = net_spin(S)\n s_ave = s\n\n #Run algorithm and sample spins\n for n in range(sample_steps):\n i = np.random.randint(0, L)\n j = np.random.randint(0, L)\n delta = delta_E(S, i, j)\n if delta <= 0:\n S[i,j] *= -1\n E += delta\n if S[i,j] < 0:\n s -= 2\n else:\n s += 2\n else:\n r = np.random.random_sample()\n if r < np.exp(-1*delta/temp):\n S[i,j] *= -1\n E += delta\n if S[i,j] < 0:\n s -= 2\n else:\n s += 2\n \n E_ave = E_ave + (1/(1+n))*(E - E_ave)\n s_ave = s_ave + (1/(1+n))*(s - s_ave)\n \n U = E_ave/N\n M = s_ave/N\n \n return S, U, M\n\n\ndef delta_E(S, i, j):\n total = 0\n aux = np.block([[S,S,S],\n [S,S,S],\n [S,S,S]])\n i += np.shape(S)[0]\n j += np.shape(S)[0]\n \n total += aux[i,j]*aux[i+1,j]\n total += aux[i,j]*aux[i-1,j]\n total += aux[i,j]*aux[i,j+1]\n total += aux[i,j]*aux[i,j-1]\n \n return total*2\n\n\ndef energy(S):\n total = 0\n aux = np.block([[S,S,S],\n [S,S,S],\n [S,S,S]])\n for i in range(np.shape(S)[0]):\n n = i + np.shape(S)[0]\n for j in range(np.shape(S)[0]):\n m = j + np.shape(S)[0]\n total += aux[n,m]*aux[n+1,m]\n total += aux[n,m]*aux[n-1,m]\n total += aux[n,m]*aux[n,m+1]\n total += aux[n,m]*aux[n,m-1]\n return -1*total/2\n\n\ndef net_spin(S):\n n = np.shape(S)[0]\n total = 0\n for i in range(n):\n for j in range(n):\n total += S[i,j]\n return total\n\n\ndef onsager(T):\n if np.any(T >= 2.2692):\n return 0\n else:\n return (1-(np.sinh(2/T))**(-4))**(1/8)\n\n\ndef graph_spins(S):\n L = np.shape(S)[0]\n for j in range(0, L):\n for i in range(0, L):\n if S[i,j] == 1:\n plt.plot(i, L-j-1, marker='s', color='white')\n else:\n plt.plot(i, L-j-1, marker='s', color='black')\n print(\"Spin up: White\")\n print(\"Spin down: Black\")\n\n\n# +\nnum_steps = 1000000\nL = 16\nT = 10\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\nx_axis = np.linspace(0, num_steps/L**2, num_steps+1)\n\nprint(\"Magnetization converging to:\", M[num_steps], \"with system size:\", L)\nplt.plot(x_axis, M)\nprint(\"Onsager's result:\", onsager(T))\n# -\n\nprint(\"Internal Energy converging to:\", U[num_steps], \"with system size:\", L)\nplt.plot(x_axis, U)\n\n# +\nnum_steps = 2000000\nL = 32\nT = 10\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\nx_axis = np.linspace(0, num_steps/L**2, num_steps+1)\n\nprint(\"Magnetization converging to:\", M[num_steps], \"with system size:\", L)\nplt.plot(x_axis, M)\nprint(\"Onsager's result:\", onsager(T))\n# -\n\nprint(\"Internal Energy converging to:\", U[num_steps], \"with system size:\", L)\nplt.plot(x_axis, U)\n\nprint(\"Magnetic Susceptability converging to:\", X[num_steps], \"with system size:\", L)\nplt.plot(x_axis, X)\n\nprint(\"Heat Capacity converging to:\", C[num_steps], \"with system size:\", L)\nplt.plot(x_axis, C)\n\nsample_steps = 100\nL = np.array([8,16,32,64])\nT = np.append([20,10],np.flip(np.arange(.1,9.9,.1),0))\nM = np.zeros(np.size(T))\nfor i in range(np.size(L)):\n num_steps = 100000*L[i]\n S = random_S(L[i])\n for j in range(np.size(T)):\n if j > 0:\n num_steps = 1000\n n = 1\n S, U, M[j] = two_dim_ising_mag(S, L[i], T[j], num_steps, sample_steps)\n plt.plot(T, M)\nplt.plot([2.2692,20],[0,0], 'k-')\nplt.plot([2.2962,2.2692],[onsager(2.2961), 0], 'k-')\nt = np.arange(0.1,2.2692,0.1)\nplt.plot(t, onsager(t), color='black')\nplt.xlabel('Temperature')\nplt.ylabel('Magnetization')\n\nnum_steps = 100000\nL = 10\nT = .1\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n\nnum_steps = 5000000\nL = 256\nT = 10\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n\nnum_steps = 5000000\nL = 256\nL = 256\nT = 8\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n\nnum_steps = 5000000\nL = 256\nT = 2.3\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n\nnum_steps = 5000000\nL = 256\nL = 256\nT = 4.0\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n\nnum_steps = 5000000\nL = 256\nL = 256\nT = 1.8\nS, U, M, X, C = two_dim_ising(L, T, num_steps)\ngraph_spins(S)\n"},"script_size":{"kind":"number","value":7611,"string":"7,611"}}},{"rowIdx":969,"cells":{"path":{"kind":"string","value":"/TitanicSurvivalExploration/Titanic_Survival_Exploration.ipynb"},"content_id":{"kind":"string","value":"e24650eeba8b1a6037f38b43d1c97acbd30f93b3"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"pydevhacker/MachineLearningNanoDegreeUdacity"},"repo_url":{"kind":"string","value":"https://github.com/pydevhacker/MachineLearningNanoDegreeUdacity"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":188679,"string":"188,679"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# Solution_1 :\n\nclass Solution(object):\n def grayCode(self, n):\n \"\"\"\n :type n: int\n :rtype: List[int]\n \"\"\"\n grays = dict()\n grays[0] = ['0']\n grays[1] = ['0', '1']\n for i in range(2, n + 1):\n n_gray = []\n for pre in grays[i - 1]:\n n_gray.append('0' + pre)\n for pre in grays[i - 1][::-1]:\n n_gray.append('1' + pre)\n grays[i] = n_gray\n return map(lambda x: int(x, 2), grays[n])\n\n'f', '', 'kernel')\ntf.flags.DEFINE_integer(\"batch_size\", \"10\", \"batch size for training\")\ntf.flags.DEFINE_string(\"logs_dir\", \"/data/logs\", \"path to logs directory\")\ntf.flags.DEFINE_string(\"data_dir\", \"/data/\", \"path to dataset\")\ntf.flags.DEFINE_float(\"learning_rate\", \"1e-4\", \"Learning rate for Adam Optimizer\")\ntf.flags.DEFINE_float(\"beta1\", \"0.9\", \"Beta 1 value to use in Adam Optimizer\")\ntf.flags.DEFINE_string(\"model_dir\", \"/data/imagenet-vgg-verydeep-19.mat\", \"Path to vgg model mat\")\ntf.flags.DEFINE_bool('debug', \"False\", \"Debug mode: True/ False\")\ntf.flags.DEFINE_string('mode', \"train\", \"Mode train/ test\")\nFLAGS = flags.FLAGS\n\nMODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat'\nIMAGE_SIZE = 128\nMAX_ITERATION = 6000\nrestore_model = False\n\n\n# # FLAGS.debug\n\n# +\ndef vgg_net(weights, image): # load the pre-trained VGG19 , https://arxiv.org/pdf/1409.1556.pdf\n layers = (\n # 'conv1_1', 'relu1_1',\n # skip conv1_1 of VGG\n 'conv1_2', 'relu1_2', 'pool1',\n\n 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',\n\n 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',\n 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',\n\n 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',\n 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',\n\n 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',\n 'relu5_3', 'conv5_4', 'relu5_4'\n )\n\n net = {}\n current = image\n for i, name in enumerate(layers):\n kind = name[:4]\n if kind == 'conv':\n kernels, bias = weights[i + 2][0][0][0][0]\n # matconvnet: weights are [width, height, in_channels, out_channels]\n # tensorflow: weights are [height, width, in_channels, out_channels]\n kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + \"_w\")\n bias = utils.get_variable(bias.reshape(-1), name=name + \"_b\")\n current = utils.conv2d_basic(current, kernels, bias)\n elif kind == 'relu':\n current = tf.nn.relu(current, name=name)\n if FLAGS.debug:\n utils.add_activation_summary(current)\n elif kind == 'pool':\n current = utils.avg_pool_2x2(current)\n net[name] = current\n return net\n\n\ndef HyperColumns(images, train_phase):\n print(\"setting up vgg initialized conv layers ...\")\n model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)\n\n weights = np.squeeze(model_data['layers'])\n\n with tf.variable_scope(\"HyperColumns\") as scope:\n # VGG takes in 3channel (RGB) images. \n # In order to input 1-channel (gray) image, \n # define a new filter that takes in gray color image and map it into 64 channels so as to fit VGG conv1_2\n W0 = utils.weight_variable([3, 3, 1, 64], name=\"W0\")\n b0 = utils.bias_variable([64], name=\"b0\")\n conv0 = utils.conv2d_basic(images, W0, b0)\n hrelu0 = tf.nn.relu(conv0, name=\"relu\")\n image_net = vgg_net(weights, hrelu0)\n\n # HyperColumns\n # https://arxiv.org/abs/1411.5752\n relu1_2 = image_net[\"relu1_2\"]\n layer_relu1_2 = tf.image.resize_bilinear(relu1_2, (IMAGE_SIZE, IMAGE_SIZE)) \n\n relu2_1 = image_net[\"relu2_1\"]\n layer_relu2_1 = tf.image.resize_bilinear(relu2_1, (IMAGE_SIZE, IMAGE_SIZE)) \n \n relu2_2 = image_net[\"relu2_2\"]\n layer_relu2_2 = tf.image.resize_bilinear(relu2_2, (IMAGE_SIZE, IMAGE_SIZE)) \n\n relu3_1 = image_net[\"relu3_1\"]\n layer_relu3_1 = tf.image.resize_bilinear(relu3_1, (IMAGE_SIZE, IMAGE_SIZE)) \n relu3_2 = image_net[\"relu3_2\"]\n layer_relu3_2 = tf.image.resize_bilinear(relu3_2, (IMAGE_SIZE, IMAGE_SIZE)) \n relu3_3 = image_net[\"relu3_3\"]\n layer_relu3_3 = tf.image.resize_bilinear(relu3_3, (IMAGE_SIZE, IMAGE_SIZE)) \n \n relu3_4 = image_net[\"relu3_4\"]\n layer_relu3_4 = tf.image.resize_bilinear(relu3_4, (IMAGE_SIZE, IMAGE_SIZE)) \n relu4_1 = image_net[\"relu4_1\"]\n layer_relu4_1 = tf.image.resize_bilinear(relu4_1, (IMAGE_SIZE, IMAGE_SIZE)) \n relu4_2 = image_net[\"relu4_2\"]\n layer_relu4_2 = tf.image.resize_bilinear(relu4_2, (IMAGE_SIZE, IMAGE_SIZE)) \n relu4_3 = image_net[\"relu4_3\"]\n layer_relu4_3 = tf.image.resize_bilinear(relu4_3, (IMAGE_SIZE, IMAGE_SIZE)) \n relu4_4 = image_net[\"relu4_4\"]\n layer_relu4_4 = tf.image.resize_bilinear(relu4_4, (IMAGE_SIZE, IMAGE_SIZE)) \n \n relu5_1 = image_net[\"relu5_1\"]\n layer_relu5_1 = tf.image.resize_bilinear(relu5_1, (IMAGE_SIZE, IMAGE_SIZE)) \n relu5_2 = image_net[\"relu5_2\"]\n layer_relu5_2 = tf.image.resize_bilinear(relu5_2, (IMAGE_SIZE, IMAGE_SIZE)) \n relu5_3 = image_net[\"relu5_3\"]\n layer_relu5_3 = tf.image.resize_bilinear(relu5_3, (IMAGE_SIZE, IMAGE_SIZE)) \n relu5_4 = image_net[\"relu5_4\"]\n layer_relu5_4 = tf.image.resize_bilinear(relu5_4, (IMAGE_SIZE, IMAGE_SIZE)) \n \n HyperColumns = tf.concat([layer_relu1_2, \\\n layer_relu2_1, layer_relu2_2, \\\n layer_relu3_1, layer_relu3_2, layer_relu3_3, layer_relu3_4, \\\n layer_relu4_1, layer_relu4_2, layer_relu4_3, layer_relu4_4, \\\n layer_relu5_1, layer_relu5_2, layer_relu5_3, layer_relu5_4 \\\n ] ,3)\n wc1 = utils.weight_variable([1, 1, 5440, 2], name=\"wc1\")\n wc1_biase = utils.bias_variable([2], name=\"wc1_biase\")\n pred_AB_conv = tf.nn.conv2d(HyperColumns, wc1, [1, 1, 1, 1], padding='SAME')\n pred_AB = tf.nn.bias_add(pred_AB_conv, wc1_biase) \n return tf.concat(values=[images, pred_AB], axis=3, name=\"pred_image\")\n\ndef train(loss, var_list):\n optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1)\n grads = optimizer.compute_gradients(loss, var_list=var_list)\n for grad, var in grads:\n utils.add_gradient_summary(grad, var)\n return optimizer.apply_gradients(grads)\n\n\n\n\n# +\nprint(\"Setting up network...\")\ntrain_phase = tf.placeholder(tf.bool, name=\"train_phase\")\nimages = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='L_images')\nlab_images = tf.placeholder(tf.float32, shape=[None, None, None, 3], name=\"LAB_images\")\npred_image = HyperColumns(images, train_phase)\n\ngen_loss_mse = tf.reduce_mean(2 * tf.nn.l2_loss(pred_image - lab_images)) / (IMAGE_SIZE * IMAGE_SIZE * 100 * 100)\ntf.summary.scalar(\"HyperColumns_loss_MSE\", gen_loss_mse)\n\ntrain_variables = tf.trainable_variables()\nfor v in train_variables:\n utils.add_to_regularization_and_summary(var=v)\n\ntrain_op = train(gen_loss_mse, train_variables)\n\n\n\n\n# -\n\nprint(\"Reading image dataset...\")\ntrain_images, testing_images, validation_images = flowers.read_dataset(FLAGS.data_dir)\nimage_options = {\"resize\": True, \"resize_size\": IMAGE_SIZE, \"color\": \"LAB\"}\nbatch_reader_train = dataset.BatchDatset(train_images, image_options)\nbatch_reader_validate = dataset.BatchDatset(validation_images, image_options)\nbatch_reader_testing = dataset.BatchDatset(testing_images, image_options)\n\n# +\nprint(\"Setting up session\")\nsess = tf.Session()\nsummary_op = tf.summary.merge_all()\nsaver = tf.train.Saver()\ntrain_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)\nvalidate_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validate')\n\nsess.run(tf.global_variables_initializer())\n\n# -\n\nif restore_model == True:\n ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path)\n print(\"Model restored...\")\n\nFLAGS.mode = 'test'\n\n# +\ncheck_variables_trainable = False\nif check_variables_trainable == True :\n print('printing out the trainable variables...')\n variables_names = [v.name for v in tf.trainable_variables()]\n values = sess.run(variables_names)\n for k, v in zip(variables_names, values):\n print (\"Variable: \", k)\n print (\"Shape: \", v.shape)\n\nmse_train_list = []\nif FLAGS.mode == 'train':\n for itr in xrange(MAX_ITERATION):\n l_image, color_images = batch_reader_train.next_batch(FLAGS.batch_size)\n feed_dict = {images: l_image, lab_images: color_images, train_phase: True}\n\n if itr % 10 == 0:\n mse, summary_str = sess.run([gen_loss_mse, summary_op], feed_dict=feed_dict)\n mse_train_list.append(mse)\n train_writer.add_summary(summary_str, itr)\n print(\"Step: %d, MSE: %g\" % (itr, mse))\n\n if itr % 100 == 0:\n saver.save(sess, FLAGS.logs_dir + \"model.ckpt\", itr)\n pred = sess.run(pred_image, feed_dict=feed_dict)\n idx = np.random.randint(0, FLAGS.batch_size)\n save_dir = os.path.join(FLAGS.logs_dir, \"image_checkpoints\")\n utils.save_image(color_images[idx], save_dir, \"gt\" + str(itr // 100))\n utils.save_image(pred[idx].astype(np.float64), save_dir, \"pred\" + str(itr // 100))\n print(\"%s --> Model saved\" % datetime.datetime.now())\n\n sess.run(train_op, feed_dict=feed_dict)\n\n if itr % 10000 == 0:\n FLAGS.learning_rate /= 2\nelif FLAGS.mode == \"test\":\n count = 10\n l_image, color_images = batch_reader_testing.get_N_images(count)\n feed_dict = {images: l_image, lab_images: color_images, train_phase: False}\n save_dir = os.path.join(FLAGS.logs_dir, \"image_pred\")\n pred = sess.run(pred_image, feed_dict=feed_dict)\n for itr in range(count):\n utils.save_image(color_images[itr], save_dir, \"gt\" + str(itr))\n utils.save_image(pred[itr].astype(np.float64), save_dir, \"pred\" + str(itr))\n print(\"--- Images saved on test run ---\")\n \n# -\n\nplot_train_loss = True\nif plot_train_loss == True:\n plt.semilogy(mse_train_list[0:MAX_ITERATION], '-ro', label=\"train loss\") # train loss\n plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n plt.xlabel('iteration index')\n plt.ylabel('loss')\n plt.show()\n\n\n\n# Adding the feature **Age** as a condition in conjunction with **Sex** improves the accuracy by a small margin more than with simply using the feature **Sex** alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions. \n# **Pclass**, **Sex**, **Age**, **SibSp**, and **Parch** are some suggested features to try.\n#\n# Use the `survival_stats` function below to to examine various survival statistics. \n# **Hint:** To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: `[\"Sex == 'male'\", \"Age < 18\"]`\n\nsurvival_stats(data, outcomes, 'Age', [\"Sex == 'male'\", \"Age < 18\"])\n\nsurvival_stats(data, outcomes, 'Pclass', [\"Sex == 'male'\", \"Age < 18\"])\n\n\n# After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction. \n# Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model. \n# **Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_2`.\n\n# +\ndef predictions_3(data):\n \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n \n predictions = []\n for _, passenger in data.iterrows():\n \n # Remove the 'pass' statement below \n # and write your prediction conditions here\n if passenger['Sex'] == 'female':\n predictions.append(1)\n elif passenger['Age'] < 10 and passenger['Pclass'] != 3:\n predictions.append(1)\n else:\n predictions.append(0)\n \n # Return our predictions\n return pd.Series(predictions)\n\n# Make the predictions\npredictions = predictions_3(data)\n# -\n\nprint(accuracy_score(outcomes, predictions))\n\n# **Answer**: Predictions have an accuracy of 79.91%.\n\nsurvival_stats(data, outcomes, 'Pclass', [\"Sex == 'female'\" ])\n\nsurvival_stats(data, outcomes, 'Parch', [\"Sex == 'female'\"])\n\nsurvival_stats(data, outcomes, 'Parch', [\"Sex == 'male'\", \"Age < 18\"])\n\nsurvival_stats(data, outcomes, 'SibSp', [\"Sex == 'female'\"])\n\nsurvival_stats(data, outcomes, 'SibSp', [\"Sex == 'male'\", \"Age < 18\"])\n\n\n# +\ndef predictions_4(data):\n \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n \n predictions = []\n for _, passenger in data.iterrows():\n \n # Remove the 'pass' statement below \n # and write your prediction conditions here\n if passenger['Sex'] == 'female' and passenger['SibSp'] < 3 and passenger['Parch'] < 4:\n predictions.append(1)\n elif passenger['Sex'] == 'male' and passenger['Age'] < 10 and passenger['Pclass'] != 3 :\n predictions.append(1)\n else:\n predictions.append(0)\n \n # Return our predictions\n return pd.Series(predictions)\n\n# Make the predictions\npredictions = predictions_4(data)\n# -\n\nprint(accuracy_score(outcomes, predictions))\n\n# ### Question 4\n# *Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions?* \n# **Hint:** Run the code cell below to see the accuracy of your predictions.\n\n# Steps taken to implement final prediciton model:\n# - If passenger is Female and we predict every female passanger chances of survial to true\n# - we can predict with accuracy of 78.68 %\n# - For Male passengers if their age is less than 10 years, there are high chances of survival\n# - accuracy goes up to 79.35 %\n# - If Male passenger is not from 'Pclass' 3, Passengers from lower class have low chances of survial\n# - accuracy increases to 79.91 %\n# - If Female passenger has less than 3 siblings, Female passengers having more siblings are less likely to survive.\n# - accuracy increases to 80.92 %\n# - If Female passenger has less than 4 children or parents is more likely to survive.\n# - accuracy goes up to 81.48 %\n\nprint(accuracy_score(outcomes, predictions))\n\n# **Answer**: Predictions have an accuracy of 81.48%.\n\n# # Conclusion\n#\n# Congratulations on what you've accomplished here! You should now have an algorithm for predicting whether or not a person survived the Titanic disaster, based on their features. In fact, what you have done here is a manual implementation of a simple machine learning model, the _decision tree_. In a decision tree, we split the data into smaller groups, one feature at a time. Each of these splits will result in groups that are more homogeneous than the original group, so that our predictions become more accurate. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. [This link](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) provides another introduction into machine learning using a decision tree.\n#\n# A decision tree is just one of many algorithms that fall into the category of _supervised learning_. In this Nanodegree, you'll learn about supervised learning techniques first. In supervised learning, we concern ourselves with using features of data to predict or model things with objective outcome labels. That is, each of our datapoints has a true outcome value, whether that be a category label like survival in the Titanic dataset, or a continuous value like predicting the price of a house.\n#\n# ### Question 5\n# *Can you think of an example of where supervised learning can be applied?* \n# **Hint:** Be sure to note the outcome variable to be predicted and at least two features that might be useful for making the predictions.\n\n# **Answer**: \n# - Handwriting recognition:\n# - Outcome : Input image corresponds to which alphabatic letter.\n# - Features : pixel intensity, edges\n# - House Price Prediction:\n# - Outcome : predicted house price\n# - Features : Area in sq ft, number of bed room, location of house.\n# - Predicting Estimated time of autonomus vehice:\n# - Outcome : Expected time to reach destination\n# - Features : Weather condition, traffic, red lights\n\n# > **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n# **File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n"},"script_size":{"kind":"number","value":17855,"string":"17,855"}}},{"rowIdx":970,"cells":{"path":{"kind":"string","value":"/NeuralNetworks/LSTM/LSTMIntro.ipynb"},"content_id":{"kind":"string","value":"995b8d8daf7e68a0d81659c242606a8e3cf378bd"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"Harsh188/100_Days_of_ML"},"repo_url":{"kind":"string","value":"https://github.com/Harsh188/100_Days_of_ML"},"star_events_count":{"kind":"number","value":8,"string":"8"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1627,"string":"1,627"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Long Short Term Memory\n\n# Credits:\n#\n# - [A Gentle Introduction to Long Short-Term Memory Networks by the Experts](https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/)\n# - [The 10 Neural Network Architectures Machine Learning Researchers Need To Learn\n# ](https://medium.com/cracking-the-data-science-interview/a-gentle-introduction-to-neural-networks-for-machine-learning-d5f3f8987786)\n\n# ## Overview\n\n# LSTMs are a type of RNNs which were created to solve the vanishing gradient problem. It does this by introducing gates and an explicitly defined memory cell. The memory cell stores the previous values and holds onto it unless a “forget gate” tells the cell to forget those values.\n\n# ## How do LSTMs work?\n#\n"},"script_size":{"kind":"number","value":1027,"string":"1,027"}}},{"rowIdx":971,"cells":{"path":{"kind":"string","value":"/Face detection/Face detection.ipynb"},"content_id":{"kind":"string","value":"0ff03377f066cc11990661d878d86054aecac5db"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Sadhana-acharya/rep"},"repo_url":{"kind":"string","value":"https://github.com/Sadhana-acharya/rep"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1401,"string":"1,401"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Assignment 10, Part 1: Tidy Data Practice\n# Use this notebook to follow along with the tasks in the `AO6-Matplotlib_Part2.ipynb` notebook.\n#\n# ## Instructions\n# For each task, use the cell below to write and test your code. You may add additional cells for any task as needed or desired. \n\n# ## Task 1a: Setup\n#\n# Import the following packages:\n# + `pandas` as `pd`\n# + `numpy` as `np`\n# + `matplotlib.pyplot` as `plt`\n#\n# Activate the `%matplotlib inline` magic.\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# %matplotlib inline\n\n# ## Task 2a: Understand the data\n# Execute the following code to display the sample data frame:\n\n# Create the data rows and columns.\ndata = [['John Smith', None, 2],\n ['Jane Doe', 16, 11],\n ['Mary Johnson', 3, 1]]\n# Create the list of labels for the data frame.\nheaders = ['', 'Treatment_A', 'Treatement_B']\n# Create the data frame.\npd.DataFrame(data, columns=headers)\n\n# Using the table above, answer the following:\n#\n# What are the variables?\n\n# + active=\"\"\n# Treatment A, Treatment B and the name of individuals\n# -\n\n# What are the observations?\n\n# + active=\"\"\n# the numerical values of both the treatments \n# -\n\n# What is the observable unit?\n\n# + active=\"\"\n#\n# -\n\n# Are the variables columns?\n\n# + active=\"\"\n# no, they are both in rows and columns\n# -\n\n# Are the observations rows? \n\n# + active=\"\"\n# no, it has one variable of individual's names\n# -\n\n# ## Task 2b: Explain causes of untidyness\n#\n# Execute the following code to display the sample data frame:\n\ndata = [['Agnostic',27,34,60,81,76,137],\n ['Atheist',12,27,37,52,35,70],\n ['Buddhist',27,21,30,34,33,58],\n ['Catholic',418,617,732,670,638,1116],\n ['Don\\'t know/refused',15,14,15,11,10,35],\n ['Evangelical Prot',575,869,1064,982,881,1486],\n ['Hindu',1,9,7,9,11,34],\n ['Historically Black Prot',228,244,236,238,197,223],\n ['Jehovah\\'s Witness',20,27,24,24,21,30],\n ['Jewish',19,19,25,25,30,95]]\nheaders = ['religion','<$10k','$10-20k','$20-30k','$30-40k','$40-50k','$50-75k']\nreligion = pd.DataFrame(data, columns=headers)\nreligion\n\n# Explain why the data above is untidy?\n\n# + active=\"\"\n# Because the variable labels are not only restricted to the column names and they fall into the rows as well. for a tidy data, rows should only have the observations.\n# -\n\n# What are the variables? \n\n# + active=\"\"\n# religion\n# <$10k\n# $10-20k\n# $20-30k\n# $30-40k\n# $40-50k\n# $50-75k\n# -\n\n# What are the observations?\n\n# + active=\"\"\n# the numerical value for each variable\n# -\n\n# ## Task 2c: Explain causes of untidyness\n#\n# Execute the following code to display the sample data frame:\n\ndata = [['AD', 2000, 0, 0, 1, 0, 0, 0, 0, None, None],\n ['AE', 2000, 2, 4, 4, 6, 5, 12, 10, None, 3],\n ['AF', 2000, 52, 228, 183, 149, 129, 94, 80, None, 93],\n ['AG', 2000, 0, 0, 0, 0, 0, 0, 1, None, 1],\n ['AL', 2000, 2, 19, 21, 14, 24, 19, 16, None, 3],\n ['AM', 2000, 2, 152, 130, 131, 63, 26, 21, None, 1],\n ['AN', 2000, 0, 0, 1, 2, 0, 0, 0, None, 0],\n ['AO', 2000, 186, 999, 1003, 912, 482, 312, 194, None, 247],\n ['AR', 2000, 97, 278, 594, 402, 419, 368, 330, None, 121],\n ['AS', 2000, None, None, None, None, 1, 1, None, None, None]]\nheaders = ['country', 'year', 'm014', 'm1524', 'm2534', 'm3544', 'm4554', 'm5564', \n 'm65', 'mu', 'f014']\ndemographics = pd.DataFrame(data, columns=headers)\ndemographics\n\n# Using the dataset above:\n#\n# Explain why the data above is untidy?\n\n# + active=\"\"\n# because the variable labels of country and year are occupying the rows as well which they should not for a data to be tidy. Also, the sex variable is not in a separate column \n# -\n\n# What are the variables? \n\n# + active=\"\"\n# sex, age group, country and year.\n# -\n\n# What are the observations?\n\n# + active=\"\"\n# from column 2 onwards are the observations\n# -\n\n# ## Task 3a: Melt data, use case #1\n#\n# Using the `pd.melt` function, melt the demographics data introduced in section 2. Be sure to:\n# - Set the colum headers correctly. \n# - Order by country \n# - Print the first 10 lines of the resulting melted dataset.\n#\n# ***Note*** The demographics dataset is provided in Task 2c above\n\n\n\n# ## Task 3b: Practice with a new dataset\n#\n# Download the [PI_DataSet.txt](https://hivdb.stanford.edu/download/GenoPhenoDatasets/PI_DataSet.txt) file from [HIV Drug Resistance Database](https://hivdb.stanford.edu/pages/genopheno.dataset.html). Store the file in the same directory as the practice notebook for this assignment.\n#\n# ***Note***: Choose the file labeled “10935 phenotype results from 1808 isolates”\n#\n# Here is the meaning of data columns:\n# - SeqID: a numeric identifier for a unique HIV isolate protease sequence. Note: disruption of the protease inhibits HIV’s ability to reproduce.\n# - The Next 8 columns are identifiers for unique protease inhibitor class drugs. \n# - The values in these columns are the fold resistance over wild type (the HIV strain susceptible to all drugs).\n# - Fold change is the ratio of the drug concentration needed to inhibit the isolate.\n# - The latter columns, with P as a prefix, are the positions of the amino acids in the protease. \n# - '-' indicates consensus.\n# - '.' indicates no sequence.\n# - '#' indicates an insertion. \n# - '~' indicates a deletion;.\n# - '*' indicates a stop codon\n# - a letter indicates one letter Amino Acid substitution. \n# - two and more amino acid codes indicates a mixture. \n#\n# Import this dataset into your notebook, view the top few rows of the data and respond to these questions:\n#\n# What are the variables? \n\n# + active=\"\"\n#\n# -\n\n# What are the observations?\n\n# + active=\"\"\n#\n# -\n\n# What are the values? \n\n# ## Task 3c: Practice with a new dataset Part 2\n#\n# Use the data retreived from task 3b, generate a data frame containing a Tidy’ed set of values for drug concentration fold change. BE sure to:\n#\n# - Set the column names as ‘SeqID’, ‘Drug’ and ‘Fold_change’.\n# - Order the data frame first by sequence ID and then by Drup name\n# - Reset the row indexes\n# - Display the first 10 elements.\n\n\n"},"script_size":{"kind":"number","value":6431,"string":"6,431"}}},{"rowIdx":972,"cells":{"path":{"kind":"string","value":"/kannada_NN_Kaggle_entry.ipynb"},"content_id":{"kind":"string","value":"8407956b06c69e3c9a50fda40438e83c0c19c5ed"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"pfvbell/kannada_neuralnetwork_kaggle"},"repo_url":{"kind":"string","value":"https://github.com/pfvbell/kannada_neuralnetwork_kaggle"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":14335,"string":"14,335"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"L8J3PscjK6IJ\" outputId=\"a0db4aff-5493-4e01-c5a5-564683dffa17\"\n#Otwieranie pliku\nfile = open('kowalecki_175ic_txt.txt')\nprint(file)\n\n# + id=\"yNnpWlrDLhxN\"\n#Otwieranie pliku oraz zamknięcie go w razie pojawienia się błędu\nreader = open('kowalecki_175ic_txt.txt')\ntry:\n reader.read()\nfinally:\n reader.close()\n\n# + id=\"i_f-QV-9MJnk\"\n#Uruchomienie pliku. Zamknięcie następuje po wyjściu z 'with'\nwith open('kowalecki_175ic_txt.txt') as reader:\n reader.read()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"rs3VebYYOWik\" outputId=\"d9781deb-6064-4e29-9517-8b28f0fa8bcb\"\n#Typy plików\nprint(type(open('kowalecki_175ic_txt.txt', 'r')))\nprint(type(open('kowalecki_175ic_txt.txt', 'rb')))\nprint(type(open('kowalecki_175ic_txt.txt', 'rb', buffering=0)))\n\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"_g96BlUdPmpC\" outputId=\"f4aa141f-9114-443e-9aed-7856397a7f7a\"\n#Czytanie plików\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n print(reader.read())\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"FnI2x8Z0QgSt\" outputId=\"ca7088cf-a1f6-4915-886f-55e1d2c57428\"\n#Czytanie po 5 bajtów z pliku\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n print(reader.readline(5))\n print(reader.readline(5))\n print(reader.readline(5))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"66sn6tKiQ9wH\" outputId=\"d444b408-1030-405d-b03f-ebab31b8f14d\"\n#Zwrócenie tekstu jako listę\nf = open('kowalecki_175ic_txt.txt')\nf.readlines()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"wY6f_zr_RQIp\" outputId=\"d70b36ff-fe36-475c-e460-eb972394fa9a\"\n#Iterowanie po każdej linii w pliku na 3 różne sposoby\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n line = reader.readline()\n while line != '':\n print(line, end='')\n line = reader.readline()\n\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n for line in reader.readlines():\n print(line, end='')\n\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n for line in reader:\n print(line, end='')\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"VhkVk5iRSeKN\" outputId=\"ca8a483c-bc23-4ec5-ee6e-c612e97d7044\"\n#Zapis do pliku\nwith open('kowalecki_175ic_txt.txt', 'r') as reader:\n read_only_text = reader.readlines()\n\nwith open('kowalecki_175ic_write.txt', 'w') as writer:\n for text in reversed(read_only_text):\n writer.write(text)\n \nwith open('kowalecki_175ic_write.txt', 'r') as reader:\n print(reader.read())\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"yY3i0FadTmr3\" outputId=\"5dca4b5f-9d61-4c9e-8c3d-0fdf382862c1\"\n#Praca z bajtami - wczytanie pliku\nwith open('kowalecki_175ic_write.txt', 'rb') as reader:\n print(reader.readline())\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"Ta1ZDJizUnku\" outputId=\"fea18754-4238-406f-81bf-182ecde30285\"\n#Wczytanie obrazu i wyświetlenie jego danych\nwith open('test.jpg', 'rb') as byte_reader:\n print(byte_reader.read(1))\n print(byte_reader.read(3))\n print(byte_reader.read(2))\n print(byte_reader.read(1))\n print(byte_reader.read(1))\n고 말했다.\n

\n'''\n\nre.findall(\"(?<=).+(?=)\", article)\n\nnew_list = []\nfor text in re.findall(\".+\", article):\n# print(text)\n# print(text.replace(\"\", \"\").replace(\"\", \"\"))\n new_list.append(text[6:-7])\nnew_list\n\nphones = \"\"\"\npark 010-1234-1234\nkim 02-3450-3459\nlee 00000000\n\"\"\"\n\n\n# +\n\n\nfor phone in phones.split(\"n\"):\n if phone:\n num = re.search(r\"[a-zA-Z]+\\s\\d{3}-\\d{3,4}-\\d{4}\", phone).group()\n print(\"이름 : \", num.split()[0])\n print(\"전화번호 : \", num.split()[1])\n \n\n# -\n\nprint(re.search(r\"([a-zA-Z]+)\\s(\\d{3}-\\d{3,4}-\\d{4})\", phone).group(1))\n\n\nprint(re.findall(r\"([a-zA-Z]+)\\s(\\d{3}-\\d{3,4}-\\d{4})\", phone))\n\ntext = \"Paris in the the spring\"\n\ntext2 = \"Paris in in the spring\"\n\nre.search(r'(\\b\\w+)\\s+\\1', text2)\n\nre.sub(r'(foo)(bar)', r'\\g<1>123\\g<2>456', 'foobar') #foo123bar\n\nimport requests\nfrom bs4 import BeautifulSoup\nurl = 'https://search.musinsa.com/category/001'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, \"html.parser\")\nitem_list = soup.select(\"div.list-box li.li_box\")\n\n\nfor item in item_list:\n print(item)\n\nimport requests\nfrom bs4 import BeautifulSoup\nresponse = requests.get(\"https://scrapying-study.firebaseapp.com/04/\")\nprint(response.text)\n\n\nimport json\nresponse = requests.get(\"https://jsonplaceholder.typicode.com/posts\")\ndata = json.loads(response.text)\ndata[0]['title']\n\n# +\n\nheaders = {\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'\n}\n\n# \"accept\": text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\n# \"accept-encoding\": gzip, deflate, br\n# \"accept-language\": ko-KR,ko;q=0.9\n# \"cache-control\": max-age=0\n# \"cookie\": JSESSIONID=D000B102B9D3882A4D4FF0D5D05548D1; NNB=IRMRKG3QFXSF6; NRTK=ag#all_gr#1_ma#-2_si#0_en#0_sp#0; nx_ssl=2; page_uid=U+e7clprvhGssnIZSmossssssGo-245854\n# \"referer\": https://news.naver.com/main/list.nhn?mode=LPOD&mid=sec&oid=003\n# sec-fetch-dest: document\n# sec-fetch-mode: navigate\n# sec-fetch-site: same-origin\n# sec-fetch-user: ?1\n# upgrade-insecure-requests: 1\n# user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\n \nresponse = requests.get(\"https://news.naver.com/main/read.nhn?mode=LPOD&mid=sec&oid=003&aid=0010260737\", headers=headers)\nprint(response.text)\n# -\n\n\n"},"script_size":{"kind":"number","value":5824,"string":"5,824"}}},{"rowIdx":973,"cells":{"path":{"kind":"string","value":"/notebooks/.ipynb_checkpoints/S5PL2-checkpoint.ipynb"},"content_id":{"kind":"string","value":"00b2c9a1fc38b005ad2ffbaa75d9e4bff9eb561b"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"MachineLearningJournalClub/EOChallenge"},"repo_url":{"kind":"string","value":"https://github.com/MachineLearningJournalClub/EOChallenge"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":7615,"string":"7,615"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [conda env:python-hawaii]\n# language: python\n# name: conda-env-python-hawaii-py\n# ---\n\nimport math\n\nmath.sin(math.radians(30))\n\nangles = [0, 1, 30, 45, 180]\n\nmath.sin(angles)\n\nangles\n\nangles/2\n\nangles*3\n\nimport requests\n\nurl = 'http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/hawaii-TAVG-Trend.txt'\n\nprint(url)\n\nresponse = requests.get(url)\n\nresponse.status_code\n\nprint(response.text)\n\nwith open('hawaii-temperature-data.txt', 'w') as open_file:\n open_file.write(response.text)\n\n\n"},"script_size":{"kind":"number","value":710,"string":"710"}}},{"rowIdx":974,"cells":{"path":{"kind":"string","value":"/tv/tvl2dcn_den.ipynb"},"content_id":{"kind":"string","value":"c48e86ff030239bfe941aee8a93de2b8d039075f"},"detected_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"bwohlberg/sporco-notebooks"},"repo_url":{"kind":"string","value":"https://github.com/bwohlberg/sporco-notebooks"},"star_events_count":{"kind":"number","value":18,"string":"18"},"fork_events_count":{"kind":"number","value":4,"string":"4"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":513741,"string":"513,741"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# ---\n\n# Greyscale ℓ2-TV Denoising\n# =========================\n#\n# This example demonstrates the use of class [tvl2.TVL2Deconv](http://sporco.rtfd.org/en/latest/modules/sporco.admm.tvl2.html#sporco.admm.tvl2.TVL2Deconv) for removing Gaussian white noise from a greyscale image using Total Variation regularization with an ℓ2 data fidelity term (ℓ2-TV denoising). (This class is primarily intended for deconvolution problems, but can be applied to denoising problems by choosing an impulse filter as the blurring kernel.)\n\n# +\nfrom __future__ import print_function\nfrom builtins import input\n\nimport numpy as np\n\nfrom sporco.admm import tvl2\nfrom sporco import util\nfrom sporco import metric\nfrom sporco import plot\nplot.config_notebook_plotting()\n# -\n\n# Load reference image.\n\nimg = util.ExampleImages().image('monarch.png', scaled=True,\n idxexp=np.s_[:,160:672], gray=True)\n\n# Construct test image corrupted Gaussian white noise with a 0.05 standard deviation.\n\nnp.random.seed(12345)\nimgn = img + np.random.normal(0.0, 0.05, img.shape)\n\n# Set regularization parameter and options for ℓ2-TV deconvolution solver. The regularization parameter used here has been manually selected for good performance.\n\nlmbda = 0.04\nopt = tvl2.TVL2Deconv.Options({'Verbose': True, 'MaxMainIter': 200,\n 'gEvalY': False})\n\n# Create solver object and solve, returning the the denoised image ``imgr``.\n\nb = tvl2.TVL2Deconv(np.ones((1,1)), imgn, lmbda, opt)\nimgr = b.solve()\n\n# Display solve time and denoising performance.\n\nprint(\"TVL2Deconv solve time: %5.2f s\" % b.timer.elapsed('solve'))\nprint(\"Noisy image PSNR: %5.2f dB\" % metric.psnr(img, imgn))\nprint(\"Denoised image PSNR: %5.2f dB\" % metric.psnr(img, imgr))\n\n# Display reference, corrupted, and denoised images.\n\nfig = plot.figure(figsize=(20, 5))\nplot.subplot(1, 3, 1)\nplot.imview(img, title='Reference', fig=fig)\nplot.subplot(1, 3, 2)\nplot.imview(imgn, title='Corrupted', fig=fig)\nplot.subplot(1, 3, 3)\nplot.imview(imgr, title=r'Restored ($\\ell_2$-TV)', fig=fig)\nfig.show()\n\n# Get iterations statistics from solver object and plot functional value, ADMM primary and dual residuals, and automatically adjusted ADMM penalty parameter against the iteration number.\n\nits = b.getitstat()\nfig = plot.figure(figsize=(20, 5))\nplot.subplot(1, 3, 1)\nplot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)\nplot.subplot(1, 3, 2)\nplot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T,\n ptyp='semilogy', xlbl='Iterations', ylbl='Residual',\n lgnd=['Primal', 'Dual'], fig=fig)\nplot.subplot(1, 3, 3)\nplot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig)\nfig.show()\n & 3 & & 5 & & 8 & & 13 \\\\ \n# & & & & & & & & & & & & & & \\\\ \n# 21 & & 34 & & 55 & & 89 & & 144 & & 233 & & 377 & & 610 \\\\ \n# & & & & & & & & & & & & & & \\\\ \n# 987 & & 1597 & & 2584 & & 4181 & & 6765 & & 10946 & & 17711 & & 28657\n# \\\\ \n# & & & & & & & & & & & & & & \\\\ \n# 46368 & & 75025 & & 121393 & & 196418 & & 317811 & & 614229 & & 832040\n# & & 1346269 \\\\ \n# & & & & & & & & & & & & & & \\\\ \n# 2178309 & & 3524578 & & 5702887 & & 9227465 & & 14930352 & & 24157817 & \n# & 39088169 & & 63245986%\n# \\end{array}$$\n#\n\n# **Exercise 2:** Write a code that computes the first N Fibonacci numbers, saves them into an array, and displays them on the screen.\n\n# **WAIT** -- before you read the next cell, try to do Exercise 2!\n\n# +\nN = 20 # Set the size of the list we will compute\n\nF=[0,1] # The first two numbers in the list\nfor i in range(2, N):\n F.append(F[i-1]+F[i-2]) # append the next item on the list\n\nprint('First',N,'Fibonacci numbers:',F)\n# -\n\n# **For fun,** we can make a little widget to control how many numbers to print out. \n\n# +\nfrom ipywidgets import interact\n\ndef printFib(N=10):\n F=[0,1] # The first two numbers in the list\n for i in range(2, N):\n F.append(F[i-1]+F[i-2]) # append the next item on the list\n print(F)\n \ninteract(printFib, N=(10,100,10));\n# -\n\n# By moving the slider above, print out the first 100 Fibonacci numbers\n#\n# As we can see, this sequence grows pretty fast. The Fibonacci numbers seem\n# to have one more digit after about every five terms in the sequence.\n\n# ## How fast does it grow?\n#\n# One of the ways to study the growth of a sequence is to look at ratios between consecutive terms. We look at ratios of pairs of numbers in the Fibonacci sequence. \n#\n# The first few values are\n# \\begin{eqnarray}\n# F_2/F_1 &=& 1 \\\\\n# F_3/F_2 &=& 2/1 = 2 \\\\\n# F_4/F_3 &=& 3/2 = 1.5 \\\\ \n# F_5/F_4 &=& 5/3 = 1.666... \\\\ \n# F_6/F_5 &=& 8/5 = 1.6 \\\\ \n# F_7/F_6 &=& 13/8 = 1.625 \n# \\end{eqnarray}\n#\n# So the ratios are levelling out somewhere around 1.6. We observe that $1.6^5 \\approx 10$, which is why after every five terms in the Fibonacci sequence, we get another digit. This tells us we have roughly **exponential growth,** where $F_n$ grows about as quickly as the exponential function $(1.6)^n$.\n#\n# We can check this computation in Python. We use $ ** $ to take a power, as in the following cell. \n\n(1.6)**5\n\n# ## The Golden Ratio\n#\n# We can print out a bunch of these ratios, and plot them, just to see that they do. The easiest way to do this is with a bit of Python code. Perhaps you can try this yourself.\n#\n# **Exercise 3** Write some code that computes the first N ratios $F_{n+1}/F_n$, save them it into an array, and displays them on the screen.\n\n# **WAIT!** Don't read any further until you try the exercises.\n\n# +\n# %matplotlib inline\nfrom matplotlib.pyplot import * \n\nN = 20\nF = [0,1]\nR = []\nfor i in range(2, N):\n F.append(F[i-1]+F[i-2]) # append the next item on the list\n R.append(F[i]/F[i-1])\n\n\nfigure(figsize=(10,6));\nplot(R,'o')\ntitle('The first '+str(N-2)+' Ratios $F_{n+1}/F_n$')\nxlabel('$n$')\nylabel('$Ratio$');\n\nprint('The first', N-2, 'ratios are:',R)\n\n# -\n\n# We see the numbers are levelling out at the value 1.6108034... This number may be familiar to you. It is called the **Golden Ratio.** \n#\n# We can compute the exact value by observing the ratios satisfy a nice algebraic equation:\n# $$\n# \\frac{F_{n+2}}{F_{n+1}}=\\frac{F_{n+1}+F_{n}}{F_{n+1}}=1+\\frac{F_{n}}{F_{n+1}}=1+\\frac{1}{\\frac{F_{n+1}}{F_{n}}},\n# $$\n# or more simply \n# $$\\frac{F_{n+2}}{F_{n+1}}=1+\\frac{1}{\\frac{F_{n+1}}{F_{n}}}.$$\n#\n# As $n$ gets larger and larger, the ratios $F_{n+2}/F_{n+1}$ and $F_{n+1}/F_{n}$ tend toward a final value, say $x$. This value must then solve the equation\n# $$x=1+\\frac{1}{x}.$$\n#\n# We rewrite this as a quadratic equation\n# $$x^2=x+1$$ \n# which we solve from the quadratic formula\n# $$ x= \\frac{1 \\pm \\sqrt{1+4}}{2} = \\frac{1 \\pm \\sqrt{5}}{2}.$$\n# It is the positive solution $x= \\frac{1 + \\sqrt{5}}{2} = 1.6108034...$ which is called the Golden Ratio.\n#\n#\n\n# The **Golden ratio** comes up in art, geometry, and Greek mythology as a perfect ratio that is pleasing to the eye (and to the gods). \n#\n# For instance, the rectangle shown below is said to have the dimensions of the Golden ratio, because the big rectangle has the same shape as the smaller rectangle inside. Mathematically, we have the ratios of lengths\n# $$ \\frac{a+b}{a} = \\frac{a}{b}.$$\n#\n# ![Golden ratio rectangle](images/Golden2.png)\n#\n# Writing $x = \\frac{a}{b}$, the above equation simplifies to\n# $$ 1 + \\frac{1}{x} = x,$$\n# which is the same quadratic equation we saw for the limit of ratios of Fibonacci numbers.\n\n# For more information about the Golden ratio see\n# https://en.wikipedia.org/wiki/Golden_ratio\n\n# ## A Formula for the Fibonacci Sequence $F_n$\n\n# Let's give the Golden ratio a special name. In honour of the ancient Greeks who used it so much, we call it `phi:'\n# $$ \\varphi = \\frac{1 + \\sqrt{5}}{2}. $$\n# We'll call the other quadratic root 'psi:'\n# $$ \\psi = \\frac{1 - \\sqrt{5}}{2}. $$\n# This number $\\psi$ is called the **conjugate** of $\\varphi$ because it looks the same, except for the negative sign in front of the $\\sqrt{5}$.\n#\n# Here's something **amazing.** It turns out that we have a remarkable formula for the Fibonnaci numbers, in terms of these two Greek numbers. The formula says\n# $$F_n = \\frac{\\varphi^n - \\psi^n}{\\sqrt{5}}.$$\n#\n\n# #### Wow!\n#\n# Seems amazing. And it is handy because now we can compute, say, the thousandth term in the sequence, $F_{1000}$ directly, without having to compute all the other terms that come before. \n#\n# But, whenever someone gives you a formula, you should check it!\n#\n# **Exercise 4:** Write a piece of code to show that the formula above, with $\\varphi,\\psi$ does produce, say, the first 20 Fibonnaci numbers.\n#\n# **WAIT!** Don't go on until you try writing a program yourself, to compute the Fibonacci numbers using only powers of $\\varphi, \\psi$.\n\n# +\n## SOLUTION (don't peak!)\n\nfrom numpy import * ## We need this to define square roots\nphi = (1 + sqrt(5))/2\npsi = (1 - sqrt(5))/2\nfor n in range(20):\n print( (phi**n - psi**n)/sqrt(5) ) \n\n# -\n\n# Looking at that computer output, it does seem to give Fibonacci numbers, with a bit of numerical error.\n#\n# ## Checking the Math\n#\n# Doing math, though, we like exact answers and we want to know why. So WHY does this formula $(\\phi^n - \\psi^n)/\\sqrt{5}$ give Fibonacci numbers?\n#\n# Well, we can check, step by step.\n#\n# For $n=0$, the formula gives \n# $$\\frac{\\varphi^0 - \\psi^0}{\\sqrt{5}} = \\frac{1-1}{\\sqrt{5}} = 0,$$ which is $F[0]$, the first Fibonacci number. \n#\n# For $n=1$, the formula gives \n# $$\\frac{\\varphi^1 - \\psi^1}{\\sqrt{5}} =\n# \\frac{\\frac{1 + \\sqrt{5}}{2} - \\frac{1 -\\sqrt{5}}{2} }{\\sqrt{5}} = \\frac{\\sqrt{5}}{\\sqrt{5}} = 1,$$ which is $F[1]$, the next Fibonacci number. \n#\n# For $n=2$, it looks harder because we get the squares $\\varphi^2, \\psi^2$ in the formula. But then remember that both $\\varphi$ and $\\psi$ solve the quadratic $x^2 = x+1$, so we know $\\varphi^2 = \\phi +1$ and $\\psi^2 = \\psi +1$. So we can write\n# $$\\frac{\\phi^2 - \\psi^2}{\\sqrt{5}} = \\frac{\\phi + 1 - \\psi -1}{\\sqrt{5}} = \\frac{\\phi - \\psi }{\\sqrt{5}} = 1,$$\n# since we already calculated this in the $n=1$ step. So this really is $F[2]=1$.\n#\n# For $n=3,4,5,\\ldots$ again it might seem like it will be hard because of the higher powers. But multiplying the formulas $\\varphi^2 = \\varphi +1$ and $\\psi^2 = \\psi +1$ by powers of $\\phi$ and $\\psi$, we get\n#\n# $$\\begin{eqnarray*}\n# \\varphi^2 &=& \\varphi +1,\\quad \\varphi^3 = \\varphi^2+\\varphi\n# ,\\quad \\varphi^4=\\varphi^3+\\varphi^2,\\qquad \\dots \\qquad %\n# \\varphi^{n+2}=\\varphi^{n+1}+{\\varphi}^n,\\quad \\text{and} \\\\\n# \\psi^2 &=&\\psi +1,\\quad \\psi^3=\\psi^2+\\psi ,\\quad \\psi^4=\\psi^3+\\psi^2,\\qquad \n# \\dots \\qquad \\psi^{n+2}=\\psi^{n+1}+\\psi^n.\n# \\end{eqnarray*}$$\n#\n# So, assuming we know the generating formula already for $n$ and $n+1$ we can write the next term as\n# $$\\frac{\\varphi^{n+2} - \\psi^{n+2}}{\\sqrt{5}} = \\frac{\\varphi^{n+1} +\\varphi^n - \\psi^{n+1} - \\psi^n}{\\sqrt{5}}\n# = \\frac{\\varphi^{n+1} - \\psi^{n+1}}{\\sqrt{5}} + \\frac{\\varphi^{n} - \\psi^{n}}{\\sqrt{5}} = F[n+1] + F[n] = F[n+2].$$\n#\n# So we do get $\\frac{\\varphi^{n+2} - \\psi^{n+2}}{\\sqrt{5}} = F[n+2]$, and the formula holds for all numbers n. \n#\n# This method of verifying the formula for all n, based on previous values of n, is an example of **mathematical induction.**\n\n# ## Why did this work?\n#\n# Well, from the Golden ratio, we have the formula $\\varphi^2 = \\varphi + 1$, which then gives the formula $\\varphi^{n+2} = \\varphi^{n+1} + \\varphi^n$. This looks a lot like the Fibonacci formula $$F[n+2] = F[n+1] + F[n].$$ Same powers of $\\psi$.\n#\n# If we take ANY linear combination of powers of $\\varphi, \\psi$, such as\n# $$f(n) = 3\\varphi^n + 4\\psi^n,$$\n# we will get a sequence that behaves like the Fibonacci sequence, with $f(n+2) = f(n+1) + f(n).$ To get the 'right' Fibonacci sequence, we just have to replace the 3 and 4 with the right coefficients.\n\n# ## From sequences to functions\n#\n# Wouldn't it be fun to extend Fibonacci numbers to a function, defined for all numbers $x$?\n#\n# The problems is the function \n# $$F[x] = \\frac{\\varphi^x - \\psi^x}{\\sqrt{5}}$$\n# is not defined for values of $x$ other than integers. \n#\n# The issue is the term $\\psi^{x}=\\left( \\frac{1-\\sqrt{5}}{2}\\right) ^{x}$, which is the power of a negative number.\n# We don't really know how to define that. For instance, what is the square root of a negative number?\n#\n# To\n# overcome this technical difficulty, we write\n#\n# $$\\psi ^{x}=\\left( -\\left( -\\psi \\right) \\right) ^{x}=\\left( -\\left( \\frac{%\n# \\sqrt{5}-1}{2}\\right) \\right) ^{x}=\\left( -1\\right) ^{x}\\left( \\frac{\\sqrt{5}%\n# -1}{2}\\right) ^{x}. $$\n#\n# Now the factor $\\left( \\frac{\\sqrt{5}-1}{2} \\right) ^{x}$ make sense since \n# the number inside the brackets is positive. We have localized the problem into the powers of $-1$ for the term $\\left(\n# -1\\right) ^{x}$. We would like to replace this term by a\n# continuous function $m(x)$ such that it takes the values $\\pm1$ on the integers. That is,\n#\n# $$m(n) =1\\quad \\text{if }n\\text{ is even }\\quad\\text{and}\\quad m(n) =-1\\quad \\text{if }n\\text{ is odd.} $$\n#\n# The cosine function works. That is\n#\n# $$m\\left( x\\right) =\\cos \\left( \\pi x\\right) \\qquad \\text{does the job.} $$\n# That is:\n# $$\\cos \\left( n\\pi \\right) =1\\quad \\text{if }n\\text{ is even}\\quad\\text{ and}\\quad %\n# \\cos \\left( n\\pi \\right) =-1\\quad \\text{if }n\\text{ is odd.}$$\n#\n# Why this is a **good** choice would lead us to complex numbers and more!\n\n# Hence, we obtain the following closed formula for our function $F[x]:$\n#\n# $$\\begin{eqnarray*}\n# F[x] &=&\\frac{{\\varphi }^{x}-\\left( -1\\right) ^{x}\\left( -\\psi\n# \\right) ^{x}}{{\\varphi -\\psi }}=\\frac{1}{\\sqrt{5}}\\left( {\\varphi }%\n# ^{x}-\\left( -1\\right) ^{x}\\left( -\\psi \\right) ^{x}\\right) \\\\\n# &=&\\frac{1}{\\sqrt{5}}\\left( \\left( \\frac{1+\\sqrt{5}}{2}\\right) ^{x}-\\cos\n# \\left( \\pi x\\right) \\left( \\frac{\\sqrt{5}-1}{2}\\right) ^{x}\\right) .\n# \\end{eqnarray*}$$\n#\n# Let's plot this function, and the Fibonacci sequence.\n#\n\n# ## A plot of the continuous Fibonacci function\n\n# +\n# %matplotlib inline\nfrom numpy import *\nfrom matplotlib.pyplot import *\n\nphi=(1+5**(1/2))/2\npsi=(5**(1/2)-1)/2\n\nx = arange(0,10)\ny = (pow(phi,x) - cos(pi*x)*pow(psi,x))/sqrt(5)\nxx = linspace(0,10)\nyy = (pow(phi,xx) - cos(pi*xx)*pow(psi,xx))/sqrt(5)\n\nfigure(figsize=(10,6));\nplot(x,y,'o',xx,yy);\ntitle('The continuous Fibonacci function')\nxlabel('$x$')\nylabel('$Fib(x)$');\n# -\n\n# ## A plot with negative values\n#\n# Well, with this general definition, we can even include negative numbers for $x$ in the function.\n#\n# Let's plot this too. \n\n# +\n# %matplotlib inline\nfrom numpy import *\nfrom matplotlib.pyplot import *\n\nphi=(1+5**(1/2))/2\npsi=(5**(1/2)-1)/2\n\nx = arange(-10,10)\ny = (pow(phi,x) - cos(pi*x)*pow(psi,x))/sqrt(5)\nxx = linspace(-10,10,200)\nyy = (pow(phi,xx) - cos(pi*xx)*pow(psi,xx))/sqrt(5)\n\n\nfigure(figsize=(10,6));\nplot(x,y,'o',xx,yy);\ntitle('The Fibonacci function, extended to negative values')\nxlabel('$x$')\nylabel('$Fib(x)$');\n\n# -\n\n# So we see we can even get negative Fibonacci numbers!\n\n# ## The Golden Ratio and Continued Fractions\n\n# We have found that the Golden ratio ${\\varphi =}\\frac{{1+}\\sqrt{5}}{2}$\n# satisfies the identity\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{\\varphi }}.\n# $$\n#\n# Substituting for ${\\varphi }$ on the denominator in the right, we obtain\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\frac{1}{{\\varphi }}}.\n# $$\n#\n# Substituting again for ${\\varphi }$ on the denominator in the right, we\n# obtain\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\frac{1}{{\\varphi }}}}.\n# $$\n#\n# Repeating this again,\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\frac{1}{{\\varphi }}}}}%\n# .$$\n#\n# And again,\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\frac{1}{%\n# {\\varphi }}}}}}.\n# $$\n#\n# And again,\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1%\n# }{{1+}\\frac{1}{{\\varphi }}}}}}}.\n# $$\n#\n# We see that this process can be $\\textit{continued indefinitely}$. This results\n# in an $\\textit{infinite expansion of a fraction}$. These type of expressions are known as \n# $\\textbf{continued fractions}$:\n#\n# $$\n# {\\varphi =1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1%\n# }{{1+}\\dfrac{1}{{1+}\\dfrac{1}{1+\\dfrac{1}{{\\vdots }}}}}}}}}.\n# $$\n#\n# We can approximate continued fractions with the finite fractions obtained by\n# stopping the development at some point. In our case, we obtain the\n# approximates\n#\n# $$\n# 1,~1+1,~1+\\frac{1}{1+1},~1+\\frac{1}{1+\\dfrac{1}{1+1}},~1+\\frac{1}{1+\\dfrac{1%\n# }{1+\\dfrac{1}{1+1}}},~1+\\frac{1}{1+\\dfrac{1}{1+\\dfrac{1}{1+\\dfrac{1}{1+1}}}}%\n# ,\\dots \n# $$\n#\n# Explicitly, these approximates are\n#\n# $$\n# 1,~2,~\\frac{3}{2},~\\frac{5}{3},~\\frac{8}{5},~\\frac{13}{8},\\dots \n# $$\n#\n# This looks like it is just the sequence of ratios $F_{n+1}/F_n$ we saw above! How can we prove this is the case for all $n$?\n#\n# We know that the sequence $R_{n} = F_{n+1}/F_n$ satisfies the recursive relation. \n#\n# $$\n# R_{n}=\\frac{F_{n+1}}{F_{n}}=1+\\frac{F_{n-1}}{F_{n}}=1+\\frac{1}{R_{n-1}}%\n# ,\\qquad \\text{with}\\qquad R_{1}=1.\n# $$\n#\n# Then, we can generate all the terms in the sequence $R_{n}$ by staring with $%\n# R_{1}=1$, and then using the relation $R_{n+1}=1+\\frac{1}{R_{n}}:$\n#\n# $$\n# \\begin{eqnarray*}\n# R_{1} &=&1 \\\\\n# R_{2} &=&1+\\frac{1}{R_{1}}=1+\\frac{1}{1}=2 \\\\\n# R_{3} &=&1+\\frac{1}{R_{2}}=1+\\frac{1}{1+R_{1}}=1+\\frac{1}{1+1} \\\\\n# R_{4} &=&1+\\frac{1}{R_{3}}=1+\\frac{1}{1+\\frac{1}{1+1}} \\\\\n# R_{5} &=&1+\\frac{1}{R_{4}}=1+\\frac{1}{1+\\frac{1}{1+\\frac{1}{1+1}}} \\\\\n# &&\\vdots \n# \\end{eqnarray*}\n# $$\n#\n# This confirms that both the sequence of rations $R_{n}$ and the sequence of\n# approximations to the continuous fraction of ${\\varphi }$ are the same\n# sequence. $\\square $\n#\n# In general, continued fractions are expressions of the form\n#\n# $$\n# a_{0}+\\frac{1}{a_{1}+\\dfrac{1}{a_{2}+\\dfrac{1}{a_{3}+\\dots }}}\n# $$\n#\n# where $a_{0}$ is an integer and $a_{1},a_{2},a_{3},\\dots $ are positive\n# integers. These type of fractions are abbreviated by the notation\n#\n# $$\n# \\left[ a_{0};a_{1},a_{2},a_{3},\\dots \\right] =a_{0}+\\frac{1}{a_{1}+\\dfrac{1}{%\n# a_{2}+\\dfrac{1}{a_{3}+\\dots }}}.\n# $$\n#\n# For example\n#\n# $$\n# \\begin{eqnarray*}\n# \\left[ 1;1,1,2\\right] &=&1+\\frac{1}{1+\\dfrac{1}{1+\\dfrac{1}{1+1}}}=\\frac{8}{%\n# 5} \\\\\n# && \\\\\n# \\left[ 1;1,1,1,1,\\dots \\right] &=&{1+}\\frac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{%\n# {1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{{1+}\\dfrac{1}{1+\\dfrac{1}{{\\vdots }%\n# }}}}}}}}={\\varphi }\n# \\end{eqnarray*}\n# $$\n#\n# For more information of continued fractions, see\n# https://en.wikipedia.org/wiki/Continued_fraction \n#\n#\n\n# ## Conclusion\n#\n# ### What have we learned?\n#\n# - a **sequence** is an ordered list of numbers, which may go on forever.\n# - the **Fibonacci sequence** 0,1,1,2,3,5,8,13,... is a famous list of numbers, well-studied since antiquity.\n# - each number in this sequence is the sum of the two coming before it in the sequence.\n# - the sequence grows fast, increasing by a **factor** of about **10** for every **five** terms.\n# - the **ratio** of pairs of Fibonacci numbers converges to the **Golden ratio,** known since the ancient Greeks as the number\n# $$\\varphi = \\frac{1 + \\sqrt{5}}{2} \\approx 1.6108.$$\n# - the Fibonacci numbers can be computed directly as the difference of powers of $\\varphi$ and its **conjugate,** $\\psi = \\frac{1 - \\sqrt{5}}{2}.$ This is sometimes faster than computing the whole list of Fibonnaci numbers.\n# - this formula with powers of $\\varphi, \\psi$ is verified using **induction.**\n# - The Fibonacci numbers can be **extended** to a **continuous function** $Fib(x)$, defined for all real numbers $x$ (including negatives). It **oscillates** (wiggles) on the negative x-axis.\n# - The **Golden Ratio** can also be expressed a **continued fraction,** which is an infinite expansion of fractions with sub-fraction terms. Many interesting numbers come from interesting continued fraction forms.\n\n# [![Callysto.ca License](https://github.com/callysto/curriculum-notebooks/blob/master/callysto-notebook-banner-bottom.jpg?raw=true)](https://github.com/callysto/curriculum-notebooks/blob/master/LICENSE.md)\n"},"script_size":{"kind":"number","value":20265,"string":"20,265"}}},{"rowIdx":975,"cells":{"path":{"kind":"string","value":"/kaggle/great/dog_breed.ipynb"},"content_id":{"kind":"string","value":"04f9e0a12d20c26cfa7b05131f3a1ba34da76a85"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"pirate-turtle/deep-learning"},"repo_url":{"kind":"string","value":"https://github.com/pirate-turtle/deep-learning"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":2647262,"string":"2,647,262"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + _uuid=\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\" _cell_guid=\"b1076dfc-b9ad-4769-8c92-a6c4dae69d19\"\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n# + _uuid=\"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a\" _cell_guid=\"79c7e3d0-c299-4dcb-8224-4455121ee9b0\"\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D,ZeroPadding2D,Input\nfrom keras.callbacks import ModelCheckpoint,EarlyStopping\n\nimport matplotlib.pyplot as plt\nimport os\nimport tensorflow as tf\n\nfrom keras.losses import categorical_crossentropy\nfrom keras.preprocessing.image import img_to_array,load_img,ImageDataGenerator\nfrom keras.utils import to_categorical\n# -\n\nfrom keras.layers import BatchNormalization\n\n\nlabels = pd.read_csv('../input/dog-breed-identification/labels.csv')\nimg_path = '../input/dog-breed-identification/train/'\nlabels = labels.assign(img_path = lambda x : img_path + x['id']+'.jpg')\nlabels\n\n# +\nfig, axes = plt.subplots(nrows=4, ncols=5, figsize=(15, 15),\n subplot_kw={'xticks': [], 'yticks': []})\n\nfor i, ax in enumerate(axes.flat):\n ax.imshow(plt.imread(labels.img_path[i]))\n ax.set_title(labels.breed[i])\n \nplt.tight_layout()\nplt.show()\n# -\n\nlabels = labels.assign(img_path = lambda x : img_path + x['id']+'.jpg')\nlabels.breed.value_counts()\n\nlabelplot = pd.value_counts(labels['breed'],ascending=True).plot(kind='barh',fontsize=\"40\",title=\"Class Distribution\",figsize=(50,100))\n\n# +\n# image size check\nfrom PIL import Image\n\nimg_size = []\nfor image in labels['img_path']:\n im=Image.open(image)\n img_size.append(im.size)\n \nimg_size.sort(reverse=True)\nimg_size\n# -\n\n#Top 20 breed\ntop_20=list(labels.breed.value_counts()[0:20].index)\ntop_20\n\ndata=labels[labels.breed.isin(top_20)]\ndata\n\nimg_pixel=np.array([img_to_array(load_img(img, target_size=(256, 256))) for img in data['img_path'].values.tolist()])\n\nimg_label=data.breed\nimg_label=pd.get_dummies(data.breed)\nimg_label.head()\n\nX=img_pixel\ny=img_label.values\nprint(X.shape)\nprint(y.shape)\n\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\nprint(X_train.shape)\nprint(y_train.shape)\nprint(X_test.shape)\nprint(y_test.shape)\n\n# +\ntrain_datagen = ImageDataGenerator(\n rotation_range=30,\n width_shift_range=0.2,\n height_shift_range=0.2,\n rescale=1./255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n fill_mode='nearest')\n\ntest_datagen=ImageDataGenerator(rescale=1./255)\n# -\n\ntraining_set=train_datagen.flow(X_train,y=y_train,batch_size=32)\ntesting_set=test_datagen.flow(X_test,y=y_test,batch_size=32)\n\n# +\n# CNN model\nmodel=Sequential()\n\nmodel.add(ZeroPadding2D((1,1),input_shape=(256,256,3)))\nmodel.add(Conv2D(32,kernel_size=(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(ZeroPadding2D(padding=(1,1)))\nmodel.add(Conv2D(32,kernel_size=(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n\nmodel.add(Flatten())\nmodel.add(Dense(64,activation='relu'))\nmodel.add(Dropout(0.2))\n\nmodel.add(Dense(20,activation='softmax'))\n\nmodel.compile(loss=categorical_crossentropy,optimizer='adam',metrics=['accuracy'])\nmodel.summary()\n# -\n\n history=model.fit_generator(training_set,\n steps_per_epoch = 16,\n validation_data = testing_set,\n validation_steps = 4,\n epochs = 50,\n verbose = 1)\n\n# +\nfrom tensorflow.keras.applications import VGG16\n\ntransfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3))\n# -\n\ntransfer_model.trainable = False\ntransfer_model.summary()\n\nfinetune_model = Sequential()\nfinetune_model.add(transfer_model)\nfinetune_model.add(Flatten())\nfinetune_model.add(Dense(64, activation='relu'))\nfinetune_model.add(Dense(20, activation='softmax'))\nfinetune_model.compile(loss=categorical_crossentropy,optimizer='adam',metrics=['accuracy'])\nfinetune_model.summary()\n\n history=finetune_model.fit_generator(training_set,\n steps_per_epoch = 16,\n validation_data = testing_set,\n validation_steps = 4,\n epochs = 50,\n verbose = 1)\n\n# +\n##\n"},"script_size":{"kind":"number","value":5465,"string":"5,465"}}},{"rowIdx":976,"cells":{"path":{"kind":"string","value":"/7/captioning.ipynb"},"content_id":{"kind":"string","value":"1c99e98ece1dd1d8e8c6ecf2ccf9cfcf94b68a07"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"onaga1958/ml-hw"},"repo_url":{"kind":"string","value":"https://github.com/onaga1958/ml-hw"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":187615,"string":"187,615"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %load_ext autoreload\n# %autoreload 2\n\nfrom QueEstaPasando import clean_data\nimport pandas as pd\nfrom QueEstaPasando import trainer_ml\n\npd.set_option('max_colwidth', 800) \ndata = pd.read_csv(\"../QueEstaPasando/data/total.csv\", index_col=0)\ndata.head()\n\nis_pos = data.loc[:, 'score'] == 'pos'\ndf_pos = data.loc[is_pos]\ndf_pos.head(80)\n\ndf_pos.head(28)\n\ndf_pos1 = pd.DataFrame(df_pos['text'])\ndf_pos1.columns = ['text']\ndf_pos1['text'] = df_pos['text']\n\ndf_pos1['created_at'] = df_pos['created_at']\n\n\ndf_pos1.head(35)\n\nt_ml = trainer_ml.Trainer_ML()\n\nt_ml.predict_model(df_pos1)\n\ndf= pd.DataFrame()\ndf['created_at'] =['2021-04-27T07:26:38.000Z']\ndf['text'] = [\"@MercantilBanco gracias por la información. Resuelto el problema. Gracias por vuestra atención.\"]\n\nt_ml.predict_model(df)\n\n\na.org/learn/machine-learning-big-data-apache-spark/discussions/all\n#\n# Please make sure to follow the guidelines before asking a question:\n#\n# https://github.com/IBM/skillsnetwork/wiki/FAQ#im-feeling-lost-and-confused-please-help-me\n#\n#\n# If running outside Watson Studio, this should work as well. In case you are running in an Apache Spark context outside Watson Studio, please remove the Apache Spark setup in the first notebook cells.\n\n# +\nfrom IPython.display import Markdown, display\ndef printmd(string):\n display(Markdown('# '+string+''))\n\n\nif ('sc' in locals() or 'sc' in globals()):\n printmd('<<<<>>>>')\n# -\n\n# !pip install pyspark==2.4.5\n\ntry:\n from pyspark import SparkContext, SparkConf\n from pyspark.sql import SparkSession\nexcept ImportError as e:\n printmd('<<<<>>>>')\n\n# +\nsc = SparkContext.getOrCreate(SparkConf().setMaster(\"local[*]\"))\n\nspark = SparkSession \\\n .builder \\\n .getOrCreate()\n\n\n# -\n\ndef assignment1(sc):\n rdd = sc.parallelize(list(range(100)))\n return rdd.count()\n\n\nprint(assignment1(sc))\n\n# !rm -f rklib.py\n# !wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\n\n# Please provide your email address and obtain a submission token on the grader’s submission page in coursera, then execute the cell\n\n# +\nfrom rklib import submit\nimport json\n\nkey = \"R1eDmiHNEei9kxIYdin0mA\"\npart = \"fnFg7\"\nemail = \"rayhanozzy@outlook.com\"\ntoken = \"WPdAV3tVjXKg7Wwe\" #you can obtain it from the grader page on Coursera (have a look here if you need more information https://youtu.be/GcDo0Rwe06U?t=276)\n\n\nsubmit(email, token, key, part, [part], json.dumps(assignment1(sc)))\n# -\n\n\nig,ax=plt.subplots(1,1,figsize=(9,5))\nsns.kdeplot(df_train[df_train['Survived']==1]['Age'], ax=ax)\nsns.kdeplot(df_train[df_train['Survived']==0]['Age'], ax=ax)\nplt.legend(['Survived == 1', 'Survived == 0'])\nplt.show()\n\n# +\nplt.figure(figsize=(8, 6))\ndf_train['Age'][df_train['Pclass'] == 1].plot(kind='kde')\ndf_train['Age'][df_train['Pclass'] == 2].plot(kind='kde')\ndf_train['Age'][df_train['Pclass'] == 3].plot(kind='kde')\n\nplt.xlabel('Age')\nplt.title('Age Distribution within classes')\nplt.legend(['1st Class', '2nd Class', '3rd Class'])\n# -\n\ncummulate_survival_ratio = []\nfor i in range(1,80):\n cummulate_survival_ratio.append(df_train[df_train['Age']0 else 0)\ndf_test['Fare'] = df_test['Fare'].map(lambda i: np.log(i) if i >0 else 0)\n\nfig, ax = plt.subplots(1,1,figsize=(8,8))\ng= sns.distplot(df_train['Fare'], color='b',\nlabel='Skewness : {:.2f}'.format(df_train['Fare'].skew()),\nax=ax)\ng = g.legend(loc='best')\n\n\n# + deletable=true editable=true\nprint(\"each image code is a 1000-unit vector:\", img_codes.shape)\nprint(img_codes[0, :10])\nprint('\\n\\n')\nprint(\"for each image there are 5-7 descriptions, e.g.:\\n\")\nprint(captions[0], sep='\\n')\n\n# + deletable=true editable=true\n#split descriptions into tokens\nfor img_i in range(len(captions)):\n for caption_i in range(len(captions[img_i])):\n sentence = captions[img_i][caption_i] \n captions[img_i][caption_i] = [\"#START#\"] + sentence.split(' ') + [\"#END#\"]\n\n# + deletable=true editable=true\n# Build a Vocabulary\n\nword_counts = Counter()\nfor image in captions:\n for caption in image:\n word_counts.update(caption[1:-1])\n\nvocab = ['#UNK#', '#START#', '#END#']\nvocab += [k for k, v in word_counts.items() if v >= 5]\nn_tokens = len(vocab)\n\nassert 10000 <= n_tokens <= 10500\n\nword_to_index = {w: i for i, w in enumerate(vocab)}\n\n# + deletable=true editable=true\nPAD_ix = -1\nUNK_ix = vocab.index('#UNK#')\n\ndef as_matrix(sequences, max_len=None):\n max_len = max_len or max(map(len, sequences))\n \n matrix = np.zeros((len(sequences), max_len), dtype='int32') + PAD_ix\n for i, seq in enumerate(sequences):\n row_ix = [word_to_index.get(word, UNK_ix) for word in seq[:max_len]]\n matrix[i, :len(row_ix)] = row_ix\n \n return matrix\n\n\n# + deletable=true editable=true\ncaptions = np.array(captions)\n\ndef generate_batch(images, captions, batch_size, max_caption_len=None):\n #sample random numbers for image/caption indicies\n random_image_ix = np.random.randint(0, len(images), size=batch_size)\n \n #get images\n batch_images = images[random_image_ix]\n \n #5-7 captions for each image\n captions_for_batch_images = captions[random_image_ix]\n \n #pick 1 from 5-7 captions for each image\n batch_captions = list(map(choice, captions_for_batch_images))\n \n #convert to matrix\n batch_captions_ix = as_matrix(batch_captions, max_len=max_caption_len)\n \n return batch_images, batch_captions_ix\n\n\n# + [markdown] deletable=true editable=true\n# ### Mah Neural Network\n\n# + deletable=true editable=true\n# network shapes. \nCNN_FEATURE_SIZE = img_codes.shape[1]\nEMBED_SIZE = 256 # Didn't make a valuable deffernce\nLSTM_UNITS = 512 # Didn't make a valuable deffernce\n\n# Input Variable\nsentences = T.imatrix() # [batch_size x time] of word ids\nimage_vectors = T.matrix() # [batch size x unit] of CNN image features\nsentence_mask = T.neq(sentences, PAD_ix)\n\n# network inputs\nl_words = InputLayer((None, None), sentences)\nl_mask = InputLayer((None, None), sentence_mask)\n\n# embeddings for words \nl_word_embeddings = EmbeddingLayer(l_words, input_size=n_tokens,\n output_size=EMBED_SIZE)\n\n# input layer for image features\nl_image_features = InputLayer((None, CNN_FEATURE_SIZE), image_vectors)\n\nl_image_features_small = DenseLayer(l_image_features, num_units=LSTM_UNITS,\n W=lasagne.init.HeNormal(gain='relu'))\nl_image_features_small = DropoutLayer(l_image_features_small)\n\ndecoder = LSTMLayer(l_word_embeddings,\n num_units=LSTM_UNITS,\n cell_init=l_image_features_small,\n mask_input=l_mask,\n grad_clipping=1e25) # try different values. With huge numbers results were a bit better\n\n# apply whatever comes next to each tick of each example in a batch. Equivalent to 2 reshapes\nbroadcast_decoder_ticks = BroadcastLayer(decoder, (0, 1))\n\npredicted_probabilities_each_tick = DenseLayer(broadcast_decoder_ticks,\n n_tokens,\n nonlinearity=lasagne.nonlinearities.softmax)\n\n# un-broadcast back into (batch,tick,probabilities)\npredicted_probabilities = UnbroadcastLayer(predicted_probabilities_each_tick,\n broadcast_layer=broadcast_decoder_ticks)\n\nnext_word_probas = get_output(predicted_probabilities)\n\nreference_answers = sentences[:, 1:]\noutput_mask = sentence_mask[:, 1:]\n\nloss = lasagne.objectives.categorical_crossentropy(\n next_word_probas[:, :-1].reshape((-1, n_tokens)),\n reference_answers.reshape((-1,))\n).reshape(reference_answers.shape)\n\nloss = (loss * output_mask).sum() / output_mask.sum()\nweights = lasagne.layers.get_all_params(predicted_probabilities, trainable=True)\nupdates = lasagne.updates.adam(loss, weights)\ntrain_step = theano.function(inputs=[image_vectors, sentences], outputs=loss, updates=updates)\nval_step = theano.function(inputs=[image_vectors, sentences], outputs=loss)\n\n# + [markdown] deletable=true editable=true\n# ### Main loop\n# * We recommend you to periodically evaluate the network using the next \"apply trained model\" block\n# * its safe to interrupt training, run a few examples and start training again\n\n# + deletable=true editable=true\nbatch_size = 32 # just for pass memory limit\nn_epochs = 100 # adjust me\nn_batches_per_epoch = int(len(img_codes) / batch_size) # to transform short epochs to long with little spam\nn_validation_batches = 5 # how many batches are used for validation after each epoch\n\n# + deletable=true editable=true\nfor epoch in range(n_epochs):\n train_loss = 0\n for _ in range(n_batches_per_epoch):\n train_loss += train_step(*generate_batch(img_codes, captions, batch_size))\n train_loss /= n_batches_per_epoch\n \n val_loss = 0\n for _ in range(n_validation_batches):\n val_loss += val_step(*generate_batch(img_codes, captions, batch_size))\n val_loss /= n_validation_batches\n \n print('\\nEpoch: {}, train loss: {}, val loss: {}'.format(epoch + 1,\n train_loss,\n val_loss))\n\n# + [markdown] deletable=true editable=true\n# ### apply trained model\n\n# + deletable=true editable=true\n#the same kind you did last week, but a bit smaller\nfrom pretrained_lenet import build_model, preprocess, MEAN_VALUES\n\n# build googlenet\nlenet = build_model()\n\n#load weights\nlenet_weights = pickle.load(open('data/blvc_googlenet.pkl', 'rb'), encoding='bytes')[b'param values']\nset_all_param_values(lenet[\"prob\"], lenet_weights)\n\n#compile get_features\ncnn_input_var = lenet['input'].input_var\ncnn_feature_layer = lenet['loss3/classifier']\nget_cnn_features = theano.function([cnn_input_var], lasagne.layers.get_output(cnn_feature_layer))\n\n# + deletable=true editable=true\nfrom matplotlib import pyplot as plt\n# %matplotlib inline\n\n#sample image\nimg = plt.imread('data/Dog-and-Cat.jpg')\nimg = preprocess(img)\n\n# + deletable=true editable=true\n# deprocess and show, one line :)\nfrom pretrained_lenet import MEAN_VALUES\nplt.imshow(np.transpose((img[0] + MEAN_VALUES)[::-1],[1,2,0]).astype('uint8'))\n\n# + [markdown] deletable=true editable=true\n# ## Generate caption\n\n# + deletable=true editable=true\nlast_word_probas_det = get_output(predicted_probabilities, deterministic=False)[:, -1]\n\nget_probs = theano.function([image_vectors, sentences], last_word_probas_det)\n\n#this is exactly the generation function from week5 classwork,\n#except now we condition on image features instead of words\ndef generate_caption(image, caption_prefix = (\"START\",), t=1, sample=True, max_len=100):\n image_features = get_cnn_features(image)\n caption = list(caption_prefix)\n for _ in range(max_len):\n \n next_word_probs = get_probs(image_features, as_matrix([caption])).ravel()\n #apply temperature\n next_word_probs = next_word_probs**t / np.sum(next_word_probs**t)\n\n if sample:\n next_word = np.random.choice(vocab, p=next_word_probs) \n else:\n next_word = vocab[np.argmax(next_word_probs)]\n\n caption.append(next_word)\n\n if next_word==\"#END#\":\n break\n \n return caption\n\n\n# + deletable=true editable=true\nfor i in range(50):\n print(' '.join(generate_caption(img, t=1.)[1:-1]))\n# -\n\n# Bad results, don't know why :(\n# I tried different variants of EMBED_SIZE, LSTM_UNITS, learning rate scheduling, grad_cliping..., check architecture, don't know what's wrong.\n\n# + [markdown] deletable=true editable=true\n#\n\n# + [markdown] deletable=true editable=true\n# Конец вывода после долгой тренеровки (другой запуск):\n#\n# Epoch: 495, train loss: 2.21678551197052, val loss: 2.194071388244629\n#\n# Epoch: 496, train loss: 2.21208044052124, val loss: 2.1524736404418947\n#\n# Epoch: 497, train loss: 2.213805365562439, val loss: 2.269657611846924\n#\n# Epoch: 498, train loss: 2.2279205179214476, val loss: 2.2288233280181884\n#\n# Epoch: 499, train loss: 2.188794708251953, val loss: 2.2174277305603027\n#\n# Epoch: 500, train loss: 2.202223610877991, val loss: 2.2282715320587156\n# Finish :)\n#\n# a base suspended in a tight tank\n# edible cat peers over flowers out of the window\n# two little girls brush their head\n# two small brown and white\n# behind a white and black and white dog watching a red ball\n# seal looking ahead\n# brown and dog and a small skull\n# wet and head of water and birds\n# grey and white small kitten lays on a blue and blue comforter\n# eating a pan of pizza\n# dog allows a tree to look like they #UNK# are vitamin and photographs\n# close to of a brown and white bird\n# chargers flying light\n# hogs setting in the back are in front of the trees\n# close up sitting around while another woman brushes her teeth while holding a sheep #UNK#\n# some big brown old ewe\n# tall dog saying him to keep its ring\n# this head in gray and white of its fur toothbrush suit is traveling down a green carpet\n# fluffy brown and white cat sits on the street\n# drink that has a small child laying on each\n# dog #UNK# treat\n# close up of a lamp\n# brown and white and black and white photo of a ball chasing a feather\n# and small dog\n# greek thing in the clear nest\n# strewn eating pink bag\n# furry rent by head\n# with two fuzzy spotted blue and white near each other in the middle of grassy area with a green pattern\n# grey and gold teddy bear sitting in a tree\n# enjoying a match\n\n# + [markdown] deletable=true editable=true\n# # Bonus Part\n# - Use ResNet Instead of GoogLeNet\n# - Use W2V as embedding\n# - Use Attention :) \n\n# + [markdown] deletable=true editable=true\n# # Pass Assignment https://goo.gl/forms/2qqVtfepn0t1aDgh1 \n"},"script_size":{"kind":"number","value":16671,"string":"16,671"}}},{"rowIdx":977,"cells":{"path":{"kind":"string","value":"/week11_homework (Feature engineering).ipynb"},"content_id":{"kind":"string","value":"0d1fae36407ea7dbff882aea199a17969d604b05"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"dmg-ai/MachineLearning_RN"},"repo_url":{"kind":"string","value":"https://github.com/dmg-ai/MachineLearning_RN"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":205940,"string":"205,940"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # **Неделя 11. feature engineering**\n\n# ## **Домашняя работа**\n\n# Импортируем необходимые модули\n\n# +\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# %matplotlib inline\n\nfrom catboost import CatBoostRegressor # нужно установить библиотеку catboost\nfrom sklearn.metrics import mean_absolute_error\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nRANDOM_STATE = 42\n# -\n\n# Загрузим датасет California Housing dataset\n\nfrom sklearn.datasets import fetch_california_housing\ncalifornia_housing = fetch_california_housing()\n\ndata = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)\ndata['target'] = california_housing.target\n\n# **Описание датасета:**\n# Датасет содержит информацию о жилых районах штата Калифорния. 20640 Записей, 8 признаков. \n#\n# **Описание признаков:**\n# 1. *MedInc* - медианный доход домохозяйства\n# 2. *HouseAge* - медианный возраст домов\n# 3. *AveRooms* - среднее число комнат\n# 4. *AveBedrms* - среднее число спален\n# 5. *Population* - число людей, проживающих в районе\n# 6. *AveOccup* - среднее число людей, проживающих в доме\n# 7. *Latitude* - широта, геогр. координаты \n# 8. *Longitude* - долгота, геогр. координаты\n#\n# **Целевая переменная:** Медианная стоимость дома в районе, единица измерения $10 тыс.\n\ndata.head()\n\n\n# В этой домашней работе вам предстоит реализовывать различные гипотезы и тестировать их. \n#\n# Метрика качества - *mean_absolute_error*.\n#\n# Разбиение на обучающую и тестовую выборку случайное.\n#\n# Cтратегия кросс-валидации - ShuffledSplit.\n#\n# **Во всех следующих заданиях нужно использовать модель, полученную с помощью *create_model()*.**\n\ndef create_model():\n \"\"\"\n create instance of CatBoostRegressor model\n \"\"\"\n return CatBoostRegressor(random_state=RANDOM_STATE, logging_level='Silent', iterations=60)\n\n\n\n# разделим выборку на обучающую и тестовую часть\n\n# +\nfrom sklearn.model_selection import train_test_split\n\ntrain, test = train_test_split(data, test_size = 0.2, random_state = RANDOM_STATE, shuffle=True)\n\nprint('train size: {}'.format(train.shape[0]))\nprint('test size: {}'.format(test.shape[0]))\n# -\n\n# #### **Задание 1.** \n# Попробуем обучить модель на датасете без дополнительных признаков и преобразований. Оцените качество модели на кросс-валидации. Модель нужно создать с помощью функции *create_model()*. В форме укажите значение метрики.\n#\n# Разделить выборку на фолды нужно с помошью sklearn.model_selection.KFold c параметрами:\n# 1. n_splits = 5\n# 2. shuffle = True\n# 3. random_state = RANDOM_STATE\n#\n# Для проведения кросс-валидации можно использовать функции cross_val_score или cross_val_predict, в которые нужно передать созданный KFold. Можете попробовать сделать кросс-валидацию с помощью самого объекта KFold.\n#\n# Метрика - mean_absolute_error. Кросс-валидацию необходимо проводить на train части датасета. \n#\n# *Не забудьте исключить target.*\n\n# +\nfrom sklearn.model_selection import KFold, cross_val_score, cross_val_predict\n\nmodel = create_model()\nkfold = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)# your code here\npredictions = cross_val_predict(model, train.drop('target', axis=1), train['target'],cv=kfold) # your code here \nscore = cross_val_score(model, train.drop('target', axis=1), train['target'], \n scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here\n\nprint(score)\n# -\n\nround(-score, 2)\n\n# ------------------------\n\n# В датасете есть интересные признаки - координаты районов. Координаты часто являются очень полезными, но не в исходном виде. Посмотрим, есть ли зависимость между target и расположением района.\n#\n# Нарисуйте диаграмму рассеивания, в которой ось x - широта, y - долгота, окрасьте точки по значению target. \n\n#your code here\nplt.figure(figsize=(10,8))\nplt.scatter(data['Longitude'], data['Latitude'], c=data['target'], s=15)\nplt.xlabel('Latitude', fontsize=16)\nplt.ylabel('Longitude', fontsize=16)\n\n# #### **Задание 2.** \n#\n# Если сопоставить диаграмму с картой штата Калифорния, то будет видно, что цены выше у крупных городов и на побережье, попробуем добавить такой признак. Посчитайте расстояние от района до следующих крупных городов:\n# 1. Лос-Анджелес (34.05, -118.24)\n# 2. Сан-Хосе (37.33, -121.88)\n# 3. Сан-Франциско (37.77, -122.41)\n# 4. Сакраменто (38.58, -121.49)\n#\n# Расстояние от каждого города до района должно быть отдельным признаком.\n#\n# Добавьте новые признаки в датасет, оцените качество на кросс-валидации как в 1 задании. В форме укажите значение метрики.\n\n# +\n#your code here \nimport geopy.distance as distance\n\ncities = {\n 'distance_LA' : (34.05, -118.24),\n 'distance_SJ' : (37.33, -121.88),\n 'distance_SF' : (37.77, -122.41),\n 'distance_SA' : (38.58, -121.49)\n}\n\nfor city in cities.keys():\n vals = []\n for coord1 in zip(data['Latitude'], data['Longitude']):\n vals.append(distance.vincenty(coord1, cities[city]).km)\n data[city] = vals\n# -\n\ndata.head()\nmodel2 = create_model()\nscore2 = cross_val_score(model2, data.drop('target', axis=1), data['target'], \n scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here\nprint(round(-score2,2))\n\n# ------------------------\n\n# #### **Задание 3.** \n#\n# Можно пойти дальше и добавить еще городов или других объектов, до которых можно посчитать расстояние, но тогда число признаков может значительно вырасти, что почти всегда приводит к плохим последствиям. Преобразуем расстояния до городов в новый категориальный признак - самый близкий город.\n#\n# Постройте такой признак, удалите признаки расстояний до городов и оцените качество на кросс-валидации как в 1 задании. В форме укажите значение метрики. \n#\n# Названия городов можно закодировать с помощью sklearn.preprocessing.LabelEncoder.\n\n# +\n#your code here \nfrom sklearn.preprocessing import LabelEncoder\n\ncols = ['distance_LA', 'distance_SJ', 'distance_SF', 'distance_SA']\ncity = []\nfor i in range(data.shape[0]): \n city.append(cols[np.argsort(data[cols].iloc[i].values)[0]])\ndata['Nearest_city'] = city\n\nle = LabelEncoder()\ndata['Nearest_city'] = le.fit_transform(data['Nearest_city'])\n# -\n\ndata.head(10)\n\nmodel3 = create_model()\nscore3 = cross_val_score(model3, data.drop(cols+['target'], axis=1), data['target'], \n scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here\nprint(round(-score3,2))\n\n# ------------------------\n\n# #### **Задание 4.** \n#\n# Попробуем добавить более сложный признак. Номер кластера, в который попадает район. Постройте такой признак, оцените качество с новым признаком на кросс-валидации. В форме укажите значение метрики.\n#\n# Постройте кластеризацию на всех признаках с помощью алгоритма KMeans со следующими параметрами:\n# 1. n_clusters = 20 \n# 2. random_state = RANDOM_STATE\n#\n# Перед кластеризацией данные необходимо отмасштабировать с помощью StandartScaler. \n# Масштабирование и вычисление параметров кластеризации необходимо производить на train фолдах, а применять их к train и test фолдам.\n#\n# Используйте все рассчитанные ранее признаки.\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\n\n# +\nkfold = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n\ntrain, test = train_test_split(data, test_size = 0.2, random_state = RANDOM_STATE, shuffle=True)\ntmp = 0\nscores_on_folds = []\nfor train_ids, test_ids in kfold.split(train): #kfold будет итеративно генерировать id train и test фолдов\n \n #разделяем выборку на train и test фолды\n train_folds = train.iloc[train_ids] \n test_fold = train.iloc[test_ids]\n \n #создаем скейлер, алгоритм кластеризации и модель\n scaler = StandardScaler()\n kmeans = KMeans(n_clusters=20, random_state=RANDOM_STATE)\n model = create_model()\n \n #your code here\n scaled_train = scaler.fit_transform(train_folds.drop('target', axis=1))\n scaled_test = scaler.transform(test_fold.drop('target', axis=1))\n \n kmeans_train = kmeans.fit_transform(scaled_train)\n kmeans_test = kmeans.transform(scaled_test)\n tmp = kmeans_train\n model.fit(kmeans_train, train_folds.target)\n \n predictions = model.predict(kmeans_test)\n score = mean_absolute_error(test_fold.target, predictions)\n scores_on_folds.append(score)\n\nprint('score_by_fold: {}'.format(scores_on_folds))\nprint('cross-validation score: {}'.format(np.mean(scores_on_folds)))\n# -\n\nround(np.mean(scores_on_folds),2)\n\n# ------------------------\n\n# #### **Задание 5.** \n#\n# Оценивать качество на тестовой выборке стоит только в самом конце, не важно генерируем мы новые признаки или подбираем гиперпараметры модели. Если часто смотреть на метрики на тестовой выборке и делать по ним выводы, то можно переобучиться под тест. Оценка качества на тесте получится недостоверной, по ней нельзя сделать вывод о работе модели с реальными данными. \n#\n# 1. Оцените качество модели на кросс-валидации на train на всех построенных в домашней работе признаках.\n# 2. Рассчитайте признаки из домашней работы для train и test выборок.\n# 3. Оцените качество на test, обучившись на train.\n#\n# Сравните качество на кросс-валидации и test. В форме укажите разницу между значением метрики на train и cv (train_score - cv_core).\n#\n# Используйте все рассчитанные раннее признаки.\n\n# +\n#your code here\n\nmodel5 = create_model()\nmodel5.fit(train.drop('target', axis=1), train['target'])\npreds = model5.predict(test.drop('target', axis=1))\nmae = mean_absolute_error(test['target'], preds)\n# -\n\nround(mae-np.mean(scores_on_folds),2)\n\n# ------------------------\n"},"script_size":{"kind":"number","value":9796,"string":"9,796"}}},{"rowIdx":978,"cells":{"path":{"kind":"string","value":"/Tensorflow Basics - Lesson 1.ipynb"},"content_id":{"kind":"string","value":"8114a573c4be46dabc78d938861c0d4265964998"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"hushenglang/tensorflow_basics"},"repo_url":{"kind":"string","value":"https://github.com/hushenglang/tensorflow_basics"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":3641,"string":"3,641"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"8W8zcTKkUyi1\" outputId=\"2b07c19f-f57f-4a84-e71c-7381021679b7\"\n# Python ≥3.5 is required\nimport sys\nassert sys.version_info >= (3, 5)\n\n# Scikit-Learn ≥0.20 is required\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\ntry:\n # # %tensorflow_version only exists in Colab.\n # %tensorflow_version 2.x\n IS_COLAB = True\nexcept Exception:\n IS_COLAB = False\n\n# TensorFlow ≥2.0 is required\nimport tensorflow as tf\nfrom tensorflow import keras\nassert tf.__version__ >= \"2.0\"\n\nif not tf.config.list_physical_devices('GPU'):\n print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n if IS_COLAB:\n print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n\n# Common imports\nimport numpy as np\nimport os\nfrom pathlib import Path\n\n# to make this notebook's output stable across runs\nnp.random.seed(42)\ntf.random.set_seed(42)\n\n# To plot pretty figures\n# %matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\n# Where to save the figures\nPROJECT_ROOT_DIR = \".\"\nCHAPTER_ID = \"rnn\"\nIMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\nos.makedirs(IMAGES_PATH, exist_ok=True)\n\ndef save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n print(\"Saving figure\", fig_id)\n if tight_layout:\n plt.tight_layout()\n plt.savefig(path, format=fig_extension, dpi=resolution)\n\n\n# + [markdown] id=\"1w6k1tuexBRf\"\n# ## Download the dataset\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"uJqDiZvKwypM\" outputId=\"b6906af9-a573-4806-bcfb-a91f768585df\"\nDOWNLOAD_ROOT = \"https://github.com/ageron/handson-ml2/raw/master/datasets/jsb_chorales/\"\nFILENAME = \"jsb_chorales.tgz\"\nfilepath = keras.utils.get_file(FILENAME,\n DOWNLOAD_ROOT + FILENAME,\n cache_subdir=\"datasets/jsb_chorales\",\n extract=True)\n\n# + id=\"wg_IxDD8xFu8\"\njsb_chorales_dir = Path(filepath).parent\ntrain_files = sorted(jsb_chorales_dir.glob(\"train/chorale_*.csv\"))\nvalid_files = sorted(jsb_chorales_dir.glob(\"valid/chorale_*.csv\"))\ntest_files = sorted(jsb_chorales_dir.glob(\"test/chorale_*.csv\"))\n\n# + id=\"em_CndBmxauM\"\nimport pandas as pd\n\ndef load_chorales(filepaths):\n return [pd.read_csv(filepath).values.tolist() for filepath in filepaths]\n\ntrain_chorales = load_chorales(train_files)\nvalid_chorales = load_chorales(valid_files)\ntest_chorales = load_chorales(test_files)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"poTYdQ1ayB7X\" outputId=\"92f8ac2b-0740-432c-ab63-4447a72c2310\"\ntrain_chorales[0]\n\n# + [markdown] id=\"q4hpXC3ayZxA\"\n# Notes range from 36 (C1 = C on octave 1) to 81 (A5 = A on octave 5), plus 0 for silence:\n\n# + id=\"L9B9fHdYyGxC\"\nnotes = set()\nfor chorales in (train_chorales, valid_chorales, test_chorales):\n for chorale in chorales:\n for chord in chorale:\n notes |= set(chord)\n\nn_notes = len(notes)\nmin_note = min(notes - {0})\nmax_note = max(notes)\n\nassert min_note == 36\nassert max_note == 81\n\n# + [markdown] id=\"-aMK2we624a-\"\n# writing a few functions to listen to these chorales :\n\n# + id=\"SQL68r3FydnR\"\nfrom IPython.display import Audio\n\ndef notes_to_frequencies(notes):\n # Frequency doubles when you go up one octave; there are 12 semi-tones\n # per octave; Note A on octave 4 is 440 Hz, and it is note number 69.\n return 2 ** ((np.array(notes) - 69) / 12) * 440\n\ndef frequencies_to_samples(frequencies, tempo, sample_rate):\n note_duration = 60 / tempo # the tempo is measured in beats per minutes\n # To reduce click sound at every beat, we round the frequencies to try to\n # get the samples close to zero at the end of each note.\n frequencies = np.round(note_duration * frequencies) / note_duration\n n_samples = int(note_duration * sample_rate)\n time = np.linspace(0, note_duration, n_samples)\n sine_waves = np.sin(2 * np.pi * frequencies.reshape(-1, 1) * time)\n # Removing all notes with frequencies ≤ 9 Hz (includes note 0 = silence)\n sine_waves *= (frequencies > 9.).reshape(-1, 1)\n return sine_waves.reshape(-1)\n\ndef chords_to_samples(chords, tempo, sample_rate):\n freqs = notes_to_frequencies(chords)\n freqs = np.r_[freqs, freqs[-1:]] # make last note a bit longer\n merged = np.mean([frequencies_to_samples(melody, tempo, sample_rate)\n for melody in freqs.T], axis=0)\n n_fade_out_samples = sample_rate * 60 // tempo # fade out last note\n fade_out = np.linspace(1., 0., n_fade_out_samples)**2\n merged[-n_fade_out_samples:] *= fade_out\n return merged\n\ndef play_chords(chords, tempo=160, amplitude=0.1, sample_rate=44100, filepath=None):\n samples = amplitude * chords_to_samples(chords, tempo, sample_rate)\n if filepath:\n from scipy.io import wavfile\n samples = (2**15 * samples).astype(np.int16)\n wavfile.write(filepath, sample_rate, samples)\n return display(Audio(filepath))\n else:\n return display(Audio(samples, rate=sample_rate))\n\n\n# + [markdown] id=\"ghy-DcIF3yt2\"\n# Now let's listen to a few chorales:\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 191} id=\"Q2My9je93lK3\" outputId=\"524b94c9-e132-49ca-81d5-962f18fe2f28\"\nfor index in range(3):\n play_chords(train_chorales[index])\n\n\n# + id=\"AdYzWcte35jK\"\ndef create_target(batch):\n X = batch[:, :-1]\n Y = batch[:, 1:] # predict next note in each arpegio, at each step\n return X, Y\n\ndef preprocess(window):\n window = tf.where(window == 0, window, window - min_note + 1) # shift values\n return tf.reshape(window, [-1]) # convert to arpegio\n\ndef bach_dataset(chorales, batch_size=32, shuffle_buffer_size=None,\n window_size=32, window_shift=16, cache=True):\n def batch_window(window):\n return window.batch(window_size + 1)\n\n def to_windows(chorale):\n dataset = tf.data.Dataset.from_tensor_slices(chorale)\n dataset = dataset.window(window_size + 1, window_shift, drop_remainder=True)\n return dataset.flat_map(batch_window)\n\n chorales = tf.ragged.constant(chorales, ragged_rank=1)\n dataset = tf.data.Dataset.from_tensor_slices(chorales)\n dataset = dataset.flat_map(to_windows).map(preprocess)\n if cache:\n dataset = dataset.cache()\n if shuffle_buffer_size:\n dataset = dataset.shuffle(shuffle_buffer_size)\n dataset = dataset.batch(batch_size)\n dataset = dataset.map(create_target)\n return dataset.prefetch(1)\n\n\n# + [markdown] id=\"qoMWXPHhM4dO\"\n# creating the training set, the validation set and the test set:\n\n# + id=\"NYvIQiH6MePb\"\ntrain_set = bach_dataset(train_chorales, shuffle_buffer_size=1000)\nvalid_set = bach_dataset(valid_chorales)\ntest_set = bach_dataset(test_chorales)\n\n# + [markdown] id=\"Oj7C2B7GPUqo\"\n# ## building the model\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"Vsueh4ehM-CW\" outputId=\"6a597ea3-70c1-48f9-b9ea-e95b749cddf4\"\nn_embedding_dims = 5\n\nmodel = keras.models.Sequential([\n keras.layers.Embedding(input_dim=n_notes, output_dim=n_embedding_dims,\n input_shape=[None]),\n keras.layers.Conv1D(32, kernel_size=2, padding=\"causal\", activation=\"relu\"),\n keras.layers.BatchNormalization(),\n keras.layers.Conv1D(48, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=2),\n keras.layers.BatchNormalization(),\n keras.layers.Conv1D(64, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=4),\n keras.layers.BatchNormalization(),\n keras.layers.Conv1D(96, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=8),\n keras.layers.BatchNormalization(),\n keras.layers.LSTM(256, return_sequences=True),\n keras.layers.Dense(n_notes, activation=\"softmax\")\n])\n\nmodel.summary()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"ougFu4UBPXO8\" outputId=\"bc57553c-36da-456c-b50d-18f2bf68c6cf\"\noptimizer = keras.optimizers.Nadam(lr=1e-3)\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\nmodel.fit(train_set, epochs=20, validation_data=valid_set)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"ctIauHk7Pxuw\" outputId=\"2b8a13ca-19f4-42c6-c186-1b127e2a6551\"\nmodel.save(\"my_bach_model.h5\")\nmodel.evaluate(test_set)\n\n\n# + id=\"jFyDUQygQDAb\"\ndef generate_chorale(model, seed_chords, length):\n arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))\n arpegio = tf.reshape(arpegio, [1, -1])\n for chord in range(length):\n for note in range(4):\n next_note = model.predict_classes(arpegio)[:1, -1:]\n arpegio = tf.concat([arpegio, next_note], axis=1)\n arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n return tf.reshape(arpegio, shape=[-1, 4])\n\n\n# + [markdown] id=\"sQ1hPIaxREvf\"\n# test this function using the first 8 chords of one of the test chorales\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 75} id=\"ESCKLsYgQqio\" outputId=\"0995d176-4066-4498-8811-11b744ffdb07\"\nseed_chords = test_chorales[2][:8]\nplay_chords(seed_chords, amplitude=0.2)\n\n# + [markdown] id=\"UKB7y8LYRTUt\"\n# generate 56 more chords, for a total of 64 chords\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 146} id=\"wzkljWHsRHoO\" outputId=\"b655df1e-b5e5-421b-8c03-f4dafa92c91c\"\nnew_chorale = generate_chorale(model, seed_chords, 56)\nplay_chords(new_chorale)\n\n\n# + [markdown] id=\"iFNLCqdcXHb5\"\n# This approach has one major flaw: it is often too conservative. Indeed, the model will not take any risk, it will always choose the note with the highest score, and since repeating the previous note generally sounds good enough, it's the least risky option, so the algorithm will tend to make notes last longer and longer. Pretty boring. Plus, if you run the model multiple times, it will always generate the same melody.\n#\n# So let's spice things up a bit! Instead of always picking the note with the highest score, we will pick the next note randomly, according to the predicted probabilities. For example, if the model predicts a C3 with 75% probability, and a G3 with a 25% probability, then we will pick one of these two notes randomly, with these probabilities. We will also add a temperature parameter that will control how \"hot\" (i.e., daring) we want the system to feel. A high temperature will bring the predicted probabilities closer together, reducing the probability of the likely notes and increasing the probability of the unlikely ones.\n\n# + id=\"TK4qJdM5RWmI\"\ndef generate_chorale_v2(model, seed_chords, length, temperature=1):\n arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))\n arpegio = tf.reshape(arpegio, [1, -1])\n for chord in range(length):\n for note in range(4):\n next_note_probas = model.predict(arpegio)[0, -1:]\n rescaled_logits = tf.math.log(next_note_probas) / temperature\n next_note = tf.random.categorical(rescaled_logits, num_samples=1)\n arpegio = tf.concat([arpegio, next_note], axis=1)\n arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n return tf.reshape(arpegio, shape=[-1, 4])\n\n\n# + [markdown] id=\"rmiWO43VXe27\"\n# generating 3 chorales using this new function: one cold, one medium, and one hot\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 75} id=\"M3P47zLfXXm3\" outputId=\"20f0a567-5318-4124-dd59-1ba58f9b7135\"\nnew_chorale_v2_cold = generate_chorale_v2(model, seed_chords, 56, temperature=0.8)\nplay_chords(new_chorale_v2_cold, filepath=\"bach_cold.wav\")\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 75} id=\"U68lpGKxXidx\" outputId=\"a3e47904-6ac8-4f75-d4a1-7dcd91f63504\"\nnew_chorale_v2_medium = generate_chorale_v2(model, seed_chords, 56, temperature=1.0)\nplay_chords(new_chorale_v2_medium, filepath=\"bach_medium.wav\")\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 75} id=\"FZHuI-WxXmV8\" outputId=\"6e59faba-1a82-433a-9872-f90482b4200f\"\nnew_chorale_v2_hot = generate_chorale_v2(model, seed_chords, 56, temperature=1.5)\nplay_chords(new_chorale_v2_hot, filepath=\"bach_hot.wav\")\n\n# + id=\"Qe9B3aPeXwcf\"\n\n"},"script_size":{"kind":"number","value":12494,"string":"12,494"}}},{"rowIdx":979,"cells":{"path":{"kind":"string","value":"/Regressão Linear/Regressão Linear com gradiente descendente.ipynb"},"content_id":{"kind":"string","value":"6a6f7bdf93a50b948c531e209db16a1cbe359e79"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"eder0782/Tutoriais-de-AM"},"repo_url":{"kind":"string","value":"https://github.com/eder0782/Tutoriais-de-AM"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":350266,"string":"350,266"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Regressão Linear com gradiente descendente\n#\n# Métodos iterativos de otimização são usadas em aprendizado de máquina em toda a parte. Aqui, nós vamos olhar o método de gradiente descendente, o método mais popular para treinar redes neurais artificiais. Em se tratando de uma simples regressão linear, o método de gradiente descendente só é recomendado quando temos dados com muitas dimensões. Nesse caso, a inversão da matriz $\\pmb{X}^T\\pmb{X}$ começa a demorar muito e resolver regressão linear pela fórmula analítica $\\pmb{\\hat{w}} = (\\pmb{X}^T \\pmb{X})^{-1} \\pmb{X}^T \\pmb{y}$ não vale mais a pena. \n#\n# Nós também veremos um pouco de regimes de aprendizado online e em lotes (*mini-batch learning*) e discutiremos como esses regimes podem ser usados para aprender utilizando bases gigantescas que não são possíveis de carregar de uma só vez para o RAM do computador (e.g. bases com +/- 20 GB).\n#\n# Para melhor entendimento do algoritmo de otimização, é mais interessante começar usando-o em um problema mais simples, então vamos introduzir o algoritmo com um problema de regressão linear simples, com apenas uma variável na matriz de dados $\\pmb{X}$.\n#\n#\n# ## Pré-requisitos\n#\n# É preciso ter um conhecimento básico de Python, incluindo o mínimo de Python orientado à objetos. Caso não saiba programar, os cursos de [Introdução à Ciência da Computação](https://br.udacity.com/course/intro-to-computer-science--cs101/) e [Fundamentos de Programação com Python](https://br.udacity.com/course/programming-foundations-with-python--ud036/) fornecem uma base suficiente sobre programação em Python e Python orientado à objetos, respectivamente. Além disso, é necessário ter conhecimento das bibliotecas de manipulação de dados Pandas e Numpy. Alguns bons tutoriais são o [Mini-curso 1](https://br.udacity.com/course/machine-learning-for-trading--ud501/) do curso de Aprendizado de Máquina para Negociação, o site [pythonprogramming.net](https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/) ou o primeiro curso do DataCamp em [Python](https://www.datacamp.com/getting-started?step=2&track=python).\n#\n# Para entender o desenvolvimento do algoritmo de regressão linear é preciso ter o conhecimento de introdução à álgebra linear. Na UnB, a primeira parte do curso de Economia Quantitativa 1 já cobre o conteúdo necessário. Caso queira relembrar ou aprender esse conteúdo, o curso online do MIT de [Introdução à Álgebra Linear](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8) fornece uma boa base sobre a matemática que será desenvolvida nos algoritmos de aprendizado de máquina.\n#\n# Conhecimento de cálculo e principalmente otimização é fundamental para o entendimento dos algoritmos de aprendizado de máquina, que muitas vezes são encarados explicitamente como problemas de otimização. Uma noção de cálculo multivariado também ajudará na compreensão dos algoritmos, visto que muitas vezes otimizaremos em várias direções.\n#\n#\n# ## Intuição e explicação matemática\n#\n# Vamos utilizar um exemplo de regressão linear bastante simples, com apenas uma variável dependente e uma independente. A relação entre elas pode ser expressa na equação $\\pmb{y} = b + \\pmb{x} w + \\pmb{\\epsilon}$ e nós queremos achar os valores $\\hat{b}$ e $\\hat{w}$ que minimizam a norma do vetor $\\pmb{\\epsilon}$, isto é, minimizamos a soma dos quadrados dos resíduos.\n#\n# A ideia pro trás dos métodos iterativos de otimização é bastante simples: nós começamos com algum chute razoável para os valores de $\\hat{b}$ e $\\hat{w}$ e vamos atualizando-os na direção certa até que chegamos no valor mínimo da nossa função custo, nesse caso, $||\\pmb{\\hat{\\epsilon}}||^2$. \n#\n# Matematicamente, nós temos que perceber que a nossa função custo, $||\\pmb{\\hat{\\epsilon}}||^2$, é uma função de $\\hat{b}$ e $\\hat{w}$:\n#\n# $$L(\\hat{b}, \\hat{w})=||\\pmb{\\hat{\\epsilon}}||^2 = \\sum{\\epsilon}^2 \\\\=\n# \\sum{(\\hat{y}} - y)^2 \\\\=\n# \\sum{(\\hat{b} + x\\hat{w}} - y)^2$$\n#\n# E que, portanto, podemos minimizá-la nesses dois parâmetros usando cálculo multivariado. Essa função custo - especifica de regressão linear - é uma função convexa, o que quer dizer que ela o único ponto de mínimo que ela tem é um mínimo global. Em outras palavras, a função custo pode ser vista como uma tigela, e o gradiente dessa função nós apontará a direção de descida mais ingrime nas direções $\\hat{b}$ e $\\hat{w}$, de forma que possamos chegar ao fundo da tigela, onde está o ponto de menor custo. Para implementar o gradiente descendente, basta atualizar **simultaneamente** os valores de $\\hat{b}$ e $\\hat{w}$, subtraindo deles as respectivas derivadas parciais da função custo vezes uma taxa de aprendizado $\\alpha$ (o sinal $:=$ significa atualizar):\n#\n# $$\\hat{b} := \\hat{b} - \\alpha \\frac{\\partial}{\\partial \\hat{b}}L(\\hat{b}, \\hat{w})$$\n#\n# $$\\hat{w} := \\hat{w} - \\alpha \\frac{\\partial}{\\partial \\hat{w}}L(\\hat{b}, \\hat{w})$$\n#\n# Ou, no caso específico da nossa função custo de soma dos erros quadrados:\n#\n# $$\\hat{b} := \\hat{b} - \\alpha \\frac{1}{2} \\sum{(\\hat{b} + \\hat{w} x - y)} $$\n#\n# $$\\hat{w} := \\hat{w} - \\alpha \\frac{1}{2} \\sum{((\\hat{b} + \\hat{w} x - y) x)} $$\n#\n# Se quisermos simplificar, podemos retirar da fórmula $\\frac{1}{2}$ que não fará diferença, uma vez que as derivadas já estão sendo multiplicadas por uma constante $\\alpha$. Se quisermos simplificar a notação mais ainda, podemos utilizar a de vetores:\n#\n# $$\\pmb{\\hat{w}} := \\pmb{\\hat{w}} - \\alpha \\nabla(L)) $$\n#\n# Em que $\\pmb{\\hat{w}}$ é o vetor dos parâmetros da regressão linear, incluindo o intercepto $\\hat{b}$. Note que esse última regra de atualização é geral para qualquer número de dimensões que tenham nossos dados.\n#\n# E pronto. É só isso. Simples assim!\n#\n# ## Visualizando gradiente descendente\n#\n# Para entender melhor como funciona o algoritmo de gradiente descendente, vamos simular alguns dados com uma relação conhecida, de forma que possamos ver gradiente descendente em ação. Nós vamos trabalhar com uma regressão linear bem simples, com apenas dois parâmetros para aprender: o intercepto $\\hat{b}$ e a inclinação com respeito a única variável, $\\hat{w}$.\n#\n# Particularmente, vamos gerar dados x e y de forma que $y = 5 + 3x + \\epsilon$, em que $\\epsilon$ é algum erro aleatório. Nós sabemos que os valores ótimos de $\\hat{w}$ e $\\hat{b}$ seriam então 3 e 2, respectivamente, então poderemos vêr quão perto deles chegarão os parâmetros aprendidos por gradiente descendente. \n#\n# Visualmente, se plotarmos os pares (x,y) teremos um gráfico como o abaixo. A nossa esperança é que a técnica de gradiente descendente consiga achar uma reta que melhor se encaixa nestes dados.\n\n# +\nimport pandas as pd\nimport numpy as np\nnp.random.seed(0)\nfrom matplotlib import pyplot as plt\n\ndados = pd.DataFrame()\ndados['x'] = np.linspace(-10,10,100)\ndados['y'] = 5 + 3*dados['x'] + np.random.normal(0,3,100)\n\nplt.scatter(dados['x'], dados['y'])\nplt.axhline(y=0, linewidth=2, color = 'k')\nplt.axvline(x=0, linewidth=2, color = 'k')\nplt.show()\n# -\n\n# Antes de implementar a regressão linear por gradiente descendente, é uma boa visualizar como é a nossa função custo quando plotada nas duas dimensões dos parâmetros $\\hat{b}$ e $\\hat{w}$ que queremos aprender:\n\n# +\nfrom IPython import display\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\n# %matplotlib notebook\n\n# define a função custo\ndef L(y, y_hat):\n return ((y-y_hat) ** 2).sum()\n\n# define valores de b_hat e w_hat\nb_hat, w_hat = np.linspace(-10,20,40), np.linspace(0,6,40)\n\n# acha o custo para cada combinação de b_hat e w_hat\nloss = np.array([L(dados['y'], i + j * dados['x']) for i in b_hat for j in w_hat]).reshape(40,40)\nb_hat, w_hat = np.meshgrid(b_hat, w_hat) # combina os b_hat e w_hat em uma grade\n\n# faz o gráfico em 3d\nfig = plt.figure()\nax = fig.gca(projection='3d')\nax.set_zticks([])\nax.set_xlabel('$\\hat{b}$')\nax.set_ylabel('$\\hat{w}$')\nax.set_zlabel('Custo', rotation=90)\nsurf = ax.plot_surface(b_hat, w_hat, loss,\n rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0)\nplt.show()\n\n# -\n\n# Como eu disse, a função de custo parece uma tigela. Se você olhar bem, vai perceber como o ponto de mínimo da tigela está onde $\\hat{b}=5$ e $\\hat{w}=3$. O gradiente dessa função é simplesmente um vetor que dá a inclinação dessa tigela em cada ponto:\n#\n# $$\\nabla(L)=\\Bigg[\\frac{\\partial L}{\\partial \\hat{b}}, \\frac{\\partial L}{\\partial \\hat{w}} \\Bigg]$$\n#\n# Se nós seguirmos na direção oposta do gradiente, então chegaremos no ponto de mínimo. Podemos traçar uma analogia com uma bolinha de gude sendo solta em uma tigela: a bolinha descerá na direção mais inclinada e eventualmente parará no ponto mais baixo da tigela. Há uma importante diferença, no entanto. Quando falamos de uma bolinha de gude desliando para o fundo de uma tigela, podemos visualizar a bolinha começando com uma pequena velocidade e acelerando ao longo do trajeto. Com gradiente descendente ocorre o oposto: inicialmente, os parâmetros $\\hat{b}$ e $\\hat{w}$ caminham rapidamente em direção ao ponto de mínimo e, quanto mais se aproximam dele, passam a caminhar cada vez mais devagar.\n#\n# Mas por que isso acontece? Pense em como a cada iteração os parâmetros $\\hat{b}$ e $\\hat{w}$ dão um passo em direção ao mínimo. O tamanho desse passo será o valor do gradiente naquele ponto multiplicado pela constante $\\alpha$. Olhe de novo para o gráfico acima e note que quanto mais próximos estamos do ponto de mínimo, menor a inclinação da função custo, **OU SEJA** menor o gradiente, **OU SEJA**, menor o passo dado em direção ao mínimo.\n#\n# Essa característica do método de gradiente descendente é ao mesmo tempo boa e ruim. É ruim pois atrasa o processo de aprendizado quando chegamos próximo do mínimo, mas é boa porque nos permite uma exploração mais minuciosa da superfície de custo em torno do ponto de mínimo. Dessa forma, podemos localizá-lo com mais precisão. Isso talvez não pareça muito importante nesse caso super simples de regressão linear com apenas dois parâmetros para aprender, mas quando estamos lidando com aprendizado de redes neurais com milhares de parâmetros e uma função custo não convexa você vai entender porque é importante essa exploração minuciosa do espaço da função custo.\n#\n# Tendo dito tudo isso, vamos agora implementar a regressão linear com gradiente descendente. Note como abaixo nós nos restringimos ao caso simples para que possamos visualizar o processo de aprendizado. Algumas pequenas mudanças são necessárias no caso de uma regressão linear com vários parâmetros para aprender.\n\n# +\nclass linear_regr(object):\n\n def __init__(self, learning_rate=0.0001, training_iters=1000, show_learning=False):\n self.learning_rate = learning_rate\n self.training_iters = training_iters\n self.show_learning = show_learning\n\n \n def fit(self, X_train, y_train, plot=False):\n \n # formata os dados\n if len(X_train.values.shape) < 2:\n X = X_train.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n # para plotar o aprendizado (é preciso conhecer a equação geradora)\n if self.show_learning:\n assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros\n self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)]\n self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)]\n \n # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável)\n self.w_hat = np.random.normal(0,5, size = X[0].shape)\n \n loss = [] # para plotar o aprendizado\n for _ in range(self.training_iters):\n \n gradient = np.zeros(self.w_hat.shape) # inicia o gradiente\n \n # atualiza o gradiente com informação de todos os pontos\n for point, yi in zip(X, y_train):\n gradient += (point * self.w_hat - yi) * point\n \n gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado\n \n # atualiza os parâmetros\n self.w_hat -= gradient\n \n l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo\n \n loss.append(l) # armazeno o custo para gráfico\n \n if self.show_learning:\n # mostra o estado atual do aprendizado\n self._show_state(X_train, y_train, loss) \n \n \n def predict(self, X_test):\n # formata os dados\n if len(X_test.values.shape) < 2:\n X = X_test.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n return np.dot(X, self.w_hat) \n \n \n def _show_state(self, X_train, y_train, loss):\n # visualiza o processo de aprendizado\n lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b\n lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w\n\n # scatter plot\n plt.subplot(221)\n plt.scatter(X_train, y_train, s= 10)\n plt.plot(X_train, self.predict(X_train), c='r')\n plt.title('$y = b + w x$')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # loss\n plt.subplot(222)\n plt.plot(range(len(loss)), loss)\n plt.title('Custo')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # b_loss\n plt.subplot(223)\n plt.plot( np.linspace(-10,20,20), self.b_loss)\n plt.scatter(self.w_hat[0], lb, c = 'k')\n plt.title('Custo em $\\hat{b}$')\n plt.tick_params(labelleft=False)\n plt.grid(True)\n \n # w_loss\n plt.subplot(224)\n plt.plot(np.linspace(0,6,20), self.w1_loss)\n plt.scatter(self.w_hat[1], lw, c = 'k')\n plt.title('Custo em $\\hat{w}$')\n plt.grid(True)\n plt.tick_params(labelleft=False)\n \n plt.tight_layout()\n display.display(plt.gcf())\n display.clear_output(wait=True)\n plt.clf() # limpa a imagem do gráfico\n\nregr = linear_regr(learning_rate=0.0005, training_iters=30, show_learning=True)\nregr.fit(dados['x'], dados['y'])\n# -\n\n# Nos gráficos acima, pode ver como evolui a posição da reta que queremos encaixar nos dados (gráfico 1), o custo (gráfico 2), o parâmetro $\\hat{b}$ (gráfico 3) e o parâmetro $\\hat{w}$ (gráfico 4). Note como com apenas 30 iterações de treino nós conseguimos que os parâmetros aprendidos chegassem muito perto dos valores de mínimo. Nós podemos dizer com confiança que nosso algoritmo de gradiente descendente foi um sucesso!\n#\n#\n# ## Hiper-parâmetros\n#\n# O algoritmo de otimização iterativa por gradiente descendente é talvez o algoritmo de aprendizado de máquina mais importante que você vai aprender: ele é extremamente poderoso, relativamente rápido e funciona nos mais diversos cenários. No entanto, tudo isso vêm a um preço e nesse caso são os hiper-parâmetros.\n#\n# Diferentemente dos parâmetros $\\pmb{\\hat{w}}$ aprendidos durante o treinamento de uma regressão linear (ou de uma rede neural, como veremos mais para frente), os hiper-parâmetros **nãos** são aprendidos pela máquina durante o treinamento e devem ser ajustados manualmente. No caso da nossa regressão linear por gradiente descendente, podemos distinguir três hiper-parâmetros: \n# * A taxa de aprendizado\n# * O número de iterações de treino\n# * Os valores iniciais de $\\pmb{\\hat{w}}$\n#\n# No caso de regressão linear, como a função custo é convexa, não importa muito onde começamos em termos de $\\pmb{\\hat{w}}$. **Se os outros dois hiper-parâmetros** forem ajustados corretamente chegaremos no mínimo independentemente do ponto de partido. Então, aqui nós não vamos dar muita atenção aos valores iniciais de $\\pmb{\\hat{w}}$ (note como na nossa implementação eles nem sequer foram feitos para serem ajustados e são simplesmente pequenos valores aleatórios).\n#\n# Agora, os dois primeiros hiper-parâmetros são muito importantes e o sucesso ou fracasso do aprendizado depende severamente de conseguirmos ajustá-los corretamente. A **taxa de aprendizado** é definitivamente o mais importante de todos, então vamos gastar um certo tempo discutindo como ela influencia no aprendizado e como ajustá-la bem.\n#\n# A taxa de aprendizado define o tamanho dos passos que daremos em direção ao mínimo em cada iteração. Se esses passos forem muito pequenos, é quase garantido que chegaremos ao ponto de mínimo da função, mas para isso talvez precisaremos de muitas iterações de treino, tornando o algoritmo desnecessariamente lento. \n#\n# Por outro lado, se colocarmos uma taxa de aprendizado muito alta, pode acontecer de sermos catapultados para cima da função custo e irmos cada vez mais longe do mínimo, resultando em uma falha completa de aprendizado. Isso acontecerá quando o passo que dermos for tão grande que pulará o ponto de mínimo e chegará em um ponto na função custo mais alto do que o de onde saímos. Nesse novo ponto, o gradiente será ainda maior, aumentando mais ainda o passo seguinte e nos arremessando ainda mais longe do ponto de mínimo a cada iteração.\n#\n# \n#\n# Podemos ver que a taxa de aprendizado não deve ser nem tão grande, nem tão pequena. Uma sugestão de ajustamento desse hiper-parâmetro é começar com 0.01 e explorar os pontos em volta dez vezes maior/menor (isto é, 0.1 e 0.001). Na maioria dos casos, uma boa taxa de aprendizado será algum dos seguintes valores: 1, 0.1, 0.01, 0.001, 0.0001, 0.00001.\n#\n# Com uma boa taxa de aprendizado, selecionar o **número de iterações de treino** é uma tarefa fácil. Mesmo assim, recomenda-se plotar o valor da função custo a cada iteração de treino, assim como fizemos no gráfico 2 acima. Dessa forma você poderá ver se a função custo já chegou em uma região em que o seu valor não diminui ou diminui pouco a cada iteração. \n#\n# No nosso caso, o gráfico da função custo a cada iteração é bastante suave, mas pode acontecer de haver tanto iterações em que o custo cai quando iterações em que o custo sobe. Se esse é o caso e a função custo flutua muito a cada iteração, recomenda-se baixar a taxa de aprendizado. Se a função custo desce suavemente e constantemente, mas muito devagar, recomenda-se aumentar a taxa de aprendizado.\n#\n# ## Problemas no aprendizado\n#\n# Lembre-se de como a função custo da regressão linear é uma tigela? Se fizermos secções horizontais nessa tigela teremos um mapa topográfico da superfície de custo, assim como no ótimo desenho abaixo feito por mim.\n#\n# \n#\n# Sabemos que a otimização por gradiente descendente dará passos na direção mais inclinada, ou seja, na direção perpendicular as curvas de nível, assim como desenhado acima. Se as curvas de nível forem círculos perfeitos (como os que eu tentei desenhar), gradiente descendente só dará passos em direção ao ponto de mínimo e convergirá rapidamente. Por outro lado, se as curvas de nível da superfície de custo forem elipses alongadas, o tempo de convergência dependerá fortemente da inicialização dos nossos parâmetros.\n#\n# \n#\n# Por exemplo, se começarmos nossa descida no ponto 2 da imagem acima, a direção perpendicular à curva de nível aponta diretamente para o ponto de mínimo e não teremos maiores problemas durante o aprendizado. Mas se começarmos em um ponto como o 1 da imagem acima, a direção perpendicular à curva de nível aponta numa direção quase 90 graus da direção ao ponto de mínimo. Como consequência, daremos muitos passos em zig-zag e a convergência demorará muito mais.\n#\n# Esse formato de elipse da função custo surge quando as variáveis dos nossos dados estão em escalas muito diferentes. Assim, uma solução simples para esse problema é deixar todos as variáveis na mesma escala. Uma forma de realizar isso é, para cada variável, subtrair a média e dividir pelo desvio padrão (normalização).\n#\n#\n# ## Gradiente descendente estocástico: aprendizado em mini-lotes\n#\n# No exemplo simples que estamos usando, simulamos apenas 100 dados. Mas imagine agora que você deseja trabalhar com dados de algum censo, em que teremos observações na ordem de dezenas de milhões. Em primeiro lugar, você provavelmente não teria memória RAM suficiente para carregar todos os dados de uma vez, mas vamos supor que isso não seja um problema e você consiga implementar facilmente um procedimento que carrega os dados por partes. Você então inicia os parâmetros da regressão linear e agora precisaria percorrer todos os milhões de dados para computar o gradiente e dar **apenas um** passo da otimização. Em outras palavas, **cada passo** da otimização por gradiente descendente demora linearmente mais conforme mais dados temos. Isso é muito ineficiente e há uma forma muito mais rápida de realizar essa otimização.\n#\n# Em primeiro lugar, considere se os seus dados tem alguma redundância, isto é, se você embaralhasse todas as observações, uma parte dos dados seria parecida com a outra? Se sim, então nós não precisamos percorrer todos os dados para computar o gradiente e podemos conseguir uma aproximação dele apenas olhando alguns exemplos dos dados. Essa é a ideia central por trás da técnica de gradiente descendente estocástico (G.D.E.).\n#\n# Para possibilitar que a otimização por gradiente descendente continue rápida mesmo com milhões de dados, nós vamos alterá-la da seguinte forma: \n#\n# 1. primeiro, embaralhamos os nossos dados de forma que se retirássemos diferentes sub-amostras deles, elas não defeririam muito.\n# 2. segundo lugar, ao invés de computar o gradiente usando todos os dados, nós vamos fazer uma estimação dele usando apenas alguns dados - digamos um lote de 5 observações. Nós então atualizaremos os parâmetros com base nessa estimação do gradiente. Na atualização seguinte, nós repetiremos esse processo, mas agora estimando o gradiente com o próximo lote de dados, e assim por diante.\n#\n# Você pode estar pensando que utilizar apenas 5 observações para estimar um gradiente nos dará uma estimativa bem ruim e você tem razão. Na verdade, essa estimativa é tão ruim que muitas vezes o gradiente estimado nos levará em uma direção errada e custo *aumentará*. No entanto, na média, o gradiente estimado nos levará na direção correta. Em resumo, com GDE precisaremos de mais iterações de treino para chegar próximo do mínimo, mas cada iteração demorará muito (muuuito muuutio) menos tempo e o aprendizado como um todo será mais rápido. A rigor, se gradiente descendente com todos os dados demora linearmente mais conforme mais dados temos, com GDE o tempo de treino é **CONSTANTE** e **não** aumenta com o a quantidade de dados! Isso porque pode acontecer de nem sequer precisarmos ver todas as observações para chegar a uma região razoável na função de custo.\n#\n# Mais ainda, como não precisamos de todos os dados de uma vez para o processo de treinamento, podemos utilizar essa técnicas para aprendizado de máquina com em base de dados gigantescas, maiores até do que nosso computador suportaria trazer para a memória de curto prazo de uma só vez.\n#\n# Ao utilizar GDE introduzimos mais um hiper-parâmetro que terá que ser ajustado manualmente: o tamanho do lote. É importante entender como esse hiper-parâmetro funciona para saber como ajustá-lo bem. Em geral, lotes maiores significam passos mais precisos em direção ao mínimo, mas ao mesmo tempo significa passos mais demorados.\n#\n# Um outro detalhe que vale a pena mencionar é que GDE normalmente não converge, mas fica vagando em alguma região próxima ao ponto de mínimo. Na prática, isso não é um problema, pois nessa região o custo já é baixo o suficiente. De qualquer forma, é uma boa visualizar um exemplo do tipo de trajeto que GDE percorrerá numa superfície de custo:\n#\n# \n#\n# E finalmente, nosso implementação de GDE. Para notar o aumento de velocidade, é necessário desligar a visualização.\n\n# +\nnp.random.seed(23)\n\nclass linear_regr(object):\n\n def __init__(self, learning_rate=0.0001, training_iters=30, batch_size=10, show_learning=False):\n self.learning_rate = learning_rate\n self.training_iters = training_iters\n self.batch_size = batch_size\n self.show_learning = show_learning\n\n \n def fit(self, X_train, y_train, plot=False):\n \n # formata os dados\n if len(X_train.values.shape) < 2:\n X = X_train.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n # para plotar o aprendizado (é preciso conhecer a equação geradora)\n if self.show_learning:\n assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros\n self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)]\n self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)]\n \n # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável)\n self.w_hat = np.random.normal(0,5, size = X[0].shape)\n \n loss = [] # para plotar o aprendizado\n for i in range(self.training_iters):\n \n # cria os mini-lotes\n offset = (i * self.batch_size) % (y_train.shape[0] - self.batch_size)\n batch_X = X[offset:(offset + self.batch_size), :]\n batch_y = y_train[offset:(offset + self.batch_size)]\n \n gradient = np.zeros(self.w_hat.shape) # inicia o gradiente\n \n # atualiza o gradiente com informação dos pontos do lote\n for point, yi in zip(batch_X, batch_y):\n gradient += (point * self.w_hat - yi) * point\n \n gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado\n \n \n # atualiza os parâmetros\n self.w_hat -= gradient\n \n l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo\n \n loss.append(l) # armazeno o custo para gráfico\n \n if self.show_learning:\n # mostra o estado atual do aprendizado\n self._show_state(X_train, y_train, loss) \n \n \n def predict(self, X_test):\n # formata os dados\n if len(X_test.values.shape) < 2:\n X = X_test.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n return np.dot(X, self.w_hat) \n \n \n def _show_state(self, X_train, y_train, loss):\n # visualiza o processo de aprendizado\n lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b\n lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w\n\n # scatter plot\n plt.subplot(221)\n plt.scatter(X_train, y_train, s= 10)\n plt.plot(X_train, self.predict(X_train), c='r')\n plt.title('$y = b + w x$')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # loss\n plt.subplot(222)\n plt.plot(range(len(loss)), loss)\n plt.title('Custo')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # b_loss\n plt.subplot(223)\n plt.plot( np.linspace(-10,20,20), self.b_loss)\n plt.scatter(self.w_hat[0], lb, c = 'k')\n plt.title('Custo em $\\hat{b}$')\n plt.tick_params(labelleft=False)\n plt.grid(True)\n \n # w_loss\n plt.subplot(224)\n plt.plot(np.linspace(0,6,20), self.w1_loss)\n plt.scatter(self.w_hat[1], lw, c = 'k')\n plt.title('Custo em $\\hat{w}$')\n plt.grid(True)\n plt.tick_params(labelleft=False)\n \n plt.tight_layout()\n display.display(plt.gcf())\n display.clear_output(wait=True)\n plt.clf() # limpa a imagem do gráfico\n\nregr = linear_regr(learning_rate=0.0003, training_iters=40, show_learning=True)\nregr.fit(dados['x'], dados['y'])\n# -\n\n# ## Explorando melhoras: acelerando GDE\n#\n# GDE sozinho já é um método bastante popular para treinar modelos de aprendizado de máquina. Mesmo assim, várias extensões e variações foram propostas com o intuito de diminuir as flutuações na função custo ou acelerar o processo de treinamento. Aqui, vamos explorar apenas uma delas, mas saiba que muitas outras existem.\n#\n# Como já dissemos, a diferença fundamental entre o método de gradiente descendente e o processo de uma bolinha de gude descendo em uma cuia é que a bolinha acumula momento, acelerando conforme desce. Em outras palavras, quando a direção de descida é a mesma, a bolinha aumenta a velocidade. Isso é definitivamente uma propriedade que gostaríamos de ter no nosso processo de aprendizado por GDE: se estamos indo na direção certa, é uma boa ideia acelerar!\n#\n# Não é tão difícil modificar GDE para incorporar momento. Para isso, basta sabermos a velocidade passada da bolinha e atualizá-la conforme o processo de descida. Além disso, nós agora vamos atualizar os parâmetros conforme a velocidade ao invés de utilizar apenas o gradiente. Eis a nova regra de atualização dos parâmetros:\n#\n# $$\\pmb{v_t} := \\gamma \\pmb{v_{t-1}} + \\alpha \\nabla(L)) $$\n#\n# $$\\pmb{\\hat{w}} := \\pmb{\\hat{w}} - \\pmb{v_t}$$\n#\n# Na primeira linha, nós atualizamos a velocidade. O termo $\\gamma v_{t-1}$ funciona como um atrito ou resistência do ar, diminuindo a velocidade em uma porcentagem $1-\\gamma$ da velocidade anterior. $\\gamma$ é mais um hiper-parâmetro que precisa ser ajustado manualmente. O termo seguinte, $\\alpha \\nabla(L))$, incorpora a informação da inclinação descida. \n#\n# E por fim, nossa implementação.\n\n# +\nnp.random.seed(23)\nclass linear_regr(object):\n\n def __init__(self, learning_rate=0.0001, training_iters=1000, gamma=0.9, batch_size=10, show_learning=False):\n self.learning_rate = learning_rate\n self.training_iters = training_iters\n self.gamma = gamma\n self.batch_size = batch_size\n self.show_learning = show_learning\n \n def fit(self, X_train, y_train, plot=False):\n \n # formata os dados\n if len(X_train.values.shape) < 2:\n X = X_train.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n # para plotar o aprendizado (é preciso conhecer a equação geradora)\n if self.show_learning:\n assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros\n self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)]\n self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)]\n \n # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável)\n self.w_hat = np.random.normal(0,5, size = X[0].shape)\n \n velocidade = np.zeros(self.w_hat.shape) # inicia a velocidade\n loss = [] # para plotar o aprendizado\n for i in range(self.training_iters):\n \n # cria os mini-lotes\n offset = (i * self.batch_size) % (y_train.shape[0] - self.batch_size)\n batch_X = X[offset:(offset + self.batch_size), :]\n batch_y = y_train[offset:(offset + self.batch_size)]\n \n gradient = np.zeros(self.w_hat.shape) # inicia o gradiente\n \n # atualiza o gradiente com informação de todos os pontos\n for point, yi in zip(batch_X, batch_y):\n gradient += (point * self.w_hat - yi) * point\n \n gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado\n velocidade = (velocidade * self.gamma) + gradient # atualiza a velocidade\n \n # atualiza os parâmetros\n self.w_hat -= velocidade\n \n l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo\n \n loss.append(l) # armazeno o custo para gráfico\n \n if self.show_learning:\n # mostra o estado atual do aprendizado\n self._show_state(X_train, y_train, loss) \n \n \n def predict(self, X_test):\n # formata os dados\n if len(X_test.values.shape) < 2:\n X = X_test.values.reshape(-1,1)\n X = np.insert(X, 0, 1, 1)\n \n return np.dot(X, self.w_hat) \n \n \n def _show_state(self, X_train, y_train, loss):\n # visualiza o processo de aprendizado\n lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b\n lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w\n\n # scatter plot\n plt.subplot(221)\n plt.scatter(X_train, y_train, s= 10)\n plt.plot(X_train, self.predict(X_train), c='r')\n plt.title('$y = b + w x$')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # loss\n plt.subplot(222)\n plt.plot(range(len(loss)), loss)\n plt.title('Custo')\n plt.tick_params(labelsize=9, labelleft=False, labelbottom = False)\n plt.grid(True)\n\n # b_loss\n plt.subplot(223)\n plt.plot( np.linspace(-10,20,20), self.b_loss)\n plt.scatter(self.w_hat[0], lb, c = 'k')\n plt.title('Custo em $\\hat{b}$')\n plt.tick_params(labelleft=False)\n plt.grid(True)\n \n # w_loss\n plt.subplot(224)\n plt.plot(np.linspace(0,6,20), self.w1_loss)\n plt.scatter(self.w_hat[1], lw, c = 'k')\n plt.title('Custo em $\\hat{w}$')\n plt.grid(True)\n plt.tick_params(labelleft=False)\n \n plt.tight_layout()\n display.display(plt.gcf())\n display.clear_output(wait=True)\n plt.clf() # limpa a imagem do gráfico\n\nregr = linear_regr(learning_rate=0.0001, training_iters=30, gamma = 0.9, show_learning=True)\nregr.fit(dados['x'], dados['y'])\n# -\n\n# ## Usando gradiente descendente na prática\n#\n# Se você prestou atenção até aqui, sabe que para implementar gradiente descendente precisamos da derivada parcial da função custo com relação aos parâmetros que queremos otimizar. No nosso exemplo de regressão linear simples, isso foi bem fácil de calcular, mas nem sempre isso será o caso. Felizmente para nós, na prática, as bibliotecas de programação especializadas em otimização já calculam essas derivadas automaticamente para nós. Mais ainda, nelas, gradiente descendente e suas variações já vem implementados! \n#\n# Para mostrar como utilizar gradiente descendente na prática vamos utilizar uma biblioteca de aprendizado de máquina desenvolvida pelo Google e agora aberta ao público: [TensorFlow](https://www.tensorflow.org/). Veja como em poucas linhas podemos implementar a técnica de gradiente descendente para resolver nosso exemplo de regressão linear. Note também como podemos rodar muito mais iterações rapidamente:\n#\n\n# +\nimport tensorflow as tf\nimport numpy as np\n\nx, y = dados['x'].values, dados['y'].values # dados\n\n# Monta a estrutura tf\n\n# valores iniciais shape \nW_hat = tf.Variable(tf.random_normal([1], 0, 5))\nb_hat = tf.Variable(tf.zeros([1]))\n\n# modelo\ny_hat = W_hat * x + b_hat\n\n# Função custo\nloss = tf.reduce_mean(tf.square(y_hat - y))\n\n# otimizador e passo no treinamento\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)\n\nsess = tf.Session() # para rodar a estrutura\nsess.run(tf.global_variables_initializer()) # inicia variáveis\n\nfor step in range(200):\n sess.run(optimizer)\n\nw_final, b_final = sess.run([W_hat, b_hat]) \nprint('Após treinamento, w_hat = %.2f e w_hat = %.2f' % (w_final[0], b_final[0]))\n\nsess.close() \n# -\n\n# ## Ligações externas\n#\n# Dada a sua importância, há muitas fontes excelentes para aprender sobre gradiente descendente:\n#\n# * Os vídeos do curso online de Neural Networks for Machine Learning, da universidade de Torono, são provavelmente a melhor fonte para estudar gradiente descendente. Todos os vídeos [desta secção do cusro](https://www.youtube.com/playlist?list=PLnnr1O8OWc6bAAkp43m0jNF_DEqwWp2o2) são excelentes para bastante sobre gradiente descendente e suas extensões.\n#\n# * Os vídeos [1](https://www.youtube.com/watch?v=LN0PLnDpGN4&index=5&t=598s&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0), [2](https://www.youtube.com/watch?v=kWq2k1gPyBs&index=6&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0) e [3](https://www.youtube.com/watch?v=7LqYTTwuu0k&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0&index=7) sobre gradiente descendente com regressão linear de uma variável (do curso de Machine Learning com o professor Ng) cobrem a maioria do conteúdo que vimos aqui com bastante visualização e de maneira intuitiva. Além disso, o vídeo [4](https://www.youtube.com/watch?v=UfNU3Vhv5CA&t=627s) do mesmo curso mostra bem a intuição de GDE\n#\n# * Os vídeos [1](https://www.youtube.com/watch?v=hMLUgM6kTp8&index=20&list=PLAwxTw4SYaPn_OWPFT9ulXLuQrImzHfOV) e [2](https://www.youtube.com/watch?v=s6jC7Wc9iMI&index=21&list=PLAwxTw4SYaPn_OWPFT9ulXLuQrImzHfOV) do custo de Deep Learning do Google resumem bem GDE, dão dicas de como acelerar o aprendizado e ainda falam sobre a extensão do algoritmo com momento - embora um pouco diferente da nossa.\n#\n#\n#\n#\n"},"script_size":{"kind":"number","value":37524,"string":"37,524"}}},{"rowIdx":980,"cells":{"path":{"kind":"string","value":"/notebooks/Hands_on_2_competition_Titanic_predict_survival.ipynb"},"content_id":{"kind":"string","value":"1134d9bcf81232fdc62bf0d3a259e0996ff12614"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"alexmasselot/crea-introduction-to-programmation"},"repo_url":{"kind":"string","value":"https://github.com/alexmasselot/crea-introduction-to-programmation"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2017-12-08T21:09:35","string":"2017-12-08T21:09:35"},"gha_updated_at":{"kind":"timestamp","value":"2017-12-08T07:49:32","string":"2017-12-08T07:49:32"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6442,"string":"6,442"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# Your purpose is now to predict if one passenger has survived based on what we know (his age, gender...)\n\n# +\nimport pandas as pd\nimport numpy as np\n\n# Load the data as a dataframe\ndf = pd.read_csv('./../data/titanic_train.csv')\n# -\n\nY_truth = list(1.0*df['Survived'])\n\n\ndef compute_accuracy(Y, Y_pred):\n \"\"\"\n This function is used below to compute the score associated to a prediction.\n Y should be a list with the result\n Y_pred should be a prediction of this list\n This function will return 1.0 if you correctly predict the survival state of all passengers.\n \"\"\"\n assert type(Y) is list\n assert type(Y_pred) is list\n assert len(Y) == len(Y_pred)\n n_success = [(1 if round(y_pred) == y else 0) for (y, y_pred) in zip(Y, Y_pred)]\n return 1.0*sum(n_success)/len(Y)\n\n\ndf\n\n# ## Making simple predictors\n#\n# We have 891 passengers. For each of them, we are going to attribute:\n#\n# * 0 if we predict they are going to die\n# * 1 if we predict they are going to survive\n#\n# The goal is then to compare our prediction against the real results to see how accurate we are.\n#\n# We are 100% accurate when we predict the correct outcome for everyone, and 0% when we predict for none.\n#\n# The ideal goal is to build the best **predicition model** based on all the available passenger data (age, class, gender...)\n#\n# ### What if we predict everyone dies?\n#\n# In this case we simply attribute a *0* to everyone.\n#\n# The output shows *~62%*, because *62%* of passengers actually lost their lives.\n\nY_pred_everyone_dies = [0.0]*len(df)\n# This will return 0.616, as asserting that everybody\n# died is true for 62% of the population.\ncompute_accuracy(Y_truth, Y_pred_everyone_dies)\n\n# ### What about using the Pclass information?\n#\n# We saw in the data exploration a bias with the ticket class. The first class passengers were more likely to survive.\n#\n# Let's therefore build a finer model, predicting a favorable outcome for the first class.\n#\n# The output now shws a better outcome, of %68%\"!\n#\n\n# +\n#We build a Y_pred_with_class list, and for each passenger, we set 0 or 1 into it\nY_pred_with_class = []\nfor passenger in df.to_dict(orient='records'):\n if passenger['Pclass'] == 1:\n yp = 1\n else:\n yp = 0\n # we then add the passenger prediction to the whole list\n Y_pred_with_class.append(yp)\n\n#we can now check how relevant it is\ncompute_accuracy(Y_truth, Y_pred_with_class)\n# -\n\n# ## You turn!!!\n# ### make a prediction based on gender\n\n# +\nY_pred_with_gender = []\n\nfor passenger in df.to_dict(orient='records'):\n ####### insert your condition here, based on the same template as above\n\n ####### end of your code\n Y_pred_with_gender.append(yp)\n\n#we can now check how relevant it is\ncompute_accuracy(Y_truth, Y_pred_with_gender)\n# -\n\n# ### Be more imaginative: create your own model!\n# ### This is a contest\n#\n# The idea is maybe to combine various factors, in the form, for example\n#\n# ####### insert your condition here, based on the same template as above\n# if passenger['Pclass']>=2:\n# if(passenger['embarked'] == 'S':\n# yp=1\n# else:\n# yp=0\n# else:\n# if(passenger['Sex'] == 'female':\n# yp=0\n# else:\n# yp=1\n#\n# ####### end of your code\n#\n\n# +\nY_pred_my_model_1 = []\n\nfor passenger in df.to_dict(orient='records'):\n ####### insert your condition here, based on the same template as above\n\n ####### end of your code\n Y_pred_my_model_1.append(yp)\n\n#we can now check how relevant it is\ncompute_accuracy(Y_truth, Y_pred_my_model_1)\n# -\n\n# You can of course copy/paste the code above, create new cell to test other models.\n#\n"},"script_size":{"kind":"number","value":3981,"string":"3,981"}}},{"rowIdx":981,"cells":{"path":{"kind":"string","value":"/Thompson Sampling/.ipynb_checkpoints/Thompson Sampling-checkpoint.ipynb"},"content_id":{"kind":"string","value":"7e66bad4164d6f55912fdcd23aa7a2aee254f437"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"mohamed-amine-guerras/Reinforcement-Learning"},"repo_url":{"kind":"string","value":"https://github.com/mohamed-amine-guerras/Reinforcement-Learning"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":42733,"string":"42,733"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ### N-Repeated Element in Size 2N Array\n\n# In a array A of size 2N, there are N+1 unique elements, and exactly one of these elements is repeated N times.\n#\n# Return the element repeated N times.\n# ##### Example 1:\n# Input: [1, 2, 3, 3]\n#\n# Output: 3\n# ##### Example 2:\n# Input: [2, 1, 2, 5, 3, 2]\n#\n# Output: 2\n# ##### Example 3:\n# Input: [5, 1, 5, 2, 5, 3, 5, 4]\n#\n# Output: 5\n\n# ### Brief analysis\n# sum is an easy way\n\ndef repeatedNTimes(A: list) -> int:\n return int((sum(A) - sum(set(A))) / (len(A) / 2 - 1))\n\n\nfrom IPython.display import Image\nImage(filename=\"/Users/xlyue/Documents/leetcode practice/1559408450984.jpg\",width=400,height=400)\nom_beta\n ad = i\n ads_selected.append(ad)\n reward = data.values[n,ad]\n if (reward == 0):\n numbers_of_rewards_0[ad] += 1\n else:\n numbers_of_rewards_1[ad] += 1\n total_reward += reward\n\ntotal_reward\n\n# ## Plot the results\n\nplt.hist(ads_selected)\nplt.title('Histogramme of Ads selections')\nplt.xlabel('Ads')\nplt.ylabel('Number of times each ad is selected')\nplt.show()\n"},"script_size":{"kind":"number","value":1322,"string":"1,322"}}},{"rowIdx":982,"cells":{"path":{"kind":"string","value":"/RNN/Untitled.ipynb"},"content_id":{"kind":"string","value":"e1ddc3b0b37ee41985640745b784bdb28c9e718e"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"ftfarias/data-science_notebooks"},"repo_url":{"kind":"string","value":"https://github.com/ftfarias/data-science_notebooks"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":16599,"string":"16,599"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [conda env:tf]\n# language: python\n# name: conda-env-tf-py\n# ---\n\n# +\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport random\n\nimport numpy as np\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom tensorflow.models.rnn.translate import data_utils\n\n\nclass Seq2SeqModel(object):\n \"\"\"Sequence-to-sequence model with attention and for multiple buckets.\n This class implements a multi-layer recurrent neural network as encoder,\n and an attention-based decoder. This is the same as the model described in\n this paper: http://arxiv.org/abs/1412.7449 - please look there for details,\n or into the seq2seq library for complete model implementation.\n This class also allows to use GRU cells in addition to LSTM cells, and\n sampled softmax to handle large output vocabulary size. A single-layer\n version of this model, but with bi-directional encoder, was presented in\n http://arxiv.org/abs/1409.0473\n and sampled softmax is described in Section 3 of the following paper.\n http://arxiv.org/abs/1412.2007\n \"\"\"\n\n def __init__(self,\n source_vocab_size,\n target_vocab_size,\n buckets,\n size,\n num_layers,\n max_gradient_norm,\n batch_size,\n learning_rate,\n learning_rate_decay_factor,\n use_lstm=False,\n num_samples=512,\n forward_only=False,\n dtype=tf.float32):\n \"\"\"Create the model.\n Args:\n source_vocab_size: size of the source vocabulary.\n target_vocab_size: size of the target vocabulary.\n buckets: a list of pairs (I, O), where I specifies maximum input length\n that will be processed in that bucket, and O specifies maximum output\n length. Training instances that have inputs longer than I or outputs\n longer than O will be pushed to the next bucket and padded accordingly.\n We assume that the list is sorted, e.g., [(2, 4), (8, 16)].\n size: number of units in each layer of the model.\n num_layers: number of layers in the model.\n max_gradient_norm: gradients will be clipped to maximally this norm.\n batch_size: the size of the batches used during training;\n the model construction is independent of batch_size, so it can be\n changed after initialization if this is convenient, e.g., for decoding.\n learning_rate: learning rate to start with.\n learning_rate_decay_factor: decay learning rate by this much when needed.\n use_lstm: if true, we use LSTM cells instead of GRU cells.\n num_samples: number of samples for sampled softmax.\n forward_only: if set, we do not construct the backward pass in the model.\n dtype: the data type to use to store internal variables.\n \"\"\"\n self.source_vocab_size = source_vocab_size\n self.target_vocab_size = target_vocab_size\n self.buckets = buckets\n self.batch_size = batch_size\n self.learning_rate = tf.Variable(\n float(learning_rate), trainable=False, dtype=dtype)\n self.learning_rate_decay_op = self.learning_rate.assign(\n self.learning_rate * learning_rate_decay_factor)\n self.global_step = tf.Variable(0, trainable=False)\n\n # If we use sampled softmax, we need an output projection.\n output_projection = None\n softmax_loss_function = None\n # Sampled softmax only makes sense if we sample less than vocabulary size.\n if num_samples > 0 and num_samples < self.target_vocab_size:\n w_t = tf.get_variable(\"proj_w\", [self.target_vocab_size, size], dtype=dtype)\n w = tf.transpose(w_t)\n b = tf.get_variable(\"proj_b\", [self.target_vocab_size], dtype=dtype)\n output_projection = (w, b)\n\n def sampled_loss(inputs, labels):\n labels = tf.reshape(labels, [-1, 1])\n # We need to compute the sampled_softmax_loss using 32bit floats to\n # avoid numerical instabilities.\n local_w_t = tf.cast(w_t, tf.float32)\n local_b = tf.cast(b, tf.float32)\n local_inputs = tf.cast(inputs, tf.float32)\n return tf.cast(\n tf.nn.sampled_softmax_loss(local_w_t, local_b, local_inputs, labels,\n num_samples, self.target_vocab_size),\n dtype)\n softmax_loss_function = sampled_loss\n\n # Create the internal multi-layer cell for our RNN.\n single_cell = tf.nn.rnn_cell.GRUCell(size)\n if use_lstm:\n single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)\n cell = single_cell\n if num_layers > 1:\n cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)\n\n # The seq2seq function: we use embedding for the input and attention.\n def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):\n return tf.nn.seq2seq.embedding_attention_seq2seq(\n encoder_inputs,\n decoder_inputs,\n cell,\n num_encoder_symbols=source_vocab_size,\n num_decoder_symbols=target_vocab_size,\n embedding_size=size,\n output_projection=output_projection,\n feed_previous=do_decode,\n dtype=dtype)\n\n # Feeds for inputs.\n self.encoder_inputs = []\n self.decoder_inputs = []\n self.target_weights = []\n for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.\n self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[batch_size],\n name=\"encoder{0}\".format(i)))\n for i in xrange(buckets[-1][1] + 1):\n self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[batch_size],\n name=\"decoder{0}\".format(i)))\n self.target_weights.append(tf.placeholder(dtype, shape=[batch_size],\n name=\"weight{0}\".format(i)))\n\n # Our targets are decoder inputs shifted by one.\n targets = [self.decoder_inputs[i + 1]\n for i in xrange(len(self.decoder_inputs) - 1)]\n\n # Training outputs and losses.\n if forward_only:\n self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(\n self.encoder_inputs, self.decoder_inputs, targets,\n self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),\n softmax_loss_function=softmax_loss_function)\n # If we use output projection, we need to project outputs for decoding.\n if output_projection is not None:\n for b in xrange(len(buckets)):\n self.outputs[b] = [\n tf.matmul(output, output_projection[0]) + output_projection[1]\n for output in self.outputs[b]\n ]\n else:\n self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(\n self.encoder_inputs, self.decoder_inputs, targets,\n self.target_weights, buckets,\n lambda x, y: seq2seq_f(x, y, False),\n softmax_loss_function=softmax_loss_function)\n\n # Gradients and SGD update operation for training the model.\n params = tf.trainable_variables()\n if not forward_only:\n self.gradient_norms = []\n self.updates = []\n opt = tf.train.GradientDescentOptimizer(self.learning_rate)\n for b in xrange(len(buckets)):\n gradients = tf.gradients(self.losses[b], params)\n clipped_gradients, norm = tf.clip_by_global_norm(gradients,\n max_gradient_norm)\n self.gradient_norms.append(norm)\n self.updates.append(opt.apply_gradients(\n zip(clipped_gradients, params), global_step=self.global_step))\n\n self.saver = tf.train.Saver(tf.all_variables())\n\n def step(self, session, encoder_inputs, decoder_inputs, target_weights,\n bucket_id, forward_only):\n \"\"\"Run a step of the model feeding the given inputs.\n Args:\n session: tensorflow session to use.\n encoder_inputs: list of numpy int vectors to feed as encoder inputs.\n decoder_inputs: list of numpy int vectors to feed as decoder inputs.\n target_weights: list of numpy float vectors to feed as target weights.\n bucket_id: which bucket of the model to use.\n forward_only: whether to do the backward step or only forward.\n Returns:\n A triple consisting of gradient norm (or None if we did not do backward),\n average perplexity, and the outputs.\n Raises:\n ValueError: if length of encoder_inputs, decoder_inputs, or\n target_weights disagrees with bucket size for the specified bucket_id.\n \"\"\"\n # Check if the sizes match.\n encoder_size, decoder_size = self.buckets[bucket_id]\n if len(encoder_inputs) != encoder_size:\n raise ValueError(\"Encoder length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(encoder_inputs), encoder_size))\n if len(decoder_inputs) != decoder_size:\n raise ValueError(\"Decoder length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(decoder_inputs), decoder_size))\n if len(target_weights) != decoder_size:\n raise ValueError(\"Weights length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(target_weights), decoder_size))\n\n # Input feed: encoder inputs, decoder inputs, target_weights, as provided.\n input_feed = {}\n for l in xrange(encoder_size):\n input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]\n for l in xrange(decoder_size):\n input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]\n input_feed[self.target_weights[l].name] = target_weights[l]\n\n # Since our targets are decoder inputs shifted by one, we need one more.\n last_target = self.decoder_inputs[decoder_size].name\n input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)\n\n # Output feed: depends on whether we do a backward step or not.\n if not forward_only:\n output_feed = [self.updates[bucket_id], # Update Op that does SGD.\n self.gradient_norms[bucket_id], # Gradient norm.\n self.losses[bucket_id]] # Loss for this batch.\n else:\n output_feed = [self.losses[bucket_id]] # Loss for this batch.\n for l in xrange(decoder_size): # Output logits.\n output_feed.append(self.outputs[bucket_id][l])\n\n outputs = session.run(output_feed, input_feed)\n if not forward_only:\n return outputs[1], outputs[2], None # Gradient norm, loss, no outputs.\n else:\n return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs.\n\n def get_batch(self, data, bucket_id):\n \"\"\"Get a random batch of data from the specified bucket, prepare for step.\n To feed data in step(..) it must be a list of batch-major vectors, while\n data here contains single length-major cases. So the main logic of this\n function is to re-index data cases to be in the proper format for feeding.\n Args:\n data: a tuple of size len(self.buckets) in which each element contains\n lists of pairs of input and output data that we use to create a batch.\n bucket_id: integer, which bucket to get the batch for.\n Returns:\n The triple (encoder_inputs, decoder_inputs, target_weights) for\n the constructed batch that has the proper format to call step(...) later.\n \"\"\"\n encoder_size, decoder_size = self.buckets[bucket_id]\n encoder_inputs, decoder_inputs = [], []\n\n # Get a random batch of encoder and decoder inputs from data,\n # pad them if needed, reverse encoder inputs and add GO to decoder.\n for _ in xrange(self.batch_size):\n encoder_input, decoder_input = random.choice(data[bucket_id])\n\n # Encoder inputs are padded and then reversed.\n encoder_pad = [data_utils.PAD_ID] * (encoder_size - len(encoder_input))\n encoder_inputs.append(list(reversed(encoder_input + encoder_pad)))\n\n # Decoder inputs get an extra \"GO\" symbol, and are padded then.\n decoder_pad_size = decoder_size - len(decoder_input) - 1\n decoder_inputs.append([data_utils.GO_ID] + decoder_input +\n [data_utils.PAD_ID] * decoder_pad_size)\n\n # Now we create batch-major vectors from the data selected above.\n batch_encoder_inputs, batch_decoder_inputs, batch_weights = [], [], []\n\n # Batch encoder inputs are just re-indexed encoder_inputs.\n for length_idx in xrange(encoder_size):\n batch_encoder_inputs.append(\n np.array([encoder_inputs[batch_idx][length_idx]\n for batch_idx in xrange(self.batch_size)], dtype=np.int32))\n\n # Batch decoder inputs are re-indexed decoder_inputs, we create weights.\n for length_idx in xrange(decoder_size):\n batch_decoder_inputs.append(\n np.array([decoder_inputs[batch_idx][length_idx]\n for batch_idx in xrange(self.batch_size)], dtype=np.int32))\n\n # Create target_weights to be 0 for targets that are padding.\n batch_weight = np.ones(self.batch_size, dtype=np.float32)\n for batch_idx in xrange(self.batch_size):\n # We set weight to 0 if the corresponding target is a PAD symbol.\n # The corresponding target is decoder_input shifted by 1 forward.\n if length_idx < decoder_size - 1:\n target = decoder_inputs[batch_idx][length_idx + 1]\n if length_idx == decoder_size - 1 or target == data_utils.PAD_ID:\n batch_weight[batch_idx] = 0.0\n batch_weights.append(batch_weight)\n return batch_encoder_inputs, batch_decoder_inputs, batch_weights\nasonably far from all the points (as determined by the value of $\\sigma$).\n#\n# For each feature $(x_i, y_i)$ in our dataset, we can calculate the similarity to each feature via the selected kernel:\n#\n# $$f_i = \\left[\\begin{align}\n# k(x_i, &x_0) \\\\\n# k(x_i, &x_1) \\\\\n# k(x_i, &x_2) \\\\\n# \\vdots & \\\\\n# k(x_i, &x_n)\n# \\end{align}\\right]$$\n#\n# notice that, under the Gaussian kernel at least, there will be one element $k(x_i, x_i)$ that evaluates to 1.\n#\n# To use the SVM, we use this $f \\in \\mathbb{R}^{n+1}$ to calculate the inner product $\\theta^{\\prime} f$ and predict $y_i=1$ if $\\theta^{\\prime} f_i \\ge 0$. We obtain the parameters for $\\theta$ by minimizing:\n#\n# $$\\min_{\\theta} \\left[ C \\sum_{i=1}^n y_i k_1(\\theta^{\\prime} f_i) + (1-y_i) k_0(\\theta^{\\prime} f_i) \\right] + \\frac{1}{2}\\sum_{j=1}^k \\theta^2_j$$\n#\n\n# ### Regularization and soft margins\n#\n# There remains a choice to be made for the values of the SVM parameters. Recall $C$, which corresponds to the inverse of the regularization parameter in a lasso model. This choice of $C$ involves a **bias-variance tradeoff**:\n#\n# * large C = low bias, high variance\n# * small C = high bias, low variance\n#\n# In a support vector machine, regularization results in a **soft margin** that allows some points to cross the optimal decision boundary (resulting in misclassifiction for those points). As C gets larger, the more stable the margin becomes, since it is allowing more points to determine the margin.\n#\n# We can think of C as a \"budget\" for permitting points to exceed the margin. We can tune C to determine the optimal hyperplane.\n#\n# Similarly, if we are using the Gaussian kernel, we must choose a value for $\\sigma^2$. When $\\sigma^2$ is large, then features are considered similar over greater distances, resulting in a smoother decision boundary, while for smaller $\\sigma^2$, similarity falls off quickly with distance.\n#\n# * large $\\sigma^2$ = high bias, low variance\n# * small $\\sigma^2$ = low bias, high variance\n\n# ### Linear kernel\n#\n# The simplest choice of kernel is to use no kernel at all, but rather to simply use the **linear combination** of the features themselves as the kernel. Hence,\n#\n# $$y = \\left\\{ \\begin{aligned} 1 &\\text{, if } \\theta^{\\prime} x \\ge 0\\\\\n# 0 &\\text{ otherwise}\\end{aligned}\\right.$$\n#\n# This approach is useful when there are a *large number of features*, but the *size of the dataset is small*. In this case, a simple linear decision boundary may be appropriate given that there is relatively little data. If the reverse is true, where there are a small number of features and plenty of data, a Gaussian kernel may be more appropriate, as it allows for a more complex decision boundary.\n\n# ## Multi-class Classification\n#\n# In the exposition above, we have addressed binary classification problems. The SVM can be generalized to multi-class classification. This involves training $K$ binary classifiers, where each of $k=1,\\ldots,K$ classes is trained against the remaining classes pooled into a single group (\"all-versus-one\"). Then for each point, we select the class for which the inner product $\\theta_k^{\\prime} x$ is largest.\n\n# ## Data Preprocessing\n#\n# It is important with many kernels to **scale** the features prior to using them in a SVM. This is because features which are numerically large relative to the others will tend to dominate the norm. So that each feature is able to contribute equally to the selection of the decision boundary, we want them all to have approximately the same range.\n#\n# In general, standardization of datasets is a common pratice for statistical learning algorithms. We often ignore the shape of the data distribution and simply center it on the mean, then scale it by dividing by their standard deviation (unless the feature is constant). This is important because the objective function in several learning algorithms (*e.g.* the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features.\n#\n# Scikit-learn's `preprocessing` module provides a `scale` function to perform this operation on a single array-like dataset:\n\n# +\nfrom sklearn import preprocessing\n\nX = np.array([[ 1., -1., 2.],\n [ 2., 0., 0.],\n [ 0., 1., -1.]])\nX_scaled = preprocessing.scale(X)\nX_scaled \n# -\n\n# Scaled data has zero mean and unit variance:\n\nX_scaled.mean(0)\n\nX_scaled.std(0)\n\n# The `preprocessing` module also provides a utility class called `StandardScaler` that allows for the computation of the mean and standard deviation on a training set. This allows one to later *reapply* the same transformation on validation and test sets.\n\nscaler = preprocessing.StandardScaler().fit(X)\nscaler\n\nscaler.mean_ \n\nscaler.scale_ \n\nscaler.transform(X) \n\n# So then, for new data, we can simply apply the `scaler` object's `transform` method:\n\nscaler.transform([[-1., 1., 0.]]) \n\n# Optionally, one can disable either centering or scaling by passing `with_mean=False` or `with_std=False`, respectively.\n\n# ### Range scaling\n#\n# An alternative standardization is scaling features to lie between a given minimum and maximum value (typically between zero and one). This is often the case where we want robustness to very small standard deviations of features or we want to preserve zero entries in sparse data.\n#\n# The `MinMaxScaler` provides this scaling.\n\n# +\nmin_max_scaler = preprocessing.MinMaxScaler()\n\nmin_max_scaler.fit_transform(X)\n# -\n\n# The same instance of the transformer can then be applied to some new test data, which results in the same scaling and shifting operations:\n\nX_test = np.array([[ -3., -1., 4.]])\nmin_max_scaler.transform(X_test)\n\n# ### Normalization\n#\n# Normalization is the process of scaling individual samples to have unit norm. This is useful if you plan to use a quadratic function such as the dot-product or any other kernel to quantify the similarity of any pair of samples.\n#\n# The function `normalize` performs this operation on a single array-like dataset, either using the l1 or l2 norms:\n\npreprocessing.normalize(X, norm='l2')\n\npreprocessing.normalize(X, norm='l1')\n\n# As with scaling, there is also a `Normalizer` class that can be used to establish normalization with respect to a training set.\n\nnormalizer = preprocessing.Normalizer().fit(X)\n\nnormalizer.transform(X_test)\n\n# ### Categorical feature encoding\n#\n# Often features are not given as continuous values, but rather as categorical classes. For example, variables may be defined as `[\"male\", \"female\"]`, `[\"Europe\", \"US\", \"Asia\"]`, `[\"Disease A\", \"Disease B\", \"Disease C\"]`. Such features can be efficiently coded as integers, for instance `[\"male\", \"US\", \"Disease B\"]` could be expressed as `[0, 1, 1]`.\n#\n# Unfortunately, an integer representation can not be used directly with estimators in scikit-learn, because these expect *continuous* input, and would therefore interpret the categories as being ordered, which for the above examples, would be inappropriate.\n#\n# One approach is to use a \"one-of-K\" or \"one-hot\" encoding, which is implemented in `OneHotEncoder`. This estimator transforms a categorical feature with `m` possible values into `m` binary features, with only one active.\n\nenc = preprocessing.OneHotEncoder()\n\nenc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) \n\nenc.transform([[0, 1, 3]]).toarray()\n\n# By default, the cardinality of each feature is inferred automatically from the dataset; this can be manually overriden using the `n_values` argument.\n\n# `LabelBinarizer` is a utility class to help create a label indicator matrix from a list of multi-class labels:\n\nlb = preprocessing.LabelBinarizer()\nlb.fit([1, 2, 6, 4, 2])\n\nlb.classes_\n\nlb.transform((1,4))\n\n# For multiple labels per instance, use MultiLabelBinarizer:\n\nlb = preprocessing.MultiLabelBinarizer()\nlb.fit_transform([(1, 2), (3,)])\n\nlb.classes_\n\n# `LabelEncoder` is a utility class to help normalize labels such that they contain only consecutive values between 0 and `n_classes-1`.\n\nle = preprocessing.LabelEncoder()\nle.fit([1,2,2,6])\n\nle.classes_\n\nle.transform([1, 1, 2, 6])\n\nle.inverse_transform([0, 0, 1, 2])\n\n# ## Missing Data Imputation\n#\n# Missing data is a common problem in most real-world scientific datasets. While the best way for dealing with missing data will always be preventing their occurrence in the first place, it usually can't be helped, particularly when data are collected passively or voluntarily, or when data collection and recording is distributed among several people. There are a variety of ways for dealing with missing data, from the very naïve to the very sophisticated, and unfortunately the more common approaches tend to be *ad hoc* and will usually do more harm than good. \n#\n# It turns out that more robust methods for imputation are not as difficult to implement as they first appear to be. Two of the best ones are Bayesian imputation and multiple imputation. In this section, we will use **multiple imputation** to account for missing data in a regression analysis. \n\n# As a motivating example, we will use a dataset of educational outcomes for children with hearing impairment. Here, we are interested in determining factors that are associated with better or poorer learning outcomes. \n#\n# ![hearing aid](images/hearing_aid.jpg)\n#\n# There is a suite of available predictors, including: \n#\n# * gender (`male`)\n# * number of siblings in the household (`siblings`)\n# * index of family involvement (`family_inv`)\n# * whether the primary household language is not English (`non_english`)\n# * presence of a previous disability (`prev_disab`)\n# * non-white race (`non_white`)\n# * age at the time of testing (in months, `age_test`)\n# * whether hearing loss is not severe (`non_severe_hl`)\n# * whether the subject's mother obtained a high school diploma or better (`mother_hs`)\n# * whether the hearing impairment was identified by 3 months of age (`early_ident`).\n\ntest_scores = pd.read_csv('../data/test_scores.csv', index_col=0)\ntest_scores.head()\n\n# For three variables in the dataset, there are incomplete records.\n\ntest_scores.isnull().sum(0)\n\n# ### Strategies for dealing with missing data\n#\n# The easiest (and worst) way to deal with missing data is to **ignore it**. That is, simply run the analysis, missing values and all, hoping for the best. If your software is any good, this approach will simply not work; the algorithm will try to operate on data that includes missing values, and propagate them, resulting in statistics and estimates that cannot be calculated, which will typically raise errors. If your software is poor, it will make some assumption or decision about the missing values, and proceed to generate results conditional on the assumption, which creates problems that may never be detected because no indication was given to any potential problem. \n#\n# The next easiest (worst) approach to analyzing data with missing values is to conduct list-wise deletion, by deleting the records that have missing values. This is called **complete case analysis**, because only records that are complete get retained for the analysis. The degree to which complete case analysis is undesirable depends on the mechanism by which data have become missing.\n\n# ### Types of Missingness\n#\n# - **Missing completely at random (MCAR)**: When data are MCAR, missing cases are, on average, identical to non-missing cases, with respect to the model. Ignoring the missingness will reduce the power of the analysis, but will not bias inference.\n# - **Missing at random (MAR)**: Missing data depends (usually probabilistically) on measured values, and hence can be modeled by variables observed in the data set. Accounting for the values which “cause” the missing data will produce unbiased results in an analysis.\n# - **Missing not at random(MNAR)**: Missing data depend on unmeasured or unknown variables. There is no information available to account for the missingness.\n#\n# The very best-case scenario for using complete case analysis, which corresponds to MCAR missingness, results in a **loss of power** due to the reduction in sample size. The analysis will lose the information contained in the non-missing elements of a partially-missing record. When data are not missing completely at random, inferences from complete case analysis may be **biased** due to systematic differences between missing and non-missing records that affects the estimates of key parameters.\n#\n# One alternative to complete case analysis is to simply fill (*impute*) the missing values with a reasonable guess a the true value, such as the mean, median or modal value of the fully-observed records. This imputation, while not recovering any information regarding the missing value itself for use in the analysis, does provide a mechanism for including the non-missing values in the analysis, thereby making use of all available information.\n\n# The `Imputer` class in scikit-learn provides methods for imputing missing values, either using the mean, the median or the most frequent value of the row or column in which the missing values are located. This class also allows for different missing value encodings.\n#\n# For example, we can replace missing entries encoded as `np.nan` using the mean value of the columns (axis 0) that contain the missing values:\n\n# +\nfrom sklearn.preprocessing import Imputer\n\nimp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n# -\n\nimp.fit([[1, 2], [np.nan, 3], [7, 6]])\n\nX = [[np.nan, 1], [6, np.nan], [3, 6]]\nimp.transform(X)\n\n# In our educational outcomes dataset, we are probably better served using mode imputation:\n\nmode_imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)\n\nmode_imp.fit(test_scores)\n\nmode_imp.transform(test_scores)[:3]\n\n# Of course, in Python it is often easier to impute data using Pandas `DataFrame` method `fillna`.\n\ntest_scores.siblings.mean()\n\nsiblings_imputed = test_scores.siblings.fillna(test_scores.siblings.mean())\n\n# This approach may be reasonable under the MCAR assumption, but may induce bias under a MAR scenario, whereby missing values may **differ systematically** relative to non-missing values, making the particular summary statistic used for imputation *biased* as a mean/median/modal value for the missing values.\n#\n# Beyond this, the use of a single imputed value to stand in place of the actual missing value glosses over the **uncertainty** associated with this guess at the true value. Any subsequent analysis procedure (*e.g.* regression analysis) will behave as if the imputed value were observed, despite the fact that we are actually unsure of the actual value for the missing variable. The practical consequence of this is that the variance of any estimates resulting from the imputed dataset will be **artificially reduced**.\n\n# ## Multiple Imputation\n#\n# One robust alternative to addressing missing data is **multiple imputation** (Schaffer 1999, van Buuren 2012). It produces unbiased parameter estimates, while simultaneously accounting for the uncertainty associated with imputing missing values. It is conceptually and mechanistically straightforward, and produces complete datasets that may be analyzed using any statistical methodology or software one chooses, as if the data had no missing values to begin with.\n#\n# Multiple imputation generates imputed values based on a **regression model**. This regression model will help us generate reasonable values, particularly if data are MAR, since it uses information in the dataset that may be informative in predicting what the true value may be. Ideally, we want predictor variables that are **correlated** with the missing variable, and with the mechanism of missingness, if any. For example, one might be able to use test scores from one subject to predict missing test scores from another; or, the probability of income reporting to be missing may vary systematically according to the education level of the individual.\n\n# To see if there is any potential information among the variables in our dataset to use for imputation, it is helpful to calculate the pairwise correlation between all the variables. Since we have discrete variables in our data, the [Spearman rank correlation coefficient](http://www.wikiwand.com/en/Spearman%27s_rank_correlation_coefficient) is appropriate.\n\ntest_scores.dropna().corr(method='spearman')\n\n# We will try to impute missing values the mother's high school education indicator variable, which takes values of 0 for no high school diploma, or 1 for high school diploma or greater. The appropriate model to predict binary variables is a **logistic regression**. We will use the scikit-learn implementation, `LogisticRegression`.\n\nfrom sklearn.linear_model import LogisticRegression\n\n# To keep things simple, we will only use variables that are themselves complete to build the predictive model, hence our subset of predictors will exclude family involvement score (`family_inv`) and previous disability (`prev_disab`).\n\nimpute_subset = test_scores.drop(labels=['family_inv','prev_disab','score'], axis=1)\n\n# Next, we scale the predictor variables to range from 0 to 1, to improve the performance of the regression model.\n\ny = impute_subset.pop('mother_hs').values\nX = preprocessing.StandardScaler().fit_transform(impute_subset.astype(float))\n\n# Next, we create a `LogisticRegression` model, and fit it using the non-missing observations.\n\n# +\nmissing = np.isnan(y)\n\nmod = LogisticRegression()\nmod.fit(X[~missing], y[~missing])\n# -\n\nmother_hs_pred = mod.predict(X[missing])\nmother_hs_pred\n\n# These values can then be inserted in place of the missing values, and an analysis can be performed on the entire dataset.\n#\n# However, this is still just a single imputation for each missing value, and hence glosses over the uncertainty associated with the derivation of the imputes. Multiple imputation proceeds by **imputing several values**, to generate several complete datasets and performing the same analysis on all of them. With a set of estimates in hand, an *average* estimate of model parameters can be obtained that more adequately accounts for the uncertainty, hopefully providing more robust inference than from a single impute.\n#\n# There are a variety of ways to generate multiple imputations. Here, we will exploit **regularization** in order to do this. The `LogisticRegression` class from scikit-learn provides facilities for regularization using either L2 (resulting in ridge regression) or L1 (resulting in LASSO regression) penalties. The degree of regularization in either case is controlled by the `C` parameter, whereby large values of `C` give more freedom to the model, while smaller values of `C` constrain the model more. We can use a selection of `C` values to obtain a range of predictions from variants of the same model. For example:\n\nmod2 = LogisticRegression(C=1, penalty='l1')\nmod2.fit(X[~missing], y[~missing])\nmod2.predict(X[missing])\n\nmod3 = LogisticRegression(C=0.4, penalty='l1')\nmod3.fit(X[~missing], y[~missing])\nmod3.predict(X[missing])\n\n# Surprisingly few imputations are required to acheive reasonable estimates, with 3-10 usually sufficient. We will use 3.\n\n# +\nmother_hs_imp = []\n\nfor C in 0.1, 0.4, 2:\n \n mod = LogisticRegression(C=C, penalty='l1')\n mod.fit(X[~missing], y[~missing])\n imputed = mod.predict(X[missing])\n mother_hs_imp.append(imputed)\n# -\n\nmother_hs_imp\n\n# ## SVM using `scikit-learn`\n#\n# The scikit-learn machine learning package for Python includes a nice implementation of support vector machines.\n\nfrom sklearn import svm\n\n# Let's begin with a fun enological example. Your textbook includes a dataset `wine.dat` that is the result of chemical analyses of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. (The response variable is incorrectly labeled `region`; it should be the grape from which the wine was derived). We might be able to correctly classify a given wine based on its chemical profile.\n#\n# To illustrate the characteristics of the SVM, we will select two attributes, which will make things easy to visualize.\n\n# +\nwine = pd.read_table(\"../data/wine.dat\", sep='\\s+')\n\nattributes = ['Alcohol',\n 'Malic acid',\n 'Ash',\n 'Alcalinity of ash',\n 'Magnesium',\n 'Total phenols',\n 'Flavanoids',\n 'Nonflavanoid phenols',\n 'Proanthocyanins',\n 'Color intensity',\n 'Hue',\n 'OD280/OD315 of diluted wines',\n 'Proline']\n\ngrape = wine.pop('region')\ny = grape.values\nwine.columns = attributes\nX = wine[['Alcohol', 'Proline']].values\n\nsvc = svm.SVC(kernel='linear')\nsvc.fit(X, y)\n# -\n\nwine.head()\n\n# It is easiest to display the model fit graphically, by evaluating the model over a grid of points.\n\n# +\nfrom matplotlib.colors import ListedColormap\n# Create color maps for 3-class classification problem, as with iris\ncmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\ncmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n\ndef plot_estimator(estimator, X, y, ax=None):\n \n try:\n X, y = X.values, y.values\n except AttributeError:\n pass\n \n if ax is None:\n _, ax = plt.subplots()\n \n estimator.fit(X, y)\n x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n np.linspace(y_min, y_max, 100))\n Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n\n # Put the result into a color plot\n Z = Z.reshape(xx.shape)\n ax.pcolormesh(xx, yy, Z, cmap=cmap_light)\n\n # Plot also the training points\n ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n ax.axis('tight')\n ax.axis('off')\n plt.tight_layout()\n\n\n# -\n\n# %matplotlib inline\nplot_estimator(svc, X, y)\n\n# The SVM gets its name from the samples in the dataset from each class that lie closest to the other class. These training samples are called **support vectors** because changing their position in *p*-dimensional space would change the location of the decision boundary. \n#\n# In scikits-learn, the indices of the support vectors for each class can be found in the `support_vectors_` attribute of the `SVC` object. Here is a 2 class problem using only classes 1 and 2 in the wine dataset.\n#\n# The support vectors are circled.\n\n# +\n# Extract classes 1 and 2\nX, y = X[np.in1d(y, [1, 2])], y[np.in1d(y, [1, 2])]\n\nplt.figure()\nplot_estimator(svc, X, y)\nplt.scatter(svc.support_vectors_[:, 0], \n svc.support_vectors_[:, 1], \n s=120, \n facecolors='none', \n edgecolors='w',\n linewidths=2,\n zorder=10)\n\n\n# -\n\n# Clearly, these classes are not linearly separable.\n#\n# As we learned, regularization is tuned via the $C$ parameter. In practice, a large $C$ value means that the number of support vectors is small (less regularization), while a small $C$ implies many support vectors (more regularization). scikit-learn sets a default value of $C=1$.\n\n# +\ndef plot_regularized(power, ax):\n svc = svm.SVC(kernel='linear', C=10**power)\n plot_estimator(svc, X, y, ax=ax)\n ax.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=80, \n facecolors='none', edgecolors='w', linewidths=2, zorder=10)\n ax.set_title('Power={}'.format(power))\n \nfig, axes = plt.subplots(2, 3, figsize=(12,10))\nfor power, ax in zip(range(-2, 4), axes.ravel()):\n plot_regularized(power, ax)\n\n\n# -\n\n# We can choose from a suite of available kernels (`linear`, `poly`, `rbf`, `sigmoid`, `precomputed`) or a custom kernel can be passed as a function. Note that the radial basis function (`rbf`) kernel is just a Gaussian kernel, but with parameter $\\gamma=1/\\sigma^2$.\n\n# +\ndef plot_poly_svc(degree=3, ax=None):\n svc_poly = svm.SVC(kernel='poly', degree=degree)\n plot_estimator(svc_poly, X, y, ax=ax)\n ax.scatter(svc_poly.support_vectors_[:, 0], svc_poly.support_vectors_[:, 1], \n s=80, facecolors='none', linewidths=2, zorder=10)\n ax.set_title('Polynomial degree {}'.format(degree))\n \nfig, axes = plt.subplots(2, 3, figsize=(12,10))\nfor deg, ax in zip(range(1, 7), axes.ravel()):\n plot_poly_svc(deg, ax)\n\n\n# +\ndef plot_rbf_svc(power=1, ax=None):\n \n svc_rbf = svm.SVC(kernel='rbf', gamma=10**power)\n plot_estimator(svc_rbf, X, y, ax=ax)\n ax.scatter(svc_rbf.support_vectors_[:, 0], svc_rbf.support_vectors_[:, 1], \n s=80, facecolors='none', linewidths=2, zorder=10)\n ax.set_title('$\\gamma=10^{%i}$' % power)\n \nfig, axes = plt.subplots(2, 3, figsize=(12,10))\nfor pow, ax in zip(range(-3, 3), axes.ravel()):\n plot_rbf_svc(pow, ax)\n# -\n\n# Of course, the radial basis function (RBF) kernel is very flexible and performs best for this dataset. However, it is easy to get carried away tuning to a training dataset--we don't really believe the resulting decision boundary, do we?\n\n# ## Cross-validation\n#\n# In order to make objective choices for either kernels or hyperparameter values, we can apply the cross-validation methods outlined in last week's lecture. Every estimator class in `scikit-learn` exposes a `score` method that can judge the quality of the fit (or the prediction) on new data.\n#\n# The `score(x,y)` method for the `SVC` class returns the *mean accuracy* of the predictions from `x` with respect to `y`, for the fitted SVM.\n\nsvc_lin = svm.SVC(kernel='linear', C=2)\nsvc_lin.fit(X, y)\nsvc_lin.score(X, y)\n\nsvc_poly = svm.SVC(kernel='poly', degree=3)\nsvc_poly.fit(X, y)\nsvc_poly.score(X, y)\n\nsvc_rbf = svm.SVC(kernel='rbf', gamma=1e-2)\nsvc_rbf.fit(X, y)\nsvc_rbf.score(X, y)\n\n\n# Each estimator in `scikit-learn` has a default estimator score method, which is an evaluation criterion for the problem they are designed to solve. For the `SVC` class, this is the **mean accuracy**, as shown above.\n#\n# Alternately, if we use cross-validation, you can specify one of a set of built-in scoring metrics. For classifiers such as support vector machines, these include:\n#\n# **accuracy**\n# :\t`sklearn.metrics.accuracy_score`\n#\n# **average_precision**\n# :\t`sklearn.metrics.average_precision_score`\n#\n# **f1**\n# :\t`sklearn.metrics.f1_score`\n#\n# **precision**\n# :\t`sklearn.metrics.precision_score`\n#\n# **recall**\n# :\t`sklearn.metrics.recall_score`\n#\n# **roc_auc**\n# :\t`sklearn.metrics.roc_auc_score`\n#\n# Regression models can use appropriate metrics, like `mean_squared_error` or `r2`.\n#\n# Finally, one can specify arbitrary loss functions to be used for assessment. The `metrics` module implements functions assessing prediction errors for specific purposes. \n\ndef custom_loss(observed, predicted):\n diff = np.abs(observed - predicted).max()\n return np.log(1 + diff)\n\n\nfrom sklearn.metrics import make_scorer\ncustom_scorer = make_scorer(custom_loss, greater_is_better=False)\n\n# Implementing cross-validation on our wine SVC is straightforward:\n\n# +\nfrom sklearn import model_selection\n\nX_train, X_test, y_train, y_test = model_selection.train_test_split(\n wine.values, grape.values, test_size=0.4, random_state=0)\n# -\n\nX_train.shape, y_train.shape\n\nX_test.shape, y_test.shape\n\nf = svm.SVC(kernel='linear', C=1)\nf.fit(X_train, y_train)\nf.score(X_test, y_test)\n\n# The following example demonstrates how to estimate the accuracy of a linear kernel support vector machine on the wine dataset by splitting the data, fitting a model and computing the score 5 consecutive times (with different splits each time):\n\nscores = model_selection.cross_val_score(f, wine.values, grape.values, cv=5)\nscores\n\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n\n# Furthermore, we can customize the scoring method by specifying the `scoring` parameter:\n\nmodel_selection.cross_val_score(f, wine.values, grape.values, cv=5,\n scoring='f1_weighted')\n\n# The module `sklearn.metric` also exposes a set of simple functions measuring prediction error given observations and prediction, such as the confusion matrix:\n\n# +\nfrom sklearn.metrics import confusion_matrix\n\nsvc_poly = svm.SVC(kernel='poly', degree=3).fit(X_train, y_train)\nconfusion_matrix(y_test, svc_poly.predict(X_test))\n# -\n\n# ## Exercise: Titanic survival\n#\n# Try to estimate a reasonable support vector classfier for the fate of passengers on the Titanic (`../data/titanic.xls`). Specifically, see if you can correctly classify the survivors based on the covariates available in the dataset.\n#\n# As an extension, use multiple imputation to allow for the inclusion of age into the analysis, and see if it makes a difference in the results.\n\n# !conda install -y xlrd\n\ntitanic = pd.read_excel(\"../data/titanic.xls\", \"titanic\")\ntitanic.head()\n\n# +\n# Write answer here\n# -\n\n# ## References\n#\n# - [Coursera's Machine Learning course](https://www.coursera.org/course/ml) by Stanford's Andrew Ng\n# - [`scikit-learn` User's Guide](http://scikit-learn.org/stable/modules/svm.html) SVM section\n# - [Scikit-learn tutorials for the Scipy 2013 conference](https://github.com/jakevdp/sklearn_scipy2013) by Jake Vanderplas\n"},"script_size":{"kind":"number","value":43191,"string":"43,191"}}},{"rowIdx":983,"cells":{"path":{"kind":"string","value":"/.ipynb_checkpoints/Lab9_20151258_20151521_20150178_final-checkpoint.ipynb"},"content_id":{"kind":"string","value":"c3f736f9172108bfa4953fa66f6d90dbecb3080a"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"bvez/AplicaTA"},"repo_url":{"kind":"string","value":"https://github.com/bvez/AplicaTA"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":272205,"string":"272,205"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Laboratorio 9\n\n#

Para este laboratorio, hemos decidido usar un dataset con los tweets que realizaron las personas frente a Apple

\n\nimport nltk\nimport re\nnltk.download('punkt')\n\n# ## Lectura del archivo de entrada\n\nimport pandas as pd\ndf = pd.read_csv(\"Apple-Twitter-Sentiment-DFE.csv\", header=0,encoding = 'utf_8')\n\ndf.sample(5)\n\n# ## Limpieza de columnas\n\n# Eliminamos las columnas que no aportan informacion en cuanto al sentimiento dentro del texto. Estas columnas son las fechas, identificadores y estados.\n\ndf2 = df.drop([\"_unit_id\",\"_unit_state\",\"date\",\"id\",\"query\",\"_last_judgment_at\"],axis=1)\ndf2.sample(5)\n\n# ## Separacion de datos\n\n# Separamos la columna \"sentiment\" que es el dato final que queremos lograr. Además dividimos los datos de forma que el 80% de los datos servirá para entrenar y el 20% restante servirá para probar.\n\n# +\nX_all = df2.drop(['sentiment'],axis=1)\ny_all = df2['sentiment']\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nnum_test = 0\nX_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test)\n# -\n\nimport numpy as np\ntitles =np.array(X_train[\"text\"])\nprint(X_train[\"text\"])\nprint(titles[1])\n\nimport re\ntext = \" \".join(titles) \ntitles\n\n# Eliminación de URLs\n\n# +\nfrom collections import Counter\n\nfor i in range(len(titles)):\n #URLs_dict = Counter( re.findall(r\"(https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9]\\.[^\\s]{2,})\",i) )\n #URLs = list(URLs_dict.keys())\n titles[i] = re.sub(r\"(https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9]\\.[^\\s]{2,})\",\"\", titles[i])\ntitles\n# -\n\n# Se obtienen todas las siglas presentes en los textos, ya sean un conjunto de 2 o más letras mayúsculas o separadas \n# por punto.\n\n# +\nfrom collections import Counter\nmy_dict = Counter( re.findall(r\"[A-Z][A-Z]+\",text) )\nsiglas = list(my_dict.keys())\nmy_dict2 = Counter(re.findall(r\"([A-Z]\\.([A-Z]\\.)+)\",text))\nsiglas2 = list(my_dict2.keys())\n\nsig =[]\nfor i in siglas2:\n sig.append(i[0])\nsiglas = sig + siglas\nsiglas\n# -\n\n# Como todos los tweets son de idioma inglés se puede quitar las palabras que no aportan (stopwords) y se puede aplicar lemmatization y stem, se intentan conservar las mayúsculas, pues, en un tweet, se usan las mayúsculas para remarcar una idea, normalmente expresa incomodidad u odio.\n\nimport nltk\nnltk.download('stopwords')\nstopwords_eng = nltk.corpus.stopwords.words('english')\nprint(stopwords_eng)\nstopwords_MAY_eng = list((\" \".join([token.upper() for token in stopwords_eng])).split())\nstopwords_MAY_eng\n\n# +\nfor i in range(len(titles)):\n titles[i] = \" \".join([token for token in titles[i].split() if (token not in stopwords_eng and token not in stopwords_MAY_eng )])\n #len(text.split()) - len(text2.split())\n\ntext2 = \" \".join([token for token in text.split() if (token not in stopwords_eng and token not in stopwords_MAY_eng )])\ntitles\n\n\n# +\nprint(\"Sin stop words: \")\nmy_dicc_No_StopWords = Counter(text2.split())\n\nprint(\"cant tokens: \",len(text2.split()))\nprint(\"tamanho vocabulario: \",len(my_dicc_No_StopWords))\n\nmy_dicc_No_StopWords.most_common(10)\n\n# +\nporter = nltk.PorterStemmer()\nimport copy\n\ntweets_st =[]\nfor i in range(len(titles)):\n copia1 = copy.deepcopy(titles[i])\n\n spliteado = copia1.split()\n tweets_stem_split = []\n for i in spliteado:\n if(i not in siglas):\n tweets_stem_split.append(porter.stem(i))\n else:\n tweets_stem_split.append(i)\n tweets_stem = \" \".join([token for token in tweets_stem_split])\n tweets_st.append(tweets_stem)\ntweets_st\n\n# +\n#print(\"Stem: \")\n#my_dicc_Stem = Counter(tweets_st.split())\n\n#print(\"cant tokens: \",len(tweets_st.split()))\n#print(\"tamanho vocabulario: \",len(my_dicc_Stem))\n\n#my_dicc_Stem.most_common(10)\n# -\n\n\n\nnltk.download('wordnet')\nwnl = nltk.WordNetLemmatizer()\n\n# +\ntweets_lemma_split = []\n\ntweets_le =[]\nfor i in range(len(titles)):\n copia2 = copy.deepcopy(titles[i])\n\n spliteado2 = copia2.split()\n tweets_lemma_split = []\n for j in spliteado2:\n if(j not in siglas):\n tweets_lemma_split.append(wnl.lemmatize(j))\n else:\n tweets_lemma_split.append(j)\n tweets_lemma = \" \".join([token for token in tweets_lemma_split])\n tweets_le.append(tweets_lemma)\ntweets_le\n\n\n# -\n\ndef clean_tokens(text):\n return text.split()\n\n\n# ## Análisis de la Representación Vectorial \n\n# +\nfrom sklearn.model_selection import cross_val_score\n\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nfrom sklearn.naive_bayes import BernoulliNB, MultinomialNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neighbors import NearestCentroid\nfrom sklearn.ensemble import RandomForestClassifier\n\n\n# -\n\ndef run_model(clf, X, y):\n scores = cross_val_score(clf, X, y, cv=5)\n print(\"%s accuracy: %0.2f (+/- %0.2f)\" % \\\n (str(clf.__class__).split('.')[-1].replace('>','').replace(\"'\",''), \n scores.mean(), scores.std() * 2))\n\n\ndef run_models(X, y):\n run_model(LinearSVC(), X, y)\n run_model(SGDClassifier(), X, y)\n run_model(Perceptron(), X, y)\n run_model(PassiveAggressiveClassifier(), X, y)\n run_model(BernoulliNB(), X, y)\n run_model(MultinomialNB(), X, y)\n run_model(KNeighborsClassifier(), X, y)\n run_model(NearestCentroid(), X, y)\n run_model(RandomForestClassifier(n_estimators=100, max_depth=10), X, y)\n\n\n# ### BAG OF WORDS\n\ntext=titles\nDocumentos = copy.deepcopy(text)\nDocumentos\n\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nvectorizer = CountVectorizer()\n\n# Se transforma el texto en una matriz de tXd, donde se indica si el token t está en el documento d\n\ntxd_matrix = vectorizer.fit_transform(Documentos)\n\n# Se transforma la matriz txd para que sea binaria\n\ntxd_matrix = CountVectorizer(binary=True,tokenizer=clean_tokens,stop_words='english').fit_transform(Documentos)\ntxd_matrix.shape\n\nrun_models(txd_matrix, y_train)\n\n# ### TFIDF VECTORIZER\n# Una matriz de métricas tfidf para cada documento del dataset\n\nDocumentosTFIDF = copy.deepcopy(text)\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n# Transformamos la matriz de solo tf (tf_matrix) y la matriz tfidf\n\ntf_matrix = TfidfVectorizer(use_idf=False).fit_transform(DocumentosTFIDF)\ntfidf_matrix = TfidfVectorizer().fit_transform(DocumentosTFIDF)\nDocumentosTFIDF\n\n# +\n#print(tf_matrix[3107])\n# -\n\nprint(tfidf_matrix)\n#nltk.download()\n\ntext1=copy.deepcopy(text)\nfor i in range(len(text1)):\n text1[i]=text1[i].split()\n\nfrom collections import Counter\nimport numpy as np\n\n# +\n#Obtener la matriz TF-IDF de los tweets\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nX_1 = TfidfVectorizer(tokenizer=clean_tokens, stop_words='english').fit_transform(DocumentosTFIDF)\nprint(X_1)\n# -\n\nprint(X_1.shape,y_train.shape)\n\n# +\nrun_models(X_1, y_train)\n\n\n#clf = LinearSVC(random_state = 0)\n#clf.fit(X_1,y_train)\n\n#prueba = [\"a\"] * X_1.shape[1]\n#prueba[0] = \"I loved this amazing version\"\n#print(prueba)\n#X_5 = TfidfVectorizer(tokenizer=clean_tokens, stop_words='english').fit_transform(prueba)\n\n#X_5\n#clf.predict(X_5)\n# -\n\n# ## Utilizando Word Vectors de Spacy\n\n# !pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz --user\n\nimport spacy\nnlp = spacy.load('en_core_web_sm')\ndoc = nlp(u'This is a sentence.')\n\n# !pip3 install --upgrade gensim --user\nfrom gensim.models import word2vec\n\nsentences = word2vec.Word2Vec(sentences=)\n\nprint(sentences)\n\n\n\n\n\n\n\n\n\n\n\n\n"},"script_size":{"kind":"number","value":8141,"string":"8,141"}}},{"rowIdx":984,"cells":{"path":{"kind":"string","value":"/_drafts/AWS Quant Tutorial/.ipynb_checkpoints/Untitled-checkpoint.ipynb"},"content_id":{"kind":"string","value":"e2340244804900327a3c759da0a78e1f9cc9d3d7"},"detected_licenses":{"kind":"list like","value":["CC0-1.0"],"string":"[\n \"CC0-1.0\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"bshabashFD/bshabashFD.github.io_old2"},"repo_url":{"kind":"string","value":"https://github.com/bshabashFD/bshabashFD.github.io_old2"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":5479,"string":"5,479"},"script":{"kind":"string","value":"# # [Amin M. Boulouma Blog](https://amboulouma.com)\n#\n# ## Advanced Python - Algorithms in Python #1\n#\n# - Help the creator channel reach 20k subscribers. He will keep uploading quality content for you: [Amin M. Boulouma Channel](https://www.youtube.com/channel/UCOZbokHO727qeStxeYSKMUQ?sub_confirmation=1)\n# - Those algorithms are best understood following the course: [Python Basics Tutorial](https://youtu.be/ne4Xsoe5Br8)\n\n# \n# # Stars and bars\n#\n# Stars and bars is a mathematical technique for solving certain combinatorial problems.\n# It occurs whenever you want to count the number of ways to group identical objects.\n#\n# ## Theorem\n#\n# The number of ways to put $n$ identical objects into $k$ labeled boxes is\n# $$\\binom{n + k - 1}{n}.$$\n#\n# The proof involves turning the objects into stars and separating the boxes using bars (therefore the name).\n# E.g. we can represent with $\\bigstar | \\bigstar \\bigstar |~| \\bigstar \\bigstar$ the following situation:\n# in the first box is one object, in the second box are two objects, the third one is empty and in the last box are two objects.\n# This is one way of dividing 5 objects into 4 boxes.\n#\n# It should be pretty obvious, that every partition can be represented using $n$ stars and $k - 1$ bars and every stars and bars permutation using $n$ stars and $k - 1$ bars represents one partition.\n# Therefore the number of ways to divide $n$ identical objects into $k$ labeled boxes is the same number as there are permutations of $n$ stars and $k - 1$ bars.\n# The [Binomial Coefficient](./combinatorics/binomial-coefficients.html) gives us the desired formula.\n#\n# ## Number of non-negative integer sums\n#\n# This problem is a direct application of the theorem.\n#\n# You want to count the number of solution of the equation \n# $$x_1 + x_2 + \\dots + x_k = n$$\n# with $x_i \\ge 0$.\n#\n# Again we can represent a solution using stars and bars.\n# E.g. the solution $1 + 3 + 0 = 4$ for $n = 4$, $k = 3$ can be represented using $\\bigstar | \\bigstar \\bigstar \\bigstar |$.\n#\n# It is easy to see, that this is exactly the stars an bars theorem.\n# Therefore the solution is $\\binom{n + k - 1}{n}$.\n#\n# ## Number of lower-bound integer sums\n#\n# This can easily be extended to integer sums with different lower bounds.\n# I.e. we want to count the number of solutions for the equation\n# $$x_1 + x_2 + \\dots + x_k = n$$\n# with $x_i \\ge a_i$.\n#\n# After substituting $x_i' := x_i - a_i$ we receive the modified equation\n# $$(x_1' + a_i) + (x_2' + a_i) + \\dots + (x_k' + a_k) = n$$\n# $$\\Leftrightarrow ~ ~ x_1' + x_2' + \\dots + x_k' = n - a_1 - a_2 - \\dots - a_k$$\n# with $x_i' \\ge 0$.\n# So we have reduced the problem to the simpler case with $x_i' \\ge 0$ and again can apply the stars and bars theorem.\n"},"script_size":{"kind":"number","value":2769,"string":"2,769"}}},{"rowIdx":985,"cells":{"path":{"kind":"string","value":"/Natural language processing/2.4.4.ipynb"},"content_id":{"kind":"string","value":"f44a65a405339d13ee9b6228664d18a1a9176940"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"yokopi521/deep_learning_practice"},"repo_url":{"kind":"string","value":"https://github.com/yokopi521/deep_learning_practice"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":3753,"string":"3,753"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n# ## This notebook contains e2e analysis used to allocate tolerances for each $\\color{red}{\\text{Global Zernike Aberration}}$ mode for a segmented telescope.\n\nimport os\nimport time\nfrom shutil import copy\nfrom astropy.io import fits\nimport astropy.units as u\nimport hcipy\nimport numpy as np\nimport pastis.util as util \nfrom pastis.config import CONFIG_PASTIS \nfrom pastis.simulators.luvoir_imaging import LuvoirA_APLC \nfrom pastis.simulators.generic_segmented_telescopes import SegmentedAPLC\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy.interpolate import griddata\nimport exoscene.image\nimport exoscene.star\nimport exoscene.planet\nfrom exoscene.planet import Planet\nfrom astropy.io import fits as pf\nfrom matplotlib.colors import TwoSlopeNorm\nimport matplotlib.gridspec as gridspec\nfrom pastis.analytical_pastis.temporal_analysis import req_closedloop_calc_batch\n\ncoronagraph_design = 'small'\nnb_seg = CONFIG_PASTIS.getint('LUVOIR', 'nb_subapertures')\nnm_aber = CONFIG_PASTIS.getfloat('LUVOIR', 'calibration_aberration') * 1e-9\nsampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')\n\n# +\ndata_dir = \"\"\nrepo_dir = \"\"\n\n\noverall_dir = util.create_data_path(data_dir, telescope='luvoir_'+coronagraph_design)\nresDir = os.path.join(overall_dir, 'matrix_numerical')\n\nos.makedirs(resDir, exist_ok=True)\n# -\n\noptics_input = os.path.join(util.find_repo_location(), CONFIG_PASTIS.get('LUVOIR', 'optics_path_in_repo'))\nluvoir = LuvoirA_APLC(optics_input, coronagraph_design, sampling)\n\nmax_LO = 20\nluvoir.create_global_zernike_mirror(max_LO)\nn_LO = luvoir.zernike_mirror.num_actuators\n\nLO_modes = np.zeros(n_LO)\nluvoir.zernike_mirror.actuators = LO_modes\n\nluvoir.zernike_mirror.flatten()\n\nunaberrated_coro_psf, ref = luvoir.calc_psf(ref=True, display_intermediate=True, norm_one_photon=True)\n\n\nnorm = np.max(ref)\ndh_intensity = (unaberrated_coro_psf / norm) * luvoir.dh_mask\ncontrast_floor = np.mean(dh_intensity[np.where(luvoir.dh_mask != 0)])\nprint(f'norm: {norm}',f'constrast floor: {contrast_floor}')\n\nnonaberrated_coro_psf, ref, efield = luvoir.calc_psf(ref=True, display_intermediate=False, return_intermediate='efield',norm_one_photon=True)\nEfield_ref = nonaberrated_coro_psf.electric_field\n\n# +\n# LO_modes = np.zeros(n_LO)\n# LO_modes[3] = 100*(nm_aber)/2\n# luvoir.zernike_mirror.actuators = LO_modes\n# aberrated_coro_psf, ref2 = luvoir.calc_psf(ref=True, display_intermediate=True)\n\n# dh_intensity_aberrated = (aberrated_coro_psf/ norm) * luvoir.dh_mask\n# aberrated_contrast = np.mean(dh_intensity_aberrated[np.where(luvoir.dh_mask != 0)])\n# print(f'contrast floor: {aberrated_contrast}')\n\n# +\nprint('Generating the E-fields for low order zernike modes in science plane')\nprint(f'Calibration aberration used: {nm_aber} m')\n\nstart_time = time.time()\nfocus_fieldS = []\nfocus_fieldS_Re = []\nfocus_fieldS_Im = []\n\nfor i in range(1, n_LO):\n print(f'Working on global zernike mode: {i}')\n \n # Apply calibration aberration to used mode\n LO_modes = np.zeros(n_LO)\n LO_modes[i] = (nm_aber)/2\n luvoir.zernike_mirror.actuators = LO_modes\n # Calculate coronagraphic E-field and add to lists\n aberrated_coro_psf, inter = luvoir.calc_psf(display_intermediate=False, return_intermediate='efield',norm_one_photon=True)\n focus_field1 = aberrated_coro_psf\n focus_fieldS.append(focus_field1)\n focus_fieldS_Re.append(focus_field1.real)\n focus_fieldS_Im.append(focus_field1.imag)\n \n# -\n\nfocus_fieldS[0]\n\nmat_LO = np.zeros([n_LO-1, n_LO-1])\nfor i in range(0, n_LO-1):\n for j in range(0, n_LO-1):\n test = np.real((focus_fieldS[i].electric_field -Efield_ref) * np.conj(focus_fieldS[j].electric_field-Efield_ref))\n dh_test = (test / norm) * luvoir.dh_mask\n contrast = np.mean(dh_test[np.where(luvoir.dh_mask != 0)])\n mat_LO[i, j] = contrast\n\nmat_LO.shape\n\n# +\nfrom matplotlib.colors import LinearSegmentedColormap\n\nplt.figure(figsize=(10,8)) \nplt.imshow((mat_LO))\nplt.title(r\"PASTIS matrix $M$ for global zernike\", fontsize=20)\nplt.xlabel(\"Mode Index\",fontsize=20)\nplt.ylabel(\"Mode Index\",fontsize=20)\nplt.tick_params(labelsize=15)\ncbar = plt.colorbar(fraction=0.046, pad=0.04)\ncbar.set_label(r\"in units of $1/{nm^2}$\",fontsize =15)\nplt.tight_layout()\n\n# +\nfilename_matrix1 = 'PASTISmatrix_n_LO_' + str(n_LO)\nhcipy.write_fits(mat_LO, os.path.join(resDir, filename_matrix1 + '.fits'))\nprint('Matrix saved to:', os.path.join(resDir, filename_matrix1 + '.fits','\\n'))\n\nfilename_matrix2 = 'EFIELD_Re_matrix_n_LO_' + str(n_LO)\nhcipy.write_fits(focus_fieldS_Re, os.path.join(resDir, filename_matrix2 + '.fits'))\nprint('Efield Real saved to:', os.path.join(resDir, filename_matrix2 + '.fits', '\\n'))\n\nfilename_matrix3 = 'EFIELD_Im_matrix_n_LO_' + str(n_LO)\nhcipy.write_fits(focus_fieldS_Im, os.path.join(resDir, filename_matrix3 + '.fits'))\nprint('Efield Imag saved to:', os.path.join(resDir, filename_matrix3 + '.fits','\\n'))\n# -\n\nevals, evecs = np.linalg.eig(mat_LO)\nsorted_evals = np.sort(evals)\nsorted_indices = np.argsort(evals)\nsorted_evecs = evecs[:, sorted_indices]\n\nc_target_log = -11\nc_target = 10**(c_target_log)\nn_repeat = 20\n\nmu_map_LO = np.sqrt(((c_target) / (n_LO-1)) / (np.diag(mat_LO)))\n\n\n# +\nz_pup_downsample = CONFIG_PASTIS.getfloat('numerical', 'z_pup_downsample') \nN_pup_z = int(luvoir.pupil_grid.shape[0] / z_pup_downsample) #N_pup_z = 100,used to define out-of-band efield\ngrid_zernike = hcipy.field.make_pupil_grid(N_pup_z, diameter=luvoir.diam)\n\nnpup = int(np.sqrt(luvoir.pupil_grid.x.shape[0]))\nnimg = int(np.sqrt(luvoir.focal_det.x.shape[0]))\n\n# Getting the flux together\nsptype = 'A0V'\nVmag = 5.0\nminlam = 500\nmaxlam = 600 \ndark_current = 0 \nCIC = 0 \nstar_flux = exoscene.star.bpgs_spectype_to_photonrate(spectype=sptype, Vmag=Vmag, minlam=minlam, maxlam=maxlam) #ph/s/m^2\nNph = star_flux.value*15**2*np.sum(luvoir.apodizer**2) / npup**2\n# -\n\nluvoir.zernike_mirror.flatten()\nnonaberrated_coro_psf ,refshit ,inter_ref = luvoir.calc_psf(ref=True, display_intermediate=False, return_intermediate='efield',norm_one_photon=True)\nEfield_ref = nonaberrated_coro_psf.electric_field\n\nluvoir.zernike_mirror.flatten()\ndefocus_ref2 = luvoir.calc_out_of_band_wfs(norm_one_photon=True) #returns wavefront on obwfs detector\ndefocus_ref2_sub_real = hcipy.field.subsample_field(defocus_ref2.real, z_pup_downsample, grid_zernike, statistic='mean')\ndefocus_ref2_sub_imag = hcipy.field.subsample_field(defocus_ref2.imag, z_pup_downsample, grid_zernike, statistic='mean')\nEfield_ref_OBWFS = (defocus_ref2_sub_real + 1j*defocus_ref2_sub_imag) * z_pup_downsample\n\n# +\nnyquist_sampling = 2.\n\n# Actual grid for LUVOIR images\ngrid_test = hcipy.make_focal_grid(\n luvoir.sampling,\n luvoir.imlamD,\n pupil_diameter=luvoir.diam,\n focal_length=1,\n reference_wavelength=luvoir.wvln,\n )\n\n# Actual grid for LUVOIR images that are nyquist sampled\ngrid_det_subsample = hcipy.make_focal_grid(\n nyquist_sampling,\n np.floor(luvoir.imlamD),\n pupil_diameter=luvoir.diam,\n focal_length=1,\n reference_wavelength=luvoir.wvln,\n )\nn_nyquist = int(np.sqrt(grid_det_subsample.x.shape[0]))\n\n# +\ndesign = 'small'\n\ndh_outer_nyquist = hcipy.circular_aperture(2 * luvoir.apod_dict[design]['owa'] * luvoir.lam_over_d)(grid_det_subsample)\ndh_inner_nyquist = hcipy.circular_aperture(2 * luvoir.apod_dict[design]['iwa'] * luvoir.lam_over_d)(grid_det_subsample)\ndh_mask_nyquist = (dh_outer_nyquist - dh_inner_nyquist).astype('bool')\n\ndh_size = len(np.where(luvoir.dh_mask != 0)[0])\ndh_size_nyquist = len(np.where(dh_mask_nyquist != 0)[0])\ndh_index = np.where(luvoir.dh_mask != 0)[0]\ndh_index_nyquist = np.where(dh_mask_nyquist != 0)[0]\n# -\n\nE0_OBWFS = np.zeros([N_pup_z*N_pup_z,1,2])\nE0_OBWFS[:,0,0] = Efield_ref_OBWFS.real\nE0_OBWFS[:,0,1] = Efield_ref_OBWFS.imag\n\nE0_coron = np.zeros([nimg*nimg,1,2])\nE0_coron[:,0,0] = Efield_ref.real \nE0_coron[:,0,1] = Efield_ref.imag\n\nfilename_matrix2 = 'EFIELD_Re_matrix_n_LO_' + str(n_LO) + '.fits'\nG_zernike_real = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix2)) \nfilename_matrix3 = 'EFIELD_Im_matrix_n_LO_' + str(n_LO) + '.fits'\nG_zernike_imag = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix3)) \n\nG_coron_zernike= np.zeros([nimg*nimg,2,n_LO-1])\nfor pp in range(0, n_LO-1):\n G_coron_zernike[:,0,pp] = G_zernike_real[pp] - Efield_ref.real\n G_coron_zernike[:,1,pp] = G_zernike_imag[pp] - Efield_ref.imag\n\n# +\nstart_time = time.time()\nfocus_fieldS = []\nfocus_fieldS_Re = []\nfocus_fieldS_Im = []\n\nfor i in range(1, n_LO):\n #print(f'Working on \"defocus\" zernike mode, segment: {i}')\n \n # Apply calibration aberration to used mode\n LO_modes = np.zeros(n_LO)\n #sm_mode[6*i-3] = (nm_aber)/2 \n LO_modes[i] = (nm_aber)/2\n luvoir.zernike_mirror.actuators = LO_modes\n zernike_meas = luvoir.calc_out_of_band_wfs(norm_one_photon=True)\n zernike_meas_sub_real = hcipy.field.subsample_field(zernike_meas.real, z_pup_downsample, grid_zernike, statistic='mean')\n zernike_meas_sub_imag = hcipy.field.subsample_field(zernike_meas.imag, z_pup_downsample, grid_zernike, statistic='mean')\n focus_field1 = zernike_meas_sub_real + 1j * zernike_meas_sub_imag\n focus_fieldS.append(focus_field1)\n focus_fieldS_Re.append(focus_field1.real)\n focus_fieldS_Im.append(focus_field1.imag)\n\n# +\nfilename_matrix = 'EFIELD_OBWFS_Re_matrix_num_LO_' + str(n_LO)\nhcipy.write_fits(focus_fieldS_Re, os.path.join(resDir, filename_matrix + '.fits'))\nprint('Efield Real saved to:', os.path.join(resDir, filename_matrix + '.fits'))\n\nfilename_matrix = 'EFIELD_OBWFS_Im_matrix_num_LO_' + str(n_LO)\nhcipy.write_fits(focus_fieldS_Im, os.path.join(resDir, filename_matrix + '.fits'))\nprint('Efield Imag saved to:', os.path.join(resDir, filename_matrix + '.fits'))\n# -\n\nfilename_matrix = 'EFIELD_OBWFS_Re_matrix_num_LO_' + str(n_LO)+'.fits'\nG_OBWFS_real = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix))\nfilename_matrix = 'EFIELD_OBWFS_Im_matrix_num_LO_' + str(n_LO)+'.fits'\nG_OBWFS_imag = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix))\n\nG_OBWFS= np.zeros([N_pup_z*N_pup_z,2,n_LO-1])\nfor pp in range(0, n_LO-1):\n G_OBWFS[:,0,pp] = G_OBWFS_real[pp]*z_pup_downsample - Efield_ref_OBWFS.real\n G_OBWFS[:,1,pp] = G_OBWFS_imag[pp]*z_pup_downsample - Efield_ref_OBWFS.imag\n\n# +\nflux = Nph\nQ_LO = np.diag(np.asarray(mu_map_LO**2))\n\nNtimes = 20\nTimeMinus = -2\nTimePlus = 5.5 #3.5\nNwavescale = 8\nNflux = 3\n\nres = np.zeros([Ntimes, Nwavescale, Nflux, 1])\nresult_wf_test =[]\n\n#i=-1\nfor wavescale in range (1,15,2):\n #i=i+1\n print('Harris modes with batch OBWFS and noise %f'% wavescale, \"i\",i) \n niter = 10\n timer1 = time.time()\n StarMag = 0.0\n #j=-1\n for tscale in np.logspace(TimeMinus, TimePlus, Ntimes):\n j=j+1\n Starfactor = 10**(-StarMag/2.5)\n print(tscale)\n tmp0 = req_closedloop_calc_batch(G_coron_zernike, G_OBWFS, E0_coron, E0_OBWFS, dark_current+CIC/tscale,\n dark_current+CIC/tscale, tscale, flux*Starfactor,0.0001*wavescale**2*Q_LO,\n niter, luvoir.dh_mask, norm) \n tmp1 = tmp0['averaged_hist']\n n_tmp1 = len(tmp1)\n result_wf_test.append(tmp1[n_tmp1-1])\n\n# +\ndelta_wf = []\nfor wavescale in range (1,15,2):\n wf = 1e3*np.sqrt(0.0001*wavescale**2)\n delta_wf.append(wf)\n\ntexp = np.logspace(TimeMinus, TimePlus, Ntimes)\n\nfont = {'family': 'serif','color' : 'black','weight': 'normal','size' : 20}\nplt.figure(figsize =(15,10))\n\nplt.title('Target contrast = %s, Vmag= %s'%(c_target, Vmag),fontdict=font)\nplt.plot(texp,result_wf_test[0:20]-contrast_floor, label=r'$\\Delta_{wf}= %d\\ pm$'%(delta_wf[0]))\nplt.plot(texp,result_wf_test[20:40]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[0]))\nplt.plot(texp,result_wf_test[40:60]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[2]))\nplt.plot(texp,result_wf_test[60:80]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[3]))\nplt.plot(texp,result_wf_test[80:100]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[4]))\nplt.plot(texp,result_wf_test[100:120]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[5]))\nplt.plot(texp,result_wf_test[120:140]-contrast_floor, label=r'$\\Delta_{wf}=%d\\ pm$'%(delta_wf[6]))\nplt.xlabel(\"$t_{WFS}$ in secs\",fontsize=20)\nplt.ylabel(\"$\\Delta$ contrast\",fontsize=20)\nplt.yscale('log')\nplt.xscale('log')\nplt.legend(loc = 'upper center',fontsize=20)\nplt.tick_params(top=False, bottom=True, left=True, \n right=True,labelleft=True, labelbottom=True,\n labelsize=20)\nplt.tick_params(axis='both',which='major',length=10, width=2)\nplt.tick_params(axis='both',which='minor',length=6, width=2)\nplt.grid()\nplt.show()\n# -\n\ndelta_wf[1]\n"},"script_size":{"kind":"number","value":13268,"string":"13,268"}}},{"rowIdx":986,"cells":{"path":{"kind":"string","value":"/I-140_Premium_RFE_Yes.ipynb"},"content_id":{"kind":"string","value":"19001496d6bf4f8497e81f4feecac34b5a8ff60c"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Kim-SeongCheol/i-140"},"repo_url":{"kind":"string","value":"https://github.com/Kim-SeongCheol/i-140"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":418894,"string":"418,894"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [conda env:tensorflow]\n# language: python\n# name: conda-env-tensorflow-py\n# ---\n\n# +\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport datetime, time\nsns.set_style('whitegrid')\n\n# %matplotlib inline\n\n# -\n\n#

데이터 불러오기

\n#\n#\n\ninput = pd.read_csv('input_data.csv', encoding = 'cp1252')\ninput.shape\n\ninput.info()\n\ninput.describe()\n\n#

Ouput data 정리

\n# 1. nan 데이터 삭제\n# 2. Total Processing Time 데이터 integer형으로 변경\n# 3. 600보다 큰 데이터 삭제\n# 4. 총 기간 일 자별 -> 월별 class_output 데이터 생성\n# 5. Approval/Denial Data와 USCIS Received Data 간의 차이 -> delta_output\n#\n#\n#\n\ninput = input.dropna(subset=['Total Processing Time', 'Approval/Denial Date', 'USCIS Received Date'])\ninput.shape\n\n# +\ninput['output'] = input['Total Processing Time'].apply(lambda x: int(x.split()[0]))\ninput = input.drop(input[input.output > 600].index)\n\ninput.shape\n# -\n\ninput.columns\n\ntoday = datetime.date(2018, 4, 10)\ninput['AF_DATE'] = input['Application Filed'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput['month_ago'] = today - input['AF_DATE'] \ninput['month_ago'] = input['month_ago'].apply(lambda x: int(x.days))\ninput['month_ago'] = input['month_ago'].apply(lambda x: int(x / 30))\n\n#

연도별 데이터 사용

\n# Approval/Denial Date 기준\n#
\n# 2015년 이후\n\ninput['Approval/Denial Date'].isnull().sum()\n\ninput['year'] = input['Approval/Denial Date'].apply(lambda x: int(x.split('/')[2]))\n#input = input.drop(input[input['year'] < 2013].index)\ninput.shape\n\ninput[['year', 'output']].groupby(['year'], as_index=False).mean().sort_values(by='output', ascending=False)\n\ninput[['USCIS Received Date', 'Approval/Denial Date']][0:10]\n\ninput['USCIS Received Date'] = input['USCIS Received Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput['Approval/Denial Date'] = input['Approval/Denial Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput['USCIS_Approval_Delta'] = input['Approval/Denial Date'] - input['USCIS Received Date'] \ninput['USCIS_Approval_Delta'] = input['USCIS_Approval_Delta'].apply(lambda x: int(x.days))\n\nsns.distplot(input['USCIS_Approval_Delta']) \n\n#

불필요 Columns 삭제

\n\ninput = input.drop(['Applicant Type', 'Service Center','Approval/Denial Date', 'USCIS Received Date',\n 'Reason for RFE','Application Status','USCIS Notice Date', 'USCIS Receipt Number','Most Recent LUD',\n 'Days Elapsed', 'Notes', 'State', 'Case Added to Tracker', 'Last Updated', 'year','Total Processing Time', 'AF_DATE'\n ], axis = 1)\ninput.shape\n\ninput.sample(10)\n\n#

Nationality 데이터 정리

\n#\n# Chinal, India 제외한 데이터 Others로 변경\n\nlen(input[input.Nationality == 'China'])\n\n\nlen(input[input.Nationality == 'India'])\n\nlen(input[(input.Nationality != 'China') & (input.Nationality != 'India')])\n\ninput.ix[(input.Nationality != 'India') & (input.Nationality != 'China'), 'Nationality'] = 'Others'\n\ninput[['Nationality', 'output']].groupby(['Nationality'], as_index=False).mean().sort_values(by='output', ascending=False)\n\nnationality_one_hot = pd.get_dummies(input['Nationality'])\ninput = input.drop('Nationality', axis = 1)\ninput = input.join(nationality_one_hot)\n\n#

Category 데이터 정리

\n#\n# 1. EB4, EB5, nan 삭제\n#
\n# 2. 카테고리 데이터 -> one hot encoding\n#\n\nset(input.Category)\n\ninput = input.dropna(subset=['Category'])\ninput.shape\n\nsns.countplot(input.Category)\n\ninput = input.drop(input[input.Category == 'EB5'].index)\ninput = input.drop(input[input.Category == 'EB4'].index)\ninput.shape\n\nsns.countplot(input.Category)\n\ninput[['Category', 'output']].groupby(['Category'], as_index=False).mean().sort_values(by='output', ascending=False)\n\ncategory_one_hot = pd.get_dummies(input['Category'])\ninput = input.drop('Category', axis = 1)\ninput = input.join(category_one_hot)\n\n#

I-140 /486 Filing, Processing Type, RFE Received? 데이터 정리

\n# 1. Binary Categorical 데이터 이므로 binary data 로 변경(0,1)\n# 2. RFE received? 데이터는 nan값이 많으므로 yes, no nan에 대해서 2,1,0 넣기?\n\ninput[\"I-140/485 Filing\"].isnull().sum()\n\ninput[\"Processing Type\"].isnull().sum()\n\n# +\ninput = input.dropna(subset = ['Processing Type'])\n\ninput.shape\n# -\n\ninput.ix[input['I-140/485 Filing'] == 'concurrent', 'I-140/485_Filing'] = int(1)\ninput.ix[input['I-140/485 Filing'] == 'non-concurrent', 'I-140/485_Filing'] = int(0)\n\ninput['Application Filed'].isnull().sum()\n\ninput['AF_Month'] = input['Application Filed'].apply(lambda x: int(x.split('/')[0]))\n\ninput[['AF_Month', 'output']].groupby(['AF_Month'], as_index=False).mean().sort_values(by='output', ascending=False)\n\ninput['AF_Month'] = input['AF_Month'].apply(lambda x: 1 if x > 6 else 0)\n\ninput['Priority Date'].isnull().sum()\n\ninput = input.dropna(subset = ['Priority Date'])\ninput.shape\n\ninput['Priority Date'] = input['Priority Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput['Application Filed'] = input['Application Filed'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput['AF_PD_Delta'] = input['Application Filed'] - input['Priority Date'] \ninput['AF_PD_Delta'] = input['AF_PD_Delta'].apply(lambda x: int(x.days))\n\n\ninput.ix[input['AF_PD_Delta'] < 0, 'AF_PD_Delta'] = 0\ninput.ix[input['AF_PD_Delta'] > 500, 'AF_PD_Delta'] = 500\ninput['AF_PD_Delta'] = input['AF_PD_Delta'].apply(lambda x: int(x/50))\n\ninput[['AF_PD_Delta', 'output']].groupby(['AF_PD_Delta'], as_index=False).mean().sort_values(by='output', ascending=False)\n\nsns.countplot(input.AF_PD_Delta)\n\ninput_premium = input[input['Processing Type'] == 'premium']\ninput_premium.shape\n\ninput_premium_yes = input_premium[input_premium['RFE Received?'] == 'yes']\ninput_premium_yes.shape\n\ninput_premium_yes = input_premium_yes.drop(['I-140/485 Filing', 'Processing Type', 'RFE Received?'], axis = 1)\n\ninput_premium_noanswer = input_premium[input_premium['RFE Received?'].isnull()]\ninput_premium_noanswer = input_premium_noanswer.drop(['I-140/485 Filing', 'Processing Type', 'RFE Received?'], axis = 1)\n\ninput_premium_noanswer.shape\n\nadditional_input_premium_yes = input_premium_noanswer[input_premium_noanswer['USCIS_Approval_Delta'] > 30]\nadditional_input_premium_yes.shape\n\ninput_premium_yes = pd.concat([input_premium_yes, additional_input_premium_yes])\ninput_premium_yes.shape\n\n#

RFE_AF_Delta 데이터 만들기

\n# RFE Received Date과 Application Filed Date의 날짜 차이
\n# regression 에서 좋은 성능을 내게 해주므로 Normalize만 시키고 continuous데이터 그대로 사용\n#\n# \n\n# RFE Received? 가 NaN인 데이터중 USCIS Received Date과 Approval/Denial Date과의 차이가 30일 이상인데이터 추가 했지만,\n# 그럴 경우 데이터 대부분이 RFE Received Date 가 nan이라 영향 주지 않음.\n\ninput_premium_yes['RFE Received Date'].isnull().sum()\n\ninput_premium_yes = input_premium_yes.dropna(subset = ['RFE Received Date'])\n\ninput_premium_yes['RFE Received Date'] = input_premium_yes['RFE Received Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1])))\ninput_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE Received Date'] - input_premium_yes['Application Filed'] \ninput_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE_AF_Delta'].apply(lambda x: int(x.days))\n\ninput_premium_yes.ix[input_premium_yes['RFE_AF_Delta'] < 0, 'RFE_AF_Delta'] = 0\ninput_premium_yes.ix[input_premium_yes['RFE_AF_Delta'] > 365, 'RFE_AF_Delta'] = 365\ninput_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE_AF_Delta'].apply(lambda x: x/365)\n\nsns.distplot(input_premium_yes.RFE_AF_Delta)\n\ninput_premium_yes['RFE_AF_Delta'].corr(input_premium_yes['output'])\n\ninput_premium_yes = input_premium_yes.drop(['Priority Date', 'Application Filed', 'RFE Received Date', 'RFE Replied Date','USCIS_Approval_Delta'], axis = 1)\n\n\n#

Classification output

\n\nsns.distplot(input_premium_yes.output)\n\ninput_premium_yes = input_premium_yes.drop(input_premium_yes[input_premium_yes.output > 150].index)\ninput_premium_yes['class_output'] = input_premium_yes['output'].apply(lambda x: int(x / 30))\n\ninput_premium_yes.shape\n\n#

데이터 스플릿

\n\n# +\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.metrics import mean_squared_error\nimport matplotlib\nimport itertools\n\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression, LinearRegression\nfrom sklearn.neural_network import MLPClassifier, MLPRegressor\nfrom sklearn.metrics import accuracy_score\n\nimport scipy\n# -\n\n# 1) Classification\n\ninput_premium_yes_dic = {}\nperiod_list = [25, 31, 37, 43, 49, 55]\n# 2년 , 2.5년, 3년, 3.5년\nfor period in period_list:\n input_premium_yes_dic[period] = input_premium_yes[input_premium_yes['month_ago'] < period]\n print(\"period %d shape : %s\" %(period-1, input_premium_yes_dic[period].shape))\n\ncol_train = list(input_premium_yes.columns)\ncol_train.remove('output')\ncol_train.remove('class_output')\ncol_train.remove('month_ago') \n\nFEATURES = col_train\nLABEL = \"class_output\"\ntop_1_acc = []\ntop_2_acc = []\nfor period in period_list:\n # Training set and Prediction set with the features to predict\n X = input_premium_yes_dic[period][FEATURES]\n y = input_premium_yes_dic[period].class_output\n \n acc_top1 = 0\n acc_top2 = 0\n for i in range(0,4):\n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n y_train = pd.DataFrame(y_train, columns = [LABEL])\n y_test = pd.DataFrame(y_test, columns = [LABEL])\n # Top- 1 Accuracy\n logreg = LogisticRegression()\n logreg.fit(x_train, y_train)\n y_pred = logreg.predict(x_test)\n acc_top1 = acc_top1 + round(logreg.score(x_test, y_test) * 100, 2)\n probs = logreg.predict_proba(x_test)\n best_n = np.argsort(probs, axis=1)\n correct_top2 = 0\n for i in range(0, y_test.shape[0]):\n if y_test.values[i][0] in list(best_n[i][-2:][::-1]):\n correct_top2 += 1\n acc_top2 = acc_top2 + (correct_top2 / y_test.shape[0] * 100)\n \n \n acc_top1 = acc_top1 / 4\n print(\"Logistic Regression %d month ago top-1 acc : %f\" % (period - 1,acc_top1))\n top_1_acc.append(acc_top1)\n \n acc_top2 = acc_top2 / 4\n print(\"Logistic Regression %d month ago top-2 acc : %f\" % (period - 1,acc_top2))\n top_2_acc.append(acc_top2)\n print(\"-\" *60)\n\n\nfig, ax = plt.subplots()\nx = np.linspace(-4, 4, 150)\nax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o')\nax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o')\nax.legend()\nplt.title(\"Logistic Regression Classification\")\nplt.xlabel(\"months ago\")\nplt.ylabel(\"Accuracy\")\nplt.show()\n\nFEATURES = col_train\nLABEL = \"class_output\"\ntop_1_acc = []\ntop_2_acc = []\nfor period in period_list:\n # Training set and Prediction set with the features to predict\n X = input_premium_yes_dic[period][FEATURES]\n y = input_premium_yes_dic[period].class_output\n # Train and Test \n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n y_train = pd.DataFrame(y_train, columns = [LABEL])\n y_test = pd.DataFrame(y_test, columns = [LABEL])\n acc_top1 = 0\n acc_top2 = 0\n for i in range(0,4):\n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n y_train = pd.DataFrame(y_train, columns = [LABEL])\n y_test = pd.DataFrame(y_test, columns = [LABEL])\n # Top- 1 Accuracy\n mlp = MLPClassifier(solver='lbfgs', alpha=5e-5,\n hidden_layer_sizes=(200, 150, 100, 50), random_state=1)\n\n mlp.fit(x_train, y_train) \n y_pred = logreg.predict(x_test)\n acc_top1 = acc_top1 + round(logreg.score(x_test, y_test) * 100, 2)\n probs = logreg.predict_proba(x_test)\n best_n = np.argsort(probs, axis=1)\n correct_top2 = 0\n for i in range(0, y_test.shape[0]):\n if y_test.values[i][0] in list(best_n[i][-2:][::-1]):\n correct_top2 += 1\n acc_top2 = acc_top2 + correct_top2 / y_test.shape[0] * 100\n \n acc_top1 = acc_top1 / 4\n print(\"Neural Network %d month ago top-1 acc : %f\" % (period - 1,acc_top1))\n top_1_acc.append(acc_top1)\n \n acc_top2 = acc_top2 / 4\n print(\"Neural Network %d month ago top-2 acc : %f\" % (period - 1,acc_top2))\n top_2_acc.append(acc_top2)\n print(\"-\" *60) \n\nfig, ax = plt.subplots()\nx = np.linspace(-4, 4, 150)\nax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o')\nax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o')\nax.legend()\nplt.title(\"Neural Network Classification\")\nplt.xlabel(\"months ago\")\nplt.ylabel(\"Accuracy\")\nplt.show()\n\n\n# 2) Regression\n\ndef rsquared(x, y):\n \"\"\" Return R^2 where x and y are array-like.\"\"\"\n\n slope, intercept, r_value, p_value, std_err = scipy.stats.mstats.linregress(x, y)\n return r_value**2\n\n\n# +\nrsq_list = []\ntop_1_acc = []\ntop_2_acc = []\ny_test_dic = {}\ny_pred_dic = {}\n\nfor period in period_list:\n FEATURES = col_train\n LABEL = \"output\"\n # Columns for tensorflow\n feature_cols = [tf.contrib.layers.real_valued_column(k) for k in FEATURES]\n # Training set and Prediction set with the features to predict\n X = input_premium_yes_dic[period][FEATURES]\n y = input_premium_yes_dic[period].output\n\n # Train and Test \n rsq = 0\n acc_top1 = 0\n acc_top2 = 0\n for i in range(0,4):\n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n y_train = pd.DataFrame(y_train, columns = [LABEL])\n y_test = pd.DataFrame(y_test, columns = [LABEL])\n\n regressor = LinearRegression()\n regressor.fit(x_train, y_train)\n y_pred = regressor.predict(x_test)\n rsq = rsq + rsquared(y_test, y_pred)\n diff = np.array(y_test).reshape(-1,) - y_pred.reshape(-1,)\n acc_top1 = acc_top1 + ((-15 < diff) & (diff < 15)).sum() / x_test.shape[0] * 100\n acc_top2 = acc_top2 + ((-30 < diff) & (diff < 30)).sum() / x_test.shape[0] * 100\n \n y_test_dic[period] = y_test\n y_pred_dic[period] = y_pred\n \n rsq = rsq / 4\n print(\"Logistic Regression %d month ago rsq : %f\" % (period -1, rsq))\n rsq_list.append(rsq)\n \n acc_top1 = acc_top1 / 4\n print(\"Logistic Regression %d month ago top-1 acc : %f\" % (period - 1,acc_top1))\n top_1_acc.append(acc_top1)\n \n acc_top2 = acc_top2 / 4\n print(\"Logistic Regression %d month ago top-2 acc : %f\" % (period - 1,acc_top2))\n top_2_acc.append(acc_top2)\n print(\"-\" *60)\n# -\n\nfig, ax = plt.subplots()\nx = np.linspace(-4, 4, 150)\nax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o')\nax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o')\nax.legend()\nplt.title(\"Logisctic Regression\")\nplt.xlabel(\"months ago\")\nplt.ylabel(\"Accuracy\")\nplt.show()\n\n# +\ni = 1\nplt.figure(figsize = (15,10))\nfor period in period_list:\n y_test = y_test_dic[period]\n y_pred = y_pred_dic[period]\n plt.subplot(320 + i)\n i = i + 1\n plt.scatter(y_test, y_pred)\n plt.xlabel('Test Data')\n plt.ylabel('Predicted Data')\n plt.title(\"%d month ago\" % (period -1))\n plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--')\n \nplt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.3, wspace=0.2)\nplt.show()\n\n# +\n\nrsq_list = []\ntop_1_acc = []\ntop_2_acc = []\ny_test_dic = {}\ny_pred_dic = {}\n\nfor period in period_list:\n FEATURES = col_train\n LABEL = \"output\"\n # Columns for tensorflow\n feature_cols = [tf.contrib.layers.real_valued_column(k) for k in FEATURES]\n # Training set and Prediction set with the features to predict\n X = input_premium_yes_dic[period][FEATURES]\n y = input_premium_yes_dic[period].output\n\n # Train and Test \n rsq = 0\n acc_top1 = 0\n acc_top2 = 0\n for i in range(0,4):\n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n y_train = pd.DataFrame(y_train, columns = [LABEL])\n y_test = pd.DataFrame(y_test, columns = [LABEL])\n\n regressor = MLPRegressor(hidden_layer_sizes=(100, 50, 25),\n activation='relu',\n solver='lbfgs',\n learning_rate='adaptive',\n max_iter=2000,\n alpha=5e-5)\n regressor.fit(x_train, y_train)\n y_pred = regressor.predict(x_test)\n rsq = rsq + rsquared(y_test, y_pred)\n diff = np.array(y_test).reshape(-1,) - y_pred.reshape(-1,)\n acc_top1 = acc_top1 + ((-15 < diff) & (diff < 15)).sum() / x_test.shape[0] * 100\n acc_top2 = acc_top2 + ((-30 < diff) & (diff < 30)).sum() / x_test.shape[0] * 100\n \n y_test_dic[period] = y_test\n y_pred_dic[period] = y_pred\n \n rsq = rsq / 4\n print(\"Logistic Regression %d month ago rsq : %f\" % (period -1, rsq))\n rsq_list.append(rsq)\n \n acc_top1 = acc_top1 / 4\n print(\"Logistic Regression %d month ago top-1 acc : %f\" % (period - 1,acc_top1))\n top_1_acc.append(acc_top1)\n \n acc_top2 = acc_top2 / 4\n print(\"Logistic Regression %d month ago top-2 acc : %f\" % (period - 1,acc_top2))\n top_2_acc.append(acc_top2)\n print(\"-\" *60)\n\n# -\n\nfig, ax = plt.subplots()\nx = np.linspace(-4, 4, 150)\nax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o')\nax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o')\nax.legend()\nplt.title(\"Neural Network Regression\")\nplt.xlabel(\"months ago\")\nplt.ylabel(\"Accuracy\")\nplt.show()\n\n# +\ni = 1\nplt.figure(figsize = (15,10))\nfor period in period_list:\n y_test = y_test_dic[period]\n y_pred = y_pred_dic[period]\n plt.subplot(320 + i)\n i = i + 1\n plt.scatter(y_test, y_pred)\n plt.xlabel('Test Data')\n plt.ylabel('Predicted Data')\n plt.title(\"%d month ago\" % (period -1))\n plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--')\n \nplt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.3, wspace=0.2)\nplt.show()\n"},"script_size":{"kind":"number","value":18602,"string":"18,602"}}},{"rowIdx":987,"cells":{"path":{"kind":"string","value":"/inverse-problems/2018/code/L06-iterative.ipynb"},"content_id":{"kind":"string","value":"667b372f787c1e22628e488408baa05ab94e5ffd"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"omaclaren/open-learning-material"},"repo_url":{"kind":"string","value":"https://github.com/omaclaren/open-learning-material"},"star_events_count":{"kind":"number","value":27,"string":"27"},"fork_events_count":{"kind":"number","value":4,"string":"4"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":7061914,"string":"7,061,914"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ### Iterative regularisation \n#\n# Simple Landweber Iteration scheme illustrated on deconvolution problem.\n\nimport numpy as np\nfrom IPython.display import set_matplotlib_formats\nset_matplotlib_formats('png', 'pdf')\n\n\n#forward mapping for convolution\ndef create_fmap_con(n=4,d=1):\n weights = np.zeros(d)\n weights[0:n] = 1./n\n A = np.zeros([d,d])\n for i in range(0,d):\n A[i,:] = np.roll(weights,i-int(n/2))\n return A\n\n\nt = np.linspace(0,4*np.pi,1000)\nx = np.sin(t)\nplt.plot(t,x,'r--')\nplt.show()\n\n#create forward mapping for convolution with greater width of smoothing\nA = create_fmap_con(n=int(len(x)/4+1),d=len(x))\ny = np.dot(A,x)\nplt.plot(t,y,'k')\nplt.plot(t,x,'r--')\nplt.show()\n\n#add almost undetectable noise\n#y_noisy = y+np.random.normal(0,0.001,size=len(y))\ny_noisy = y+np.random.normal(0,0.01,size=len(y))\nplt.plot(t,y_noisy,'k')\nplt.plot(t,y,'r--')\nplt.show()\n\n#invert noisy\nplt.plot(t,x,'r--')\nplt.plot(t,np.dot(np.linalg.inv(A),y_noisy),'k')\nplt.show()\n\n# #### Iterative approach\n\nfrom scipy.sparse.linalg import svds\n\nU, s, VT = svds(A, k=1)\ns1 = s\nprint(s1)\n\nniter = 1000\nxs = np.zeros((niter,len(x)))\nfor i in range(0,niter-1):\n #update rule\n xs[i+1,:] = xs[i,:] + A.T@(y_noisy-A@xs[i,:])\n\nniter = 1000\nxs = np.zeros((niter,len(x)))\nx_norms = np.zeros(niter)\ndata_norms = np.zeros(niter)\nfor i in range(0,niter-1):\n \n #calc norms\n x_norms[i] = np.linalg.norm(xs[i,:],2)\n #x_norms_1[i] = np.linalg.norm(xs[i,:],1)\n data_norms[i] = np.linalg.norm(y_noisy - np.dot(A,xs[i,:]))\n \n #update rule\n xs[i+1,:] = xs[i,:] + A.T@(y_noisy-A@xs[i,:])\n\nplt.plot(t,xs[0,:],'r')\nplt.plot(t,xs[1,:],'b')\nplt.plot(t,xs[2,:],'g')\nplt.plot(t,xs[3,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs[6,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs[10,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs.T,'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs[29,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs[299,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nplt.plot(t,xs[999,:],'k')\nplt.plot(t,x,'r--')\nplt.show()\n\nn = 30\nplt.plot(data_norms[0:n]**2,linewidth=5)\nplt.ylabel(r'$||Ax-y||^2$',fontsize=14)\nplt.xlabel(r'iteration',fontsize=14)\nplt.show()\n\nn = 30\nplt.plot(np.arange(5,n),data_norms[5:n]**2,linewidth=5)\nplt.ylabel(r'$||Ax-y||^2$',fontsize=14)\nplt.xlabel(r'iteration',fontsize=14)\nplt.show()\n\n#n = 30\nn = 100\nplt.plot(np.arange(15,n),data_norms[15:n]**2,linewidth=5)\nplt.ylabel(r'$||Ax-y||^2$',fontsize=14)\nplt.xlabel(r'iteration',fontsize=14)\nplt.show()\n\nn = 999\nplt.plot(x_norms[0:n]**2,data_norms[0:n]**2,'-+',markersize=10,linewidth=2)\nplt.ylabel(r'$||Ax-y||^2$',fontsize=14)\nplt.xlabel(r'$||x||^2$',fontsize=14)\nplt.show()\n\n\n"},"script_size":{"kind":"number","value":2965,"string":"2,965"}}},{"rowIdx":988,"cells":{"path":{"kind":"string","value":"/nbody_simulation/.ipynb_checkpoints/cosmic_web-checkpoint.ipynb"},"content_id":{"kind":"string","value":"b419113e38be3ff7b4b1de3b80adf7029b7f0bac"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"pitt1321/IndrasNet"},"repo_url":{"kind":"string","value":"https://github.com/pitt1321/IndrasNet"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2015-12-09T07:20:59","string":"2015-12-09T07:20:59"},"gha_updated_at":{"kind":"timestamp","value":"2015-11-06T15:18:26","string":"2015-11-06T15:18:26"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":215405,"string":"215,405"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %matplotlib inline\nimport numpy \nfrom matplotlib import pyplot as plt\nfrom matplotlib import rcParams\nrcParams['font.family'] = 'serif'\nrcParams['font.size'] = 16\nfrom matplotlib import animation\nfrom JSAnimation.IPython_display import display_animation\n\n# +\n#Parameters\n\nnx = 81\ndx = 0.25\ndt = 0.0002\ngamma = 1.4 \nnt = int(0.01/dt)+1\nx=numpy.linspace(-10,10,nx)\n\n\n# +\n#Define rho with the initial conditions\n\ndef rho_initial(nx):\n \n rho = numpy.zeros(nx)\n rho[0:(nx-1)/2] = 1 #kg/m^3\n rho[(nx-1)/2:nx] = 0.125 #kg/m^3\n \n return rho\n\nrho_initial = rho_initial(nx)\n#print(rho)\n\n# +\n#Define u with the initial conditions\n\n#def u_initial():\n \nu_initial = numpy.zeros(nx)\n \n# return u \n\n#u = u_initial()\n#print(u)\n\n# +\n#Define p with the initial conditions\n\ndef p_initial(nx):\n \n p = numpy.zeros(nx)\n p[0:(nx-1)/2] = 100*1000 #N/m^2\n p[(nx-1)/2:nx] = 10*1000 #N/m^2\n \n return p\n\np_initial = p_initial(nx)\n#print(p)\n# -\n\ndef getE(p, rho, u):\n \n e = p/((gamma-1)*rho)\n e_t = e + (1/2*u**2)\n \n return e_t\n\n\ne_t_initial = getE(p_initial, rho_initial, u_initial)\n#print(e_t_initial)\n\nrho_initial.shape\n\n\ndef getU_vector(rho, u, e_t):\n\n u_vector = numpy.zeros((nx, 3))\n u_vector[:, 0] = rho\n u_vector[:, 1] = rho*u\n u_vector[:, 2] = rho*e_t\n \n return u_vector\n\n\nu_vector_initial = getU_vector(rho_initial, u_initial, e_t_initial)\n\n\n# +\n#u_vector_initial[:,1]/rho_initial==u_initial\n# -\n\ndef computeF(u_vector):\n\n f_vector = numpy.zeros((nx, 3))\n \n f_vector[:, 0] = u_vector[:, 1]\n f_vector[:, 1] = u_vector[:, 1]**2/u_vector[:, 0] + (gamma-1)*(u_vector[:, 2] - 0.5*u_vector[:, 1]**2/u_vector[:, 0])\n f_vector[:, 2] = (u_vector[:, 2] + (gamma-1)*(u_vector[:, 2] - 0.5*u_vector[:, 1]**2/u_vector[:, 0]))*(u_vector[:, 1]/u_vector[:, 0])\n \n return f_vector\n\n\ndef richtmyer (u, nt, dt, dx):\n un = numpy.zeros((nt, nx, 3))\n un[:,:,:] = u.copy()\n ustar = u.copy()\n \n for t in range(1, nt):\n \n #predictor\n f = computeF(u)\n \n ustar[:-1,:] = 0.5*(u[1:,:] + u[:-1,:]) - dt/(2*dx)*(f[1:,:] - f[:-1,:])\n \n #corrector\n fstar = computeF(ustar)\n \n un[t,1:-1,:] = u[1:-1,:] - dt/dx*(fstar[1:-1,:] - fstar[:-2,:])\n \n u = un[t,:,:].copy()\n \n return un\n\n\na = richtmyer(u_vector_initial, nt, dt, dx)\nprint(numpy.shape(a))\n\nnumpy.where(x==2.5)\n\n# +\n#find final velocity\nv_final=a[nt-1,:,1]/a[nt-1,:,0]\n\nprint(\"the velocity at x = 2.5 is:\")\nprint(v_final[50]) #nx=50\n\n# +\n#find final density\nrho_final = a[nt-1,:,0]\n\nprint(\"the density at x = 2.5 is:\")\nprint(rho_final[50])\n\n# +\n#find final pressure\n\np_final = (gamma-1)*(a[nt-1, :, 2]-0.5*(a[nt-1, :, 1])**2/a[nt-1,:,0])\n\nprint(\"the pressure at x = 2.5 is:\")\nprint(p_final[50])\n\n# +\n#diplay animation\nfig = plt.figure();\nax = plt.axes(xlim=(-10,10),ylim=(0,2),xlabel=('Distance'),ylabel=('Traffic density'));\nline, = ax.plot([],[],color='#003366', lw=2);\n\ndef animate(data):\n x = numpy.linspace(-10,10,nx)\n y = a[data, :, 0]\n line.set_data(x,y)\n return line,\n\nanim = animation.FuncAnimation(fig, animate, frames=nt-1, interval=50)\ndisplay_animation(anim, default_mode='once')\n# -\n\n\n= RAmax: \n z = data[i][0]\n ra = data[i][1]\n dec = data[i][2]\n Mly = 3.26 * (3000.0*z - 607.8 * z**2 - 156.3 * z**3 + 138.3*z**4)/0.71\n x = Mly*np.cos(ra*np.pi/180)\n y = Mly*np.sin(ra*np.pi/180)\n galaxies.append([x,y])\n\n return np.array(galaxies)\n\n\ngalaxies = galaxy(data_cf, RAmin=0, RAmax=60)\n\n\ndef randGalaxy(galaxies, zmin,zmax,RAmin,RAmax):\n \"\"\"\n Creates a random galaxy distribution to match data distribution\n -----------------------------------------------------------------\n Inputs:\n galaxies: a numpy array of [x,y] positions of galaxies- output of galaxy function\n zmin/max: the minimum/maximum red-shift of the data set (float). \n RAmin/max: the min/max right ascension of the data set (float). \n \n Output:\n numpy array of [x,y] coordinates of random galaxies in desired range\n \"\"\"\n\n #random z numbers in given range:\n zrand = zmax*np.sqrt(np.random.uniform(0,1,len(galaxies)))\n #random ra numbers in given range: \n rarand = np.random.uniform(RAmin,RAmax,len(galaxies))\n \n #known conversion from redshift to distance from Earth \n Mly = 3.26 * (3000.0*zrand - 607.8 * zrand**2 - 156.3 * zrand**3 + 138.3*zrand**4)/0.71\n \n #x,y coordinates of randomly distributed galaxies: \n xrand = Mly*np.cos(rarand*np.pi/180)\n yrand = Mly*np.sin(rarand*np.pi/180)\n \n rgalaxies=zip(xrand,yrand)\n \n return np.array(rgalaxies)\n\n\n\nrgalaxies = randGalaxy(galaxies, zmin=zmin, zmax=zmax, RAmin=0, RAmax=60)\n\n\ndef galaxyPairFinder(galaxies,randomGalaxies):\n \"\"\"\n Finds the distance between all pairs of galaxies from\n both the data and the random distribution.\n -------------------------------------------------\n Inputs:\n galaxies -- numpy array of [x,y] positions of galaxies from SDSS data (output of galaxy function)\n randomGalaxies -- numpy array of [x,y] positions of random galaxies (output of randGalaxy function)\n \"\"\"\n d = []\n dr= [] \n for i in range(0,len(galaxies)): \n for j in range(0,len(galaxies)): \n if j > i: \n d.append(np.sqrt((galaxies[i][0] - galaxies[j][0])**2 + (galaxies[i][1] - galaxies[j][1])**2))\n #want every unique pair of real data\n dr.append(np.sqrt((galaxies[i][0] - randomGalaxies[j][0])**2 +(galaxies[i][1] - randomGalaxies[j][1])**2))\n #every possible pair of random galaxies with real galaxy data, do not have to worry about uniqueness \n return np.array([d,dr])\n \n\n\ngpairs = galaxyPairFinder(galaxies, rgalaxies)\n\n\ndef twoPointCorr(galaxyPairs,Nbins): \n \"\"\"\n Calculates the two-point correlation function of a set of galaxies,\n using already created pairs of distances\n -------------------------------------------------\n Inputs:\n galaxyPairs -- numpy array of distances between real and random galaxies [d, dr] (output of galaxyPairFinder function)\n Nbins -- desired number of bins to use\n \n Output:\n numpy array of bins and xi values [r, xi]\n \"\"\"\n \n #find min/max distances between random galaxies\n rmin = min(galaxyPairs[1])\n rmax = max(galaxyPairs[1])\n\n r = np.linspace(rmin,rmax,Nbins) #set up bins\n \n #create empty arrays to build using bins\n ddr = []\n rrr = []\n for i in range(0, len(r)-1): \n counterD=0.0\n counterR=0.0 \n for w in galaxyPairs[0]: \n #for each value of distances between real galaxies, determine whether it belongs in current bin \"w\"\n if w>=r[i] and w= r[i] and k Mpc\n rmax = max(galaxyPairs[0])*(1/(0.70*3.26))\n\n signal = twoPoint[:,1] #get values corresponding to positive frequencies\n fourier = np.fft.fft(signal)\n \n n = signal.size \n waveLength = (rmax - rmin)/Nbins \n \n freq =np.linspace(0,(n/2+1)*2.*(1/waveLength),n/2+1)\n fourier_pos = fourier[:n/2+1]\n \n return np.array([freq,fourier_pos.real])\n\n\n# **Combining all functions of 2 / 3 **\n\ndef largeScaleStructure(data,RAmin,RAmax,Nbins):\n \"\"\"\n Combines functions related to the large scale structure and plots:\n 1. Galaxy data in used field from SDSS\n 2. Distribution of random galaxies\n 3. Two-point correlation function\n 4. Fourier Transform of Two-Point Correlation Function (Power Spectrum)\n Inputs:\n data -- data to use, taken from SDSS\n RAmin/max -- desired minimum and maximum of RA range\n Nbins -- amount of bins to use for two-point correlation function\n \n \"\"\"\n zmin = min(data[:,0])\n zmax = max(data[:,0])\n \n galaxies = galaxy(data,RAmin,RAmax)\n randomGalaxies = randGalaxy(galaxies,zmin,zmax,RAmin,RAmax)\n galaxyPairs = galaxyPairFinder(galaxies,randomGalaxies)\n twoPoint = twoPointCorr(galaxyPairs,Nbins)\n powerSpectra = powerSpectrum(galaxyPairs,Nbins,twoPoint)\n \n #plot galaxies from sdss: \n plt.scatter(galaxies[:,0],galaxies[:,1],s=2)\n plt.title('Galaxy Distribution: %f Mpc\n #plt.xlim(0,500000)\n #plt.ylim(-2,2)\n plt.title('Two-Point Correlation Function')\n plt.xlabel(r'$f [h^{-1}Mpc]$')\n plt.ylabel(r'$\\xi(r)$')\n #plt.yscale('log')\n plt.savefig('tpcf.pdf')\n plt.show()\n \n #plot power spectrum\n plt.clf()\n plt.scatter(powerSpectra[0],powerSpectra[1])\n plt.title('Power Spectrum')\n plt.xlabel(r'$f [h{Mpc}^{-1}]$')\n plt.ylabel(r'$F(\\xi(r))$')\n plt.xlim(0,36)\n plt.ylim(-20,40)\n plt.savefig('power_spectrum.pdf')\n plt.show()\n\n\n\nlargeScaleStructure(data_cf, RAmin=0, RAmax=30, Nbins=100)\n"},"script_size":{"kind":"number","value":10707,"string":"10,707"}}},{"rowIdx":989,"cells":{"path":{"kind":"string","value":"/3D_representations_on_simulated_rf_matrices_dataset.ipynb"},"content_id":{"kind":"string","value":"896fc8c31c668780975c2bb41c0b1c94b4683867"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"TahiriNadia/ML_DL_Classification_Trees"},"repo_url":{"kind":"string","value":"https://github.com/TahiriNadia/ML_DL_Classification_Trees"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":156555,"string":"156,555"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"view-in-github\" colab_type=\"text\"\n# \"Open\n\n# + [markdown] id=\"DftcBgmBCYUX\"\n# Домашнее задание №3, часть 1: Анализ данных. Pandas + Визуализации.\n\n# + id=\"OPEWrRsrCR7a\"\n\n\n# + id=\"sPefesl5ChCg\"\n\n\n# + id=\"LEN7daAlChGM\"\n\n\n# + [markdown] id=\"w-CSGTpUCiAl\"\n# Черновик.\nopular synthetic dataset called `iris` for this notebook.\n#\n# The way it usually goes in sklearn is that you make a classifier, train it, and then use this trained model to make predictions.\n#\n# ```\n# clf = sklearn.SomeClassifier()\n# clf.fit(training_features, training_answers)\n# predictions = clf.predict(prediction_features)\n# ```\n\nfrom sklearn.datasets import load_iris\nfrom sklearn import tree\niris = load_iris()\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(iris.data, iris.target)\n\nimport graphviz \ndot_data = tree.export_graphviz(clf, out_file=None) \ngraph = graphviz.Source(dot_data) \ngraph\n\ndot_data = tree.export_graphviz(clf, out_file=None, \n feature_names=iris.feature_names, \n class_names=iris.target_names, \n filled=True, rounded=True, \n special_characters=True) \ngraph = graphviz.Source(dot_data) \ngraph \n\nclf.predict(iris.data[:1, :])\n\nclf.predict_proba(iris.data[:1, :])\n\n# Exercise for you!\n#\n# 1. Try doing the same stuff with a Random Forest and Adaboost\n# 2. Read about cross validation here: http://scikit-learn.org/stable/modules/cross_validation.html. Try figure out the best depth for the decision tree using cross validation.\n\n\nd +[None]):\n with open(file, 'r', encoding = 'utf-8') as fin:\n for line in fin.readlines():\n encoded_line = encode(line,subword)\n for word in encoded_line.split(' '):\n word_dict.add(word.lower())\n\nprint('word nums : %d' % (len(word_dict)))\n\n# +\n# with open(embed_file,encoding='ISO-8859-1') as f:\n# with open(embed_file+'.u8','w+',encoding='utf-8') as f2:\n# for line in f.readlines():\n# try:\n# uft_str = line.encode(\"iso-8859-1\").decode('utf-8') \n# f2.write(uft_str)\n# except :\n# pass\n\n# +\n# 加载embedding\nimport numpy as np\ndef read_vectors(path, topn): # read top n word vectors, i.e. top is 10000\n lines_num, dim = 0, 0\n vectors = {}\n iw = []\n wi = {}\n with open(path,encoding='utf-8') as f:\n first_line = True\n for line in f:\n if first_line:\n first_line = False\n dim = int(line.strip().split()[1])\n continue\n lines_num += 1\n tokens = line.rstrip().split(' ')\n if tokens[0] in word_dict:\n vectors[tokens[0]] = np.asarray([float(x) for x in tokens[1:]])\n vectors[tokens[0]] = vectors[tokens[0]]\n iw.append(tokens[0])\n if topn != 0 and lines_num >= topn:\n break\n for i, w in enumerate(iw):\n wi[w] = i\n return vectors, iw, wi, dim\n\nvectors = read_vectors(embed_file,0)\nprint(len(vectors[0]))\n# -\n\n\n\n\n\n# +\n# 计算一个文件的embedding\nrandom_embedding = dict()\nstop_words = set(['!','?','。','.',','])\ndef get_embeddings(file,subword=None):\n embeddings = []\n valids = []\n count = 0\n with open(file, encoding='utf-8') as fin:\n for line in fin.readlines():\n encoded_line = encode(line,subword)\n tmp = np.zeros([300])\n count = 0\n for word in encoded_line.split(' '):\n if word in vectors[0]:\n tmp += vectors[0][word]\n count += 1\n else:\n if word in random_embedding:\n noisy = random_embedding[word]\n else: \n noisy = np.random.normal(size=[300])\n random_embedding[word] = noisy\n tmp+=noisy\n count += 1\n if count > 0:\n tmp = tmp / sum(np.sqrt(tmp*tmp))\n valids.append(1)\n else:\n valids.append(0)\n embeddings.append(tmp)\n return embeddings,valids\nref_embeddings = get_embeddings(ref_file,None)\ndef distances(embedA,embedB,validA,validB):\n res = []\n \n for a,b,c,d in zip(embedA,embedB,validA,validB):\n dis = (sum(a*b)) / (np.sqrt(sum(a*a))* np.sqrt(sum(b*b))+1e-10)\n res.append(dis)\n \n return sum(res) / len(res)\nembeddings = []\nfinal_names = eval_files + [ref_file]\nvalids = []\nfor file,subword in zip(eval_files + [ref_file], eval_subword +[None]):\n print(file)\n embedding,valid = get_embeddings(file,subword)\n embeddings.append(embedding)\n valids.append(valid)\n\nmatrix = []\nfor i in range(len(embeddings)-1,len(embeddings)):\n row = []\n for j in range(0,len(embeddings)):\n diss = distances(embeddings[i],embeddings[j],valids[i],valids[j])\n row.append(diss)\n matrix.append(row)\nprint(matrix)\n\n# +\n# # 计算一个文件的embedding Gready\n# def get_embeddings(file,subword=None):\n# embeddings = []\n# valids = []\n# count = 0\n# with open(file, encoding='utf-8') as fin:\n# for line in fin.readlines():\n# encoded_line = encode(line,subword)\n# tmp = []\n# count = 0\n# for word in encoded_line.split(' '):\n# if word in vectors[0]:\n# tmp.append(vectors[0][word])\n# count += 1\n# else:\n# noisy = np.random.uniform(size=[300])\n# noisy = noisy/sum(np.sqrt(noisy*noisy))\n# tmp.append(noisy)\n# count += 1\n# if count > 0:\n# valids.append(1)\n# else:\n# valids.append(0)\n# embeddings.append(tmp)\n# return embeddings,valids\n# ref_embeddings = get_embeddings(ref_file,None)\n\n# def distances(embedA,embedB,validA,validB):\n# res = []\n \n# for a,b,c,d in zip(embedA,embedB,validA,validB):\n# if c*d == 0:\n# print('error')\n# for r in a:\n# score1 = -1\n# for r1 in b:\n# dis = (sum(r*r1)+1e-10) / (np.sqrt(sum(r*r))* np.sqrt(sum(r1*r1))+1e-10)\n# score1 = max(dis,score1)\n# for r in b:\n# score2 = -1\n# for r1 in a:\n# dis = (sum(r*r1)+1e-10) / (np.sqrt(sum(r*r))* np.sqrt(sum(r1*r1))+1e-10)\n# score2 = max(dis,score2)\n# res.append(score1+score2)\n \n# return sum(res) / len(res) / 2\n# embeddings = []\n# final_names = eval_files + [ref_file]\n# valids = []\n# for file,subword in zip(eval_files + [ref_file], eval_subword +[None]):\n# print(file)\n# embedding,valid = get_embeddings(file,subword)\n# embeddings.append(embedding)\n# valids.append(valid)\n\n# matrix = []\n# for i in range(len(embeddings)-1,len(embeddings)):\n# row = []\n# for j in range(0,len(embeddings)):\n# diss = distances(embeddings[i],embeddings[j],valids[i],valids[j])\n# row.append(diss)\n# matrix.append(row)\n# print(matrix)\n\n# +\n\n \n \n# -\n\nprint(matrix)\n\n\n\n\n0-4200-8a5d-27ec4c842e13\"\nmodel_2.summary()\n\n# + id=\"d_KSwJrV6sLR\"\n# model_2.optimizer = optimizer\ntrain_cubes = train_cubes.cache()\nval_cubes=val_cubes.cache()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"BeKp0bIFGUL-\" outputId=\"cf95f7b6-c98e-4357-bd18-65be6ff339ff\"\nepochs=10\nhistory=model_2.fit(train_cubes, validation_data =val_cubes,\n epochs=epochs,\n # steps_per_epoch=split//batch_size,\n # validation_steps = int(length - split)//batch_size\n )\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 336} id=\"OyyA_uZJrQeu\" outputId=\"b39897cc-4c6b-4972-8a17-49ce214727a5\"\nimport matplotlib.pyplot as plt\n\nplt.style.use('grayscale')\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']\naccuracy = history.history['accuracy']\nval_accuracy = history.history['val_accuracy']\nepoch_scale = range(1,len(loss)+1)\nfig = plt.figure(figsize=(15,5))\nax=fig.add_subplot(1,2,1,)\nax.set_title('a) Loss')\nax.plot(epoch_scale, loss,'*-')\nax.plot(epoch_scale, val_loss, '*-')\nax.legend([\"training\", \"validation\"])\n# ax.axes.margins(0)\nax1=fig.add_subplot(1,2,2)\nax1.set_title('b) Accuracy')\nax1.plot(epoch_scale, accuracy, 'o-')\nax1.plot(epoch_scale, val_accuracy, '*-')\nax1.legend([\"training\", \"validation\"])\n\nfig.show()\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"CEDB4W9gGOaY\" outputId=\"031a4257-46ee-4675-e1fa-a9c50a6c5e10\"\ninitial_epoch=epochs\nepochs=epochs+10\nhistory=model_2.fit(train_cubes, validation_data =val_cubes,\n epochs=epochs,\n initial_epoch = initial_epoch,\n # steps_per_epoch=split//batch_size,\n # validation_steps = int(length - split)//batch_size\n )\n\n# + id=\"QZu9Rr2zuWBv\"\nloss=[*loss, *history.history['loss']]\nval_loss =[*val_loss, *history.history['val_loss']]\naccuracy =[*accuracy, *history.history['accuracy']]\nval_accuracy =[*val_accuracy, *history.history['val_accuracy']]\nepoch_scale = range(1,len(loss)+1)\n\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 336} id=\"KkzbYCYq7y5F\" outputId=\"0d5443a9-ca62-4193-fbd7-c502450b75de\"\n\nfig = plt.figure(figsize=(15,5))\nax=fig.add_subplot(1,2,1,)\nax.set_title('a) Loss')\nax.plot(epoch_scale, loss,'*-')\nax.plot(epoch_scale, val_loss, '*-')\nax.legend([\"training\", \"validation\"])\n# ax.axes.margins(0)\nax1=fig.add_subplot(1,2,2)\nax1.set_title('b) Accuracy')\nax1.plot(epoch_scale, accuracy, 'o-')\nax1.plot(epoch_scale, val_accuracy, '*-')\nax1.legend([\"training\", \"validation\"])\n\nfig.show()\n\n# + [markdown] id=\"jZ_J4yaxgYyx\"\n# for 20 epochs the same result is getting\n# so 10 epochs is enaugh\n\n# + [markdown] id=\"XgYxakD9MMdr\"\n# We got 98% accuracy for our 3D images of RF-Matrices, and these images is invariant to the number of trees in the matrices or order of entry of trees to the matrix eather\n\n# + [markdown] id=\"dN405zd6nTyy\"\n# ##Confusion matrix\n\n# + id=\"p8fpkqi1nXag\"\nds = tf.data.Dataset.from_tensor_slices((test_x,test_y))\nds = ds.map(to_3DCube)\ntest_cubes = ds.map(lambda ind,val,l:[tf.SparseTensor(ind,tf.squeeze(val),shape_3D),l])\ntest_cubes= test_cubes.map(lambda c,l:[tf.expand_dims(tf.sparse.to_dense(c,default_value=0,validate_indices=False), axis=-1),l])\n\ntest_array = np.array(list(test_cubes.as_numpy_iterator()),dtype=object)\ntst_x = np.stack(test_array[:,0])\ntst_y = np.stack(test_array[:,1])\n\n\n# + id=\"KUquE58Bu54j\"\n\n# Use the model to predict the labels\ntest_predictions = model_2.predict(tst_x,steps=tst_x.shape[0])\ntest_y_pred = np.argmax(test_predictions, axis=1)\ntest_y_true = np.argmax(tst_y, axis=1)\n\n# + colab={\"base_uri\": \"https://localhost:8080/\"} id=\"DW49EsZ2nclT\" outputId=\"68443929-6690-4921-e486-4bd4ca40dcfd\"\nfrom sklearn.metrics import confusion_matrix\nconfMat=confusion_matrix( test_y_pred,test_y_true)\nprint( np.sum(np.diag(confMat))/len(test_y_true))\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 585} id=\"IkCdkpU7njJ3\" outputId=\"aa9da859-31fe-4a9a-f6c1-e4f376242195\"\nimport itertools\nimport io\nfrom tensorflow.image import decode_png\n\ndef plot_confusion_matrix(cm, class_names):\n \"\"\"\n Returns a matplotlib figure containing the plotted confusion matrix.\n\n Args:\n cm (array, shape = [n, n]): a confusion matrix of integer classes\n class_names (array, shape = [n]): String names of the integer classes\n \"\"\"\n figure = plt.figure(figsize=(8, 8))\n plt.imshow(cm, interpolation='nearest', cmap=plt.cm.gray_r)\n plt.title(\"Confusion matrix\")\n plt.colorbar()\n tick_marks = np.arange(len(class_names))\n plt.xticks(tick_marks, class_names, rotation=90)\n plt.yticks(tick_marks, class_names)\n\n # Normalize the confusion matrix.\n cm = np.around(cm.astype(np.float) / cm.sum(axis=1)[:, np.newaxis], decimals=2)\n\n # Use white text if squares are dark; otherwise black.\n threshold = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n color = \"white\" if cm[i, j] > threshold else \"black\"\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=color)\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n # return figure\n\nplot_confusion_matrix(confMat,class_names=('one','two','three','four','five'))\n"},"script_size":{"kind":"number","value":12900,"string":"12,900"}}},{"rowIdx":990,"cells":{"path":{"kind":"string","value":"/churn_model.ipynb"},"content_id":{"kind":"string","value":"123d68c0d3e5d02f40f83a6b0b423d95d7a3028e"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"tejasbangera/Churn-Prediction-Model"},"repo_url":{"kind":"string","value":"https://github.com/tejasbangera/Churn-Prediction-Model"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":245986,"string":"245,986"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport numpy as np\nbreast_cancer = load_breast_cancer()\n\nfeatures = breast_cancer.data\ntarget = breast_cancer.target\n\ntrain_features, test_features, train_target, test_target = train_test_split(features, target, test_size=0.33)\n\ndtr = DecisionTreeClassifier()\n\n\n# -\n\nsimulate_blocks = 100000\n\n# +\n#dtr.fit(train_features, train_target)\n# -\n\nmodel_path = \"breast-cancer.model\"\n# Save the model\nimport pickle\n# with open(model_path, 'wb') as handle:\n# pickle.dump(dtr, handle)\nwith open(model_path, 'rb') as handle:\n dtr = pickle.load(handle)\n\n# +\nfrom sklearn.tree import export_graphviz\nfrom IPython.display import Image\nimport pydotplus\n\ndot_data = export_graphviz(dtr)\ngraph = pydotplus.graph_from_dot_data(dot_data) \nImage(graph.create_png())\n# -\n\nfrom hummingbird.ml import convert, load\nhb_model = convert(dtr, 'pytorch')\n\n# +\n\nfeatures = np.tile(features, (simulate_blocks, 1))\n# -\n\n# %%timeit -r 3\n# Run predictions on CPU\nhb_model.predict(features)\n\n# Run predictions on GPU\nhb_model.to('cuda')\n\n# %%timeit -r 3\nhb_model.predict(features)\n\n\nonary = {'Name': names_list, 'Expertise': expertise_list,\n 'Stars': stars_list, 'Rating_Count': rating_count_list}\n\n# ### Dictionary to Pandas Dataframe\n\ndf = pd.DataFrame.from_dict (dent_dictionary)\n\n# dataframe before cleaning\ndf \n\n# ### Clean the Data\n\ndf['Stars'] = df['Stars'].apply(lambda x: x.replace('star rating', ''))\n\n# dataframe after cleaning\ndf \n\ndf['Expertise'] = df['Expertise'].apply(lambda x: x.replace('\\n', ','))\n\ndf\n\n# ### Save Data in Excel\n\ndf.to_excel('yelp_cleaned_data.xlsx', index= False)\n"},"script_size":{"kind":"number","value":2087,"string":"2,087"}}},{"rowIdx":991,"cells":{"path":{"kind":"string","value":"/code/fmri_experiment/main_fmri.ipynb"},"content_id":{"kind":"string","value":"eac12ef5a3307b73cf11d9f8595d1908228159a2"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"kingjr/b2b"},"repo_url":{"kind":"string","value":"https://github.com/kingjr/b2b"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":7448451,"string":"7,448,451"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom helperFunctions import *\nimport numpy as np \n\n\nclass Model:\n def __init__(self):\n self.model = None\n self.target = None\n self.features = None\n self.data = None\n self.testX = None\n self.testY = None\n self.trainX = None\n self.trainY = None\n \n def _readDataset(self, filename):\n self.data = pd.read_csv(filename)\n\n def _dropNulls(self):\n self.data.drop([\"education\"], axis = 1, inplace = True) #dropping this improved accuracy\n self.data.dropna(inplace = True)\n \n def _saveProcessedData(self):\n self.features = self.data.drop(\"TenYearCHD\", axis = 1)\n self.target = self.data.TenYearCHD\n self.data.to_csv(\"../data/processedData.csv\")\n \n def _trainTestSplit(self):\n self.trainX, self.testX, self.trainY, self.testY = train_test_split(self.features, self.target, test_size=0.2)\n \n def preProcessing(self, filename):\n self._readDataset(filename)\n self._dropNulls()\n self.data.reset_index(drop = True)\n self._saveProcessedData()\n self._trainTestSplit()\n #display(self.data.corr())\n\n\n# -\n\nmodel = Model()\nmodel.preProcessing(\"../data/framingham.csv\")\nmodel.data.TenYearCHD.hist()\nprint()\n\nmodel.data.hist(\"age\", \"TenYearCHD\")\n\nmodel.data.hist(\"male\", \"TenYearCHD\")\n\nmodel.data.hist(\"male\")\n\nmodel.data.corr(\"spearman\")\n\n\nes, or likewise, a high number of features.\n#\n# Here I will use the Exhaustive feature selection algorithm from mlxtend in a classification (Paribas) and regression (House Price) dataset.\n\n# 참고 자료 - http://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/\n\n# +\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# %matplotlib inline\n\nimport warnings\n\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom sklearn.metrics import roc_auc_score\n\nfrom mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS\n# -\n\nwarnings.filterwarnings(action='ignore')\n\n# +\nfile_path = '/Users/wontaek/Documents/Lecture_dataset/BNP_Paribas_Cardif_claims/train.csv'\ndata = pd.read_csv(file_path, nrows=50000)\ndata.shape\n\n# In practice, feature selection should be done after data pre-processing,\n# so ideally, all the categorical variables are encoded into numbers,\n# and then you can assess how deterministic they are of the target\n\n# here for simplicity I will use only numerical variables\n# select numerical columns:\n\nnumerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\nnumerical_vars = list(data.select_dtypes(include=numerics).columns)\ndata = data[numerical_vars]\ndata.shape\n\n\n# separate train and test sets\nX_train, X_test, y_train, y_test = train_test_split(\n data.drop(labels=['target', 'ID'], axis=1),\n data['target'],\n test_size=0.3,\n random_state=0)\n\nX_train.shape, X_test.shape\n\n\n\n# find and remove correlated features\n# in order to reduce the feature space a bit\n# so that the algorithm takes shorter\n\ndef correlation(dataset, threshold):\n col_corr = set() # Set of all the names of correlated columns\n corr_matrix = dataset.corr()\n for i in range(len(corr_matrix.columns)):\n for j in range(i):\n if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n colname = corr_matrix.columns[i] # getting the name of column\n col_corr.add(colname)\n return col_corr\n\ncorr_features = correlation(X_train, 0.8)\nprint('correlated features: ', len(set(corr_features)) )\n\n\n# removed correlated features\nX_train.drop(labels=corr_features, axis=1, inplace=True)\nX_test.drop(labels=corr_features, axis=1, inplace=True)\n\nX_train.shape, X_test.shape\n# -\n\nX_train.columns[0:10]\n\n# 조합을 만들어서 feature를 선택하는 방법이다.\n# - 최소 조합의 개수와 최대 조합의 개수를 선택해서 하는 방식\n# - feature가 많을 수록 경우의 수도 많으니 오래 걸린다.\n\n# +\n# exhaustive feature selection\n# I indicate that I want to select 10 features from\n# the total, and that I want to select those features\n# based on the optimal roc_auc\n\n# in order to shorter search time for the demonstration\n# i will ask the algorithm to try all possible 1,2,3 and 4\n# feature combinations from a dataset of 4 features\n\n# if you have access to a multicore or distributed computer\n# system you can try more greedy searches\n\nefs1 = EFS(RandomForestClassifier(n_jobs=4, random_state=0), \n min_features=1,\n max_features=5, \n scoring='roc_auc',\n print_progress=True,\n cv=2)\n\nefs1 = efs1.fit(np.array(X_train[X_train.columns[0:5]].fillna(0)), y_train)\n\n\n# -\n\ndef run_randomForests(X_train, X_test, y_train, y_test):\n rf = RandomForestClassifier(n_estimators=200, random_state=39, max_depth=4)\n rf.fit(X_train, y_train)\n print('Train set')\n \n pred = rf.predict_proba(X_train)\n print('Random Forests roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n print('Test set')\n \n pred = rf.predict_proba(X_test)\n print('Random Forests roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))\n\n\nefs1.subsets_\n\nefs1.best_idx_\n\nselected_feat= X_train.columns[list(efs1.best_idx_)]\nselected_feat\n\n# +\n# evaluate performance of classifier using selected features\n\nrun_randomForests(X_train[selected_feat].fillna(0),\n X_test[selected_feat].fillna(0),\n y_train, y_test)\n# -\n\n# regression도 동일하게 진행한다.\nin], X[train],\n self.alphas,\n self.independent_alphas)\n self.G_.append(G)\n\n # Fit encoder\n H = ols.fit(X[test], Y[test] @ G.T).coef_\n self.H_.append(H)\n\n # Aggregate ensembling\n self.G_ = np.mean(self.G_, 0)\n self.H_ = np.mean(self.H_, 0)\n self.E_ = np.diag(self.H_)\n\n return self\n\n def score(self, X, Y, scoring=None, multioutput='raw_values'):\n scoring = self.scoring if scoring is None else scoring\n if multioutput != 'raw_values':\n raise NotImplementedError\n # Transform with decoder\n YG = Y @ self.G_.T\n # Make standard and knocked-out encoders predictions\n XE = X @ np.diag(self.E_).T\n # Compute R for each column X\n return rn_score(YG, XE, scoring=scoring, multioutput='raw_values')\n\n \ndef ridge_cv(X, Y, alphas, independent_alphas=False, Uv=None):\n \"\"\" Similar to sklearn RidgeCV but\n (1) can optimize a different alpha for each column of Y\n (2) return leave-one-out Y_hat\n \"\"\"\n if isinstance(alphas, (float, int)):\n alphas = np.array([alphas, ], np.float64)\n if Y.ndim == 1:\n Y = Y[:, None]\n n, n_x = X.shape\n n, n_y = Y.shape\n # Decompose X\n if Uv is None:\n U, s, _ = linalg.svd(X, full_matrices=0)\n v = s**2\n else:\n U, v = Uv\n UY = U.T @ Y\n\n # For each alpha, solve leave-one-out error coefs\n cv_duals = np.zeros((len(alphas), n, n_y))\n cv_errors = np.zeros((len(alphas), n, n_y))\n for alpha_idx, alpha in enumerate(alphas):\n # Solve\n w = ((v + alpha) ** -1) - alpha ** -1\n c = U @ np.diag(w) @ UY + alpha ** -1 * Y\n cv_duals[alpha_idx] = c\n\n # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))\n G_diag = (w * U ** 2).sum(axis=-1) + alpha ** -1\n error = c / G_diag[:, np.newaxis]\n cv_errors[alpha_idx] = error\n\n # identify best alpha for each column of Y independently\n if independent_alphas:\n best_alphas = (cv_errors ** 2).mean(axis=1).argmin(axis=0)\n duals = np.transpose([cv_duals[b, :, i]\n for i, b in enumerate(best_alphas)])\n cv_errors = np.transpose([cv_errors[b, :, i]\n for i, b in enumerate(best_alphas)])\n else:\n _cv_errors = cv_errors.reshape(len(alphas), -1)\n best_alphas = (_cv_errors ** 2).mean(axis=1).argmin(axis=0)\n duals = cv_duals[best_alphas]\n cv_errors = cv_errors[best_alphas]\n\n coefs = duals.T @ X\n Y_hat = Y - cv_errors\n return coefs, best_alphas, Y_hat\n\n\nclass Forward():\n def __init__(self, alphas=alphas, independent_alphas=True,\n scoring='r', multioutput='uniform_average'):\n self.alphas = alphas\n self.independent_alphas = independent_alphas\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'Forward'\n\n def fit(self, X, Y):\n # Fit encoder\n self.H_, H_alpha, _ = ridge_cv(X, Y, self.alphas,\n self.independent_alphas)\n\n self.E_ = np.sum(self.H_**2, 0)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n # Make standard and knocked-out encoders predictions\n XH = X @ self.H_.T\n # Compute R for each column of Y\n return rn_score(Y, XH, scoring=scoring, multioutput=multioutput)\n\n def predict(self, X):\n return X @ self.H_.T\n\n\nclass Backward():\n def __init__(self, alphas=alphas, independent_alphas=True,\n scoring='r'):\n self.alphas = alphas\n self.independent_alphas = independent_alphas\n self.scoring = scoring\n self.__name__ = 'Backward'\n\n def fit(self, X, Y):\n # Fit encoder\n self.H_, H_alpha, _ = ridge_cv(Y, X, self.alphas,\n self.independent_alphas)\n\n self.E_ = np.sum(self.H_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput='raw_values'):\n scoring = self.scoring if scoring is None else scoring\n if multioutput != 'raw_values':\n raise NotImplementedError\n # Make standard and knocked-out encoders predictions\n YH = Y @ self.H_.T\n # Compute R for each column of Y\n return rn_score(X, YH, scoring=scoring, multioutput=multioutput)\n\n def predict(self, X):\n return 0\n\n\ndef canonical_correlation(model, X, Y, scoring, multioutput):\n \"\"\"Score in canonical space\"\"\"\n\n # check valid model\n for xy in 'xy':\n for var in ('mean', 'std', 'rotations'):\n assert hasattr(model, '%s_%s_' % (xy, var))\n assert model.x_rotations_.shape[1] == model.y_rotations_.shape[1]\n\n # check valid data\n if Y.ndim == 1:\n Y = Y[:, None]\n if X.ndim == 1:\n X = X[:, None]\n assert len(X) == len(Y)\n\n # Project to canonical space\n X = X - model.x_mean_\n X /= model.x_std_\n X = np.nan_to_num(X, 0)\n XA = X @ model.x_rotations_\n\n Y = Y - model.y_mean_\n Y /= model.y_std_\n Y = np.nan_to_num(Y, 0)\n YB = Y @ model.y_rotations_\n\n return rn_score(XA, YB, scoring=scoring, multioutput=multioutput)\n\n\ndef validate_number_components(n, X, Y):\n n_max = min(X.shape[1], Y.shape[1])\n if n == -1:\n n = n_max\n elif n >= 0. and n < 1.:\n n = int(np.floor(n_max * n))\n n = 1 if n == 0 else n\n\n assert n == int(n) and n > 0 and n <= n_max\n return int(n)\n\n\nclass GridPLS(BaseEstimator, RegressorMixin):\n \"\"\"Optimize n_components by minimizing Y_pred error\"\"\"\n def __init__(self, n_components=components, cv=5,\n scoring='r', multioutput='uniform_average', tol=1e-15):\n self.n_components = n_components\n self.cv = cv\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'GridPLS'\n\n def fit(self, X, Y):\n\n N = self.n_components\n if not isinstance(N, (list, np.ndarray)):\n N = [N, ]\n\n components = np.unique([validate_number_components(n, X, Y)\n for n in N])\n # Optimize n_components on Y prediction!\n if len(components) > 1:\n models = GridSearchCV(SkPLS(), dict(n_components=components))\n best = models.fit(X, Y).best_estimator_\n self.n_components_ = best.n_components\n\n x_valid = range(X.shape[1])\n y_valid = range(Y.shape[1])\n else:\n best = PLS(n_components=components[0],\n scoring=self.scoring,\n multioutput=self.multioutput)\n best.fit(X, Y)\n self.n_components_ = best.n_components_\n x_valid = best.x_valid_\n y_valid = best.y_valid_\n\n self.x_mean_ = np.zeros(X.shape[1])\n self.x_std_ = np.zeros(X.shape[1])\n self.x_rotations_ = np.zeros((X.shape[1], self.n_components_))\n self.y_mean_ = np.zeros(Y.shape[1])\n self.y_std_ = np.zeros(Y.shape[1])\n self.y_rotations_ = np.zeros((Y.shape[1], self.n_components_))\n\n self.x_mean_[x_valid] = best.x_mean_\n self.x_std_[x_valid] = best.x_std_\n self.x_rotations_[x_valid, :] = best.x_rotations_\n self.y_mean_[y_valid] = best.y_mean_\n self.y_std_[y_valid] = best.y_std_\n self.y_rotations_[y_valid, :] = best.y_rotations_\n\n self.E_ = np.sum(self.x_rotations_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n return canonical_correlation(self, X, Y,\n scoring, multioutput)\n def transform(self, X):\n return self.best.transform(X[:, self.x_valid_])\n \n def fit_transform(self, X, Y):\n return self.fit(X, Y).best.transform(X)\n\n\nclass GridCCA(BaseEstimator, RegressorMixin):\n \"\"\"Optimize n_components by minimizing Y_pred error\"\"\"\n def __init__(self, n_components=components, cv=5,\n scoring='r', multioutput='uniform_average', tol=1e-15):\n self.n_components = n_components\n self.cv = cv\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'GridCCA'\n\n def fit(self, X, Y):\n\n N = self.n_components\n if not isinstance(N, (list, np.ndarray)):\n N = [N, ]\n components = np.unique([validate_number_components(n, X, Y)\n for n in N])\n # Optimize n_components on Y prediction!\n if len(components) > 1:\n models = GridSearchCV(SkCCA(), dict(n_components=components))\n best = models.fit(X, Y).best_estimator_\n self.n_components_ = best.n_components\n x_valid = range(X.shape[1])\n y_valid = range(Y.shape[1])\n else:\n best = CCA(n_components=components[0],\n scoring=self.scoring,\n multioutput=self.multioutput)\n best.fit(X, Y)\n self.n_components_ = best.n_components_\n\n x_valid = best.x_valid_\n y_valid = best.y_valid_\n\n self.x_mean_ = np.zeros(X.shape[1])\n self.x_std_ = np.zeros(X.shape[1])\n self.x_rotations_ = np.zeros((X.shape[1], self.n_components_))\n self.y_mean_ = np.zeros(Y.shape[1])\n self.y_std_ = np.zeros(Y.shape[1])\n self.y_rotations_ = np.zeros((Y.shape[1], self.n_components_))\n\n self.x_mean_[x_valid] = best.x_mean_\n self.x_std_[x_valid] = best.x_std_\n self.x_rotations_[x_valid, :] = best.x_rotations_\n self.y_mean_[y_valid] = best.y_mean_\n self.y_std_[y_valid] = best.y_std_\n self.y_rotations_[y_valid, :] = best.y_rotations_\n\n self.E_ = np.sum(self.x_rotations_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n return canonical_correlation(self, X, Y,\n scoring, multioutput)\n def transform(self, X):\n return self.best.transform(X[:, self.x_valid_])\n \n def fit_transform(self, X, Y):\n return self.fit(X, Y).best.transform(X)\n\n\nclass GridRegCCA(BaseEstimator, RegressorMixin):\n def __init__(self, alphas=np.logspace(-4, 4., 20), cv=5,\n n_components=[-1, ],\n scoring='r', multioutput='uniform_average', tol=1e-15):\n self.alphas = alphas\n self.cv = cv\n self.scoring = scoring\n self.n_components = n_components\n self.multioutput = multioutput\n self.__name__ = 'GridRegCCA'\n\n def fit(self, X, Y):\n\n self.x_valid_ = np.where(X.std(0) > 0)[0]\n self.y_valid_ = np.where(Y.std(0) > 0)[0]\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n\n N = self.n_components\n if not isinstance(N, (list, np.ndarray)):\n N = [N, ]\n components = np.unique([validate_number_components(n, X, Y)\n for n in N])\n grid = {'alpha': self.alphas,\n 'n_components': components}\n\n # Optimize n_components on Y prediction!\n if np.prod(list(map(np.shape, grid.values()))) > 1:\n models = GridSearchCV(RegCCA(scoring=self.scoring,\n multioutput=self.multioutput),\n grid)\n best = models.fit(X, Y).best_estimator_\n else:\n best = RegCCA(alpha=grid['alpha'][0], n_components=components[0],\n scoring=self.scoring, multioutput=self.multioutput)\n best.fit(X, Y)\n self.n_components_ = best.n_components\n self.alpha_ = best.alpha\n\n self.x_mean_ = best.x_mean_\n self.x_std_ = best.x_std_\n self.x_rotations_ = best.x_rotations_\n self.y_mean_ = best.y_mean_\n self.y_std_ = best.y_std_\n self.y_rotations_ = best.y_rotations_\n\n self.E_ = np.sum(self.x_rotations_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n return canonical_correlation(self, X, Y,\n scoring, multioutput)\n\n def transform(self, X):\n return self.best.transform(X[:, self.x_valid_])\n \n def fit_transform(self, X, Y):\n return self.fit(X, Y).best.transform(X)\n\nclass CCA(SkCCA):\n \"\"\"overwrite scikit-learn CCA to get scoring function in\n canonical space\"\"\"\n\n def __init__(self, n_components=-1,\n scoring='r', multioutput='uniform_average', tol=1e-15):\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'CCA'\n super().__init__(n_components=n_components, tol=tol)\n\n def fit(self, X, Y):\n\n self.x_valid_ = np.where(X.std(0) > 0)[0]\n self.y_valid_ = np.where(Y.std(0) > 0)[0]\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n\n N = self.n_components\n self.n_components = validate_number_components(N, X, Y)\n super().fit(X, Y)\n self.n_components_ = self.n_components\n self.n_components = N\n self.E_ = np.sum(self.x_rotations_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n return canonical_correlation(self, X, Y,\n scoring, multioutput)\n\n def transform(self, X):\n return super().transform(X[:, self.x_valid_])\n \n def fit_transform(self, X, Y):\n return self.fit(X, Y).transform(X)\n\n \nclass PLS(SkPLS):\n \"\"\"overwrite scikit-learn PLSRegression to get scoring function in\n canonical space\"\"\"\n\n def __init__(self, n_components=-1,\n scoring='r', multioutput='uniform_average', tol=1e-15):\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'PLS'\n super().__init__(n_components=n_components, tol=tol)\n\n def fit(self, X, Y):\n self.x_valid_ = np.where(X.std(0) > 0)[0]\n self.y_valid_ = np.where(Y.std(0) > 0)[0]\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n N = self.n_components\n self.n_components = validate_number_components(N, X, Y)\n super().fit(X, Y)\n self.n_components_ = self.n_components\n self.n_components = N\n self.E_ = np.sum(self.x_rotations_**2, 1)\n return self\n\n def score(self, X, Y, scoring=None, multioutput=None):\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n scoring = self.scoring if scoring is None else scoring\n multioutput = self.multioutput if multioutput is None else multioutput\n return canonical_correlation(self, X, Y,\n scoring, multioutput)\n\n def transform(self, X):\n return super().transform(X[:, self.x_valid_])\n \n def fit_transform(self, X, Y):\n return self.fit(X, Y).transform(X)\n\n\nclass RegCCA(CCA):\n \"\"\"Wrapper to get sklearn API for Regularized CCA \"\"\"\n def __init__(self, alpha=0., n_components=-1,\n scoring='r', multioutput='uniform_average',\n tol=1e-15):\n self.alpha = alpha\n self.n_components = n_components\n assert (n_components > 0) or (n_components == -1)\n self.tol = tol\n self.scoring = scoring\n self.multioutput = multioutput\n self.__name__ = 'RegCCA'\n\n def fit(self, X, Y):\n\n self.x_valid_ = np.where(X.std(0) > 0)[0]\n self.y_valid_ = np.where(Y.std(0) > 0)[0]\n X = X[:, self.x_valid_]\n Y = Y[:, self.y_valid_]\n\n # Set truncation\n dx, dy = X.shape[1], Y.shape[1]\n dz_max = min(dx, dy)\n dz = dz_max if self.n_components == -1 else self.n_components\n dz = min(dz, dz_max)\n self.n_components_ = dz\n self.x_rotations_ = np.zeros((dx, dz))\n self.y_rotations_ = np.zeros((dy, dz))\n\n # Preprocess\n self.x_std_ = X.std(0)\n self.y_std_ = Y.std(0)\n self.x_mean_ = X.mean(0)\n self.y_mean_ = Y.mean(0)\n\n X = (X - self.x_mean_) / self.x_std_\n Y = (Y - self.y_mean_) / self.y_std_\n\n # compute cca\n comps = self._compute_kcca([X, Y],\n reg=self.alpha, numCC=dz)\n self.x_rotations_ = comps[0]\n self.y_rotations_ = comps[1]\n self.E_ = np.sum(self.x_rotations_**2, 1)\n\n return self\n\n def _compute_kcca(self, data, reg=0., numCC=None):\n \"\"\"Adapted from https://github.com/gallantlab/pyrcca\n\n Copyright (c) 2015, The Regents of the University of California\n (Regents). All rights reserved.\n\n Permission to use, copy, modify, and distribute this software and its\n documentation for educational, research, and not-for-profit purposes,\n without fee and without a signed licensing agreement, is hereby\n granted, provided that the above copyright notice, this paragraph and\n the following two paragraphs appear in all copies, modifications, and\n distributions. Contact The Office of Technology Licensing, UC Berkeley,\n 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510)\n 643-7201, for commercial licensing opportunities.\n\n Created by Natalia Bilenko, University of California, Berkeley.\n \"\"\"\n\n kernel = [d.T for d in data]\n\n nDs = len(kernel)\n nFs = [k.shape[0] for k in kernel]\n numCC = min([k.shape[1] for k in kernel]) if numCC is None else numCC\n\n # Get the auto- and cross-covariance matrices\n crosscovs = [np.dot(ki, kj.T) for ki in kernel for kj in kernel]\n\n # Allocate left-hand side (LH) and right-hand side (RH):\n LH = np.zeros((sum(nFs), sum(nFs)))\n RH = np.zeros((sum(nFs), sum(nFs)))\n\n # Fill the left and right sides of the eigenvalue problem\n for i in range(nDs):\n RH[sum(nFs[:i]): sum(nFs[:i+1]),\n sum(nFs[:i]): sum(nFs[:i+1])] = (crosscovs[i * (nDs + 1)]\n + reg * np.eye(nFs[i]))\n\n for j in range(nDs):\n if i != j:\n LH[sum(nFs[:j]): sum(nFs[:j+1]),\n sum(nFs[:i]): sum(nFs[:i+1])] = crosscovs[nDs * j + i]\n\n LH = (LH + LH.T) / 2.\n RH = (RH + RH.T) / 2.\n\n maxCC = LH.shape[0]\n try:\n r, Vs = linalg.eigh(LH, RH, eigvals=(maxCC - numCC, maxCC - 1))\n except linalg.LinAlgError: # noqa\n r = np.zeros(numCC)\n Vs = np.zeros((sum(nFs), numCC))\n\n r[np.isnan(r)] = 0\n rindex = np.argsort(r)[::-1]\n comp = []\n Vs = Vs[:, rindex]\n for i in range(nDs):\n comp.append(Vs[sum(nFs[:i]):sum(nFs[:i + 1]), :numCC])\n return comp\n\n\ndef score_knockout(model, X, Y, XY_train=None, scoring='r', fix_grid=True):\n assert isinstance(model, (CCA, PLS, GridCCA, GridPLS, RegCCA,\n GridRegCCA, B2B, Forward, Backward))\n assert len(X) == len(Y)\n assert scoring in ('r', 'r2')\n is_b2b = isinstance(model, B2B)\n dim_x = X.shape[1]\n\n # Compute standard scores\n score_full = model.score(X, Y,\n scoring=scoring,\n multioutput='raw_values')\n score_delta = np.zeros(dim_x)\n\n # Compute knock out scores\n for f in range(dim_x):\n\n # Setup knockout matrix\n knockout = np.eye(dim_x)\n knockout[f] = 0\n\n model_ = model\n # refit the model\n if XY_train is not None:\n X_train, Y_train = XY_train\n model_ = deepcopy(model)\n if isinstance(model, (GridPLS, GridCCA, GridRegCCA)) and fix_grid:\n n = model.n_components_\n model_.n_components = -1 if n == X.shape[1] else n\n if is_b2b:\n model_.fit_H(X_train @ knockout, Y_train)\n else:\n model_.fit(X_train @ knockout, Y_train)\n\n # Score\n score_ko = model_.score(X @ knockout, Y,\n scoring=scoring,\n multioutput='raw_values')\n\n # Aggregate predicted dimensions\n if is_b2b:\n score_delta[f] = score_full[f] - score_ko[f]\n elif len(score_full) != len(score_ko):\n print('Different dims!')\n score_delta[f] = score_full.mean() - score_ko.mean()\n else:\n score_delta[f] = (score_full - score_ko).mean()\n\n return score_delta\n\n\nclass Manova():\n def __init__(self, statistics='F Value'):\n self.statistics = statistics\n \n def fit(self, X, Y):\n from statsmodels.multivariate.manova import MANOVA\n model = MANOVA(Y, X)\n manova_results = model.mv_test().summary_frame.reset_index()\n idx = manova_results.Statistic == \"Wilks' lambda\"\n self.coef_ = np.array([v for v in manova_results.loc[idx, 'F Value'].values])\n\n\n\nclass Composite():\n def __init__(self, models):\n self.models = models\n \n def fit(self, X, Y):\n self.models_ = list()\n for y, model in zip(Y.T, self.models):\n self.models_.append(model.fit(X, y))\n return self\n\n def predict(self, X):\n Y_pred = [model.predict(X) for model in self.models_]\n return np.transpose(Y_pred)\n \ndef get_model(model_name, n_features, n_voxels):\n \n\n max_comp = min(n_voxels, n_features)\n comp_sweep = np.unique(np.floor(np.linspace(1, max_comp, 20))).astype(int)\n alpha_sweep = np.logspace(-4, 4, 20)\n models = dict(\n B2B=B2B(alpha_sweep),\n B2B_ensemble=B2B(alpha_sweep, ensemble=20),\n UnbiasedB2B=UnbiasedB2B(alpha_sweep, ensemble=KFold(3, shuffle=False)), # FIXME use b2b H=LinearRegregrssion\n UnbiasedB2B_ensemble=UnbiasedB2B(alpha_sweep, ensemble=20), # FIXME use b2b H=LinearRegregrssion\n Backward=Backward(alpha_sweep),\n Forward=Forward(alpha_sweep),\n CCA=GridCCA(comp_sweep),\n RegCCA=GridRegCCA(alpha_sweep, n_components=[max_comp]),\n PLS=GridPLS(comp_sweep),\n B2B_CCALinearSVR=B2B(alpha_sweep, \n ensemble=20,\n G=make_pipeline(CCA(4), Composite([LinearSVR() for i in range(4)]))),\n )\n return models[model_name]\n \n# if __name__ == '__main__':\n\n# X = np.zeros((10, 100))\n# Y = np.zeros((10, 200))\n# assert validate_number_components(0, X, Y) == 1\n# assert validate_number_components(1, X, Y) == 1\n# assert validate_number_components(.5, X, Y) == 50\n\n# def make_data():\n# n = 1000\n# dx = 4\n# dy = 5\n# X = np.random.randn(n, dx)\n# E = np.eye(dx)\n# E[2:] = 0\n# N = np.random.randn(n, dx)\n# N2 = np.random.randn(n, dy) / 10.\n# F = np.random.randn(dx, dy)\n# Y = (X @ E + N) @ F + N2\n\n# train, test = range(0, n, 2), range(1, n, 2)\n# return X, Y, train, test\n\n# X, Y, train, test = make_data()\n\n# models = (B2B, Forward, Backward, CCA, RegCCA, PLS,\n# GridCCA, GridPLS, GridRegCCA)\n# canonicals = (CCA, RegCCA, PLS, GridCCA, GridPLS, GridRegCCA)\n\n# for scoring in ('r', 'r2'):\n# for mo in ('uniform_average', 'variance_weighted'):\n# params = dict(scoring=scoring, multioutput=mo)\n\n# for model in models:\n# if model in (B2B, Backward):\n# model = model(scoring=scoring)\n# else:\n# model = model(scoring=scoring, multioutput=mo)\n# model.fit(X[train], Y[train])\n# assert len(model.E_) == X.shape[1]\n# score = model.score(X[test], Y[test], multioutput='raw_values')\n\n# if isinstance(model, canonicals):\n# assert len(score) == model.n_components_\n# elif isinstance(model, (B2B, Backward)):\n# assert len(score) == X.shape[1]\n# elif isinstance(model, Forward):\n# assert len(score) == Y.shape[1]\n\n# for fix_grid in (False, True):\n# # assert np.mean(score) > .3\n# score_delta = score_knockout(model, X[test], Y[test],\n# scoring=scoring,\n# fix_grid=fix_grid)\n# assert len(score_delta) == X.shape[1]\n# print(model.__name__, score_delta)\n# score_delta = score_knockout(model, X[test], Y[test],\n# (X[train], Y[train]),\n# scoring=scoring,\n# fix_grid=fix_grid)\n# assert len(score_delta) == X.shape[1]\n# print(model.__name__, score_delta)\n\n# for model in canonicals:\n# for n_components in (-1, 1, 2):\n# if model in (CCA, RegCCA, PLS):\n# m = model(n_components)\n# else:\n# m = model([n_components, ])\n# m.fit(X[train], Y[train])\n# assert len(m.E_) == X.shape[1]\n# score = m.score(X[test], Y[test], multioutput='raw_values')\n# assert len(score) == m.n_components_\n# print(model.__name__, score_delta)\n\n# +\nimport os\nimport pathlib\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.stats import pearsonr, norm, wilcoxon\n\nfrom sklearn.model_selection import cross_val_predict, cross_val_score\nfrom sklearn.linear_model import LinearRegression, RidgeCV\nfrom sklearn.model_selection import KFold, ShuffleSplit\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn import neighbors\nfrom sklearn.metrics import make_scorer\n\nimport nibabel as nib\nimport nilearn\nfrom nilearn import surface\nfrom nilearn import plotting\nfrom nilearn.decoding.searchlight import search_light\nfrom nistats.design_matrix import make_first_level_design_matrix as make_design\nfrom nistats.first_level_model import run_glm, FirstLevelModel\nfrom nistats.contrasts import compute_contrast\nfrom nistats.hemodynamic_models import compute_regressor\n\nfrom wordfreq import zipf_frequency\nimport spacy\n# -\n\n# # Paths\n\n# +\nderiv_path = pathlib.Path('/private/home/jeanremi/project/mous_fmri/fmriprep/')\ndata_path = pathlib.Path('/private/home/jeanremi/data/mous/raw/')\n\nfunc_suffix = '_task-visual_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'\nfunc_fname = str(deriv_path / '%s' / 'func'/ ('%s' + func_suffix))\n\nconfounds_suffix = '_task-visual_desc-confounds_regressors.tsv'\nconfounds_fname = str(deriv_path / '%s' / 'func'/ ('%s' + confounds_suffix))\n\nevents_fname = str(data_path / '%s' / 'func' / ('%s' + '_task-visual_events.tsv'))\n\n\n# -\n\ndef read_events(event_fname):\n # Read MRI events\n events = pd.read_csv(event_fname, sep='\\t')\n\n # Add context: sentence or word list?\n contexts = dict(WOORDEN='word_list', ZINNEN='sentence')\n for key, value in contexts.items():\n sel = events.value.str.contains(key)\n events.loc[sel, 'context'] = value\n events.loc[sel, 'condition'] = value\n\n # Clean up MRI event mess\n sel = ~events.context.isna()\n start = 0\n context = 'init'\n for idx, row in events.loc[sel].iterrows():\n events.loc[start:idx, 'context'] = context\n start = idx\n context = row.context\n events.loc[start:, 'context'] = context\n\n # Add event condition: word, blank, inter stimulus interval etc\n conditions = (('50', 'pulse'), ('blank', 'blank'), ('ISI', 'isi'))\n for key, value in conditions:\n sel = events.value == key\n events.loc[sel, 'condition'] = value\n\n events.loc[events.value.str.contains('FIX '), 'condition'] = 'fix'\n\n # Extract words from file\n sel = events.condition.isna()\n words = events.loc[sel, 'value'].apply(lambda s: s.strip('0123456789 '))\n events.loc[sel, 'word'] = words\n\n # Remove empty words\n sel = (events.word.astype(str).apply(len) == 0) & (events.condition.isna())\n events.loc[sel, 'word'] = pd.np.nan\n events.loc[sel, 'condition'] = 'blank'\n events.loc[~events.word.isna(), 'condition'] = 'word'\n \n # Define sequence\n events.loc[events.word=='QUESTION', 'condition'] = 'question'\n events.loc[events.word=='QUESTION', 'word'] = np.nan\n words = events.query('condition==\"word\"')\n events['sequence'] = np.cumsum(events.condition=='fix')\n \n for s, words in events.query('condition==\"word\"').groupby('sequence'):\n events.loc[words.index, 'word_position'] = range(len(words))\n \n # Fix bids\n events['trial_type'] = events['type']\n\n return events\n\n\n# # GLM: one Subject\n\n# +\n# subject = 'sub-V1002'\n\n# img = nib.load(func_fname % (subject, subject))\n\n# events = read_events(events_fname % (subject, subject))\n\n# confounds = pd.read_csv(confounds_fname % (subject, subject), sep='\\t', index_col=None)\n\n# confounds = confounds.fillna(method='ffill').fillna(method='bfill')\n\n# model = FirstLevelModel(t_r=2., n_jobs=12)\n# model.fit(img, events, confounds)\n\n# p001_unc = norm.isf(0.001)\n# zmap = model.compute_contrast('Picture-Response')\n# plotting.plot_glass_brain(zmap,\n# colorbar=False,\n# threshold=p001_unc,\n# plot_abs=False)\n# -\n\n# # Across subjects\n\n# +\nfrom nilearn.decoding.searchlight import GroupIterator\nfrom joblib import Parallel, delayed\nimport warnings\n\ndef searchlight_multiscore(X, Y, estimator, A, cv=3, scorer=None,\n groups=None, n_jobs=1, verbose=1,\n direction='decod'):\n \"\"\"Same as nilearn but acces multidimensional scoring\n scorer is a callable\"\"\"\n group_iter = GroupIterator(A.shape[0], n_jobs)\n parallel = Parallel(n_jobs=n_jobs, verbose=verbose)\n iter_func = delayed(_group_iter_searchlight) \n scores = parallel(iter_func(A.rows[idx], estimator, X, Y, \n groups, scorer, cv, direction) \n for idx in group_iter)\n return np.concatenate(scores, axis=0)\n\ndef _group_iter_searchlight(A, estimator, X, Y, groups, scorer, cv, direction):\n if isinstance(cv, int):\n cv = KFold(cv)\n all_scores = list()\n for row in A:\n if direction == 'decod':\n x, y = X[:, row], Y\n else:\n x, y = X, Y[:, row]\n \n if x.std(0).sum()==0 or y.std(0).sum()==0:\n all_scores.append(None)\n continue\n try:\n scores = list()\n for train, test in cv.split(x):\n estimator.fit(x[train], y[train])\n if scorer == 'knockout':\n score = score_knockout(estimator, x[test], y[test])\n elif isinstance(scorer, str):\n score = getattr(estimator, scorer)\n elif scorer is not None:\n y_pred = estimator.predict(x[test])\n score = scorer(y[test], y_pred)\n else:\n score = estimator.score(x[test], y[test])\n scores.append(score)\n except:\n all_scores.append(None)\n continue\n all_scores.append(np.mean(scores, 0))\n \n # deal with missing data\n shapes = [np.shape(s) for s in all_scores if s is not None]\n assert len(set(shapes)) == 1\n for i, scores in enumerate(all_scores):\n if scores is None:\n all_scores[i] = np.zeros(shapes[0]) * np.nan\n return np.array(all_scores)\n\n\n# +\ndef correlate(X, Y):\n if X.ndim == 1:\n X = X[:, None]\n if Y.ndim == 1:\n Y = Y[:, None]\n X = X - X.mean(0)\n Y = Y - Y.mean(0)\n SX2 = (X**2).sum(0)**.5\n SY2 = (Y**2).sum(0)**.5\n SXY = (X * Y).sum(0)\n return SXY / (SX2 * SY2)\n\ndef scale(X):\n shape = X.shape\n if len(shape) == 1:\n X = X[:, None]\n X = X - np.nanmean(X, 0, keepdims=True)\n std = np.nanstd(X, 0, keepdims=True)\n non_zero = np.where(std>0)\n X[:, non_zero] /= std[non_zero]\n X[np.isnan(X)] = 0\n X[~np.isfinite(X)] = 0\n \n return X.reshape(shape)\n\ndef convolve_events(events, frame_times, hrf_model='glover'):\n \n # Define potential causal factors\n events = events.query('condition==\"word\"')\n \n word_freq = events.word.apply(lambda w: zipf_frequency(w, 'nl'))\n word_length = events.word.apply(len)\n\n nlp = spacy.load(\"nl_core_news_sm\")\n pos = events.word.apply(lambda w: nlp(w)[0].pos_ in ('VERB', 'ADJ', 'NOUN', 'ADV'))\n \n regressors = dict(\n #word=np.ones(len(events)),\n word_length=scale(word_length.values),\n word_freq=scale(word_freq.values),\n word_function=scale(pos.values),\n dummy=scale(word_length.values)+scale(word_freq.values)+np.random.randn(len(events)),\n )\n\n # Convolve then with standard HRF\n reg_signals = list()\n reg_names = list()\n for name, values in regressors.items():\n\n signal, name_ = compute_regressor(\n np.c_[events.onset, np.ones(len(events)), values].T,\n hrf_model=hrf_model, \n frame_times=frame_times,\n oversampling=16)\n\n reg_signals.append(signal)\n reg_names.extend([name + n.split('cond')[1] for n in name_])\n reg_signals = np.concatenate(reg_signals, 1)\n \n return reg_signals, reg_names\n\n\n# -\n\ndef main(subject, remove_confounds=False):\n\n # Read Bold\n files = [f for f in os.listdir(deriv_path / subject / 'func')\n if f.endswith('-preproc_bold.nii.gz')\n and '_task-visual_space-MNI152' in f]\n assert len(files)\n img = nib.load(str(deriv_path / subject / 'func' / files[0]))\n \n # Read events and convolve with HRF response\n tr = 2.\n frame_times = np.arange(img.shape[-1]) * tr\n events = read_events(events_fname % (subject, subject))\n reg_signals, reg_names = convolve_events(events, \n frame_times, \n hrf_model='glover')\n \n # Prepare linear modeling\n estimator = make_pipeline(StandardScaler(), \n RidgeCV(np.logspace(-2, 8, 20)))\n cv = KFold(5, shuffle=False)\n \n # Loop across hemisphere\n fsaverage = nilearn.datasets.fetch_surf_fsaverage()\n out = dict()\n for hemi in ('left', ): # 'right'\n \n # Volume to surface\n mesh = fsaverage['pial_%s' % hemi]\n \n radius = 8.\n bold = surface.vol_to_surf(img, mesh, radius=radius).T\n bold = scale(bold)\n mesh = fsaverage['infl_%s' % hemi]\n coords, _ = surface.load_surf_mesh(mesh)\n radius = 8.\n nn = neighbors.NearestNeighbors(radius=radius)\n A = nn.fit(coords).radius_neighbors_graph(coords).tolil()\n\n nn = neighbors.NearestNeighbors(radius=2.)\n A_small = nn.fit(coords).radius_neighbors_graph(coords).tolil()\n\n # Remove confounds variable from bold signal\n if remove_confounds:\n keys = ['csf', 'white_matter']\n keys.extend(['trans_' + x for x in 'xyz'])\n keys.extend(['rot_' + x for x in 'xyz'])\n keys.extend(['trans_%s_derivative1' % x for x in 'xyz'])\n keys.extend(['rot_%s_derivative1' % x for x in 'xyz'])\n\n confounds = pd.read_csv(confounds_fname % (subject, subject), \n sep='\\t', index_col=None)\n confounds = confounds[keys].fillna(method='ffill').fillna(method='bfill')\n confounds = scale(confounds.values)\n\n bold_pred = cross_val_predict(estimator,\n X=confounds,\n y=bold,\n cv=cv,\n n_jobs=-1)\n bold = scale(bold - bold_pred)\n \n # Get manova small\n scores = searchlight_multiscore(X=scale(reg_signals),\n Y=bold,\n estimator=Manova(),\n A=A_small,\n cv=cv,\n scorer='coef_',\n direction='encod',\n n_jobs=-1)\n for score, name in zip(scores.T, reg_names):\n out['_'.join(('Manova_small', hemi, name))] = score\n \n # Get manova\n scores = searchlight_multiscore(X=scale(reg_signals),\n Y=bold,\n estimator=Manova(),\n A=A,\n cv=cv,\n scorer='coef_',\n direction='encod',\n n_jobs=-1)\n for score, name in zip(scores.T, reg_names):\n out['_'.join(('Manova', hemi, name))] = score\n \n \n # Get encoding coefficients\n betas = LinearRegression().fit(X=reg_signals, \n y=bold).coef_\n for beta, name in zip(betas.T, reg_names):\n out['betas_%s_%s' % (hemi, name)] = beta\n\n # Get Encoding scores\n for idx, name in enumerate(reg_names):\n scores = list()\n for train, test in cv.split(bold):\n estimator.fit(reg_signals[train, idx][:, None], bold[train])\n bold_pred = estimator.predict(reg_signals[test, idx][:, None])\n r = correlate(bold[test], bold_pred)\n scores.append(r)\n out['encod_%s_%s' % (hemi, name)] = np.mean(scores, 0)\n\n # Get decoding scores\n scores = searchlight_multiscore(X=bold,\n Y=scale(reg_signals), \n estimator=estimator,\n A=A,\n cv=cv,\n scorer=correlate, \n n_jobs=-1)\n\n for score, name in zip(scores.T, reg_names):\n out['decod_%s_%s' % (hemi, name)] = score\n \n # B2b unbiased E_hat \n model = get_model('UnbiasedB2B', reg_signals.shape[1], A.sum(1).min())\n model.ensemble = cv\n scores = searchlight_multiscore(X=scale(reg_signals),\n Y=bold,\n estimator=model,\n A=A,\n cv=cv,\n scorer='E_',\n direction='encod',\n n_jobs=-1)\n for score, name in zip(scores.T, reg_names):\n out['_'.join(('UnbiasedB2B_betas', hemi, name))] = score\n\n # paper models\n models = ('Forward', 'Backward', \n 'PLS', \n 'RegCCA', #'CCA',\n 'B2B', 'B2B_ensemble',#'UnbiasedB2B',\n 'B2B_CCALinearSVR',\n )\n \n \n for model_name in models:\n print(model_name)\n model = get_model(model_name, reg_signals.shape[1], A.sum(1).min())\n scores = searchlight_multiscore(X=scale(reg_signals),\n Y=bold,\n estimator=model,\n A=A,\n cv=cv,\n scorer='knockout',\n direction='encod',\n n_jobs=-1)\n\n for score, name in zip(scores.T, reg_names):\n out['_'.join((model_name, hemi, name, 'knockout'))] = score\n \n if model_name not in ('Forward', 'Backward'):\n continue\n scores = searchlight_multiscore(X=scale(reg_signals),\n Y=bold,\n estimator=model,\n A=A,\n cv=cv,\n scorer=None,\n direction='encod',\n n_jobs=-1)\n # FIXME: for all but B2B, there is only one full score\n if scores.ndim == 1:\n scores = np.concatenate([scores[:, None]] * reg_signals.shape[1], axis=1)\n \n for score, name in zip(scores.T, reg_names):\n out['_'.join((model_name, hemi, name))] = score\n \n return out\n\n# +\n# subject = 'sub-V1001'\n# out = mini(subject, False)\n\n# +\n# fsaverage = nilearn.datasets.fetch_surf_fsaverage()\n# hemi = 'left'\n# var = 'word_freq'\n# plotting.plot_surf_stat_map(\n# stat_map=out['_'.join(('encod', hemi, var))],\n# surf_mesh=fsaverage['infl_%s' % hemi], \n# hemi=hemi,\n# cmap=cmap,\n# );\n\n# +\nfrom submitit import AutoExecutor\n\nsubjects = sorted([f.split('.html')[0] for f in os.listdir('fmriprep')\n if f[:3] == 'sub' and f.endswith('.html')])\nsubjects = [s for s in subjects if 'sub-V' in s]\nexecutor = AutoExecutor('b2b_results/')\nexecutor.update_parameters(timeout_min=60*10,\n slurm_partition='learnfair,scavenge,uninterrupted',\n slurm_constraint='pascal',\n mem_gb=128,\n slurm_cpus_per_task=10,\n gpus_per_node=1)\njobs = executor.map_array(main, subjects)\n# -\n\nresults = pd.DataFrame([j.results()[0] \n for j in jobs if j.state=='COMPLETED'])\n\n\n# +\n\ndef add_colorbar(figure, axes, vmax, cmap, threshold=None):\n from matplotlib.colors import Normalize, LinearSegmentedColormap\n from matplotlib.cm import ScalarMappable\n from matplotlib.colorbar import make_axes\n \n vmin = -vmax\n our_cmap = plt.get_cmap(cmap)\n norm = Normalize(vmin=vmin, vmax=vmax)\n\n nb_ticks = 5\n ticks = np.linspace(vmin, vmax, nb_ticks)\n bounds = np.linspace(vmin, vmax, our_cmap.N)\n\n if threshold is not None:\n cmaplist = [our_cmap(i) for i in range(our_cmap.N)]\n # set colors to grey for absolute values < threshold\n istart = int(norm(-threshold, clip=True) *\n (our_cmap.N - 1))\n istop = int(norm(threshold, clip=True) *\n (our_cmap.N - 1))\n for i in range(istart, istop):\n cmaplist[i] = (0.5, 0.5, 0.5, 1.)\n our_cmap = LinearSegmentedColormap.from_list(\n 'Custom cmap', cmaplist, our_cmap.N)\n\n # we need to create a proxy mappable\n proxy_mappable = ScalarMappable(cmap=our_cmap, norm=norm)\n # proxy_mappable.set_array(surf_map_faces)\n cax, kw = make_axes(axes, location='right', fraction=.1,\n shrink=.6, pad=.0)\n cbar = figure.colorbar(\n proxy_mappable, cax=cax, ticks=ticks,\n boundaries=bounds, spacing='proportional',\n format='%.2g', orientation='vertical')\n\n\n# +\ndef plot(data, hemi='left', threshold=None, cmap='cold_hot', **kwargs):\n fig, ax = plt.subplots(1, figsize=[4.5, 5], subplot_kw={'projection': '3d'}, dpi=150)\n \n\n coords, verts = surface.load_surf_mesh(fsaverage['infl_%s' % hemi])\n coords2 = coords[:, [2, 1, 0]]\n coords2[:, 2] *= -1\n coords2[:, 2] -= 120\n \n xlim, ylim, zlim = zip(np.r_[coords, coords2].min(0), \n np.r_[coords, coords2].max(0))\n opt = dict(threshold=threshold,\n view='lateral',\n cmap=cmap,\n colorbar=False,\n bg_map=fsaverage['sulc_%s' % hemi])\n for k, v in kwargs.items():\n opt[k] = v\n \n \n for view in (coords2, coords):\n plotting.plot_surf_stat_map(stat_map=data,\n surf_mesh=(view, verts),\n axes=ax, \n **opt)\n ax.set_xlim(*xlim)\n ax.set_ylim(ylim[0]*1.1, ylim[1]*1.1)\n ax.set_zlim(*zlim)\n \n # desaturate cortex\n from matplotlib.collections import PolyCollection\n\n for poly in ax.get_children():\n from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n if not isinstance(poly, Poly3DCollection):\n continue\n facecolors = poly._facecolors[:, :3]\n idx = np.where(np.apply_along_axis(lambda r: len(set(r)), 1, facecolors)==1)[0]\n poly._facecolors[idx, :3] /= 3.\n poly._facecolors[idx, :3] += .2\n\n # add colorbar\n vmax = np.nanmax(data) if 'vmax' not in kwargs.keys() else kwargs['vmax']\n add_colorbar(fig, ax, vmax, cmap=cmap, threshold=threshold)\n \n return fig\n\ndef fig_to_img(fig):\n from matplotlib.backends.backend_agg import FigureCanvasAgg\n canvas = FigureCanvasAgg(fig)\n canvas.draw()\n width, height = fig.get_size_inches() * fig.get_dpi()\n img = np.fromstring(canvas.tostring_rgb(), dtype='uint8')\n return img.reshape(int(height), int(width), 3)\n\ndef crop(img):\n white = np.mean(img, 2) != 255\n first_row = np.where(white.sum(1))[0][0] - 10\n last_row = np.where(white[::-1].sum(1))[0][0] - 10\n first_column = np.where(white.sum(0))[0][0] - 10\n return img[first_row:-last_row][:, first_column:]\n\n\n# -\n\nfrom matplotlib.colors import LinearSegmentedColormap\nimport colorcet\nblack = 10\ncolors = np.r_[colorcet.cm.fire(np.linspace(.95, 0, 128-black//2))[:, [2, 1, 0, 3]],\n np.c_[np.zeros((black, 3)), np.ones((black, 1))],\n colorcet.cm.fire(np.linspace(0, .95, 128-black//2))]\nluminosity = (1 - np.clip(np.linspace(-5, 5, len(colors))**2, 0, 1)) ** 2\ncolors[:, :3] = np.clip(colors[:, :3] + luminosity[:, None]/2., 0, 1)\ncmap = LinearSegmentedColormap.from_list('ice_fire', colors)\n# plt.matshow(np.random.randn(100, 100), cmap=cmap, vmin=-4, vmax=4)\n# plt.colorbar()\n\nfsaverage = nilearn.datasets.fetch_surf_fsaverage()\n\n# +\nfeatures = ('word_length', 'word_freq', 'word_function', 'dummy')\nanalyses = ['Forward', 'PLS', 'RegCCA', 'B2B']\nanalyses += ['B2B_CCALinearSVR',]# 'B2B_CCASVR']\n \nimgs = dict()\nfor feature in features:\n print(feature)\n for analysis in analyses:\n \n hemi = 'left'\n key = '_'.join((analysis, hemi, feature, 'knockout'))\n X = np.array([d for d in results[key].values])\n X = np.nan_to_num(X)\n \n # p-values\n valid = np.nanstd(X, 0)>0\n p = np.ones(X.shape[1])\n _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n \n # only display r gain\n mean = np.nanmean(X, 0)\n values = mean * ((mean>0) * (p < .01))\n \n fig = plot(values, threshold=.0001, cmap=cmap,\n vmax=.05 if feature != 'word' else 1.)\n imgs[key] = crop(fig_to_img(fig))\n \n fig = plot(-np.log10(p), threshold=2, vmax=15, cmap=cmap)\n imgs[key + '_pval'] = crop(fig_to_img(fig))\n# -\n\nfeatures = ('word_length', 'word_freq', 'word_function', 'dummy')\nhemi = 'left'\nfor pval in [False, True]:\n fig = plt.figure(figsize=[7, 9], constrained_layout=False)\n gs = fig.add_gridspec(ncols=len(features)*2+1, nrows=len(analyses), wspace=0)\n axes = [[gs[r, (c*2):(c+1)*2] for c in range(len(features))] for r in range(len(analyses))]\n\n for analysis, axs in zip(analyses, axes):\n for feature, ax in zip(features, axs):\n\n ax = fig.add_subplot(ax)\n key = '_'.join((analysis, hemi, feature, 'knockout'))\n if pval:\n key += '_pval'\n img = imgs[key]\n colorbar = img[:, int(img.shape[1]*3.7/5):, :]\n img = img[:, :int(img.shape[1]*3.1/5), :]\n ax.imshow(img)\n for s in ('top', 'right', 'bottom', 'left'):\n ax.spines[s].set_visible(False)\n ax.set_xticks([])\n ax.set_yticks([])\n if feature == features[0]:\n ylabel = analysis\n if ylabel == 'B2B_CCALinearSVR':\n ylabel = '$B2B_{SVM}$'\n ax.set_ylabel(ylabel)\n if analysis == analyses[0]:\n ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')]))\n\n ax = fig.add_subplot(gs[:, -1])\n ax.imshow(colorbar)\n for s in ('top', 'right', 'bottom', 'left'):\n ax.spines[s].set_visible(False)\n ax.set_xticks([])\n ax.set_yticks([])\n if pval:\n ax.set_ylabel('$-log_{10}(p)$', labelpad=-10)\n else:\n ax.set_ylabel('$\\Delta R$', labelpad=-10)\n ax.yaxis.set_label_position(\"right\")\n if not pval:\n fig.savefig('fmri_delta_r.pdf', dpi=150, facecolor='white')\n fig.savefig('fmri_delta_r.png', dpi=150, facecolor='white')\n else:\n fig.savefig('fmri_pvals.pdf', dpi=150, facecolor='white')\n fig.savefig('fmri_pvals.png', dpi=150, facecolor='white')\n\n# # ROI\n\nrois = dict()\nfor hemi in ('left',): #'right'\n analysis = 'Forward'\n\n key = '_'.join((analysis, hemi, feature))\n X = np.array([d for d in results[key].values])\n X = np.nan_to_num(X)\n\n # p-values\n valid = np.nanstd(X, 0)>0\n p = np.ones(X.shape[1])\n _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n \n rois[hemi] = p<.001\n \n # plot\n fig = plot(np.nanmean(X, 0) * (p<.001), threshold=0.001, \n vmax=.04, cmap=cmap, hemi=hemi)\n roi_img = crop(fig_to_img(fig))\n\nfeatures = ('word_length', 'word_freq', 'word_function', 'dummy')\nmodels = ('Forward', 'PLS', 'RegCCA', 'B2B', 'B2B_CCALinearSVR')\nsummary = list()\nfor feature in features:\n for model in models:\n scores = list()\n for hemi in ('left', ):\n key = '_'.join((model, hemi, feature, 'knockout'))\n scores.append([s[rois[hemi]] for s in results[key].values])\n scores = np.nanmean(np.concatenate(scores, axis=1), axis=1)\n for i, score in enumerate(scores):\n summary.append(dict(subject=i, r=score, model=model, feature=feature))\nsummary = pd.DataFrame(summary)\n\n# +\nfig, axes = plt.subplots(1, len(features)+1, \n #sharex=True, sharey=True, \n figsize=[7, 2], facecolor='white')\naxes[0].set_visible(False)\n\nax = fig.add_subplot(256)\nax.text(0, 2, 'Fwd', color='C0', horizontalalignment='center', fontsize=12)\nax.text(1, 2, 'PLS', color='C1', horizontalalignment='center', fontsize=12)\nax.text(0, 0, 'CCA', color='C2', horizontalalignment='center', fontsize=12)\nax.text(1, 0, 'B2B', color='C3', horizontalalignment='center', fontsize=12)\nax.text(.5, -2, '$B2B_{SVM}$', color='C4', horizontalalignment='center', fontsize=12)\n\nax.set_ylim(-2.1, 4)\nax.set_xlim(-1, 2)\nax.axis('off')\nax = fig.add_subplot(251)\nax.imshow(roi_img[9:249, 10:304])\nax.axis('off')\nax.set_title('ROI')\n\nfor feature, ax in zip(features, axes[1:]):\n d = summary.query('feature==@feature')\n sns.stripplot(x='model', y='r', data=d, jitter=.3, s=2, ax=ax)\n \n # legend clean up\n ax.axhline(0,color='k', ls=':')\n ax.legend().set_visible(False)\n \n ylim = .1\n ax.set_ylim(-.03, ylim)\n yticks = np.around(np.arange(-.03, .101, .01), 2)\n ax.set_yticks(yticks)\n if ax == axes[1]:\n ax.set_ylabel('ΔR', labelpad=-10).set_rotation(0)\n ax.set_yticklabels(np.around(yticks, 2))\n ax.set_yticklabels(['%.2f' % f if f in (0., .1) else '' \n for f in yticks])\n else:\n ax.set_ylabel('')\n ax.set_yticklabels([])\n ax.set_xticks([])\n ax.set_xlabel('')\n ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')]))\n ax.set_xlabel('Models')\n\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n ax.spines['bottom'].set_visible(False)\n \n # statistical comparison\n if feature == 'dummy':\n pass\n\n space = np.ptp(ax.get_ylim()) / 30\n k = 0\n for idx, m1 in enumerate(models):\n\n r = list()\n _, p = wilcoxon(d.query('model == @m1').r.values)\n # print('%s: %s: %.4f' % (feature, m1, p))\n \n for jdx, m2 in enumerate(models):\n if m1 == m2:\n continue\n if m2 != 'B2B': # _CCALinearSVR\n continue\n r = list()\n d.query('model==@m1').r.values\n for _, subject in d.query('model in [@m1, @m2]').groupby('subject'):\n if len(subject)==2:\n r.append([subject.query('model==@m1').r.values[0],\n subject.query('model==@m2').r.values[0]])\n r1, r2 = np.transpose(r)\n u, p = wilcoxon(r1, r2)\n print('%s: %s versus %s: %.4f' % (feature, m1, m2, p))\n if p < .05: # and 'B2B_CCALinearSVR' == m2:\n k += 1\n y = .8 * ylim + k * space\n if np.median(r1) > np.median(r2):\n color = 'C%i' % idx\n else:\n color = 'C%i' % jdx\n \n ax.plot(np.linspace(0, 4, 5)[[idx, jdx]], \n [y, y], color=color, lw=1.)\n if k:\n ax.text(1.5, .085, '*', color='C3', fontsize=20, horizontalalignment='center')\n ax.set_ylim(-.03, .101)\nfig.tight_layout(h_pad=0, w_pad=-1)\n\nfig.savefig('fmri_strip.pdf', dpi=150, facecolor='white')\nfig.savefig('fmri_strip.png', dpi=150, facecolor='white')\n# -\n\n# # Controls betas manova decod etc\n\n# # manova\n\n# +\n# results_manova = pd.DataFrame([j.results()[0] for j in jobs_manova if j.state=='COMPLETED'])\n# results_manova = pd.DataFrame([j.results()[0] for j in jobs_manova_small if j.state=='COMPLETED'])\n\n# +\n# analysis = 'Manova_small'\n# hemi = 'left'\n# key = '_'.join((analysis, hemi, 'dummy'))\n\n# for feature in ('word_length', 'word_freq', 'word_function', 'dummy'):\n# key = '_'.join((analysis, hemi, feature))\n# X = np.array([d for d in results[key].values])\n\n# X = np.nan_to_num(X)\n\n# # p-values\n# valid = np.nanstd(X, 0)>0\n# p = np.ones(X.shape[1])\n# _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n\n# # plot\n# fig = plot(-np.log10(p), threshold=3, vmax=13,\n# cmap=cmap, hemi=hemi)\n# imgs[key] = crop(fig_to_img(fig))\n# -\n\nfeatures = ['word_length', 'word_freq', 'word_function', 'dummy']\nfor feature in features:\n print(feature)\n for analysis in ('decod', 'encod', 'betas', 'Manova', 'UnbiasedB2B_betas'):\n \n hemi = 'left'\n key = '_'.join((analysis, hemi, feature))\n X = np.array([d for d in results[key].values])\n X = np.nan_to_num(X)\n \n vmax = .12\n if analysis in ('decod', 'encod', 'Forward', 'Backward'):\n fig = plot(np.clip(np.nanmean(X, 0), -vmax, vmax), \n threshold=0.008, vmax=vmax, cmap=cmap)\n imgs[key] = crop(fig_to_img(fig))\n \n # p-values\n valid = np.nanstd(X, 0)>0\n p = np.ones(X.shape[1])\n _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n \n fig = plot(np.clip(-np.log10(p), 0, 13), \n threshold=3, vmax=13, cmap=cmap)\n imgs[key + '_pval'] = crop(fig_to_img(fig))\n\n# +\nanalysis = 'Manova'\nhemi = 'left'\nkey = '_'.join((analysis, hemi, 'dummy'))\ndummy = np.array([d for d in results[key].values])\n\nfor feature in ('word_length', 'word_freq', 'word_function'):\n key = '_'.join((analysis, hemi, feature))\n X = np.array([d for d in results[key].values])\n\n X = np.nan_to_num(X - dummy)\n\n # p-values\n valid = np.nanstd(X, 0)>0\n p = np.ones(X.shape[1])\n _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n\n # plot\n fig = plot(np.clip(-np.log10(p), 0, 13), \n threshold=3, vmax=13, cmap=cmap)\n imgs['dummy_vs_' + key + '_pval'] = crop(fig_to_img(fig)) \n\n# +\nanalyses = ('decod',\n 'betas', 'UnbiasedB2B_betas', \n 'Manova', 'dummy_vs_Manova')\nanalysis_names = dict(\n Backward='Decode\\nR',\n decod='Decode\\nR',\n encod='Encod 1 Feature\\nR',\n betas='Forward\\n$\\hat H$',\n Manova='Manova\\n$F$',\n UnbiasedB2B_betas='Unbiased B2B\\n$\\hat S$',\n #B2B='B2B\\n$\\Delta R$',\n dummy_vs_Manova='Manova\\n$F - F_{dummy}$',\n\n)\nfeatures = ['word_length', 'word_freq', 'word_function', 'dummy']\n\nfig = plt.figure(figsize=[8, 12], constrained_layout=False)\n\ngs = fig.add_gridspec(ncols=2*len(features)+1, \n nrows=len(analyses), \n wspace=0)\naxes = iter([[gs[r, (c*2):(c+1)*2] \n for c in range(len(features))]\n for r in range(len(analyses))])\nc_axes = [fig.add_subplot(gs[:1, -1]),\n fig.add_subplot(gs[2:4, -1])]\n\ngroup_analyses = (\n ('decod', ),\n ('betas', 'UnbiasedB2B_betas',\n 'Manova', 'dummy_vs_Manova'),\n)\n\nfor group, analyses_ in enumerate(group_analyses):\n for analysis in analyses_:\n axs = next(axes)\n for feature, ax in zip(features, axs):\n if analysis == 'dummy_vs_Manova' and feature == 'dummy':\n continue\n\n ax = fig.add_subplot(ax)\n key = '_'.join((analysis, hemi, feature))\n if analysis == 'B2B':\n key += '_knockout'\n if group == 1:\n key += '_pval'\n cmap_label = '$-log_{10}(p)$'\n else:\n cmap_label = '$R$'\n \n img = imgs[key]\n colorbar = img[:, int(img.shape[1]*3.7/5):, :]\n img = img[:, :int(img.shape[1]*3.1/5), :]\n ax.imshow(img)\n for s in ('top', 'right', 'bottom', 'left'):\n ax.spines[s].set_visible(False)\n ax.set_xticks([])\n ax.set_yticks([])\n if feature == features[0]:\n ax.set_ylabel(analysis_names[analysis])\n if analysis == analyses_[0]:\n ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')]))\n\n ax = c_axes[group]\n ax.imshow(colorbar)\n for s in ('top', 'right', 'bottom', 'left'):\n ax.spines[s].set_visible(False)\n ax.set_xticks([])\n ax.set_yticks([])\n ax.set_ylabel(cmap_label, labelpad=-10)\n ax.yaxis.set_label_position(\"right\")\n\nfig.tight_layout(w_pad=0, h_pad=.01)\nfig.savefig('fmri_controls.pdf', dpi=150, facecolor='white')\nfig.savefig('fmri_controls.png', dpi=150, facecolor='white')\n# -\n\n\n\n# # all vertices\n\n# +\nfeatures = ['word_length', 'word_freq', 'dummy']\nanalyses = ['Forward', 'PLS', 'RegCCA', 'CCA', 'B2B']\n\nsummary = list()\nfor feature in features:\n for analysis in analyses:\n scores = list()\n for hemi in ('left', 'right'):\n key = '_'.join((analysis, hemi, feature, 'knockout'))\n scores.append(np.array([d for d in results[key].values]))\n scores = np.concatenate(scores, axis=1)\n scores = np.nanmean(scores, axis=1)\n \n for subject, s in enumerate(scores):\n summary.append(dict(feature=feature, analysis=analysis, score=s, subject=subject))\nsummary = pd.DataFrame(summary)\n# -\n\nsummary = list()\nfor feature in features:\n for analysis in analyses:\n scores = list()\n for hemi in ('left', 'right'):\n key = '_'.join((analysis, hemi, feature, 'knockout'))\n scores.append(np.array([d for d in results[key].values]))\n scores = np.concatenate(scores, axis=1)\n scores = np.nanmean(scores, axis=0)\n \n for vertex, s in enumerate(scores):\n summary.append(dict(feature=feature, analysis=analysis, score=s, vertex=vertex))\nsummary = pd.DataFrame(summary)\n\nsummary = list()\nfor feature in features:\n print(feature)\n for analysis in analyses:\n scores = list()\n for hemi in ('left', 'right'):\n key = '_'.join((analysis, hemi, feature, 'knockout'))\n scores.append(np.array([d for d in results[key].values]))\n scores = np.concatenate(scores, axis=1)\n \n p_vals = np.ones(scores.shape[1])\n for idx, s in enumerate(scores.T):\n valid = ~np.isnan(s)\n if not sum(valid):\n continue\n _, p_vals[idx] = wilcoxon(s[valid])\n \n for vertex, p in enumerate(p_vals):\n summary.append(dict(feature=feature, analysis=analysis, p=p, vertex=vertex))\nsummary = pd.DataFrame(summary)\n\nimport seaborn as sns\n\n# +\nfig, axes = plt.subplots(len(features), 3, sharey=True, sharex=True, figsize=[6, 6])\nfor feature, axs in zip(features, axes):\n for c, (analysis, ax) in enumerate(zip(('Forward', 'PLS', 'CCA'), axs)):\n y = -np.log10(summary.query('analysis==\"B2B\" and feature==@feature').p)\n x = -np.log10(summary.query('analysis==@analysis and feature==@feature').p)\n ax.scatter(x, y, s=.05, color='C%i' % c)\n ax.plot([0, 15], [0, 15], 'k', lw=.5)\n ax.set_xlim([0, 15])\n ax.set_ylim([0, 15])\n ax.set_aspect('equal')\n if feature == features[0]:\n ax.set_title(analysis)\n elif feature == features[-1]:\n ax.set_xlabel('$\\Delta$ R')\n if c == 0:\n ax.set_ylabel('B2B $\\Delta$ R')\n \nfig.tight_layout()\n\n# +\nfig, axes = plt.subplots(len(features), 3, sharey=True, sharex=True, figsize=[6, 6])\nfor feature, axs in zip(features, axes):\n for c, (analysis, ax) in enumerate(zip(('Forward', 'PLS', 'CCA'), axs)):\n y = summary.query('analysis==\"B2B\" and feature==@feature').score\n x = summary.query('analysis==@analysis and feature==@feature').score\n ax.scatter(x, y, s=.05, color='C%i' % c)\n ax.plot([-1, 1], [-1, 1], 'k', lw=.5)\n ax.set_xlim([-.02, .12])\n ax.axhline(0, color='k', lw=.5)\n ax.axvline(0, color='k', lw=.5)\n ax.set_ylim([-.02, .12])\n ax.axhline(0, color='k', lw=.5)\n ax.axvline(0, color='k', lw=.5)\n ax.set_aspect('equal')\n if feature == features[0]:\n ax.set_title(analysis)\n elif feature == features[-1]:\n ax.set_xlabel('$\\Delta$ R')\n if c == 0:\n ax.set_ylabel('B2B $\\Delta$ R')\n \nfig.tight_layout()\n\n\n# -\n\n# # Old Averages\n\n# +\ndef plot(left, right, views=3, threshold=None, **kwargs):\n if views == 3:\n fig, axes = plt.subplots(1, 3, subplot_kw={'projection': '3d'}, figsize=[11, 3])\n axes = dict(left=axes[:2], right=axes[[2, 1]])\n else:\n fig, axes = plt.subplots(1, 2, subplot_kw={'projection': '3d'}, figsize=[7, 3])\n axes = dict(left=axes[:2], right=[None, axes[1]])\n \n for hemi, data in dict(left=left, right=right).items():\n \n coords, verts = surface.load_surf_mesh(fsaverage['infl_%s' % hemi])\n side = -1 if hemi == 'left' else 1\n coords[:, 0] += side * coords[:, 0].max()\n \n opt = dict(threshold=threshold,\n colorbar=False,\n surf_mesh=(coords, verts),\n bg_map=fsaverage['sulc_%s' % hemi])\n for k, v in kwargs.items():\n opt[k] = v\n if axes[hemi] is not None:\n plotting.plot_surf_stat_map(stat_map=data, hemi=hemi, axes=axes[hemi][0], **opt)\n axes[hemi][0].set_xlim(-83, 83)\n axes[hemi][0].set_ylim(-108, 108)\n axes[hemi][0].set_zlim(-73, 73)\n plotting.plot_surf_stat_map(stat_map=data, view='posterior', axes=axes[hemi][1], **opt)\n axes[hemi][1].set_xlim(-83, 83)\n axes[hemi][1].set_ylim(-108, 108)\n axes[hemi][1].set_zlim(-73, 73)\n \n fig.tight_layout(w_pad=0)\n return fig\n\ndef get_pvals(analysis, key, hemis=('left', 'right')):\n p_vals = list()\n for hemi in hemis:\n X = np.array([d for d in results['_'.join((analysis, hemi, key))].values])\n valid = X.std(0)>0\n p = np.ones(X.shape[1])\n r, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid])))\n p_vals.append(-np.log10(p))\n return p_vals\n\n\n# -\n\n# # check fmriprep SLURM\n\n# ls /private/home/jeanremi/data/mous/freesurfer -halt\n\n# +\nimport os\nimport pandas as pd\nfailed = [f for f in os.listdir('jobs') if f.endswith('.err')]\n\ndf = list()\nfor job in failed:\n with open('jobs/' + job) as f:\n\n \n err = '\\n'.join(f.readlines())\n if 'fMRIPrep finished without errors' in err:\n success = True\n err_type = ''\n else:\n success = False\n if 'DUE TO TIME LIMIT' in err:\n err_type = 'time'\n elif err == '':\n err_type = 'unknown'\n elif 'fmriprep: error: argument' in err:\n err_type = 'not subject'\n \n with open('jobs/' + job.replace('.err', '.out')) as f:\n out = '\\n'.join(f.readlines())\n try:\n start = out.split('200509-')[1].split(',')[0]\n stop = out.split('200509-')[-2].split(',')[0]\n except IndexError:\n start, stop = None, None\n task_subject = out.split('task ')[1].split('\\n')[0]\n try:\n task, subject = task_subject.split()\n except ValueError:\n task = task_subject\n subject = None\n \n df.append(dict(job=job,\n success=success, \n err_type=err_type, \n task=task, \n subject=subject,\n start=start, stop=stop))\ndf = pd.DataFrame(df)\ndf\n# -\n\nprint(df.query('not success'))\nprint(len(df.query('success')))\n"},"script_size":{"kind":"number","value":72285,"string":"72,285"}}},{"rowIdx":992,"cells":{"path":{"kind":"string","value":"/Mod_2/hypothesis_testing/hypothesis_testing.ipynb"},"content_id":{"kind":"string","value":"0381aac2b98263fcdae16110f3127c52a4652378"},"detected_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"license_type":{"kind":"string","value":"permissive"},"repo_name":{"kind":"string","value":"chibz3/nyc-mhtn-ds-051120-lectures"},"repo_url":{"kind":"string","value":"https://github.com/chibz3/nyc-mhtn-ds-051120-lectures"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2020-05-11T20:07:17","string":"2020-05-11T20:07:17"},"gha_updated_at":{"kind":"timestamp","value":"2020-05-11T19:59:54","string":"2020-05-11T19:59:54"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":41123,"string":"41,123"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Hypothesis testing \n# -\n\n# ## Intuition for Hypothesis Testing Example\n#\n# Cristian has recently claimed that his lucky quarter is actually distinctly different than every other kind of quarter. Due to the unique weight distribution from the quarter's design there is actually a greater chance for the quarter to land tails than other fair coins.\n#\n# Do we believe him?\n#\n# I sure don't. But lets be good data scientists and put this claim to the test.\n#\n# Let's flip the coin once and if it comes up tails then I'll change my mind.\n#\n# Would you change your mind?\n#\n# How many tails would I have to flip in order to convince you that this coin actual isn't fair? How many to know for sure that it isn't fair?\n#\n# What is a reasonable threshold to set?\n\n# imports\nfrom scipy import stats\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# ### High Level Hypothesis Testing\n# 1. Start with a Scientific Question (yes/no)\n# 2. Take the skeptical stance (Null hypothesis) \n# 3. State the complement (Alternative)\n# 4. Create a model of the situation Assuming the Null Hypothesis is True!\n# 5. Decide how surprised you would need to be in order to change your mind\n\n# ## Definitions\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **What is statistical hypothesis testing?**\n#\n# When we perform experiments, we typically do not have access to all the members of a population, and need to take **samples** of measurements to make inferences about the population. \n#\n# A statistical hypothesis test is a method for testing a hypothesis about a parameter in a population using data measured in a sample. \n#\n# We test a hypothesis by determining the chance of obtaining a sample statistic if the null hypothesis regarding the population parameter is true. \n#\n# > The goal of hypothesis testing is to make a decision about the value of a population parameter based on sample data.\n#\n#\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Why do we care about hypothesis testing?**\n#\n# Scenarios: \n# * Chemistry - do inputs from two different barley fields produce different yields?\n# * Astrophysics - do star systems with near-orbiting gas giants have hotter stars?\n# * Economics - demography, surveys, etc.\n# * Medicine - BMI vs. Hypertension, etc.\n# * Business - which ad is more effective given engagement?\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# **Intuition** \n#\n# Suppose you have a large dataset for a population. The data is normally distributed with mean 0 and standard deviation 1.\n#\n# Along comes a new sample with a sample mean of 2.9.\n#\n# > The idea behind hypothesis testing is a desire to quantify our belief as to whether our sample of observations came from the same population as the original dataset. \n#\n# According to the empirical (68–95–99.7) rule for normal distributions there is only roughly a 0.003 chance that the sample came from the same population, because it is roughly 3 standard deviations above the mean. \n#\n# \n# \n# To formalize this intuition, we define an threshold value for deciding whether we believe that the sample is from the same underlying population or not. This threshold is $\\alpha$, the **significance threshold**. \n#\n# This serves as the foundation for hypothesis testing where we will reject or fail to reject the null hypothesis.\n#\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Hypothesis testing \n#\n# Regardless of the type of statistical hypothesis test you're performing, there are five main steps to executing them:\n#\n# 1. Set up a null and alternative hypothesis \n#\n# 2. Choose a significance level $\\alpha$ (or use the one assigned). \n#\n# 3. Determine the critical test statistic value or p-value. **(Find the rejection region for the null hypothesis.)**\n#\n# 4. Calculate the value of the test statistic. \n#\n# 5. Compare the test statistic value to the critical test statistic value to reject the null hypothesis or not.\n\n# + [markdown] slideshow={\"slide_type\": \"subslide\"}\n# \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# **Decision Rule**: \n#\n# The decision rule tells us when we can reject the null hypothesis. \n#\n# It depends on 3 factors: \n# 1. The alternative hypothesis \n# * Is this an upper-tailed, lower-tailed, or two-tailed test?\n# 2. The test statistic \n# 3. The level of significance $\\alpha$. \n#\n#\n# Upper-tailed test (right-tailed test): \n# * The null hypothesis is rejected if the test statistic is greater than the critical value. \n#\n# Lower-tailed test (left-tailed test): \n# * The null hypothesis is rejected if the test statistic is smaller than the critical value.\n#\n# Two-tailed test:\n# * The null hypothesis is rejected if the test statistic is either larger than an upper critical value or smaller than a lower critical value.\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# # Language of Hypothesis testing \n#\n# **Significance Level $\\alpha$**\n#\n# The significance level $\\alpha$ is the threshold at which you're okay with rejecting the null hypothesis. It is the probability of rejecting the null hypothesis when it is true. \n#\n# The most commonly used $\\alpha$ in science is $\\alpha = 0.05$. When you set $\\alpha = 0.05$, you're saying \"I'm okay with rejecting the null hypothesis if there is less than a 5% chance that the results I am seeing are actually due to randomness\". \n#\n# **p-values**\n#\n# The p-value is the probability of observing a test statistic at least as large as the one observed, by random chance, assuming that the null hypothesis is true. \n#\n# If $p \\lt \\alpha$, we reject the null hypothesis. \n#\n# If $p \\geq \\alpha$, we fail to reject the null hypothesis.\n#\n# > **We do not accept the alternative hypothesis, we only reject or fail to reject the null hypothesis in favor of the alternative.**\n#\n#\n# **What if the experiment we perform fails to reject the null hypothesis?**\n#\n# * We do not throw out failed experiments! \n# * We say \"this methodology, with this data, does not produce significant results\" \n# * Maybe we need more data!\n# -\n\n# ## Type 1 Errors (False Positives) and Type 2 Errors (False Negatives)\n# Most tests for the presence of some factor are imperfect. And in fact most tests are imperfect in two ways: They will sometimes fail to predict the presence of that factor when it is after all present, and they will sometimes predict the presence of that factor when in fact it is not. Clearly, the lower these error rates are, the better, but it is not uncommon for these rates to be between 1% and 5%, and sometimes they are even higher than that. (Of course, if they're higher than 50%, then we're better off just flipping a coin to run our test!)\n#\n# Predicting the presence of some factor (i.e. counter to the null hypothesis) when in fact it is not there (i.e. the null hypothesis is true) is called a \"false positive\". Failing to predict the presence of some factor (i.e. in accord with the null hypothesis) when in fact it is there (i.e. the null hypothesis is false) is called a \"false negative\".\n#\n#\n# How does changing our alpha value change the rate of type 1 and type 2 errors?\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Let's continue our discussion of hypothesis tests with an example.\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# Suppose that African elephants have weights distributed normally around a mean of 9000 lbs with a standard deviation of 900 lbs. _Pachyderm Adventures_ has recently measured the weights of **35** Gabonese elephants and has calculated their average weight at 8637 lbs. \n#\n# Is the average weight of Gabonese elephants different that the average weight of African elephants? Use significance level $\\alpha = 0.05$. \n#\n# **What are the null and alternative hypotheses? What is the significance level of the test?**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# * Null hypothesis\n# * The average weight of Gabonese elephants is the same as the average weight of African elephants.\n#\n# * Alternative hypothesis\n# * The average weight of Gabonese elephants is different than the average weight of African elephants.\n#\n# The significance level of our test is $\\alpha = 0.05$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **What should be our test statistic? Are we running an upper, lower, or two-tailed test? Why?**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Since we know the population standard deviation, the size of our sample is greater than 30, and we are comparing the sample mean to the population mean, we are going to run a one-sample z-test. \n#\n# Since we want to know if the sample mean is **different** from the population mean, we are running a two-tailed test. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **What's the value of the critical test statistic that we should use for our test?**\n\n# + slideshow={\"slide_type\": \"notes\"}\n# critical z-statistic\nalpha = 0.05\n\n# point percent function is the inverse of the cumulative density function which can be understood as the quantile\nstats.norm.ppf(alpha/2), stats.norm.ppf(1-alpha/2)\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Since we are performing a two-tailed one-sample z-test and $\\alpha = 0.05$, if the z-score we compute is greater than 1.96 or smaller than -1.96, then we can reject the null hypothesis at significance level 0.05 in favor of the alternative hypothesis. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Perform the test.**\n#\n# Compute the relevant test statistic for the sample.\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Compute the z-statistic for the sample. \n#\n# $$\\text{z-statistic} = \\frac{\\bar{x} - \\mu}{\\sigma/\\sqrt{n}}, $$ where $\\bar x$ is the sample mean, $\\mu$ is the population mean, $\\sigma$ is the population standard deviation, and $n$ is the sample size. \n\n# + slideshow={\"slide_type\": \"notes\"}\nn = 35\nsigma = 900\n\nx_bar = 8637\nmu = 9000\n\nse = sigma/np.sqrt(n)\nz = (x_bar - mu)/se\nprint(z)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Make a decision: do we reject the null hypothesis or not?**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > z = -2.39 is smaller than -1.96, thus we can reject the null hypothesis in favor of the alternative hypothesis at significance level $\\alpha = 0.05$. \n# - - -\n#\n# Another way of getting to same answer: \n\n# + slideshow={\"slide_type\": \"notes\"}\nstats.norm.cdf(z)\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > The area of the tail corresponding to this z-score is 0.0085. This is below 0.025. Thus we reject the null hypothesis in favor of the alternative at significance level $\\alpha = 0.05$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Would we be able to reject the null hypothesis if our significance threshold was $\\alpha = 0.01$?**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# The area of the tail corresponding to the calculated z-statistic z = -2.386 is `stats.norm.cdf(z) = 0.0085`. \n#\n# > Since the area of the tail corresponding to the z-score we obtained is 0.0085, which is greater than 0.005, we fail to reject the null hypothesis in favor of the alternative at a significance level of $\\alpha = 0.01$. \n\n# + slideshow={\"slide_type\": \"notes\"}\n# critical z-statistic\nalpha = 0.01\nstats.norm.ppf(alpha/2), stats.norm.ppf(1-alpha/2)\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Alternatively, since we are performing a two-tailed one-sample z-test and $\\alpha = 0.01$, if the z-score we compute is greater than 2.58 or smaller than -2.58, then we can reject the null hypothesis at significance level 0.05 in favor of the alternative hypothesis. \n#\n# >Since the calculated z-statistic is -2.386, we fail to reject the null hypothesis in favor of the alternative at a significance level of $\\alpha = 0.01$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # z-tests vs t-tests\n#\n# According to the **Central Limit Theorem**, the sampling distribution of a statistic, like the sample mean, will follow a normal distribution _as long as the sample size is sufficiently large_. \n#\n# __What if we don't have large sample sizes?__\n#\n# When we do not know the population standard deviation or we have a small sample size, the sampling distribution of the sample statistic will follow a t-distribution. \n# * Smaller sample sizes have larger variance, and t-distributions account for that by having heavier tails than the normal distribution.\n# * t-distributions are parameterized by degrees of freedom, fewer degrees of freedom fatter tails. Also converges to a normal distribution as dof >> 0\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # One-sample z-tests and one-sample t-tests\n#\n# One-sample z-tests and one-sample t-tests are hypothesis tests for the population mean $\\mu$. \n#\n# How do we know whether we need to use a z-test or a t-test? \n#\n# \n#\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **When we perform a hypothesis test for the population mean, we want to know how likely it is to obtain the test statistic for the sample mean given the null hypothesis that the sample mean and population mean are not different.** \n#\n# The test statistic for the sample mean summarizes our sample observations. How do test statistics differ for one-sample z-tests and t-tests? \n#\n# A t-test is like a modified z-test. \n#\n# * Penalize for small sample size: \"degrees of freedom\"\n#\n# * Use sample standard deviation $s$ to estimate the population standard deviation $\\sigma$.\n#\n# \n#\n#\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# A one-sample t-test estimates the population mean (one parameter). A sample with size $n$ provides $n$ pieces of information, or degrees of freedom, for estimating the population mean and its variability. \n#\n# One degree of freedom is used to estimate the mean, the remaining $n-1$ degrees of freedom are used to estimate variability. \n#\n# >The one-sample t-test for samples of size $n$ has $n-1$ degrees of freedom.\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# \n#\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# ## One-sample z-test\n#\n# * For large enough sample sizes $n$ with known population standard deviation $\\sigma$, the test statistic of the sample mean $\\bar x$ is given by the **z-statistic**, \n# $$Z = \\frac{\\bar{x} - \\mu}{\\sigma/\\sqrt{n}}$$ where $\\mu$ is the population mean. \n#\n# * Our hypothesis test tries to answer the question of how likely we are to observe a z-statistic as extreme as our sample's given the null hypothesis that the sample and the population have the same mean, given a significance threshold of $\\alpha$. This is a one-sample z-test. \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# ## One-sample t-test\n#\n# * For small sample sizes or samples with unknown population standard deviation, the test statistic of the sample mean is given by the **t-statistic**, \n# $$ t = \\frac{\\bar{x} - \\mu}{s/\\sqrt{n}} $$ Here, $s$ is the sample standard deviation, which is used to estimate the population standard deviation, and $\\mu$ is the population mean. \n#\n# * Our hypothesis test tries to answer the question of how likely we are to observe a t-statistic as extreme as our sample's given the null hypothesis that the sample and population have the same mean, given a significance threshold of $\\alpha$. This is a one-sample t-test.\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# ## Compare and contrast z-tests and t-tests. \n# In both cases, it is assumed that the samples are normally distributed. \n#\n# A t-test is like a modified z-test:\n# 1. Penalize for small sample size; use \"degrees of freedom\" \n# 2. Use the _sample_ standard deviation $s$ to estimate the population standard deviation $\\sigma$. \n#\n# T-distributions have more probability in the tails. As the sample size increases, this decreases and the t distribution more closely resembles the z, or standard normal, distribution. By sample size n = 1000 they are virtually indistinguishable from each other. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Here's an example: \n#\n# A coffee shop relocates from Manhattan to Brooklyn and wants to make sure that all lattes are consistent before and after their move. They buy a new machine and hire a new barista. In Manhattan, lattes are made with 4 oz of espresso. A random sample of 25 lattes made in their new store in Brooklyn shows a mean of 4.6 oz and standard deviation of 0.22 oz. Are their lattes different now that they've relocated to Brooklyn?\n#\n# **What's the null and alternative hypothesis to test in this case? What kind of test should we run? Why?** \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > $H_0$: Lattes are the same. \n#\n# > $H_1$: Lattes are different. \n#\n# >> Should run a one-sample t-test. Unknown population standard deviation. Small sample size. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Two-sample t-tests \n#\n# Sometimes, we are interested in determining whether two population means are equal. In this case, we use two-sample t-tests.\n#\n# There are two types of two-sample t-tests: **paired** and **independent** (unpaired) tests. \n#\n# What's the difference? \n#\n# **Paired tests**: How is a sample affected by a certain treatment? The individuals in the sample remain the same and you compare how they change after treatment. \n#\n# **Independent tests**: When we compare two different, unrelated samples to each other, we use an independent (or unpaired) two-sample t-test.\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# The test statistic for an unpaired two-sample t-test is slightly different than the test statistic for the one-sample t-test. \n#\n# Assuming equal variances, the test statistic for a two-sample t-test is given by: \n#\n# $$ t = \\frac{\\bar{x_1} - \\bar{x_2}}{\\sqrt{s^2 \\left( \\frac{1}{n_1} + \\frac{1}{n_2} \\right)}}$$\n#\n# where $s^2$ is the pooled sample variance, \n#\n# $$ s^2 = \\frac{\\sum_{i=1}^{n_1} \\left(x_i - \\bar{x_1}\\right)^2 + \\sum_{j=1}^{n_2} \\left(x_j - \\bar{x_2}\\right)^2 }{n_1 + n_2 - 2} $$\n#\n# Here, $n_1$ is the sample size of sample 1 and $n_2$ is the sample size of sample 2. \n#\n# An independent two-sample t-test for samples of size $n_1$ and $n_2$ has $(n_1 + n_2 - 2)$ degrees of freedom. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Sample problem: Unpaired two-sample t-test \n#\n# You measure the delivery times of ten different restaurants in two different neighborhoods, A and B. You want to know if restaurants in the different neighborhoods have the same delivery times. It's okay to assume both samples have equal variances. \n#\n# ``` python\n# delivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8]\n# delivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3]\n# ```\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Let's practice solving hypothesis test problems!\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Example 1\n# Let's revisit our Gabonese elephant weight example. \n#\n# Suppose that African elephants have weights distributed normally around a mean of 9000 lbs with a standard deviation of 900 lbs. _Pachyderm Adventures_ has recently measured the weights of **35** Gabonese elephants and has calculated their average weight at 8637 lbs. \n#\n# Is the average weight of Gabonese elephants _less_ than the average weight of African elephants? Use significance level $\\alpha = 0.05$. \n#\n# **What are the null and alternative hypothesis in this case?**\n#\n# **What kind of test do we need to run?**\n#\n# **What's the critical test statistic value we should use?**\n#\n# **Perform the test and make a decision regarding the null hypothesis.**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# * Null hypothesis\n# * The average weight of Gabonese elephants is the same as the average weight of African elephants.\n#\n# * Alternative hypothesis\n# * The average weight of Gabonese elephants is less than the average weight of African elephants.\n# - - - \n#\n# We need to run a lower-tailed one-sample z-test. \n\n# + slideshow={\"slide_type\": \"notes\"}\n\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Example 2\n# Next, let's finish working through our coffee shop example... \n#\n# A coffee shop relocates from Manhattan to Brooklyn and wants to make sure that all lattes are consistent before and after their move. They buy a new machine and hire a new barista. In Manhattan, lattes are made with 4 oz of espresso. A random sample of 25 lattes made in their new store in Brooklyn shows a mean of 4.6 oz and standard deviation of 0.22 oz. Are their lattes different now that they've relocated to Brooklyn? Use a significance level of $\\alpha = 0.01$. \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# State null and alternative hypothesis\n# 1. Null: the amount of espresso in the lattes is the same as before the move.\n# 2. Alternative: the amount of espresso in the lattes is different before and after the move. \n#\n# What kind of test? \n# * two-tailed one-sample t-test\n# * small sample size\n# * unknown population standard deviation \n# * two-tailed because we want to know if amounts are same or different \n\n# + slideshow={\"slide_type\": \"notes\"}\nx_bar = 4.6 \nmu = 4 \ns = 0.22 \nn = 25 \n\ndf = n-1\n\nt = (x_bar - mu)/(s/n**0.5)\nprint(\"The t-statistic for our sample is {}.\".format(round(t, 2)))\n\n# + slideshow={\"slide_type\": \"notes\"}\n# critical t-statistic values\nstats.t.ppf(0.005, df), stats.t.ppf(1-0.005, df)\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Can we reject the null hypothesis? \n#\n# > Yes. t > |t_critical|. we can reject the null hypothesis in favor of the alternative at $\\alpha = 0.01$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Example 3\n#\n# I'm buying jeans from store A and store B. I know nothing about their inventory other than prices. \n#\n# ``` python\n# store1 = [20,30,30,50,75,25,30,30,40,80]\n# store2 = [60,30,70,90,60,40,70,40]\n# ```\n#\n# Should I go just to one store for a less expensive pair of jeans? I'm pretty apprehensive about my decision, so $\\alpha = 0.1$. It's okay to assume the samples have equal variances.\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **State the null and alternative hypotheses**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Null: Store A and B have the same jean prices. \n#\n# > Alternative: Store A and B do not have the same jean prices. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **What kind of test should we run? Why?** \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Run a two-tailed two independent sample t-test. Sample sizes are small. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Perform the test.**\n\n# + slideshow={\"slide_type\": \"notes\"}\nstore1 = [20,30,30,50,75,25,30,30,40,80]\nstore2 = [60,30,70,90,60,40,70,40]\n\nstats.ttest_ind(store1, store2)\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **Make decision.**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > We fail to reject the null hypothesis at a significance level of $\\alpha = 0.1$. We do not have evidence to support that jean prices are different in store A and store B. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# ## Example 4 \n#\n# Next, let's finish working through the restaurant delivery times problem. \n#\n# You measure the delivery times of ten different restaurants in two different neighborhoods. You want to know if restaurants in the different neighborhoods have the same delivery times. It's okay to assume both samples have equal variances. Set your significance threshold to 0.05. \n#\n# ``` python\n# delivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8]\n# delivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3]\n# ```\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# State null and alternative hypothesis. What type of test should we perform? \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Null hypothesis: The delivery times for restaurants in neighborhood A are equal to delivery times for restaurants in neighborhood B. \n#\n# > Alternative hypothesis: Delivery times for restaurants in neighborhood A are not equal to delivery times for restaurants in neighborhood B. \n#\n# > Two-sided unpaired two-sample t-test\n\n# + slideshow={\"slide_type\": \"notes\"}\ndelivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8]\ndelivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3]\n\n# + slideshow={\"slide_type\": \"notes\"}\nstats.ttest_ind(delivery_times_A, delivery_times_B)\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > We cannot reject the null hypothesis that restaurant A and B have equal delivery times. p-value > $\\alpha$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Level Up: More practice problems!\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# A rental car company claims the mean time to rent a car on their website is 60 seconds with a standard deviation of 30 seconds. A random sample of 36 customers attempted to rent a car on the website. The mean time to rent was 75 seconds. Is this enough evidence to contradict the company's claim at a significance level of $\\alpha = 0.05$? \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Null hypothesis:\n#\n# Alternative hypothesis:\n#\n\n# + slideshow={\"slide_type\": \"notes\"}\n# one-sample z-test \n\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Reject?:\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# Consider the gain in weight (in grams) of 19 female rats between 28 and 84 days after birth. \n#\n# Twelve rats were fed on a high protein diet and seven rats were fed on a low protein diet.\n#\n# ``` python\n# high_protein = [134, 146, 104, 119, 124, 161, 107, 83, 113, 129, 97, 123]\n# low_protein = [70, 118, 101, 85, 107, 132, 94]\n# ```\n#\n# Is there any difference in the weight gain of rats fed on high protein diet vs low protein diet? It's OK to assume equal sample variances. \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Null and alternative hypotheses? \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > null: \n#\n# > alternative: \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# What kind of test should we perform and why? \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# > Test:\n\n# + slideshow={\"slide_type\": \"notes\"}\n\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# We fail to reject the null hypothesis at a significance level of $\\alpha = 0.05$. \n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# **What if we wanted to test if the rats who ate a high protein diet gained more weight than those who ate a low-protein diet?**\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Null:\n#\n# alternative:\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Kind of test? \n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Critical test statistic value? \n\n# + slideshow={\"slide_type\": \"notes\"}\n\n\n# + [markdown] slideshow={\"slide_type\": \"notes\"}\n# Can we reject?\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # Summary \n#\n# Key Takeaways:\n#\n# * A statistical hypothesis test is a method for testing a hypothesis about a parameter in a population using data measured in a sample. \n# * Hypothesis tests consist of a null hypothesis and an alternative hypothesis.\n# * We test a hypothesis by determining the chance of obtaining a sample statistic if the null hypothesis regarding the population parameter is true. \n# * One-sample z-tests and one-sample t-tests are hypothesis tests for the population mean $\\mu$. \n# * We use a one-sample z-test for the population mean when the population standard deviation is known and the sample size is sufficiently large. We use a one-sample t-test for the population mean when the population standard deviation is unknown or when the sample size is small. \n# * Two-sample t-tests are hypothesis tests for differences in two population means. \n"},"script_size":{"kind":"number","value":28259,"string":"28,259"}}},{"rowIdx":993,"cells":{"path":{"kind":"string","value":"/Basic Python/Variable.ipynb"},"content_id":{"kind":"string","value":"5612c86d5474ad4b0cec8bfc77654d6b2747fa12"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"kausikporey/Python"},"repo_url":{"kind":"string","value":"https://github.com/kausikporey/Python"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6398,"string":"6,398"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nnum = 5\n\nid(num)\n\na = 5\nb = a\n\nid(a)\n\nid(b)\n\nid(10)\n\nPI = 22/7\n\nPI\n\nPI = 2\n\nPI\n\ntype(PI)\n\nnum = 3.2\nnum = int(num)\n\ntype(num)\n\nnum\n\nlist(range(2,10))\n\nlist(range(5))\n\nlist(range(2,10,2))\n\nd = {'Kausik':'Samsung','Rahul':'Iphone','Goutam':'Moto'}\n\nd.keys()\n\nd.values()\n\nd['Rahul']\n\ny = 5 + 6j\n\ny\ning) in a retriever. This builds on top of ideas in the [ContextualCompressionRetriever](/docs/modules/data_connection/retrievers/contextual_compression/).\n\n# +\n# #!pip install cohere\n\n# +\n# #!pip install faiss\n\n# OR (depending on Python version)\n\n# #!pip install faiss-cpu\n\n# +\n# get a new token: https://dashboard.cohere.ai/\n\nimport os\nimport getpass\n\nos.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Cohere API Key:\")\n# -\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n\n# +\n# Helper function for printing docs\n\n\ndef pretty_print_docs(docs):\n print(\n f\"\\n{'-' * 100}\\n\".join(\n [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n )\n )\n\n\n# + [markdown] jp-MarkdownHeadingCollapsed=true\n# ## Set up the base vector store retriever\n# Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs.\n\n# +\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.document_loaders import TextLoader\nfrom langchain.vectorstores import FAISS\n\ndocuments = TextLoader(\"../../../state_of_the_union.txt\").load()\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\ntexts = text_splitter.split_documents(documents)\nretriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever(\n search_kwargs={\"k\": 20}\n)\n\nquery = \"What did the president say about Ketanji Brown Jackson\"\ndocs = retriever.get_relevant_documents(query)\npretty_print_docs(docs)\n# -\n\n# ## Doing reranking with CohereRerank\n# Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `CohereRerank`, uses the Cohere rerank endpoint to rerank the returned results.\n\n# +\nfrom langchain.llms import OpenAI\nfrom langchain.retrievers import ContextualCompressionRetriever\nfrom langchain.retrievers.document_compressors import CohereRerank\n\nllm = OpenAI(temperature=0)\ncompressor = CohereRerank()\ncompression_retriever = ContextualCompressionRetriever(\n base_compressor=compressor, base_retriever=retriever\n)\n\ncompressed_docs = compression_retriever.get_relevant_documents(\n \"What did the president say about Ketanji Jackson Brown\"\n)\npretty_print_docs(compressed_docs)\n# -\n\n# You can of course use this retriever within a QA pipeline\n\nfrom langchain.chains import RetrievalQA\n\nchain = RetrievalQA.from_chain_type(\n llm=OpenAI(temperature=0), retriever=compression_retriever\n)\n\nchain({\"query\": query})\n\n\n"},"script_size":{"kind":"number","value":3158,"string":"3,158"}}},{"rowIdx":994,"cells":{"path":{"kind":"string","value":"/_5_task.ipynb"},"content_id":{"kind":"string","value":"2e5cf8f1ef1bab2403ff7daec0f76e3295df9a93"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"qu4n7/geek_prob"},"repo_url":{"kind":"string","value":"https://github.com/qu4n7/geek_prob"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":10372,"string":"10,372"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n#

Can a large scale dewatering project replace a pumping test?

\n#

A show case of the Tim groundwater familiy by an assessment of the Vlaketunnel dewatering in 2011.

\n#\n# The primary goal of this notebook is to illustrate the use of the Tim groundwater family, especially the use of QGIS-Tim, TimML and data available on the internet.\n#\n# This notebook is presented during a workshop at the NHV Spring meeting on April 13, 2023.\n#\n# Prepared by Mark Bakker (TU Delft) and Hendrik Meuwese (Waterboard Scheldestromen).\n# \n\n# ## Some Python imports\n\n# +\n# import general packages\nfrom io import StringIO\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n# install timml if it is not installed already\ntry:\n import timml as tml\nexcept:\n # !pip install timml\n import timml as tml\n\n# import specific functions for this notebook. not used now to make notebook run on colab\n# import vlaketunnel_functions as vlake_func\n\n# some plotting parameters\nplt.rcParams[\"figure.figsize\"] = (12, 4) # set default figure size\nplt.rcParams[\"contour.negative_linestyle\"] = 'solid' # set default line style\nplt.rcParams[\"figure.autolayout\"] = True # same at tight_layout after every plot\n# -\n\n# # This is TimML in Python\n#\n# A short example of TimML in the Python interface.\n#\n# The code cell below is copied from the example notebook (https://github.com/mbakker7/timml/blob/master/notebooks/timml_notebook0_sol.ipynb). It creates a groundwater model with uniform flow and an extraction of 400 m$^3$/day by a well.\n#\n# Do you want to know more about Tim? For Dutch people, TKI TIM is active now: https://publicwiki.deltares.nl/display/TKIP/DEL156+TKI+TIM\n\n# +\nml = tml.ModelMaq(kaq=10, z=[10, 0])\nrf = tml.Constant(ml, xr=-1000, yr=0, hr=41)\nuf = tml.Uflow(ml, slope=0.001, angle=0)\nw = tml.Well(ml, xw=-400, yw=0, Qw=50., rw=0.2)\nml.solve()\n\nml.contour(win=[-1000, 100, -500, 500], ngr=50, levels=np.arange(39, 42, 0.1), ) #figsize=(6, 6))\nml.tracelines(-800 * np.ones(1), -200 * np.ones(1), np.zeros(1), hstepmax=20, color='C1')\n# -\n\n# The example is a simple synthetic model. \n# The construction of more complicated TimML models is facilitated by a QGIS plus-in. We will use QGIS to create a TimML model later in this Notebook.\n#\n# QGIS-TIM models may be exported to Python scripts and imported in a notebook. Some pre-processing of model input and observations is done in a separate notebook.\n#\n# First we give an overview of the modeling case.\n\n# # Short overview of the case and model input\n#\n\n# ## Why was dewatering started?\n# The eastern part of the Vlaketunnel (A58 highway in province Zeeland) lifted up by 10-15 cm on November 12, 2010. The tunnel was closed immediately. Dumper trucks delivered sand to try to stabilize the tunnel. \n# A large-scale dewatering was started on November 26, 2010.\n#\n# ![image.png](attachment:8e8328db-5651-45ab-ae31-f7ded25d32f5.png)\n\n# ## Boundary conditions\n#\n# ### GeoTop\n# Lithology according to BRO GeoTop v1.5\n#\n# ![image.png](attachment:f3552384-e244-4cb8-a202-8ef5a598984e.png)\n#\n# ### Layer composition\n#\n# | top (m NAP) | bottom (m NAP) | hydrogeology | dewatering | channel | kh |\n# |--- |--- |--- |--- |--- | --- |\n# | -1 | -7 | semi-confining toplayer | | yes | c=1000 |\n# | -7 | -15 | upper part aquifer | yes | yes | kh=5 |\n# | -15 | -30 | middle part aquifer | | | kh=15 |\n# | -30 | -40 | lower part aquifer | | | kh=5 |\n\n# ### Discharge\n#\n# Actual discharges are: 325 m$^3$/hour in the eastern part and 75 m$^3$/hour in the western part.\n\n# +\nq_east_total = 325 * 24 # m^3/d\nq_east_nr_wells = 4\nprint(f'EAST discharge per well = {q_east_total/q_east_nr_wells} m3/day')\n\nq_west_total = 75 * 24 # m^3/d\nq_west_nr_wells = 2\nprint(f'WEST discharge per well = {q_west_total/q_west_nr_wells} m3/day')\n# -\n\n# ## Observed drawdowns, relative to center of eastern dewatering site\n#\n# Major dewatering is on eastern shore, see observed drawdown of 8 m at x=0.\n#\n#\n# Red color for observations west of Kanaal door Zuid-Beveland, maroon color for eastern shore. Mind the difference of drawdown near $x=1700$ on both shores. \n\n# +\n# import on your laptop\n# import pickle\n# with open(r'data/df_dh.pkl', 'rb') as f:\n# df_dh = pickle.load(f)\n\n# binder does not support pickle, we use text import\ndata_as_string = StringIO(\"\"\"name;x;y;screen_top;tube_nr;dh_obs;ha;va;color;r\neastern_tunnel;59313.0;387345.0;-10.00;1.0;-8.000000;left;top;maroon;0.000000\npb6;58774.0;386980.0;-10.00;1.0;-2.000000;left;top;r;650.957756\nB48F0233-001;58803.0;388144.0;-6.45;1.0;-0.549122;left;top;r;947.892926\nB48F0233-002;58803.0;388144.0;-8.81;2.0;-0.307027;left;bottom;r;947.892926\nB48F0233-003;58803.0;388144.0;-14.89;3.0;-0.338581;right;top;r;947.892926\nB48F0203-002;59400.0;389050.0;-8.60;2.0;-0.821750;left;bottom;maroon;1707.218205\nB48F0203-003;59400.0;389050.0;-14.40;3.0;-0.767000;right;top;maroon;1707.218205\nB48F0232-002;58615.0;389021.0;-9.57;2.0;-0.029118;left;bottom;r;1815.538488\nB48F0232-003;58615.0;389021.0;-14.61;3.0;-0.026286;right;top;r;1815.538488\nB48F0231-002;57381.0;389003.0;-10.58;2.0;0.000676;left;bottom;r;2545.896306\nB48F0231-003;57381.0;389003.0;-14.27;3.0;-0.016892;right;top;r;2545.896306\"\"\")\ndf_dh = pd.read_table(data_as_string, header=0, sep=\";\", index_col=0)\n# -\n\ndf_dh.plot.scatter(x='r', y='dh_obs', c='color', xlabel='distance to eastern dewatering site (m)', ylabel='drawdown (m)', figsize=(10,4), grid=True);\n\n\n# # Create TimML model using QGIS-Tim\n#\n# QGIS-Tim is a graphical user interface in QGIS for TimML (steady-state) and TTim (transient) models (https://deltares.gitlab.io/imod/qgis-tim/index.html). QGIS-Tim can export a Python file with the model input. \n#\n# ## Model set-up\n# Because of the limited time during this workshop, the QGIS-Tim is prepared. The result is posted in the code cell below.\n\n# +\n#Video.from_file(\"data/screen_capture_qgis_tim_compressed.mp4\", width=320, height=320)\n#Video.from_file(\"data/screen_capture_qgis_tim_full.mp4\", width=320, height=320)\n# -\n\n# ## Functions to create the model and display the model results\n# These are commonly stored in a separate Python file. They are included here to make it easier to run the notebook on google colab.\n\n# +\ndef create_model(kaq=[0.1, 5.0, 15.0, 5.0], c=[1000.0, 2.0, 2.0, 2.0],hstar=0, c_channel_bot=30,\n do_plot=True, df_dh=None):\n \"\"\"\n Create a TimML model for Vlaketunnel case\n\n Parameters\n ----------\n kaq : list, optional\n Kh of aquifers. The default is [0.1, 5.0, 15.0, 5.0].\n c : list, optional\n c of aquitards. The default is [1000.0, 2.0, 2.0, 2.0].\n hstar : float, optional\n Top boundary condition of semi-confining toplayer. The default is 0.\n c_channel_bot : float, optional\n resistance of Kanaal door Zuid-Beveland. The default is 30.\n do_plot : boolean, optional\n Plot results? The default is True.\n df_dh: pd.DataFrame, optional\n Information about observed drawdowns, required for plotting. The default is None.\n\n Returns\n -------\n ml : timml model\n The model\n\n \"\"\"\n \n # create model\n ml = tml.ModelMaq(\n kaq=kaq,\n z=[1.0, -3.0, -7.0, -7.0, -14.0, -14.0, -30.0, -30.0, -40.0],\n c=c,\n topboundary=\"semi\",\n npor=[None, None, None, None, None, None, None, None],\n hstar=hstar,\n )\n\n # add dewatering\n dewatering_east_xys = [[59224, 387382], [59359, 387375], [59360, 387311], [59234, 387298], ]\n q_east_total = 325*24\n \n q_west_total = 75*24\n dewatering_west_xys = [[58781, 387375], [58785, 387307],]\n \n for dewatering_xys, q_total in zip([dewatering_east_xys, dewatering_west_xys], [q_east_total, q_west_total]):\n # loop over both dewatering locations\n for dewatering_xy in dewatering_xys:\n # loop over the modelled wells, in pratice a lot of more wells are used. Current model has focus on regional effect, therefore limited number of wells are considered sufficient\n dewatering_east = tml.Well(\n xw=dewatering_xy[0],\n yw=dewatering_xy[1],\n Qw=q_total/len(dewatering_xys),\n rw=0.5,\n res=1.0,\n layers=1,\n label=None,\n model=ml,\n )\n\n c_channel = ml.aq.c.copy()\n c_channel[0] = c_channel_bot\n\n channel_0 = tml.PolygonInhomMaq(\n kaq=ml.aq.kaq,\n z=ml.aq.z,\n c=c_channel,\n topboundary=\"semi\",\n npor=[None, None, None, None, None, None, None, None],\n hstar=0.0,\n # compared to QGIS-Tim export the channel is extended to the north in order to cover the northern observation wells better\n xy= [ [58921, 390500], [59065, 390500], [59110, 387996], [59146, 387447], [59263, 386809], [59317, 386260], [59110, 386251], [58966, 386863], [58921, 388617], ],\n order=4,\n ndeg=6,\n model=ml,\n )\n ml.solve()\n \n if do_plot and (df_dh is not None):\n plot_model_results(ml, df_dh)\n \n return ml\n\ndef plot_model_input(ml):\n \"\"\"\n Plot model input in schematic section\n\n Parameters\n ----------\n ml : timml Model\n The model\n\n Returns\n -------\n None.\n\n \"\"\"\n # some plotting constants\n xmin=-1\n xchannel=-0.25\n xhinter=-0.2\n xmax=1\n zaqmid = np.mean([ml.aq.zaqtop,ml.aq.zaqbot],axis=0)\n\n # plot layers\n plt.hlines(y=ml.aq.zlltop,xmin=xmin,xmax=xmax,color='darkgray')\n plt.hlines(y=ml.aq.zaqbot,xmin=xmin,xmax=xmax,color='darkgray')\n\n # plot kh\n for kh, z in zip(ml.aq.kaq, zaqmid):\n plt.annotate(f'kh={kh:0.1f}m/d',(0,z),ha='center')\n # plot c\n for c, z in zip(ml.aq.c, ml.aq.zaqtop):\n plt.annotate(f'c={c:0.1f}d',(0.5,z),ha='center',va='center')\n # plot channel\n plt.plot([xmin,xchannel],[ml.aq.inhomlist[0].hstar]*2,color='blue')\n plt.annotate(f'h_ch={ml.aq.inhomlist[0].hstar:0.1f}',(xchannel,ml.aq.inhomlist[0].hstar),ha='right',va='bottom')\n plt.annotate(f'c_ch={ml.aq.inhomlist[0].c[0]:0.1f}',(xchannel,ml.aq.zaqtop[0]),ha='right',va='bottom')\n\n # plot hinterland\n plt.plot([xhinter,xmax],[ml.aq.hstar]*2,color='darkblue')\n plt.annotate(f'h_polder={ml.aq.hstar:0.1f}',(xhinter,ml.aq.hstar),ha='left',va='bottom')\n\n plt.xlim([xmin, xmax])\n\n\ndef plot_model_results(ml, df_dh):\n \"\"\"\n Plot results of TimML model of Vlaketunnel case\n\n Parameters\n ----------\n ml : timml Model, \n The model.\n df_dh : pd.DataFrame \n Observed drawdowns\n\n Returns\n -------\n None.\n\n \"\"\"\n \n # contour plot\n plt.subplot(221)\n ml.contour(win=[57000, 60000, 386900, 389100], ngr=50, layers=1,\n levels=[-5,-2,-1,-0.5,-0.1], labels=True, decimals=2, legend=False, newfig=False);\n plt.scatter(df_dh.x, df_dh.y, 20, c=df_dh.color)\n for index, row in df_dh.iterrows():\n plt.annotate(f'{row.dh_obs:0.2f}', (row.x, row.y),ha=row.ha,va=row.va)\n plt.title('contours in layer 1');\n \n # plot model input\n plt.subplot(222)\n plot_model_input(ml)\n \n for plotid in (223, 224):\n plt.subplot(plotid)\n if plotid == 223:\n # first plot, get model results\n df_dh['ml_layer'] = None\n df_dh['dh_calc'] = None\n for index, row in df_dh.iterrows():\n df_dh.loc[index,'ml_layer'] = np.where(ml.aq.zaqtop > row.screen_top)[0][-1]\n df_dh.loc[index,'dh_calc'] = ml.headalongline(row.x, row.y, row.ml_layer)[0][0]\n # plot all model results\n plot_df = df_dh\n else:\n # second plot, only plot outside dewatering area\n plot_df = df_dh.loc[df_dh.r > 100]\n\n plt.scatter(plot_df.r, plot_df.dh_obs, 50, c=plot_df.color, alpha=0.3, label='observed')\n plt.scatter(plot_df.r, plot_df.dh_calc, 40, marker='+', label='modelled')\n plt.legend()\n plt.title('heads from screened modellayer');\n plt.grid()\n\n\n# -\n\n# ## The model is now a function\n\n# The Python script exported by QGIS-Tim is relatively long, because each well is stored separately. The code is included in the function `create_model`. The function builds the same model as created in QGIS-Tim. Some of the model input can be changed through the input arguments of the function. When no arguments are given, the model uses the default parameters from QGIS-Tim. The only change is that for the dewatering, all heads are computed with respect to the situation before the start of dewatering. Hence, waterlevel in the canal and polders is specified as 0.\n#\n# The mode is built and solved as follows:\n\nml = create_model(do_plot=False) # create and solve model, but don't plot results\n\n# The aquifer parameters of the model may be visualized with the `plot_model_input` function.\n\nplot_model_input(ml)\n\n# A contour plot of the computed head changed caused by the dewatering are shown below. The figure also included the measured head changes (note that the head change is the opposite of the drawdown). \n\nml.contour(win=[57000, 60000, 386900, 389100], ngr=50, layers=1,\n levels=[-5,-2,-1,-0.5,-0.1], labels=True, decimals=2, legend=False, newfig=False);\nplt.scatter(df_dh.x, df_dh.y, 20, c=df_dh.color)\nfor index, row in df_dh.iterrows():\n plt.annotate(f'{row.dh_obs:0.2f}', (row.x, row.y),ha=row.ha,va=row.va)\nplt.title('contours in layer 1');\n\n# Both plots, and sections over the observation locations are combined in one plotting function: `plot_model_results`.\n\nplot_model_results(ml, df_dh)\n\n# ## Next: vary the aquifer parameters to better match the observed head changes\n#\n# The calculated head change is (far) larger than the observed head change. Different aquifer parameters are specified as input to the `create_model` function. The function automtically calls the plot function by default.\n\n# ### First attempt: larger bottom resistance of Kanaal door Zuid-Beveland\n\nml = create_model(kaq=[0.1, 2.0, 10.0, 5.0], c_channel_bot=250, df_dh=df_dh)\n\n# ### Second attempt: higher resistance of semi-confining top layer below polders\n\nml = create_model(kaq=[0.1, 2.0, 10.0, 5.0], c_channel_bot=250, c=[5000.0, 2.0, 2.0, 2.0], df_dh=df_dh)\n\n# # Up to you!\n#\n# Which model input gives the best representation of the observation?\n#\n# Is there one best solution? Is this relatively simple schematization a reasonable representation?\n"},"script_size":{"kind":"number","value":14775,"string":"14,775"}}},{"rowIdx":995,"cells":{"path":{"kind":"string","value":"/classifiers/decision-tree/team_v_team_cross_validated_over_and_under_sampled_decision_tree.ipynb"},"content_id":{"kind":"string","value":"f693ce23c935561ed26d62fd45134f1d29596c3d"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"psalire/cmpe255-term-project"},"repo_url":{"kind":"string","value":"https://github.com/psalire/cmpe255-term-project"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":6808693,"string":"6,808,693"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Economics Problem Set 1\n#\n#\n#\n\n# ## Question 1\n#\n#\n# ### 1. State variables:\n#\n# Stock of oil $S_t$\n# Price $p_t$\n#\n# ### 2. Control variables:\n#\n# Amount of oil to sell $x_t$\n#\n# ### 3. Transition equation:\n#\n# $$S_{t+1} = S_t - x_t$$\n#\n# ### 4. Sequence problem\n#\n# Max $E_{t}\\{\\Sigma_{s=1}^{\\infty}p_{t+s}x_{t+s}(\\frac{1}{1+r})^s) \\}$\n# subject to $S_{t+s+1} = S_{t+s} - x_{t+s}$ $\\forall s$ and $S_t = B$ and $S_{t+s} \\geqslant 0$\n#\n# The Bellman equation is of the form:\n#\n# $$V(S) = Max_x\\{px + \\frac{1}{1+r} V(S - x)\\}$$\n#\n# ### 5. The Euler equation\n#\n# $$p_{t+s} = p_{t+1+s}(\\frac{1}{1+r})$$\n#\n# ### 6. The solution\n#\n# Since the payoff function is linear, the solution is piecewise.\n#\n# If $p_{t+1+s} = p_{t+s}$ for all s, then $x_t = B$, i.e. we sell everything today. This is because we get the same absolute payoff from selling a marginal unit in any period, but we discount the future, so we sell everything today.\n#\n# If $p_{t+1+s} > (1+r)p_{t+s}$ then $x_{t+s} = 0$ for all s. This is actually a violation of the transversality condition, and so the model is not stationary in this case.\n#\n# A necessary condition for an interior solution is $p_{t+s}(1+r) = p_{t+s+1}$\n#\n\n# ## Question two\n#\n# ### 1. State variables:\n#\n# Capital today: $k_t$\n# Shock today: $z_t$\n#\n# ### 2. Control variables:\n#\n# Consumption today: $c_t$\n#\n# ### 3. Bellman Equation:\n#\n# $$V(z_t, k_t) = Max_c\\{U(C_{t}) + \\beta E_tV(z_{t+1}, k_{t+1})\\}$$\n#\n# subject to the resource contraint:\n#\n# $$k_{t+1} + c_{t} = z_{t}k_{t}^\\alpha + (1-\\delta)k_{t}$$\n#\n#\n\n# ### Import some packages\n\n# +\n#Imports\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# to print plots inline\n# %matplotlib inline\n# -\n\n# ### Set Parameters\n#\n# Parameters:\n# * $\\gamma$ : Coefficient of Relative Risk Aversion\n# * $\\beta$ : Discount factor\n# * $\\delta$ : Rate of depreciation\n# * $\\alpha$ : Curvature of production function\n# * $\\sigma_z$ : Standard dev of productivity shocks\n# * $\\mu$ : Centre of log normal distribution\n# * $\\rho$ : Persistence parameter\n#\n\ngamma = 0.5\nbeta = 0.96\ndelta = 0.05\nalpha = 0.4\nsigmaz = 0.2\nmu = 0\nrho = 0\n\n# ### Create Grid Space\n\n# +\n'''\n------------------------------------------------------------------------\nCreate Grid for State Space - Capital and Shock\n------------------------------------------------------------------------\nlb_k = scalar, lower bound of capital grid\nub_k = scalar, upper bound of capital grid\nsize_k = integer, number of grid points in capital state space\nk_grid = vector, size_k x 1 vector of capital grid points \n------------------------------------------------------------------------\n'''\nlb_k = 10\nub_k = 13\nsize_k = 60 # Number of grid points of k\nsize_z = 60 # Number of grid points of z\nk_grid = np.linspace(lb_k, ub_k, size_k)\n\nimport ar1_approx as ar1\nln_z_grid, pi = ar1.addacooper(size_z, mu, rho, sigmaz)\nz_grid = np.exp(ln_z_grid)\npi_z = np.transpose(pi)\n\n\n\n# +\n'''\n------------------------------------------------------------------------\nCreate grid of current utility values \n------------------------------------------------------------------------\nC = matrix, current consumption (c=z_tk_t^a - k_t+1 + (1-delta)k_t)\nU = matrix, current period utility value for all possible\n choices of w and w' (rows are w, columns w')\n------------------------------------------------------------------------\n'''\n\nC = np.zeros((size_k, size_k, size_z))\nfor i in range(size_k): # loop over k_t\n for j in range(size_k): # loop over k_t+1\n for q in range(size_z): #loop over z_t\n C[i, j, q] = z_grid[q]* k_grid[i]**alpha + (1 - delta)*k_grid[i] - k_grid[j]\n# replace 0 and negative consumption with a tiny value \n# This is a way to impose non-negativity on cons\nC[C<=0] = 1e-15\nif gamma == 1:\n U = np.log(C)\nelse:\n U = (C ** (1 - gamma)) / (1 - gamma)\nU[C<0] = -9999999\n\n# -\n\n# ### Value function iteration\n\n# +\n'''\n------------------------------------------------------------------------\nValue Function Iteration \n------------------------------------------------------------------------\nVFtol = scalar, tolerance required for value function to converge\nVFdist = scalar, distance between last two value functions\nVFmaxiter = integer, maximum number of iterations for value function\nV = vector, the value functions at each iteration\nVmat = matrix, the value for each possible combination of w and w'\nVstore = matrix, stores V at each iteration \nVFiter = integer, current iteration number\nTV = vector, the value function after applying the Bellman operator\nPF = vector, indicies of choices of w' for all w \nVF = vector, the \"true\" value function\n------------------------------------------------------------------------\n'''\nVFtol = 1e-6 \nVFdist = 7.0 \nVFmaxiter = 500 \nV = np.zeros((size_k, size_z)) # initial guess at value function\nVmat = np.zeros((size_k, size_k, size_z)) # initialize Vmat matrix\nVstore = np.zeros((size_k, size_z, VFmaxiter)) #initialize Vstore array\nVFiter = 1 \nwhile VFdist > VFtol and VFiter < VFmaxiter:\n print('This is the distance', VFdist, VFiter)\n for i in range(size_k): # loop over k_t\n for j in range(size_k): # loop over k_t+1\n for q in range(size_z): #loop over z_t\n EV = 0\n for qq in range(size_z):\n EV += pi_z[q, qq]*V[j, qq]\n Vmat[i, j, q] = U[i, j, q] + beta * EV\n \n Vstore[:,:, VFiter] = V.reshape(size_k, size_z,) # store value function at each iteration for graphing later\n TV = Vmat.max(1) # apply max operator over k_t+1\n PF = np.argmax(Vmat, axis=1)\n VFdist = (np.absolute(V - TV)).max() # check distance\n V = TV\n VFiter += 1 \n \n\n\nif VFiter < VFmaxiter:\n print('Value function converged after this many iterations:', VFiter)\nelse:\n print('Value function did not converge') \n\n\nVF = V # solution to the functional equation\n# -\n\n# Plot value function\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[1:], VF[1:, 0], label='$z$ = ' + str(z_grid[0]))\nax.plot(k_grid[1:], VF[1:, 5], label='$z$ = ' + str(z_grid[5]))\nax.plot(k_grid[1:], VF[1:, 15], label='$z$ = ' + str(z_grid[15]))\nax.plot(k_grid[1:], VF[1:, 19], label='$z$ = ' + str(z_grid[19]))\n# Now add the legend with some customizations.\nlegend = ax.legend(loc='lower right', shadow=False)\n# Set the fontsize\nfor label in legend.get_texts():\n label.set_fontsize('large')\nfor label in legend.get_lines():\n label.set_linewidth(1.5) # the legend line width\nplt.xlabel('Size of Capital')\nplt.ylabel('Value Function')\nplt.title('Value Function')\nplt.show()\n\n#Plot optimal consumption rule as a function of capital\noptK = k_grid[PF]\noptC = z_grid * k_grid ** (alpha) + (1 - delta) * k_grid - optK\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[:], optC[:][18], label='Consumption')\n# Now add the legend with some customizations.\n#legend = ax.legend(loc='upper left', shadow=False)\n# Set the fontsize\nfor label in legend.get_texts():\n label.set_fontsize('large')\nfor label in legend.get_lines():\n label.set_linewidth(1.5) # the legend line width\nplt.xlabel('Size of Capital')\nplt.ylabel('Optimal Consumption')\nplt.title('Policy Function, consumption - growth model')\nplt.show()\n\n\n# +\n#Plot optimal capital in period t + 1 rule as a function of cake size\noptK = k_grid[PF]\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[:], optK[:][18], label='Capital in period t+1')\n# Now add the legend with some customizations.\n#legend = ax.legend(loc='upper left', shadow=False)\n# Set the fontsize\nfor label in legend.get_texts():\n label.set_fontsize('large')\nfor label in legend.get_lines():\n label.set_linewidth(1.5) # the legend line width\nplt.xlabel('Size of Capital in period t')\nplt.ylabel('Optimal Capital in period t+1')\nplt.title('Policy Function, capital next period - growth model')\nplt.show()\n\n\n# -\n\n# ## Question 3\n\n# ### The Bellman equation\n#\n# $$V(z_t, k_t) = Max_c\\{U(C_{t}) + \\beta E_{z_{t+1} | z_t} V(z_{t+1}, k_{t+1})\\}$$\n#\n# subject to the resource contraint:\n#\n# $$k_{t+1} + c_{t} = z_{t}k_{t}^\\alpha + (1-\\delta)k_{t}$$\n\n# ### Set Parameters\n#\n# Parameters:\n# * $\\gamma$ : Coefficient of Relative Risk Aversion\n# * $\\beta$ : Discount factor\n# * $\\delta$ : Rate of depreciation\n# * $\\alpha$ : Curvature of production function\n# * $\\sigma_v$ : Standard deviation of iid shock to log z\n# * $\\rho$ : Persistence parameter\n# * $\\sigma_v$: stdev of iid shock\n\ngamma = 0.5\nbeta = 0.96\ndelta = 0.05\nalpha = 0.4\nsigma_v = 0.1\nmu = 0\nrho = 0.8\n\n# ### Create Grid Space\n\n# +\n'''\n------------------------------------------------------------------------\nCreate Grid for State Space - Capital and Shock\n------------------------------------------------------------------------\nlb_k = scalar, lower bound of capital grid\nub_k = scalar, upper bound of capital grid\nsize_k = integer, number of grid points in capital state space\nk_grid = vector, size_k x 1 vector of capital grid points \n------------------------------------------------------------------------\n'''\nlb_k = 10\nub_k = 13\nsize_k = 30 # Number of grid points of k\nsize_z = 30 # Number of grid points of z\nk_grid = np.linspace(lb_k, ub_k, size_k)\n\nimport ar1_approx as ar1\nln_z_grid, pi = ar1.addacooper(size_z, mu, rho, sigma_v)\nz_grid = np.exp(ln_z_grid)\npi_z = np.transpose(pi)\n\n\n# +\n'''\n------------------------------------------------------------------------\nCreate grid of current utility values \n------------------------------------------------------------------------\nC = matrix, current consumption (c=z_tk_t^a - k_t+1 + (1-delta)k_t)\nU = matrix, current period utility value for all possible\n choices of k and k'\n------------------------------------------------------------------------\n'''\n\nC = np.zeros((size_k, size_k, size_z))\nfor i in range(size_k): # loop over k_t\n for j in range(size_k): # loop over k_t+1\n for q in range(size_z): #loop over z_t\n C[i, j, q] = z_grid[q]* k_grid[i]**alpha + (1 - delta)*k_grid[i] - k_grid[j]\n# replace 0 and negative consumption with a tiny value \n# This is a way to impose non-negativity on cons\nC[C<=0] = 1e-15\nif gamma == 1:\n U = np.log(C)\nelse:\n U = (C ** (1 - gamma)) / (1 - gamma)\nU[C<0] = -9999999\n\n# -\n\n# ### Value function iteration\n\n# +\n'''\n------------------------------------------------------------------------\nValue Function Iteration \n------------------------------------------------------------------------\nVFtol = scalar, tolerance required for value function to converge\nVFdist = scalar, distance between last two value functions\nVFmaxiter = integer, maximum number of iterations for value function\nV = vector, the value functions at each iteration\nVmat = matrix, the value for each possible combination of w and w'\nVstore = matrix, stores V at each iteration \nVFiter = integer, current iteration number\nTV = vector, the value function after applying the Bellman operator\nPF = vector, indicies of choices of w' for all w \nVF = vector, the \"true\" value function\n------------------------------------------------------------------------\n'''\nVFtol = 1e-6 \nVFdist = 7.0 \nVFmaxiter = 500 \nV = np.zeros((size_k, size_z)) # initial guess at value function\nVmat = np.zeros((size_k, size_k, size_z)) # initialize Vmat matrix\nVstore = np.zeros((size_k, size_z, VFmaxiter)) #initialize Vstore array\nVFiter = 1 \nwhile VFdist > VFtol and VFiter < VFmaxiter:\n print('This is the distance', VFdist, VFiter)\n for i in range(size_k): # loop over k_t\n for j in range(size_k): # loop over k_t+1\n for q in range(size_z): #loop over z_t\n EV = 0\n for qq in range(size_z):\n EV += pi_z[q, qq]*V[j, qq]\n Vmat[i, j, q] = U[i, j, q] + beta * EV\n \n Vstore[:,:, VFiter] = V.reshape(size_k, size_z,) # store value function at each iteration for graphing later\n TV = Vmat.max(1) # apply max operator over k_t+1\n PF = np.argmax(Vmat, axis=1)\n VFdist = (np.absolute(V - TV)).max() # check distance\n V = TV\n VFiter += 1 \n \n\n\nif VFiter < VFmaxiter:\n print('Value function converged after this many iterations:', VFiter)\nelse:\n print('Value function did not converge') \n\n\nVF = V # solution to the functional equation\n# -\n\n# Plot value function\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[1:], VF[1:, 0], label='$z$ = ' + str(z_grid[0]))\nax.plot(k_grid[1:], VF[1:, 5], label='$z$ = ' + str(z_grid[5]))\nax.plot(k_grid[1:], VF[1:, 15], label='$z$ = ' + str(z_grid[15]))\nax.plot(k_grid[1:], VF[1:, 19], label='$z$ = ' + str(z_grid[19]))\n# Now add the legend with some customizations.\nlegend = ax.legend(loc='lower right', shadow=False)\n# Set the fontsize\nfor label in legend.get_texts():\n label.set_fontsize('large')\nfor label in legend.get_lines():\n label.set_linewidth(1.5) # the legend line width\nplt.xlabel('Size of Capital')\nplt.ylabel('Value Function')\nplt.title('Value Function')\nplt.show()\n\n#Plot optimal consumption rule as a function of capital\noptK = k_grid[PF]\noptC = z_grid * k_grid ** (alpha) + (1 - delta) * k_grid - optK\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[:], optC[:][12], label='Consumption')\nax.plot(k_grid[:], optC[:][18], label='Consumption')\nax.plot(k_grid[:], optC[:][19], label='Consumption')\n# Now add the legend with some customizations.\n#legend = ax.legend(loc='upper left', shadow=False)\nplt.xlabel('Size of Capital')\nplt.ylabel('Optimal Consumption')\nplt.title('Policy Function, consumption - growth model')\nplt.show()\n\n#Plot optimal capital in period t + 1 rule as a function of cake size\noptK = k_grid[PF]\nplt.figure()\nfig, ax = plt.subplots()\nax.plot(k_grid[:], optK[:][4], label='Capital in period t+1')\nax.plot(k_grid[:], optK[:][12], label='Capital in period t+1')\nax.plot(k_grid[:], optK[:][14], label='Capital in period t+1')\n# Now add the legend with some customizations.\n#legend = ax.legend(loc='upper left', shadow=False)\n# Set the fontsize\nfor label in legend.get_texts():\n label.set_fontsize('large')\nfor label in legend.get_lines():\n label.set_linewidth(1.5) # the legend line width\nplt.xlabel('Size of Capital in period t')\nplt.ylabel('Optimal Capital in period t+1')\nplt.title('Policy Function, capital next period - growth model')\nplt.show()\n\n# ## Question 4\n\n# ## 1. Bellman Equation:\n#\n# $$V(w) = Max\\{V^U(w), V^J(w)\\}$$\n# where:\n# $$V^U(w)= b + \\beta E V(w)$$\n# and\n# $$V^J(w) = E_0 \\sum_{t=0}^{\\infty} \\beta^t w = \\frac{w}{1 - \\beta} $$\n\n# Declare parameters\n# Preference parameters\nbeta = 0.96\nb = 0.05\n# Taste shock parameters: AR(1) process:\nmu = 0\nsigma = .15\nsize_w = 100\nrho = 0\n\n# +\n'''\n------------------------------------------------------------------------\nCreate Grid for State Space\n------------------------------------------------------------------------\nub_w = scalar, upper bound grid\nsize_w = integer, number of grid points in state space\nw_grid = vector, size_w x 1 vector of grid points\n------------------------------------------------------------------------\n'''\n\nimport ar1_approx as ar1\nln_w_grid, pi_t = ar1.addacooper(size_w, mu, rho, sigma)\nw_grid = np.exp(ln_w_grid)\npi = np.transpose(pi_t)\n# -\n\n'''\n------------------------------------------------------------------------\nCreate grid of current utility values \n------------------------------------------------------------------------\nU = matrix, current period utility value for all possible\n choices of w and w' (rows are w, columns w')\n------------------------------------------------------------------------\n'''\nU = np.zeros(size_w) \nfor i in range(size_w): # loop over w\n U[i] = (w_grid[i])/(1-beta)\n\n\n# +\n'''\n------------------------------------------------------------------------\nValue Function Iteration \n------------------------------------------------------------------------\nVFtol = scalar, tolerance required for value function to converge\nVFdist = scalar, distance between last two value functions\nVFmaxiter = integer, maximum number of iterations for value function\nV = matrix, the value functions at each iteration\nTV = matrix, the value function after applying the Bellman operator\nPF_discrete = matrix, matrix of policy function: eat=1, not eat=0 \nVstore = array, stores V at each iteration \nVFiter = integer, current iteration number\nEV = scalar, expected value function for a given state\nU_eat = matrix, utility from eating cake now\nVwait = matrix, value of waiting to eat the cake\nVF = vector, the \"true\" value function\n------------------------------------------------------------------------\n'''\nVFtol = 1e-8 \nVFdist = 7.0 \nVFmaxiter = 500 \nV = np.zeros(size_w) # initial guess at value function\nTV = np.zeros(size_w)\nPF_discrete = np.zeros(size_w)\nVstore = np.zeros((size_w, VFmaxiter)) #initialize Vstore array\nVFiter = 1 \nwhile VFdist > VFtol and VFiter < VFmaxiter:\n print('This is the distance', VFdist, VFiter)\n for i in range(size_w): # loop over w\n EV = 0\n for ii in range(size_w): # loop over w\n EV += pi[i, ii] * V[ii] # note can move one space because of how we constructed grid\n U_emp = U[i]\n Vun = b + beta * EV \n TV[i] = max(U_emp, Vun)\n PF_discrete[i] = U_emp >= Vun # = 1 if take job\n \n Vstore[:, VFiter] = TV # store value function at each iteration for graphing later \n VFdist = (np.absolute(V - TV)).max() # check distance\n V = TV\n VFiter += 1 \n\nif VFiter < VFmaxiter:\n print('Value function converged after this many iterations:', VFiter)\nelse:\n print('Value function did not converge') \n\n\nVF = V # solution to the functional equation\n# -\n\n# ### Threshold\n\n'''\n------------------------------------------------------------------------\nFind threshold policy functions \n------------------------------------------------------------------------\n'''\nthreshold_w = w_grid[np.argmax(PF_discrete)]\nprint(threshold_w)\n\n# Plot value function \nplt.figure()\nfig, ax = plt.subplots()\nax.plot(w_grid[:], VF[:])\n# Set the fontsize\nplt.xlabel('Wage offer')\nplt.ylabel('Value Function')\nplt.title('Value Function - search model')\nplt.show()\n\n# +\n#Set grid of b\ngrid_b = np.linspace(0.05, 1, 20)\nthreshold_vec = np.zeros(20)\n\n#Begin for loop\nfor q in range(20):\n\n VFtol = 1e-8 \n VFdist = 7.0 \n VFmaxiter = 500 \n V = np.zeros(size_w) # initial guess at value function\n TV = np.zeros(size_w)\n PF_discrete = np.zeros(size_w)\n Vstore = np.zeros((size_w, VFmaxiter)) #initialize Vstore array\n VFiter = 1 \n while VFdist > VFtol and VFiter < VFmaxiter:\n print('This is the distance', VFdist, VFiter)\n for i in range(size_w): # loop over w\n EV = 0\n for ii in range(size_w): # loop over w\n EV += pi[i, ii] * V[ii] # note can move one space because of how we constructed grid\n U_emp = U[i]\n Vun = grid_b[q] + beta * EV \n TV[i] = max(U_emp, Vun)\n PF_discrete[i] = U_emp >= Vun # = 1 if take job\n \n Vstore[:, VFiter] = TV # store value function at each iteration for graphing later \n VFdist = (np.absolute(V - TV)).max() # check distance\n V = TV\n VFiter += 1 \n\n if VFiter < VFmaxiter:\n print('Value function converged after this many iterations:', VFiter)\n else:\n print('Value function did not converge') \n\n\n VF = V # solution to the functional equation\n threshold_vec[q]=w_grid[np.argmax(PF_discrete)]\n# -\n\nprint(threshold_vec)\n\n# Plot resevation wage as function of benefits \nplt.figure()\nfig, ax = plt.subplots()\nax.plot(grid_b[:], threshold_vec[:])\n# Set the fontsize\nplt.xlabel('Benefits')\nplt.ylabel('Threshold wage offer')\nplt.title('Threshold wage - search model')\nplt.show()\n"},"script_size":{"kind":"number","value":20187,"string":"20,187"}}},{"rowIdx":996,"cells":{"path":{"kind":"string","value":"/muhammad-njyb-python-1.ipynb"},"content_id":{"kind":"string","value":"51784872dbe5df53083398d71820d686980cc0c4"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"Kurakuratempur/gettoknow"},"repo_url":{"kind":"string","value":"https://github.com/Kurakuratempur/gettoknow"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"timestamp","value":"2020-09-27T10:32:40","string":"2020-09-27T10:32:40"},"gha_updated_at":{"kind":"timestamp","value":"2020-09-27T09:54:10","string":"2020-09-27T09:54:10"},"gha_language":{"kind":"string","value":"Jupyter Notebook"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":10422,"string":"10,422"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python [default]\n# language: python\n# name: python3\n# ---\n\n# # Backpropagation\n\nfrom IPython.display import Image\nImage(\"mlp.png\", height=200, width=600)\n\n# # Variables & Terminology\n# * ## $W_{i}$ - weights of the $i$th layer\n# * ## $B_{i}$ - biases of the $i$th layer\n# * ## $L_{a}^{i}$ - _activation_ (Inner product of weights and inputs of previous layer) of the $i$th layer.\n# * ## $L_{o}^{i}$ - _output_ of the $i$th layer. (This is $f(L_{a}^{i})$, where $f$ is the activation function)\n#\n# # MLP with one input, one hidden, one output layer\n# * ## $X, y$ are the training samples\n# * ## $\\mathbf{W_{1}}$ and $\\mathbf{W_{2}}$ are the weights for first (hidden) and the second (output) layer.\n# * ## $\\mathbf{B_{1}}$ and $\\mathbf{B_{2}}$ are the biases for first (hidden) and the second (output) layer.\n# * ## $L_{a}^{0} = L_{o}^{0}$, since the first (zeroth) layers is just the input.\n#\n# # Activations and outputs\n# * ## $L_{a}^{1} = X\\mathbf{W_{1}} + \\mathbf{B_{1}}$\n# * ## $L_{o}^{1} = \\frac{1}{1 + e^{-L_{a}^{1}}}$\n# * ## $L_{a}^{2} = L_{o}^{1}\\mathbf{W_{2}} + \\mathbf{B_{2}}$\n# * ## $L_{o}^{2} = \\frac{1}{1 + e^{-L_{a}^{2}}}$\n# * ## Loss $E = \\frac{1}{2} \\sum_{S}(y - L_{o}^{2})^{2}$\n#\n# ----\n# Derivation of backpropagation learning rule:\n\nfrom IPython.display import YouTubeVideo\nYouTubeVideo(\"LOc_y67AzCA\")\n\nimport numpy as np\nfrom utils import backprop_decision_boundary, backprop_make_classification, backprop_make_moons\nfrom sklearn.metrics import accuracy_score\nfrom theano import tensor as T\nfrom theano import function, shared\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\nplt.rc('figure', figsize=(8, 6))\n# %matplotlib inline\n\n# +\nx, y = T.dmatrices('xy')\n\n# weights and biases\nw1 = shared(np.random.rand(2, 3), name=\"w1\")\nb1 = shared(np.random.rand(1, 3), name=\"b1\")\nw2 = shared(np.random.rand(3, 2), name=\"w2\")\nb2 = shared(np.random.rand(1, 2), name=\"b2\")\n\n# layer activations\nl1_activation = T.dot(x, w1) + b1.repeat(x.shape[0], axis=0)\nl1_output = 1.0 / (1 + T.exp(-l1_activation))\nl2_activation = T.dot(l1_output, w2) + b2.repeat(l1_output.shape[0], axis=0)\nl2_output = 1.0 / (1 + T.exp(-l2_activation))\n\n# losses and gradients\nloss = 0.5 * T.sum((y - l2_output) ** 2)\ngw1, gb1, gw2, gb2 = T.grad(loss, [w1, b1, w2, b2])\n\n# functions\nalpha = 0.2\npredict = function([x], l2_output)\ntrain = function([x, y], loss, updates=[(w1, w1 - alpha * gw1), (b1, b1 - alpha * gb1),\n (w2, w2 - alpha * gw2), (b2, b2 - alpha * gb2)])\n# -\n\n# make dummy data\nX, Y = backprop_make_classification()\nbackprop_decision_boundary(predict, X, Y)\ny_hat = predict(X)\nprint(\"Accuracy: \", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1)))\n\nfor i in range(500):\n l = train(X, Y)\n if i % 100 == 0:\n print(l)\nbackprop_decision_boundary(predict, X, Y)\ny_hat = predict(X)\nprint(\"Accuracy: \", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1)))\n\n# # Exercise: Implement an MLP with two hidden layers, for the following dataset\n\nX, Y = backprop_make_moons()\nplt.scatter(X[:, 0], X[:, 1], c=np.argmax(Y, axis=1))\n\n# ### Hints:\n# 1. Use two hidden layers, one containing 3 and the other containing 4 neurons\n# 2. Use learning rate $\\alpha$ = 0.2\n# 3. Try to make the network converge in 1000 iterations \n\n# +\n# enter code here\n# -\n\n# ### Tips & Tricks for backprogation:\n# [Efficient BackProp, LeCun et al](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\nwah ini dan jalankan perhitungannya.\n#\n# | barang | harga |\n# |-------------|--------|\n# | ayam | 20000 |\n# | ikan kembung| 15000 |\n# | sayur kangkung | 10000 |\n# | susu | 22000 |\n\n# + colab={} colab_type=\"code\" id=\"px1m7D9NTd9C\"\nAyam = 20000\nKembung = 15000\nKangkung = 10000\nSusu = 22000\nDiskon = 0.25\nBiaya = (1*Ayam + 4*Kembung + 3*Kangkung + 2*Susu)\nBiayafinal = Biaya - Biaya*Diskon\n\njawaban_12 = Biayafinal\n\n# + [markdown] colab_type=\"text\" id=\"apXGBudnTd9E\"\n# 13. Surti, remaja anak bapak kades dan si Tejo, jejaka yang baru saja mudik berencana untuk menikah 2 tahun dari sekarang. Jika biaya pernikahan dan resepsi di kampung mereka adalah 48.000.000, berapa uang yang harus ditabung mereka berdua per bulannya agar 2 tahun lagi mereka bisa menikah?\n#\n# asumsi: tidak ada inflasi, dan semua harga selalu konstan.\n\n# + colab={} colab_type=\"code\" id=\"SJZeQlRGTd9E\"\nbiayanikah = 48000000\nestimasiwaktu = 24 #bulan\ntabungan = biayanikah / estimasiwaktu #total tabungan berdua, per orang berapa tidak cukup informasinya\n\njawaban_13 = tabungan\n\n# + [markdown] colab_type=\"text\" id=\"t6zq8ndETd9G\"\n# 14. Sebuah bioskop ingin memutar film dan menampilkan judul film tersebut di website mereka. Namun judul film tersebut semuanya memakai huruf kecil. Bantulah bioskop tersebut\n#\n# Hint: Pakai method di dalam string\n\n# + colab={} colab_type=\"code\" id=\"QppBED31Td9H\"\njudul = 'the lord of the rings: the return of the king'\njawaban_14 = print(judul.title())\n\n# + [markdown] colab_type=\"text\" id=\"SGZoVCxvTd9J\"\n# 15. Carilah ada berapa kata 'gandalf' di dalam teks berikut. (tidak case sensitive)\n\n# + colab={} colab_type=\"code\" id=\"Phdw2URbTd9J\"\nteks = \"Centuries later, during the War of the Ring, Gandalf leads Aragorn, Legolas, Gimli, and King Théoden to Isengard, \\\n where they reunite with Merry and Pippin. Gandalf retrieves the defeated Saruman's palantír. Pippin later looks \\\n into the seeing-stone and is seen by Sauron. From Pippin's description of his visions, Gandalf surmises that Sauron \\\n will attack Gondor's capital Minas Tirith. He rides there to warn Gondor's steward Denethor, taking Pippin with him.\"\n\na = teks.count(\"Gandalf\")\nb = teks.count(\"gandalf\")\ntotal = a + b\njawaban_15 = total\n# -\n\n\n"},"script_size":{"kind":"number","value":5913,"string":"5,913"}}},{"rowIdx":997,"cells":{"path":{"kind":"string","value":"/Iris.ipynb"},"content_id":{"kind":"string","value":"f757a0cec578c5f74b237efac68701679d683c4b"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"thomasdubois18/Data_Science"},"repo_url":{"kind":"string","value":"https://github.com/thomasdubois18/Data_Science"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":785257,"string":"785,257"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"Ro5oRBZgdhbU\" colab_type=\"text\"\n# # return Anahtar Sözcüğü\n#\n# Kabaca fonksiyonun o noktada durduran ve önündeki değeri dışarıya döndüren anahtar sözcüktür. \n\n# + id=\"PKNTQ6laevFp\" colab_type=\"code\" outputId=\"d91db9a1-b61e-434b-c366-2c45012a770f\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 136}\n#@title Fonksiyon dışında kullanılamaz.\n\nreturn \n\n\n# + id=\"kn7xutzmfC0i\" colab_type=\"code\" outputId=\"6eb95766-0373-4e2a-ae2a-e672df4c9115\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\n#@title Basit kullanımı\ndef deneme():\n return 1\n\nprint(deneme())\n\n\n# + id=\"s2rXkGiCfVwZ\" colab_type=\"code\" outputId=\"4b321431-0503-4f7c-c87c-27962e1e1922\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\n#@title Önündeki değeri fonksiyonun dışına döndürür\ndef deneme():\n return 'cagatay'\n\n\ndeger = deneme()\n\nprint(deger)\n\n\n# + id=\"v97XmU_ffw_c\" colab_type=\"code\" outputId=\"12b72c3a-9fd7-4c12-b59e-bc08728290ae\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\n#@title Kullanıldığı satırda fonksiyon biter, sonra yazılanlar anlamsızdır\ndef deneme():\n a = 5 \n return a\n a = 456\n\nprint(deneme())\n\n\n# + id=\"c1gDX_qDgJnZ\" colab_type=\"code\" outputId=\"71ad3139-e1e1-4e0e-c781-eb0d44c71200\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\n#@title Önünde değer yerine işlem varsa, bu işlemin gerçekleşmesini bekler ve sonucunu döndürür\ndef deneme():\n return (1 + 4 + 9)*0 > 1 and True\n\nprint(deneme())\n\n\n# + id=\"ykKWPtPNiLLZ\" colab_type=\"code\" outputId=\"801387f1-b2ef-447e-9901-147622d17056\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\n#@title Önünde hiçbir şey yoksa None döndürür\ndef deneme():\n return \n\nprint(deneme())\n\n\n# + [markdown] id=\"GgrMiEnEgfKG\" colab_type=\"text\"\n# **Python oldukça esnektir, yalnızca değer döndürmek zorunda değilsiniz.**\n\n# + id=\"iGnJpusOgtgu\" colab_type=\"code\" outputId=\"3cdc5eef-b596-41e0-ec2d-e8d1b974fbc4\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\ndef deneme():\n return 1, 2, 'cagatay'\n\nprint(deneme())\n\n\n# + id=\"WpSQdCKi2Bsb\" colab_type=\"code\" outputId=\"3c0a7ba2-c197-4faa-b156-dbe5322f9e58\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\ndef deneme(a):\n if a:\n return 'cagatay'\n else:\n return 1.4\n\nprint(deneme(False))\n\n\n# + id=\"jododA_3g0N_\" colab_type=\"code\" outputId=\"fc605d9b-c3a1-431e-9640-ad71ad74ce76\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\ndef deneme():\n a = {'a': 1,\n 'b': None,\n 'c': 3.2}\n\n return a\n\nprint(deneme())\n\n\n# + id=\"tmiTcuZ1hFtD\" colab_type=\"code\" outputId=\"bf134d7d-e0b8-43a1-ba1c-db4e1a2f2f4d\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 36}\nclass A:\n def __init__(self):\n self.ad = 'cagatay' \n\ndef deneme():\n obje = A()\n return obje\n\ndisaridaki_obje = deneme()\n\nprint(disaridaki_obje.ad)\n\n\n# + id=\"lauoTmtdiEBO\" colab_type=\"code\" cellView=\"both\" outputId=\"683957fa-7186-454b-e552-0bcbc4532432\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 54}\ndef baska():\n def deneme():\n print('deneme')\n\n return deneme\n\ncikti = baska()\n\nprint(cikti())\nmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n\n# Fortes correlations ! mise à part peut etre pour l'épaisseur des sepales. Il peut être interessant de faire une ACP pour voir les nouvelles variables ainsi construites ! Ce n'est pas forcement interessant de part le nombre de variables faible mais peu etre interessant étant donné la correlation.\n\n# # ACP\n\ndf.head()\n\nliste_df = list(df)\n\ndf[liste_df[0:-2]].head()\n\nn = df[liste_df[0:-2]].shape[0]\np = df[liste_df[0:-2]].shape[1] #car target te label\nprint(n,p)\n\n# On standardise les données\n\n# +\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\ndf_standardise = sc.fit_transform(df[liste_df[0:-2]])\ndf_standardise\n\nprint(np.mean(df_standardise,axis=0)) #presque 0 car erreur de trancature\nprint(np.std(df_standardise,axis=0)) #1\n\n# -\n\nfrom sklearn.decomposition import PCA\nacp = PCA(svd_solver='full')\ncoord = acp.fit_transform(df_standardise)\nprint(acp.n_components_) #car on a tout gardé ici\n\neigval = (n-1)/n*acp.explained_variance_\nprint(eigval) #valeur corrigée\nplt.plot(np.arange(1,p+1),eigval)\nplt.title(\"Scree plot\")\nplt.ylabel(\"Eigen values\")\nplt.xlabel(\"Factor number\")\nplt.show()\n\n# Compliqué à choisir car peu de variables, mais on peut choisir entre 2 et 3.\n\nprint(acp.singular_values_**2/n) #ou valeurs singulieres\n\nprop_var = acp.explained_variance_ratio_ #proportion de variance expliquée\nprop_var = np.cumsum(prop_var)\nprint(prop_var)\n#cumul de variance expliquée\nplt.plot(np.arange(1,p+1),prop_var)\nplt.title(\"Explained variance vs. # of factors\")\nplt.ylabel(\"Cumsum explained variance ratio\")\nplt.xlabel(\"Factor number\")\nplt.show()\n\n# Cette fois on semble plus s'orrienter vers 2-3 valeurs.\n\n#seuils pour test des bâtons brisés\nbs = 1/np.arange(p,0,-1)\nbs = np.cumsum(bs)\nbs = bs[::-1]\n#test des bâtons brisés\nprint(pd.DataFrame({'Val.Propre':eigval,'Seuils':bs}))\n\n# De part la règle de Kaiser, pour une ACP normée, la somme des valeurs propres étant égale au nombre de variables, leur moyenne vaut 1. On considère ainsi qu’un axe est intéressant si sa valeur propre est supérieure 1.\n#\n# Ici on est tenté entre 1 variable et 2 car la deuxieme est à 0,9...\n\ncolor_list = ['blue', 'red', 'green']\ncolors = [color_list[c] for c in df['target']]\n#positionnement des individus dans le premier plan\nfig, axes = plt.subplots(figsize=(12,12))\naxes.set_xlim(-5,5) #même limites en abscisse\naxes.set_ylim(-5,5) #et en ordonnée\n#placement des étiquettes des observations\nfor i in range(n):\n plt.annotate(df.index[i],(coord[i,0],coord[i,1]),color=colors[i])\n#ajouter les axes\nplt.plot([-5,5],[0,0],color='silver',linestyle='-',linewidth=1)\nplt.plot([0,0],[-5,5],color='silver',linestyle='-',linewidth=1)\n#affichage\nplt.show()\n\n# Pas sur qu'on soit mieux qu'avec les variables de départ, surement du au faite que l'on avait peu de variables (4).\n# La classe bleue est très bien séparée, les deux autres beaucoup moins.\n\n# # Résolution par Apprentissage\n#\n\n# ## Naive Bayes\n\n# \"modèle à caractéristiques statistiquement indépendantes \" : créé donc des classes en partant du principe que chaque paramètre est indépendant\n\nfrom sklearn.naive_bayes import GaussianNB\n\ndf2 = df[liste_df[0:-2]]\n\nclf = GaussianNB()\nclf.fit(df2, df['target']) #on entraine notre modele\n\nclf.get_params()\n\nresult = clf.predict(df2)\nresult\n\n# On a testé les résultats sur le même jeu de données, testons la qualité des prédictions :\n\nnb_error = 0\nfor i in (result - target):\n if i !=0:\n nb_error += 1\nprint(\"Nombre de valeurs fausses : \"+str(nb_error))\nprint(\"Pourcentage de valeurs justes : \"+str((len(result)-nb_error)*100/len(result)))\n\n# on a quand meme 6 erreurs de prédiction sur 150\n\nfrom sklearn.metrics import accuracy_score\naccuracy_score(result, target)\n\n# Nous donne un score plus \"travaillé\"\n#\n#\n# On peut décider de vouloir savoir où sont les erreurs : \n\nfrom sklearn.metrics import confusion_matrix\nmat_conf = confusion_matrix(target, result)\nmat_conf\n\nsns.heatmap(mat_conf, square=True, annot=True, cbar=False\n , xticklabels=list(iris_data.target_names)\n , yticklabels=list(iris_data.target_names))\nplt.xlabel('predicted values')\nplt.ylabel('real values');\n\n# 0 erreurs sur Setosa, si on regarde sur les Graphs ci dessus on avait bien identifié que les setosas étaient très bien séparés ! Donc, logique !\n\n# # Apprentissage Validation\n\n# On utilise une méthode par apprentissage validation en créant des jeux de données 70% / 30%\n\nfrom sklearn.model_selection import train_test_split # version 0.18.1\n# split la data en 70%/30%\ndata_test = train_test_split(df2, target\n , random_state=0\n , train_size=0.7)\n#data_test est une liste de 4 DF :\ndata_train, data_test, target_train, target_test = data_test\n\nclf = GaussianNB()\nclf.fit(data_train, target_train)\nresult = clf.predict(data_test)\ntarget = target_test\n\nnb_error = 0\nfor i in (result - target):\n if i !=0:\n nb_error += 1\nprint(\"Nombre de valeurs fausses : \"+str(nb_error))\nprint(\"Pourcentage de valeurs justes : \"+str((len(result)-nb_error)*100/len(result)))\n\naccuracy_score(result, target_test)\n\nmat_conf = confusion_matrix(target, result)\nsns.heatmap(mat_conf, square=True, annot=True, cbar=False\n , xticklabels=list(iris_data.target_names)\n , yticklabels=list(iris_data.target_names))\nplt.xlabel('predicted values')\nplt.ylabel('real values');\n\n# Perfect !\n\n# # Affichage des territoires de classification\n\n# Méthode : On créé une espece de matrice avec toutes les valeurs possibles et on voit quelles valeurs leur attribu notre algo.\n\ndata_sepales = df[['sepal length (cm)','sepal width (cm)']]\n\ntarget = df['target']\n\n# +\n# On réapprend\nclf = GaussianNB()\nclf.fit(data_sepales, target)\nh = 0.1 #epaisseur de notre \"grillage\" de valeurs\nμ = 0.5 #valeur dont on repousse un peu les predictions par rapport aux valeurs max et min\n#attention ne pas trop pousser !\n# On cherche les valeurs min/max de longueurs (x)/largeurs (y) des sépales\nx_min = df['sepal length (cm)'].min() - μ\nx_max = df['sepal length (cm)'].max() + μ\n\ny_min = df['sepal width (cm)'].min() - μ\ny_max = df['sepal width (cm)'].max() + μ\n\nx = np.arange(x_min, x_max, h) #plages de valeurs utilisées celon x\ny = np.arange(y_min, y_max, h) #plages de valeurs utilisées celon y\n# -\n\n# On créé alors une meshgrid de ces valeurs (espèce de matrice 2D de nos plages de valeurs)\n\nxx, yy = np.meshgrid(x,y )\n\ndata_vizu = list(zip(xx.ravel(), yy.ravel()) ) #ligne tres technique... explication ci apres :\n\n# Dans xx on contient len(y) vecteurs possédant chacun toutes les valeurs de x :\n\nprint(xx)\nprint(len(xx))\nprint(len(xx[0]))\n\n# pour yy c'est l'inverse : chaque vecteur contient une unique valeur de y repété len(x) fois\n\nprint(yy)\nprint(len(yy))\nprint(len(yy[0]))\n\n# ravel concatene les vetceurs d'une matrice :\n\nprint(xx.ravel())\nprint(len(xx.ravel()))\nprint(len(xx)*len(xx[0]))\n\n# zip quand a lui récupere pour une suite de liste la premiere valeur de chaque liste pour faire une premiere suite\n#\n# puis la deuxieme valeur de chaque liste pour en faire une nouvelle\n#\n# etc\n#\n# Ainsi :\n\ndata_vizu[:10]\n\n# +\nz = clf.predict(data_vizu)\n\nfig = plt.figure(figsize=(8, 5))\n\ncolor_list = ['blue', 'red', 'green']\ncolors = [color_list[c] for c in z]\n\n\nplt.scatter(xx.ravel(), yy.ravel(), c=colors)\nplt.xlim(xx.min() - .07, xx.max() + .07)\nplt.ylim(yy.min() - .07, yy.max() + .07)\nplt.xlabel('petal length (cm)')\nplt.ylabel('petal width (cm)')\n# -\n\n# Cela parait très joli, mais est-ce efficace ? On peut afficher nos valeurs connus pour voir si ce modèle est fiable ou non\n#\n# Pour cela on met z au meme format que xx et yy pour utiliser colormesh\n\nzz= z.reshape(xx.shape)\n\nzz\n\n# +\nfig = plt.figure(figsize=(8, 5))\n\nplt.pcolormesh(xx, yy, zz) # Affiche les déductions en couleurs pour les couples x,y\n\n# Plot also the training points\ncolor_list = ['blue', 'red', 'green']\ncolors = [color_list[c] for c in target]\nplt.scatter(df['sepal length (cm)'],df['sepal width (cm)'], c=colors)\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xlabel('petal length (cm)')\nplt.ylabel('petal width (cm)')\n# -\n\n# A noter qu'on aurait pu utiliser directement colormesh au lieu de scatter\n#\n# On remarque que notre prédiction n'est pas vraiment parfaite, c'est déjà ce que l'on avait remarqué précédemment lorsque l'on utilise uniquement les pétales et pas les sépales\n\n# # Méthode des K plus proches voisins\n\n# Cette méthode d'apprentissage utilise comme son nom l'indique la distance avec les voisins les plus proches pour déterminer les classes\n#\n# K represente le nombre de voisins que l'on veut utiliser.\n#\n# Avec k (trop) faible on risque d'avoir affaire à du sous apprentissage (underfitting) et donc pas de prédictions.\n#\n# Avec k (trop) fort on risque d'avoir affaire à du sur apprentissage (overfitting) et donc trop coller à notre échantillon.\n\n# +\nfrom sklearn import neighbors\n\nclf = neighbors.KNeighborsClassifier()\n# -\n\nfrom ipywidgets import interact\n@interact(k=(0,30))\ndef k_change(k=5):\n clf = neighbors.KNeighborsClassifier(n_neighbors=k)\n clf.fit(data_sepales, target)\n z = clf.predict(data_vizu)\n zz = z.reshape(xx.shape)\n fig = plt.figure(figsize=(8, 5))\n plt.pcolormesh(xx, yy, zz)\n \n\n color_list = ['blue', 'red', 'green']\n colors = [color_list[c] for c in target]\n plt.scatter(df['sepal length (cm)'],df['sepal width (cm)'], c=colors)\n \n plt.xlim(xx.min(), xx.max())\n plt.ylim(yy.min(), yy.max())\n plt.xlabel('petal length (cm)')\n plt.ylabel('petal width (cm)')\n\n\n# Etant donné que l'on a plusieurs valeurs possibles pour k (ici on a choisi de 1 à 30 mais les extremes sont forcément 'mauvais') on peu essayer de trouver un K \"optimal\"\n\n@interact(p=(30,70))\ndef p_change(p=50):\n # split the data in 80%/20% in each set\n data_test = train_test_split(data_sepales, target\n , random_state=0\n , train_size=p/100)\n #data_test est une liste de 4 DF :\n data_train, data_test, target_train, target_test = data_test\n result = []\n k_values = range(1,30)\n\n for k in k_values:\n clf = neighbors.KNeighborsClassifier(n_neighbors=k)\n clf.fit(data_train, target_train)\n z = clf.predict(data_test)\n score = accuracy_score(z, target_test)\n result.append(score)\n\n fig = plt.figure(figsize=(8, 5))\n plt.plot(k_values, result)\n\n\n# En essayant de faire varier la proportion de l'échantillonage apprentissage/validation on voit que la valeur optimale du k reste très fou mais il semble y avoir une valeur interessante aux alentours de 20 !\n\n# # Et si on passait par un modèle d'apprentissage non supervisé ?\n\n# Pour cela il ne faut donc pas utiliser la donnée target qui renferme les 3 espèces.\n#\n# On peut alors voir si les groupes qui font être formés vont être les mêmes !\n\n# ## On commence par réduire le nombre de variables \n\n# En effet bien souvent pour les modèles d'apprentissage non supervisé on utilise beaucoup de paréamètres pour ne perdre aucune information, puis on essaye de créer des nouvelles variables à partir de celles de départ, on utilise ici un ACP (Analyse en Composantes Principales).\n#\n# Ici il suffit de choisir le nombre de composantes finales voulues, de donner notre jeu de données puis d'obtenir ces nouvelles composantes.\n\ndf_non_sup = df[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']]\n\n# +\nfrom sklearn.decomposition import PCA\n\nmodel = PCA(n_components=2)\nmodel.fit(df_non_sup)\ndf_reduc = pd.DataFrame(model.transform(df_non_sup), columns = ['C1', 'C2'])\n\n# -\n\n# Ainsi on a transformé notre data de 4 composantes original :\n\ndf_non_sup.head()\n\n# En une nouvelle data contenant \"autant d'informations\" mais avec seulement 2 composantes (que l'on a nommé C1 et C2) :\n\ndf_reduc.head()\n\n# Il pourrait être interessant de voir si les 2 nouvelles composantes différencient bien les 3 espèces de fleurs.\n\ndf_reduc['label'] = df['label']\n\ndf_reduc.head()\n\n# On peut voir ca soit de manière automatique :\n\nsns.lmplot(\"C1\", \"C2\", hue='label', data=df_reduc, fit_reg=False)\n\n# Soit en voulant gérer un peu plus les choses :\n\n# +\ncolor_list = ['blue', 'red', 'green']\ncolors = [color_list[c] for c in iris_data.target]\n\nplt.scatter(df_reduc['C1'], df_reduc['C2'], c=colors)\nplt.xlabel('C1')\nplt.ylabel('C2')\n\nfor ind, s in enumerate(iris_data.target_names):\n # on dessine de faux points, car la légende n'affiche que les points ayant un label\n plt.scatter([], [], label=s, color=color_list[ind])\n\nplt.legend(scatterpoints=1, frameon=False, labelspacing=1, \n bbox_to_anchor=(1.3, 0.5) , loc=\"center right\", title='Species')\n# -\n\n# On remarque donc que nos 2 nouvelles composantes conservent bien les classes, et les bonnes !\n\n# # Clustering\n\n# On va maintenant créer des regroupement par clustering, on va commencer par choisir Kmeans :\n#\n#\n\n# +\nfrom sklearn.cluster import KMeans\n\nmodel_kmeans = KMeans(n_clusters=3, random_state=0)\nmodel_kmeans.fit(df_reduc[['C1', 'C2']])\n\ngroups_kmeans = model_kmeans.predict(df_reduc[['C1', 'C2']])\n\ndf_reduc['group_kmeans'] = groups_kmeans\n# -\n\nsns.lmplot(\"C1\", \"C2\", data=df_reduc, hue='label',\n col='group_kmeans', fit_reg=False)\n\n# Ca ne match vraiment pas bien... Il se trouve que Kmean fonctionne tres bien avec des formes... en forme de cercle ! Or nous avons ici plutot des elipses, on essaye alors GMM (Gaussian Mixture Models) qui est la méthode la plus rapide et qui s'adapte à beaucoup de \"formes\" de groupes, attention toutefois l'ACP est nécéssaire au préalable car il utilisera toutes composantes !\n\n# +\nfrom sklearn.mixture import GaussianMixture\n\nmodel_GMM = GaussianMixture (n_components=3, covariance_type='full')\nmodel_GMM.fit(df_reduc[['C1', 'C2']])\ngroups_GMM = model_GMM.predict(df_reduc[['C1', 'C2']])\n\ndf_reduc['group_GMM'] = groups_GMM\n# -\n\nsns.lmplot(\"C1\", \"C2\", data=df_reduc, hue='label',\n col='group_GMM', fit_reg=False)\n\n# Malgré 3 erreurs c'est bien mieux ! De toute facon les deux groupes étant très proches cela reste un très bon groupement.\n"},"script_size":{"kind":"number","value":17520,"string":"17,520"}}},{"rowIdx":998,"cells":{"path":{"kind":"string","value":"/Project_4/Final_Version.ipynb"},"content_id":{"kind":"string","value":"ae9ceb156d72aa2df4273a41bc397364c1f7aeea"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"nlzakh02/Zakharova_Metis"},"repo_url":{"kind":"string","value":"https://github.com/nlzakh02/Zakharova_Metis"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":1183910,"string":"1,183,910"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + [markdown] colab_type=\"text\" id=\"JUgOrRxl0sKq\"\n# ## Google Colab\n#\n# Google provides a free cloud service based on Jupyter Notebooks that supports free CPU and GPU. It allows you to develop deep learning applications using popular libraries such as PyTorch, TensorFlow, Keras, and OpenCV (without installation). All these libraries are pre-installed on Google Colab along wilt Python.\n# -\n\n# ### 1. Notebook Creation\n#\n# Login with your account and got to [google colab](https://colab.research.google.com). You will be prompted to either create a new notebook or you can also upload your `.ipynb` notebook from your Github, Google Drive or your local machine.\n#\n# \n\n# Once you have created the notebook, you can rename it by clicking on notebook name in the upper right corner.\n# \n#\n#\n# All your notebooks will be saved in your Google Drive inside the directory `Colab Notebooks`.\n\n# ### 2. Dataset\n#\n# In upcoming assignments you would need data to train your model. The best way to use colab with your dataset is to upload your dataset to google drive and the mount your drive. You can do so with the following command\n\n# + colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 34} colab_type=\"code\" id=\"nyfGOBXU7cjL\" outputId=\"f2c83b83-9633-403c-fe4e-5750acd9c33d\"\nfrom google.colab import drive \ndrive.mount(\"/content/gdrive\", force_remount=True)\n\n# + [markdown] colab_type=\"text\" id=\"hAvnynvP0sKv\"\n# Now you should see your drive on the left-hand side of the screen!.(You may need to hit \"refresh\" if it doesn't occur immediately)\n#\n# \n\n# + [markdown] colab_type=\"text\" id=\"YddOu3Bc0sKx\"\n# ### 3. Installing python libraries\n#\n# In general you would not need to install anything, but incase you have then you can do so with the following command.\n# -\n\n# !pip3 install torch torchvision\n\n# \n\n# ### 4. Download Notebooks\n#\n# Your notebooks are automatically saved in your google drive. But if you need to download them, you can do so by `File` -> `Download .ipynb`\nils\n\n# logging for gensim (set to INFO)\nimport logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n# -\n\ncnts = counts.transpose()\n\n# Convert a sparse matrix into a gensim corpus\ncorps = matutils.Sparse2Corpus(cnts)\n\n# Associating counts with words\nid2word = dict((v, k) for k, v in count.vocabulary_.items())\n\nlda1 = models.LdaModel(corpus=corps, num_topics=3, minimum_probability=0.03, id2word=id2word, passes=10, alpha='auto', eta='auto') #\n\nlda1.print_topics(num_words=15)\n\n# Transform speeches from word space to topic space\nlda_corps = lda1[corps]\n#lda_corps\n\n# Store topic vectors for each document in a list of lists\nlda_docs = [doc for doc in lda_corps]\n\n# Check topic space vectors for first 10 documents\nlda_docs[0:10]\n\n# # Clustering with K-means\n\n# Create dataframe with results of transformation into topic space for clustering and plotting\nlda_d = [dict(doc) for doc in lda_corps]\nd = pd.DataFrame(lda_d).fillna(0)\nd.columns = ['dim1', 'dim2', 'dim3']\nd1 = d.copy()\nd.head(2)\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\n\n# +\n# Scatter Plot\nscale = 100\nfigure, tax = ternary.figure(scale=scale)\nfigure.set_size_inches(10, 10)\n\ntax.boundary(linewidth=2.0)\ntax.gridlines(multiple=5, color=\"blue\")\n\n# Set Axis labels and Title\nfontsize = 15\ntax.set_title(\"Clustering of Speeches\", fontsize=20)\ntax.left_axis_label(\"Government as Concept\", fontsize=fontsize)\ntax.right_axis_label(\"Emotional, Aspirational\", fontsize=fontsize)\ntax.bottom_axis_label(\"Act of Governing\", fontsize=fontsize)\n\n# Plot a few different styles with a legend\np_set = d[['dim1', 'dim2', 'dim3']]\npoints = [tuple(x*100) for x in p_set.values]\n\n#points = random_points(30, scale=scale)\ntax.scatter(points, marker='s', color=\"red\", s=50)\n\ntax.legend()\ntax.ticks(axis='lbr', linewidth=1, multiple=10)\ntax.clear_matplotlib_ticks()\n\ntax.show()\n# -\n\n# ### Clustering scaled data\n\nfrom sklearn.preprocessing import scale\nkmdata = scale(d)\n\n# +\n# List for saving silhouette score for each number of clusters\nsc = []\n# List for saving sum of squared distances for samples to their closest cluster center for each number of clusters\nsse = []\n# List with numbers of clusters to be tested\nks = list(range(2, 58))\n\nfor k in ks:\n km = KMeans(n_clusters=k)\n km.fit(d)\n label = km.predict(d)\n sc.append(silhouette_score(d, label))\n sse.append(km.inertia_)\n# -\n\n# Plot silhouette score\nplt.plot(ks, sc)\n#plt.xlim((0,10))\n#plt.ylim((0.6, 1))\n\n# Plot sum of squared distances for samples to their closest cluster center\nplt.plot(ks, sse)\n#plt.xlim((3, 8))\n#plt.ylim((0, 2))\n\n# ### Best number of clusters identified is 6\n\nkm = KMeans(n_clusters=6)\nkm.fit(kmdata)\n\nkm.cluster_centers_\n\nkm.labels_\n\nd[\"class\"] = km.labels_\ncolors = [\"red\", \"blue\", \"green\", \"black\", \"magenta\", \"cyan\"]\nd[\"colors\"] = d[\"class\"].map(lambda x: colors[x-1])\nd.head()\n\n# +\n# Scatter Plot\nscale = 100\nfigure, tax = ternary.figure(scale=scale)\nfigure.set_size_inches(10, 10)\n\ntax.boundary(linewidth=2.0)\ntax.gridlines(multiple=5, color=\"blue\")\n\n# Set Axis labels and Title\nfontsize = 20\ntax.set_title(\"Clustering of Speeches\", fontsize=20)\ntax.left_axis_label(\"Government as Concept\", fontsize=fontsize)\ntax.right_axis_label(\"Emotional, Aspirational\", fontsize=fontsize)\ntax.bottom_axis_label(\"Act of Governing\", fontsize=fontsize)\n\n# Plot a few different styles with a legend\np_set = d[['dim1', 'dim2', 'dim3']]\npoints = [tuple(x*100) for x in p_set.values]\n\n#points = random_points(30, scale=scale)\ntax.scatter(points, marker='s', color=d[\"colors\"].values, s=150)\n\ntax.legend()\ntax.ticks(axis='lbr', linewidth=1, multiple=10)\ntax.clear_matplotlib_ticks()\n\ntax.show()\n# -\n\n\n\n# # Other Dimentionality Reduction Methods Trialed\n\n# ## Principal Component Analysis (PCA)\n\n# +\nn = list(range(59))[1:]\nfrom sklearn.decomposition import PCA\nvar = []\n\nfor i in n:\n reducer = PCA(n_components=i)\n reduced_X = reducer.fit(counts.toarray())\n var.append(sum(reduced_X.explained_variance_ratio_))\n# -\n\nplt.plot(n, var);\n\n# ## Sparse PCA\n\nfrom sklearn.decomposition import MiniBatchSparsePCA, SparsePCA\n\npca = MiniBatchSparsePCA(n_components=7, alpha=0.2, batch_size=5, ridge_alpha=0.2)\npca_data = pca.fit(counts.toarray())\n\npca1 = pca.transform(counts.toarray())\n#pca1\n\nt = pd.DataFrame(pca.components_, columns=count.get_feature_names()).T\n\ng = t[(t.T != 0).any()][0].sort_values(ascending=False)[:20]\nprint(\"First 20 n-grams in 1st component: \", g)\n\nplt.imshow(WordCloud().generate_from_frequencies(g.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng1 = t[(t.T != 0).any()][1].sort_values(ascending=False)[:20]\nprint(\"First 20 n-grams in 2nd component: \", g1)\n\nplt.imshow(WordCloud().generate_from_frequencies(g1.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng2 = t[(t.T != 0).any()][2].sort_values(ascending=False)[:20]\nprint(\"First 20 n-grams in 3rd component: \", g2)\n\nplt.imshow(WordCloud().generate_from_frequencies(g2.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng3 = t[(t.T != 0).any()][3].sort_values(ascending=False)[:20]\nprint(\"First 20 n-grams in 4th component: \", g3)\n\nplt.imshow(WordCloud().generate_from_frequencies(g3.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng4 = t[(t.T != 0).any()][4].sort_values(ascending=False)[:20]\nprint(\"First 20 n-grams in 5th component: \", g4)\n\nplt.imshow(WordCloud().generate_from_frequencies(g4.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng5 = t[(t.T != 0).any()][5].sort_values(ascending=False)[:15]\nprint(\"First 20 n-grams in 6th component: \", g5)\n\nplt.imshow(WordCloud().generate_from_frequencies(g5.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\ng6 = t[(t.T != 0).any()][6].sort_values(ascending=False)[:15]\nprint(\"First 20 n-grams in 7th component: \", g6)\n\nplt.imshow(WordCloud().generate_from_frequencies(g6.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\n# ## Non-negative Matrix Factorization (NMF)\n\nfrom sklearn.decomposition import NMF\n\nnmf = NMF(n_components=6, alpha=1.)\nnmf_x = nmf.fit(counts.toarray())\nnmf.reconstruction_err_\n\nn = pd.DataFrame(nmf.components_, columns=count.get_feature_names()).T\nf = n[(n.T != 0).any()][0].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 1st component: \", f)\n\nplt.imshow(WordCloud().generate_from_frequencies(f.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nf1 = n[(n.T != 0).any()][1].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 2nd component: \", f1)\n\nplt.imshow(WordCloud().generate_from_frequencies(f1.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nf2 = n[(n.T != 0).any()][2].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 3rd component: \", f2)\n\nplt.imshow(WordCloud().generate_from_frequencies(f2.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nf3 = n[(n.T != 0).any()][3].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 4th component: \", f3)\n\nplt.imshow(WordCloud().generate_from_frequencies(f3.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nf4 = n[(n.T != 0).any()][4].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 5th component: \", f4)\n\nplt.imshow(WordCloud().generate_from_frequencies(f4.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nf5 = n[(n.T != 0).any()][5].sort_values(ascending=False)[:15]\nprint(\"First 15 n-grams in 6th component: \", f5)\n\nplt.imshow(WordCloud().generate_from_frequencies(f5.to_dict()), interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\n# # Other Clustering Methods Trialed\n\n# ## K-means Clustering with the Scaled Data\n\nfrom sklearn.preprocessing import scale\nkmdata = scale(d1)\n\n# +\n# List for saving silhouette score for each number of clusters\nsc = []\n# List for saving sum of squared distances for samples to their closest cluster center for each number of clusters\nsse = []\n# List with numbers of clusters to be tested\nks = list(range(2, 58))\n\nfor k in ks:\n km = KMeans(n_clusters=k)\n km.fit(kmdata)\n label = km.predict(kmdata)\n sc.append(silhouette_score(kmdata, label))\n sse.append(km.inertia_)\n# -\n\n# Plot silhouette score\nplt.plot(ks, sc)\n#plt.xlim((0,10))\n#plt.ylim((0.6, 1))\n\n# Plot sum of squared distances for samples to their closest cluster center\nplt.plot(ks, sse)\n#plt.xlim((4, 10))\n#plt.ylim((0, 5))\n\n# ### The best number of clusters appears to be 6.\n\n# ## DBSCAN Clustering\n\nfrom sklearn.cluster import DBSCAN\n\ndbs = DBSCAN(eps=0.1, min_samples=3, metric=\"euclidean\")\n\n# Cluster unscaled data\ndbs.fit(d)\n\n# 4 clusters were identified, one point was identified as not belonging to any cluster\nset(dbs.labels_)\n\n# Cluster scaled data\ndbs.fit(kmdata)\n\n# 4 clusters were identified, one point was identified as not belonging to any cluster\nset(dbs.labels_)\n\n\n\n# ## Mean Shift Clustering\n\nfrom sklearn.cluster import MeanShift\nms = MeanShift()\n\n# Cluster unscaled data\nms.fit(d)\n\n# Identified 5 clusters\nms.cluster_centers_\n\nms.labels_\n\n# Cluster scaled data\nms.fit(kmdata)\n\n# Identified 4 clusters\nms.cluster_centers_\n\nms.labels_\n\n\n"},"script_size":{"kind":"number","value":11524,"string":"11,524"}}},{"rowIdx":999,"cells":{"path":{"kind":"string","value":"/HW Solution/HW7.ipynb"},"content_id":{"kind":"string","value":"c5764406628043731b7ba7a02b8b9ee8e01c30bc"},"detected_licenses":{"kind":"list like","value":[],"string":"[]"},"license_type":{"kind":"string","value":"no_license"},"repo_name":{"kind":"string","value":"fayrek/Python-Lab-Fall-2020"},"repo_url":{"kind":"string","value":"https://github.com/fayrek/Python-Lab-Fall-2020"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_event_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_language":{"kind":"null"},"language":{"kind":"string","value":"Jupyter Notebook"},"is_generated":{"kind":"bool","value":false,"string":"false"},"is_vendor":{"kind":"bool","value":false,"string":"false"},"conversion_extension":{"kind":"string","value":".py"},"size":{"kind":"number","value":65956,"string":"65,956"},"script":{"kind":"string","value":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: Python 3\n# name: python3\n# ---\n\n# + [markdown] id=\"view-in-github\" colab_type=\"text\"\n# \"Open\n\n# + id=\"kqd-PgheVVuG\" colab_type=\"code\" colab={}\n\n\n# + id=\"e8Zn3-MGlBL1\" colab_type=\"code\" outputId=\"287b270b-d86f-48f1-9140-4c4fbdf60336\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000}\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n# !pip install -q tensorflow-gpu==2.0.0-beta1\nimport tensorflow as tf\n\nfrom tensorflow.keras import datasets, layers, models\nimport tensorflow.keras\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.layers import Dropout, Input\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.metrics import categorical_crossentropy\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nprint(tf.__version__)\n\nimport random\nimport glob\nimport os\nimport pathlib\nimport time\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nfrom packaging import version\nimport IPython.display as display\nimport pandas as pd\n\nfrom google.colab import drive\ndrive.mount('/content/drive')\n\n\n# Load the TensorBoard notebook extension\n# %load_ext tensorboard\n# Clear any logs from previous runs\n# !rm -rf ./logs/ \n\npath= '/content/drive/My Drive/DATA/LEGO-brick-images'\ndata_root = pathlib.Path(path)\ndataset_path = pathlib.Path(path + \"/dataset.csv\")\ntrain_path = path+ '/train'\nvalid_path = path+ '/valid'\nprint (train_path)\ndf = pd.read_csv(dataset_path, skipinitialspace=True, skip_blank_lines=True,encoding='utf-8', index_col='id')\n\nlabel_names = [( str(f)) for f in df.index]\n#label_names = [\"11214 Bush 3M friction with Cross axle\",\"18651 Cross Axle 2M with Snap friction\",\"2357 Brick corner 1x2x2\",\"3003 Brick 2x2\",\"3004 Brick 1x2\",\"3005 Brick 1x1\",\"3022 Plate 2x2\",\"3023 Plate 1x2\",\"3024 Plate 1x1\",\"3040 Roof Tile 1x2x45deg\",\"3069 Flat Tile 1x2\",\"32123 half Bush\",\"3673 Peg 2M\",\"3713 Bush for Cross Axle\",\"3794 Plate 1X2 with 1 Knob\",\"6632 Technic Lever 3M\"]\n\nprint (label_names)\nclass_size=len(label_names)\n\ntrain_datagen = ImageDataGenerator(\n rescale=1./255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True)\n\ntest_datagen = ImageDataGenerator(\n rescale=1./255,\n shear_range=0.2,\n zoom_range=0.2,\n vertical_flip=True)\n\nvalid_datagen = ImageDataGenerator(rescale=1./255)\n\ntrain_batches = train_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32)\nvalid_batches = valid_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32)\ntest_batches = test_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32)\n \nimage_model = tf.keras.applications.InceptionV3(include_top=False,weights='imagenet',input_tensor=Input(shape=(224,224,3)))\n#vgg16_model = tf.keras.applications.vgg16.VGG16(weights='imagenet', include_top=False, input_tensor=Input(shape=(224,224,3)))\n\n\n# Create the model\nmodel = Sequential()\n \n# Add the vgg convolutional base model\nmodel.add(image_model)\n \n# Add new layers\nmodel.add(Flatten())\nmodel.add(Dense(1024, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(class_size, activation='softmax'))\n \n# Show a summary of the model. Check the number of trainable parameters\nmodel.summary()\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer=tensorflow.keras.optimizers.RMSprop(lr=1e-4),\n metrics=['acc'])\n\n# Define the Keras TensorBoard callback.\nlogdir=\"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\ntensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir,\n histogram_freq=1,\n write_graph=True,\n write_images=True,\n write_grads=True,\n batch_size=32)\n\nhistory = model.fit_generator(\n train_batches,\n steps_per_epoch=train_batches.samples/train_batches.batch_size ,\n epochs=5,\n validation_data=valid_batches,\n validation_steps=valid_batches.samples/valid_batches.batch_size,\n verbose=1,\n callbacks=[tensorboard_callback])\nmodel.evaluate(test_batches)\n# %tensorboard --logdir logs\n\nsaved_model_path = \"/content/drive/My Drive/tmp/saved_models/\"+str(int(time.time()))\nkeras.experimental.export_saved_model(model, saved_model_path)\n\n# + id=\"_S7pf2u1ITGI\" colab_type=\"code\" colab={}\n\n\n# + [markdown] id=\"0MxNFlxaNIii\" colab_type=\"text\"\n#\n\n# + [markdown] colab_type=\"text\" id=\"7Z2jcRKwUHqV\"\n# This notebook provides recipes for loading and saving data from external sources.\n\n# + [markdown] id=\"RGBAVArKA2U2\" colab_type=\"text\"\n#\n\n# + id=\"JLOUroipA1Jm\" colab_type=\"code\" colab={}\n\n\n# + id=\"0fd3FxU-Rv_9\" colab_type=\"code\" outputId=\"939dcc5f-803a-4d69-fb61-632fd2f057bd\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 760}\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport matplotlib.pylab as plt\n\n# !pip install -q tensorflow-gpu==2.0.0-beta1\nimport tensorflow as tf\nfrom tensorflow import keras\n\nimport numpy as np\nimport PIL.Image as Image\nfrom google.colab import drive\nimport pathlib\nimport csv\ndrive.mount('/content/drive')\nfrom tensorflow.keras import layers\npath= \"/content/drive/My Drive/DATA/LEGO brick images\"\nwith open(path+\"/labels.csv\", 'r') as f:\n reader = csv.reader(f,quoting=csv.QUOTE_ALL)\n label_names = list(reader)\nlabel_names=label_names[0]\nprint (label_names)\nsaved_model_path = \"/content/drive/My Drive/tmp/saved_models/1563634289/\"\ntest_path = '/content/drive/My Drive/DATA/LEGO-brick-images_Archive/test6.JPG'\nIMAGE_SHAPE = (224, 224)\nimg =Image.open(test_path).resize(IMAGE_SHAPE)\nprint(img.format)\nprint(img.mode)\nprint(img.size)\nimg=img.convert('RGB')\n\n\n#print(img.shape)\nimg = np.array(img)/255.0\nimgr = tf.reshape(img, [1,224, 224, 3])\nprint(imgr.shape)\nclassifier = tf.keras.experimental.load_from_saved_model(saved_model_path)\n\nresult = classifier.predict(imgr)\nprint(result.shape)\nclassifier.summary()\n#print(classifier.predict(img).shape)\nprint(np.argmax(result[0]))\npredicted_class = np.argmax(result[0], axis=-1)\nprint(predicted_class)\nimg = tf.reshape(img, [224, 224, 3])\nplt.imshow(img)\nplt.axis('off')\npredicted_class_name = label_names[predicted_class]\n_ = plt.title(\"Prediction: \" + predicted_class_name.title())\n\n\n# + id=\"LQz0cHX1vhFb\" colab_type=\"code\" colab={}\n# !pip install -q tensorflow-gpu==2.0.0-beta1\n# %load_ext tensorboard\n\n# + id=\"JXYrSRhB-hXL\" colab_type=\"code\" outputId=\"0f507b03-3cfa-4cc5-894a-511ac876b009\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 102}\n# !ls\n# !ls 'drive/My Drive/'tmp/saved_models/\n# !saved_model_cli show --dir 'drive/My Drive/tmp/saved_models/1563479506' --tag_set serve\n\n# + id=\"OceayWUALABE\" colab_type=\"code\" outputId=\"6544402d-d317-44eb-9f21-fb81d2c24de5\" colab={\"resources\": {\"http://localhost:6006/\": {\"data\": \"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 x.W/1)\n score = self.softmax(score)\n\n # score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16)\n # score = self.bn1(score) # element-wise add, size=(N, 512, x.H/16, x.W/16) \n # score = self.relu(self.deconv2(score)) # size=(N, 256, x.H/8, x.W/8)\n # score = self.bn2(score) # element-wise add, size=(N, 256, x.H/8, x.W/8) \n # score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4)\n # score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2)\n # score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W)\n # score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1)\n\n return score # size=(N, n_class, x.H/1, x.W/1)\n\n# load pretrained weights and define FCN8s\n\nif pretrainOnCAMO:\n vgg_model = torch.load('/content/vggmodel')\n fcn_model = torch.load('/content/fcnmodel')\nelse:\n vgg_model = VGGNet(requires_grad=True, remove_fc=True)\n fcn_model = FCN8s(pretrained_net=vgg_model, n_class=num_class)\n\n\nts = time.time()\nvgg_model = vgg_model.cuda()\nfcn_model = fcn_model.cuda()\nfcn_model = nn.DataParallel(fcn_model, device_ids=num_gpu)\nprint(\"Finish cuda loading, time elapsed {}\".format(time.time() - ts))\n\n# criterion=new_loss\n# criterion = nn.BCEWithLogitsLoss()\n# criterion = FocalLoss()\n# criterion = DiceLoss()\noptimizer = optim.Adam(fcn_model.parameters(), lr=lr)\nscheduler = lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\nprint(fcn_model) \n\n\n# + [markdown] id=\"6YmWQoDPL7LD\" colab_type=\"text\"\n#\n# ## Training\n\n# + id=\"UADtIeFHD-hk\" colab_type=\"code\" colab={}\ndef train():\n for epoch in range(epochs):\n scheduler.step()\n\n ts = time.time()\n for iter, batch in enumerate(train_loader):\n optimizer.zero_grad()\n\n if use_gpu:\n inputs = Variable(batch['X'].cuda())\n labels = Variable(batch['Y'].cuda())\n else:\n inputs, labels = Variable(batch['X']), Variable(batch['Y'])\n outputs = fcn_model(inputs)\n # # !!!!!!!\n # print(outputs)\n \n # print(\"!!!!!!\")\n # print(labels)\n # # !!!!!!!\n # weights=[1/((labels==1).numel()),1/((labels==0).numel())]\n # pos_weight=torch.tensor((labels==0).numel()/(labels==1).numel()).cuda()*1.5\n criterion=nn.BCEWithLogitsLoss()\n # criterion = nn.L1Loss()\n # loss=criterion.forward(input=m(outputs),target=labels.type(torch.LongTensor).cuda())\n\n labels/=max(labels.max(),1)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n if iter % 10 == 0:\n print(\"epoch{}, iter{}, loss: {}\".format(epoch, iter, loss.data.item()))\n \n print(\"Finish epoch {}, time elapsed {}\".format(epoch, time.time() - ts))\n \n\n val(epoch)\n fcn_model.train()\n \n highest_pixel_acc = max(pixel_acc_list)\n highest_mIOU = max(mIOU_list)\n highest_f_measure = max(f_measure_list)\n lowest_mae = min(mae_list) \n \n highest_pixel_acc_epoch = pixel_acc_list.index(highest_pixel_acc)\n highest_mIOU_epoch = mIOU_list.index(highest_mIOU)\n highest_f_measure_epoch = f_measure_list.index(highest_f_measure)\n lowest_mae_epoch = mae_list.index(lowest_mae)\n \n print(\"The highest mIOU is {} and is achieved at epoch-{}\".format(highest_mIOU, highest_mIOU_epoch))\n print(\"The lowest MAE is {} and is achieved at epoch-{}\".format(lowest_mae, lowest_mae_epoch))\n print(\"The highest f-measure is {} and is achieved at epoch-{}\".format(highest_f_measure, highest_f_measure_epoch))\n\n\ndef save_result_comparison(input_np, output_np, gt_path):\n print(gt_path)\n means = np.array([103.939, 116.779, 123.68]) / 255.\n \n global global_index\n \n original_im_RGB = np.zeros((256,256,3)) \n original_im_RGB[:,:,0] = input_np[0,0,:,:] \n original_im_RGB[:,:,1] = input_np[0,1,:,:]\n original_im_RGB[:,:,2] = input_np[0,2,:,:]\n\n original_im_RGB[:,:,0] = original_im_RGB[:,:,0] + means[0]\n original_im_RGB[:,:,1] = original_im_RGB[:,:,1] + means[1]\n original_im_RGB[:,:,2] = original_im_RGB[:,:,2] + means[2]\n\n original_im_RGB[:,:,0] = original_im_RGB[:,:,0]*255.0\n original_im_RGB[:,:,1] = original_im_RGB[:,:,1]*255.0\n original_im_RGB[:,:,2] = original_im_RGB[:,:,2]*255.0\n \n im_seg_RGB = np.zeros((256,256,3))\n\n # the following version is designed for 11-class version and could still work if the number of classes is fewer.\n for i in range(256):\n for j in range(256):\n if output_np[i,j] == 0:\n im_seg_RGB[i,j,:] = [0, 0, 0]\n elif output_np[i,j] == 1: \n im_seg_RGB[i,j,:] = [255, 255, 255]\n elif output_np[i,j] == 2: \n im_seg_RGB[i,j,:] = [192, 192, 128] \n elif output_np[i,j] == 3: \n im_seg_RGB[i,j,:] = [128, 64, 128] \n elif output_np[i,j] == 4: \n im_seg_RGB[i,j,:] = [0, 0, 192] \n elif output_np[i,j] == 5: \n im_seg_RGB[i,j,:] = [128, 128, 0] \n elif output_np[i,j] == 6: \n im_seg_RGB[i,j,:] = [192, 128, 128] \n elif output_np[i,j] == 7: \n im_seg_RGB[i,j,:] = [64, 64, 128] \n elif output_np[i,j] == 8: \n im_seg_RGB[i,j,:] = [64, 0, 128] \n elif output_np[i,j] == 9: \n im_seg_RGB[i,j,:] = [64, 64, 0] \n elif output_np[i,j] == 10: \n im_seg_RGB[i,j,:] = [0, 128, 192] \n \n # horizontally stack original image and its corresponding segmentation results \n gt_image = Image.open(gt_path).convert('RGB')\n gt_image = gt_image.resize((256, 256))\n slicing_vertical = np.ones((256, 2, 3)) * 255.0\n hstack_image = np.hstack((original_im_RGB, slicing_vertical, im_seg_RGB, slicing_vertical, gt_image)) \n return hstack_image\n \ndef save_image(image_stack):\n global global_index\n stack = []\n slicing_horizontal = np.ones((2, 772, 3)) * 255.0\n for i in image_stack:\n stack.append(i)\n stack.append(slicing_horizontal)\n vstack_image = np.vstack(stack)\n new_im = Image.fromarray(np.uint8(vstack_image))\n \n file_name = folder_to_save_validation_result + str(global_index) + '.jpg'\n \n global_index = global_index + 1\n \n new_im.save(file_name) \n\n# train() \n\n\n# + [markdown] id=\"P_icuXVoL9aL\" colab_type=\"text\"\n# ## Validation\n\n# + id=\"Cl0WVeoTD-be\" colab_type=\"code\" colab={}\ndef val(epoch):\n fcn_model.eval()\n total_ious = []\n pixel_accs = []\n f_measures = []\n maes = []\n numberOfImage = 4\n \n for iter, batch in enumerate(val_loader): ## batch is 1 in this case\n if use_gpu:\n inputs = Variable(batch['X'].cuda())\n else:\n inputs = Variable(batch['X']) \n\n output = fcn_model(inputs) \n \n # only save the 1st image for comparison\n\n if iter <= numberOfImage:\n print('---------iter={}'.format(iter))\n if iter == 0:\n vstack_image = [] \n # generate images\n images = output.data.max(1)[1].cpu().numpy()[:,:,:]\n image = images[0,:,:] \n image = save_result_comparison(batch['X'], image, batch['N'][0])\n vstack_image.append(image)\n print(batch['N'])\n if iter == numberOfImage:\n save_image(vstack_image)\n\n \n output = output.data.cpu().numpy()\n\n N, _, h, w = output.shape \n pred = output.transpose(0, 2, 3, 1).reshape(-1, num_class).argmax(axis=1).reshape(N, h, w) \n target = batch['l'].cpu().numpy().reshape(N, h, w)\n\n for p, t in zip(pred, target):\n total_ious.append(iou(p, t))\n pixel_accs.append(pixel_acc(p, t))\n f_measures.append(F_Measure(p, t))\n maes.append(MAE(p, t))\n\n # Calculate average IoU\n total_ious = np.array(total_ious).T # n_class * val_len\n ious = np.nanmean(total_ious, axis=1)\n pixel_accs = np.array(pixel_accs).mean()\n f_measures = np.nanmean(np.array(f_measures))\n maes = np.array(maes).mean()\n print(\"epoch{}, MAE: {}, meanIoU: {}, f_measure: {}, IoUs: {}\".format(epoch, maes, np.nanmean(ious), f_measures, ious))\n \n global pixel_acc_list\n global mIOU_list\n global f_measure_list\n global mae_list\n \n pixel_acc_list.append(pixel_accs)\n mIOU_list.append(np.nanmean(ious))\n f_measure_list.append(f_measures)\n mae_list.append(maes)\n\n\n# borrow functions and modify it from https://github.com/Kaixhin/FCN-semantic-segmentation/blob/master/main.py\n# Calculates class intersections over unions\ndef iou(pred, target):\n ious = []\n target=target/max(target.max(),1)\n for cls in range(num_class):\n pred_inds = pred == cls\n target_inds = target == cls\n intersection = pred_inds[target_inds].sum()\n union = pred_inds.sum() + target_inds.sum() - intersection\n # if(cls==1):\n # print(pred_inds.sum())\n # print(target_inds.sum())\n # print(intersection)\n if union == 0:\n ious.append(float('nan')) # if there is no ground truth, do not include in evaluation\n else:\n ious.append(float(intersection) / max(union, 1))\n # print(\"cls\", cls, pred_inds.sum(), target_inds.sum(), intersection, float(intersection) / max(union, 1))\\\n \n return ious\n\n\ndef pixel_acc(pred, target):\n correct = (pred == target).sum()\n total = (target == target).sum()\n return correct / total\n\ndef F_Measure(pred, target):\n beta_sqr = 0.3\n target=target/max(target.max(),1)\n cls = 1\n pred_inds = pred == cls\n target_inds = target == cls\n TP = pred_inds[target_inds].sum()\n FP = pred_inds.sum() - TP\n FN = target_inds.sum() - TP\n P = TP / (TP + FP)\n R = TP / (TP + FN)\n denominator = (beta_sqr*P + R)\n # print(P, R)\n if denominator == 0:\n return float('nan') # if there is no ground truth, do not include in evaluation\n else:\n f_measure = (beta_sqr + 1) * P * R / denominator\n return f_measure\n\ndef MAE(pred, target):\n target=target/max(target.max(),1)\n pred = torch.from_numpy(pred).float()\n target = torch.from_numpy(target).float()\n # print(type(target[0][0]))\n loss = nn.L1Loss()\n mae= loss(pred, target)\n return mae\n\n\n\n# + [markdown] id=\"VZz-zbB7MAtW\" colab_type=\"text\"\n# ## Execution\n\n# + id=\"ujsJzHISD-Ru\" colab_type=\"code\" colab={\"base_uri\": \"https://localhost:8080/\", \"height\": 1000} outputId=\"bfc8c141-88b7-4a6b-b145-4c322ca4448c\"\n## perform training and validation\nglobal_index = 0\npixel_acc_list = []\nmIOU_list = []\nf_measure_list = []\nmae_list = []\n# val(0) # show the accuracy before training\ntrain()\n\n# + id=\"20qijkJUChVB\" colab_type=\"code\" 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/Homework 9 -Correct Importance Sampling and Random Numbers/Homework 9 (Corrected Importance Sampling).ipynb
0399190505b6acbfb825c8e922ebb0765d9be28c
[]
no_license
KeoniCastellano/2017-cmp
https://github.com/KeoniCastellano/2017-cmp
0
0
null
2018-08-29T21:34:12
2018-08-29T21:15:41
null
Jupyter Notebook
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.py
36,159
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # %matplotlib inline import numpy as np import matplotlib.pyplot as plt from numpy import exp, pi,sqrt from scipy import integrate from random import random f = lambda x: (x**(-1/2))/(exp(x)+1) # + ''' We first graph the function to determine the region that we are looking at. ''' x = np.linspace(0.0001,1,1000) y = f(x) plt.figure(figsize=(15,15)) plt.title('Graph of Function') plt.axis([0,1,0,1]) plt.xlabel('x', size = 15) plt.ylabel('f(x)', size = 15) plt.plot(x,y) plt.show() # - # We can form a probability distribution using the weighted function $w(x) = x^{-\frac{1}{2}}$ by normalizing it on the interval we are looking at. # So $p(x) = \frac{x^{-\frac{1}{2}}}{\int_0^1{x^{-\frac{1}{2}}}dx} = \frac{1}{2}x^{-\frac{1}{2}}$. The cumulative distribution function is $F(x)=\int_0^x{\frac{1}{2}y^{-\frac{1}{2}}dy} = x^{\frac{1}{2}}.$ Thus, $x$ should be weighted according to $F^{-1}(x) = x^2.$ ''' This is the algorithm for importance sampling ''' w = lambda x: x**(-1/2) const = integrate.quad(w,0,1)[0] def IS(N): ratio = 0 for i in range(N): s = random() ratio += (f(s**2)/w(s**2)) I = ratio*const/N return I print('The value of the integral using importance sampling is %.4f' %IS(1000000))
1,513
/Introduction_to_Pandas/3_Position_and_Label_Based_Indexing.ipynb
40cbd1f75e13489036517377c0bdf5325c792c6b
[]
no_license
sneelapu77/IIITB_MachineLearning
https://github.com/sneelapu77/IIITB_MachineLearning
1
2
null
2018-10-06T16:31:30
2018-09-16T06:15:09
null
Jupyter Notebook
false
false
.py
92,155
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as n #File format to read .xls data = pd.read_excel("crim_usa_2016.xls") data #Creating a new variable and storing the values of row 2 using location indexing. new_header = data.iloc[2] #storing the newly created variable in the column header. data.columns = new_header data #deleting the unwanted rows using index method data.drop(data.index[:3], inplace=True) #deleting the unwanted rows using index method data.drop(data.index[20:42], inplace=True) data #command used to display max 50 columns pd.set_option("display.max_columns",50) #Command to display the column headers(Raw Format). data.columns #Command to drop mltiple columns. data.drop(["Rape\n(revised \ndefinition3)", "Rape\n(revised \ndefinition) \nrate3"], axis=1, inplace = True) data crime_rate = data.groupby(['Population1'])['Violent \ncrime \nrate '].sum() import matplotlib.pyplot as plt crime_rate.plot(kind = "barh", title = "CRIME RATE") # Plotting graph data.plot(x = "Population1", y = "Violent crime rate", label = "Violent crime rate") plt.xlabel("Population") plt.ylabel("Violent Crime Rate") plt.legend() plt.title("Violent crime rate in relation to population size") plt.show() import seaborn as sns sns.set_style("dark") data.plot.barh() #Removing special characters in column headers data.columns = data.columns.str.strip().str.replace(' ', '_').str.replace('\n', '').str.replace(' \n', '') data.columns data data.columns = data.columns.str.strip().str.replace('_',' ') data.columns data #Arranging the row number in sequence data.index = range(20) data #Storing the data set to a new xls file. data.to_excel("new1.xls")
1,935
/CarND-LaneLines-P1/.ipynb_checkpoints/P1-checkpoint.ipynb
3fb2d15947e78ebf4f058f99a5e9bc457920f256
[]
no_license
erhiteshkumar/Udacity
https://github.com/erhiteshkumar/Udacity
0
0
null
null
null
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.py
21,972
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:py27] # language: python # name: conda-env-py27-py # --- # # Feature Selection Lab # # In this lab we will explore feature selection on the [Titanic Dataset](https://www.kaggle.com/c/titanic/data). # # We encourage you to conduct EDA across the data before building a logistic regression to determine whether or not a given individual survived. # # You'll then experiment with various feature selection techniques to improve your performance. You'll need the sklearn documentation: http://scikit-learn.org/stable/modules/feature_selection.html import pandas as pd import numpy as np # %matplotlib inline import matplotlib.pyplot as plt # ## 1. Import the data and EDA # # We'll be working with the titanic datasets - go ahead and import it from the "assets" folder. While you're at it, take some time to do EDA and see what the data looks like! train = pd.read_csv('train.csv') train.shape train.describe(include='all') # # cabin is not in my selection, I remove this column and then clean my new dataset import copy dataset_org = copy.deepcopy(train) train.drop('Cabin', axis=1, inplace=True) train.head(2) # + train = train.dropna() train.shape # + new_data = train.loc[train['Age'] > 0] new_data.shape # - # + new_data = new_data.loc[new_data['Fare'] > 0] new_data.shape # - # + mask = new_data['Embarked'].isin(['S', 'C','Q']) new_data2 = new_data[mask] new_data2.shape # - # + mask = new_data['Sex'].isin(['male', 'female']) new_data2 = new_data[mask] new_data2.shape # - # + mask = new_data['Pclass'].isin([1, 2,3]) new_data2 = new_data[mask] new_data2.shape # - new_data2 # ## 2. Feature selection # # Let's use the `SelectKBest` method in scikit learn to see which are the top 5 features. # # - What are the top 5 features for `Xt`? # # => store them in a variable called `kbest_columns` # # create dummy variable new_data2=pd.concat([new_data2, pd.get_dummies(new_data2['Sex'])], axis=1) new_data2=pd.concat([new_data2, pd.get_dummies(new_data2['Embarked'])], axis=1) new_data2=pd.concat([new_data2, pd.get_dummies(new_data2['Parch'])], axis=1) new_data2=pd.concat([new_data2, pd.get_dummies(new_data2['SibSp'])], axis=1) new_data2=pd.concat([new_data2, pd.get_dummies(new_data2['Pclass'])], axis=1) new_data2.shape # + col_name = ['PassengerId', 'Survived', 'Pclass', 'Name','Sex', 'Age','SibSp','Parch','Ticket','Fare','Embarked','female', 'male','Embarked_C','Embarked_Q','Embarked_S','Parch_0','Parch_1','Parch_2','Parch_3','Parch_4','Parch_5', 'Parch_6','SibSp_0','SibSp_1','SibSp_2','SibSp_3', 'SibSp_4','SibSp_5','Pclass_1','Pclass_2','Pclass_3'] len(col_name) # - new_data2.columns=['PassengerId', 'Survived', 'Pclass', 'Name','Sex', 'Age','SibSp','Parch','Ticket','Fare','Embarked','female','male','Embarked_C','Embarked_Q','Embarked_S','Parch_0','Parch_1','Parch_2','Parch_3','Parch_4','Parch_5', 'Parch_6','SibSp_0','SibSp_1','SibSp_2','SibSp_3','SibSp_4','SibSp_5','Pclass_1','Pclass_2','Pclass_3'] new_data2.columns # # these are the independent variable # + feature_cols = [ u'female',u'Age',u'Fare'] X = new_data2[feature_cols] X # store response vector in "y" y = new_data2['Survived'] y # check X's type print type(X) #print type(X.values) # check y's type print type(y) print type(y.values) # + from sklearn.datasets import load_iris from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 X_new = SelectKBest(chi2, k=2).fit_transform(X, y) X_new.shape kbest_columns = X_new # + from sklearn.metrics import classification_report, confusion_matrix, accuracy_score from sklearn.cross_validation import train_test_split from sklearn.linear_model import LogisticRegression, LogisticRegressionCV from sklearn.grid_search import GridSearchCV from sklearn import metrics from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y) # STEP 1: split X and y into training and testing sets (using random_state for reproducibility) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99) # STEP 2: train the model on the training set (using K=1) logreg_cv = LogisticRegressionCV(solver='liblinear',Cs = 15, cv = 5,penalty = 'l2') logreg_cv.fit(X_train, y_train) # STEP 3: test the model on the testing set, and check the accuracy y_pred_class = logreg_cv.predict(X_test) print metrics.accuracy_score(y_test, y_pred_class) # - new_data2.shape # + feature_cols = [ u'Age',u'Fare', u'male', u'Embarked_Q', u'Embarked_S', u'Parch_1',u'Parch_2', u'Parch_3', u'Parch_4', u'Parch_5', u'Parch_6',u'SibSp_1', u'SibSp_2', u'SibSp_3', u'SibSp_4', u'SibSp_5',u'Pclass_2', u'Pclass_3'] X = new_data2[feature_cols] X # store response vector in "y" y = new_data2['Survived'] y # check X's type print type(X) #print type(X.values) # check y's type print type(y) print type(y.values) # - len(feature_cols) # + from sklearn.feature_selection import SelectKBest X_new = SelectKBest(k=5).fit_transform(X_train, y_train) kbest_columns_all_variable = SelectKBest(k=5).fit(X_train, y_train) # - kbest_columns_all_variable X_new # + from sklearn.metrics import classification_report, confusion_matrix, accuracy_score from sklearn.cross_validation import train_test_split from sklearn.linear_model import LogisticRegression, LogisticRegressionCV from sklearn.grid_search import GridSearchCV from sklearn import metrics from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y) # STEP 1: split X and y into training and testing sets (using random_state for reproducibility) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) # STEP 2: train the model on the training set (using K=1) logreg_cv = LogisticRegressionCV(solver='liblinear',Cs = 15, cv = 5,penalty = 'l2') logreg_cv.fit(X_train, y_train) # STEP 3: test the model on the testing set, and check the accuracy y_pred_class = logreg_cv.predict(X_test) print metrics.accuracy_score(y_test, y_pred_class) # - # ## 3. Recursive Feature Elimination # # `Scikit Learn` also offers recursive feature elimination as a class named `RFECV`. Use it in combination with a logistic regression model to see what features would be kept with this method. # # => store them in a variable called `rfecv_columns` # + from sklearn.svm import SVC #from sklearn.model_selection import StratifiedKFold from sklearn.feature_selection import RFECV # Create the RFE object and compute a cross-validated score. logreg_cv = LogisticRegressionCV(solver='liblinear',Cs = 15, cv = 5,penalty = 'l2') #svc = SVC(kernel="linear") # The "accuracy" scoring is proportional to the number of correct # classifications rfecv = RFECV(estimator=logreg_cv, step=1, cv=2, scoring='accuracy') rfecv.fit_transform(X_train, y_train) rfecv_columns = rfecv.fit(X_train, y_train) # - print("Optimal number of features : %d" % rfecv.n_features_) # ## 4. Logistic regression coefficients # # Let's see if the Logistic Regression coefficients correspond. # # - Create a logistic regression model # - Perform grid search over penalty type and C strength in order to find the best parameters # - Sort the logistic regression coefficients by absolute value. Do the top 5 correspond to those above? # # => choose which ones you would keep and store them in a variable called `lr_columns` import numpy as np from sklearn import datasets from sklearn.linear_model import Ridge from sklearn.grid_search import GridSearchCV ## Load the Dataset dataset = new_data2 ## Prepare a Range of Alpha Values to Test C_vals = [ 1, 5, 10, 100, 1000] penalties = ['l1','l2'] logreg_cv = LogisticRegressionCV(solver='liblinear' ,cv=5) # + ## Create and Fit a GridSearchCV Model grid = GridSearchCV(estimator=logreg_cv, param_grid=dict(penalty = penalties, Cs =C_vals )) grid_coef = grid.fit(X_train, y_train) print(grid) # - grid.best_estimator_ ## Summarize the Results of the Grid Search print(grid.best_score_) print(grid.best_estimator_.Cs) # ## 5. Compare features sets # # Use the `best estimator` from question 4 on the 3 different feature sets: # # - `kbest_columns` # - `rfecv_columns` # - `lr_columns` # - `all_columns` # # Questions: # # - Which scores the highest? (use cross_val_score) # - Is the difference significant? # # Discuss results. kbest_columns_all_variable.scores_ rfecv_columns.grid_scores_ grid.best_score_ # ## Bonus 1 # # Use a bar chart to display the logistic regression coefficients. Start from the most negative on the left. # ## Bonus 2 # # Use Sebastian Raschka's [MLxtend library](http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/) to implement a feature selection tactic discussed in class: sequential forward or backward search or floating sequential forward/backward search.
9,107
/Rhavif Budiman - EDA Practice Case.ipynb
d05edcfe730d416692c632412af6cfdf265ad84f
[]
no_license
rhavifbudiman/IYKRA-DataFellowship-EDAPracticeCase
https://github.com/rhavifbudiman/IYKRA-DataFellowship-EDAPracticeCase
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # RHAVIF BUDIMAN # # EDA PRACTICE CASE # ## IYKRA DATA FELLOWSHIP BATCH 5 # github link https://github.com/rhavifbudiman/IYKRA-DataFellowship-EDAPracticeCase # List of questions of this project: # 1. Whether this data is clean? # 2. How is the point distribution of the epl team? and which team is an anomaly? # 3. Which team has the best attack? # 4. Which team has the best defence? # 5. Which team is good in the financial aspect? # ### Answer # ## Preparation #import library import pandas as pd import statsmodels.api as sm import seaborn as sns from matplotlib import pyplot as plt # ## Question 1 . Whether this data is clean? # read in the data data = pd.read_csv('https://raw.githubusercontent.com/Syukrondzeko/Fellowship-5/main/epl_1819.csv') #check data data.head(7) # check the size data.shape # + active="" # this data have 20 row and 44 column # - data.duplicated(subset='Team').sum() # this data have 0 duplicated data # check the type for all columns in data that we have data.info() # you can read that we have so much info ahahaha, but you must careful because some of them are integer but declared in this info is object is like string that can contain numeric and alphabetic value, we must see that variable! the most easiest thing used excel like me ahahaha, you can compare between int64 type and object type data['attack_passes'].head(5) data['attack_posession'].head(5) # so we can see the difference, the difference because the separator for thousand, if you crosschek it using excel, all of thousand information using comma (,) not dot(.) so we must change it back to dot (.) , the easiest way to change it we back to read_csv function with a restriction, all thousand using comma convert to dot # read in the data data = pd.read_csv('epl_1819.csv', thousands = ",") data['attack_passes'].head(5) # check the type for all columns in data that we have data.info() # its already alright, its a little bit not clean but its already great data! # ## Question 2 . How is the point distribution of the EPL team? Which team is an anomaly? colname = list(data.columns.values) list(enumerate(colname)) colnamen = colname.remove('Team') colnamen = colname.remove('category') # + plt.figure(figsize = (15,30)) for i in enumerate(colname): plt.subplot(9,5,i[0]+1) sns.distplot(data[i[1]]) import warnings warnings.filterwarnings("ignore", category=FutureWarning) # - # if the normal distribution the model position in middle, so only a few variable normal in this case, to see details, you can see graph before this line, so we pick one of the anomaly distribution. I pick 'general_squad_size'column, in distribution graph we can see is has a little bit model in left side, so we must check it again using boxplot to see the outlier of this column plt.boxplot(data['general_squad_size']) # we can see general_squad_size has outlier, so treatment to this case is remove the outlier or replace the value of the outlier # ## Question 3 . Which team has the best attack? # after seeing the data, i conclude to make model from team with best attack is # # best attack = attack_scored + attack_passes + attack_passes_through + attack_passes_long + attack_passes_back + attack_crosses + attack_corners_taken + (attack_shots_on_target / attack_shots) + attack_posession + attack_pass_accuracy # # but all of that value must be standarized first # + #make list attack variable listat = ['attack_scored','attack_passes', 'attack_passes_through', 'attack_passes_long','attack_passes_back','attack_crosses', 'attack_shots','attack_posession','attack_pass_accuracy','attack_corners_taken','attack_shots_on_target'] #standarize the data from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() # # copy the team names and scale the variables data_atk = data[['Team']].copy() data_atk[listat] = scaler.fit_transform(data[listat]) d = data_atk d['best_attack'] = d['attack_scored'] + d['attack_passes'] + d['attack_passes_through'] + d['attack_passes_long'] + d['attack_passes_back'] + d['attack_crosses'] + d['attack_corners_taken'] + (d['attack_shots_on_target'] / d['attack_shots']) + d['attack_posession'] + d['attack_pass_accuracy'] d.head(5).sort_values(by='best_attack', ascending=False) # - # so the best attack team is manchester city with 8.65 attack points # ## Question 4 . Which team has the best defence? # after seeing the data, i conclude to make model from team with best defence is # # best defence= defence_goals_conceeded+ defence_saves+ defence_blocks+ defence_interceptions+defence_tackles+defence_tackles_last_man+defence_clearances+defence_clearances_headed+defence_penalty_conceeded # # but all of that value must be standarized first # + #make list defence listdf = ['defence_goals_conceeded','defence_saves', 'defence_blocks', 'defence_interceptions','defence_tackles','defence_tackles_last_man', 'defence_clearances','defence_clearances_headed','defence_penalty_conceeded'] data_df = data[['Team']].copy() data_df[listdf] = scaler.fit_transform(data[listdf]) s = data_df s['best_defence'] = s['defence_goals_conceeded']+s['defence_saves']+ s['defence_blocks'] + s['defence_interceptions'] + s['defence_tackles']+s['defence_tackles_last_man']+s['defence_clearances']+s['defence_clearances_headed']+s['defence_penalty_conceeded'] s.head(5).sort_values(by='best_defence', ascending=False) # - # so the best defence team is arsenal with 4.311 defence points # ## Question 5 . Which team has the best finance? # after seeing the data, i conclude to make model from team with best financial aspect rating is # best_finance = finance _live_games_televised + finance _tv_revenue+finance _tv_revenue+finance _team_market # + #make list finance listfn = ['finance _live_games_televised','finance _tv_revenue', 'finance _team_market', 'finance _market_average'] data_fn = data[['Team']].copy() data_fn[listfn] = scaler.fit_transform(data[listfn]) f = data_fn f['best_finance_rating'] = f['finance _live_games_televised']+f['finance _tv_revenue']+ f['finance _tv_revenue'] + f['finance _team_market'] f.head(5).sort_values(by='best_finance_rating', ascending=False) # - # so after counting the rating, the best team with good financial aspect is liverpool
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Day 17 In-class assignment: Shotgun Sequencing # # Today we are going to think about how we would write a program to do shotgun sequencing. # ### Student Names # //Put the names of everybody in your group here! # ### Learning Goals # # * Understand genome sequencing # * Get some practice working with functions, strings and loops # # Work in pairs and Shotgun Assemble by hand # # We are going to try to assemble some sequences from a famous book in class. Your instructor(s) will hand out strips of paper. See if you can assemble the original text from the strips of paper! # # **Question 1:** How hard was this task to do by hand? Did you manage to get the entire text? (Also, please take a picture of what you have submit the picture to d2l with this notebook.) # # // put your answer here # **Question 2:** Write some pseudocode or a flowchart showing the steps you would take to write your own code to assemble strips of paper. We strongly suggest that you describe this pseudocode as a bunch of high-level function calls with general descriptions of what your functions will look like. # // Include your pseudocode here or reference a picture that you will submit with your notebook to d2l. # **Question 3:** What functions do you think will be the most difficult to write? Why? # // put your answer here. # Before moving on, verify your answers to questions 1-3 with an instructor. # # Code Review and Testing # # In this section we are going to program a basic shotgun assembler. With your group, read though the program and try to understand what it is doing. Code review is an extreamly important skill in computational science - it's very common for people to inherit code from coworkers, or to find examples that they would like to use on the web. Dissecting the code helps you to learn, and is a great way to make yourself a better programmer. Some questions to ask yourself while looking at code include: # # 1. Can you identify any clever techniques that you could use in your own coding practices? # 2. Can you identify any bad techniques that you could change to do better? # 3. What assumptions do you think the programmer is making about the model or problem? Are these assumptions well documented? # 4. Can you come up with nominally "valid" test cases that will break the code? # # Let's write the first piece of an assembler. To start, consider the following example strings we will use as data. Can you see how these strings go together? start_string_list = ['er_way__in_short_the_period_was_so_far_like_the_pr', \ '__in_short_the_period_was_so_far_like_the_present_', \ 'he_present_period_that_some_of_its_noisiest_author', \ '_period_that_some_of_its_noisiest_authorities_insi'] # # Shotgun Assembly Program: # # Let us assume we have inherited the following code from a previous student in our research lab. The student has most of the code written but did not get a chance to finish the ```slide_left_right``` function. The student let you know that everything is working and all you need to do to make a usable shotgun assembler is to finish ```slide_left_right```. Run the program on the above string to see what it does. #define the internal slide function # If the strings overlap, return the combined longer string and the length of the overlap def slide_left_right(left,right): # remember to calculate the overlap here - THIS NEEDS TO BE FINISHED! overlap=10 return([left[:-overlap]+right, overlap ]) def string_overlap(s1, s2): #Slide string 1 over string 2 and calculate the overlap [s3_1, i1] = slide_left_right(s1,s2) #Slide string 2 over string 1 and calculate the overlap [s3_2, i2] = slide_left_right(s2,s1) #Check to see which overlap is biggest. if(i1 > i2): return(s3_1) if(i2 == 0): #Return empty string if the strings do not overlap return('') return(s3_2) #new shotgun function def shotgun(cuts): #use the first cut to initialize the string assembled_string = cuts[0] #Make a list of the remaining cuts unassembled_cuts = cuts[1:] #Counter to help us keep track if the list did not change previous_list_len = 0; #Keep looping until we found all overlapping regions. #i.e. there are still some unassembled_cuts and the list length is changing while(unassembled_cuts and previous_list_len != len(unassembled_cuts)): #reset list of unmatching cuts non_overlapping_cuts = [] #Record the previous list length previous_list_len = len(unassembled_cuts) #Loop though unassembled cuts and add them to the assembled_string for cut in unassembled_cuts: s_tmp = string_overlap(assembled_string, cut) #If the temporary string is empty store the cut in the non_overlapping_cuts list if(s_tmp == ''): non_overlapping_cuts.append(cut) else: assembled_string = s_tmp; unassembled_cuts = non_overlapping_cuts.copy() return(assembled_string) #Run the shotgun assembly print(shotgun(start_string_list)) # **Question 4:** Discuss this code with your partner or group and describe what it does. What is the ```slide_left_right``` function supposed to do? What are the inputs and outputs to ```slide_left_right```? # //Put your answer here. # # The purpose of this function is to "slide" the two strings past each other, compare the overlapping segments, and return the string that corresponds to the maximum overlap between the two strings (i.e., stitch the strings together). The inputs are the two strings; the outputs are a single combined string and a number indicating the number of characters in those strings that overlap. # **Question 5:** Assume for now, that the student who wrote this code is correct and it will work. Help the student finish the program by writing the ```slide_right_left``` function. #copy, paste and modify the slide_left_right function from above def slide_left_right(left,right): overlap = 0; for i in range(1,len(left)+1): if(left[-i:] == right[:i]): overlap = i; if overlap == 0: if right in left: return left, len(left) return left[:-overlap]+right, overlap # ### Unit Testing # # It is difficult to write code and have it work the first time. Before trying to run your function with the rest of the code, it is a good idea to test just the single function. This is sometimes called "unit testing", and is a standard programming practice. For example, this code checks the ```slide_left_right``` function. Before you execute this code, see if you can predict what the output will be! # Before running the code see if you can predict what the output will be. s3, i = slide_left_right(start_string_list[0],start_string_list[1]) print(s3) print(i) # Did that work? Sometimes it is hard to tell. A better unit test is simple. Consider the following example, and before you run it predict what you think the output will be: #Before running the code see if you can predict what the output will be. s3,i= slide_left_right('aaabbb','bbbbcccc') print(s3) print(i) # Using the much simpler imput strings helps you quickly see if the code is working. # **Question 6: (Unit Testing, four parts).** Now, let's write your own test cases. Since this is your first time, we will be very specfic about what tests you should write. However, you want to be able to write your own unit tests. Always write tests for odd, but valid, inputs. For example, write a unit test for each of the four cases below. # # In each case shown below you should be able to just copy the test case from above and make some minor changes. The goal is to learn something different for each case. Write a test case for each of the following conditions: # **1) Write a test case to see what happens if you pass ```slide_left_right``` two strings that are the same.** # + #Test to see if the function works with exactly the same strings #Put your test code here [s3, i] = slide_left_right('aaa','aaa') print(s3) print(i) # - # **2) Write a test case to see what happends if the strings do not overlap (i.e., they are entirely different).** #Test to see if the code works for non-overlapping strings #Put your test code here [s3, i] = slide_left_right('aaa','bbb') print(s3) print(i) # **3) Write a test case to see what happens if the right string is entirely inside the left string** #Test to see if the code works if the right string is entirely inside the left one #Put your test code here [s3, i] = slide_left_right('aaabbbccc','bbb') print(s3) print(i) # **4) Write a test case to see what happens if the right string is empty. (i.e. '')** #Different Overlapping strings #Put your test code here [s3, i] = slide_left_right('aaabbbccc','') print(s3) print(i) # **Question 7:** As a group, discuss the unit tests shown above. What did you learn from these test cases? Can you think of any additional test cases that you haven't implemented? And, finally, did the code do what you expected it to do? # //Write your answers here # ## Before you go any further: # # Make sure that you check with one of the instructors to make sure your answers make sense! # # Full Test: # # Before moving to this section, make sure you are comfortable with the above tests. Now let's test the entire shotgun program again using the new function you have written: #Run the shotgun assembly on the orginial string list shotgun(start_string_list) # Make sure this got the answer you are expecting. Let's try a different order to the strings to make sure that is working as well: # + second_string_list = [start_string_list[0], \ start_string_list[3], \ start_string_list[2], \ start_string_list[1]] print(shotgun(second_string_list)) # - # Before moving on, make sure you got the same answer for both inputs. Adjust the function ```slide_left_right``` as needed to make sure this happens! # # Generate Genome Cuts # # Now let's download a really big test to see if the code can do what you did with the slips of paper in class. # # First, download the "Example-Assembly_Exercise.ipynb" notebook from the d2l website. Run the notebook to generate the "Genome_cuts.txt" file. You do not need to understand the notebook in order to generate the file. However, it would be a good exercise to read though the notebook and see what pieces make sense. # # Open the "Genome_cuts.txt" file using the following command: cuts = open("Genome_cuts.txt").read() cuts=cuts.split('\n') # Run the shotgun assembler on the code and talk about the outputs with your group members: print(shotgun(cuts)) # ** Question 8:** Although it is possible that the randomly-generated "Genome_cuts.txt" produced input to the function that work, most likely the above string is not fully assembled (i.e., the assembly doesn't produce the paragraph of text you would expect). Why do you think this may not have worked? # //put your answer here # ** Question 9:** When writing unit tests. It is generally best to come up with the smallest test that reproduces the error. This small tests help you isolate the problem. Come up with a very short unit test that demonstrates the failure exhibited above. # //put your answer here # ** Question 10:** Sequencing machines are not always correct. How would you change the program to account for errors in some of the letters? # //put your answer here # # Feedback on this assignment # **Question 11:** What questions do you (or does your group) have after this assignment? # *Put your answer here.* # # Submit this assignment # Log into the course Desire2Learn website (d2l.msu.edu) and go to the "In-class assignments" and the "Day 17" folder. Upload this notebook there. You only have to upload one notebook per group - just make sure that everybody's name is at the top of the notebook!
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + id="3Ql6keKSXwbE" from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.model_selection import GridSearchCV from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report import pandas as pd import numpy as np import json # + id="ZKQcTPtmXwbI" df = pd.read_csv('train.csv', header = 0) df = df._get_numeric_data() numeric_headers = list(df.columns.values) numeric_headers.pop() X = df[numeric_headers] X= X.drop('label', axis=1) X = X.to_numpy() y = df['label'] y=y.apply(lambda row: int(row)) y=y.to_numpy() scaler = StandardScaler() X=scaler.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) # + id="k8Dh-Cx_XwbI" names = ["Nearest_Neighbors", "SVM", "MLP", "Adaboost", "Random_Forest"] # + id="bMfu9eMhXwbI" classifiers = [ KNeighborsClassifier(), SVC(), MLPClassifier(), AdaBoostClassifier(), RandomForestClassifier()] # + id="0bQrdxPgXwbI" paramsGrid={} paramsGrid["SVM"]={ 'C':[0.0001,0.1,1,10,100], 'gamma':[0.001,0.1,1] } paramsGrid["Nearest_Neighbors"]={ 'n_neighbors':[1,5,10,50,100,500,1000] } paramsGrid["Adaboost"]={ 'n_estimators': [50,100,150,200,250,300] } paramsGrid["MLP"]={ 'learning_rate_init':[0.001,0.1,0.01], 'early_stopping':[True], 'hidden_layer_sizes':[100,200,500] } paramsGrid["Random_Forest"]={ 'min_samples_leaf': [2,10,30,50], 'min_samples_split': [2,10,30,50], 'n_estimators': [50,100,150,200,250,300] } # + id="ru61WcJvbG2F" def evaluate(model, test_features, test_labels): ypred = model.predict(test_features) errors = abs(ypred - test_labels) accuracy=np.sum([pred == true for pred, true in zip(ypred, test_labels)])/len(test_labels) print('Model Performance') print('Average Error: {:0.4f} degrees.'.format(np.mean(errors))) print('Accuracy = {:0.2f}%.'.format(accuracy)) return accuracy from google.colab import files # + colab={"base_uri": "https://localhost:8080/", "height": 854} id="4VerWBThgEbE" outputId="5dee07f3-7847-4eee-d031-d6641bdbbd24" results={} for name, clf in zip(names, classifiers): print(f"For classifier {name}") grid = GridSearchCV(estimator = clf, param_grid = paramsGrid[name], cv = 3, n_jobs = -1, verbose = 2) grid.fit(X_train, y_train) print("\tBest parameters set found on development set:") print() print(f"\t{grid.best_params_}") print() best_grid = grid.best_estimator_ grid_accuracy = evaluate(best_grid, X_test, y_test) print("\tGrid scores on development set:") print() print(f"\t\t{grid_accuracy}") print() results[name]={ "best_params":grid.best_params_, "grid_accuracy":grid_accuracy } with open(f"Results_tuning.json", "w+") as f: json.dump(results,f) files.download(f"Results_tuning.json") /label> # </div> # </div> # # <div class="form-group col-md-4"> # <label for="inputState">State</label> # <select id="inputState" class="form-control"> # <option selected>Choose...</option> # <option>TN</option> # <option>KN</option> # </select> # </div> # <div class="form-group col-md-2"> # <label for="inputZip">Zip</label> # <input type="text" class="form-control" id="inputZip"> # </div> # </div> # # <hr /> # # <!-- input-group and input-group-text--> # <div class="row"> # <div class="col-md-4"> # <div class="form-group"> # <div class="input-group"> # <div class="input-group-text">$</div> # <input type="text" class="form-control" placeholder="Amount" /> # <div class="input-group-text">.00</div> # </div> # </div> # </div> # <div class="col-md-3"> # <button type="submit" class="btn btn-primary mt-3" > Donate</button> # </div> # </div> # # <!-- Bootstrap table --> # <table class="table table-striped mt-4"> # <thead class="table-dark"> # <tr> # <!-- scope is used to specify headings --> # <th scope="col">#</th> # <th scope="col">First</th> # <th scope="col">Last</th> # <th scope="col">Handle</th> # </tr> # </thead> # <tbody> # <tr> # <th scope="row">1</th> # <td>Mark</td> # <td>Otto</td> # <td>@mdo</td> # </tr> # <tr > # <th scope="row">2</th> # <td>Jacob</td> # <td>Thornton</td> # <td>@fat</td> # </tr> # <tr class="table-success"> # <th scope="row">3</th> # <td>Larry</td> # <td>the Bird</td> # <td>@twitter</td> # </tr> # </tbody> # </table> # # <div class="form-check mt-3"> # <input type="checkbox" class="form-check-input" id="exampleCheck1"> # <label class="form-check-label" for="exampleCheck1">Check me out</label> # </div> # # <button type="submit" class="btn btn-primary mt-3" >Submit</button> # </form> # </div> # # # <!-- Optional JavaScript --> # <!-- jQuery first, then Popper.js, then Bootstrap JS --> # <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script> # <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script> # <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script> # # <script id="main" type="text/javascript"> # # </script> # # </body> # </html> # ``` # ### Output: # # <div class="alert alert-block alert-info"> # # <html> # <head> # <title> My WebPage </title> # </head> # <body> # <p><a href ="html/3_forms.html" > Click here for output </a></p> # </body> # </html> # </div>
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- s = 'abc' print(s) print(type(s)) s = "abc" print(s) print(type(s)) s_sq = 'abc' s_dq = "abc" print(s_sq == s_dq) s_sq = 'a\'b"c' print(s_sq) s_sq = 'a\'b\"c' print(s_sq) s_dq = "a'b\"c" print(s_dq) s_dq = "a\'b\"c" print(s_dq) s_sq = 'a\'b"c' s_dq = "a'b\"c" print(s_sq == s_dq) # + # s = 'abc # xyz' # SyntaxError: EOL while scanning string literal # - s = 'abc\nxyz' print(s) s_tq = '''abc xyz''' print(s_tq) print(type(s_tq)) s_tq = '''abc''' print(s_tq) s_tq_sq = '''\'abc\' "xyz"''' print(s_tq_sq) s_tq_dq = """'abc' \"xyz\"""" print(s_tq_dq) print(s_tq_sq == s_tq_dq) s_tq = '''abc xyz''' print(s_tq) s_multi = ('abc\n' 'xyz') print(s_multi) ="PwKx7HnE9duw" colab_type="code" colab={} tf.global_variables_initializer().run() #init variables # + id="ESCuh_hg9f04" colab_type="code" colab={} #Julia Set #Compute the new values of z : z^2 + c zs_ = zs*zs + c # + id="8XEmPNlV905h" colab_type="code" colab={} # Have we diverged with this new value? not_diverged = tf.abs(zs_) < 4 # + id="y2qWUDkR91bo" colab_type="code" colab={} # Operation to update the zs and the iteration count # Note: We keep computing zs after they diverge! This # is very wasteful! There are better, if a little # less simple, ways to do this. # step = tf.group( zs.assign(zs_), ns.assign_add(tf.cast(not_diverged, tf.float32)) ) # + id="1VtXhm-m95kF" colab_type="code" colab={} #run for i in range(200): step.run() # + id="J5mPwCwR97Rx" colab_type="code" outputId="25cfb1c7-aa43-42a0-b843-a43619d21fdb" colab={"base_uri": "https://localhost:8080/", "height": 751} #plot import matplotlib.pyplot as plt fig = plt.figure(figsize=(16,10)) def processFractal(a): """Display an array of iteration counts as a colorful picture of a fractal.""" a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1]) img = np.concatenate([10+20*np.cos(a_cyclic), 30+50*np.sin(a_cyclic), 155-80*np.cos(a_cyclic)], 2) img[a==a.max()] = 0 a = img a = np.uint8(np.clip(a, 0, 255)) return a plt.imshow(processFractal(ns.eval())) plt.tight_layout(pad=0) plt.show() # + id="0G1b-Crc99O5" colab_type="code" colab={} sess.close() # + id="9PbmFYLt_QHM" colab_type="code" colab={}
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # %matplotlib inline import matplotlib.pyplot as plt import numpy as np import scipy.optimize as opt import sklearn.linear_model import sklearn.model_selection import random random.seed(137) rest = random.random() def weight(word): # overfitted if word == '[email protected]': return 100.0 if word == 'car': return random.random() if word == 'dog': return - random.random() return random.random() def has(word, text): return word in text def feature(index): return 1 # + [markdown] slideshow={"slide_type": "slide"} # # Applied Machine Learning # # ## Linear Models # + [markdown] slideshow={"slide_type": "slide"} # ### Recap # # - We have some dataset # - We identify the problem and define the loss function # - Then we minimize the total loss (empirical risk, or objective) using available (training) data # - We vary parameters to minimize the objective function # - The minimizing parameters are then used to predict unknown values # + [markdown] slideshow={"slide_type": "slide"} # ### A text classification problem # # Lets consider the **20 newsgroups** dataset: # - from sklearn.datasets import fetch_20newsgroups data = fetch_20newsgroups() text, label = data['data'][0], data['target_names'][data['target'][0]] print(label) print('----') print(text[:300]) # + [markdown] slideshow={"slide_type": "slide"} # ### A linear model for classification # # Let us consider a function that tells if the `text` comes from `rec.autos` # - weight('car')*has('car', text) + weight('dog')*has('dog', text) + rest # Alternatively say `car` is `0` and `dog` is `1`: weight(0)*feature(0) + weight(1)*feature(1) + rest # + [markdown] slideshow={"slide_type": "-"} # How do we find those `weight` ($w$) for all the words? # + [markdown] slideshow={"slide_type": "slide"} # ### Gradient Descent # # - Last time we used `opt.fmin` and it magically found the solution # - The method is simple though # - Start with random weights $w_0$ # - Iterate: $w_{i+1} = w_{i} - \alpha \times \nabla \mathsf{objective}(w_i)$ # - All we need to know is the gradient of objective # + [markdown] slideshow={"slide_type": "slide"} # ### Gradient of loss # # - Last time we considered a regression problem and used $(y-p)^2$ # - The gradient w.r.t $p$ is obvious: $- 2 (y - p)$ # + [markdown] slideshow={"slide_type": "slide"} # ### Gradient check # # How can we ensure the gradient is correct? # + def loss(y, p): return (y-p)**2 def gradient(y, p): return -2*(y-p) p = 0.1 y = 0.3 eps = 0.001 gradient(y, p), (loss(y, p+eps) - loss(y, p-eps)) / (2*eps) # + [markdown] slideshow={"slide_type": "slide"} # ### Gradient descent in code # + current_p = random.random() alpha = 0.1 for i in range(5): current_p = current_p - alpha*gradient(y, current_p) print(i, current_p) # + current_p = random.random() alpha = 0.1 xs = list(range(20)) ys = [] for _ in xs: current_p = current_p - alpha*gradient(y, current_p) ys.append(current_p) plt.plot(xs, ys); plt.hlines(y, xs[0], xs[-1]); # + [markdown] slideshow={"slide_type": "slide"} # ### Classification loss # # - We will use something called **logistic loss** # + def loss(y, p): return np.log2(1.0 + np.exp(-y*p)) loss(-1, -100.0), loss(-1, +100.0) # + [markdown] slideshow={"slide_type": "slide"} # ### Logistic Regression in sklearn # - model = sklearn.linear_model.SGDClassifier(loss='log', tol=1e-6) example_1 = [1,0]; label_1 = [1] example_2 = [0,1]; label_2 = [0] model.fit([example_1, example_2], np.ravel([label_1, label_2])) model.coef_ # + [markdown] slideshow={"slide_type": "slide"} # ### Overfitting # # - We can always come up with a model that fits data perfectly # - weight('[email protected]') # - For some reason that's not what we want. Why? # - First, we need to measure if such a thing happens # + [markdown] slideshow={"slide_type": "slide"} # ### Splitting the data # # - Obviously we should not test what we fit against # - We should fit (train) the model on some part of data # - Next, we check the model against the rest # + [markdown] slideshow={"slide_type": "slide"} # ### Leave-on-out # # - Generate as many samples as there are examples # - Gives you a good estimate if you don't have a lot of data # - Gets impractical on huge datasets # - loo = sklearn.model_selection.LeaveOneOut() for train, test in loo.split([1,2,3,4,5]): print(train, test) # + [markdown] slideshow={"slide_type": "slide"} # ### Cross validation # # - Split the dataset into a few (say 5) non-overlapping parts # - Four parts go to training data and one part goes to test data # - Do the above 5 times to train the model and test it # - Makes a decent way to *detect* overfitting # + [markdown] slideshow={"slide_type": "slide"} # ### Cross validation in sklearn # # Let's consider indices of data # - xval = sklearn.model_selection.KFold(n_splits=3) for train, test in xval.split([1,2,3,4,5,6]): print(train, test) # + [markdown] slideshow={"slide_type": "slide"} # ### This thing is an ill-posed problem # # - A mathematical problem is ill-posed when the solution is not unique # - That's exactly the case of regression/classification/... # - We need to make the problem well-posed: *regularization* # + [markdown] slideshow={"slide_type": "slide"} # ### Structural risk minimization # # - Structural risk is empirical risk plus regularizer # - Instead of minimizing empirical risk we find some tradeoff # - Regularizer is a function of model we get # - $\mathsf{objective} = \mathsf{loss} + \mathsf{regularizer}$ # + [markdown] slideshow={"slide_type": "slide"} # ### Regularizer # # - A functions that reflects the complexity of a model # - What is the complexity of a set of 'if ... then'? # - Not obvious for linear model but easy to invent something # + [markdown] slideshow={"slide_type": "slide"} # ### $\ell_1$ regularizer # # - Derivative is const # - Forces weight to be zero if it doesn't hurt performance much # - Use if you believe some features are useless # - classification_model = sklearn.linear_model.SGDClassifier(loss='log', penalty='l1'); regression_model = sklearn.linear_model.SGDRegressor(penalty='l1'); # + [markdown] slideshow={"slide_type": "slide"} # ### $\ell_2$ regularizer # # - Derivative is linear # - Forces weights to get *similar* magnitude if it doesn't hurt performance much # - Use if you believe all features are more or less important # - classification_model = sklearn.linear_model.SGDClassifier(loss='log', penalty='l2'); regression_model = sklearn.linear_model.SGDRegressor(penalty='l2'); # + [markdown] slideshow={"slide_type": "slide"} # ### Elastic net # # - Just a weighted sum of $\ell_1$ and $\ell_2$ regularizers # - An attempt to get useful properties of both # - regression_model = sklearn.linear_model.SGDRegressor(penalty='elasticnet') # + [markdown] slideshow={"slide_type": "slide"} # ### Limitations of linearity # # - In low-dimensional spaces linear models are not very 'powerful' (can we define that?) # - The higher dimensionality, the more powerful linear model becomes # + [markdown] slideshow={"slide_type": "slide"} # ### Sparse features # # - We say features are sparse when most of the values are zero # - Examples: visited hosts, movies that user liked, ... # - Sparse features are efficient in high-dimensional setting # - [0, 0, ..., 1, ..., 0, 0, 1, 0]; # + [markdown] slideshow={"slide_type": "slide"} # ### One hot encoding, hashing trick # # - One way to encode categorical things like visited hosts # - We enumerate all the hosts # - We put 1 to position of every host, 0 otherwise # - Hashing trick: instead of enumerating them just hash # - print(hash('hse.ru')) print(hash('hse.ru') % 2**16) # + [markdown] slideshow={"slide_type": "slide"} # ### Hashing vectorizer in sklearn # + from sklearn.feature_extraction.text import HashingVectorizer vectorizer = HashingVectorizer(n_features=10, binary=True) features = vectorizer.fit_transform(['hello there', 'hey there']) print(features.todense()) # + [markdown] slideshow={"slide_type": "slide"} # ### When do we use linear models? # # - It is definitely the first thing to try if you have some text data # - In general a good choice for any sparse data # - This approach is pretty much the fastest one # - Even if some method outperforms, you still get a good baseline # + [markdown] slideshow={"slide_type": "slide"} # ### Self-assesment questions # # 1. You noticed that your linear model learned a weight of **95.3** for the word `the`. *Is there a problem? [Y]/N* # 2. The train loss is **0.43** and the test loss is **0.39**. *Is it an example of ..? [a) overfitting] b) underfitting c) I don't know* # 3. You've got asically infinite amounts of data. *Do you have to use regularization? Y/N* # 4. You believe your dataset is pretty noisy and some features are broken. *You use a) L1 b) L2 c) no regularization* # 5. Why do we hash words? *a) it's simpler b) it's faster c) it's more reliable* # + [markdown] slideshow={"slide_type": "slide"} # ### Homework 1 # # - No score, just has to be done # - Load dataset, create linear model, train, and explain the results # - The template is provided: `HSE-AML-HW1.ipynb` # - Hint: check the code examples for `KFold`, `HashingVectorizer`, `LogisticRegression`
9,598
/f1_standings_project.ipynb
28506a4fe696135d6ef50ce9288b614edef02884
[]
no_license
andrewsloan/Formula-One-Standings
https://github.com/andrewsloan/Formula-One-Standings
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
14,977
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ### Importing all the proper tools and retrieving data from API # + import pandas as pd import requests import json from IPython.display import clear_output import os url = 'http://ergast.com/api/f1/2018/21/driverStandings.json' json_data = requests.get(url).json() driver_standings = json_data["MRData"]["StandingsTable"]["StandingsLists"][0]["DriverStandings"] #api data is stored in driver_standings # - # ### The data necessary to construct the dataframe is stored in various datatypes within the API. I created a class to organize the data in a manner from which we can create the dataframe with all items. # + class Driver_info: def __init__(self, sub_data, category): self.list_name = [] self.sub_data = sub_data self.category = category def construct_list(self): if self.sub_data == "Driver": for i in driver_standings: self.list_name.append(i[self.sub_data][self.category]) # Driver information is a dictionary within DriverStandings elif self.sub_data == "Constructors": for i in driver_standings: self.list_name.append(i[self.sub_data][0][self.category]) # Constructor information is a list within a dictionary within DriverStandings else: for i in driver_standings: self.list_name.append(int(i[self.category])) # all else is within the main DriverStandings dictionary first_name = Driver_info('Driver', 'givenName') first_name.construct_list() last_name = Driver_info('Driver', 'familyName') last_name.construct_list() dob = Driver_info('Driver', 'dateOfBirth') dob.construct_list() nationality = Driver_info('Driver', 'nationality') nationality.construct_list() constructor = Driver_info('Constructors', 'name') constructor.construct_list() position = Driver_info('n/a', 'position') position.construct_list() points = Driver_info('n/a', 'points') points.construct_list() d = {'First Name': first_name.list_name, 'Last Name': last_name.list_name, 'DOB': dob.list_name, 'Nationality': nationality.list_name, 'Constructor': constructor.list_name, 'Position': position.list_name, 'Points': points.list_name } #d represents all the data to be included in the dataframe # - # ### The user will sort the data in a manner of their choice. The sort_standings class assures the data is sorted in a proper manner. class Sort_standings: def sort_action (self): sorter = input('How would you like to sort the standings?\n(First Name, Last Name, DOB, Nationality, Constructor, Points) ') os.system('cls' if os.name == 'nt' else 'clear') clear_output() while sorter not in df.columns: print (f"\n'{sorter}' is not valid. Please enter term in proper format.") sorter = input('\nHow would you like to sort the standings?\n(First Name, Last Name, DOB, Nationality, Constructor, Points) ') os.system('cls' if os.name == 'nt' else 'clear') clear_output() return sorter # ### The Dataframe df = pd.DataFrame(data=d) df.set_index("Position", inplace=True) sort_standings = Sort_standings() sort_by = sort_standings.sort_action() df = df.sort_values(sort_by, ascending=False if sort_by == 'Points' else True) #Points will be most to least #Oldest to youngest for DOB #All others are alphabetical df boston.feature_names boston_tensor = torch.from_numpy(boston.data) boston_tensor.size() boston_tensor[:2] boston_tensor[:10,:5] # ### 3d- tensor # + from PIL import Image panda = np.array(Image.open('../data/images/panda.jpg').resize((224,224))) panda_tensor = torch.from_numpy(panda) panda_tensor.size() # - plt.imshow(panda); # ### Slicing Tensor sales = torch.FloatTensor([1000.0,323.2,333.4,444.5,1000.0,323.2,333.4,444.5]) sales[:5] sales[:-5] plt.imshow(panda_tensor[:,:,0].numpy()); plt.imshow(panda_tensor[25:175,60:130,0].numpy()); # ### Select specific element of tensor #torch.eye(shape) produces an diagonal matrix with 1 as it diagonal #elements. sales = torch.eye(3,3) sales[0,1] # ### 4D Tensor from glob import glob #Read cat images from disk data_path='/Users/vishnu/Documents/fastAIPytorch/fastai/courses/dl1/data/dogscats/train/cats/' cats = glob(data_path+'*.jpg') #Convert images into numpy arrays cat_imgs = np.array([np.array(Image.open(cat).resize((224,224))) for cat in cats[:64]]) cat_imgs = cat_imgs.reshape(-1,224,224,3) cat_tensors = torch.from_numpy(cat_imgs) cat_tensors.size() # ### Tensor addition and multiplication # + #Various ways you can perform tensor addition a = torch.rand(2,2) b = torch.rand(2,2) c = a + b d = torch.add(a,b) #For in-place addition a.add_(5) #Multiplication of different tensors a*b a.mul(b) #For in-place multiplication a.mul_(b) # - # ### On GPU # + a = torch.rand(10000,10000) b = torch.rand(10000,10000) a.matmul(b) #Time taken : 3.23 s # - #Move the tensors to GPU a = a.cuda() b = b.cuda() a.matmul(b) #Time taken : 11.2 µs # ### Variables from torch.autograd import Variable x = Variable(torch.ones(2,2),requires_grad=True) y = x.mean() y.backward() x.grad x.grad_fn x.data y.grad_fn # ### Create data for our neural network def get_data(): train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, 7.042,10.791,5.313,7.997,5.654,9.27,3.1]) train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, 2.827,3.465,1.65,2.904,2.42,2.94,1.3]) dtype = torch.FloatTensor X = Variable(torch.from_numpy(train_X).type(dtype),requires_grad=False).view(17,1) y = Variable(torch.from_numpy(train_Y).type(dtype),requires_grad=False) return X,y # ### Create learnable parameters def get_weights(): w = Variable(torch.randn(1),requires_grad = True) b = Variable(torch.randn(1),requires_grad=True) return w,b # ### Implement Neural Network def simple_network(x): y_pred = torch.matmul(x,w)+b return y_pred # ### Implement Neural Network in Pytorch import torch.nn as nn f = nn.Linear(17,1) # Much simpler. f # ### Implementing Loss Function def loss_fn(y,y_pred): loss = (y_pred-y).pow(2).sum() for param in [w,b]: if not param.grad is None: param.grad.data.zero_() loss.backward() return loss.data[0] # + ### Implementing Optimizer # - def optimize(learning_rate): w.data -= learning_rate * w.grad.data b.data -= learning_rate * b.grad.data # ## Loading Data # ### Defining Dataset from torch.utils.data import Dataset class DogsAndCatsDataset(Dataset): def __init__(self,): pass def __len__(self): pass def __getitem__(self,idx): pass class DogsAndCatsDataset(Dataset): def __init__(self,root_dir,size=(224,224)): self.files = glob(root_dir) self.size = size def __len__(self): return len(self.files) def __getitem__(self,idx): img = np.asarray(Image.open(self.files[idx]).resize(self.size)) label = self.files[idx].split('/')[-2] return img,label # ### Defining DataLoader to iterate over Dogs and Cats Dataset # + from torch.utils.data import Dataset, DataLoader dataloader = DataLoader(DogsAndCatsDataset,batch_size=32,num_workers=2) for imgs , labels in dataloader: #Apply your DL on the dataset. pass # - subplot(1, 5, 1); plt.imshow(train_img[4].reshape(28,28), cmap = plt.cm.gray, interpolation='nearest', clim=(0, 255)); plt.xlabel('784 Components', fontsize = 12) plt.title('Original Image', fontsize = 14); # 331 principal components plt.subplot(1, 5, 2); plt.imshow(explainedVariance(.99, train_img)[4].reshape(28, 28), cmap = plt.cm.gray, interpolation='nearest', clim=(0, 255)); plt.xlabel('331 Components', fontsize = 12) plt.title('99% of Explained Variance', fontsize = 14); # 154 principal components plt.subplot(1, 5, 3); plt.imshow(explainedVariance(.95, train_img)[4].reshape(28, 28), cmap = plt.cm.gray, interpolation='nearest', clim=(0, 255)); plt.xlabel('154 Components', fontsize = 12) plt.title('95% of Explained Variance', fontsize = 14); # 87 principal components plt.subplot(1, 5, 4); plt.imshow(explainedVariance(.90, train_img)[4].reshape(28, 28), cmap = plt.cm.gray, interpolation='nearest', clim=(0, 255)); plt.xlabel('87 Components', fontsize = 12) plt.title('90% of Explained Variance', fontsize = 14); # 59 principal components plt.subplot(1, 5, 5); plt.imshow(explainedVariance(.85, train_img)[4].reshape(28, 28), cmap = plt.cm.gray, interpolation='nearest', clim=(0, 255)); plt.xlabel('59 Components', fontsize = 12) plt.title('85% of Explained Variance', fontsize = 14); # - # ## PCA to Speed up Machine Learning Algorithms (Logistic Regression) # Mention how long it takes for me to run classification with 99, 95, 90, 85 (maybe make a table). Go that PCA is not necessary in every data science workflow # # # Need to put the steps for applying PCA for machine learning applications # 1. Fit PCA on training set. <b>Note: we are fitting PCA on the training set only</b> # 2. Apply the mapping (transform) to both the training set and the test set. # 3. Train your machine learning algorithm (in this case logistic regression) on the transformed training set # 4. Test your machine learning algorithm on the transformed test set. # # [Logistic Regression Sklearn Documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) <br> # One thing I like to mention is the importance of parameter tuning. While it may not have mattered much for the toy digits dataset, it can make a major difference on larger and more complex datasets you have. <b>Please see the parameter: solver (if you think the algorithm is too slow)</b> # <b>Step 1: </b> Import the model you want to use # In sklearn, all machine learning models are implemented as Python classes from sklearn.linear_model import LogisticRegression # <b>Step 2:</b> Make an instance of the Model # <b>time it on my computer with and without PCA for viewers benefit</b> # all parameters not specified are set to their defaults # default solver is incredibly slow thats why we change it # solver = 'lbfgs' logisticRegr = LogisticRegression() # <b>Step 3:</b> Training the model on the data, storing the information learned from the data # Model is learning the relationship between x (digits) and y (labels) logisticRegr.fit(train_img_PCA, train_lbl) # <b>Step 4:</b> Predict the labels of new data (new images) # Uses the information the model learned during the model training process # Returns a NumPy Array # Predict for One Observation (image) logisticRegr.predict(test_img_PCA[0].reshape(1,-1)) # Predict for Multiple Observations (images) at Once logisticRegr.predict(test_img_PCA[0:10]) # ## Measuring Model Performance # accuracy (fraction of correct predictions): correct predictions / total number of data points # Basically, how the model performs on new data (test set) # (maybe look into F1 score with this just to change it up a bit, dont want viewers to think accuracy is only useful metric) score = logisticRegr.score(test_img_PCA, test_lbl) print(score) # http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html or F1
11,740
/AppliedDataScienceCapstone - Part2.ipynb
4900bf19fef618944ac8731c20a478e54983e144
[]
no_license
rycooley57/Coursera_Capstone
https://github.com/rycooley57/Coursera_Capstone
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
15,646
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: myenv # language: python # name: myenv # --- # + from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf import glob import matplotlib.pyplot as plt import numpy as np import os import PIL from scipy import misc from tensorflow.keras import layers import time from IPython import display # To generate GIFs import imageio print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) tf.debugging.set_log_device_placement(True) # + #get training data noise to 200, Epoch to 90, kernel initializer #(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data() BATCH_SIZE = 256 #256 #64 IMG_HEIGHT = 76 #32 #76 #152 #218 IMG_WIDTH = 64 #28 #64 #124 #178 BUFFER_SIZE = 60000 #NOTE: IN TENSOR FLOW [batch,height,width,channel] height comes before width def decode_img(img): # convert the compressed string to a 3D uint8 tensor img = tf.image.decode_jpeg(img, channels=3) # Use `convert_image_dtype` to convert to floats in the [0,1] range. #img = tf.image.convert_image_dtype(img, tf.float32) img = tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH]) img = tf.dtypes.cast(img, tf.float32) img = (img - 127.5) / 127.5 # resize the image to the desired size. return img#tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH]) def process_path(file_path): # load the raw data from the file as a string img = tf.io.read_file(file_path) # decode the image img = decode_img(img) return img img_files = glob.glob('training_faces/*.jpg')[:BUFFER_SIZE] dataset = tf.data.Dataset.from_tensor_slices(img_files) # Set `num_parallel_calls` so multiple images are loaded/processed in parallel. AUTOTUNE = tf.data.experimental.AUTOTUNE processed_dataset = dataset.map(process_path, num_parallel_calls=AUTOTUNE) for f in processed_dataset.take(1): print(f.numpy().shape) # + #[batch_size, height, width, color_channels] #train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32') # Normalize the images to [-1, 1] not sure why, we'll try 0 and 1 and see the difference #train_images = (train_images - 127.5) / 127.5 # Batch and shuffle the data #train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE) def prepare_for_training(ds, cache=True, shuffle_buffer_size=1000): # If this is a small dataset, it only loads it once, and keeps it in memory. # we use `.cache(filename)` to cache preprocessing work for datasets that don't # fit in memory. if cache: if isinstance(cache, str): ds = ds.cache(cache) else: ds = ds.cache() ds = ds.shuffle(buffer_size=shuffle_buffer_size) # Repeat forever #ds = ds.repeat() ds = ds.batch(BATCH_SIZE) # `prefetch` lets the dataset fetch batches in the background while the model # is training. #ds = ds.prefetch(buffer_size=AUTOTUNE) return ds train_dataset = prepare_for_training(processed_dataset,shuffle_buffer_size=BUFFER_SIZE) image_batch = next(iter(train_dataset)) plt.imshow( (image_batch.numpy()[0] + 1) / 2.0 ) # + NOISE_SHAPE = 300 init = tf.random_normal_initializer(mean=0.0, stddev=0.02) def generator_model(): model = tf.keras.Sequential() model.add(layers.Dense(19*16*256, use_bias=False, input_shape=(NOISE_SHAPE,), kernel_initializer=init)) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Reshape((19, 16, 256))) assert model.output_shape == (None, 19, 16, 256) # Note: None is the batch size model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False, kernel_initializer=init)) assert model.output_shape == (None, 19, 16, 128) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False, kernel_initializer=init)) assert model.output_shape == (None, 38, 32, 64) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh', kernel_initializer=init)) assert model.output_shape == (None, 76, 64, 3) return model generator = generator_model() generator.summary() noise = tf.random.normal([1, NOISE_SHAPE]) generated_image = generator(noise, training=False) plt.imshow((generated_image[0] + 1) / 2.0) # + init = tf.random_normal_initializer(mean=0.0, stddev=0.02) def discriminator_model(): model = tf.keras.Sequential() model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=[76, 64, 3], kernel_initializer=init)) model.add(layers.LeakyReLU()) model.add(layers.Dropout(0.3)) model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same', kernel_initializer=init)) model.add(layers.LeakyReLU()) model.add(layers.Dropout(0.3)) model.add(layers.Flatten()) model.add(layers.Dense(1)) return model discriminator = discriminator_model() decision = discriminator(generated_image) print (decision) # + #smoothing class=1 to [0.7, 1.2] def smooth_positive_labels(label): return label - 0.3 + (tf.random.uniform(label.shape) * 0.5) #smoothing class=0 to [0.0, 0.3] def smooth_negative_labels(label): return label + (tf.random.uniform(label.shape) * 0.3) # This method returns a helper function to compute cross entropy loss cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True) def discriminator_loss(real_output, fake_output): real_label = tf.ones_like(real_output) fake_label = tf.zeros_like(fake_output) real_loss = cross_entropy(smooth_positive_labels(real_label), real_output) fake_loss = cross_entropy(smooth_negative_labels(fake_label), fake_output) total_loss = real_loss + fake_loss return total_loss def generator_loss(fake_output): return cross_entropy(tf.ones_like(fake_output), fake_output) generator_optimizer = tf.keras.optimizers.Adam(1e-4) discriminator_optimizer = tf.keras.optimizers.Adam(1e-4) # - checkpoint_dir = './training_checkpoints' checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer, discriminator_optimizer=discriminator_optimizer, generator=generator, discriminator=discriminator) manager = tf.train.CheckpointManager(checkpoint,checkpoint_dir, max_to_keep=3, checkpoint_name='ckpt') # + EPOCHS = 190 noise_dim = NOISE_SHAPE num_examples_to_generate = 9 # We will reuse this seed overtime (so it's easier) # to visualize progress in the animated GIF) seed = tf.random.normal([num_examples_to_generate, noise_dim]) imgs_dir = './epoch_images/' # + # This annotation causes the function to be "compiled". @tf.function def train_step(images): noise = tf.random.normal([BATCH_SIZE, noise_dim]) with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape: generated_images = generator(noise, training=True) real_output = discriminator(images, training=True) fake_output = discriminator(generated_images, training=True) gen_loss = generator_loss(fake_output) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) def train(dataset, epochs): for epoch in range(140,epochs): start = time.time() batch_count = 0 for image_batch in dataset: train_step(image_batch) batch_count += image_batch.numpy().shape[0] display.clear_output(wait=True) print('Batches processed {0}'.format(batch_count)) print('Epoch: {0}'.format(epoch + 1)) # Produce images for the GIF as we go display.clear_output(wait=True) generate_and_save_images(generator, epoch + 1, seed) # Save the model every 10 epochs if (epoch + 1) % 10 == 0: manager.save() print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start)) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_images(generator, epochs, seed) def generate_and_save_images(model, epoch, test_input): # `training` is set to False. # This is so all layers run in inference mode (batchnorm). predictions = model(test_input, training=False) fig = plt.figure(figsize=(3,3)) for i in range(predictions.shape[0]): plt.subplot(3, 3, i+1) plt.imshow( (predictions[i] + 1) / 2.0) plt.axis('off') plt.savefig(imgs_dir+'image_at_epoch_{:04d}.png'.format(epoch)) plt.show() # + checkpoint.restore(manager.latest_checkpoint) if manager.latest_checkpoint: print("Restored from {}".format(manager.latest_checkpoint)) else: print("Initializing from scratch.") train(train_dataset, EPOCHS) # + anim_file = 'dcgan.gif' with imageio.get_writer(anim_file, mode='I') as writer: filenames = glob.glob(imgs_dir+'image*.png') filenames = sorted(filenames) last = -1 for i,filename in enumerate(filenames): frame = 2*(i**0.5) if round(frame) > round(last): last = frame else: continue image = imageio.imread(filename) writer.append_data(image) image = imageio.imread(filename) writer.append_data(image) import IPython if IPython.version_info > (6,2,0,''): display.Image(filename=anim_file) # - tf.saved_model.save(generator, "./models")
10,279
/AutoGluon+NVDIA_Rapids.ipynb
cfd41945d7f37e76b2a65ddc11d835cf3ab84496
[]
no_license
swarnava-96/NVDIA-RAPIDS
https://github.com/swarnava-96/NVDIA-RAPIDS
0
0
null
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Jupyter Notebook
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.py
789,210
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="scfLT2i0MLyD" # # AutoGluon with NVDIA Rapids # # + id="B0C8IV5TQnjN" colab={"base_uri": "https://localhost:8080/"} outputId="4c8aea0b-95bb-4d0e-a4f8-2fdb31da1ea7" # !nvidia-smi # + id="3Jeh6EJBaBkv" colab={"base_uri": "https://localhost:8080/"} outputId="ef5c1588-5b84-47bf-e52f-b1f4fb9dae84" # This get the RAPIDS-Colab install files and test check your GPU. Run this and the next cell only. # Please read the output of this cell. If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps. # !git clone https://github.com/rapidsai/rapidsai-csp-utils.git # !python rapidsai-csp-utils/colab/env-check.py # + id="JI7UTXbhaBon" colab={"base_uri": "https://localhost:8080/"} outputId="c4d0477b-b3f3-43e3-ef2b-9deeab27b8c7" # This will update the Colab environment and restart the kernel. Don't run the next cell until you see the session crash. # !bash rapidsai-csp-utils/colab/update_gcc.sh import os os._exit(00) # + id="qg2SasWKaBsB" colab={"base_uri": "https://localhost:8080/"} outputId="051faf4b-cf63-4ba9-b8fc-04e4976c51fd" # This will install CondaColab. This will restart your kernel one last time. Run this cell by itself and only run the next cell once you see the session crash. import condacolab condacolab.install() # + id="fKSMDrN_aB-v" colab={"base_uri": "https://localhost:8080/"} outputId="5e5dd88f-3b0d-4740-b93f-831584e0e6d3" # you can now run the rest of the cells as normal import condacolab condacolab.check() # + id="m0jdXBRiDSzj" colab={"base_uri": "https://localhost:8080/"} outputId="4beba58c-9efe-44f7-d5d6-621b432fc346" # Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py <release> <packages>' # The <release> options are 'stable' and 'nightly'. Leaving it blank or adding any other words will default to stable. # The <packages> option are default blank or 'core'. By default, we install RAPIDSAI and BlazingSQL. The 'core' option will install only RAPIDSAI and not include BlazingSQL, # !python rapidsai-csp-utils/colab/install_rapids.py stable # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="_ks5dh89SCp5" outputId="51ac12e2-20e2-453d-bf3b-f760a9e224dd" # !pip install "autogluon.tabular[all]==0.1.1b20210312" # !pip install AutoViz # !pip install xlrd # + id="Q5wlr-Q7aDf1" import pandas as pd dfe = pd.read_csv('/content/titanic_train.csv') # + colab={"base_uri": "https://localhost:8080/"} id="cq9ycjkuf8ge" outputId="8fc4e094-58c6-4d5e-e382-99cf4f820375" from autoviz.AutoViz_Class import AutoViz_Class #Instantiate the AutoViz class AV = AutoViz_Class() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="tETBQD9hf_nD" outputId="ec3c9e4f-611d-4c99-9b04-ae2c4757baf6" dfe.head() # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="bp1QydiUgIxy" outputId="02eab9af-3f40-4967-dbbf-0aa4ee3c15ce" ftc = AV.AutoViz(filename='', sep ='' , depVar ='Survived', dfte = dfe, header = 0, verbose = 0, lowess = False, chart_format ='png', max_rows_analyzed = 100000, max_cols_analyzed = 30 ) # + id="9z3M248ygVOm" from autogluon.tabular import TabularDataset, TabularPredictor from autogluon.tabular.models.lr.lr_rapids_model import LinearRapidsModel from autogluon.tabular.models.knn.knn_rapids_model import KNNRapidsModel train_data = TabularDataset('/content/titanic_train.csv') test_data = TabularDataset('/content/titanic_test.csv') label = 'Survived' # + colab={"base_uri": "https://localhost:8080/", "height": 419} id="6f74y47egr_J" outputId="e2b42ff3-2483-4835-cb21-19567deb2615" train_data # + colab={"base_uri": "https://localhost:8080/"} id="nZIDrl43gxBr" outputId="544b3f65-34bd-4a7a-fb9e-97683b6a4ce8" #using integrated RAPIDS models along with some boosting models predictor = TabularPredictor( label=label, eval_metric='accuracy', learner_kwargs={'ignored_columns': ['PassengerId']} ).fit( train_data, presets='best_quality', hyperparameters={'XGB': {'ag_args_fit': {'num_gpus': 1}}, 'GBM': [{}, {'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, 'GBMLarge'], 'CAT': {'ag_args_fit': {'num_gpus': 1}}, KNNRapidsModel: {}, LinearRapidsModel: {}, }, ) # + colab={"base_uri": "https://localhost:8080/", "height": 419} id="pbmcEB_6g3My" outputId="d043b5e5-5a89-4d86-9697-0f0635413a25" test_data # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="z_6UoQ3vhVlm" outputId="da520943-d212-41ff-d0fa-807273efe8a3" import pandas as pd submission = test_data[['PassengerId']] test_pred_proba = predictor.predict(test_data) test_pred_proba=pd.DataFrame(test_pred_proba,columns=['Survived']) submission = pd.concat([submission, test_pred_proba], axis=1) submission.to_csv('submission.csv', index=False) submission.head() # + colab={"base_uri": "https://localhost:8080/", "height": 545} id="xApjbi9ehkHz" outputId="6d9939ef-e21c-4c60-9059-cd4e4ed8a604" predictor.leaderboard(silent=True)
5,385
/d02_task.ipynb
11fe22e72b9018f94277f2c7c009c0e7b43f34e2
[]
no_license
moscow-dust/Rosalind-C
https://github.com/moscow-dust/Rosalind-C
0
0
null
null
null
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Jupyter Notebook
false
false
.py
2,123,561
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import numpy as np import pandas as pd food=pd.read_csv('food.csv') food.head() food.shape food.pivot_table(index='City',columns='Gender',values='Spends') food.pivot_table(index=['City','Item'],columns=['Gender','Frequency'],values='Spends')
516
/Predict_0mode_curve.ipynb
1aeb057b342393851bb173d1c4da398241e48aa7
[]
no_license
imzhangtianyi/dispersion
https://github.com/imzhangtianyi/dispersion
2
0
null
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Jupyter Notebook
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.py
14,978
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # + import matplotlib.pyplot as plt import cPickle from dispersion_features import extract_features cls0 = cPickle.load(open('mode0_classifier.pkl', 'rb')) w = [] l = [] for i in range(1,901): name = 'a{}'.format(i) x = extract_features(name).zetas().values if cls0.predict(x).any(): w.append(extract_features(name).properties()['W'].values[0]) l.append(extract_features(name).properties()['L'].values[0]) plt.loglog(map(lambda x: x*100, l),w) plt.xlabel('Wave number [m$^-$$^1$]') plt.ylabel('Frequency [$s^-$$^1$]') plt.show() # -
831
/src/Ensemble (accuracy 0.74).ipynb
16a982332ffa58b13de3a6bfbe1646a483128053
[ "MIT" ]
permissive
yuchen996/Motion_Recognition-BidirectionalLSTM-DATA2040_Final_Project
https://github.com/yuchen996/Motion_Recognition-BidirectionalLSTM-DATA2040_Final_Project
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + colab={"base_uri": "https://localhost:8080/"} id="YQdMNNpbOM1x" outputId="09180f0f-2821-4780-a732-71026e13e8cc" # import os # import numpy as np # import shutil # import random # root_dir = 'final2040/HMDB_more/' # test_ratio = 0.20 # train_dir = 'final2040/HMDB_more/train2/' # test_dir = 'final2040/HMDB_more/test2/' # for cls in classes: # os.makedirs(train_dir + cls) # os.makedirs(test_dir + cls) # src = root_dir + cls # allFileNames = os.listdir(src) # np.random.shuffle(allFileNames) # train_FileNames, test_FileNames = np.split(np.array(allFileNames),[int(len(allFileNames)* (1 - test_ratio))]) # train_FileNames = [src+'/'+ name for name in train_FileNames.tolist()] # test_FileNames = [src+'/' + name for name in test_FileNames.tolist()] # print("*****************************") # print('Total files: ', len(allFileNames)) # print('Training: ', len(train_FileNames)) # print('Testing: ', len(test_FileNames)) # print("*****************************") # for name in train_FileNames: # shutil.copy(name, train_dir + cls) # for name in test_FileNames: # shutil.copy(name, test_dir + cls) # print("Copying Done!") # + colab={"base_uri": "https://localhost:8080/"} id="_wZMKaF-AmTM" outputId="665afd4d-ef9a-4052-e528-84267c7a360a" pip install keras-video-generators # + id="JRTzE66vB5cO" import os import glob import tensorflow as tf import numpy as np from tensorflow import keras from keras_video import VideoFrameGenerator from google.colab import drive # + colab={"base_uri": "https://localhost:8080/"} id="PaRw-ktwBYp6" outputId="818992c2-0de3-453b-928b-2ab8adc0b4a6" from google.colab import drive drive.mount("/content/gdrive") # + colab={"base_uri": "https://localhost:8080/"} id="8n_KhqlcBqvh" outputId="64876877-70eb-4ce8-fa2b-86d10508c955" # cd /content/gdrive/Shareddrives/ # + colab={"base_uri": "https://localhost:8080/"} id="mmapMt3hEhSH" outputId="6e2bb7bf-f699-4101-a209-cbc0500f22a4" for i in glob.glob('final2040/HMDB_more/test2/*'): print(i.split(os.path.sep)[3]) # + colab={"base_uri": "https://localhost:8080/"} id="4yONuZy5Asq_" outputId="6c772da6-82c5-4117-dd88-fecfa6833a91" # use sub directories names as classes classes = [i.split(os.path.sep)[3] for i in glob.glob('final2040/HMDB_more/test2/*')] classes.sort() print(classes) # some global params SIZE = 224 CHANNELS = 3 NBFRAME = 5 BS = 10 # pattern to get videos and classes glob_pattern='final2040/HMDB_more/test2/{classname}/*.avi' # for data augmentation data_aug = keras.preprocessing.image.ImageDataGenerator() # + colab={"base_uri": "https://localhost:8080/"} id="hRiit10lA7ro" outputId="d4c85ed6-d912-44cd-ba0d-25c59444f967" # Create video frame generator train = VideoFrameGenerator( classes=classes, glob_pattern=glob_pattern, nb_frames=NBFRAME, split=0.999, shuffle=True, batch_size=1, target_shape= (SIZE,SIZE), nb_channel=CHANNELS, transformation=data_aug, use_frame_cache=True) # + colab={"base_uri": "https://localhost:8080/"} id="Sin0zG2BWNrl" outputId="d56fb327-2b49-482e-8654-265cc311941e" #get validation data valid = train.get_validation_generator() # + id="J8jQt89vWaFe" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="0bf69758-631a-4609-815f-c93991703bc9" import keras_video.utils keras_video.utils.show_sample(train) # + id="cQo7Axy_iwbJ" colab={"base_uri": "https://localhost:8080/", "height": 395} outputId="eeb9ccb5-7fc0-4959-99cc-4b1b90f627e7" from tensorflow.keras.models import load_model INSHAPE=(NBFRAME,) + (SIZE, SIZE) + (CHANNELS,) nbout = len(classes) print(nbout) model = load_model("final2040/saved_model/51classes_0419_densenet.h5") model.summary() # + id="7MA2NsGVmopS" model.evaluate(valid) # + id="AfRbkcwIzLsL" colab={"base_uri": "https://localhost:8080/"} outputId="1ceb3cb6-36df-4154-9fe9-0de95c14096e" optimizer = keras.optimizers.SGD(0.001) model.compile( optimizer, 'categorical_crossentropy', metrics=['acc']) model.evaluate(valid) # + colab={"base_uri": "https://localhost:8080/"} id="WJlhXHPB2C5I" outputId="3c94d458-823a-4943-b2d0-01fd9a240ee4" # a, b = train[2] # a.shape # + colab={"base_uri": "https://localhost:8080/", "height": 35} id="X9CDlfMQ2UPs" outputId="ed733518-5dfa-44c6-b982-cfaa935e1082" res = model.predict(a) res = np.argmax(res) classes[res] # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="SGjhcQou2dbx" outputId="4d274acf-0092-4420-dc10-cc515b0ff7a5" import matplotlib.pyplot as plt for i in range(10): a, b = train[i] sequences = a labels = b rows = len(sequences) index = 1 res = model.predict(a) res = np.argmax(res) print("The predicted catogoy is: "+classes[res]) plt.figure(figsize=(10, 22*rows)) for batchid, sequence in enumerate(sequences): classid = np.argmax(labels[batchid]) classname = train.classes[classid] cols = len(sequence) for image in sequence: plt.subplot(rows, cols, index) plt.title(classname) plt.imshow(image) plt.axis('off') index += 1 plt.show() # + id="_uBNn1Xaq6tF" # import numpy as np # res = model.predict(train) # res = np.argmax(res, axis=1) # cls = [] # for i in res: # cls.append(classes[i]) # cls # + id="_rsm7P-pzFKP" # keras_video.utils.show_sample(train, index=0, random=False, row_width=22, row_height=5) # + id="SADaYMWdJlEq" colab={"base_uri": "https://localhost:8080/"} outputId="6cd93cac-a24e-4403-e752-615c2f81d9ce" # save model import os def save_model(model, name): model_name = '{}.h5'.format(name) save_dir = os.path.join(os.getcwd(), 'final2040/saved_model') # Save model and weights if not os.path.isdir(save_dir): os.makedirs(save_dir) model_path = os.path.join(save_dir, model_name) model.save(model_path) print('Saved trained model at %s ' % model_path) save_model(modelEns, 'Ensemble1_with2') # + id="V_G-EU_nS5ZM" # import matplotlib.pyplot as plt # acc = history.history['acc'] # val_acc = history.history['val_acc'] # loss = history.history['loss'] # val_loss = history.history['val_loss'] # epochs = range(len(acc)) # plt.plot(epochs, loss, label='Training loss') # plt.plot(epochs, val_loss, label='Validation loss') # plt.title('Training and validation loss') # plt.legend(loc=0) # #plt.figure() # plt.savefig('final2040/output_figures/25classes_tune_loss.png') # + id="z-5ns5UKeV7H" # plt.plot(epochs, acc, label='Training accuracy') # plt.plot(epochs, val_acc, label='Validation accuracy') # plt.title('Training and validation accuracy') # plt.legend(loc=0) # #plt.figure() # plt.savefig('final2040/output_figures/25classes_tune_acc.png') # + [markdown] id="n4O9fkFlpTtz" # ## Ensemble # + id="LXSWmwMspIWg" from sklearn.ensemble import VotingClassifier from sklearn.metrics import accuracy_score from tensorflow.keras.models import load_model def get_model(mod): if mod == 0: model = load_model("final2040/saved_model/51classes_0.68_mobilenet.h5") # elif mod == 1: # model = load_model("final2040/saved_model/alexnet-51class.h5") elif mod == 2: model = load_model("final2040/saved_model/51classes_0.72.h5") return model # def get_model(): # model = load_model("../input/resmodel/resmodel_3.h5") # return model clf1 = tf.keras.wrappers.scikit_learn.KerasClassifier( lambda: get_model(0), epochs=0, verbose=False) # res2_clf = tf.keras.wrappers.scikit_learn.KerasClassifier( # lambda: get_model(1), # epochs=0, # verbose=False) clf3 = tf.keras.wrappers.scikit_learn.KerasClassifier( lambda: get_model(2), epochs=0, verbose=False) for x in [clf1, clf3]: x._estimator_type = "classifier" voting = VotingClassifier( estimators=[('1', clf1), #('2', clf2), ('3', clf3)], voting='soft', flatten_transform=True) # for clf in (clf1, res2_clf, res3_clf, voting): # clf.fit(X_train, y_train) # y_pred = clf.predict(X_test) # print(clf.__class__.__name__, accuracy_score(y_test, y_pred)) voting.fit(train) # + colab={"base_uri": "https://localhost:8080/"} id="euHBghc9n9jD" outputId="dd70aca2-f944-447e-98fc-de25ddd61605" # save model import os def save_model(model, name): model_name = '{}.h5'.format(name) save_dir = os.path.join(os.getcwd(), 'final2040/saved_model') # Save model and weights if not os.path.isdir(save_dir): os.makedirs(save_dir) model_path = os.path.join(save_dir, model_name) model.save(model_path) print('Saved trained model at %s ' % model_path) save_model(model, '16classes_0417_0.8687') # + id="A8gipk2NxCnH" from tensorflow.keras.models import load_model def ensembleModels(models, model_input): # collect outputs of models in a list yModels=[model(model_input) for model in models] # averaging outputs yAvg=tf.keras.layers.average(yModels) # build model from same input and avg output modelEns = tf.keras.Model(inputs=model_input, outputs=yAvg, name='ensemble') return modelEns m1 = load_model("final2040/saved_model/51classes_0419_1.h5") m1._name = 'mob1' m2 = load_model("final2040/saved_model/51classes_0419_densenet.h5") m2._name = 'dense2' m3 = load_model("final2040/saved_model/51classes_0419_2.h5") m3._name = 'alex3' models = [m1, m2, m3] model_input = tf.keras.layers.Input(shape=models[0].input_shape[1:]) modelEns = ensembleModels(models, model_input) # + colab={"base_uri": "https://localhost:8080/"} id="GqZLdA0wnCJq" outputId="0e193d8e-e017-4cd0-895f-7e9af3687bed" m3.compile( tf.keras.optimizers.SGD(0.001), 'categorical_crossentropy', metrics=['acc']) m3.evaluate(valid) # + colab={"base_uri": "https://localhost:8080/"} id="QOWITMmp-30y" outputId="3b894712-aa6c-414e-c3e2-6eba1803b1e7" modelEns.compile( tf.keras.optimizers.SGD(0.001), 'categorical_crossentropy', metrics=['acc']) modelEns.evaluate(valid) # + colab={"base_uri": "https://localhost:8080/", "height": 737} id="UWyaTe3bFhBj" outputId="0e133739-4ea8-4ac7-f5e8-6c2996053be7" for i in range(10): a, b = valid[i] sequences = a labels = b rows = len(sequences) index = 1 res = modelEns.predict(a) res = np.argmax(res) print("The predicted catogoy is: "+classes[res]) plt.figure(figsize=(10, 22*rows)) for batchid, sequence in enumerate(sequences): classid = np.argmax(labels[batchid]) classname = train.classes[classid] cols = len(sequence) for image in sequence: plt.subplot(rows, cols, index) plt.title(classname) plt.imshow(image) plt.axis('off') index += 1 plt.show() # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="fPxgo4TYNAuV" outputId="87a32c1c-e640-4347-ddeb-747882796131" for i in range(10): a, b = valid[i] sequences = a labels = b rows = len(sequences) index = 1 res = model.predict(a) res = np.argmax(res) print("The predicted catogoy is: "+classes[res]) plt.figure(figsize=(10, 22*rows)) for batchid, sequence in enumerate(sequences): classid = np.argmax(labels[batchid]) classname = train.classes[classid] cols = len(sequence) for image in sequence: plt.subplot(rows, cols, index) plt.title(classname) plt.imshow(image) plt.axis('off') index += 1 plt.show()
12,034
/.ipynb_checkpoints/rnn-checkpoint.ipynb
d10cad5fcf15e1c1ac58af435dca8b34200734e8
[]
no_license
RayestGeeta/Stock-Predict
https://github.com/RayestGeeta/Stock-Predict
0
0
null
null
null
null
Jupyter Notebook
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false
.py
90,990
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # 导入包 import pandas as pd import torch from torch import nn import numpy as np import matplotlib.pyplot as plt from torch.utils.data import DataLoader,Dataset, TensorDataset # 读取数据 data = pd.read_csv('suning.csv', encoding = 'gbk') # 删除空值的行 data = data.drop(data[data['涨跌额'].str.contains('None')].index) # + # 把数据设置成统一的float数据类型 data["涨跌额"] = data['涨跌额'].astype("float") data["最高价"] = data['最高价'].astype("float") data["最高价"] = data['最高价'].astype("float") data["开盘价"] = data['开盘价'].astype("float") data["涨跌幅"] = data['涨跌幅'].astype("float") data["换手率"] = data['换手率'].astype("float") # 导入数据和预测值 datas = data[data.columns[4:7]].values labels = data[data.columns[3]].values # + # 将数据导入 torch的数据集数据类型 dataset = TensorDataset(torch.tensor(np.array(datas.reshape(-1, 3))[3000:]), torch.tensor(np.array(labels))[3000:]) # 将dataset导入dataloader(来进行批训练) dataloader = DataLoader(dataset, batch_size=len(dataset),shuffle=True, drop_last=False) class rnn(nn.Module): def __init__(self):#面向对象中的继承 super(rnn, self).__init__() # rnn 网络层 self.rnn = nn.RNN(1, 2,2) # 全连接层 self.linear = nn.Linear(3, 10) self.linear1 = nn.Linear(10,8) self.linear3 = nn.Linear(8,2) self.linear4 = nn.Linear(2,1) def forward(self,x): x1,_ = self.rnn(x.reshape(-1 ,3,1)) a,b,c = x1.shape out = self.linear4(x1.view(-1,c))#因为线性层输入的是个二维数据,所以此处应该将lstm输出的三维数据x1调整成二维数据,最后的特征维度不能变 out1 = out.view(a,b,-1)#因为是循环神经网络,最后的时候要把二维的out调整成三维数据,下一次循环使用 # 全连接层 out = self.linear(x) out = self.linear1(out) out = self.linear3(out) out = self.linear4(out) return out # 构建模型 rnn = rnn() print(rnn) # 设定优化器和误差函数 optimizer = torch.optim.Adam(rnn.parameters(), lr=0.02) # optimize all cnn parameters loss_func = nn.MSELoss() # 一共运行20轮 for epoch in range(20): # 从dataloader读取数据 for step,(b_x, b_y) in enumerate(dataloader): prediction = rnn(b_x.float()) # rnn的输出 loss = loss_func(prediction, b_y.float()) # 计算误差 #print(loss.data.numpy()) optimizer.zero_grad() # 梯度清0 loss.backward() # 误差反向传播 optimizer.step() # 误差更新 # 画图 plt.plot(rnn(b_x.float()).view(-1).data.numpy()[:50]) plt.plot(b_y[:50].data.numpy()) # - plt.plot(rnn(b_x.float()).view(-1).data.numpy()[:50]) plt.plot(b_y[:50].data.numpy()) plt.title('Predict') plt.xlabel('date') plt.ylabel('price') plt.savefig('rnn.png')
2,828
/examples/notebooks/qgis_layer_style_file.ipynb
bb8e0f56b3d4a851da0beb7bd575228a208c6542
[ "MIT" ]
permissive
luxizhou/geemap
https://github.com/luxizhou/geemap
0
0
null
null
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # <a href="https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/qgis_layer_style_file.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a> # # Uncomment the following line to install [geemap](https://geemap.org) if needed. # + # # !pip install geemap # - import os import geemap # ## Create a QGIS Layer Style File for NLCD data out_nlcd_qml = os.path.join(os.path.expanduser('~/Downloads'), 'nlcd_style.qml') geemap.create_nlcd_qml(out_nlcd_qml) # ## Create a QGIS Layer Style File from an Earth Engine color table # # Separated by Tab ee_class_table = """ Value Color Description 0 1c0dff Water 1 05450a Evergreen needleleaf forest 2 086a10 Evergreen broadleaf forest 3 54a708 Deciduous needleleaf forest 4 78d203 Deciduous broadleaf forest 5 009900 Mixed forest 6 c6b044 Closed shrublands 7 dcd159 Open shrublands 8 dade48 Woody savannas 9 fbff13 Savannas 10 b6ff05 Grasslands 11 27ff87 Permanent wetlands 12 c24f44 Croplands 13 a5a5a5 Urban and built-up 14 ff6d4c Cropland/natural vegetation mosaic 15 69fff8 Snow and ice 16 f9ffa4 Barren or sparsely vegetated 254 ffffff Unclassified """ out_qml = os.path.join(os.path.expanduser('~/Downloads'), 'image_style.qml') geemap.vis_to_qml(ee_class_table, out_qml)
1,656
/notebooks/pub/JS_Divergence_B1083_CombinedPopulations-Shuffle_Final.ipynb
a22d1901feb121d5cb37e9b52620398db7b1d46e
[]
no_license
theilmbh/NeuralTDA
https://github.com/theilmbh/NeuralTDA
5
2
null
2020-03-09T16:53:58
2019-12-28T13:25:18
Jupyter Notebook
Jupyter Notebook
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.py
384,287
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + ### # The goal of this notebook is to: # - Take two neural populations # - Compute the JS divergence between stimuli pairs for each population (the same stimuli pairs) # - Compute the mutual information between the distributions of JS divergences import glob import os from importlib import reload import pickle import datetime import numpy as np import scipy as sp import pandas as pd import h5py as h5 from tqdm import tqdm_notebook as tqdm import matplotlib.pyplot as plt # %matplotlib inline import neuraltda.topology2 as tp2 import neuraltda.spectralAnalysis as sa import neuraltda.simpComp as sc import pycuslsa as pyslsa daystr = datetime.datetime.now().strftime('%Y%m%d') figsavepth = '/home/brad/DailyLog/'+daystr+'/' print(figsavepth) # + # Set up birds and block_paths birds = ['B1083', 'B1056', 'B1235', 'B1075'] bps = {'B1083': '/home/brad/krista/B1083/P03S03/', 'B1075': '/home/brad/krista/B1075/P01S03/', 'B1235': '/home/brad/krista/B1235/P02S01/', 'B1056': '/home/brad/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/', 'B1056': '/home/brad/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/', 'B1083-5': '/home/brad/krista/B1083/P03S05/'} learned_stimuli = {'B1083': ['M_scaled_burung', 'N_scaled_burung', 'O_scaled_burung', 'P_scaled_burung'], 'B1056': ['A_scaled_burung', 'B_scaled_burung', 'C_scaled_burung', 'D_scaled_burung'], 'B1235': [], 'B1075': []} peck_stimuli = {'B1083': {'L': ['N_40k','P_40k'], 'R': ['M_40k', 'O_40k']}, 'B1056': {'L': ['B_scaled_burung', 'D_scaled_burung'], 'R': ['A_scaled_burung', 'C_scaled_burung']}, 'B1235': {'L': ['F_scaled_burung', 'H_scaled_burung'], 'R': ['E_scaled_burung', 'G_scaled_burung'],}, 'B1075': {'L': ['F_40k', 'H_40k'], 'R': ['E_40k', 'G_40k']}, 'B1083-5': {'L': ['N_40k','P_40k'], 'R': ['M_40k', 'O_40k']}} unfamiliar_stimuli = {'B1083': ['I_40k', 'J_40k', 'K_40k', 'L_40k'], 'B1083-5': ['I_40k', 'J_40k', 'K_40k', 'L_40k'], 'B1235': ['A_scaled_burung', 'B_scaled_burung', 'C_scaled_burung', 'D_scaled_burung'], 'B1075': ['A_40k', 'B_40k', 'C_40k', 'D_40k'], 'B1056': ['E_scaled_burung', 'F_scaled_burung', 'G_scaled_burung', 'H_scaled_burung'] } #bps = {'B1056': '/home/AD/btheilma/krista/B1056/klusta/phy020516/Pen01_Lft_AP100_ML1300__Site03_Z2500__B1056_cat_P01_S03_1/', # 'B1235': '/home/AD/btheilma/krista/B1235/P02S01/'} #test_birds = ['B1056', 'B1235'] #test_birds = ['B1075', 'B1235'] #test_birds = ['B1056', 'B1235'] #test_birds =['B1056', 'B1083'] #test_birds = ['B1083'] #test_birds = ['B1083', 'B1083-5'] #test_birds = ['B1056', 'B1235', 'B1083', 'B1083-5'] #test_birds = ['B1056'] test_birds = ['B1083', 'B1083-5'] # Binning Parameters windt = 10.0 # milliseconds dtovr = 0.5*windt # milliseconds segment_info = [0, 0] # use full Trial cluster_group = ['Good'] # use just good clusters comment = 'JS_MI_BTWNPOP' # BootStrap Populations bdfs = {} # Dictionary to store bdf # - # Loop through each bird in our list and bin the data for bird in test_birds: block_path = bps[bird] bfdict = tp2.dag_bin(block_path, windt, segment_info, cluster_group=cluster_group, dt_overlap=dtovr, comment=comment) bdf = glob.glob(os.path.join(bfdict['raw'], '*.binned'))[0] print(bdf) bdfs[bird] = bdf # + # extract left vs right stims # extract population tensors for the populations of interest # Do not sort the stims population_tensors_familiar = {} stimuli = [] for bird in test_birds: stimuli = peck_stimuli[bird]['L'] + peck_stimuli[bird]['R'] print(stimuli) bdf = bdfs[bird] population_tensors_familiar[bird] = [] # open the binned data file with h5.File(bdf, 'r') as f: #stimuli = f.keys() print(list(f.keys())) for stim in stimuli: poptens = np.array(f[stim]['pop_tens']) population_tensors_familiar[bird].append([poptens, stim]) # + # extract Unfamiliar stims # extract population tensors for the populations of interest # Do not sort the stims population_tensors_unfamiliar = {} stimuli = [] for bird in test_birds: stimuli = unfamiliar_stimuli[bird] print(stimuli) bdf = bdfs[bird] population_tensors_unfamiliar[bird] = [] # open the binned data file with h5.File(bdf, 'r') as f: #stimuli = f.keys() print(list(f.keys())) for stim in stimuli: poptens = np.array(f[stim]['pop_tens']) population_tensors_unfamiliar[bird].append([poptens, stim]) # + # flatten the list of population tensors for each population threshold = 6 def threshold_poptens(tens, thresh): ncell, nwins, ntrials = tens.shape frs = np.mean(tens, axis=1) tfr = thresh*frs tfrtens = np.tile(tfr[:, np.newaxis, :], (1, nwins, 1)) bintens = 1*np.greater(tens, tfrtens) return bintens def shuffle_binmat(binmat): ncells, nwin = binmat.shape for i in range(ncells): binmat[i, :] = np.random.permutation(binmat[i, :]) return binmat def get_JS(i, j, Li, Lj, speci, specj, beta): js = (i, j, sc.sparse_JS_divergence2_fast(Li, Lj, speci, specj, beta)) print((i, j)) return js def get_Lap(trial_matrix, sh): if sh == 'shuffled': mat = shuffle_binmat(trial_matrix) else: mat = trial_matrix ms = sc.binarytomaxsimplex(trial_matrix, rDup=True) scg1 = sc.simplicialChainGroups(ms) L = sc.sparse_laplacian(scg1, dim) return L def get_M(i, j, L1, L2): mspec = sc.compute_M_spec(L1, L2) print((i, j)) return (i, j, mspec) def get_JS_spec(i, j, speci, specj, specm, beta): js = (i, j, sc.sparse_JS_divergence2_spec(speci, specj, specm, beta)) return js def compute_withins_vs_between(mtx, ntrials, nstim, diag=0): stim_per_group = int(nstim//2) btwn_data = mtx[0:stim_per_group*ntrials, stim_per_group*ntrials:] within1 = mtx[0:stim_per_group*ntrials, 0:stim_per_group*ntrials][np.triu_indices(stim_per_group*ntrials, diag)] within2 = mtx[stim_per_group*ntrials:, stim_per_group*ntrials:][np.triu_indices(stim_per_group*ntrials, diag)] within = np.concatenate((within1, within2)) return (btwn_data, within1, within2) # + poptens = {'familiar': population_tensors_familiar, 'unfamiliar': population_tensors_unfamiliar} # + poptens['familiar']['B1083-5'] combined_poptens = poptens['familiar']['B1083'] combined_poptens.extend(poptens['familiar']['B1083-5']) print(len(combined_poptens)) # + # mirroring cuda code #Left vs right reload(sc) from joblib import Parallel, delayed dim = 1 betas = [1] all_spectra = [] ntrials = 20 # Only do half the trials for each stim bird_tensors = combined_poptens SCG = [] spectra = [] laplacians_save = [] print('Computing Laplacians..') for bird_tensor, stim in bird_tensors: binmatlist = [] print(stim) ncells, nwin, _ = bird_tensor.shape bin_tensor = threshold_poptens(bird_tensor, threshold) laps = Parallel(n_jobs=24)(delayed(get_Lap)(bin_tensor[:, :, trial], 'shuffled') for trial in range(ntrials)) laplacians_save.append((bird, stim, laps)) laplacians = sum([s[2] for s in laplacians_save], []) N = len(laplacians) # compute spectra print('Computing Spectra...') spectra = Parallel(n_jobs=24)(delayed(sc.sparse_spectrum)(L) for L in laplacians) all_spectra.extend(spectra) # Precompute M spectra pairs = [(i, j) for i in range(N) for j in range(i, N)] print('Computing M spectra...') M_spec = Parallel(n_jobs=24)(delayed(get_M)(i, j, laplacians[i], laplacians[j]) for (i, j) in pairs) M_spec = {(p[0], p[1]): p[2] for p in M_spec} # Save computed spectra with open(os.path.join(figsavepth, 'Mspectra_{}-{}-{}-{}.pkl'.format('B1083Combined', ntrials, 'shuff', 'fam')), 'wb') as f: pickle.dump(M_spec, f) with open(os.path.join(figsavepth, 'Lapspectra_{}-{}-{}-{}.pkl'.format('B1083Combined', ntrials, 'shuff', 'fam')), 'wb') as f: pickle.dump(laplacians_save, f) # + # compute density matrices for beta in betas: print('Computing JS Divergences with beta {}...'.format(beta)) jsmat = np.zeros((N, N)) jsdat = Parallel(n_jobs=24)(delayed(get_JS_spec)(i, j, spectra[i], spectra[j], M_spec[(i,j)], beta) for (i, j) in pairs) for d in jsdat: jsmat[d[0], d[1]] = d[2] with open(os.path.join(figsavepth, 'JSpop_fast_B1083Combined-{}-{}-{}_LvsR-fam-shuff.pkl'.format(dim, beta, ntrials)), 'wb') as f: pickle.dump(jsmat, f) # - plt.figure(figsize=(12, 12)) plt.imshow(jsmat + jsmat.T) plt.savefig(os.path.join(figsavepth, "JSDivAltogether_shuffle.pdf")) print(combined_poptens) 5,0)) plt.boxplot(winsorized_Income_Comp_Of_Resources) plt.title("winsorized_Income_Comp_Of_Resources") plt.show() # + # Winsorize Schooling from scipy.stats.mstats import winsorize plt.figure(figsize=(7,4)) plt.subplot(1,2,1) original_Schooling = df['schooling'] plt.boxplot(original_Schooling) plt.title("original_Schooling") plt.subplot(1,2,2) winsorized_Schooling = winsorize(df['schooling'],(0.025,0.01)) plt.boxplot(winsorized_Schooling) plt.title("winsorized_Schooling") plt.show() # - win_list = [winsorized_Life_Expectancy,winsorized_Adult_Mortality,winsorized_Infant_Deaths,winsorized_Alcohol, winsorized_Percentage_Exp,winsorized_HepatitisB,winsorized_Under_Five_Deaths,winsorized_Polio,winsorized_Tot_Exp,winsorized_Diphtheria,winsorized_HIV,winsorized_GDP,winsorized_thinness_1to19_years,winsorized_thinness_5to9_years,winsorized_Income_Comp_Of_Resources,winsorized_Schooling] for variable in win_list: q75, q25 = np.percentile(variable, [75 ,25]) iqr = q75 - q25 min_val = q25 - (iqr*1.5) max_val = q75 + (iqr*1.5) print("Number of outliers after winsorization : {}".format(len(np.where((variable > max_val) | (variable < min_val))[0]))) # Adding winsorized variables to the data frame. df['winsorized_Life_Expectancy'] = winsorized_Life_Expectancy df['winsorized_Adult_Mortality'] = winsorized_Adult_Mortality df['winsorized_Infant_Deaths'] = winsorized_Infant_Deaths df['winsorized_Alcohol'] = winsorized_Alcohol df['winsorized_Percentage_Exp'] = winsorized_Percentage_Exp df['winsorized_HepatitisB'] = winsorized_HepatitisB df['winsorized_Under_Five_Deaths'] = winsorized_Under_Five_Deaths df['winsorized_Polio'] = winsorized_Polio df['winsorized_Tot_Exp'] = winsorized_Tot_Exp df['winsorized_Diphtheria'] = winsorized_Diphtheria df['winsorized_HIV'] = winsorized_HIV df['winsorized_GDP'] = winsorized_GDP df['winsorized_thinness_1to19_years'] = winsorized_thinness_1to19_years df['winsorized_thinness_5to9_years'] = winsorized_thinness_5to9_years df['winsorized_Income_Comp_Of_Resources'] = winsorized_Income_Comp_Of_Resources df['winsorized_Schooling'] = winsorized_Schooling # + # Distribution of each numerical variable. all_col = ['life_expectancy','winsorized_Life_Expectancy','adult_mortality','winsorized_Adult_Mortality','infant_deaths', 'winsorized_Infant_Deaths','alcohol','winsorized_Alcohol','percentage_expenditure','winsorized_Percentage_Exp','hepatitis_b', 'winsorized_HepatitisB','under-five_deaths','winsorized_Under_Five_Deaths','polio','winsorized_Polio','total_expenditure', 'winsorized_Tot_Exp','diphtheria','winsorized_Diphtheria','hiv/aids','winsorized_HIV','gdp','winsorized_GDP','thinness_1-19_years','winsorized_thinness_1to19_years','thinness_5-9_years', 'winsorized_thinness_5to9_years','income_composition_of_resources','winsorized_Income_Comp_Of_Resources', 'schooling','winsorized_Schooling'] plt.figure(figsize=(10,70)) for i in range(len(all_col)): plt.subplot(18,2,i+1) plt.hist(df[all_col[i]]) plt.title(all_col[i]) plt.show() # - # ### Plotting Average Life Expectancy vs Country df_country = df.groupby('country')['life_expectancy'].mean() df_country.plot(kind='bar', figsize=(70,20), fontsize=25) plt.title("Life_Expectancy w.r.t Country",fontsize=55) plt.xlabel("Country",fontsize=50) plt.ylabel("Avg Life_Expectancy",fontsize=50) plt.show() # ### Plotting Average Life Expectancy vs Year plt.figure(figsize=(7,5)) plt.bar(df.groupby('year')['year'].count().index,df.groupby('year')['life_expectancy'].mean(),color='blue',alpha=0.65) plt.xlabel("Year",fontsize=12) plt.ylabel("Avg Life_Expectancy",fontsize=12) plt.title("Life_Expectancy w.r.t Year") plt.show() # ### Plotting Average Life Expectancy vs Status plt.figure(figsize=(5,5)) plt.bar(df.groupby('status')['status'].count().index,df.groupby('status')['life_expectancy'].mean()) plt.xlabel("Status",fontsize=12) plt.ylabel("Avg Life_Expectancy",fontsize=12) plt.title("Life_Expectancy w.r.t Status") plt.show() # ### Scatter plot between the target variable(winsorized variables) and all continuous variables. # + plt.figure(figsize=(18,40)) plt.subplot(6,3,1) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Adult_Mortality"]) plt.title("LifeExpectancy vs AdultMortality") plt.subplot(6,3,2) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Infant_Deaths"]) plt.title("LifeExpectancy vs Infant_Deaths") plt.subplot(6,3,3) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Alcohol"]) plt.title("LifeExpectancy vs Alcohol") plt.subplot(6,3,4) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Percentage_Exp"]) plt.title("LifeExpectancy vs Percentage_Exp") plt.subplot(6,3,5) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_HepatitisB"]) plt.title("LifeExpectancy vs HepatitisB") plt.subplot(6,3,6) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Under_Five_Deaths"]) plt.title("LifeExpectancy vs Under_Five_Deaths") plt.subplot(6,3,7) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Polio"]) plt.title("LifeExpectancy vs Polio") plt.subplot(6,3,8) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Tot_Exp"]) plt.title("LifeExpectancy vs Tot_Exp") plt.subplot(6,3,9) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Diphtheria"]) plt.title("LifeExpectancy vs Diphtheria") plt.subplot(6,3,10) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_HIV"]) plt.title("LifeExpectancy vs HIV") plt.subplot(6,3,11) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_GDP"]) plt.title("LifeExpectancy vs GDP") plt.subplot(6,3,12) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_thinness_1to19_years"]) plt.title("LifeExpectancy vs thinness_1to19_years") plt.subplot(6,3,13) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_thinness_5to9_years"]) plt.title("LifeExpectancy vs thinness_5to9_years") plt.subplot(6,3,14) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Income_Comp_Of_Resources"]) plt.title("LifeExpectancy vs Income_Comp_Of_Resources") plt.subplot(6,3,15) plt.scatter(df["winsorized_Life_Expectancy"], df["winsorized_Schooling"]) plt.title("LifeExpectancy vs Schooling") plt.show() # - # #### Conclusions: # 1. Nature of scatter plot, we found approx same variation among following pairs, # A. income_comp_of_resources and scooling # B. thinneess_5to9_years and thinness_1to19_years # C. Under_five_death and infant_death # ### Identifying Correlation and plotting heat map # Correlation of winsorized variables df_win = df.iloc[:,21:] df_win['country'] = df['country'] df_win['year'] = df['year'] df_win['status'] = df['status'] df_win_num = df_win.iloc[:,:-3] cormat = df_win_num.corr() # Using heatmap to observe correlations plt.figure(figsize=(12,12)) sns.heatmap(cormat, square=True, annot=True, linewidths=.5) plt.title("Correlation matrix among winsorized variables") plt.show() # ### Conclusions # 1. Based on the heat map above, we found correlation factor of 0.98, 0.94 and 0.88 for under_five_death vs infant_death, thinness_5to9_deaths vs thinness_1to19_deaths and income_comp_of_resources vs scooling respectively. # 2. Same behaviour was observed in scatter plot also. # 3. We've planned to drop one of the two attributes (based on more null values observed) that are having same behaviour towards target attibute # A. under_five_death among under_five_death vs infant_death # B. thinness_5to9_deaths among thinness_5to9_deaths vs thinness_1to19_deaths # C. income_comp_of_resources among income_comp_of_resources vs scooling # 4. Our final dataset will be having 2928 rows and 18 columns # # Model Part df.head() # ### Dropping three attributes based on their correlation with other three attributes # + X = df[['winsorized_Adult_Mortality', 'winsorized_Alcohol', 'winsorized_Percentage_Exp', 'winsorized_HepatitisB', 'winsorized_Under_Five_Deaths', 'winsorized_Polio', 'winsorized_Tot_Exp', 'winsorized_Diphtheria', 'winsorized_HIV', 'winsorized_GDP', 'winsorized_thinness_5to9_years', 'winsorized_Income_Comp_Of_Resources','status']] Y = df['winsorized_Life_Expectancy'] # - from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3) #we are not using random_state variable to ensure split is being done randomly # #### Decision Tree Regressor from sklearn.tree import DecisionTreeRegressor tree_reg = DecisionTreeRegressor() tree_reg.fit(X_train,Y_train) y_pred = tree_reg.predict(X_test) tree_r2_score = r2_score(Y_test,y_pred) print('R2 score for this model is', tree_r2_score) print('MSE',mean_squared_error(Y_test, y_pred)) # #### Linear Regression lr = LinearRegression() lr.fit(X_train, Y_train) Y_pred = lr.predict(X_test) print('R2 score for this model is',r2_score(Y_test, Y_pred)) print('MSE',mean_squared_error(Y_test, Y_pred)) # #### Random Forest Regressor # from sklearn.ensemble import RandomForestRegressor rf = RandomForestRegressor() rf.fit(X_train, Y_train) rf_pred=rf.predict(X_test) r3 = r2_score(Y_test, rf_pred) print('R2 score for this model is', r3) print('MSE',mean_squared_error(Y_test, rf_pred)) rf.feature_importances_ import seaborn as sns # Helper function for plotting feature importance def plot_features(columns, importances, n=10): df = (pd.DataFrame({"features": columns, "feature_importance": importances}) .sort_values("feature_importance", ascending=False) .reset_index(drop=True)) sns.barplot(x="feature_importance", y="features", data=df[:n], orient="h") plot_features(X_train.columns, rf.feature_importances_) # ### Conclusions: # 1. We tried to fit our data in three different models namely Decision Tree, Linear Regression and Random Forest Regressor. # 2. Among all three models, we achieved r2 value of 0.9633 in random forest regressor with mean square error of 3.25. # 3. Feature importance of HIV attribute is highest among all other attributes as target attribute prediction.
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dhillon1/CAPSTONE-II-WordNXT
https://github.com/dhillon1/CAPSTONE-II-WordNXT
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7,690
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## WORDNXT :: Next Word Predictor # #### NOTE: This piece of code is first part of the project that is RNN model. This model has been trained on LSTN, a text specific model. There will be more changes in upcoming developments of this code and project as a whole. import tensorflow as tf from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.layers import Embedding, LSTM, Dense from tensorflow.keras.models import Sequential from tensorflow.keras.utils import to_categorical from tensorflow.keras.optimizers import Adam import pickle import numpy as np import os import string # + #pip install tensorflow-gpu --user # + #sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True)) # + #from tensorflow.python.client import device_lib #print(device_lib.list_local_devices()) # + file = open("WikiQA-train.txt", "r", encoding = "utf8") lines = [] for i in file: lines.append(i) data = "" for i in lines: data = ' '. join(lines) data = data.replace('\n', '').replace('\r', '').replace('\ufeff', '') translator = str.maketrans(string.punctuation, ' '*len(string.punctuation)) #map punctuation to space new_data = data.translate(translator) z = [] for i in data.split(): if i not in z: z.append(i) data = ' '.join(z) # + tokenizer = Tokenizer() tokenizer.fit_on_texts([data]) # saving the tokenizer for predict function. pickle.dump(tokenizer, open('tokenizer1.pkl', 'wb')) sequence_data = tokenizer.texts_to_sequences([data])[0] vocab_size = len(tokenizer.word_index) + 1 sequences = [] for i in range(1, len(sequence_data)): words = sequence_data[i-1:i+1] sequences.append(words) sequences = np.array(sequences) X = [] y = [] for i in sequences: X.append(i[0]) y.append(i[1]) X = np.array(X) y = np.array(y) y = to_categorical(y, num_classes=vocab_size) # - model = Sequential() model.add(Embedding(vocab_size, 10, input_length=1)) model.add(LSTM(1000, return_sequences=True)) model.add(LSTM(1000)) model.add(Dense(1000, activation="relu")) model.add(Dense(vocab_size, activation="softmax")) # + from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.callbacks import ReduceLROnPlateau from tensorflow.keras.callbacks import TensorBoard checkpoint = ModelCheckpoint("nextword1.h5", monitor='loss', verbose=1, save_best_only=True, mode='auto') reduce = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=3, min_lr=0.0001, verbose = 1) logdir='logsnextword1' tensorboard_Visualization = TensorBoard(log_dir=logdir) # - model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=0.001)) model.fit(X, y, epochs=10, batch_size=64, callbacks=[checkpoint, reduce, tensorboard_Visualization]) id nonlinearity def sigmoid(x): output = 1/(1+np.exp(-x)) return output # convert output of sigmoid function to its derivative def sigmoid_output_to_derivative(output): return output*(1-output) # training dataset generation int2binary = {} binary_dim = 8 largest_number = pow(2,binary_dim) binary = np.unpackbits( np.array([list(range(largest_number))],dtype=np.uint8).T,axis=1) for i in range(largest_number): int2binary[i] = binary[i] # input variables alpha = 0.1 input_dim = 2 hidden_dim = 16 output_dim = 1 # initialize neural network weights synapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1 synapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1 synapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1 synapse_0_update = np.zeros_like(synapse_0) synapse_1_update = np.zeros_like(synapse_1) synapse_h_update = np.zeros_like(synapse_h) # + # generate a simple addition problem (a + b = c) a_int = np.random.randint(largest_number/2) # int version a = int2binary[a_int] # binary encoding b_int = np.random.randint(largest_number/2) # int version b = int2binary[b_int] # binary encoding # true answer c_int = a_int + b_int c = int2binary[c_int] # - # training logic for j in range(10000): # generate a simple addition problem (a + b = c) a_int = np.random.randint(largest_number/2) # int version a = int2binary[a_int] # binary encoding b_int = np.random.randint(largest_number/2) # int version b = int2binary[b_int] # binary encoding # true answer c_int = a_int + b_int c = int2binary[c_int] # where we'll store our best guess (binary encoded) d = np.zeros_like(c) overallError = 0 layer_2_deltas = list() layer_1_values = list() layer_1_values.append(np.zeros(hidden_dim)) # moving along the positions in the binary encoding for position in range(binary_dim): # generate input and output X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]]) y = np.array([[c[binary_dim - position - 1]]]).T # hidden layer (input ~+ prev_hidden) layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h)) # output layer (new binary representation) layer_2 = sigmoid(np.dot(layer_1,synapse_1)) # did we miss?... if so, by how much? layer_2_error = y - layer_2 layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2)) overallError += np.abs(layer_2_error[0]) # decode estimate so we can print it out d[binary_dim - position - 1] = np.round(layer_2[0][0]) # store hidden layer so we can use it in the next timestep layer_1_values.append(copy.deepcopy(layer_1)) future_layer_1_delta = np.zeros(hidden_dim) for position in range(binary_dim): X = np.array([[a[position],b[position]]]) layer_1 = layer_1_values[-position-1] prev_layer_1 = layer_1_values[-position-2] # error at output layer layer_2_delta = layer_2_deltas[-position-1] # error at hidden layer layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1) # let's update all our weights so we can try again synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta) synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta) synapse_0_update += X.T.dot(layer_1_delta) future_layer_1_delta = layer_1_delta synapse_0 += synapse_0_update * alpha synapse_1 += synapse_1_update * alpha synapse_h += synapse_h_update * alpha synapse_0_update *= 0 synapse_1_update *= 0 synapse_h_update *= 0 # print out progress if(j % 1000 == 0): print("Error:" + str(overallError)) print("Pred:" + str(d)) print("True:" + str(c)) out = 0 for index,x in enumerate(reversed(d)): out += x*pow(2,index) print(str(a_int) + " + " + str(b_int) + " = " + str(out)) print("------------") # + import copy, numpy as np np.random.seed(0) # compute sigmoid nonlinearity def sigmoid(x): output = 1/(1+np.exp(-x)) return output # convert output of sigmoid function to its derivative def sigmoid_output_to_derivative(output): return output*(1-output) # training dataset generation int2binary = {} binary_dim = 8 largest_number = pow(2,binary_dim) binary = np.unpackbits( np.array([list(range(largest_number))],dtype=np.uint8).T,axis=1) for i in range(largest_number): int2binary[i] = binary[i] # input variables alpha = 0.1 input_dim = 2 hidden_dim = 16 output_dim = 1 # initialize neural network weights synapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1 synapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1 synapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1 synapse_0_update = np.zeros_like(synapse_0) synapse_1_update = np.zeros_like(synapse_1) synapse_h_update = np.zeros_like(synapse_h) # training logic for j in range(10000): # generate a simple addition problem (a + b = c) a_int = np.random.randint(largest_number/2) # int version a = int2binary[a_int] # binary encoding b_int = np.random.randint(largest_number/2) # int version b = int2binary[b_int] # binary encoding # true answer c_int = a_int + b_int c = int2binary[c_int] # where we'll store our best guess (binary encoded) d = np.zeros_like(c) overallError = 0 layer_2_deltas = list() layer_1_values = list() layer_1_values.append(np.zeros(hidden_dim)) # moving along the positions in the binary encoding for position in range(binary_dim): # generate input and output X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]]) y = np.array([[c[binary_dim - position - 1]]]).T # hidden layer (input ~+ prev_hidden) layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h)) # output layer (new binary representation) layer_2 = sigmoid(np.dot(layer_1,synapse_1)) # did we miss?... if so, by how much? layer_2_error = y - layer_2 layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2)) overallError += np.abs(layer_2_error[0]) # decode estimate so we can print it out d[binary_dim - position - 1] = np.round(layer_2[0][0]) # store hidden layer so we can use it in the next timestep layer_1_values.append(copy.deepcopy(layer_1)) future_layer_1_delta = np.zeros(hidden_dim) for position in range(binary_dim): X = np.array([[a[position],b[position]]]) layer_1 = layer_1_values[-position-1] prev_layer_1 = layer_1_values[-position-2] # error at output layer layer_2_delta = layer_2_deltas[-position-1] # error at hidden layer layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1) # let's update all our weights so we can try again synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta) synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta) synapse_0_update += X.T.dot(layer_1_delta) future_layer_1_delta = layer_1_delta synapse_0 += synapse_0_update * alpha synapse_1 += synapse_1_update * alpha synapse_h += synapse_h_update * alpha synapse_0_update *= 0 synapse_1_update *= 0 synapse_h_update *= 0 # print out progress if(j % 1000 == 0): print("Error:" + str(overallError)) print("Pred:" + str(d)) print("True:" + str(c)) out = 0 for index,x in enumerate(reversed(d)): out += x*pow(2,index) print(str(a_int) + " + " + str(b_int) + " = " + str(out)) print("------------") # - except: columns.append('{}th Most Common Venue'.format(ind+1)) # create a new dataframe neighborhoods_venues_sorted = pd.DataFrame(columns=columns) neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood'] for ind in np.arange(toronto_grouped.shape[0]): neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues) neighborhoods_venues_sorted # - # ### Cluster Neighborhoods¶ # # #### Run k-means to cluster the neighborhood into 5 clusters. # + # set number of clusters kclusters = 5 toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1) # run k-means clustering kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering) # check cluster labels generated for each row in the dataframe kmeans.labels_[0:10] # - # #### Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood. # + toronto_merged = toronto_data # add clustering labels toronto_merged['Cluster Labels'] = kmeans.labels_ # merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood') toronto_merged.head() # check the last columns! # - # #### Finally, let's visualize the resulting clusters # + # create map map_clusters = folium.Map(location=[toronto_coords[0], toronto_coords[1]], zoom_start=12) # set color scheme for the clusters x = np.arange(kclusters) ys = [i+x+(i*x)**2 for i in range(kclusters)] colors_array = cm.rainbow(np.linspace(0, 1, len(ys))) rainbow = [colors.rgb2hex(i) for i in colors_array] # add markers to the map markers_colors = [] for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']): label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True) folium.CircleMarker( [lat, lon], radius=5, popup=label, color=rainbow[cluster-1], fill=True, fill_color=rainbow[cluster-1], fill_opacity=0.7).add_to(map_clusters) map_clusters # - # ### Examine Clusters¶ # ### Cluster 1 toronto_merged.loc[toronto_merged['Cluster Labels'] == 0, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]] # ### Cluster 2 toronto_merged.loc[toronto_merged['Cluster Labels'] == 1, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))] # ### Cluster 3 toronto_merged.loc[toronto_merged['Cluster Labels'] == 2, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]] # ### Cluster 4 toronto_merged.loc[toronto_merged['Cluster Labels'] == 3, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]] # ### Cluster 5 toronto_merged.loc[toronto_merged['Cluster Labels'] == 4, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]
14,185
/PythonDS_Day5.ipynb
03075464ff5997a10bb7c93da84b18092ec4ad3e
[]
no_license
JuliaLo1979/60-Days-Python-Data-Science-Coding-Marathon
https://github.com/JuliaLo1979/60-Days-Python-Data-Science-Coding-Marathon
0
0
null
null
null
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Jupyter Notebook
false
false
.py
5,109
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import numpy as np #scores for 6 students english_score = np.array([55,89,76,65,48,70]) math_score = np.array([60,85,60,68,np.nan,60]) chinese_score = np.array([65,90,82,72,66,77]) #English if np.isnan(english_score).sum() == 0: print('English has no nan') print('平均:', np.mean(english_score)) print('最大值:', np.amax(english_score)) print('最小值:', np.amin(english_score)) print('標準差:', np.std(english_score)) else: print('English has nan') print('平均:', np.nanmean(english_score)) print('最大值:', np.nanmax(english_score)) print('最小值:', np.nanmin(english_score)) print('標準差:', np.nanstd(english_score)) print() #math if np.isnan(math_score).sum() == 0: print('math has no nan') print('平均:', np.mean(math_score)) print('最大值:', np.amax(math_score)) print('最小值:', np.amin(math_score)) print('標準差:', np.std(math_score)) else: print('math has nan') print('平均:', np.nanmean(math_score)) print('最大值:', np.nanmax(math_score)) print('最小值:', np.nanmin(math_score)) print('標準差:', np.nanstd(math_score)) print() #chinese if np.isnan(chinese_score).sum() == 0: print('chinese has no nan') print('平均:', np.mean(chinese_score)) print('最大值:', np.amax(chinese_score)) print('最小值:', np.amin(chinese_score)) print('標準差:', np.std(chinese_score)) else: print('chinese has nan') print('平均:', np.nanmean(chinese_score)) print('最大值:', np.nanmax(chinese_score)) print('最小值:', np.nanmin(chinese_score)) print('標準差:', np.nanstd(chinese_score)) # - math_score[4] = 55 math_score #math if np.isnan(math_score).sum() == 0: print('math has no nan') print('平均:', np.mean(math_score)) print('最大值:', np.amax(math_score)) print('最小值:', np.amin(math_score)) print('標準差:', np.std(math_score)) else: print('math has nan') print('平均:', np.nanmean(math_score)) print('最大值:', np.nanmax(math_score)) print('最小值:', np.nanmin(math_score)) print('標準差:', np.nanstd(math_score)) print() # + a = np.corrcoef(chinese_score, english_score) b = np.corrcoef(chinese_score, math_score) print(a) print(b) if a[0,1]>b[0,1]: print('與國文成績相關係數最高的學科是英文') else: print('與國文成績相關係數最高的學科是數學') # -
2,480
/5.4.2High Condition Number.ipynb
fffb0546f881735fae65968f847d99db07f9a02b
[]
no_license
darkraai/PyTorch-Models
https://github.com/darkraai/PyTorch-Models
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
101,888
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # <a href="http://cocl.us/pytorch_link_top"> # <img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " /> # </a> # # <img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/cc-logo-square.png" width="200" alt="cognitiveclass.ai logo" /> # <h1>Loss Function with a High Condition Number with and Without Momentum</h1> # <h2>Table of Contents</h2> # <p>In this lab, we will generate data that will produce a Loss Function with a High Condition Number. You will create two models; one with the momentum term and one without the momentum term.</p> # # <ul> # <li><a href="#Makeup_Data">Make Some Data </a></li> # <li><a href="#Model_Cost">Create two Models, Two Optimizers and a Cost Function</a></li> # <li><a href="#BGD">Train the Model: Batch Gradient Descent</a></li> # </ul> # <p>Estimated Time Needed: <strong>30 min</strong></p> # # <hr> # <h2>Preparation</h2> # We'll need the following libraries: # + # Import the libraries we need for this lab import torch import numpy as np import matplotlib.pyplot as plt from mpl_toolkits import mplot3d from torch.utils.data import Dataset, DataLoader from torch import nn, optim torch.manual_seed(1) # - # The class <code>plot_error_surfaces</code> is just to help you visualize the data space and the parameter space during training and has nothing to do with Pytorch. # + # Define the class for plot out the surface class plot_error_surfaces(object): # Constructor def __init__(self, w_range, b_range, X, Y, n_samples=30, go=True): W = np.linspace(-w_range, w_range, n_samples) B = np.linspace(-b_range, b_range, n_samples) w, b = np.meshgrid(W, B) Z = np.zeros((n_samples, n_samples)) count1 = 0 self.y = Y.numpy() self.x = X.numpy() for w1, b1 in zip(w, b): count2 = 0 for w2, b2 in zip(w1, b1): Z[count1, count2] = np.mean((self.y - w2 * self.x + b2) ** 2) count2 += 1 count1 += 1 self.Z = Z self.w = w self.b = b self.LOSS_list = {} # Setter def set_para_loss(self, model, name, loss): if (not (name in self.LOSS_list)): self.LOSS_list[name] = [] w = list(model.parameters())[0].item() b = list(model.parameters())[1].item() self.LOSS_list[name].append({"loss": loss, "w": w, "b": b}) # Plot the diagram def plot_ps(self, iteration=0): plt.contour(self.w, self.b, self.Z) count = 1 if (len(self.LOSS_list) > 0): for key, value in self.LOSS_list.items(): w = [v for d in value for (k, v) in d.items() if "w" == k] b = [v for d in value for (k, v) in d.items() if "b" == k] plt.scatter(w, b, cmap='viridis', marker='x', label=key) plt.title('Loss Surface Contour not to scale, Iteration: ' + str(iteration)) plt.legend() plt.xlabel('w') plt.ylabel('b') plt.show() # - # <!--Empty Space for separating topics--> # <h2 id="Makeup_Data">Make Some Data</h2> # Generate values from -2 to 2 that create a line with a slope of 0.1 and a bias of 10000. This is the line that you need to estimate. Add some noise to the data: # + # Define a class to create the dataset class Data(Dataset): # Constructor def __init__(self): self.x = torch.arange(-2, 2, 0.1).view(-1, 1) self.f = 1 * self.x + 10000 self.y = self.f + 0.1 * torch.randn(self.x.size()) self.len = self.x.shape[0] # Getter def __getitem__(self, index): return self.x[index], self.y[index] # Get Length def __len__(self): return self.len # - # Create a dataset object: # + # Create a dataset object dataset = Data() # - # Plot the data # + # Plot the data plt.plot(dataset.x.numpy(), dataset.y.numpy(), 'rx', label='y') plt.plot(dataset.x.numpy(), dataset.f.numpy(), label='f') plt.xlabel('x') plt.ylabel('y') plt.legend() # - # <!--Empty Space for separating topics--> # <h2 id="Model_Cost">Create the Model and Total Loss Function (Cost)</h2> # Create a linear regression class # + # Define linear regression class class linear_regression(nn.Module): # Constructor def __init__(self, input_size, output_size): super(linear_regression, self).__init__() self.linear = nn.Linear(input_size, output_size) # Prediction def forward(self, x): yhat = self.linear(x) return yhat # - # We will use PyTorch's build-in function to create a criterion function; this calculates the total loss or cost # + # Use the build-in function to create a criterion function criterion = nn.MSELoss() # - # Create a linear regression object, and an SGD optimizer object with no momentum. # + # Create a linear regression object and the optimizer without momentum model = linear_regression(1, 1) optimizer = optim.SGD(model.parameters(), lr=0.01) # - # Create a linear regression object, and an SGD optimiser object with momentum . # + # Create a linear regression object and the optimizer with momentum model_momentum = linear_regression(1, 1) optimizer_momentum = optim.SGD(model_momentum.parameters(), lr=0.01, momentum=0.2) # - # Create a dataloader object: # + # Create a data loader trainloader = DataLoader(dataset=dataset, batch_size=1, shuffle=True) # - # PyTorch randomly initializes your model parameters. If we use those parameters, the result will not be very insightful as convergence will be extremely fast. In order to prevent that, we will initialize the parameters such that it will take longer to converge. # + # Set parameters model.state_dict()['linear.weight'][0] = -5000 model.state_dict()['linear.bias'][0] = -100000 model_momentum.state_dict()['linear.weight'][0] = -5000 model_momentum.state_dict()['linear.bias'][0] = -100000 # - # Create a plotting object, not part of PyTorch, only used to help visualize # + # Plot the surface get_surface = plot_error_surfaces(5000, 100000, dataset.x, dataset.y, 100, go=False) get_surface.plot_ps() # - # <!--Empty Space for separating topics--> # <h2 id="BGD">Train the Model via Stochastic Gradient Descent</h2> # Run 1 epochs of stochastic gradient descent and view parameter space. # + # Train the model def train_model(epochs=1): for epoch in range(epochs): for i, (x, y) in enumerate(trainloader): #no momentum yhat = model(x) loss = criterion(yhat, y) #momentum yhat_m = model_momentum(x) loss_m = criterion(yhat_m, y) #apply optimization to momentum term and term without momentum #for plotting #get_surface.get_stuff(model, loss.tolist()) #get_surface.get_stuff1(model_momentum, loss_m.tolist()) get_surface.set_para_loss(model=model_momentum, name="momentum" ,loss=loss_m.tolist()) get_surface.set_para_loss(model=model, name="no momentum" , loss=loss.tolist()) optimizer.zero_grad() optimizer_momentum.zero_grad() loss.backward() loss_m.backward() optimizer.step() optimizer_momentum.step() get_surface.plot_ps(iteration=i) train_model() # - # The plot above shows the different parameter values for each model in different iterations of SGD. The values are overlaid over the cost or total loss surface. The contour lines somewhat miss scaled but it is evident that in the vertical direction they are much closer together implying a larger gradient in that direction. The model trained with momentum shows somewhat more displacement in the hozontal direction. # The plot below shows the log of the cost or total loss, we see that the term with momentum converges to a minimum faster and to an overall smaller value. We use the log to make the difference more evident. # + # Plot the loss loss = [v for d in get_surface.LOSS_list["no momentum"] for (k, v) in d.items() if "loss" == k] loss_m = [v for d in get_surface.LOSS_list["momentum"] for (k, v) in d.items() if "loss" == k] plt.plot(np.log(loss), 'r', label='no momentum' ) plt.plot(np.log(loss_m), 'b', label='momentum' ) plt.title('Cost or Total Loss' ) plt.xlabel('Iterations ') plt.ylabel('Cost') plt.legend() plt.show() # - # <!--Empty Space for separating topics--> # <a href="http://cocl.us/pytorch_link_bottom"> # <img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/notebook_bottom%20.png" width="750" alt="PyTorch Bottom" /> # </a> # <h2>About the Authors:</h2> # # <a href="https://www.linkedin.com/in/joseph-s-50398b136/">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD. # Other contributors: <a href="https://www.linkedin.com/in/michelleccarey/">Michelle Carey</a>, <a href="www.linkedin.com/in/jiahui-mavis-zhou-a4537814a">Mavis Zhou</a> # <hr> # Copyright &copy; 2018 <a href="cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu">cognitiveclass.ai</a>. This notebook and its source code are released under the terms of the <a href="https://bigdatauniversity.com/mit-license/">MIT License</a>.
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # <div align=center>FinTech Assignment #4 : Bitcoin Transaction and Mining</div> # ### <div align=center>Submission by Subhayu Chakravarty</div> # ### Importing required libraries import hashlib # # Mining Bitcoin Exercise # ## 1. Mining # + # Initial Block Header transaction='Cesare sends one bitcoin to Shimon' previous_transaction_hash='85738f8f9a7f1b04b5329c590ebcb9e425925c6d0984089c43a022de4f19c281' timestamp='2018-01-07 21:05:34' bits='3' nonce='0' hashed_transaction=hashlib.sha256(transaction.encode('utf-8')).hexdigest() block_header=hashed_transaction+' '+previous_transaction_hash+' '+timestamp+' '+bits print('\nInitial block header without nonce :') print(block_header) print('\nBlock header with nonce=0:') print(block_header+' 0') hashed_block_header=hashlib.sha256((block_header+' 0').encode('utf-8')).hexdigest() print('\nHashed block header:') print(hashed_block_header) # - # Finding the winning nonce counter=0 while counter<100000: nonce=str(counter) h=block_header+' '+nonce hashed_header=hashlib.sha256(h.encode('utf-8')).hexdigest() if hashed_header[0:3]=='000': print(hashed_header) print('\nWinning Nonce:') print(nonce) break counter+=1 # ## <div align=center>The End</div>
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/Python Assignment 4.ipynb
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## Hospital readmissions data analysis and recommendations for reduction # # ### Background # In October 2012, the US government's Center for Medicare and Medicaid Services (CMS) began reducing Medicare payments for Inpatient Prospective Payment System hospitals with excess readmissions. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions. # # ### Exercise overview # # In this exercise, you will: # + critique a preliminary analysis of readmissions data and recommendations (provided below) for reducing the readmissions rate # + construct a statistically sound analysis and make recommendations of your own # # More instructions provided below. Include your work **in this notebook and submit to your Github account**. # # ### Resources # + Data source: https://data.medicare.gov/Hospital-Compare/Hospital-Readmission-Reduction/9n3s-kdb3 # + More information: http://www.cms.gov/Medicare/medicare-fee-for-service-payment/acuteinpatientPPS/readmissions-reduction-program.html # + Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet # **** import pandas as pd import numpy as np import matplotlib.pyplot as plt import bokeh.plotting as bkp from mpl_toolkits.axes_grid1 import make_axes_locatable from scipy import stats # %matplotlib inline # read in readmissions data provided hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv') hospital_read_df.head(2) # **** # ## Preliminary analysis # deal with missing and inconvenient portions of data clean_hospital_read_df = hospital_read_df[(hospital_read_df['Number of Discharges'] != 'Not Available')] clean_hospital_read_df.loc[:, 'Number of Discharges'] = clean_hospital_read_df['Number of Discharges'].astype(int) clean_hospital_read_df = clean_hospital_read_df.sort('Number of Discharges') # + # generate a scatterplot for number of discharges vs. excess rate of readmissions # lists work better with matplotlib scatterplot function x = [a for a in clean_hospital_read_df['Number of Discharges'][81:-3]] y = list(clean_hospital_read_df['Excess Readmission Ratio'][81:-3]) fig, ax = plt.subplots(figsize=(8,5)) ax.scatter(x, y,alpha=0.2) ax.fill_between([0,350], 1.15, 2, facecolor='red', alpha = .15, interpolate=True) ax.fill_between([800,2500], .5, .95, facecolor='green', alpha = .15, interpolate=True) ax.set_xlim([0, max(x)]) ax.set_xlabel('Number of discharges', fontsize=12) ax.set_ylabel('Excess rate of readmissions', fontsize=12) ax.set_title('Scatterplot of number of discharges vs. excess rate of readmissions', fontsize=14) ax.grid(True) fig.tight_layout() # - # **** # # ## Preliminary report # # **A. Initial observations based on the plot above** # + Overall, rate of readmissions is trending down with increasing number of discharges # + With lower number of discharges, there is a greater incidence of excess rate of readmissions (area shaded red) # + With higher number of discharges, there is a greater incidence of lower rates of readmissions (area shaded green) # # **B. Statistics** # + In hospitals/facilities with number of discharges < 100, mean excess readmission rate is 1.023 and 63% have excess readmission rate greater than 1 # + In hospitals/facilities with number of discharges > 1000, mean excess readmission rate is 0.978 and 44% have excess readmission rate greater than 1 # # **C. Conclusions** # + There is a significant correlation between hospital capacity (number of discharges) and readmission rates. # + Smaller hospitals/facilities may be lacking necessary resources to ensure quality care and prevent complications that lead to readmissions. # # **D. Regulatory policy recommendations** # + Hospitals/facilties with small capacity (< 300) should be required to demonstrate upgraded resource allocation for quality care to continue operation. # + Directives and incentives should be provided for consolidation of hospitals and facilities to have a smaller number of them with higher capacity and number of discharges. # **** # # ## Exercise # # Include your work on the following **in this notebook and submit to your Github account**. # # A. Do you agree with the above analysis and recommendations? Why or why not? # # B. Provide support for your arguments and your own recommendations with a statistically sound analysis: # # 1. Setup an appropriate hypothesis test. # 2. Compute and report the observed significance value (or p-value). # 3. Report statistical significance for $\alpha$ = .01. # 4. Discuss statistical significance and practical significance # # # # You can compose in notebook cells using Markdown: # + In the control panel at the top, choose Cell > Cell Type > Markdown # + Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet # # **** df = clean_hospital_read_df df['Provider Number'].nunique() len(df) dfl=df[(df['Number of Discharges'] < 100) & (df['Number of Discharges'] > 0)] dfh =df[df['Number of Discharges'] > 1000] col = 'Excess Readmission Ratio' col_ex = 'Expected Readmission Rate' col_pd = 'Predicted Readmission Rate' dfl[col].hist(normed=1,bins=15) dfh[col].hist(normed=1,bins=15) # + ln = len(dfl) hn = len(dfh) lmn = dfl[col].mean() hmn = dfh[col].mean() lstd = dfl[col].std() hstd = dfh[col].std() print('Excess Readmission Ratio:\n') print('< 100 discharges:') print('Number =',ln,'\nMean Ratio =',lmn,'\nStd dev =',lstd) print('') print('> 100 discharges:') print('Number =',hn,'\nMean Ratio =',hmn,'\nStd dev =',hstd) # - mn_diff = lmn - hmn std_diff = np.sqrt(lstd**2/ln + hstd**2/hn) print(' Difference of means =',mn_diff) print('Std Dev of difference =',std_diff) print(' Significance to 1%:',2.35*std_diff) z = mn_diff/std_diff print('z score =',z) p_value = 2 * stats.norm.cdf(0, mn_diff, std_diff) p_value
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # ## What is Regression Analysis? # Regression analysis is a common statistical method used in finance to determine the relationship between variables. The process helps understand the factors that are important and irrelevant and how they affect each other. # # Let’s cover the key terms: # # - **Dependent variable:** This is the target response variable we’re trying to predict or understand. # # - **Independent variable(s):** These are the independent input factors that we think might influence the dependent variable. # # # For instance, if we want to predict the price of homes, the home price prediction would be the dependent variable, and the independent variable or independent variables would be the independent variables. Examples of independent variables or factors influencing the home price might be square feet, the number of rooms, garage, finished basement, etc. # # This notebook is meant to be read in conjunction with the blogpost found at https://analyzingalpha.com/linear-regression-python # # Get Data Using the FRED API # ### Links # - Blog Post # - YouTube Video # ## Install the FRED API # !pip install fredapi # !pip install statsmodels # + ## Import Packages # + import fredapi as fa import numpy as np import pandas as pd from plotly.subplots import make_subplots import plotly.graph_objects as go import statsmodels.api as sm from local_settings import fred as settings # - fred = fa.Fred(settings['api_key']) curcir = fred.get_series('CURRCIR') ; curcir.name = 'curcir' gdp = fred.get_series('GDP') ; gdp.name = 'gdp' sp500 = fred.get_series('sp500') ; sp500.name = 'sp500' #sp500 = fred.get_series('WILL5000INDFC') ; sp500.name = 'sp500' df = pd.merge(sp500, gdp, left_index=True, right_index=True) df = df.merge(curcir, left_index=True, right_index=True) df = df.dropna() df['tl_gdp'] = sm.OLS(df['sp500'].values, sm.add_constant(df['gdp'].values)).fit().fittedvalues df.head() # + fig = go.Figure() fig.add_trace(go.Scatter(name='GDP vs. SP500', x=df['gdp'], y=df['sp500'], mode='markers')) fig.add_trace(go.Scatter(name='Best Fit Line', x=df['gdp'], y=df['tl_gdp'], mode='lines', line=dict(color='orange'))) fig.add_trace(go.Scatter(name='GDP vs. SP500', x=df.index, y=df['sp500'], mode='markers', opacity=0, showlegend=False, hoverinfo='skip', xaxis="x2")) fig.update_layout(xaxis1={'side':'bottom'}, xaxis2={'showgrid':False, 'overlaying':'x', 'side':'top', 'tickangle':45 }) fig.update_layout(title="Simple Linear Regression", xaxis2_range=[df.index[0], df.index[-1]]) fig.show() # + import plotly.graph_objects as go # Get X values x_vals = fig.data[0]['x'] # Get Errors errors = {} for d in fig.data: errors[d['mode']]=d['y'] errors # Make line shape for each error shapes = [] for i, x in enumerate(x_vals): shapes.append(go.layout.Shape( type='line', name='Error', x0=x, y0=errors['lines'][i], x1=x, y1=errors['markers'][i], line=dict( color='black', width=1), opacity=0.5, layer='above') ) fig.update_layout(shapes=shapes, title='Simple Linear Regression w/ Error') fig.show() # + import numpy as np import matplotlib.pyplot as plt import pandas as pd rng = np.random.RandomState(1) x = 8 * rng.rand(50) y = np.sin(x) + 0.1 * rng.randn(50) #Create single dimension x= x[:,np.newaxis] y= y[:,np.newaxis] inds = x.ravel().argsort() # Sort x values and get index x = x.ravel()[inds].reshape(-1,1) y = y[inds] #Sort y according to x sorted index x = [item for x in x for item in x] y= [item for y in y for item in y] import statsmodels.api as sm model = sm.OLS(y, x).fit() ypred = model.predict(x) fig = go.Figure() fig.add_trace(go.Scatter(name='Sine Example', x=x, y=y, mode='markers')) fig.add_trace(go.Scatter(name='Best Fit Line', x=x, y=ypred, mode='lines', line=dict(color='orange'))) fig.update_layout(title="Linear Regression") # + import statsmodels.api as sm model = sm.OLS(y, x).fit() ypred = model.predict(x) fig.show() # - model.summary() # + rng = np.random.RandomState(1) x = 8 * rng.rand(50) y = np.sin(x) + 0.1 * rng.randn(50) #Create single dimension x= x[:,np.newaxis] y= y[:,np.newaxis] inds = x.ravel().argsort() # Sort x values and get index x = x.ravel()[inds].reshape(-1,1) y = y[inds] #Sort y according to x sorted index from sklearn.preprocessing import PolynomialFeatures polynomial_features= PolynomialFeatures(degree=3) xp = polynomial_features.fit_transform(x) model = sm.OLS(y, xp).fit() ypred = model.predict(xp) x = [item for x in x for item in x] y= [item for y in y for item in y] fig = go.Figure() fig.add_trace(go.Scatter(name='Sine Example', x=x, y=y, mode='markers')) fig.add_trace(go.Scatter(name='Best Fit Line', x=x, y=ypred, mode='lines', line=dict(color='orange'))) fig.update_layout(title="Polynomial Regression") # - model = sm.OLS(df['sp500'].values, sm.add_constant(df['gdp'].values)).fit() model.summary() pd.DataFrame(index=df.index, data=model.resid).head() #df2['tl'] = sm.OLS(df['sp500'].values, sm.add_constant(df2[['gdp', 'curcir']].values)).fit().fittedvalues df['tl_curcir'] = sm.OLS(df['sp500'].values, sm.add_constant(df[ 'curcir'].values)).fit().fittedvalues df.head() # + fig = go.Figure() #fig.add_trace(go.Scatter(name='GDP vs. SP500', x=df2['gdp'], y=df2['sp500'], mode='markers')) fig.add_trace(go.Scatter(name='CURCIR vs. SP500', x=df['curcir'], y=df['sp500'], mode='markers')) fig.add_trace(go.Scatter(name='Best Fit Line', x=df['curcir'], y=df['tl_curcir'], mode='lines', line=dict(color='orange'))) fig.add_trace(go.Scatter(name='CURCIR vs. SP500', x=df.index, y=df['sp500'], mode='markers', opacity=0, showlegend=False, hoverinfo='skip', xaxis="x2")) fig.update_layout(xaxis1={'side':'bottom'}, xaxis2={'showgrid':False, 'overlaying':'x', 'side':'top', 'tickangle':45 }) fig.update_layout(title="Simple Linear Regression", xaxis2_range=[df.index[0], df.index[-1]]) fig.show() # - model2 = sm.OLS(df['sp500'].values, sm.add_constant(df[ 'curcir'].values)).fit() model2.summary() model3 = sm.OLS(df['sp500'].values, sm.add_constant(df[['gdp', 'curcir']].values)).fit() model3.summary() # ## Visualizing Multiple Linear Regression # # We can perform simple linear regression and graph them separately like the below. # df['tl_gdp_curcir'] = model3 = sm.OLS(df['sp500'].values, sm.add_constant(df[['gdp', 'curcir']].values)).fit().fittedvalues # + fig = make_subplots(rows=1, cols=2) fig.add_trace(go.Scatter(name="GDP vs. SP500", x=df['gdp'], y=df['sp500'], mode='markers', ), row=1,col=1 ) fig.add_trace(go.Scatter(name='Best Fit', x=df['gdp'], y=df['tl_gdp']), row=1, col=1) fig.add_trace(go.Scatter(name="CURCIR vs. SP500", x=df['curcir'], y=df['sp500'], mode='markers', ), row=1,col=2 ) fig.add_trace(go.Scatter(name='Best Fit', x=df['curcir'], y=df['tl_curcir']), row=1, col=2) fig.update_layout(title="Simple Linear Regression, Multiple Plots ", xaxis2_range=[df.index[0], df.index[-1]]) fig.show() # - # But in truth, having two linear models is nice, but the linear regression line is just the best fit line for each independent simple linear regression model we covered above. # # It’s time to put on our 3d glasses. # # Let’s create a multiple linear regression model 3d graph where the y-values are the s&p500, and the x and z values are GDP and currency in circulation, respectively. # # + x_min, x_max = df['gdp'].min(), df['gdp'].max() y_min, y_max = df['sp500'].min(), df['sp500'].max() z_min, z_max = df['curcir'].min(), df['curcir'].max() p_min, p_max = df['tl_gdp_curcir'].min(), df['tl_gdp_curcir'].max() xrange = np.arange(x_min, x_max, int((x_max-x_min) / 10)) yrange = np.arange(y_min, y_max, int((y_max-y_min) / 10)) zrange = np.arange(z_min, z_max, int((z_max-z_min) / 10)) prange = np.arange(p_min, p_max, int((p_max-p_min) / 10)) fig = go.Figure() fig.add_trace(go.Scatter3d(name='SP500 vs. GDP & Currency', x=df['gdp'], y=df['sp500'], z=df['curcir'], mode='markers', )) fig.add_trace(go.Scatter3d(name='Best Fit Line', x=xrange, y=prange, z=zrange)) fig.update_layout(scene = dict( xaxis_title='GDP', yaxis_title='SP500', zaxis_title='Currency in Circulation' )) fig.update_layout(title="Multiple Linear Regression") fig.show() # - # The straight-line moves up and to the right, my favorite direction (trading joke). We can see as both GDP and Currency in Circulation increase, so does the S&P 500 price. # # # Why don’t we add some random data to see how that affects our model. Let’s add a random one-dimensional array between 1 and 1000 to our model. # np.random.seed(1337) # used to replicate randomness rand = np.random.choice([1, 1000, 20], df.shape[0]) df.loc[:, 'rand'] = rand df.head() # We know this is random and won’t help our regression model. Let’s see how it performs. # model4 = sm.OLS(df['sp500'].values, sm.add_constant(df[['gdp', 'curcir', 'rand']].values)).fit() model4.summary() # The r-squared didn't improve, which should be obvious -- we added random data. But how do we know if a feature is statistically significant? How do we know this new input feature helps our predicted value? Let’s dive deeper. # # Well, there’s more to it than this, but a good rule of thumb is that if the p-value is 0.05 or lower, the coefficient and independent variable are said to be statistically significant. # # In other words, if the p-value is small and the increase in r2 is large, add the variable to the input features; otherwise, discard. # # # We can see above that our p-value for x3, our random data, is 0.785, so we should remove it from our model -- even if it improves our target variable, which it didn’t. # # There’s another issue that we need to discuss. Look at the notes from the summary. # # ## Multicollinearity in Regression # Multi-what? When we perform linear regression, the independent variables should be … well… independent. We should understand that a regression coefficient represents the change in the predicted response for each 1 unit change in the independent variable, _holding all other independent variables constant_. # # There are additional problems and different types of multicollinearity, but in short, you can’t trust the p-values to identify statistically significant variables. # # So how do we know if the independent features are independent? # # We can detect multicollinearity with VIF. # # # ### Variance Inflation Factor # # Variance inflation factor or VIF detects multicollinearity in regression analysis. # # A VIF of 1 indicates two variables are not correlated, a VIF greater than 1 and less than 5 indicates a moderate correlation, and a VIF of 5 or above indicates a high correlation. # We can use `Statsmodels` to determine the VIF for each feature. # from statsmodels.stats.outliers_influence import variance_inflation_factor vif = pd.DataFrame() df2 = df[['gdp','curcir', 'rand']] vif['feature'] = df2.columns vif['VIF'] = [variance_inflation_factor(df2.values, i) for i in range(len(df2.columns))] vif # ## Cross Validation # # The goal of a regression model in most cases is to predict future values. We’ve used all of the data until now when building/training our linear regression model. We’re overfitting because we’re building a model using observed data and asking how well it will predict that historical data. # # If we use our linear regression model with next quarter’s GDP to predict the _future_ S&P 500 price, then we’re finally making a prediction. # # We should be breaking up the data into a training and test set, or even better yet, training sets and test sets. We’ll use different slices of history, the training sets, to make predictions about different periods in history, which are our testing sets. # # This would help us determine if the currency in circulation or GDP was better for predicting equity prices. As we saw, GDP was the winner in the first example, and currency in circulation bested GDP over a more extended period, but what about in the middle? # # It’s plain to see that this type of train/test set is more robust and often comes up with a better regression model leading to a more accurate predicted response. This is a common practice in scientific computing and machine learning. The only concern with machine learning models is that such models are prone to overfitting -- we’ll discuss this in a bit. # # I will use `sklearn` to create the training data, and test data splits. I’ll also use the linear regression model from `sklearn`, but linear regression works with both packages and can use either. We’re going to need to import a lot more libraries, and this time, instead of using `plotly`, we’ll use `matplotlib` in conjunction with `seaborn`. # # We’ll first grab the imports. # ### Get Imports import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.metrics import r2_score from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.feature_selection import RFE from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold # Next, let's organize the columns and review the data. columns = ['gdp','curcir', 'rand', 'sp500'] df = df[columns] df.head() # ### Scale & Normalize Data # Machine learning algorithms work better when features share a similar scale and are normally distributed. Let’s scale and standardize the variables between 0 and 1 using `sklearn.preprocessing.MinMaxScaler`. scaler = MinMaxScaler() scaled = scaler.fit_transform(df[columns]) df2 = pd.DataFrame(scaled) df2.columns = columns #scaler returns nd.array df2.head() # Notice that all of the numbers are now scaled between one and zero. Also, remember that overfitting thing? We just did it… # # Overfitting means that our model fits too closely to a particular set of data and may fail to predict observed values reliably. # # In the above case, we scaled and fit the data to the entire data set. We can’t train on our test data because we’ll be making predictions on data that we used to create our regression model. # # We should only ever use `MinMaxScaler.fit_transform` with training data and use `MinMaxScaler.transform` with test data. The reason is that we can’t scale and normalize our data based on test data. We should only scale and fit on training data. # # ### Split Data Into Train and Test Sets # There are other ways to overfit, too. We’ll discuss a few more ways shortly. For now, let’s separate our data into training and testing sets. We’ll train on 70% of the data and test on the remaining 30%. We'll also scale our data properly instead of overfitting like we did above. # # Always remember to only call `transform` and not `fit_transform` on the test data. You should never fit to testing data! # + # Create training data. train_size = 0.7 df_train, df_test = train_test_split(df, train_size=train_size, test_size=round(1-train_size,2), shuffle=False ) # Scale the test and train data. scaler = MinMaxScaler() df_train[columns] = scaler.fit_transform(df_train[columns]) df_test[columns] = scaler.transform(df_test[columns]) # fit_transform # Separate into training and testing sets y_train = df_train.pop('sp500') X_train = df_train y_test = df_test.pop('sp500') X_test = df_test print(X_train.head()) print(y_train.head()) print(X_test.head()) print(y_test.head()) # - # Now let's fix our multicollinearity issue identified by VIF. # ### Recursive Feature Elimination # Instead of manually removing our features, imagine if we had numerous and weren’t sure which ones we should eliminate? Machine learning to the rescue. # # Recursive feature elimination does just that. It’s simple to do. We furnish a hyperparameter of the number of parameters we want, and it does the hard work for us. -- A hyperparameter is a parameter for parameters. # # Let’s see it in action. # from sklearn.feature_selection import RFE lm = LinearRegression() rfe = RFE(lm, n_features_to_select=1) rfe = rfe.fit(X_train, y_train) print(X_train.columns) print(rfe.support_) print(rfe.ranking_) # Notice that our `n_features_to_select` hyperparameter was set to one, causing RFE to select only GDP. We can also see the rankings are 1, 2, 3 for GDP, currency in circulation, and our random variable, respectively. # ### Create Linear Regression Model # Let’s now understand a little more what we did above, and create another linear regression model below. # # We'll create a LinearRegression object and fit the training data to it. I’ll then use that trained LinearRegression object to predict the y_values. I’ll then compare the y_pred to the actual values (y_test) and print out our r2. `sklearn` requires the data be in a 1d array. We didn't need to do this above because the RFE took care of it for us. # # + lm = LinearRegression() # Only use GDP as determined by RME & VIF # lm required 1d array lm.fit(X_train['gdp'].values.reshape(-1,1), y_train) # Use test data for prediction y_pred = lm.predict(X_test['gdp'].values.reshape(-1,1)) r2 = r2_score(y_test, y_pred) print(r2) # - # RFE selects the best features recursively and applies the LinearRegression model to it. With this in mind, we should -- and will -- get the same answer for both linear regression models. y_pred = rfe.predict(X_test) r2 = r2_score(y_test, y_pred) print(r2) # I wanted to show you both ways of creating a `LinearRegression` model. Keep in mind that RFE can be used with all sorts of estimators such as a `DecisionTreeClassifer`. # ### Cross-Validation Using K-Folds in Python # Instead of splitting the data into one train set and one test set, we can slice the data into multiple periods and create multiple training and test sets. Let’s use four k-folds as an example. We’ll create a KFold object with four splits. The splits will segregate utilizing the test data indices. The first set will take the first 16 elements; the second will be the following 16 elements, the next 15 elements, and finally, our most recent 15 elements. Our array length is 62 and not evenly divisible by 4. # + kf = KFold(n_splits = 4) for train_index, test_index in kf.split(X_train): print("Train: ", train_index,"\nTest: ", test_index, "\n\n") # - # Notice how we now have four groups of test and train data. We can quickly estimate our r2 for each test group. scores = cross_val_score(lm, X_train, y_train, scoring='r2', cv=kf) scores # ## An Overfitting Conclusion # We see that the original linear regression model, which we thought was terrific, turns out to not be that great at predicting future S&P prices. There is some predictive power, but it isn't enough for me to put my money behind it. # # The good news is that you now have everything you need to perform simple and multiple linear regression in Python to create even better predictive models -- for the markets or whatever you choose. # # I hope you enjoyed, and if you have any questions, please let me know in the comments below. #
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: pytorch # language: python # name: pytorch # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/RheagalFire/Assignment/blob/master/Solution_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="c611e4cb" #read dataframe import pandas as pd listings=pd.read_csv('listings.csv') # + id="9ca6cee1" #read geodataframe # #!pip install geopandas import geopandas as gd geo=gd.read_file('neighbourhoods.geojson') # + id="40db58f2" #importing Map plot Library # #!pip install -U plotly import plotly.express as px # + colab={"base_uri": "https://localhost:8080/", "height": 225} id="1b56f07d" outputId="547fdb1a-8df7-4bd5-a221-3a5752931dc4" geo=geo[['neighbourhood','geometry']].set_index("neighbourhood") geo.head() # + colab={"base_uri": "https://localhost:8080/", "height": 195} id="9383cdb2" outputId="73a2bce5-ee85-4b58-9cf4-ee84556688f2" df=pd.DataFrame(listings['neighbourhood_cleansed'].value_counts()) #df['neigbourhood']=df.index df.reset_index(inplace=True) df.rename(columns={'index':'neighbourhood_cleansed','neighbourhood_cleansed':'no_of_listings'},inplace=True) df.head() # + colab={"base_uri": "https://localhost:8080/", "height": 323} id="9514333f" outputId="cf576e5c-bc74-48bf-8014-c23f6a445649" import matplotlib.pyplot as plt listings['neighbourhood_cleansed'].value_counts().plot(kind = 'bar', color = ['gold'], figsize = (40, 10)) plt.title('Histogram of Listings', fontsize = 20) plt.xlabel('Neighbourhood') plt.ylabel('Number of Listings') plt.show() # + colab={"base_uri": "https://localhost:8080/", "height": 322} id="9212a1cc" outputId="4812276d-e1c6-4557-e37f-7f306ccde7c2" listings.groupby('neighbourhood_cleansed')['number_of_reviews'].sum().plot(kind = 'bar', color = ['purple'], figsize = (40, 10)) plt.title('Histogram of Reviews', fontsize = 20) plt.xlabel('Neighbourhood') plt.ylabel('Number of reviews') plt.show() # + [markdown] id="2f9e7f79" # ### Ans A Top 10 Neighbourhood according to Listings and Reviews # + colab={"base_uri": "https://localhost:8080/", "height": 302} id="76705de8" outputId="2e64615a-0a56-4cb5-8b79-b54ab8ba2d1d" listings['neighbourhood_cleansed'].value_counts().head(10).plot(kind='bar') plt.xlabel('Zip_Codes') plt.ylabel('No. of Listings') plt.show() # + colab={"base_uri": "https://localhost:8080/", "height": 195} id="979839d7" outputId="07dfd6cb-6fab-4981-d2b5-44e2fb8a7923" ans_a=pd.DataFrame(listings['neighbourhood_cleansed'].value_counts()) ans_a.reset_index(inplace=True) ans_a.rename(columns={'index':'neighbourhood','neighbourhood_cleansed':'Listings'},inplace=True) ans_a['neighbourhood']=ans_a['neighbourhood'].astype(str).astype(object) ans_a.head() # + [markdown] id="feb37a7c" # The top Neighbourhood according to number of listing is locality with zip code **78704** # + [markdown] id="b6372b2f" # ### A.2 Mapping The Listing Pointers with thier Neighbourhood Polygons # + colab={"base_uri": "https://localhost:8080/", "height": 195} id="9cad9694" outputId="0eb8e55b-b23f-4b52-e0dd-77f3e6580ce1" ans_geo=geo.copy() ans_geo.reset_index(inplace=True) ans_geo pd.merge(ans_geo,ans_a,on='neighbourhood').head() # + [markdown] id="439932ef" # #### Reviews # + colab={"base_uri": "https://localhost:8080/", "height": 301} id="068446e6" outputId="f41d6faa-f415-4209-a955-d8191cc673fa" listings.groupby('neighbourhood_cleansed')['number_of_reviews'].sum().sort_values(ascending=False).head(10).plot(kind='bar') plt.xlabel('Zip Codes') plt.ylabel('No. of Reviews') plt.show() # + [markdown] id="b815ae89" # The Top Neibhbourhood according to number of reviews is again with zip code **78704** # + colab={"base_uri": "https://localhost:8080/", "height": 195} id="4b085e00" outputId="08bf183c-57df-44a2-e9bc-d6fd25b29ba8" a={} for n in listings['neighbourhood_cleansed'].unique(): a[n]=listings.loc[listings['neighbourhood_cleansed']==n]['room_type'].value_counts() room_count=pd.DataFrame.from_dict(a,orient='index') room_count.reset_index(inplace=True) room_count.rename(columns={'index':'neighbourhood_cleansed'},inplace=True) room_count.fillna(value=0,inplace=True) room_count.head() # + colab={"base_uri": "https://localhost:8080/", "height": 195} id="5191e6aa" outputId="faee0741-41ec-49c9-825c-4db2754cb966" final_df=pd.merge(df,room_count,on='neighbourhood_cleansed') final_df.head() # + [markdown] id="4cedc1dc" # ### Ans B # + [markdown] id="df8b3809" # ### Map Visualization Broken Down By Single Rooms # + colab={"base_uri": "https://localhost:8080/", "height": 542} id="a6a02a92" outputId="2831ecff-d36f-44de-91d6-e46c2db3466c" fig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='Private room',labels={'no_of_listings':'no_of_listings'},hover_data=["no_of_listings"],color_continuous_scale="Viridis") fig.update_geos(fitbounds="locations", visible=False) fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}) fig.show() # + [markdown] id="19fc7e88" # ### Map Visualization Broken Down By Entire home/apt # + id="cc281e95" colab={"base_uri": "https://localhost:8080/", "height": 542} outputId="68fb6cf5-7d34-41b9-b4e4-9fa7dc8cbe52" fig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='Entire home/apt',labels={'no_of_listings':'no_of_listings'},hover_data=["no_of_listings"],color_continuous_scale='blackbody') fig.update_geos(fitbounds="locations", visible=False) fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}) fig.show() # + [markdown] id="62ff0b31" # ### AnsC: Top 10 Hosts # + id="09130ebd" colab={"base_uri": "https://localhost:8080/", "height": 395} outputId="680d5bb5-ed92-498d-be4e-1c582973bee5" listings['host_name'].value_counts().head(10).plot(kind='bar') plt.xlabel('Hosts') plt.ylabel('Listings') plt.show() # + id="4e592d8e" colab={"base_uri": "https://localhost:8080/"} outputId="afcab754-7f9c-495f-f812-a44bfca0ebb9" listings.groupby('neighbourhood_cleansed')['host_name'].value_counts() # + id="86e6d11f" b={} for n in listings['neighbourhood_cleansed'].unique(): b[n]=listings.loc[listings['neighbourhood_cleansed']==n]['host_name'].value_counts() name_count=pd.DataFrame.from_dict(b,orient='index') name_count.fillna(0,inplace=True) # + id="8ea7c8d2" import numpy as np make=np.argmax(name_count.values,axis=1) # + id="cb4c08b2" top_by_zip=name_count.columns[make] # + id="38088e8f" link={} for val,name in zip(name_count.index,top_by_zip): link[val]=name # + [markdown] id="d41f604e" # ### C.2 Top Hosts Region-Wise # + id="9777279e" colab={"base_uri": "https://localhost:8080/", "height": 195} outputId="c0f510ec-25f2-4ebe-c936-186afd7265c1" to_add=pd.DataFrame.from_dict(link,orient='index') to_add.reset_index(inplace=True) to_add.rename(columns={'index':'neighbourhood_cleansed',0:'Top Host'},inplace=True) to_add.head() # + [markdown] id="c40e6242" # ### Ans D Analysis and Insights # + [markdown] id="0d3101c3" # Lets Create a Hovering Map So that whenever you hover over a particular region you can have maximum info about it. # + id="d5a71c19" final_df=pd.merge(final_df,to_add,on='neighbourhood_cleansed') # + id="3c9e86e8" def remove_dollar(row): if row[0] == '$': return row[1:] return row listings['price'] = listings['price'].apply(lambda row: float(remove_dollar(row).replace(',',''))) # + id="dadba878" colab={"base_uri": "https://localhost:8080/", "height": 195} outputId="f66dd8a4-688b-4621-8336-45eab3c8c01f" # Listing price and count by zip zip_rate = listings.groupby(['neighbourhood_cleansed'])[['price','review_scores_value']].agg('mean') zip_rate.reset_index(inplace=True) zip_rate['price']=zip_rate['price'].round(2) zip_rate['review_scores_value']=zip_rate['review_scores_value'].round(2) zip_rate.rename(columns={'index':'neighbourhood_cleansed','price':'Mean Price($)','review_scores_value':'Score'},inplace=True) zip_rate.head() # + id="fa94e1a7" colab={"base_uri": "https://localhost:8080/", "height": 195} outputId="e0dffc1b-c81d-43f3-8a3d-5364ac48bd93" final_df=pd.merge(final_df,zip_rate,on='neighbourhood_cleansed') final_df.head() # + id="fc83d854" colab={"base_uri": "https://localhost:8080/", "height": 542} outputId="bc492584-1880-4a38-b016-f279034cbe8f" fig = px.choropleth(final_df, geojson=geo,locations=geo.index,color='no_of_listings',labels={'no_of_listings':'no_of_listings'},hover_data=["Entire home/apt","Private room","Hotel room","Shared room","Top Host","Mean Price($)","Score"]) fig.update_geos(fitbounds="locations", visible=False) fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}) fig.show() # + [markdown] id="193c1d51" # - According to the Price Value and Room-Type we can Redirect User to Particular Host of suitable region. # - Build out a Recommendations that allows filteration Based on The Above given Properties. # - Increase engagement of areas whose rating are good but Listings are Less. # - Make A seosanlized Report of What is Favourable during Which Month. # + id="641535e4" rts for export to FC # cohorts = fc_interface.\ # prepare_cohorts_for_metadata_export(paths_to_samples_info, google_bucket_id, \ # blacklist=["CCLF_AA1012-Tumor-SM-F67DF"]) # # cohorts_sample_set_metadata = "cohort_files/fc_upload_sample_set_cohorts.txt" # res = fc_interface.upload_entities_from_tsv(namespace, workspace, cohorts_sample_set_metadata) # + editable=false run_control={"frozen": true} # # Export metadata # (r1, r2, r3, r4, r5, r6) = fc_interface.export_batch_metadata_to_fc('TSCA21', namespace, workspace) # + editable=false run_control={"frozen": true} # ### Cohort of all tumors # res = fc_interface.upload_cohort_all_tumors(paths_to_samples_info, google_bucket_id, \ # 'Cum_Tumors_22_all', namespace, workspace, ['DW039-Tumor-SM-DB2IF']) # - # --- # --- as.layers` to build the model. # All fully connected layers should include bias terms. For initialization, just use the default initializer used by the `tf.keras.layers` functions. # # Architecture: # * Fully connected layer with input size 784 and output size 256 # * LeakyReLU with alpha 0.01 # * Fully connected layer with output size 256 # * LeakyReLU with alpha 0.01 # * Fully connected layer with output size 1 # # The output of the discriminator should thus have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image. # # Implement `discriminator()` in `cs231n/gan_tf.py` # Test to make sure the number of parameters in the discriminator is correct: # + from cs231n.gan_tf import discriminator def test_discriminator(true_count=267009, discriminator=discriminator): model = discriminator() cur_count = count_params(model) if cur_count != true_count: print('Incorrect number of parameters in discriminator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count)) else: print('Correct number of parameters in discriminator.') test_discriminator() # - # ## Generator # Now to build a generator. You should use the layers in `tf.keras.layers` to construct the model. All fully connected layers should include bias terms. Note that you can use the tf.nn module to access activation functions. Once again, use the default initializers for parameters. # # Architecture: # * Fully connected layer with inupt size tf.shape(z)[1] (the number of noise dimensions) and output size 1024 # * `ReLU` # * Fully connected layer with output size 1024 # * `ReLU` # * Fully connected layer with output size 784 # * `TanH` (To restrict every element of the output to be in the range [-1,1]) # # Implement `generator()` in `cs231n/gan_tf.py` # Test to make sure the number of parameters in the generator is correct: # + from cs231n.gan_tf import generator def test_generator(true_count=1858320, generator=generator): model = generator(4) cur_count = count_params(model) if cur_count != true_count: print('Incorrect number of parameters in generator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count)) else: print('Correct number of parameters in generator.') test_generator() # - # # GAN Loss # # Compute the generator and discriminator loss. The generator loss is: # $$\ell_G = -\mathbb{E}_{z \sim p(z)}\left[\log D(G(z))\right]$$ # and the discriminator loss is: # $$ \ell_D = -\mathbb{E}_{x \sim p_\text{data}}\left[\log D(x)\right] - \mathbb{E}_{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$ # Note that these are negated from the equations presented earlier as we will be *minimizing* these losses. # # **HINTS**: Use [tf.ones](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/ones) and [tf.zeros](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/zeros) to generate labels for your discriminator. Use [tf.keras.losses.BinaryCrossentropy](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/losses/BinaryCrossentropy) to help compute your loss function. # # Implement `discriminator_loss, generator_loss` in `cs231n/gan_tf.py` # Test your GAN loss. Make sure both the generator and discriminator loss are correct. You should see errors less than 1e-8. # + from cs231n.gan_tf import discriminator_loss def test_discriminator_loss(logits_real, logits_fake, d_loss_true): d_loss = discriminator_loss(tf.constant(logits_real), tf.constant(logits_fake)) print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss)) test_discriminator_loss(answers['logits_real'], answers['logits_fake'], answers['d_loss_true']) # + from cs231n.gan_tf import generator_loss def test_generator_loss(logits_fake, g_loss_true): g_loss = generator_loss(tf.constant(logits_fake)) print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss)) test_generator_loss(answers['logits_fake'], answers['g_loss_true']) # - # # Optimizing our loss # Make an `Adam` optimizer with a 1e-3 learning rate, beta1=0.5 to mininize G_loss and D_loss separately. The trick of decreasing beta was shown to be effective in helping GANs converge in the [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) paper. In fact, with our current hyperparameters, if you set beta1 to the Tensorflow default of 0.9, there's a good chance your discriminator loss will go to zero and the generator will fail to learn entirely. In fact, this is a common failure mode in GANs; if your D(x) learns too fast (e.g. loss goes near zero), your G(z) is never able to learn. Often D(x) is trained with SGD with Momentum or RMSProp instead of Adam, but here we'll use Adam for both D(x) and G(z). # # Implement `get_solvers` in `cs231n/gan_tf.py` from cs231n.gan_tf import get_solvers # + [markdown] tags=["pdf-ignore"] # # Training a GAN! # Well that wasn't so hard, was it? After the first epoch, you should see fuzzy outlines, clear shapes as you approach epoch 3, and decent shapes, about half of which will be sharp and clearly recognizable as we pass epoch 5. In our case, we'll simply train D(x) and G(z) with one batch each every iteration. However, papers often experiment with different schedules of training D(x) and G(z), sometimes doing one for more steps than the other, or even training each one until the loss gets "good enough" and then switching to training the other. # # **If you are a Colab user, it is recommeded to change colab runtime to GPU.** # - # #### Train your GAN! This should take about 10 minutes on a CPU, or about 2 minutes on GPU. # + from cs231n.gan_tf import run_a_gan # Make the discriminator D = discriminator() # Make the generator G = generator() # Use the function you wrote earlier to get optimizers for the Discriminator and the Generator D_solver, G_solver = get_solvers() # Run it! images, final = run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss) # + numIter = 0 for img in images: print("Iter: {}".format(numIter)) show_images(img) plt.show() numIter += 20 print() # - # **Please tag the cell below on Gradescope while submitting.** print('Vanilla GAN Final images') show_images(final) plt.show() # # Least Squares GAN # We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alternative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss: # $$\ell_G = \frac{1}{2}\mathbb{E}_{z \sim p(z)}\left[\left(D(G(z))-1\right)^2\right]$$ # and the discriminator loss: # $$ \ell_D = \frac{1}{2}\mathbb{E}_{x \sim p_\text{data}}\left[\left(D(x)-1\right)^2\right] + \frac{1}{2}\mathbb{E}_{z \sim p(z)}\left[ \left(D(G(z))\right)^2\right]$$ # # # **HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`score_real` and `score_fake`). # # Implement `ls_discriminator_loss, ls_generator_loss` in `cs231n/gan_tf.py` # Test your LSGAN loss. You should see errors less than 1e-8. # + from cs231n.gan_tf import ls_discriminator_loss, ls_generator_loss def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true): d_loss = ls_discriminator_loss(tf.constant(score_real), tf.constant(score_fake)) g_loss = ls_generator_loss(tf.constant(score_fake)) print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss)) print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss)) test_lsgan_loss(answers['logits_real'], answers['logits_fake'], answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true']) # - # Create new training steps so we instead minimize the LSGAN loss: # + # Make the discriminator D = discriminator() # Make the generator G = generator() # Use the function you wrote earlier to get optimizers for the Discriminator and the Generator D_solver, G_solver = get_solvers() # Run it! images, final = run_a_gan(D, G, D_solver, G_solver, ls_discriminator_loss, ls_generator_loss) # + numIter = 0 for img in images: print("Iter: {}".format(numIter)) show_images(img) plt.show() numIter += 20 print() # - # **Please tag the cell below on Gradescope while submitting.** print('LSGAN Final images') show_images(final) plt.show() # # Deep Convolutional GANs # In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like "sharp edges" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks as our discriminators and generators. # # #### Discriminator # We will use a discriminator inspired by the TensorFlow MNIST classification [tutorial](https://www.tensorflow.org/get_started/mnist/pros), which is able to get above 99% accuracy on the MNIST dataset fairly quickly. *Be sure to check the dimensions of x and reshape when needed*, fully connected blocks expect [N,D] Tensors while conv2d blocks expect [N,H,W,C] Tensors. Please use `tf.keras.layers` to define the following architecture: # # Architecture: # * Conv2D: 32 Filters, 5x5, Stride 1, padding 0 # * Leaky ReLU(alpha=0.01) # * Max Pool 2x2, Stride 2 # * Conv2D: 64 Filters, 5x5, Stride 1, padding 0 # * Leaky ReLU(alpha=0.01) # * Max Pool 2x2, Stride 2 # * Flatten # * Fully Connected with output size 4 x 4 x 64 # * Leaky ReLU(alpha=0.01) # * Fully Connected with output size 1 # # Once again, please use biases for all convolutional and fully connected layers, and use the default parameter initializers. Note that a padding of 0 can be accomplished with the 'VALID' padding option. # # Implement `dc_discriminator` in `cs231n/gan_tf.py` # + from cs231n.gan_tf import dc_discriminator # model = dc_discriminator() test_discriminator(1102721, dc_discriminator) # - # #### Generator # For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. Please use `tf.keras.layers` for your implementation. You might find the documentation for [tf.keras.layers.Conv2DTranspose](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Conv2DTranspose) useful. The architecture is as follows. # # Architecture: # * Fully connected with output size 1024 # * `ReLU` # * BatchNorm # * Fully connected with output size 7 x 7 x 128 # * `ReLU` # * BatchNorm # * Resize into Image Tensor of size 7, 7, 128 # * Conv2D^T (transpose): 64 filters of 4x4, stride 2 # * `ReLU` # * BatchNorm # * Conv2d^T (transpose): 1 filter of 4x4, stride 2 # * `TanH` # # Once again, use biases for the fully connected and transpose convolutional layers. Please use the default initializers for your parameters. For padding, choose the 'same' option for transpose convolutions. For Batch Normalization, assume we are always in 'training' mode. # # Implement `dc_generator` in `cs231n/gan_tf.py` # + from cs231n.gan_tf import dc_generator test_generator(6595521, generator=dc_generator) # - # We have to recreate our network since we've changed our functions. # ### Train and evaluate a DCGAN # This is the one part of A3 that significantly benefits from using a GPU. It takes 3 minutes on a GPU for the requested five epochs. Or about 50 minutes on a dual core laptop on CPU (feel free to use 3 epochs if you do it on CPU). # + # Make the discriminator D = dc_discriminator() # Make the generator G = dc_generator() # Use the function you wrote earlier to get optimizers for the Discriminator and the Generator D_solver, G_solver = get_solvers() # Run it! images, final = run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, num_epochs=3)# origin:5 # - numIter = 0 for img in images: print("Iter: {}".format(numIter)) show_images(img) plt.show() numIter += 20 print() # **Please tag the cell below on Gradescope while submitting.** print('DCGAN Final images') show_images(final) plt.show() # + [markdown] tags=["pdf-inline"] # ## INLINE QUESTION 1 # # We will look at an example to see why alternating minimization of the same objective (like in a GAN) can be tricky business. # # Consider $f(x,y)=xy$. What does $\min_x\max_y f(x,y)$ evaluate to? (Hint: minmax tries to minimize the maximum value achievable.) # # Now try to evaluate this function numerically for 6 steps, starting at the point $(1,1)$, # by using alternating gradient (first updating y, then updating x using that updated y) with step size $1$. **Here step size is the learning_rate, and steps will be learning_rate * gradient.** # You'll find that writing out the update step in terms of $x_t,y_t,x_{t+1},y_{t+1}$ will be useful. # # Breifly explain what $\min_x\max_y f(x,y)$ evaluates to and record the six pairs of explicit values for $(x_t,y_t)$ in the table below. # # ### Your answer: # # $y_0$ | $y_1$ | $y_2$ | $y_3$ | $y_4$ | $y_5$ | $y_6$ # ----- | ----- | ----- | ----- | ----- | ----- | ----- # 1 | 2 | 1 | -1 | -2 | -1 | 1 # $x_0$ | $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | $x_6$ # 1 | -1 | -2 | -1 | 1 | 2 | 1 # # 产生了一个循环,可能的原因是在局部最大局部最小的过程中,y从一个局部最大在x极小的影响下到了另一个局部最大,在调整后y又回到了原来的位置 # + [markdown] tags=["pdf-inline"] # ## INLINE QUESTION 2 # Using this method, will we ever reach the optimal value? Why or why not? # # ### Your answer: # 我认为是不行的,因为数据集的本质分布和生成器的分布理论上是不同的,所以存在一个最优判别分类器,而当判别器训练过快会使得判别器参数更新梯度消失,无法更新。 # + [markdown] tags=["pdf-inline"] # ## INLINE QUESTION 3 # If the generator loss decreases during training while the discriminator loss stays at a constant high value from the start, is this a good sign? Why or why not? A qualitative answer is sufficient. # # ### Your answer: # 我认为是个好现象,此时说明生成器在不断优化,而同时判别器能够持续为生成器优化提梯度。 # -
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# --- # jupyter: # jupytext: # text_representation: # extension: .r # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: R # language: R # name: ir # --- # + [markdown] toc=true # <h1>Table of Contents<span class="tocSkip"></span></h1> # <div class="toc"><ul class="toc-item"><li><span><a href="#Pipeline-overview" data-toc-modified-id="Pipeline-overview-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Pipeline overview</a></span></li><li><span><a href="#Install-and-load-all-required-packages" data-toc-modified-id="Install-and-load-all-required-packages-2"><span class="toc-item-num">2&nbsp;&nbsp;</span>Install and load all required packages</a></span></li><li><span><a href="#Demo-Example" data-toc-modified-id="Demo-Example-3"><span class="toc-item-num">3&nbsp;&nbsp;</span>Demo Example</a></span></li><li><span><a href="#Save-Your-session" data-toc-modified-id="Save-Your-session-4"><span class="toc-item-num">4&nbsp;&nbsp;</span>Save Your session</a></span></li><li><span><a href="#Load-the-metabolomics-data,-metabolites-and-samples-meta-information" data-toc-modified-id="Load-the-metabolomics-data,-metabolites-and-samples-meta-information-5"><span class="toc-item-num">5&nbsp;&nbsp;</span>Load the metabolomics data, metabolites and samples meta information</a></span></li><li><span><a href="#Data-normalization,-transformation,-and-missing-imputation" data-toc-modified-id="Data-normalization,-transformation,-and-missing-imputation-6"><span class="toc-item-num">6&nbsp;&nbsp;</span>Data normalization, transformation, and missing imputation</a></span><ul class="toc-item"><li><span><a href="#Replace-non-numeric-values-by-NA" data-toc-modified-id="Replace-non-numeric-values-by-NA-6.1"><span class="toc-item-num">6.1&nbsp;&nbsp;</span>Replace non-numeric values by NA</a></span></li><li><span><a href="#Remove-metabolites-with-a-constant-or-single-value-across-samples" data-toc-modified-id="Remove-metabolites-with-a-constant-or-single-value-across-samples-6.2"><span class="toc-item-num">6.2&nbsp;&nbsp;</span>Remove metabolites with a constant or single value across samples</a></span></li><li><span><a href="#Calculate-the-percentage-of-missinginess" data-toc-modified-id="Calculate-the-percentage-of-missinginess-6.3"><span class="toc-item-num">6.3&nbsp;&nbsp;</span>Calculate the percentage of missinginess</a></span></li><li><span><a href="#delete-metaboloite-if-it-was-missed-accros-more-than-50%-of-samples" data-toc-modified-id="delete-metaboloite-if-it-was-missed-accros-more-than-50%-of-samples-6.4"><span class="toc-item-num">6.4&nbsp;&nbsp;</span>delete metaboloite if it was missed accros more than 50% of samples</a></span></li><li><span><a href="#Impute-the-remaining-missed-metaboloties-using-KKN" data-toc-modified-id="Impute-the-remaining-missed-metaboloties-using-KKN-6.5"><span class="toc-item-num">6.5&nbsp;&nbsp;</span>Impute the remaining missed metaboloties using KKN</a></span></li><li><span><a href="#Log-transformation" data-toc-modified-id="Log-transformation-6.6"><span class="toc-item-num">6.6&nbsp;&nbsp;</span>Log transformation</a></span></li><li><span><a href="#Check-data-distrubution-after-and-before-data-distribution" data-toc-modified-id="Check-data-distrubution-after-and-before-data-distribution-6.7"><span class="toc-item-num">6.7&nbsp;&nbsp;</span>Check data distrubution after and before data distribution</a></span><ul class="toc-item"><li><span><a href="#Metabolites" data-toc-modified-id="Metabolites-6.7.1"><span class="toc-item-num">6.7.1&nbsp;&nbsp;</span>Metabolites</a></span></li><li><span><a href="#Samples" data-toc-modified-id="Samples-6.7.2"><span class="toc-item-num">6.7.2&nbsp;&nbsp;</span>Samples</a></span></li></ul></li><li><span><a href="#Which-metabolites-was-removed?" data-toc-modified-id="Which-metabolites-was-removed?-6.8"><span class="toc-item-num">6.8&nbsp;&nbsp;</span>Which metabolites was removed?</a></span></li></ul></li><li><span><a href="#PCA" data-toc-modified-id="PCA-7"><span class="toc-item-num">7&nbsp;&nbsp;</span>PCA</a></span></li><li><span><a href="#Source-of-variation" data-toc-modified-id="Source-of-variation-8"><span class="toc-item-num">8&nbsp;&nbsp;</span>Source of variation</a></span><ul class="toc-item"><li><span><a href="#Overall-source-of-variation" data-toc-modified-id="Overall-source-of-variation-8.1"><span class="toc-item-num">8.1&nbsp;&nbsp;</span>Overall source of variation</a></span></li><li><span><a href="#Source-of-variation-per-super-pathway" data-toc-modified-id="Source-of-variation-per-super-pathway-8.2"><span class="toc-item-num">8.2&nbsp;&nbsp;</span>Source of variation per super pathway</a></span></li><li><span><a href="#Source-of-variation-per-sub-pathway" data-toc-modified-id="Source-of-variation-per-sub-pathway-8.3"><span class="toc-item-num">8.3&nbsp;&nbsp;</span>Source of variation per sub pathway</a></span></li><li><span><a href="#Box-plot" data-toc-modified-id="Box-plot-8.4"><span class="toc-item-num">8.4&nbsp;&nbsp;</span>Box plot</a></span></li></ul></li><li><span><a href="#Biomarker-analysis" data-toc-modified-id="Biomarker-analysis-9"><span class="toc-item-num">9&nbsp;&nbsp;</span>Biomarker analysis</a></span><ul class="toc-item"><li><span><a href="#Differentiated-metabolites-(DMs)-using-Limma-(Statistical-Summary)" data-toc-modified-id="Differentiated-metabolites-(DMs)-using-Limma-(Statistical-Summary)-9.1"><span class="toc-item-num">9.1&nbsp;&nbsp;</span>Differentiated metabolites (DMs) using Limma (Statistical Summary)</a></span></li><li><span><a href="#DMs-using-T-test-(Statistical-Summary)" data-toc-modified-id="DMs-using-T-test-(Statistical-Summary)-9.2"><span class="toc-item-num">9.2&nbsp;&nbsp;</span>DMs using T-test (Statistical Summary)</a></span><ul class="toc-item"><li><span><a href="#Venn-digram" data-toc-modified-id="Venn-digram-9.2.1"><span class="toc-item-num">9.2.1&nbsp;&nbsp;</span>Venn digram</a></span></li></ul></li><li><span><a href="#DMs-using-lm-adjusted-for-sex-and-age" data-toc-modified-id="DMs-using-lm-adjusted-for-sex-and-age-9.3"><span class="toc-item-num">9.3&nbsp;&nbsp;</span>DMs using lm adjusted for sex and age</a></span><ul class="toc-item"><li><span><a href="#Venn-digram" data-toc-modified-id="Venn-digram-9.3.1"><span class="toc-item-num">9.3.1&nbsp;&nbsp;</span>Venn digram</a></span></li></ul></li><li><span><a href="#DMs-using-elastic-net" data-toc-modified-id="DMs-using-elastic-net-9.4"><span class="toc-item-num">9.4&nbsp;&nbsp;</span>DMs using elastic net</a></span><ul class="toc-item"><li><span><a href="#Find-the-best-alpha" data-toc-modified-id="Find-the-best-alpha-9.4.1"><span class="toc-item-num">9.4.1&nbsp;&nbsp;</span>Find the best alpha</a></span></li><li><span><a href="#Find-the-best-lamda" data-toc-modified-id="Find-the-best-lamda-9.4.2"><span class="toc-item-num">9.4.2&nbsp;&nbsp;</span>Find the best lamda</a></span></li></ul></li></ul></li><li><span><a href="#Metabolites-Pathways-analysis" data-toc-modified-id="Metabolites-Pathways-analysis-10"><span class="toc-item-num">10&nbsp;&nbsp;</span>Metabolites Pathways analysis</a></span></li><li><span><a href="#Machine-learning" data-toc-modified-id="Machine-learning-11"><span class="toc-item-num">11&nbsp;&nbsp;</span>Machine learning</a></span></li><li><span><a href="#Cytoscape-correlation-analysis" data-toc-modified-id="Cytoscape-correlation-analysis-12"><span class="toc-item-num">12&nbsp;&nbsp;</span>Cytoscape correlation analysis</a></span></li><li><span><a href="#Complex-Heatmap" data-toc-modified-id="Complex-Heatmap-13"><span class="toc-item-num">13&nbsp;&nbsp;</span>Complex Heatmap</a></span><ul class="toc-item"><li><span><a href="#Neuropathy" data-toc-modified-id="Neuropathy-13.1"><span class="toc-item-num">13.1&nbsp;&nbsp;</span>Neuropathy</a></span></li><li><span><a href="#Non-neuropathy" data-toc-modified-id="Non-neuropathy-13.2"><span class="toc-item-num">13.2&nbsp;&nbsp;</span>Non neuropathy</a></span></li></ul></li><li><span><a href="#Demographic-table" data-toc-modified-id="Demographic-table-14"><span class="toc-item-num">14&nbsp;&nbsp;</span>Demographic table</a></span></li><li><span><a href="#My-functions" data-toc-modified-id="My-functions-15"><span class="toc-item-num">15&nbsp;&nbsp;</span>My functions</a></span></li><li><span><a href="#print-session-information" data-toc-modified-id="print-session-information-16"><span class="toc-item-num">16&nbsp;&nbsp;</span>print session information</a></span></li></ul></div> # - # # Pipeline overview # This pipeline is designed to summarize all the preprocessing and down stream analysis for the metabolomics data in one single notebook. You can use it as a template for your analysis. Please feel free to add more functions and distribute it with others. # You can contact me if you have any questions: Fadhl Alakwaa [email protected] # # ![image.png](attachment:image.png) # # # Install and load all required packages # + # Load the libararies library(glmnet) library(ggplot2) #library(MetaboAnalystR) # It will take long time to install, you can comment this line library("scales") library(pheatmap) library(tidyverse) library(reshape2) library(magrittr) library("ggpubr") library(lsmeans) library(multcomp) library(corrplot) library("Hmisc") library(ComplexHeatmap) library(circlize) #install.packages("glmnet") #install.packages("ggplot2") #devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = TRUE, build_manual =T) #install.packages("scales") #install.packages("pheatmap") #install.packages("tidyverse") #install.packages("reshape") #install.packages("magrittr") #install.packages("ggpubr") #install.packages("ggplot2") #install.packages("lsmeans") #install.packages("multcomp") #install.packages("ggplot2") #install.packages("corrplot") #install.packages("Hmisc") #BiocManager::install("ComplexHeatmap") #install.packages("circlize") # - # # Demo Example # As an example, we showed all steps on a metabolomics data detecected from lean and obese pataients. Some obese patients had neuropathy and others did not have it. We are interested in metaboloties that are different between two obese groups. # ![image.png](attachment:image.png) # # Save Your session # Save Your session anytime so you can easily resume save.image("MetabolonR.RDat") load("MetabolonR.RDat") # # Load the metabolomics data, metabolites and samples meta information # You have to load the actual/relative abundances of the metabolites in (.csv) format (Samples are in rows and metabolites in columns). You need also to upload the annotation file of your metabolites; and the clinical information for your samples. #Load the data metabolomics_meta= read.csv("metabolomics_meta_R21_SOV.csv") samples_meta= read.csv("samples_meta_R21_SOV.csv",na.strings=c("NA","NaN", "")) # samples unique ids is the sample id metabolomics_data= read.csv("metabolomics_data_R21_SOV.csv",check.names=F,row.names=1) # metabolomics id is the comp id dim(metabolomics_data) dim(metabolomics_meta) dim(samples_meta) print("********************Metabolomics data") head(metabolomics_data) print("********************Metaboloites annotation") head(metabolomics_meta) print("********************Samples annotation") head(samples_meta) # # Data normalization, transformation, and missing imputation # I used some script from MetaboanalystR package # # We will used KNN for imputation and log2 function for log normalization # ## Replace non-numeric values by NA # + int.mat <- metabolomics_data rowNms <- rownames(int.mat) colNms <- colnames(int.mat) naNms <- sum(is.na(int.mat)) num.mat <- suppressWarnings(apply(int.mat, 2, as.numeric)) if (sum(is.na(num.mat)) > naNms) { num.mat <- apply(int.mat, 2, function(x) as.numeric(gsub(",", "", x))) if (sum(is.na(num.mat)) > naNms) { print("Non-numeric values were found and replaced by NA.") } else { print("All data values are numeric.") } } # - # ## Remove metabolites with a constant or single value across samples int.mat <- num.mat rownames(int.mat) <- rowNms colnames(int.mat) <- colNms varCol <- suppressWarnings(apply(int.mat, 2, var, na.rm = T)) constCol <- (varCol == 0 | is.na(varCol)) constNum <- sum(constCol, na.rm = T) if (constNum > 0) { print(paste(constNum, "features with a constant or single value across samples were found and deleted.")) int.mat <- int.mat[, !constCol] } # ## Calculate the percentage of missinginess totalCount <- nrow(int.mat) * ncol(int.mat) naCount <- sum(is.na(int.mat)) naPercent <- round(100 * naCount/totalCount, 1) print(paste("A total of ", naCount, " (", naPercent, "%) missing values were detected.", sep = "")) # ## delete metaboloite if it was missed accros more than 50% of samples good.inx <- apply(is.na(int.mat), 2, sum)/nrow(int.mat) < 0.5 int.mat1 <- as.data.frame(int.mat[,good.inx]) print(paste(sum(!good.inx), "variables were removed for threshold", round(100 * 0.5, 2), "percent.")) # ## Impute the remaining missed metaboloties using KKN new.mat2 <- t(impute::impute.knn(t(int.mat1))$data) # ## Log transformation min.val <- min(abs(new.mat2[new.mat2 != 0]))/2 norm.data <- log2((new.mat2 + sqrt(new.mat2^2 + min.val))/2) head(norm.data) print(paste(dim(metabolomics_data)[2]-dim(norm.data)[2] ,"is the total number of removed metaboloites")) print(paste(dim(norm.data)[2] ,"out of",dim(metabolomics_data)[2],"is kept")) # ## Check data distrubution after and before data distribution # ### Metabolites # + par(mfrow=c(2,2)) plot(density(apply(new.mat2, 2, mean, na.rm = TRUE)), col = "darkblue", las = 2, lwd = 2, main = "", xlab = "", ylab = "") mtext("Before Normalization", 3, 1) plot(density(apply(norm.data, 2, mean, na.rm = TRUE)), col = "darkblue", las = 2, lwd = 2, main = "", xlab = "", ylab = "") mtext("After Normalization", 3, 1) rangex.pre <- range(new.mat2[, 1:20], na.rm = T) rangex.norm <- range(norm.data[, 1:20], na.rm = T) boxplot(new.mat2[, 1:20], names = colnames(new.mat2[, 1:20]), ylim = rangex.pre, las = 2, col = "lightgreen", horizontal = T) mtext("Before Normalization", 3, 1) boxplot(norm.data[, 1:20], names = colnames(norm.data[, 1:20]), ylim = rangex.norm, las = 2, col = "lightgreen", horizontal = T) mtext("After Normalization", 3, 1) # - # ### Samples par(mfrow=c(1,2)) boxplot(new.mat2[1:20,1:20], names = rownames(new.mat2[1:20,1:20]), ylim = rangex.pre, las = 2, col = "lightgreen", horizontal = T) mtext("Samples Before Normalization", 3, 1) boxplot(norm.data[1:20,1:20 ], names = rownames(norm.data[1:20,1:20 ]), ylim = rangex.norm, las = 2, col = "lightgreen", horizontal = T) mtext("Samples After Normalization", 3, 1) Normalized_data= norm.data dim(Normalized_data) head(Normalized_data) # ## Which metabolites was removed? # + metabolites_meta_old=read.csv("metabolomics_meta_R21_SOV.csv") removed_metabolites=(setdiff(metabolites_meta_old$BIOCHEMICAL,colnames(Normalized_data))) subpathway_meta_number=metabolites_meta_old %>% group_by(SUB_PATHWAY) %>% summarise(total_number_of_metabolites=n()) removed_metabolites_per_pathway=metabolites_meta_old %>% dplyr::select('BIOCHEMICAL','SUB_PATHWAY')%>% filter(BIOCHEMICAL %in% removed_metabolites)%>% group_by(SUB_PATHWAY) %>% summarise(number_of_removed_metabolites=n()) %>%arrange(desc(number_of_removed_metabolites)) merge(removed_metabolites_per_pathway,subpathway_meta_number, by.x='SUB_PATHWAY',by.y='SUB_PATHWAY') %>% arrange(desc(number_of_removed_metabolites))%>% head #metabolites_meta_old[match(removed_metabolites,as.character(metabolites_meta_old$BIOCHEMICAL)),] %>% head # - # We need to update metabolites meta file metabolomics_meta=(metabolomics_meta[!metabolomics_meta$BIOCHEMICAL %in% (removed_metabolites),] ) rownames(metabolomics_meta)=NULL head(metabolomics_meta) dim(metabolomics_meta) # make sure that samples names in samples meta file ans merabolomics files are matched match(rownames(Normalized_data),samples_meta$SAMPLE_NAME) # # PCA # + df_pca <- prcomp(Normalized_data) df_out <- as.data.frame(df_pca$x) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GROUP_ID,shape=samples_meta$OBESITY ))+ geom_point()+ggtitle("")+labs(color='')+ geom_point(size=8,alpha=0.5)+ #Size and alpha just for fun theme( plot.title = element_text(hjust = 0.5,size=15,face = "bold"), axis.text.x = element_text( size = 15, angle = 45, hjust = .5, vjust = 0.5, face = "plain"), axis.text.y = element_text( size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"), axis.title.x = element_text( size = 15, angle = 0, hjust = .5, vjust = 0, face = "bold"), axis.title.y = element_text( size = 15, angle = 90, hjust = .5, vjust = .5, face = "bold"), #legend.title=element_text(size=20), legend.title=element_blank(), # remove legend title name legend.text = element_text(size=15,face="plain"), strip.text = element_text(size = 15,face="plain") , legend.position="right", # for transparent background panel.background = element_rect(fill = "transparent"), # bg of the panel plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot panel.grid.major = element_blank(), # get rid of major grid panel.grid.minor = element_blank(), # get rid of minor grid legend.background = element_rect(fill = "transparent"), # get rid of legend bg legend.box.background = element_rect(fill = "transparent"), # get rid of legend panel bg axis.line = element_line(colour = "black") # adding a black line for x and y axis ) +xlab(paste0("PC 1 (", round(df_pca$sdev[1],1),"%)"))+ ylab(paste0("PC 2 (", round(df_pca$sdev[2],1),"%)")) # ggsave("PCA.tiff", plot = last_plot(), device = NULL, path = NULL, # scale = 1, width = 10, units = c("in"), # dpi = 300, limitsize = TRUE,bg = "transparent") # - df_pca <- prcomp(Normalized_data) df_out <- as.data.frame(df_pca$x) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GROUP_ID ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$OBESITY ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$NEUROPATHY ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$GENDER ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) ggplot(df_out,aes(x=PC1,y=PC2,color=samples_meta$AGE ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) ggplot(df_out,aes(x=PC1,y=PC2,color=Normalized_data_label$AGE1 ))+geom_point()+ggtitle("PCA ALL meta")+labs(color='')+ theme(plot.title = element_text(hjust = 0.5)) # # Source of variation # Here you want to know the source of variability in your data (is it sex, age, obesity or neuropathy). The sources of variation plot present the relative contribution of each factor such as Age, gender, race and others included in the ANOVA model towards explaining the variability of the data for a feature analyzed by the ANOVA. # ![image.png](attachment:image.png) # # ## Overall source of variation # It seems, as we expect, obesity is the major source of variation in metabolomics data, as you can see from below figure # + # We are interested to look at the effect of the below factors on changing the metabolites level interested_factors=c("SAMPLE_NAME","AGE","OBESITY","GENDER","NEUROPATHY") samples_meta_interested=subset(samples_meta,select= interested_factors) #samples_meta_interested$ers_quartile=factor(samples_meta_interested$ers_quartile) #rownames(samples_meta_interested)=samples_meta_interested$SAMPLE_NAME #samples_meta_interested$SAMPLE_NAME=NULL str(samples_meta_interested) #head(samples_meta_interested) F_for_all1=matrix(0,nrow=ncol(samples_meta_interested),ncol = ncol(Normalized_data)) cobre_all<-cbind(samples_meta_interested,Normalized_data) #design <- model.matrix(~cobre_all$group_hmdb,data=cobre_all) for (i in 1:ncol(Normalized_data)){ lm.out2 = lm(cobre_all[,(i+ncol(samples_meta_interested))] ~ AGE+OBESITY+GENDER+NEUROPATHY, data=cobre_all, na.action=na.omit) # aa=summary(lm.out2) F_for_all1[nrow(F_for_all1),i]=mean(aa$coefficients[,2]) #do not care about sequentional library("car") zz=Anova(lm.out2) zz=na.omit(zz) F_for_all1[1:(nrow(F_for_all1)-1),i]=zz$`F value` } zz1=apply(F_for_all1,1,mean) zz1=as.data.frame(t(zz1)) dim(zz1) colnames(zz1)=c(c(interested_factors[-1],"error")) library(ggplot2) library(reshape) zz2=melt(zz1) ggplot(data=zz2,aes(x=variable,y=value))+ geom_bar(stat="identity",fill="steelblue")+ labs(title="Sources of variation (Overall) ",x="", y = "Mean F Ratio")+ theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) #ggsave("add_SOV.pdf", plot = last_plot(), #R21_SOV_obesity.pdf # scale = 1, units = "in", # dpi = 300, limitsize = TRUE) # - # ## Source of variation per super pathway # The number beside pathway names are the number of metaboloties involoved in that pathway # + rownames(F_for_all1)=c(c(interested_factors[-1],"error")) colnames(F_for_all1)=colnames(Normalized_data) #head(F_for_all1) ss=t(F_for_all1) ss_ALL=data.frame(ss,pathway=metabolomics_meta$SUB_PATHWAY) #head(ss_ALL) library(data.table) ss_ALL1=data.frame(ss,pathway=metabolomics_meta$SUPER_PATHWAY) ss_ALL1 %>% group_by(pathway) %>% mutate(N_metaboliesinP=(n())) %>%summarise_all(funs(mean)) %>% mutate(pathway=paste(pathway,N_metaboliesinP,sep=" ")) %>% dplyr::select(-N_metaboliesinP,-error) %>%as.data.frame()%>% melt()%>% arrange(pathway,desc(value)) %>% ggplot(aes(x=pathway,y=value,fill=variable))+ geom_bar(stat="identity")+ labs(title="Sources of variation (super pathway) ",x="", y = "Mean F Ratio")+ theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=7,angle = 45, hjust =1)) + scale_fill_manual(values=c( "#00ba38", "#00bfc4","#619cff", "#b79f00")) # - # ## Source of variation per sub pathway # + ss_ALL %>% group_by(pathway) %>% mutate(N_metaboliesinP=(n())) %>%summarise_all(funs(mean)) %>% mutate(pathway=paste(pathway,N_metaboliesinP,sep=" ")) %>% dplyr::select(-N_metaboliesinP,-error) %>%as.data.frame()%>% melt()%>% arrange(pathway,desc(value)) %>% ggplot(aes(x=pathway,y=value,fill=variable))+ geom_bar(stat="identity")+ labs(title="Sources of variation (sub pathway)",x="", y = "Mean F Ratio")+ theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=7,angle = 45, hjust =1)) + scale_fill_manual(values=c( "#00ba38", "#00bfc4","#619cff", "#b79f00")) #ggsave("add_SOV_sub_pathways.pdf", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf # scale = 1, units = "in",width=30, # dpi = 300, limitsize = TRUE) # + # Metaboloties changed by neuropathy ss_ALL %>% rownames_to_column('gene') %>% filter(NEUROPATHY > quantile(NEUROPATHY, 0.991))%>% arrange(desc(NEUROPATHY))%>%column_to_rownames('gene') NEUROPATHY_ALL=ss_ALL %>% rownames_to_column('gene') %>% filter(NEUROPATHY > quantile(NEUROPATHY, 0.991))%>% arrange(desc(NEUROPATHY))%>%column_to_rownames('gene')%>%rownames # - # Metaboloties changed by obesity ss_ALL %>% rownames_to_column('gene') %>% filter(OBESITY > quantile(OBESITY, 0.991))%>% arrange(desc(OBESITY))%>%column_to_rownames('gene') # Metaboloties changed by age ss_ALL %>% rownames_to_column('gene') %>% filter(AGE > quantile(AGE, 0.991))%>% arrange(desc(AGE))%>%column_to_rownames('gene') # Metaboloties changed by SEX ss_ALL %>% rownames_to_column('gene') %>% filter(GENDER > quantile(AGE, 0.991))%>% arrange(desc(GENDER))%>%column_to_rownames('gene') # ## Box plot # You can plot any metaboloties of intereset in a box plot with p value using ggpubr package Normalized_data_label=merge(Normalized_data,samples_meta[c('GROUP_ID','SAMPLE_NAME','OBESITY','AGE','GENDER','NEUROPATHY')], by.x="row.names",by.y="SAMPLE_NAME") rownames(Normalized_data_label)=Normalized_data_label[,1] Normalized_data_label=Normalized_data_label[-1] head(Normalized_data_label) Normalized_data_label$AGE1=factor(Normalized_data_label$AGE) table(Normalized_data_label$AGE1) levels(Normalized_data_label$AGE1) levels(Normalized_data_label$AGE1)=c(rep("20-40",15),rep("41-60",20),rep("61-74",9)) levels(Normalized_data_label$AGE1) # + Normalized_data_label %>% rownames_to_column('gene') %>% dplyr::select(gene,NEUROPATHY_ALL,NEUROPATHY) %>% column_to_rownames('gene')%>% melt() %>% # add p_values #library("ggpubr") ggplot(aes(x=variable, y=value,fill=NEUROPATHY)) + geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+ stat_summary(fun.y="mean", color="white", geom="point",shape=18, size=3,position=position_dodge(width=0.75))+ theme(text = element_text(size=12), axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=12))+ stat_compare_means( label="p.signif",method = "anova",size = 5)+ facet_wrap( ~ variable, scales="free")+xlab("")+ylab("")#+ #geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge()) #ggsave("NEUROPATHY_ALL_boxplot-NEUROPATHY.pdf", plot = last_plot(), # scale = 1, units = "in",width=10, # dpi = 300, limitsize = TRUE) # + Normalized_data_label %>% rownames_to_column('gene') %>% dplyr::select(gene,NEUROPATHY_ALL,NEUROPATHY,GENDER) %>% column_to_rownames('gene')%>% melt() %>% # add p_values #library("ggpubr") ggplot(aes(x=variable, y=value,fill=NEUROPATHY)) + geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+ stat_summary(fun.y="mean", color="white", geom="point",shape=18, size=3,position=position_dodge(width=0.75))+ theme(text = element_text(size=10), axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=8))+ stat_compare_means( label="p.signif",method = "t.test",size = 5)+ facet_wrap( ~ variable+GENDER, scales="free")+xlab("")+ylab("")#+ #geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge()) #ggsave("NEUROPATHY_ALL_boxplot-NEUROPATHY-GENDER.pdf", plot = last_plot(), # scale = 1, units = "in",width=10, # dpi = 300, limitsize = TRUE) # + Normalized_data_label %>% rownames_to_column('gene') %>% dplyr::select(gene,NEUROPATHY_ALL,GROUP_ID) %>% column_to_rownames('gene')%>% melt() %>% # add p_values #library("ggpubr") ggplot(aes(x=variable, y=value,fill=GROUP_ID)) + geom_boxplot()+#coord_cartesian(ylim = c(15, 30))+ stat_summary(fun.y="mean", color="white", geom="point",shape=18, size=3,position=position_dodge(width=0.75))+ theme(text = element_text(size=12), axis.text.x = element_text(angle=0),legend.title=element_blank(),strip.text = element_text(size=12))+ stat_compare_means( label="p.signif",method = "anova",size = 5)+ facet_wrap( ~ variable, scales="free")+xlab("")+ylab("")#+ #geom_point(aes(fill = GROUP_ID), size = 1, shape = 21, position = position_jitterdodge()) #ggsave("NEUROPATHY_ALL_boxplot-dotpoints.pdf", plot = last_plot(), # scale = 1, units = "in",width=10, # dpi = 300, limitsize = TRUE) # - # # Biomarker analysis # ## Differentiated metabolites (DMs) using Limma (Statistical Summary) # limma did not identify any DMs between obese patients with and without neuropathy # # match(rownames(Normalized_data),samples_meta$SAMPLE_NAME) # + library(limma) type = as.character(samples_meta$GROUP_ID) design <- model.matrix(~0+factor(type)) colnames(design) <- levels(factor(type)) contrast<-makeContrasts(Obese_N-Obese_NoN, Obese_N-Lean_NoN, Obese_NoN-Lean_NoN, levels=design) fit <- lmFit(as.matrix(t(Normalized_data)), design) fit2 <- contrasts.fit(fit, contrast) fit2 <- eBayes(fit2) # - # + Obese_N.vs.Obese_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 1) Obese_N.vs.Lean_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 2) Obese_NoN.vs.Lean_NoN=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05,coef = 3) head(Obese_N.vs.Obese_NoN) head(Obese_N.vs.Lean_NoN) head(Obese_NoN.vs.Lean_NoN) dim(Obese_N.vs.Obese_NoN) dim(Obese_N.vs.Lean_NoN) dim(Obese_NoN.vs.Lean_NoN) # - fold_change=data.frame(apply(topTable(fit2, adjust.method='fdr', number=999999999,p.value=1)[,c(1,2,3)],2, function(x){2^x}),check.names=F) #Not log FC head(fold_change) class(fold_change) limma_dataframe_FC=topTable(fit2, adjust.method='fdr', number=999999999,p.value=0.05) #,confint=T head(limma_dataframe_FC) # ## DMs using T-test (Statistical Summary) # Here we used pairwise.t.test function to find significant metabolites between different groups. You can adjusted for your comparison testing using parameter p.adjust.method # # Bonferroni correction ("bonferroni") # # Holm (1979) ("holm") # # Hochberg (1988) ("hochberg") # # Hommel (1988) ("hommel") # # Benjamini & Yekutieli (2001) ("BY") # # # match(samples_meta$SAMPLE_NAME,rownames(Normalized_data)) # + temp=Normalized_data %>% data.frame(check.names=F)%>% rownames_to_column("gene") %>% mutate(group=samples_meta$GROUP_ID) %>% column_to_rownames("gene") df2 <- temp %>% gather(Column, Value, -group) #head(df2) p_value <- df2 %>% split(.$Column) %>% map(function(x) pairwise.t.test(x$Value, x$group, paired = F,var.eq=F,pool.sd=F, p.adjust.method ="none"))%>% map_df( "p.value") xx=data.frame(p_value,check.names=F) rownames(xx)=c("Obese_N.vs.Lean_NoN","Obese_NoN.vs.Lean_NoN","Obese_N.vs.Obese_N","Obese_NoN.vs.Obese_N") xx=xx[-3,] #head(xx) xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,2))%>% arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% dim%>% .[1]%>% paste("Number of metaboloties between obese patients with neuropathy and lean is",.) Obese_N.vs.Lean_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,2))%>% arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% rownames() xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,3)) %>%arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% dim %>%.[1]%>% paste("Number of metaboloties between obese patients with neuropathy and lean is",.) Obese_NoN.vs.Lean_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,3))%>% arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% rownames() xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,4)) %>%arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% dim%>%.[1]%>% paste("Number of metaboloties between obese patients with neuropathy and with out is",.) Obese_N.vs.Obese_NoN= xx %>%t() %>% data.frame(check.names=F) %>% rownames_to_column("gene") %>% select_at(c(1,4))%>% arrange_at(1,desc=F) %>% filter (.[[2]] <= 0.05)%>% column_to_rownames("gene") %>% rownames() p_adjusted_value=xx %>%t() %>% data.frame(check.names=F) head(p_adjusted_value) # - # ### Venn digram # + library(limma) # Venn digram universe <- sort(unique(c(Obese_NoN.vs.Lean_NoN, Obese_N.vs.Lean_NoN, Obese_N.vs.Obese_NoN))) Counts <- matrix(0, nrow=length(universe), ncol=3) for (i in 1:length(universe)) { Counts[i,1] <- universe[i] %in% Obese_NoN.vs.Lean_NoN Counts[i,2] <- universe[i] %in% Obese_N.vs.Lean_NoN Counts[i,3] <- universe[i] %in% Obese_N.vs.Obese_NoN } colnames(Counts) <- c("Obese_NoN.vs.\nLean_NoN","Obese_N.vs.\nLean_NoN","Obese_N.vs.\nObese_NoN") cols<-c("Red", "Green", "Blue") #tiff("venn.tiff") vennDiagram(vennCounts(Counts), circle.col=cols,cex=0.9) #dev.off() # - # ## DMs using lm adjusted for sex and age # Here we used glht function in multcomp package, you can correct for multiple comparsion using adjusted("fdr") option # + # Load drugs information to include them in the model drugs_Evan=read.csv("Fadhl Project Bariatric Meds 12_2_2019_Drugs information.csv") drugs_Evan=drugs_Evan %>% dplyr::select(Study_ID,contains("total")) %>% dplyr::rename('CLIENT_IDENTIFIER'='Study_ID', 'beta_blocker'='beta_blocker_total', 'statin'='total_Statin') head(drugs_Evan) dim(drugs_Evan) drugs_Evan1=read.csv("Fadhl Project IWMC Meds 12_2_2019_Drugs2.csv",stringsAsFactors=F) #drugs_Evan1$Patient.ID=as.character(drugs_Evan1$Ptient.ID) drugs_Evan1=drugs_Evan1 %>% dplyr::filter(Patient.ID %in% as.character(samples_meta$CLIENT_IDENTIFIER) & Medication.Type %in% c('Beta blockers', 'HMG Co-A redcutase inhibitors' ) )%>% distinct(Patient.ID,Medication.Type, .keep_all = TRUE) %>% group_by(Patient.ID) %>% mutate(Medication.Type = paste0(Medication.Type, collapse = ",")) %>%dplyr::select(Patient.ID,Medication.Type) %>% distinct(Patient.ID, .keep_all = TRUE) %>% mutate(beta_blocker_total=unlist(lapply( (str_split(Medication.Type,',')), function(x) {'Beta blockers' %in% x })), total_Statin=unlist(lapply( (str_split(Medication.Type,',')), function(x) {'HMG Co-A redcutase inhibitors' %in% x })))%>% dplyr::rename('CLIENT_IDENTIFIER'='Patient.ID','beta_blocker'='beta_blocker_total', 'statin'='total_Statin') %>% dplyr::select(-Medication.Type) head(drugs_Evan1,10) dim(drugs_Evan1) str(drugs_Evan1) #length(intersect(drugs_Evan1$Patient.ID,samples_meta$CLIENT_IDENTIFIER)) # + samples_meta1=samples_meta %>%left_join(.,rbind(drugs_Evan,data.frame(drugs_Evan1))) head(samples_meta1) #tail(samples_meta1) dim(samples_meta1) # + # Drugs library(dplyr) #samples_meta1 %>% group_by(NEUROPATHY) %>% count(statin) %>%mutate(percentage = (n / sum(n)*100)) #samples_meta1 %>% group_by(GROUP_ID) %>% count(statin) %>%mutate(percentage = (n / sum(n)*100)) samples_meta1 %>% filter(!GROUP_ID=='Lean_NoN')%>%group_by(GROUP_ID,GENDER) %>% count(statin) %>% #drop_na %>% mutate(percentage = (n / sum(n)*100))%>% mutate(statin= as.factor(statin)) %>% mutate(statin = plyr::revalue(statin, c("FALSE"='NO',"TRUE"='YES','NA'='NA'))) %>% ggplot( aes(x=GROUP_ID,y= percentage,fill =statin,label=paste0(round(percentage,1),'%') ) ) + geom_bar(stat="identity")+ facet_wrap( ~ GENDER, scales="free")+ geom_text( size=5, position = position_stack(vjust = 0.5) )+ theme( plot.title = element_text(hjust = 0.5,size=20,face = "bold"), axis.text.x = element_text(color = "grey20", size = 15, angle = 45, hjust = .5, vjust = 0.5, face = "bold"), axis.text.y = element_text(color = "grey20", size = 15, angle = 0, hjust = 1, vjust = 0, face = "bold"), axis.title.x = element_text(color = "grey20", size = 20, angle = 0, hjust = .5, vjust = 0, face = "bold"), axis.title.y = element_text(color = "grey20", size = 20, angle = 90, hjust = .5, vjust = .5, face = "bold"), legend.title=element_text(size=20), legend.text = element_text(size=20,face="bold"), strip.text = element_text(size = 20,face="bold") )+xlab("")+ylab("Percentage %")+ guides(fill=guide_legend(title="statins"))+ggtitle("Percentage of patients are taking statins") # ggsave("statins.tiff", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf # scale = 1, units = "in", # dpi = 300, limitsize = TRUE) # Drugs library(dplyr) #samples_meta1 %>% group_by(NEUROPATHY) %>% count(beta_blocker) %>%mutate(percentage = (n / sum(n)*100)) #samples_meta1 %>% group_by(GROUP_ID) %>% count(beta_blocker) %>%mutate(percentage = (n / sum(n)*100)) samples_meta1 %>% filter(!GROUP_ID=='Lean_NoN')%>%group_by(GROUP_ID,GENDER) %>% count(beta_blocker) %>% mutate(percentage = (n / sum(n)*100))%>%#drop_na %>% mutate(statin= as.factor(beta_blocker)) %>% mutate(beta_blocker = plyr::revalue(statin, c("FALSE"='NO',"TRUE"='YES','NA'='NA'))) %>% ggplot( aes(x=GROUP_ID,y= percentage,fill =beta_blocker,label=paste0(round(percentage,1),"%") ) ) + geom_bar(stat="identity")+ facet_wrap( ~ GENDER, scales="free")+ geom_text( size=5, position = position_stack(vjust = 0.5) )+ theme( plot.title = element_text(hjust = 0.5,size=20,face = "bold"), axis.text.x = element_text(color = "grey20", size = 15, angle = 45, hjust = .5, vjust = 0.5, face = "bold"), axis.text.y = element_text(color = "grey20", size = 15, angle = 0, hjust = 1, vjust = 0, face = "bold"), axis.title.x = element_text(color = "grey20", size = 20, angle = 0, hjust = .5, vjust = 0, face = "bold"), axis.title.y = element_text(color = "grey20", size = 20, angle = 90, hjust = .5, vjust = .5, face = "bold"), legend.title=element_text(size=20), legend.text = element_text(size=20,face="bold"), strip.text = element_text(size = 20,face="bold") )+xlab("")+ylab("Percentage %")+ guides(fill=guide_legend(title="beta blocker"))+ ggtitle("Percentage of patients are taking \n beta blocker") # ggsave("beta_blocker.tiff", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf # scale = 1, units = "in", # dpi = 300, limitsize = TRUE) # + #library(lsmeans) #library(multcomp) options(scipen=999) interested_factors=c("SAMPLE_NAME","AGE","GENDER","GROUP_ID","beta_blocker","statin") samples_meta_interested=subset(samples_meta1,select= interested_factors) F_for_all=matrix(0,ncol=length(levels(Normalized_data_label$GROUP_ID)),nrow = ncol(Normalized_data)) cobre_all<-cbind(samples_meta_interested,Normalized_data) for (i in 1:ncol(Normalized_data)){ lm.out2 = lm(cobre_all[,(i+ncol(samples_meta_interested))] ~ GROUP_ID+AGE+GENDER+beta_blocker+statin, data=cobre_all, na.action=na.omit) # l2 <- glht(lm.out2, linfct = mcp(GROUP_ID = "Tukey")) summary(l2,test=adjusted("fdr")) #F_for_all[i,1]=as.numeric((unlist(summary(l2)[10]))[12]) #F_for_all[i,2]=as.numeric((unlist(summary(l2)[10]))[13]) #F_for_all[i,3]=as.numeric((unlist(summary(l2)[10]))[14]) F_for_all[i,1]=as.numeric((unlist(summary(l2,test=adjusted("bonferroni"))[10]))[12]) F_for_all[i,2]=as.numeric((unlist(summary(l2,test=adjusted("bonferroni"))[10]))[13]) F_for_all[i,3]=as.numeric((unlist(summary(l2,test=adjusted("bonferroni"))[10]))[14]) } #head(F_for_all) rownames(F_for_all)=colnames(Normalized_data) colnames(F_for_all)=c('Obese_N - Lean_NoN','Obese_NoN - Lean_NoN','Obese_NoN - Obese_N ') #head(F_for_all) F_for_all %>% data.frame %>% filter (.[3] < 0.05) %>% dim %>%.[1]%>% paste("Number of metaboloties between obese patients with neuropathy and without is",.) F_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[4] < 0.05) %>% column_to_rownames('gene') # - Obese_N.vs.Lean_NoN= F_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[2] < 0.05) %>% column_to_rownames('gene')%>% rownames Obese_NoN.vs.Lean_NoN= F_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[3] < 0.05) %>% column_to_rownames('gene')%>% rownames Obese_N.vs.Obese_NoN= F_for_all %>% data.frame %>% rownames_to_column('gene') %>% filter (.[4] < 0.05) %>% column_to_rownames('gene')%>% rownames # ### Venn digram # + library(limma) # Venn digram universe <- sort(unique(c(Obese_NoN.vs.Lean_NoN, Obese_N.vs.Lean_NoN, Obese_N.vs.Obese_NoN))) Counts <- matrix(0, nrow=length(universe), ncol=3) for (i in 1:length(universe)) { Counts[i,1] <- universe[i] %in% Obese_NoN.vs.Lean_NoN Counts[i,2] <- universe[i] %in% Obese_N.vs.Lean_NoN Counts[i,3] <- universe[i] %in% Obese_N.vs.Obese_NoN } colnames(Counts) <- c("Obese_NoN vs \nLean_NoN","Obese_N vs \nLean_NoN","Obese_N vs\n Obese_NoN") cols<-c("Red", "Green", "Blue") #tiff("venn.tiff") #pdf("venn_adjusted.pdf",width=10,height=10) vennDiagram(vennCounts(Counts), circle.col=cols,cex=1) #dev.off() # + length(universe[which(Counts[,3]==1 & Counts[,1]==0 & Counts[,2]==0)]) Obese_N.vs.Obese_NoN_M=universe[which(Counts[,3]==1 & Counts[,1]==0 & Counts[,2]==0)] Obese_N.vs.Obese_NoN_M Obese_N.vs.Obese_NoN_M_FC=fold_change[Obese_N.vs.Obese_NoN_M,'Obese_N...Obese_NoN',drop=F] Obese_N.vs.Obese_NoN_M_FC_pathway= merge(Obese_N.vs.Obese_NoN_M_FC,metabolomics_meta[c("SUB_PATHWAY","BIOCHEMICAL","SUPER_PATHWAY")], by.x="row.names",by.y="BIOCHEMICAL") %>% mutate(logFC=log2(Obese_N...Obese_NoN),pathway=paste0(SUPER_PATHWAY," ","(",SUB_PATHWAY,")")) Obese_N.vs.Obese_NoN_M_FC_pathway$Row.names=as.character(Obese_N.vs.Obese_NoN_M_FC_pathway$Row.names) #pdf("lm_adjusted_6_FC_SUPER_PATHWAY.pdf",width=12) ggplot(Obese_N.vs.Obese_NoN_M_FC_pathway ,aes(x=reorder(stringr::str_wrap(Row.names,40),logFC),y=logFC,fill=SUPER_PATHWAY))+ geom_bar(stat="identity",width=0.8,position=position_dodge(width=0.1))+ labs(title=" ",x="", y = "Log2(Fold Change)")+ theme(plot.title = element_text(hjust = 0.5))+ theme(axis.text.x = element_text(size=20,angle = 45, hjust =1)) + theme(legend.position="right")+ ggtitle("Obese_N vs Obese_NoN ")+ theme( plot.title = element_text(hjust = 0.5,size=20,face = "bold"), axis.text.x = element_text(color='black', size = 20, angle = 60,face='plain'), axis.text.y = element_text(color='black', size = 20, angle = 0), axis.title.x = element_text( size = 20, angle = 0,face='bold'), axis.title.y = element_text( size = 20, angle = 90), legend.title=element_text(size=20), legend.text = element_text(size=20), # strip.text = element_text(size = 12,face="bold") )+guides(fill=guide_legend(title="Pathway"))+coord_flip() #dev.off() # ggsave("lm_adjusted_6_FC_SUPER_PATHWAY.tiff", plot = last_plot(), #R21_SOV_sub_pathways_obesity.pdf # scale = 1.2, units = "in",width=11,height=10, # dpi = 300, limitsize = TRUE) # - # ## DMs using elastic net # Here we used glmnet package to identify DMs between obese patients with and without neuropathy. # Elastic net method selects metabolites that have non-zero coefficients at different values of lambda, guided by two penalty parameters alpha and lambda. Alpha sets the degree of mixing between lasso (when alpha=1) and the ridge regression (when alpha=0). Lambda controls the shrunk rate of coefficients regardless of the value of alpha. When lambda equals zero, no shrinkage is performed and the algorithm selects all the features. As lambda increases, the coefficients are shrunk more strongly and the algorithm retrieves all features with non-zero coefficients. # # temp=Normalized_data_label %>% rownames_to_column("gene") %>% filter(!GROUP_ID=='Lean_NoN') %>% dplyr::select(colnames(Normalized_data),gene,GROUP_ID) %>% column_to_rownames("gene") x <- data.matrix(temp[,-ncol(temp)]) # metabolites y <- factor(temp[,ncol(temp)],levels = c("Obese_N", "Obese_NoN")) # lables #y <- temp$GROUP_ID #write.csv(temp,"Normalized_data_label_neuropathy.csv") table(y) # ### Find the best alpha # + # Select alpha of the elastic net #http://ellisp.github.io/blog/2016/08/13/fitbit-lasso #library(magrittr) set.seed(123) alphas <- seq(from = 0, to = 1, length.out = 10) res <- matrix(0, nrow = length(alphas), ncol = 6) # five columns for results - five repeats of each CV run for(i in 1:length(alphas)){ for(j in 2:6){ cvmod <- cv.glmnet(x, y, alpha = alphas[i],family='binomial',nfolds=10, standardize=TRUE, type.measure='class') res[i, c(1, j)] <- c(alphas[i], sqrt(min(cvmod$cvm))) } } res <- data.frame(res) res$average_rmse <- apply(res[ , 2:6], 1, mean) res <- res[order(res$average_rmse), ] names(res)[1] <- "alpha" res %>% dplyr::select(-average_rmse) %>% gather(trial, rmse, -alpha) %>% ggplot(aes(x = alpha, y = rmse)) + geom_point() + geom_smooth(se = FALSE) + labs(y = "Root mean square error") + ggtitle("Cross validation best RMSE for differing values of alpha") # best alpha varies according to the random seed set earlier but with seed 123 it is 0.22 bestalpha <- res[1, 1] bestalpha # - # ### Find the best lamda # + # select Lamda #https://stats.stackexchange.com/questions/97777/variablity-in-cv-glmnet-results lambdas = NULL metabolites_list=list() #metabolites_list for (j in 1:10) { for (i in 1:100) { #fit <- cv.glmnet(x, y, family='binomial',nfolds=10, # standardize=TRUE, type.measure='class') #cv.glmnet does NOT search for values for alpha. A specific value should be supplied, else alpha=1 is assumed by default. fit <- cv.glmnet(x, y, family='binomial',nfolds=10, standardize=TRUE, type.measure='class',alpha=bestalpha) errors = data.frame(fit$lambda,fit$cvm) lambdas <- rbind(lambdas,errors) } # take mean cvm for each lambda lambdas <- aggregate(lambdas[, 2], list(lambdas$fit.lambda), mean) # select the best one bestindex = which(lambdas[2]==min(lambdas[2])) bestlambda = lambdas[bestindex,1] #bestlambda lambdas = NULL cv.lasso<- glmnet(x, y,lambda=bestlambda,family='binomial',standardize=TRUE) #coef(cv.lasso, s=bestlambda) zz=as.matrix(coef(cv.lasso, s=bestlambda)[,1]) zz1=as.matrix(zz[order(abs(zz[,1]),decreasing = T),]) zz2=data.frame(zz1[zz1!=0 & rownames(zz1)!="(Intercept)",1]) colnames(zz2)="coffiecents" metabolites_list[[j]]=rownames(zz2) } # - metabolites_list_consensus=Reduce(intersect,metabolites_list) metabolites_list_consensus print(paste("Elastic net identify",length(metabolites_list_consensus), "metaboloites belong to lipid, amino acids and xenobiotic")) metabolomics_meta_LASSO=metabolomics_meta[match(metabolites_list_consensus,metabolomics_meta$BIOCHEMICAL), c('BIOCHEMICAL','SUPER_PATHWAY','SUB_PATHWAY','KEGG','HMDB_ID','PUBCHEM','CAS')] palette=c('#83d532','#f47f2a','#00a1e3','#e72621','#fbb416','#771965','#003a63') head(metabolomics_meta_LASSO) #write.csv(metabolomics_meta_LASSO,"metabolomics_meta_LASSO.csv") # + mycols=c('#83d532','#f47f2a','#00a1e3','#e72621','#fbb416','#771965','#003a63',"#868686FF", "#0073C2FF") metabolomics_meta_LASSO %>% group_by(SUPER_PATHWAY) %>% summarise(volume = n()) %>% mutate(share=volume/sum(volume)) %>% ungroup() %>% arrange(desc(volume)) %>% mutate(SUPER_PATHWAY=factor(SUPER_PATHWAY, levels = as.character(SUPER_PATHWAY))) %>% ggplot(aes(x=2, y= share, fill=SUPER_PATHWAY)) + geom_bar( stat = "identity", color = "white") + coord_polar("y", start = 0)+ geom_text(aes(label = paste0(round(share*100,0),"%")), position = position_stack(vjust = 0.5))+ #coord_polar(theta = "y") + xlim(0.5, 2.5)+ scale_fill_manual(values = mycols) + theme_void()+guides(fill=guide_legend(title="Pathway")) #guides(fill = guide_legend(reverse = TRUE)) ggsave("pie chart.pdf", plot = last_plot(), scale = 1, units = "in",width=10, dpi = 300, limitsize = TRUE) # - # # Metabolites Pathways analysis # We used the 62 metabolites identified by elastic net and the ConsensusPathDB, the online tool to identify the significant enriched pathways # http://cpdb.molgen.mpg.de/ # # # + library(ggrepel) #library(animation) library(stringr) overlapping=read.csv(file="lasso_62_pathways.csv",check.names = T) overlapping_filtered <- overlapping #cbind(overlapping_filtered,overlapping_filtered$Avergae.Overlap..metabolites..percentage/100*5) zz=str_wrap(overlapping_filtered$pathway,width = 50) #pdf("Ryan_pathway_analysis.pdf",width=15,height=8) p6 <- ggplot() + geom_point( data=overlapping_filtered, mapping=aes(x = overlapping_filtered$size, y =-log(overlapping_filtered$q.value), color=overlapping_filtered$source, size=overlapping_filtered$size)) + scale_size(range = c(10, 30),guide = 'none')+ labs(x = "Size of metabolomics pathway", y = "-Log(q-value)",color="Pathway source",size="# of overlaped genes") + ggtitle("Metabolites pathways analysis")+ #scale_fill_continuous(low = "orange", high = "orange4")+ #geom_label(aes(label=overlapping_filtered$pathway_name),color = 'white', size = 3.5) #geom_text(data=overlapping_filtered,aes(label=overlapping_filtered$pathway_name),size=3)+ geom_label_repel(aes(x = overlapping_filtered$size, y =-log(overlapping_filtered$q.value), color=overlapping_filtered$source, label =str_wrap(overlapping_filtered$pathway,width=20)) , min.segment.length = unit(2, 'lines'), #nudge_x = ifelse(overlapping_filtered$num_overlapping_metabolites == 11, 2, 0), #nudge_y = ifelse(overlapping_filtered$num_overlapping_metabolites == 5, 0.1, 0) , #nudge_y = ifelse(overlapping_filtered$Q.joint== 0.00023, 0.2, 0), size = 3.5,force=1, arrow = arrow(length = unit(0.02, "npc")),segment.color = 'red', box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines"),show_guide = F) + #scale_x_continuous(limits = c(4, 23))+ #scale_y_continuous(limits = c(1.5, 10))+ #scale_x_continuous(breaks=c(4:30))+ #theme(legend.position = "right")+ theme(plot.title = element_text(hjust = 0.5),axis.text=element_text(size=14,face="bold"), axis.title=element_text(size=14,face="bold")) #geom_text( show.legend = F ) print(p6) #ggsave(file="bench_query_sort.pdf", width=10, dpi=300) #dev.off() # - # # Machine learning # We will use the 62 metabolites identified by elastic net to build a classification model to predict the neuropathy stataus of obese patients. I used the script from my liliko r package # # https://cran.r-project.org/src/contrib/Archive/lilikoi/lilikoi_0.1.0.tar.gz # # https://academic.oup.com/gigascience/article/7/12/giy136/5237705 # # lasso_metabolites_data= Normalized_data_label %>% rownames_to_column('gene')%>% filter(!GROUP_ID=='Lean_NoN') %>% droplevels() %>% dplyr::select(label=GROUP_ID,gene,metabolites_list_consensus ) %>%column_to_rownames('gene') head(lasso_metabolites_data) table(lasso_metabolites_data$label) result=machine_learning(lasso_metabolites_data[,!colnames(lasso_metabolites_data) %in% 'label'], metabolites_list_consensus,lasso_metabolites_data$label); # + x1=varImp(result$models[[1]], scale = TRUE)$importance x2=varImp(result$models[[2]], scale = TRUE)$importance[1] x3=varImp(result$models[[3]], scale = TRUE)$importance[1] x4=varImp(result$models[[4]], scale = TRUE)$importance x5=varImp(result$models[[5]], scale = TRUE)$importance x6=varImp(result$models[[6]], scale = TRUE)$importance[1] x7=varImp(result$models[[7]], scale = TRUE)$importance x1=x1[ order(rownames(x1)) , ,drop=F] x2=x2[ order(rownames(x2)) , ,drop=F] x3=x3[ order(rownames(x3)) , ,drop=F] x4=x4[ order(rownames(x4)) , ,drop=F] x5=x5[ order(rownames(x5)) , ,drop=F] x6=x6[ order(rownames(x6)) , ,drop=F] x7=x7[ order(rownames(x7)) , ,drop=F] pathways_heatmap=data.frame(x1,x2,x3,x4,x5,x6,x7) colnames(pathways_heatmap)=c('RPART', 'LDA', 'SVM', 'RF', 'GBM', 'PAM', 'LOG') # check this line witnh the code you have developed for the Group lasso rownames(pathways_heatmap)=colnames(Normalized_data)[match(rownames(pathways_heatmap),make.names(colnames(Normalized_data)))] #pathways_heatmap range01 <- function(x){(x-min(x))/(max(x)-min(x))} pathways_heatmap_scaled=range01(pathways_heatmap) #pathways_heatmap_scaled library(pheatmap) pheatmap(as.matrix(pathways_heatmap_scaled),fontsize=8, breaks= seq(0, 1, by=0.1), color= colorRampPalette(c("white", "red"))(length(seq(0, 1, by=0.1))), #filename = "pathways_heatmap_ER1.pdf" ) # + # Out of the 62 metabolites, we selected 12, which have the largest aggregative impact on modles performance pathways_heatmap_scaled %>% rownames_to_column("d")%>%mutate(add=PAM+LDA+SVM+RF+LOG+GBM+RPART)%>% arrange(-add) %>% mutate(quintile = ntile(add, 5)) %>% head pathways_heatmap_scaled_lasso=pathways_heatmap_scaled %>% rownames_to_column("d")%>%mutate(add=PAM+LDA+SVM+RF+LOG+GBM+RPART)%>% arrange(-add) %>% mutate(quintile = ntile(add, 5)) %>% filter(quintile==5) %>% .[[1]] pathways_heatmap_scaled_lasso length(pathways_heatmap_scaled_lasso) # - pheatmap(as.matrix(pathways_heatmap_scaled[rownames(pathways_heatmap_scaled)%in% pathways_heatmap_scaled_lasso,]),fontsize=8, breaks= seq(0, 1, by=0.1), color= colorRampPalette(c("white", "red"))(length(seq(0, 1, by=0.1))), #filename = "pathways_heatmap_ER1_lasso_selected.pdf", fontsize_row=12,fontsize_col=15 ) # # Cytoscape correlation analysis # We will generate the correlation between most important metabolites (12) that were identified by elastic net and lipids metabolites to feed them to cytoscape. The first file is the edgje file which has the Spearman correlation values between metabolites and the second one is the nodes annotation file. # # # + only_NP=Normalized_data_label %>% rownames_to_column('epi_study_id')%>% filter(GROUP_ID=='Obese_N') %>% droplevels %>% dplyr::select(-GROUP_ID,-OBESITY,-AGE,-GENDER,-NEUROPATHY) %>% column_to_rownames('epi_study_id') only_DB= Normalized_data_label %>% rownames_to_column('epi_study_id')%>% filter(GROUP_ID=='Obese_NoN')%>%droplevels %>% dplyr::select(-GROUP_ID,-OBESITY,-AGE,-GENDER,-NEUROPATHY) %>% column_to_rownames('epi_study_id') # - # + metabolomics_meta[match(colnames(only_NP) , metabolomics_meta$BIOCHEMICAL),] %>% filter(SUPER_PATHWAY =='Lipid') %>% filter(!BIOCHEMICAL %in% metabolites_list_consensus)%>% dplyr::select(BIOCHEMICAL) %>% .[[1]] %>% as.character %>%length lipid_metabolites=metabolomics_meta[match(colnames(only_NP), metabolomics_meta$BIOCHEMICAL),] %>% filter(SUPER_PATHWAY =='Lipid') %>% filter(!BIOCHEMICAL %in% metabolites_list_consensus)%>% dplyr::select(BIOCHEMICAL) %>% .[[1]] %>% as.character # + only_NP_lasso=only_NP %>% rownames_to_column('gene')%>% dplyr::select(pathways_heatmap_scaled_lasso,gene)%>% column_to_rownames('gene') dim(only_NP_lasso) only_NP_lipid=only_NP %>% rownames_to_column('gene')%>% dplyr::select(lipid_metabolites,gene)%>% column_to_rownames('gene') dim(only_NP_lipid) # + bac=as.matrix(t(only_NP_lasso)) fug=as.matrix(t(only_NP_lipid)) P_K_K=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[3]][1,2] }) })) rownames(P_K_K)=rownames(bac) colnames(P_K_K)=rownames(fug) # r -value R_K_K=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[1]][1,2] }) })) rownames(R_K_K)=rownames(bac) colnames(R_K_K)=rownames(fug) # - # filtered only significant and strong correlation (p<0.05, -0.35 < r < 0.35 ) index= data.frame(which(P_K_K >= 0.05,arr.ind = TRUE)) index1= data.frame((which((R_K_K <= 0.35 & R_K_K >= -0.35 ),arr.ind = TRUE))) rownames(index) <- c() rownames(index1) <- c() # select only signficant correlation(p<0.05) and strong correlation (-0.35<t<0.35) for(i in 1:nrow(index)){ R_K_K[index[i,]$row, index[i,]$col] <- 0 } for(i in 1:nrow(index1)){ R_K_K[index1[i,]$row, index1[i,]$col] <- 0 } diag(R_K_K) <- 0 R_K_K[lower.tri(R_K_K)] = 0 P_K_K[lower.tri(P_K_K)] = 0 mat_Cyto_R=R_K_K mat_Cyto_P=P_K_K head(mat_Cyto_R) dim(mat_Cyto_R) #convert a correlataion matrix into a table #https://stackoverflow.com/questions/47037504/r-converting-a-data-frame-of-row-column-indices-to-a-matrix options(scipen = 999) my_cor_matrix_Cyto <- flat_cor_mat(mat_Cyto_R, mat_Cyto_P) my_cor_matrix_Cyto=my_cor_matrix_Cyto %>% filter(!cor==0 & !p>0.05) head(my_cor_matrix_Cyto) dim(my_cor_matrix_Cyto) # Create edge file colnames(my_cor_matrix_Cyto)=c('Source','Target','r','p') write.csv(my_cor_matrix_Cyto,"my_cor_matrix_Cyto.csv") #Creat nodes file nodes=unique(c(my_cor_matrix_Cyto$Source,my_cor_matrix_Cyto$Target)) nodes_C=metabolomics_meta[match(nodes,metabolomics_meta$BIOCHEMICAL),c('BIOCHEMICAL', 'KEGG', 'HMDB_ID', 'SUPER_PATHWAY','SUB_PATHWAY')] %>% dplyr::rename('id'='BIOCHEMICAL') head(nodes_C) dim(nodes_C) write.csv(nodes_C,"nodes_Cytoscape.csv") # # Complex Heatmap # I used the below code to generate the below beautiful figure. I tried to make the code simple but from the name of the package, it should be complex. # # # # ![image.png](attachment:image.png) # # # ## Neuropathy # + bac=as.matrix(t(only_NP_lasso)) fug=as.matrix(t(only_NP_lipid)) P_K_K=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[3]][1,2] }) })) rownames(P_K_K)=rownames(bac) colnames(P_K_K)=rownames(fug) # r -value R_K_K=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[1]][1,2] }) })) rownames(R_K_K)=rownames(bac) colnames(R_K_K)=rownames(fug) index= data.frame(which(P_K_K >= 0.05,arr.ind = TRUE)) index1= data.frame((which((R_K_K <= 0.35 & R_K_K >= -0.35 ),arr.ind = TRUE))) rownames(index) <- c() rownames(index1) <- c() #head(index) for(i in 1:nrow(index)){ R_K_K[index[i,]$row, index[i,]$col] <- 0 } for(i in 1:nrow(index1)){ R_K_K[index1[i,]$row, index1[i,]$col] <- 0 } # remove zeros sum columns r=R_K_K ZeroColumn=colnames(r)[which(colSums(r)==0)] #ZeroColumn r=r[,!colnames(r) %in% ZeroColumn] ZeroRows=rownames(r)[which(rowSums(r)==0)] r=r[!rownames(r) %in% ZeroRows,] row_ann=rownames(r) %>% data.frame() %>% dplyr::rename('lasso'='.') %>%inner_join(.,fold_change%>% rownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>% dplyr::rename('FC'='Obese_NoN...Lean_NoN') %>% inner_join(.,limma_dataframe_FC %>% rownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>% dplyr::rename('logFC'='Obese_NoN...Lean_NoN')%>% mutate(sign=sign(logFC)) %>% inner_join(.,metabolomics_meta %>% dplyr::select(lasso=BIOCHEMICAL,SUB_PATHWAY), by='lasso') %>% column_to_rownames('lasso') head(row_ann) col_ann=colnames(r) %>% data.frame() %>% dplyr::rename('lasso'='.') %>%inner_join(.,fold_change%>% rownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>% dplyr::rename('FC'='Obese_NoN...Lean_NoN') %>% inner_join(.,limma_dataframe_FC %>% rownames_to_column('lasso') %>% dplyr::select(lasso,'Obese_NoN...Lean_NoN'),by='lasso')%>% dplyr::rename('logFC'='Obese_NoN...Lean_NoN')%>% mutate(sign=sign(logFC))%>% inner_join(.,metabolomics_meta %>% dplyr::select(lasso=BIOCHEMICAL,SUB_PATHWAY), by='lasso') %>% column_to_rownames('lasso') # - # ## Non neuropathy # + only_DB_lasso=only_DB %>% rownames_to_column('gene')%>% dplyr::select(rownames(r),gene)%>% column_to_rownames('gene') dim(only_DB_lasso) only_DB_lipid=only_DB %>% rownames_to_column('gene')%>% dplyr::select(colnames(r),gene)%>% column_to_rownames('gene') dim(only_DB_lipid) bac=as.matrix(t(only_DB_lasso)) fug=as.matrix(t(only_DB_lipid)) P_K_K_DB=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[3]][1,2] }) })) rownames(P_K_K_DB)=rownames(bac) colnames(P_K_K_DB)=rownames(fug) # r -value R_K_K_DB=t(sapply(1:nrow(bac), function(x) { sapply(1:nrow(fug), function(y) { rcorr(bac[x,],fug[y,],type=c("spearman"))[[1]][1,2] }) })) rownames(R_K_K_DB)=rownames(bac) colnames(R_K_K_DB)=rownames(fug) R_K_K_DB_KEEP= R_K_K_DB P_K_K_DB_KEEP= P_K_K_DB index= data.frame(which(P_K_K_DB >= 0.05,arr.ind = TRUE)) index1= data.frame((which((R_K_K_DB <= 0.35 & R_K_K_DB >= -0.35 ),arr.ind = TRUE))) rownames(index) <- c() rownames(index1) <- c() dim(index) #https://stackoverflow.com/questions/47037504/r-converting-a-data-frame-of-row-column-indices-to-a-matrix # select only signficant correlation(p<0.05) and strong correlation (-0.5<t<0.5) for(i in 1:nrow(index)){ R_K_K_DB[index[i,]$row, index[i,]$col] <- 0 } for(i in 1:nrow(index1)){ R_K_K_DB[index1[i,]$row, index1[i,]$col] <- 0 } # remove zeros sum columns r_DB=R_K_K_DB #ZeroColumn=colnames(r_DB)[which(colSums(r_DB)==0)] #ZeroColumn #r_DB=r[,!colnames(r_DB) %in% ZeroColumn] # - ####arrange columns r based in subpathways col_ann=col_ann %>% rownames_to_column('gene') %>% arrange(SUB_PATHWAY) %>% column_to_rownames('gene') col_order =rownames(col_ann) r_NP <- r[, col_order] r_DB <- r_DB[, col_order] match(colnames(r_NP),colnames(r_DB)) ###### color for columns n=length(unique(col_ann$SUB_PATHWAY)) library(randomcoloR) paletteCol <- distinctColorPalette(n) names(paletteCol)=unique(col_ann$SUB_PATHWAY) head(paletteCol) ######colors for rows n=length(unique(row_ann$SUB_PATHWAY)) #library(randomcoloR) paletteRow <- distinctColorPalette(n) names(paletteRow)=unique(row_ann$SUB_PATHWAY) haed(paletteRow) # + ################ANNOTATION OF THE ROWS AND COLUMNS ha_row = HeatmapAnnotation(Sub_pathway=row_ann[,4],which='row',col= list(Sub_pathway=paletteRow), FC = anno_barplot(row_ann[,1],gp = gpar(fill = ifelse(row_ann[,3]==1,c('red'),c('green') ), col = ifelse(row_ann[,3]==1,c('red'),c('blue') ) ),which = "row")) ha_row draw(ha_row) ha_col_buttom = HeatmapAnnotation(FC = anno_barplot(col_ann[,1],gp = gpar(fill = ifelse(col_ann[,3]==1,c('red'),c('blue') ), col = ifelse(col_ann[,3]==1,c('red'),c('blue') ))) ) ha_col_top = HeatmapAnnotation(Sub_pathway=col_ann[,4],col= list(Sub_pathway=paletteCol),show_legend = T,border=2) # + ####################################Draw two heatmaps mat=r_NP ht1=Heatmap(as.matrix((mat)), name = "NP", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), cluster_rows = F, cluster_columns = F, border='black', show_column_names = F, column_title = "NP", #cluster_columns = dend, row_names_side = "right", column_names_side = "bottom", row_names_gp = gpar(fontsize = 15), column_names_gp = gpar(fontsize = 10,rot=90), #km = 3, #gap = unit(3, "mm") #bottom_annotation = ha_col_buttom, top_annotation = ha_col_top, #left_annotation = ha_row, show_heatmap_legend = T, width = unit(30, "inch"), height = unit(2, "inch"), column_dend_side='top', #cluster_column_slices = TRUE column_dend_height = unit(10, "inch"), row_dend_width = unit(10, "inch"), #row_hclust_side = "right" # use_raster = TRUE, raster_device = "tiff" #heatmap_legend_param(list(title_position = "topcenter")), # heatmap_legend_param = list(color_bar = "continuous",at=c(-1,0,1)) ) mat=r_DB ht2=Heatmap(as.matrix((mat)), name = "DB", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), cluster_rows = F, border='black', cluster_columns = F, column_title = "DB", #cluster_columns = dend, row_names_side = "right", column_names_side = "bottom", row_names_gp = gpar(fontsize = 15), column_names_gp = gpar(fontsize = 8,rot=90), column_names_rot = 45, #km = 3, #gap = unit(3, "mm") bottom_annotation = ha_col_buttom, #top_annotation = ha_col_top, #left_annotation = ha_row, show_heatmap_legend = T, width = unit(30, "inch"), height = unit(2, "inch"), column_dend_side='top', #cluster_column_slices = TRUE column_dend_height = unit(10, "inch"), row_dend_width = unit(10, "inch"), #row_hclust_side = "right" # use_raster = TRUE, raster_device = "tiff" #heatmap_legend_param(list(title_position = "topcenter")), ) ht_list=ht1 + ht2 #pdf("heatmap_no_clustering_NP_DB1.pdf", width = 80, height = 20) draw(ht_list, annotation_legend_side = "left", legend_title_gp = gpar(fontsize = 10, fontface = "plain"),heatmap_legend_side = "top", gap = unit(10, "cm") ) #dev.off() # + ########### only Acyl Carnitine ####arrange columns r based in subpathways col_ann=col_ann %>% rownames_to_column('gene') %>% arrange(SUB_PATHWAY) %>% filter(str_detect(SUB_PATHWAY,'Acyl Carnitine'))%>% column_to_rownames('gene') col_order =rownames(col_ann) r_NP <- r[, col_order] r_DB <- r_DB[, col_order] match(colnames(r_NP),colnames(r_DB)) ###### color for columns n=length(unique(col_ann$SUB_PATHWAY)) library(randomcoloR) paletteCol <- distinctColorPalette(n) names(paletteCol)=unique(col_ann$SUB_PATHWAY) #paletteCol ######colors for rows n=length(unique(row_ann$SUB_PATHWAY)) #library(randomcoloR) paletteRow <- distinctColorPalette(n) names(paletteRow)=unique(row_ann$SUB_PATHWAY) #paletteRow #mat=r1 #dim(mat) # 4 x 98 ################ANNOTATION OF THE ROWS AND COLUMNS ha_row = HeatmapAnnotation(which='row', FC = anno_barplot(row_ann[,1],gp = gpar(fontsize=20,fill = ifelse(row_ann[,3]==1,c('red'),c('blue') ), col = ifelse(row_ann[,3]==1,c('red'),c('blue') ) ))) #ha_row #draw(ha_row) ha_col_buttom = HeatmapAnnotation(FC = anno_barplot(height = unit(1, "inch"),col_ann[,1],gp = gpar(fontsize=20,fill = ifelse(col_ann[,3]==1,c('red'),c('blue') ), col = ifelse(col_ann[,3]==1,c('red'),c('blue') ))) ) ha_col_top = HeatmapAnnotation(Sub_pathway=col_ann[,4],col= list(Sub_pathway=paletteCol),show_legend = T,border=2) mat=r_NP ht1=Heatmap(as.matrix((mat)), name = "NP", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), cluster_rows = F, cluster_columns = F, border='black', show_column_names = F, column_title = "NP", #cluster_columns = dend, row_names_side = "right", column_names_side = "bottom", row_names_gp = gpar(fontsize = 20), column_names_gp = gpar(fontsize = 20,rot=90), #km = 3, #gap = unit(3, "mm") #bottom_annotation = ha_col_buttom, top_annotation = ha_col_top, #right_annotation = ha_row, show_heatmap_legend = T, width = unit(20, "inch"), height = unit(3, "inch"), column_dend_side='top', #cluster_column_slices = TRUE column_dend_height = unit(20, "inch"), row_dend_width = unit(20, "inch"), #row_hclust_side = "right" # use_raster = TRUE, raster_device = "tiff" #heatmap_legend_param(list(title_position = "topcenter")), # heatmap_legend_param = list(color_bar = "continuous",at=c(-1,0,1)) ) mat=r_DB ht2=Heatmap(as.matrix((mat)), name = "DB", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), cluster_rows = F, border='black', cluster_columns = F, column_title = "DB", #cluster_columns = dend, row_names_side = "right", column_names_side = "bottom", row_names_gp = gpar(fontsize = 20), column_names_gp = gpar(fontsize = 20,rot=90), column_names_rot = 45, #km = 3, #gap = unit(3, "mm") bottom_annotation = ha_col_buttom, #top_annotation = ha_col_top, #right_annotation = ha_row, show_heatmap_legend = T, width = unit(20, "inch"), height = unit(5, "inch"), column_dend_side='top', #cluster_column_slices = TRUE column_dend_height = unit(20, "inch"), row_dend_width = unit(20, "inch"), #row_hclust_side = "right" # use_raster = TRUE, raster_device = "tiff" #heatmap_legend_param(list(title_position = "topcenter")), ) ht_list=ht1 + ht2 #pdf("heatmap_no_clustering_NP_DB1_Acyl Carnitine1.pdf", width = 60, height = 20) draw(ht_list, annotation_legend_side = "left", legend_title_gp = gpar(fontsize = 40, fontface = "plain"),heatmap_legend_side = "top", gap = unit(10, "cm") ) #dev.off() # - # # Demographic table # I used qwraps2 to generate the summary of the samples such as the mean, SD, and its corresponding p value using t-test, Fisher, and ANOVA # # #https://cran.rstudio.com/web/packages/qwraps2/vignettes/summary-statistics.html library(magrittr) #install.packages("qwraps2") library(qwraps2) table1=read.csv("Fadhl Project with Metabolic Variables 12_3_2019.csv",stringsAsFactors=F) table1 =table1 %>% filter(Sample_ID %in% samples_meta$CLIENT_IDENTIFIER) %>% dplyr::select(Sample_ID,matches("Sex|Age|BMI|BP|weight|Waist|Cholesterol|Triglycerides|LDL"))%>% dplyr::rename('CLIENT_IDENTIFIER'='Sample_ID') dim(table1) #head(table1) samples_meta2= samples_meta %>% left_join(.,table1,by='CLIENT_IDENTIFIER') head(samples_meta2) # + our_summary2 <- list("AGE" = list( "AGE mean (sd)" = ~ qwraps2::mean_sd(.data$AGE)), "BMI" = list( "BMI mean (sd)" = ~ qwraps2::mean_sd(.data$BMI.x)), "systav_10c" = list( "Blood pressure: Systolic mean (sd)" = ~ qwraps2::mean_sd(.data$SBP..mmHg.)), "diasav_10c" = list( "Blood pressure: Diastolic mean (sd)" = ~ qwraps2::mean_sd(.data$DBP..mmHg.)), "Sex" = list( "Sex: Female" = ~ qwraps2::n_perc(.data$Sex == 'Female'), "Sex: Male" = ~ qwraps2::n_perc(.data$Sex == "Male")), "Weigh" = list( "Weigh mean (sd)" = ~ qwraps2::mean_sd(.data$Weight..Kg.)), "Waist" = list( "Waist mean (sd)" = ~ qwraps2::mean_sd(.data$Waist.Circumference..cm.,na_rm = T)), "Cholesterol" = list( "Cholesterol mean (sd)" = ~ qwraps2::mean_sd(.data$Cholesterol,na_rm = T)), "Tgly" = list( "Tgly mean (sd)" = ~ qwraps2::mean_sd(.data$Triglycerides,na_rm = T)), "LDL_Chol" = list( "LDL_Chol mean (sd)" = ~ qwraps2::mean_sd(.data$LDL,na_rm = T)) ) whole <- summary_table(samples_meta2, our_summary2) #whole by_cyl <- summary_table(dplyr::group_by(samples_meta2, GROUP_ID), our_summary2) #both <- cbind(whole, by_cyl) both=by_cyl both # - # difference in means mpvals <- list(lm(AGE ~ GROUP_ID, data = samples_meta2), lm(BMI.x ~ GROUP_ID, data = samples_meta2), lm(SBP..mmHg. ~ GROUP_ID, data = samples_meta2), lm(DBP..mmHg. ~ GROUP_ID, data = samples_meta2), lm(Weight..Kg. ~ GROUP_ID, data = samples_meta2), lm(Waist.Circumference..cm. ~ GROUP_ID, data = samples_meta2), lm(Cholesterol ~ GROUP_ID, data = samples_meta2), lm(Triglycerides ~ GROUP_ID, data = samples_meta2), #lm(Crea ~ GROUP_ID, data = samples_meta2), #lm(HbA1ci ~ GROUP_ID, data = samples_meta2), # lm(HDL_Chol ~ GROUP_ID, data = samples_meta2), #lm(U_Alb_m ~ GROUP_ID, data = samples_meta2), lm(LDL ~ GROUP_ID, data = samples_meta2)) %>% lapply(aov) %>% lapply(summary) %>% lapply(function(x) x[[1]][["Pr(>F)"]][1]) %>% lapply(frmtp) %>% do.call(c, .) mpvals fpvalSex <- frmtp(fisher.test(table(samples_meta2$GROUP_ID, samples_meta2$Sex))$p.value) fpvalSex both <- cbind(both, "P-value" = "") both[grepl("mean \\(sd\\)", rownames(both)), "P-value"] <- mpvals both[grepl("Sex", rownames(both)), "P-value"] <- fpvalSex both write.csv(both,"Table1.csv",fileEncoding = "UTF-8") # # My functions # + #' A machine learning Function #' #' This function for classification using 7 different machine learning algorithms #' and it plot the ROC curves and the AUC, SEN, and specificty #' @param PDSmatrix from PDSfun function and selected_Pathways_Weka from featuresSelection function #' @keywords classifcation #' @export #' @examples machine_learning(PDSmatrix,selected_Pathways_Weka) #' machine_learning(PDSmatrix,selected_Pathways_Weka) #' #' #' machine_learning<-function(PDSmatrix,selected_Pathways_Weka,Label){ require(caret) require(pROC) require(ggplot2) require(gbm) prostate_df=data.frame(((PDSmatrix[,selected_Pathways_Weka])),Label=Label, check.names=T) colnames(prostate_df)[which(names(prostate_df) == "Label")]='subtype' performance_training=matrix( rep( 0, len=21), nrow = 3) #AUC SENS SPECF performance_testing=matrix( rep( 0, len=56), nrow = 8) # ROC SENS SPEC performance=matrix(rep( 0, len=56), nrow = 8) # ROC SENS SPEC performance_training_list <- list() performance_testing_list <- list() # var.cart= list() # var.lda= list() # var.svm= list() # var.rf= list() # var.gbm= list() # var.pam= list() # var.log= list() model=list() ###############Shuffle stat first #rand <- sample(nrow(prostate_df)) #prostate_df=prostate_df[rand, ] ###############Randomly Split the data in to training and testing set.seed(2000) trainIndex <- createDataPartition(prostate_df$subtype, p = 1,list = FALSE,times = 1) irisTrain <- prostate_df[ trainIndex,] irisTest <- prostate_df[ trainIndex,] #irisTrain$subtype=as.factor(paste0("X",irisTrain$subtype)) #irisTest$subtype=as.factor(paste0("X",irisTest$subtype)) ################################Training and tunning parameters # prepare training scheme #control <- trainControl(method="cv", number=10,classProbs = TRUE,summaryFunction = twoClassSummary # ,sampling='smote') control <- trainControl(method="LOOCV", number=10,classProbs = TRUE,summaryFunction = twoClassSummary ,sampling='smote') #1- RPART ALGORITHM set.seed(7) #This ensures that the same resampling sets are used,which will come in handy when we compare the resampling profiles between models. #assign(paste0("fit.cart",k),train(subtype~., data=irisTrain, method="rpart", trControl=control,metric="ROC")) # supress the warning messgae #options(warn=-1) #options(warn=0) #?suppressWarnings() garbage <- capture.output(fit.cart <- train(subtype~., data=irisTrain, method = 'rpart', trControl=control,metric="ROC")) #fit.cart <- train(subtype~., data=irisTrain, method = 'rpart', trControl=control,metric="ROC") #loclda model[[1]]=fit.cart performance_training[1,1]=max(fit.cart$results$ROC)#AUC performance_training[2,1]=fit.cart$results$Sens[which.max(fit.cart$results$ROC)]# sen performance_training[3,1]=fit.cart$results$Spec[which.max(fit.cart$results$ROC)]# spec #Model Testing cartClasses <- predict( fit.cart, newdata = irisTest,type="prob") cartClasses1 <- predict( fit.cart, newdata = irisTest) cartConfusion=confusionMatrix(data = cartClasses1, irisTest$subtype) cart.ROC <- roc(predictor=as.numeric(unlist(cartClasses[1])),response=irisTest$subtype,levels=rev(levels(irisTest$subtype))) performance_testing[1,1]=as.numeric(cart.ROC$auc)#AUC performance_testing[2,1]=cartConfusion$byClass[1]#SENS performance_testing[3,1]=cartConfusion$byClass[2]#SPEC performance_testing[4,1]=cartConfusion$overall[1]#accuracy performance_testing[5,1]=cartConfusion$byClass[5]#precision performance_testing[6,1]=cartConfusion$byClass[6]#recall = sens performance_testing[7,1]=cartConfusion$byClass[7]#F1 performance_testing[8,1]=cartConfusion$byClass[11]#BALANCED ACCURACY #2-LDA ALGORITHM set.seed(7) #assign(paste0("fit.lda",k),train(subtype~., data=irisTrain, method="pls", trControl=control,metric="ROC")) garbage <- suppressWarnings(capture.output(fit.lda <- train(subtype~., data=irisTrain, method = 'lda', trControl=control,metric="ROC",trace=F))) #loclda) #fit.lda <- train(subtype~., data=irisTrain, method = 'lda', trControl=control,metric="ROC") #loclda model[[2]]=fit.lda performance_training[1,2]=max(fit.lda$results$ROC)#AUC performance_training[2,2]=fit.lda$results$Sens[which.max(fit.lda$results$ROC)]# sen performance_training[3,2]=fit.lda$results$Spec[which.max(fit.lda$results$ROC)]# spec #Model Testing ldaClasses <- predict( fit.lda, newdata = irisTest,type="prob") ldaClasses1 <- predict( fit.lda, newdata = irisTest) ldaConfusion=confusionMatrix(data = ldaClasses1, irisTest$subtype) lda.ROC <- roc(predictor=as.numeric(unlist(ldaClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,2]=as.numeric(lda.ROC$auc)#AUC performance_testing[2,2]=ldaConfusion$byClass[1]#SENS performance_testing[3,2]=ldaConfusion$byClass[2]#SPEC performance_testing[4,2]=ldaConfusion$overall[1]#accuracy performance_testing[5,2]=ldaConfusion$byClass[5]#precision performance_testing[6,2]=ldaConfusion$byClass[6]#recall = sens performance_testing[7,2]=ldaConfusion$byClass[7]#F1 performance_testing[8,2]=ldaConfusion$byClass[11]#BALANCED ACCURACY #3- SVM ALGORITHM set.seed(7) garbage <- capture.output(fit.svm <- train(subtype~., data=irisTrain, method="svmRadial", trControl=control,metric="ROC")) #fit.svm <- train(subtype~., data=irisTrain, method="svmRadial", trControl=control,metric="ROC") #assign(paste0("fit.svm",k),train(subtype~., data=irisTrain, method="svmRadical", trControl=control,metric="ROC")) model[[3]]=fit.svm performance_training[1,3]=max(fit.svm$results$ROC) #AUC performance_training[2,3]=fit.svm$results$Sens[which.max(fit.svm$results$ROC)]# sen performance_training[3,3]=fit.svm$results$Spec[which.max(fit.svm$results$ROC)]# spec #Model Testing svmClasses <- predict( fit.svm, newdata = irisTest,type="prob") svmClasses1 <- predict( fit.svm, newdata = irisTest) svmConfusion=confusionMatrix(data = svmClasses1, irisTest$subtype) svm.ROC <- roc(predictor=as.numeric(unlist(svmClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,3]=as.numeric(svm.ROC$auc)#AUC performance_testing[2,3]=svmConfusion$byClass[1]#SENS performance_testing[3,3]=svmConfusion$byClass[2]#SPEC performance_testing[4,3]=svmConfusion$overall[1]#accuracy performance_testing[5,3]=svmConfusion$byClass[5]#precision performance_testing[6,3]=svmConfusion$byClass[6]#recall = sens performance_testing[7,3]=svmConfusion$byClass[7]#F1 performance_testing[8,3]=svmConfusion$byClass[11]#BALANCED ACCURACY #4-RF ALGORITHM set.seed(7) garbage <- capture.output(fit.rf <- train(subtype~., data=irisTrain, method="rf", trControl=control,metric="ROC")) #fit.rf <- train(subtype~., data=irisTrain, method="rf", trControl=control,metric="ROC") model[[4]]=fit.rf performance_training[1,4]=max(fit.rf$results$ROC) #AUC performance_training[2,4]=fit.rf$results$Sens[which.max(fit.rf$results$ROC)]# sen performance_training[3,4]=fit.rf$results$Spec[which.max(fit.rf$results$ROC)]# spec #Model Testing rfClasses <- predict( fit.rf, newdata = irisTest,type="prob") rfClasses1 <- predict( fit.rf, newdata = irisTest) rfConfusion=confusionMatrix(data = rfClasses1, irisTest$subtype) rf.ROC <- roc(predictor=as.numeric(unlist(rfClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,4]=as.numeric(rf.ROC$auc)#AUC performance_testing[2,4]=rfConfusion$byClass[1]#SENS performance_testing[3,4]=rfConfusion$byClass[2]#SPEC performance_testing[4,4]=rfConfusion$overall[1]#accuracy performance_testing[5,4]=rfConfusion$byClass[5]#precision performance_testing[6,4]=rfConfusion$byClass[6]#recall = sens performance_testing[7,4]=rfConfusion$byClass[7]#F1 performance_testing[8,4]=rfConfusion$byClass[11]#BALANCED ACCURACY #5- GBM ALGORITHM set.seed(7) garbage <- suppressWarnings(capture.output(fit.gbm <- train(subtype~., data=irisTrain, method="gbm", trControl=control,metric="ROC"))) # fit.gbm <- train(subtype~., data=irisTrain, method="gbm", trControl=control,metric="ROC") model[[5]]=fit.gbm performance_training[1,5]=max(fit.gbm$results$ROC) #AUC performance_training[2,5]=fit.gbm$results$Sens[which.max(fit.gbm$results$ROC)]# sen performance_training[3,5]=fit.gbm$results$Spec[which.max(fit.gbm$results$ROC)]# spec #Model Testing gbmClasses <- predict( fit.gbm, newdata = irisTest,type="prob") gbmClasses1 <- predict( fit.gbm, newdata = irisTest) gbmConfusion=confusionMatrix(data = gbmClasses1, irisTest$subtype) gbm.ROC <- roc(predictor=as.numeric(unlist(gbmClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,5]=as.numeric(gbm.ROC$auc)#AUC performance_testing[2,5]=gbmConfusion$byClass[1]#SENS performance_testing[3,5]=gbmConfusion$byClass[2]#SPEC performance_testing[4,5]=gbmConfusion$overall[1]#accuracy performance_testing[5,5]=gbmConfusion$byClass[5]#precision performance_testing[6,5]=gbmConfusion$byClass[6]#recall = sens performance_testing[7,5]=gbmConfusion$byClass[7]#F1 performance_testing[8,5]=gbmConfusion$byClass[11]#BALANCED ACCURACY #6- PAM ALGORITHM set.seed(7) garbage <- capture.output(fit.pam <- train(subtype~., data=irisTrain, method="pam", trControl=control,metric="ROC"))#plr) #loclda) #fit.pam <- train(subtype~., data=irisTrain, method="pam", trControl=control,metric="ROC")#plr model[[6]]=fit.pam performance_training[1,6]=max(fit.pam$results$ROC) #AUC performance_training[2,6]=fit.pam$results$Sens[which.max(fit.pam$results$ROC)]# sen performance_training[3,6]=fit.pam$results$Spec[which.max(fit.pam$results$ROC)]# spec #Model Testing pamClasses <- predict( fit.pam, newdata = irisTest,type="prob") pamClasses1 <- predict( fit.pam, newdata = irisTest) pamConfusion=confusionMatrix(data = pamClasses1, irisTest$subtype) pam.ROC <- roc(predictor=as.numeric(unlist(pamClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,6]=as.numeric(pam.ROC$auc)#AUC performance_testing[2,6]=pamConfusion$byClass[1]#SENS performance_testing[3,6]=pamConfusion$byClass[2]#SPEC performance_testing[4,6]=pamConfusion$overall[1]#accuracy performance_testing[5,6]=pamConfusion$byClass[5]#precision performance_testing[6,6]=pamConfusion$byClass[6]#recall = sens performance_testing[7,6]=pamConfusion$byClass[7]#F1 performance_testing[8,6]=pamConfusion$byClass[11]#BALANCED ACCURACY #7- logistic regression set.seed(7) garbage <- suppressWarnings(capture.output(fit.log <- train(subtype~., data=irisTrain, method="glmnet", trControl=control,metric="ROC"))) #fit.log <- train(subtype~., data=irisTrain, method="glm", trControl=control,metric="ROC")# model[[7]]=fit.log performance_training[1,7]=max(fit.log$results$ROC) #AUC performance_training[2,7]=fit.log$results$Sens[which.max(fit.log$results$ROC)]# sen performance_training[3,7]=fit.log$results$Spec[which.max(fit.log$results$ROC)]# spec #Model Testing logClasses <- predict( fit.log, newdata = irisTest,type="prob") logClasses1 <- predict( fit.log, newdata = irisTest) logConfusion=confusionMatrix(data = logClasses1, irisTest$subtype) log.ROC <- roc(predictor=as.numeric(unlist(logClasses[1])),response=irisTest$subtype, levels=rev(levels(irisTest$subtype))) performance_testing[1,7]=as.numeric(log.ROC$auc)#AUC performance_testing[2,7]=logConfusion$byClass[1]#SENS performance_testing[3,7]=logConfusion$byClass[2]#SPEC performance_testing[4,7]=logConfusion$overall[1]#accuracy performance_testing[5,7]=logConfusion$byClass[5]#precision performance_testing[6,7]=logConfusion$byClass[6]#recall = sens performance_testing[7,7]=logConfusion$byClass[7]#F1 performance_testing[8,7]=logConfusion$byClass[11]#BALANCED ACCURACY # performance_testing_list[[k]]<<- performance_testing # performance_training_list[[k]]<<- performance_training performance_testing_list[[1]] <- performance_testing performance_training_list[[1]] <- performance_training #performance_training=matrix( rep( 0, len=21), nrow = 3) #AUC SENS SPECF #performance_testing=matrix( rep( 0, len=56), nrow = 8) # ROC SENS SPEC #####plot the variable importance #par(mfrow=c(7,1)) # plot(plot(varImp(fit.cart, scale = FALSE,top=20),main="RPART")) # plot(plot(varImp(fit.lda, scale = FALSE,top=20),main="LDA")) # plot(plot(varImp(fit.svm, scale = FALSE,top=20),main="SVM")) # plot(plot(varImp(fit.rf, scale = FALSE,top=20),main="RF")) # plot(plot(varImp(fit.gbm, scale = FALSE,top=20),main="GBM")) # plot(plot(varImp(fit.pam, scale = FALSE,top=20),main="PAM")) # plot(plot(varImp(fit.log, scale = FALSE,top=20),main="LOG")) #############plot ROC smooth_method="density" #"binormal" #"density" #plot(cart.ROC, col="red" ) #pdf("ROC_ER1.pdf",width=10,height=10) plot(smooth(cart.ROC,method=smooth_method),col="red",cex.lab=1.5) #plot(cart.ROC,col="red",print.auc=T) par(new=TRUE) #plot( lda.ROC, col="green" ) #plot.roc(lda.ROC,col="green",print.auc=T) #plot.roc(smooth(lda.ROC,method="binormal"),col="green",print.auc=T) #plot.roc(smooth(lda.ROC,method="density"),col="green",print.auc=T) #plot.roc(smooth(lda.ROC,method="fitdistr"),col="green",print.auc=T) #plot.roc(smooth(lda.ROC,method="logcondens"),col="green",print.auc=T) plot(smooth(lda.ROC,method=smooth_method),col="green",cex.lab=1.5) #plot(lda.ROC,col="green",print.auc=T) par(new=TRUE) #plot(svm.ROC, col="black" ) plot(smooth(svm.ROC,method=smooth_method),col="black",cex.lab=1.5) #plot(svm.ROC,col="black",print.auc=T) par(new=TRUE) #plot(rf.ROC, col="orange" ) plot(smooth(rf.ROC,method=smooth_method),col="orange",cex.lab=1.5) #plot(rf.ROC,col="orange",print.auc=T) par(new=TRUE) #plot(gbm.ROC, col="blue" ) plot(smooth(gbm.ROC,method=smooth_method),col="blue",cex.lab=1.5) #plot(gbm.ROC,col="blue",print.auc=T) par(new=TRUE) #plot( pam.ROC, col="hotpink" ) plot(smooth(pam.ROC,method=smooth_method),col="hotpink",cex.lab=1.5) #plot(pam.ROC,col="hotpink",print.auc=T) par(new=TRUE) #plot(log.ROC, col="lightgoldenrod2", main="Testing ROC" ) plot(smooth(log.ROC,method=smooth_method),col="lightgoldenrod2",main="Testing ROC",cex.lab=1.5) #plot(log.ROC,col="lightgoldenrod2",main="Testing ROC",print.auc=T) legend(0.2, 0.4, legend=c('RPART','LDA','SVM','RF','GBM','PAM','LOG'), col=c("red", "green","black","orange","blue","hotpink","lightgoldenrod2"), lty=1:2, cex=1) #dev.off() ######################performance plotting #require(ggplot) require(reshape2) list_test=performance_testing_list list_train=performance_training_list AUC_train=lapply(list_train, function(x) x[1,]) AUC_test=lapply(list_test, function(x) x[1,]) SENS_train=lapply(list_train, function(x) x[2,]) SENS_test=lapply(list_test, function(x) x[2,]) SPEC_train=lapply(list_train, function(x) x[3,]) SPEC_test=lapply(list_test, function(x) x[3,]) F1_test=lapply(list_test, function(x) x[7,]) Balanced_accuracy_test=lapply(list_test, function(x) x[8,]) output1 <- do.call(rbind,lapply(AUC_train,matrix,ncol=7,byrow=TRUE)) output2 <- do.call(rbind,lapply(AUC_test,matrix,ncol=7,byrow=TRUE)) output3 <- do.call(rbind,lapply(SENS_train,matrix,ncol=7,byrow=TRUE)) output4 <- do.call(rbind,lapply(SENS_test,matrix,ncol=7,byrow=TRUE)) output5 <- do.call(rbind,lapply(SPEC_train,matrix,ncol=7,byrow=TRUE)) output6 <- do.call(rbind,lapply(SPEC_test,matrix,ncol=7,byrow=TRUE)) output7 <- do.call(rbind,lapply(F1_test,matrix,ncol=7,byrow=TRUE)) output8 <- do.call(rbind,lapply(Balanced_accuracy_test,matrix,ncol=7,byrow=TRUE)) AUC_train_mean=apply(output1,2,mean) AUC_test_mean=apply(output2,2,mean) AUC=data.frame(AUC=t(cbind(t(AUC_train_mean),t(AUC_test_mean)))) SENS_train_mean=apply(output3,2,mean) SENS_test_mean=apply(output4,2,mean) SENS=data.frame(SENS=t(cbind(t(SENS_train_mean),t(SENS_test_mean)))) SPEC_train_mean=apply(output5,2,mean) SPEC_test_mean=apply(output6,2,mean) SPEC=data.frame(SPEC=t(cbind(t(SPEC_train_mean),t(SPEC_test_mean)))) F1_test_mean=apply(output7,2,mean) F1=data.frame(F1=t(t(F1_test_mean))) Balanced_accuracy_test_mean=apply(output8,2,mean) Balanced_accuracy=data.frame(Balanced_accuracy=t(t(Balanced_accuracy_test_mean))) trainingORtesting=t(cbind(t(rep("training",7)),t(rep("testing",7)))) testing_only=t(t(rep("testing",7))) performance_data=data.frame(AUC=AUC,SENS=SENS,SPEC=SPEC,trainingORtesting, Algorithm=(rep(t(c('RPART','LDA','SVM','RF','GBM','PAM','LOG')),2)) ) performance_data_test=data.frame(AUC=data.frame(AUC=t((t(AUC_test_mean)))), SENS=data.frame(SENS=t((t(SENS_test_mean)))), SPEC=data.frame(SPEC=t((t(SPEC_test_mean)))), F1=F1, Balanced_accuracy=Balanced_accuracy ,testing_only,Algorithm=(rep(t(c('RPART','LDA','SVM','RF','GBM','PAM','LOG')),1)) ) #print(performance_data_test) textLabels = geom_text( aes(x=Algorithm, label=round(value,2),fill=variable), position = position_dodge(width = 1), vjust = -0.5, size = 2 ) #performance_data melted_performance_data=suppressMessages(melt(performance_data) ) melted_performance_data_test=suppressMessages(melt(performance_data_test) ) #melted_performance_data #pdf("pdf1_ER1.pdf",width=10,height=10) p1=ggplot(data=melted_performance_data[trainingORtesting=='training',], aes(x=Algorithm, y=value,fill=variable)) + geom_bar(stat="identity",position=position_dodge()) +xlab("")+ylab("")+ggtitle("Training")+theme(plot.title = element_text(hjust = 0.5) ,axis.text=element_text(size=15,face="bold"),axis.title=element_text(size=14,face="bold"))+labs(fill="")+ textLabels print(p1) #dev.off() #pdf("pdf2_ER1.pdf",width=10,height=10) p2=ggplot(data=melted_performance_data[trainingORtesting=='testing',], aes(x=Algorithm, y=value,fill=variable)) + geom_bar(stat="identity",position=position_dodge()) +xlab("")+ylab("")+ggtitle("Testing")+theme(plot.title = element_text(hjust = 0.5) ,axis.text=element_text(size=15,face="bold"),axis.title=element_text(size=14,face="bold"))+labs(fill="")+ textLabels print(p2) # dev.off() # pdf("pdf3_ER1.pdf",width=10,height=10) p3=ggplot(data=melted_performance_data_test, aes(x=Algorithm, y=value,fill=variable)) + geom_bar(stat="identity",position=position_dodge()) +xlab("")+ylab("")+ggtitle("Testing")+theme(plot.title = element_text(hjust = 0.5) ,axis.text=element_text(size=10,face="bold"),axis.title=element_text(size=14,face="bold"))+labs(fill="")+ textLabels print(p3) # dev.off() #Which algorithm performs better based on the its AUC on testing res=list() res$melted_performance_data= melted_performance_data res$models=model res$performance=performance_testing res$train_inx= trainIndex #res$melted_performance_data_test= melted_performance_data_test #print the performance metrics for the best algorithms best_model=res$models[which.max(res$performance[1,])] # the best model has the high AUC method=(unlist(best_model)[[1]]) if (method=='glmnet'){method='log'} if (method=='svmRadial'){method='svm'} #pdf("best_model_performance_ER1.pdf",width=10,height=10) dd=filter(melted_performance_data_test,Algorithm==toupper(method)) p4=ggplot(data=dd, aes(x=Algorithm, y=value,fill=variable)) + geom_bar(stat="identity",position=position_dodge()) +xlab("")+ylab("")+ggtitle("Testing")+theme(plot.title = element_text(hjust = 0.5) ,axis.text=element_text(size=15,face="bold"),axis.title=element_text(size=14,face="bold"))+labs(fill="") print(p4) #dev.off() return(res) } # - flat_cor_mat <- function(cor_r, cor_p){ #This function provides a simple formatting of a correlation matrix #into a table with 4 columns containing : # Column 1 : row names (variable 1 for the correlation test) # Column 2 : column names (variable 2 for the correlation test) # Column 3 : the correlation coefficients # Column 4 : the p-values of the correlations library(tidyr) library(tibble) cor_r <- rownames_to_column(as.data.frame(cor_r), var = "row") cor_r <- gather(cor_r, column, cor, -1) cor_p <- rownames_to_column(as.data.frame(cor_p), var = "row") cor_p <- gather(cor_p, column, p, -1) cor_p_matrix <- left_join(cor_r, cor_p, by = c("row", "column")) cor_p_matrix } # # print session information # print session information, so anybody can easily reporuduce ypur results using the same verion of the packags you used sessionInfo(package = NULL)
95,076
/Python-Addicts/Rearrange_Pos_and_Negatives.ipynb
a19de2bdb60364c8f2673f2cfdeea9679d4248fc
[]
no_license
glryz/Pythoncular
https://github.com/glryz/Pythoncular
2
1
null
2021-09-04T19:39:55
2021-09-04T17:51:43
null
Jupyter Notebook
false
false
.py
3,684
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## Functions and Parameters # def profileDet(username,followers=1): print("username: "+username) print("followers are "+str(followers)) profileDet("Raj",1000) profileDet("Arushi") class Shark: def swim(self): print("The Shark is swimming") def be_awesome(self): print("shark is being awesome") def shark_dead(self): print("shark is dead") doby = Shark() doby.swim() doby.be_awesome() doby.shark_dead() class Vehicle: def __init__(self): print("vehicle created,constructor is called") def __del__(self): print("vehicle deleted, destructor is called") # + car = Vehicle() del car # -
973
/main.ipynb
e6cb3b3b38789d1c86da893c47add25f8b2b88bc
[ "MIT" ]
permissive
shivamiitgoa/EDA-and-Clurstering-on-Iris-Dataset
https://github.com/shivamiitgoa/EDA-and-Clurstering-on-Iris-Dataset
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
1,619,409
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] _uuid="606ced8841f97a693b77254486a15ab5241bf0d3" # # Project 2: Exploratory Data Analysis and Unsupervised Learning # + _uuid="474d4d75dc784dc027a803680ad81fd3cb21dc06" # Ignoring warning import warnings warnings.simplefilter('ignore') # Importing useful libraries import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from sklearn import datasets from sklearn.cluster import AgglomerativeClustering from mpl_toolkits.mplot3d import Axes3D # %matplotlib inline # Fixing random state for reproducibility np.random.seed(160010010) # + [markdown] _uuid="3142e3b123c3a46da9ff45581c428c342c701700" # ## Preparing iris data # + _uuid="8b8c0ddb1e6e044616a5be463ddb80183ca7b628" iris = datasets.load_iris() iris_data = pd.DataFrame(iris.data) iris_data['target'] = pd.Series(iris.target) iris_data.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species'] target_classes = ['setosa','versicolor','virginica'] print("The number of observations are :", iris_data.shape[0]) print("Name of columns are :", iris_data.columns.values) print("Here are some rows from our final dataframe:") print(iris_data.head()) # + [markdown] _uuid="c5d178251b56bc9c10679353db231c76cc8a425a" # ## Question 1 # + [markdown] _uuid="eaa652c736ffb16b291e8ca8c7100576540254b4" # ### 1.1 Perform a visual exploration of the Iris dataset using scatterplots # + _uuid="347a27688282850512c6a360860119eed26e111a" # this formatter will label the colorbar with the correct target names formatter = plt.FuncFormatter(lambda i , *args: target_classes[i]) plt.scatter(x=iris_data.sepal_length,y=iris_data.sepal_width,c=iris_data.species) plt.colorbar(ticks=[0,1,2],format=formatter) plt.xlabel("sepal length (cm)") plt.ylabel("sepal width (cm)") # + _uuid="d21c753ea154ea33d514f19c35c5d5070a9a4888" plt.scatter(x=iris_data.petal_length,y=iris_data.petal_width,c=iris_data.species) plt.colorbar(ticks=[0,1,2],format=formatter) plt.xlabel("petal length (cm)") plt.ylabel("petal width (cm)") # + [markdown] _uuid="7ec00a1c61a6eaad16e2060649233e3907e03a40" # ### 1.2 Use pairplot() for the whole dataset to look at all of our features simultaneously # + _uuid="f35fe20360bec256b22eed1f025438ccf3c6a44c" # Giving each species name in our dataframe iris_data_with_labels = iris_data.copy(deep=True) iris_data_with_labels.species = pd.Series([target_classes[x] for x in iris_data.species]) print("New dataframe :") print(iris_data_with_labels.head()) # + _uuid="ca1a9e56430d6ebe62a38dbbc752b96ff41be922" sns.pairplot(iris_data_with_labels, hue="species") # making matrix plot between each variables and coloring points based on its # category # + [markdown] _uuid="8d77acceea35f6f846f9d0e2e3982f78813b2995" # ### Correlation matrix # + _uuid="634e50f724395a0550fbe05036ca6bfec7aba4d0" corr = iris_data.corr() sns.heatmap(corr,annot=True) # + [markdown] _uuid="c43203162005605bf23408d35db48e5a43152d8f" # ### 1.3 Explain what insights you can get from the plots # We can observe from our pairplot that if we petal length and petal width of setosa is very different from that of other two species. Petal length and petal width are also very correlated. In the scatter plot of petal length with petal width, we can see that setosa is seperated from other two species and there is a indistinguishable boundary between versicolor and virginica. So if our goal has been to seperate setosa from the two other species, then petal length and petal width will be considered the ideal features. # # Sepal length and sepal width of all three species are not very seperate. By observing the pairplot, we can see that in almost all scatter plots, setosa is seperate from the other two species. In some scatter plots, versicolor and virginica and seperated by indisguishable boundary and in other plots, they are intermingled. # + [markdown] _uuid="7b48b3ef12df59f8179435d0d7a97bd70e202e28" # ### 1.4 What conclusions could be drawn regarding the correlations among the numerical features in our dataset. # We can observe from our correlation matrix that, there is a high correlation between "sepal length and petal length", "sepal length and petal width", and "petal length and petal width". So we can say that sepal lenth, petal length, and petal width are highly correlated among themselves but they are not correlated significantly with the sepal width. # + [markdown] _uuid="42acb35c776c793fc9e41834ebed92f7c0caebae" # ## Question 2 # + [markdown] _uuid="c537a23f2cdb2aef41efcea395f1d9b7ddf6960e" # ### 2.1 Visualize the features of Iris images using histograms ,boxplots # + _uuid="1e20735547dfa80625809583c22807b7cbb43a25" # Creating histogram iris_data_with_labels.hist(bins=10) # + [markdown] _uuid="03e7da0829ccb37e1add35ecda35ed63041e7455" # ### Creating boxplots # + _uuid="a84f114296bb690871d7dba246a945b40be723f0" sns.boxplot(x='species', y='sepal_length', data=iris_data_with_labels, order=["virginica", "versicolor", "setosa"]) # + _uuid="46dbbe2b70e877250365e9d3357f0e13eaf51b4c" sns.boxplot(x='species', y='sepal_width', data=iris_data_with_labels, order=["virginica", "versicolor", "setosa"]) # + _uuid="2a24b597f8b1825711615317373dacb341accbe1" sns.boxplot(x='species', y='petal_length', data=iris_data_with_labels, order=["virginica", "versicolor", "setosa"]) # + _uuid="b8e4bbb6a4aaf62c710a320e54451ec9f17b2e81" sns.boxplot(x='species', y='petal_width', data=iris_data_with_labels, order=["virginica", "versicolor", "setosa"]) # + [markdown] _uuid="97cc8eadffa75cc2d3bd8a3f6f7dee31d385ea05" # ### 2.2 State your inferences about the iris dataset # From histograms and boxplots, we can see that petal width and petal length of different species are quite separate. sepal length is also little seperate. But sepal width is not separate for different species. And we can see a trend in petal width, petal length and sepal length of flowers of different species, as we are moving from viginica to setosa, these lengths are decreasing. # + [markdown] _uuid="8ba5f57048a6786d2f92de2aa80beaa7278b08b3" # ## Question 3: Visualizing the dataset using 3D-plots # + [markdown] _uuid="e12cdfa2f4a43c4750f75480d4e814549d8b8a09" # ### 3.1 Analyse the Iris dataset by plotting a 3D view using any three features # + _uuid="3494b08cf8b9ce2aa379bb7e7bd51c98bf21729a" # Since petal width, petal length and sepal length are correlated with the species of flower, we are going # to plot these variables on the 3 axes fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data.petal_width, iris_data.petal_length, iris_data.sepal_length, c=iris_data.species) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') plt.show() # + [markdown] _uuid="c62cb435b9d93eaddf08ddfc34763bb2341a8e4d" # ### 3.2 Explain your observations # We can see that all the three species are clustered. The cluster of setosa is separate from the other two species. And there is an indistinguishable separation between versicolor and virginica. # # I have chosen sepal length, petal width and petal length as our three axes because the clusters formed by taking these three features are more saperate than taking any other set of three features. # + [markdown] _uuid="8bf5cf9d414f27b9fc7b2976b254d80b8aa662d0" # ## Question 4: Implement k-means clustering algorithm and test using the Iris dataset # + _uuid="3e720b14f21b47ecd9b723464ede4774ae61ace6" import random import numpy as np def EuclidianDistance(x,y): # This function will return Euclidian distance between x and y, # where x and y are n-dimensional vector xi = np.array(list(x)) yi = np.array(list(y)) return np.sqrt(np.sum(np.square(xi - yi))) def Calculate_Mean_Square_Error(assignment_of_nodes,current_centers,dataframe): # This funciton will calculate the mean square error or J # When provided with centers, assignment of nodes and dataframe result = 0 length_of_dataframe = dataframe.shape[0] for x in range(length_of_dataframe): result += EuclidianDistance(dataframe.loc[dataframe.index[x], :], current_centers.loc[current_centers.index[int(assignment_of_nodes[x])], :]) ** 2 result = result / length_of_dataframe return result def KmeansCluster(dataframe, number_of_clusters, maximum_number_of_iteration = 100000): # This function will assign a group to every data point, and then it will return # the assignment, all the assignments that was calculated in each iteration, and # a list of value of J in each iteration. length_of_dataframe = dataframe.shape[0] width_of_dataframe = dataframe.shape[1] # choose k random points and make them centers random_indices = random.sample(list(range(length_of_dataframe)), number_of_clusters) current_centers = dataframe.loc[random_indices, :] # assign label to each of the observation points all_assignments = [] mean_square_list = [] assignment_of_nodes = np.zeros(length_of_dataframe) previous_assignment_of_nodes = np.copy(assignment_of_nodes) # iterate till maximum number of times or when the assignment of nodes is not changing for ix in range(maximum_number_of_iteration): # assign group to every data point for i in range(length_of_dataframe): current_assignment = 0 for j in range(number_of_clusters): current_distance = EuclidianDistance(dataframe.loc[dataframe.index[i], :], current_centers.loc[current_centers.index[current_assignment], :]) new_distance = EuclidianDistance(dataframe.loc[dataframe.index[i], :], current_centers.loc[current_centers.index[j], :]) if new_distance < current_distance: current_assignment = j assignment_of_nodes[i] = current_assignment this_assignment = list(assignment_of_nodes) this_assignment = [int(x) for x in this_assignment] all_assignments.append(this_assignment) mean_square_list.append(Calculate_Mean_Square_Error(assignment_of_nodes, current_centers,dataframe)) if np.sum(previous_assignment_of_nodes == assignment_of_nodes) == dataframe.shape[0]: break previous_assignment_of_nodes = np.copy(assignment_of_nodes) # calculating the center again for i in range(number_of_clusters): current_centers.loc[current_centers.index[i]] = dataframe.loc[assignment_of_nodes == i, :].mean(0) # post-processing results assignment_of_nodes = list(assignment_of_nodes) assignment_of_nodes = [int(x) for x in assignment_of_nodes] return (assignment_of_nodes, all_assignments, mean_square_list) # + _uuid="a32b9ebaa7bfb54028757aa597f6d8ab0bc9aed9" clusters, all_assignment, mean_square_list = KmeansCluster(iris_data[list(iris_data.columns[:-1])],3) # + _uuid="97849acb387c0aa6f1cf2203a133c4d861987f21" # Ploting final cluster assignment newframe = iris_data[list(iris_data.columns[:-1])] label_classes = ['class-0','class-1','class-2'] newframe["clusters"] = pd.Series([label_classes[i] for i in clusters]) sns.pairplot(newframe, hue='clusters') # + [markdown] _uuid="360579080b9fc319503173bba651f477d99dbe9a" # ### 4.1 Perform change of color code for clusters at each iterations # + _uuid="08ec415447757e71e8aac979beefdebd5c29dbe3" # %matplotlib notebook import time #initialise the graph and settings fig = plt.figure() ax = fig.add_subplot(111) plt.ion() fig.show() fig.canvas.draw() ax = fig.gca(projection='3d') number_of_iteration_completed = 0 for current_cluster in all_assignment: ax.clear() # - Clear current_frame = iris_data[list(iris_data.columns[:-1])] current_frame["clusters"] = pd.Series(current_cluster) ax.scatter(current_frame.petal_width, current_frame.petal_length, current_frame.sepal_length, c=current_frame.clusters) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') number_of_iteration_completed += 1 title = "After " + str(number_of_iteration_completed) + " iterations" ax.set_title(title) fig.canvas.draw() time.sleep(2) # Plotting final assignment final_frame = iris_data[list(iris_data.columns[:-1])] final_frame["clusters"] = pd.Series(clusters) # %matplotlib inline fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(final_frame.petal_width, final_frame.petal_length, final_frame.sepal_length, c=final_frame.clusters) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title('Final Assignment') # + [markdown] _uuid="0fb0f6d744fbb6ddcb219c7d3b76c867d8c0a58d" # ### 4.2 Compute the sum of squared error (i.e. function J from class notes) for each iteration # + _uuid="ed18ced7d54b363b4f3e5d033e7209e0ab5dc8da" number_of_iterations = len(mean_square_list) for i in range(number_of_iterations): print("Value of J after " + str(i+1) + " iterations is " + str(mean_square_list[i])) # + [markdown] _uuid="9b5c5e886c4790c272f5f9c6d57230c05fa1aa46" # ### 4.3 Visualize the sum of squared error and check for convergence of the k-means algorithm using line plot (error vs. iteration: iteration numbers on x- axis and error values on y-axis) # + _uuid="064949eaf7c321ade4ab863521b278ff5060bcb5" # %matplotlib inline plt.plot(list(range(1,len(mean_square_list) + 1)), mean_square_list, 'k') plt.xlabel('Number of iterations') plt.ylabel('Value of J(error)') plt.title('How value of J is changing with the number of iterations.') # + [markdown] _uuid="1c3566e98716459d2aead5313a3ebcce2a4d6750" # ### 4.4 Suggest different ways to choose the number of iterations to get quality clusters # * We will stop looping when J is not changing much, like if difference between J of i'th iteration and (i+1)'th iteration is less than 0.001 # * We will stop looping if centers are not changing. # * We will stop looping when assignment of nodes to clusters are not changing. # * We can also set a maximum number of iterations combinded with above two methods. # + [markdown] _uuid="f2b248e31db7d96d4daccd32f1b5290acf6f3433" # ## Question 5: Compare the results of both k-means and agglomerative clustering algorithms # + [markdown] _uuid="7a4298faeaaeda8c1ca4db124eed348b28f77448" # ### First we will run both algorithms on out dataset and store the results for further questions # + _uuid="3b16a233086ef62860870f390d47679d65470df2" from sklearn.cluster import KMeans from sklearn.cluster import AgglomerativeClustering iris_data_without_labels = iris_data[list(iris_data.columns[:-1])] # Run kmeans algorithm and print the result kmeans_model = KMeans(n_clusters=3) kmeans_result = kmeans_model.fit(iris_data_without_labels) print("Labels assigned to our data by kmeans : ", kmeans_result.labels_) # Run agglomerative clustering algorithm and print the result agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3) agglomerative_clustering_result = agglomerative_clustering_model.fit(iris_data_without_labels) print("Labels assigned to our data by agglomerative clustering : ", agglomerative_clustering_result.labels_) # Printing True labels print("True labels : ",np.array(iris_data.species)) # + [markdown] _uuid="9116115c7445a27d8e22f763efceac05df042f17" # ### 5.1 Compare the performance of k-means and agglomerative clustering methods on the iris dataset. # + [markdown] _uuid="e0fab8b40178544ecaab554516a5d64c87ca4059" # #### Comparing the results of both algorithms with true labels # + _uuid="36ce791301c0e15ecbd75b26f2d4598ca4664760" print("Result of kmeans : ") kmeans_unique_class , kmeans_unique_class_counts = np.unique(kmeans_result.labels_, return_counts=True) for x , y in zip(kmeans_unique_class, kmeans_unique_class_counts): print("The number of observations assigned class",x,"is",y) print("Result of agglomerative clustering : ") agglomerative_clustering_unique_class , agglomerative_clustering_unique_class_counts = np.unique(agglomerative_clustering_result.labels_, return_counts=True) for x , y in zip(agglomerative_clustering_unique_class, agglomerative_clustering_unique_class_counts): print("The number of observations assigned class",x,"is",y) print("True labels :") target_classes = ['setosa','versicolor','virginica'] true_unique_class , true_unique_class_counts = np.unique(np.array(iris_data.species), return_counts=True) for x , y in zip(true_unique_class, true_unique_class_counts): print("The number of observations assigned class",target_classes[x],"is",y) # + [markdown] _uuid="07651a6b790c0041b1ca888e3d7da1311b02f3fe" # #### Analysis: # We have observed the number to points assigned to each classes by different algorithms and comparing them with true classes. We can see that these numbers are similar in both algorithms but they differ significantly with the actual number of data points in each classes. Though the number of points assigned to each classes are similar, but kmeans are giving comparatively better results than the another one. # + [markdown] _uuid="00490edb70915ca6a0f6344fe463a12e64feac46" # ### 5.2 Compare the two algorithms with respect to the cluster formation; for example, plot the results of the two algorithms using 3-D scatter plots, and explain. # + _uuid="37744aeddf347114ab79c131b090dd14aa96f9e1" # we are taking petal width, petal length and sepal length as our 3 axes # Creating plot for kmeans fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=pd.Series(kmeans_result.labels_)) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("kmeans") plt.show() # Creating plot for agglomerative clustering fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=pd.Series(agglomerative_clustering_result.labels_)) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("agglomerative clustering") plt.show() # Creating plot with true labels fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=iris_data.species) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("true labels") plt.show() # + [markdown] _uuid="59fa83551dcab45c2ed2df307f537b0f0c057e8e" # #### Analysis: # We can make following observations from the above 3D plots : # * All points belonging to Setosa, are grouped in a single class by both algorithms. # * Some points beloging to versicolor, are assigned to the other class by both algorithms. # * The results produced by both of the algorithms are very similar. # + [markdown] _uuid="33b140f32036b2d3f86d5b4241fa6ac7c2a48f8c" # ### 5.3 Study the effect of initial configuration for the two algorithms. # + [markdown] _uuid="696b7533494d58c19f576ebe58855edaa5d32585" # #### 5.3.1 Effect of initial configuration for k-means # + _uuid="a9bf601948b7cee082020df4b4c63f9896e5fbda" # Case 1 - All initial points are same case_1_init = np.array([(iris_data_without_labels.loc[0,:]) for x in range(3)]) case_1_kmeans_model = KMeans(n_clusters=3,init=case_1_init,n_init=1) case_1_kmeans_model_result = case_1_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_1_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("All initial points are same") plt.show() # Case 2 - All initial points are in same class case_2_init = np.array([(iris_data_without_labels.loc[x,:]) for x in range(3)]) case_2_kmeans_model = KMeans(n_clusters=3,init=case_2_init,n_init=1) case_2_kmeans_model_result = case_2_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_2_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("All initial points are in same class") plt.show() # Case 3 - With init = 'random' case_3_kmeans_model = KMeans(n_clusters=3,init='random',n_init=1) case_3_kmeans_model_result = case_3_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_3_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("With init = 'random'") plt.show() # Case 4 - With init = 'k-means++' case_4_kmeans_model = KMeans(n_clusters=3,init='k-means++',n_init=1) case_4_kmeans_model_result = case_4_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_4_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("With init = 'k-means++'") plt.show() # + [markdown] _uuid="558fa59768ee20c393ca90e314a9e6f5b942dc22" # Since there is no difference between the plots with the different initialization, we are going to limit maximun iteration to 5 # + _uuid="b97ebd467991392a0f42185ad1f22114331db8df" # Case 1 - All initial points are same case_1_init = np.array([(iris_data_without_labels.loc[0,:]) for x in range(3)]) case_1_kmeans_model = KMeans(n_clusters=3,init=case_1_init,n_init=1, max_iter=5) case_1_kmeans_model_result = case_1_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_1_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("All initial points are same") plt.show() # Case 2 - All initial points are in same class case_2_init = np.array([(iris_data_without_labels.loc[x,:]) for x in range(3)]) case_2_kmeans_model = KMeans(n_clusters=3,init=case_2_init,n_init=1, max_iter=5) case_2_kmeans_model_result = case_2_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_2_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("All initial points are in same class") plt.show() # Case 3 - With init = 'random' case_3_kmeans_model = KMeans(n_clusters=3,init='random',n_init=1, max_iter=5) case_3_kmeans_model_result = case_3_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_3_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("With init = 'random'") plt.show() # Case 4 - With init = 'k-means++' case_4_kmeans_model = KMeans(n_clusters=3,init='k-means++',n_init=1, max_iter=5) case_4_kmeans_model_result = case_4_kmeans_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_4_kmeans_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("With init = 'k-means++'") plt.show() # + [markdown] _uuid="fc325a1599306aea8fb279caa941d26dd08e5e63" # #### Analysis # By limiting the maximum iteration to 5, we can see that if we choose centriods badly then it will take more iterations to make better clusters. # + [markdown] _uuid="7ad949f2294b48b4988493c7ac8cb13090a5f19f" # #### 5.3.2 Study of the effect of initial configurations on agglomerative clustering algorithm # + _uuid="2790e2155e8b9e5fe329fdcf80ab19266454b08f" # Case 1 - Choosing 'ward' linkage case_1_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='ward') case_1_agglomerative_clustering_model_result = case_1_agglomerative_clustering_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_1_agglomerative_clustering_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("Case 1 - Choosing 'ward' linkage") plt.show() # Case 2 - Choosing 'complete' linkage case_2_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='complete') case_2_agglomerative_clustering_model_result = case_2_agglomerative_clustering_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_2_agglomerative_clustering_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("Case 2 - Choosing 'complete' linkage") plt.show() # Case 3 - Choosing 'average' linkage case_3_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='average') case_3_agglomerative_clustering_model_result = case_3_agglomerative_clustering_model.fit(iris_data_without_labels) # Creating plot fig = plt.figure() ax = fig.gca(projection='3d') ax.scatter(iris_data_without_labels.petal_width, iris_data_without_labels.petal_length, iris_data_without_labels.sepal_length, c=case_3_agglomerative_clustering_model_result.labels_) ax.set_xlabel('petal width (cm)') ax.set_ylabel('petal length (cm)') ax.set_zlabel('sepal length (cm)') ax.set_title("Case 3 - Choosing 'average' linkage") plt.show() # + [markdown] _uuid="4fb05978daf62c3ed4490544ce54c8249fc1741e" # Here we can see there are difference between how running agglomerative clustering with different linkage assigned classes to some points of versicolor and virginica. So to explore further, we will also look on each cases at the number of data points present in each classes. # + _uuid="d7db1536230cb708355d716d53d5d337358bdccc" # Case 1 - Choosing 'ward' linkage case_1_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='ward') case_1_agglomerative_clustering_model_result = case_1_agglomerative_clustering_model.fit(iris_data_without_labels) print("Case 1 - Choosing 'ward' linkage") case_1_agglomerative_clustering_unique_class , case_1_agglomerative_clustering_unique_class_counts = np.unique(case_1_agglomerative_clustering_model_result.labels_, return_counts=True) for x , y in zip(case_1_agglomerative_clustering_unique_class , case_1_agglomerative_clustering_unique_class_counts): print("The number of observations assigned class",x,"is",y) # Case 2 - Choosing 'complete' linkage case_2_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='complete') case_2_agglomerative_clustering_model_result = case_2_agglomerative_clustering_model.fit(iris_data_without_labels) print("Case 2 - Choosing 'complete' linkage") case_2_agglomerative_clustering_unique_class , case_2_agglomerative_clustering_unique_class_counts = np.unique(case_2_agglomerative_clustering_model_result.labels_, return_counts=True) for x , y in zip(case_2_agglomerative_clustering_unique_class , case_2_agglomerative_clustering_unique_class_counts): print("The number of observations assigned class",x,"is",y) # Case 3 - Choosing 'average' linkage case_3_agglomerative_clustering_model = AgglomerativeClustering(n_clusters=3,linkage='average') case_3_agglomerative_clustering_model_result = case_3_agglomerative_clustering_model.fit(iris_data_without_labels) print("Case 3 - Choosing 'average' linkage") case_3_agglomerative_clustering_unique_class , case_3_agglomerative_clustering_unique_class_counts = np.unique(case_3_agglomerative_clustering_model_result.labels_, return_counts=True) for x , y in zip(case_3_agglomerative_clustering_unique_class , case_3_agglomerative_clustering_unique_class_counts): print("The number of observations assigned class",x,"is",y) # + [markdown] _uuid="3902c2082adb5275cfb7b2abec9e8735dc03b203" # From above results, we can see that it is the 'complete' linkage that is giving us bad results comparitive to other two linkages. And we can see that here the difference is results is significant. So one method to select which linkage to use is apply all three linkages and analyse which linkage is giving us best results. # + [markdown] _uuid="cea1500b15f5d0e3c7f2be1ce151e2315e19a422" # ## Question 6: Selecting k. Come up with an empirical strategy. # + [markdown] _uuid="ffcc3378e9b5cb33d6ed4453656509e5498a9997" # ### 6.1 How do you choose k for the k-means algorithm? # * Elbow method : Run k-mean algorithm for with different values of k and then we will choose k till which error value is decreasing sharply. # * Run agglomerative clustering algorithm on taking a smaller set of orginal data : Agglomerative clustering algorithms is more expensive in terms of time than k-means algorithm. But it gives us an idea about the number of clusters present in dataset. # * While choosing k we will make sure that k << m (where m = number of data points). # * Visualizion of data points are used to get an idea about number of clusters. # * Use other information about the dataset if it's available. For example suppose you have a data of weights of people and you want to make one cluster for men and one for women. Here you should use k = 2 # ### 6.2 How do you choose k for the agglomerative clustering algorithm? # * Dendrogram fromed from the dataset is used to have an idea about choosing a nice k. # * Run agglomerative clustering algorithm for with different values of k and then we will choose k till which error value is decreasing sharply. # * While choosing k we will make sure that k << m (where m = number of data points). # * Visualizion of data points are used to get an idea about number of clusters. # * Use other information about the dataset if it's available. For example suppose you have a data of weights of people and you want to make one cluster for men and one for women. Here you should use k = 2 # #### There are some methods which are common to all clustering algorithms, so some methods are written for both algorithms. # + _uuid="ad6c5140689af5c8ee8fd89dc034645683d91eff" error_list_for_different_k = [] for number_of_clusters in range(1,11): # Run kmeans algorithm and save the error to list kmeans_model = KMeans(n_clusters=number_of_clusters) kmeans_result = kmeans_model.fit(iris_data_without_labels) error_list_for_different_k.append(kmeans_result.inertia_) plt.plot(list(range(1,11)), error_list_for_different_k) plt.show() # + [markdown] _uuid="8af27f40318c60671c704e92849476e2d97746ba" # #### Elbow method in action # From above plot, we can see that the error value is decreasing sharply till k = 3. And we also have context of problem that we want to cluster 3 different species of iris, so we have chosen k = 3
33,129
/week6/.ipynb_checkpoints/data-checkpoint.ipynb
e16d72e2ff305e6ba842c6072e7299e03ba37a18
[]
no_license
zingjanet/code1161base
https://github.com/zingjanet/code1161base
0
0
null
2017-06-08T22:55:18
2017-05-05T02:20:19
Jupyter Notebook
Jupyter Notebook
false
false
.py
381,422
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # + deletable=true editable=true import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import os # - # %matplotlib inline plt.rcParams['figure.figsize'] = (20, 10) saved_style_state = matplotlib.rcParams.copy() os.path.isfile("Outcomes_Scores.csv") filepath = "Outcomes_Scores.csv" basix_data = pd.read_csv(filepath) print "loading from file" print "done" basix_data.head() basix_data.columns row_one = basix_data.iloc[1] row_one basix_data["ENERGY SCORE"] basix_data["ENERGY SCORE"].plot() basix_data["ENERGY SCORE"][basix_data["ENERGY SCORE"] < 120].hist() basix_data["ENERGY SCORE"][basix_data["ENERGY SCORE"] < 40] basix_data['DATA SET'].value_counts().plot(kind="bar") basix_data['DATA SET'][basix_data["ENERGY SCORE"] < 40].value_counts().plot(kind="bar") basix_data.LGA.value_counts().plot(kind="bar") basix_data['LGA'][basix_data["ENERGY SCORE"] < 40].value_counts().plot(kind="bar") failed_energy_data = basix_data["ENERGY SCORE"][basix_data["ENERGY SCORE"] < 40] plt.hist(failed_energy_data, bins=10, facecolor='blue', alpha=0.2) plt.hist(failed_energy_data, bins=50, facecolor='green', alpha=1) plt.show()
1,436
/[hw7_2]transfer_learning.ipynb
22f5e9c12aaf35f88ddb9a7eb8fa475b73b9dee3
[]
no_license
dotapetro/MLStuff
https://github.com/dotapetro/MLStuff
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
1,050,168
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Fitting tranists using MCMC # # This guide shows how to fit transits using a fairly uncommon technique. Typically this fitting process is done using the enitre light curve (unfolded). In this notebook, I perform folding and binning before fitting the transit. If you are having trouble fitting a transit using the traditional method (no binning and folding), this program can probably help you to obtain good initial estimates on some unknown parameters. # # # Advantages to folding & binning before fitting are: # * Less data points (>1000 times faster solving, ~1 min vs >24 hours) # * Less free variables to solve for, so better accuracy in results # # Disadvantages: # * No error estimation on t0 or period since its required to be known beforehand during folding # * requires knowing an accurate period and t0 beforehand. # # # # The parameters we solve for in this notebook are: # * radius ratio # * limb darkening coefficents (u1, u2) # * inclination angle # * semi-major axis (not direectly solved, is calculated from the other solved params) # # # Params that need to be known to use this program: # * stellar mass # * stellar radius # * orbital period # * t0 import numpy as np import matplotlib.pyplot as plt import os from os.path import join as opj import exoplanet as xo import pymc3 as pm from copy import deepcopy from astropy.constants import R_sun, M_sun, R_earth, M_earth cdir = os.getcwd() os.chdir('/media/rd1/kwillis/class_rv_lc') from kepler_utils import phase_fold_time, global_view os.chdir(cdir) import corner import pymc3_ext as pmx from IPython.core.display import display, HTML display(HTML("<style>.container { width:100% !important; }</style>")) # + # %matplotlib notebook # #%matplotlib inline # + def bjd2bkjd(bjd): return bjd - 2454833.0 def bkjd2bjd(bkjd): return bkjd + 2454833.0 def mask_transits(t0_bkjd, period_day, duration_day, time_bkjd, flux, lc=None, return_index=True): # http://www.raa-journal.org/docs/Supp/ms4253tab1.txt if lc != None: time_bkjd = np.array(lc.astropy_time.value) flux = lc.flux t0f_bkjd = np.copy(t0_bkjd) tots_bkjd = get_transit_times(t0f_bkjd, period_day, duration_day, time_bkjd, flux) # For each tranist find all datapoints within the tranist duration window ntot_idx = np.ones(len(flux), dtype=bool) for tot in tots_bkjd: tot_idx = (time_bkjd > tot - duration_day / 2) & (time_bkjd < tot + duration_day / 2) ntot_idx = ntot_idx & ~tot_idx if return_index: return ~ntot_idx # Returns where transits occur else: return time_bkjd[ntot_idx], flux[ntot_idx] def get_transit_times(t0_bkjd, period_day, duration_day, time_bkjd, flux): t0f_bkjd = np.copy(t0_bkjd) while t0f_bkjd > time_bkjd[0]: t0f_bkjd -= period_day #t0f_bkjd += period_day while t0f_bkjd < time_bkjd[0]: t0f_bkjd += period_day #t0f_bkjd -= period_day return np.arange(t0f_bkjd, time_bkjd.max(), step=period_day) # Folding and binning functions def signal(time_array, flux, period_id, t0=0.0, num_bins=2000): # fold and bin transit time_array = phase_fold_time(time_array, period_id, t0) sorted_i = np.argsort(time_array) time_array = time_array[sorted_i] flux = flux[sorted_i] global_view0 = global_view(time_array, flux, period_id, num_bins=num_bins) t = np.linspace(np.min(time_array), np.max(time_array), num_bins) return t, global_view0 def signal_no_bin(time_array, flux, period_id, t0): time_array = phase_fold_time(time_array, period_id, t0) sorted_i = np.argsort(time_array) time_array = time_array[sorted_i] flux = flux[sorted_i] return time_array, flux def create_mcmc_model(t, f, ferr, pm, ig, enable_lc_log=False, optimize_q=True): import pymc3 as pm with pm.Model() as model: # The baseline flux #mean = pm.Normal("mean", mu=ig['cont'], sd=ig['cont_sd']) t0 = ig['t0'] period = ig['P'] # quadratic limb darkening paramters u1 = pm.Uniform('u1', lower=ig['u1_lb'], upper=ig['u1_ub'], shape=1, testval=np.array([ig['u1']])) u2 = pm.Uniform('u2', lower=ig['u2_lb'], upper=ig['u2_ub'], shape=1, testval=np.array([ig['u2']])) u = pm.math.concatenate([u1, u2]) # radius ratio rplanet/rstar rr = pm.Uniform("r_ratio", lower=ig['r_ratio_lb'], upper=ig['r_ratio_ub'], shape=1, testval=np.array([ig['r_ratio_sv']])) # orbit plane inclinatation incl = pm.Uniform("incl", lower=ig['incl_lb'], upper=ig['incl_ub'], shape=1, testval=np.array([ig['incl_sv']])) # Star 1 radius R1 = ig['r_star'] # planet radius R2 = pm.Deterministic("R2", rr * R1) # Set up a Keplerian orbit for the planets orbit = xo.orbits.KeplerianOrbit(period=period, t0=t0, incl=incl, r_star=R1, m_star=ig['m_star']) pm.Deterministic("a", orbit.a) # Compute the model light curve using starry light_curves = xo.LimbDarkLightCurve(u).get_light_curve(orbit=orbit, r=R2, t=t) light_curve = pm.math.sum(light_curves, axis=-1) + ig['cont'] # Here we track the value of the model light curve for plotting purposes if enable_lc_log: pm.Deterministic("light_curves", light_curves) # In this line, we simulate the dataset that we will fit sim = xo.eval_in_model(light_curve) # The likelihood function assuming known Gaussian uncertainty pm.Normal("obs", mu=light_curve, sd=ferr, observed=f) ############################################################################ # Optimize map_soln = model.test_point if optimize_q: map_soln = pmx.optimize(map_soln, [incl]) map_soln = pmx.optimize(map_soln, [u1, u2]) map_soln = pmx.optimize(map_soln, [incl]) map_soln = pmx.optimize(map_soln) return model, pm, map_soln, sim def plot_pre_mcmc(t, f, ferr, map_soln): # for plotting after optimization per = ig["P"] t0 = ig["t0"] plt.figure(figsize=(7, 5)) plt.plot(t, f-1, ".k", ms=4, label="data") #if len(t0s) == 1: for i, l in enumerate("a"): plt.plot(t, map_soln["light_curves"][:, i], lw=1, label="planet {0}".format(l)) plt.xlim(t.min(), t.max()) plt.ylabel("relative flux") plt.xlabel("time [days]") plt.legend(fontsize=10) plt.title("map model") # - # # Load saved normalized light curve data # # You probably wont save data in the same way that I have here, so you will need to edit this cell to load your data properly. # # Things you should save when you save your data: # * time # * normalized flux # * flux error # * t0 # * orbital period # * transit duration # + ########### User Params ############# target_name = '11904151' ####################################### lc_data_dir = opj('/media/rd1/kwillis/light_curve_routines/data/norm_lcs', target_name + '_LC_data.npz') lc_lf = np.load(lc_data_dir) data = {'lc_flux': lc_lf['flux_norm'], 'lc_flux_err': lc_lf['flux_err_norm'], 'lc_time': lc_lf['time_norm'], } len(data['lc_time']), lc_lf['t0_pri_day'], lc_lf['p_day'], lc_lf['d_day'] # - # # Fold and bin the light curve using the best t0 and period you found elsewhere # + ########### User Params ############# bincnt = 3000 # How many bins in your fold. Note that the output will be smaller than this, since we will crop the fold using the param below fold_edge_crop_pct = 42 # Percent of datapoints to crop out at the left and right edge. Example: 30% crop with a bincnt of 10 --> [YYYNNNNYYY] --> [NNNN] so, final output LC would hav 4 datapoints ####################################### # Fold then bin t_fold, f_fold = signal(np.array(lc_lf['time_norm']), np.array(lc_lf['flux_norm']), lc_lf['p_day'][0], t0=lc_lf['t0_pri_day'][0], num_bins=bincnt) # Fold error t_fold, fe_fold = signal(np.array(lc_lf['time_norm']), np.array(lc_lf['flux_err_norm']), lc_lf['p_day'][0], t0=lc_lf['t0_pri_day'][0], num_bins=bincnt) l_idx = int(np.ceil(len(t_fold) * fold_edge_crop_pct / 100)) data = {'lc_flux': f_fold[l_idx:-l_idx], 'lc_flux_err': fe_fold[l_idx:-l_idx] / 100, 'lc_time': t_fold[l_idx:-l_idx], } plt.figure(figsize=(13,7)) plt.plot(data['lc_time'], data['lc_flux'], '.k') len(data['lc_time']), len(data['lc_flux']), len(data['lc_flux_err']) # - # # If required, do some unit conversions and calulations to derive some fitting parameters # # Here I needed to convert some radius values and calculate a radius ratio estimate # # R_sun and R_earth are constants loaded from astropy, in unit meters # + R1_est = 1.48e9 / R_sun.value / 2 R2_est = 1.47 * (R_earth / R_sun).value r_ratio_est = R2_est / R1_est R1_est, R2_est, r_ratio_est, 1/r_ratio_est # - # # State your intial guesses and solving bounds # # lb & ub are lower and upper bound. Make sure that your starting value is between these bounds! # + ########### User Params ############# ig = {'cont': 1.0, # Continuum level 't0': 0.0, # t0 [day] 'P': 0.837491225, # period [day] 'u2': 0.0, # Limb darkening param - edge curvature 'u2_lb': -1.0, 'u2_ub': 2.0, 'u1': 0.0, # Limb darkening param - edge curvature 'u1_lb': -1.0, 'u1_ub': 2.0, 'incl_sv': 89.0 / 180 * np.pi, # orbit inclination [rad] 'incl_lb': 80.0 / 180 * np.pi, 'incl_ub': 90.0 / 180 * np.pi, 'r_ratio_sv': 0.01267, # radius ratio (r_planet / r_star) - depth of transit 'r_ratio_lb': 0.000, 'r_ratio_ub': 1.000, 'r_star': R1_est, # radius of primary star [R_sun] 'm_star': 0.92, # mass of primary star [M_sun] } # - # # Create orbit model and optimize your parameters # + model, pm, map_soln, f_sim = create_mcmc_model(data['lc_time'], data['lc_flux'], data['lc_flux_err'], pm, ig, enable_lc_log=1, optimize_q=1) plot_pre_mcmc(data['lc_time'], data['lc_flux'], data['lc_flux_err'], map_soln) # - # # Run MCMC on your orbit model # # Params you can change: # * tune: how many samples per chain to get MCMC algo in tune with your data. I set a very high value (3000), but could probably get same results with 300. # * draws: how many samples per chain to try. Basically how many fits the algo will attempt. THe more you have, the better. However, too many past a certain point does you no good. Some difficult problems may require 30k draws, while easy ones can be done in <1000. Typically, these fits will need at least 3000 to look good in the corner plot and get good error estimates. # * chains: should be at least as big as the number of variables you are solving for. Think of chaings like the number of time syou will try to solve the system with N "draws". When all chains, which solve in parallel, are converging in on the same answer, the MCMC algo is happy. # + np.random.seed(42) with model: trace = pm.sample(tune=200, draws=3000, start=map_soln, chains=6, init="adapt_full", step=xo.get_dense_nuts_step(target_accept=0.9)) # - # # Show a summary of the solved parameters # + mcmc_summary = pm.summary(trace, kind='stats') mcmc_summary # + flat_samples = np.copy(pm.trace_to_dataframe(trace, varnames=['r_ratio', 'incl', 'u1', 'u2'])) fig = corner.corner(flat_samples, labels=mcmc_summary.index.to_numpy(), quantiles=[0.16, 0.5, 0.84], show_titles=True, #fig=fig, #title_kwargs={"fontsize": fontsize}, #label_kwargs={"fontsize": fontsize} ); # - # # Make final plot showing the fit from the optimizer (LSE) and MCMC (MLE) # + trace_lcs = np.copy(pm.trace_to_dataframe(trace, varnames=['light_curves'])) plt.figure(figsize=(13,7)) ax = plt.subplot(2, 1, 1) plt.plot(data['lc_time'], data['lc_flux'] - 1, '.k') # plot 2000 random light curves from the MCMC trace. Gives you a good visual of the error in the fit. sample_cnt = 2000 if sample_cnt > trace_lcs.shape[0]: sample_cnt = trace_lcs.shape[0] - 10 for trace_lc in trace_lcs[np.random.choice(np.arange(trace_lcs.shape[0]), 2000)]: plt.plot(data['lc_time'], trace_lc - 1 + np.median(data['lc_flux'] - trace_lc), '-r', alpha=0.01, lw=6) # plot the average light curve fit from MCMC mle_lc = np.nanmedian(trace_lcs, axis=0) plt.plot(data['lc_time'], mle_lc - 1 + np.median(data['lc_flux'] - mle_lc), '-r', label='MLE Solution', lw=1, alpha=1.0) plt.plot(data['lc_time'], np.ravel(map_soln['light_curves']) - 1 + np.median(data['lc_flux'] - np.ravel(map_soln['light_curves'])), '--', c=[0, 1, 0], label='LSE Solution', lw=3, alpha=0.7) plt.legend() plt.ylim(np.min(data['lc_flux'] - 1), np.max(data['lc_flux'] - 1)) plt.subplot(2, 1, 2, sharex=ax, sharey=ax) df = (data['lc_flux'] - mle_lc) - 1 plt.title('Residual RMS = ' + str(np.around(np.std(df), 6))) plt.plot(data['lc_time'], df, '.k'); # -
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # ## 파일 다운 링크 # ftp://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html # # 1. 파일 다운 후 다운된 파일을 열기 # # 2. 빨간 동그라미 쳐진 링크 클릭 # ![%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-04-19%20%E1%84%8B%E1%85%A9%E1%84%92%E1%85%AE%209.56.04.png](attachment:%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-04-19%20%E1%84%8B%E1%85%A9%E1%84%92%E1%85%AE%209.56.04.png) # # + ## 'rb'와 str()을 활용한 UnicodeDecodeError 해결코드 import re import pandas as pd pattern = re.compile('^\S+ \S+ \S+ \[(.*)\] "(.*)" (\S+) (\S+)$') def parse_access_log(path): for line in open(path,'rb'): for m in pattern.finditer(str(line)): yield m.groups() columns = ['time', 'request', 'status', 'bytes'] pd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns) # + ## cp850으로 encoding하여 해결한 UnicodeDecodeError 해결코드 import re import pandas as pd pattern = re.compile('^\S+ \S+ \S+ \[(.*)\] "(.*)" (\S+) (\S+)$') def parse_access_log(path): for line in open(path, encoding='cp850'): for m in pattern.finditer(line): yield m.groups() columns = ['time', 'request', 'status', 'bytes'] pd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns) # - df = pd.DataFrame(parse_access_log('NASA_access_log_Jul95'), columns=columns) df.time = pd.to_datetime(df.time, format='%d/%b/%Y:%X', exact=False) df.head(2) df.to_csv('access_log.csv', index=False) # !head -3 access_log.csv
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd file_one = "../db/northamerica_station_information.csv" file_two = "../db/city_gasday_fcst.csv" file_one_df = pd.read_csv(file_one) file_one_df.head() file_one_df.rename(columns = {"identifier": "Station"}, inplace = True) file_one_df.head() file_two_df = pd.read_csv(file_two, encoding="ISO-8859-1") file_two_df.head() merge_table = pd.merge(file_one_df, file_two_df, on="Station", how="right") merge_table.head() del merge_table["HDD"] del merge_table["CDD"] merge_table.head() new_df = merge_table.loc[merge_table["country"] == "US",:] new_df.head() new_df1 = new_df.rename(columns = {"Station":"station_id","name":"station_name","Production Date":"production_date","Date":"forecast_date", "Fcst Mn":"fcst_mn","Fcst Mx":"fcst_mx","Fcst Avg":"fcst_avg","Norm Mn":"norm_mn","Norm Max":"norm_mx" }) new_df1.reset_index().rename(columns = {new_df1.index.name: "id"}) # + # new_df1['id'] = range(1, len(new_df1) + 1) # - new_df1['id'] = new_df1.index new_df1.set_index('id') new_df1.loc() new_df1.head() new_df1 = new_df1.reset_index() new_df1.head() del new_df1["index"] del new_df1["id"] new_df1.head() day_df = new_df1.loc[new_df1["forecast_date"] == "12/6/2018",:] day_df day_df["station_id"].nunique() day_df = day_df.reset_index() day_df.head() del day_df["index"] day_df.head() from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String, Float from sqlalchemy.ext.automap import automap_base import pymysql pymysql.install_as_MySQLdb() # + Base = declarative_base() class Data(Base): __tablename__ = 'data' id = Column(Integer, primary_key=True) station_id = Column(String(255)) lat = Column(Integer) lon = Column(Integer) station_name = Column(String(255)) state = Column(String(255)) country = Column(String(255)) production_date = Column(Integer) forecast_date = Column(Integer) #fcst_mn = Column(Integer) #fcst_mx = Column(Integer) fcst_avg = Column(Integer) norm_mn = Column(Integer) norm_mx = Column(Integer) # - engine = create_engine("sqlite:///data.sqlite") conn = engine.connect() Base.metadata.create_all(engine) from sqlalchemy.orm import Session session = Session(bind=engine) for i in range(len(day_df)): data = Data(station_id=day_df["station_id"][i], lat=day_df["lat"][i], lon=day_df["lon"][i], station_name=day_df["station_name"][i], state=day_df["state"][i], country=day_df["country"][i], production_date=day_df["production_date"][i], forecast_date=day_df["forecast_date"][i], #fcst_mn=new_df1["fcst_mn"][i], #fcst_mx=new_df1["fcst_mx"][i], fcst_avg=day_df["fcst_avg"][i], norm_mn=day_df["norm_mn"][i], norm_mx=day_df["norm_mx"][i]) session.add(data) session.commit() qaz = engine.execute("SELECT * FROM data") for record in qaz: print(record) type(day_df["fcst_mn"][1]) # + # from sqlalchemy import create_engine # import pymysql # pymysql.install_as_MySQLdb() # + # engine = create_engine("mysql://root:@localhost/Project2") # + # conn = engine.connect() # + # new_df.to_sql(name='forecast', con=engine, if_exists='append', index=False) # + # data = pd.read_sql("SELECT * FROM forecast", conn) # + # data.head() # + # engine.table_names() # + from sqlalchemy import create_engine # Imports the methods needed to abstract classes into tables from sqlalchemy.ext.declarative import declarative_base # Allow us to declare column types from sqlalchemy import Column, Integer, String, Float # PyMySQL import pymysql pymysql.install_as_MySQLdb() # + Base = declarative_base() class Forecast(Base): __tablename__ = 'forecast' station_id = Column(String, primary_key=True) r(range(train_df.shape[0]), train_df.price.values, color = color[6]) plt.xlabel('the number of train data', fontsize=12) plt.ylabel('price', fontsize=12) plt.show() # 由散点图看出,在这个特征上,有一些离群点,移除掉它们,重新画图。 # + ulimit = np.percentile(train_df.price.values, 99.5) #print(ulimit) train_df['price'].loc[train_df['price'] > ulimit] = ulimit plt.figure(figsize=(8,6)) sns.distplot(train_df.price.values, bins=50, kde=True) plt.xlabel('price', fontsize=12) plt.show() # - # 这个分布向右倾斜的厉害,我们可以使用 `numpy.log` 函数使其变的近似正态分布。 # + train_df['price'].loc[train_df['price'] > ulimit] = ulimit plt.figure(figsize=(8,6)) sns.distplot(np.log(train_df.price.values), bins=50, kde=True) plt.xlabel('price', fontsize=12) plt.show() # - order = ['low', 'medium', 'high'] sns.stripplot(train_df.interest_level, train_df.price.values, jitter=True, order=order) plt.title("Price VS Interest Level") plt.show() # low interest的price看起来分布比较均匀,中度(medium)感兴趣的价格分布更窄,high interest level的 price分布最窄,基本分布在 1500~ 8000 之间 # # **violinplot** 提供不同类别条件下特征更多的分部信息 # 核密度估计(KDE) # 三个4分位数(quartile):1/4, 1/2, 3/4 # 1.5倍四分数间距(nterquartile range, IQR) # IQR :第三四分位数和第一分位数的区别(即Q1~Q3的差距),表示变量的分散情况,播放差更稳健的统计量 order = ['low', 'medium', 'high'] sns.violinplot(x="interest_level", y = 'price', data = train_df, order = order) plt.xlabel("interest level", fontsize = 12) plt.ylabel('price', fontsize = 12) plt.show() # #### Longitude & Latitude # 经度和维度是虽是数值型变量,但其物理含义是房屋的地理位置。 # + llimit = np.percentile(train_df.latitude.values, 1) ulimit = np.percentile(train_df.latitude.values, 99) train_df['latitude'].loc[train_df['latitude'] < llimit] = llimit train_df['latitude'].loc[train_df['latitude'] > ulimit] = ulimit plt.figure(figsize=(8,6)) sns.distplot(train_df.latitude.values, bins=50, kde=True) plt.xlabel('latitude', fontsize=12) plt.show() # - # 大部分地方纬度都在40.60~40.90之间 # + llimit = np.percentile(train_df.longitude.values, 1) ulimit = np.percentile(train_df.longitude.values, 99) train_df['longitude'].loc[train_df['longitude'] < llimit] = llimit train_df['longitude'].loc[train_df['longitude'] > ulimit] = ulimit plt.figure(figsize=(8, 6)) sns.distplot(train_df.longitude.values, bins=50, kde=True) plt.xlabel('longitude', fontsize=12) plt.show() # - # 地方经度都分布在-73.850~74.025之间,因此这个数据是跟纽约城相关的 # + from mpl_toolkits.basemap import Basemap from matplotlib import cm west, south, east, north = -74.025, 40.60, -73.850, 40.86 fig = plt.figure(figsize=(18,15)) ax = fig.add_subplot(111) m = Basemap(projection='merc', llcrnrlat=south, urcrnrlat=north, llcrnrlon=west, urcrnrlon=east, lat_ts=south, resolution='i') x, y = m(train_df['longitude'].values, train_df['latitude'].values) m.hexbin(x, y, gridsize=400, bins='log', cmap=cm.YlOrRd_r); # - sns.lmplot(x = "longitude" , y = "latitude" , fit_reg = False , hue = 'interest_level', hue_order = ['low', 'medium', 'high'] , size = 9, scatter_kws = {'alpha':0.4,'s':30}, data = train_df[(train_df.longitude > train_df.longitude.quantile(0.005)) &(train_df.longitude < train_df.longitude.quantile(0.995)) &(train_df.latitude > train_df.latitude.quantile(0.005)) &(train_df.latitude < train_df.latitude.quantile(0.995))] ) plt.xlabel('Longitude') plt.ylabel('Latitude') # 上述显示去掉了经度和纬度偏大或偏小的数据点。可以看出higt interet的房屋在一小段很集中。 # # 还有一种作图,我就不列出来了,需要安装工具包: # # ```python # import gpxpy as gpx import gpxpy.gpx # # gpx = gpxpy.gpx.GPX() # # for index, row in train.iterrows(): # # #print (row['latitude'], row['longitude']) # # if row['interest_level'] == 'high': #opting for all nominals results in poor performance of Google Earth gps_waypoint = gpxpy.gpx.GPXWaypoint(row['latitude'],row['longitude'],elevation=10) gpx.waypoints.append(gps_waypoint) # # filename = "GoogleEarth.gpx" FILE = open(filename,"w") FILE.writelines(gpx.to_xml()) FILE.close() # ``` # ### 类别型特征 # #### display_address # + cnt_srs = train_df.groupby('display_address')['display_address'].count() for i in [2, 10, 50, 100, 500]: print('Display_address that appear less than {} times: {}%'.format(i, round((cnt_srs < i).mean() * 100, 2))) plt.figure(figsize=(12, 6)) plt.hist(cnt_srs.values, bins=100, log=True, alpha=0.9) plt.xlabel('Number of times display_address appeared', fontsize=12) plt.ylabel('log(Count)', fontsize=12) plt.show() # - # 大部分display_address出现次数都少于100次,没有display_address出现次数超过500次的 # # 让我们看看前10个display_address: # + top10_addr = train_df.display_address.value_counts().nlargest(10).index.tolist() fig = plt.figure(figsize=(8, 6)) ax = sns.countplot(x="display_address", hue="interest_level", data=train_df[train_df.display_address.isin(top10_addr)]) plt.xlabel('Display_address') plt.ylabel('Number of advert occurences') plt.tick_params( axis='x', #变化应用于x轴 which='both', # major ticket和minor tickets都会受到影响 bottom='on', # 打开沿着底端边缘的tickets top='off', # 关闭沿着顶端边缘的tickets labelbottom='on') # 打开底端的label plt.xticks(rotation='vertical') ### Adding percentitles over bars height = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches] ncol= int(len(height) / 3) total = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)] for i, p in enumerate(ax.patches): ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), ha = "center") # - # #### Created train_df["created"] = pd.to_datetime(train_df["created"]) train_df["date_created"] = train_df["created"].dt.date train_df["year_created"] = train_df["created"].dt.year train_df["month_created"] = train_df["created"].dt.month train_df['hour_created'] = train_df['created'].dt.hour train_df['weekday_created'] = train_df['created'].dt.weekday train_df['quarter_created'] = train_df['created'].dt.quarter train_df['weekend_created'] = ((train_df['weekday_created'] == 5) & (train_df['weekday_created'] == 6)) # + cnt_srs = train_df['date_created'].value_counts().sort_index() plt.figure(figsize=(20,4)) ax = sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8) ax.xaxis_date() plt.xticks(rotation='vertical') plt.show() # + cnt_srs = train_df['date_created'].value_counts() plt.figure(figsize=(12,4)) ax = plt.subplot(111) ax.bar(cnt_srs.index, cnt_srs.values, alpha=0.8) ax.xaxis_date() plt.xticks(rotation='vertical') plt.show() # - # **注意**:让我们看看测试集是否与训练集在同一个时间段 # + test_df["created"] = pd.to_datetime(test_df["created"]) test_df["date_created"] = test_df["created"].dt.date cnt_srs = test_df['date_created'].value_counts() plt.figure(figsize=(12,4)) ax = plt.subplot(111) ax.bar(cnt_srs.index, cnt_srs.values, alpha=0.8) ax.xaxis_date() plt.xticks(rotation='vertical') plt.show() # - # 更细致地来看看是数据在以小时为单位的范围分布情况 # + train_df["hour_created"] = train_df["created"].dt.hour cnt_srs = train_df['hour_created'].value_counts() plt.figure(figsize=(12,6)) sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[2]) plt.xticks(rotation='vertical') plt.show() # - # 数据主要分布在一天中的头几个小时,或许是这时候网络流量比较少,数据更新正在进行。 hourDF = train_df.groupby(['hour_created', 'interest_level'])['hour_created'].count().unstack() hourDF[['low', 'medium', 'high']].plot(kind='bar', stacked=True) order = ['low', 'medium', 'high'] sns.countplot(x="hour_created", hue="interest_level", data = train_df) hourDF = train_df.groupby(['month_created', 'interest_level'])['month_created'].count().unstack() hourDF[['low', 'medium', 'high']].plot(kind='bar', stacked=True) # #### building_id # + top10_building_id = train_df.building_id.value_counts().nlargest(10).index.tolist() fig = plt.figure(figsize=(8, 6)) ax = sns.countplot(x="building_id", hue="interest_level", data=train_df[train_df.building_id.isin(top10_building_id)]) plt.xlabel('building_id') plt.ylabel('Number of advert occurences') plt.tick_params( axis='x', #变化应用于x轴 which='both', # major ticket和minor tickets都会受到影响 bottom='on', # 打开沿着底端边缘的tickets top='off', # 关闭沿着顶端边缘的tickets labelbottom='on') # 打开底端的label plt.xticks(rotation='vertical') ### Adding percentitles over bars height = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches] ncol= int(len(height) / 3) total = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)] for i, p in enumerate(ax.patches): ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), ha = "center") # - # #### manager_id # + top10_managers = train_df.manager_id.value_counts().nlargest(10).index.tolist() fig = plt.figure(figsize=(8, 6)) ax = sns.countplot(x="manager_id", hue="interest_level", data=train_df[train_df.manager_id.isin(top10_managers)]) plt.xlabel('manager_id') plt.ylabel('Number of advert occurences') plt.tick_params( axis='x', #变化应用于x轴 which='both', # major ticket和minor tickets都会受到影响 bottom='on', # 打开沿着底端边缘的tickets top='off', # 关闭沿着顶端边缘的tickets labelbottom='on') # 打开底端的label plt.xticks(rotation='vertical') ### Adding percentitles over bars height = [0 if np.isnan(p.get_height()) else p.get_height() for p in ax.patches] ncol= int(len(height) / 3) total = [height[i] + height[i + ncol] + height[i + 2 * ncol] for i in range(ncol)] for i, p in enumerate(ax.patches): ax.text(p.get_x() + p.get_width() / 2, height[i] + 5, '{:0.1%}'.format(height[i] /total[i % ncol]), ha = "center") # - # #### listing_id sns.distplot(train_df.listing_id.values, bins = 50, kde = True) plt.xlabel('listing_id') plt.show() order = ['low', 'medium', 'high'] sns.stripplot(train_df.interest_level, train_df.listing_id, jitter=True, order=order) plt.title("Listing_ID VS Interest Level") plt.show() order = ['low', 'medium', 'high'] sns.violinplot(x="interest_level", y = 'listing_id', data = train_df, order = order) plt.xlabel("Interest Level", fontsize = 12) plt.ylabel('Listing_ID', fontsize = 12) plt.show() # #### Number of Photos # # 图片数据非常大,我们首先来看一下数量特征 # + train_df["num_photos"] = train_df["photos"].apply(len) cnt_srs = train_df['num_photos'].value_counts() plt.figure(figsize=(12,6)) sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8) plt.xlabel('Number of Photos', fontsize=12) plt.ylabel('Number of Occurrences', fontsize=12) plt.show() # - ulimit = np.percentile(train_df['num_photos'], 99) train_df['num_photos'].loc[train_df['num_photos'] > ulimit] = ulimit plt.figure(figsize=(12,6)) sns.violinplot(x="num_photos", y="interest_level", data=train_df, order =['low','medium','high']) plt.xlabel('Number of Photos', fontsize=12) plt.ylabel('Interest Level', fontsize=12) plt.show() # #### Number of features # 看看特征的数量和它的分布 # + train_df["num_features"] = train_df["features"].apply(len) cnt_srs = train_df['num_features'].value_counts() plt.figure(figsize=(12,6)) sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8) plt.ylabel('Number of Occurrences', fontsize=12) plt.xlabel('Number of features', fontsize=12) plt.show() # - ulimit = np.percentile(train_df['num_features'], 99) train_df['num_features'].loc[train_df['num_features'] > ulimit] = ulimit plt.figure(figsize=(12,10)) sns.violinplot(y="num_features", x="interest_level", data=train_df, order =['low','medium','high']) plt.xlabel('Interest Level', fontsize=12) plt.ylabel('Number of features', fontsize=12) plt.show() # #### description words counts # + train_df['num_description_words'] = train_df['description'].apply(lambda x: len(x.strip().split(" "))) train_df['len_description'] = train_df['description'].apply(len) #print(train_df['len_description'].head(10)) #print(train_df['num_description_words'].head(10)) #print(train_df['description'].iloc[0]) #print(len(train_df['description'].iloc[0])) # + fig = plt.figure() order = ['low', 'medium', 'high'] sns.stripplot(train_df['interest_level'], train_df['len_description'], jitter = True, order = order) plt.title('Length of description VS Interest_level') plt.show() # - plt.figure() sns.violinplot(x="len_description", y="interest_level", data = train_df, order = order) plt.xlabel('Length of description') plt.ylabel('Interest Level') plt.show() plt.figure(figsize=(400,10)) ax = sns.countplot(train_df.len_description) plt.xticks(rotation='vertical') plt.xlabel('Length of description') plt.ylabel('Number of occurrences') plt.show() plt.figure(figsize=(8,5)) order = ['low', 'medium', 'high'] sns.stripplot(train_df['interest_level'], train_df['num_description_words'], jitter = True, order = order) plt.title('Num description words VS Interest_level') #plt.xticks(rotation='vertical') plt.xlabel('Number of words of description') plt.ylabel('Number of occurrences') plt.show() plt.figure() sns.violinplot(x="num_description_words", y="interest_level", data = train_df, order = order) plt.xlabel('Number of words of description') plt.ylabel('Interest Level') plt.show() # + plt.figure(figsize=(50,10)) ulimit = np.percentile(train_df.num_description_words.values, 99) llimit = np.percentile(train_df.num_description_words.values, 1) train_df.num_description_words.loc[train_df.num_description_words > ulimit] = ulimit train_df.num_description_words.loc[train_df.num_description_words < llimit] = llimit ax = sns.countplot(train_df.num_description_words) plt.xticks(rotation='vertical') plt.xlabel('Number of words of description') plt.ylabel('Number of occurrences') plt.show() # - # ### 词云(display_address, street_address, features) # + from wordcloud import WordCloud text = '' text_da = '' text_street = '' #i = 0; for ind, row in train_df.iterrows(): #if(0 == i % 1000): # print(i) for feature in row['features']: text = " ".join([text, "_".join(feature.strip().split(" "))]) text_da = " ".join([text_da, "_".join(row['display_address'].strip().split(" "))]) text_street = " ".join([text_street, "_".join(row['street_address'].strip().split(" "))]) i = i + 1; text = text.strip() text_da = text_da.strip() text_street = text_street.strip() # - plt.figure(figsize=(12, 6)) wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40) wordcloud.generate(text) wordcloud.recolor(random_state=0) plt.imshow(wordcloud) plt.title('WordCloud for fatures', fontsize=30) plt.axis('off') plt.show() # 允许养猫和允许养狗,其实可以合并成允许养宠物! plt.figure() wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40) wordcloud.generate(text_da) wordcloud.recolor(random_state=0) plt.imshow(wordcloud) plt.title("WordCloud for Display Address", fontsize=30) plt.axis("off") plt.show() # 都是纽约比较繁华的街道! # wordcloud for street address plt.figure() wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40) wordcloud.generate(text_street) wordcloud.recolor(random_state=0) plt.imshow(wordcloud) plt.title("Wordcloud for street address", fontsize=30) plt.axis("off") plt.show() # ### 特征之间的相关性 # + contFeaturelist = [] contFeaturelist.append('bathrooms') contFeaturelist.append('bedrooms') contFeaturelist.append('price') print(contFeaturelist) # + correlationMatrix = train_df[contFeaturelist].corr().abs() plt.subplots() sns.heatmap(correlationMatrix, annot=True) #Mask unimportant features sns.heatmap(correlationMatrix, mask=correlationMatrix < 1, cbar = False) plt.show() # - # ## 案例分析 # # ### Rent Listing Inqueries 数据集的特征工程 # # #### 导入相应的包 # # import必要的工具包,用于文件的读取和特征编码 # + import numpy as np import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from scipy import sparse from sklearn.preprocessing import LabelEncoder from sklearn.cluster import KMeans from nltk.metrics import distance as distance from sklearn.model_selection import StratifiedKFold from MeanEncoder import MeanEncoder # - # #### 读取数据 dpath = './' train = pd.read_json(dpath + 'train.json') test = pd.read_json(dpath + 'test.json') train.head().T # #### 标签interest_level # # **从类别型的标签interest_level编码为数字** # # 从前面的分析和常识来看,listing_id对确定interest_level没有用,去掉特征编码对训练集和测试集都要做,所以干脆将二者连起来一起处理 # + y_map = {'low' : 2 , 'medium' : 1 , 'high' : 0} train['interest_level'] = train['interest_level'].apply(lambda x: y_map[x]) #y_train = train.interest_level.values y_train = train.interest_level train = train.drop(['listing_id' , 'interest_level'] , axis = 1) listing_id = test.listing_id.values test = test.drop('listing_id' , axis = 1) ntrain = train.shape[0] # concat函数是在pandas底下的方法,可以将数据根据不同的轴作简单的融合 train_test = pd.concat((train , test) , axis = 0).reset_index(drop = True) # - print(y_train.head()) # #### price,bathrooms,bedrooms # # 数值型特征,+ / - / * / / ,特征的单调变换对XGBoost不必要 # remove some noise ulimit = np.percentile(train_test.price.values , 99.99) print(ulimit) train_test['price'].loc[train_test['price'] > ulimit] = ulimit # remove some noise ulimit = np.percentile(train_test.bathrooms.values , 99.99) print(ulimit) outliers = train_test["bathrooms"].loc[train_test["bathrooms"] > ulimit] print(outliers) #pandas.loc 选取指定列进行操作 #df.loc[行标签,列标签]df.loc['a':'b']#选取ab两行数据df.loc[:,'one']#选取one列的数据 #下面的代码是分别将bathrooms列中,值为112,10,20的值分别置换成1.5,1,2 train_test.loc[train_test["bathrooms"] == 112 , "bathrooms"] = 1.5 train_test.loc[train_test["bathrooms"] == 10 , "bathrooms"] = 1 train_test.loc[train_test["bathrooms"] == 20 , "bathrooms"] = 2 ulimit = np.percentile(train_test.bathrooms.values , 99.99) print(ulimit) outliers = train_test["bathrooms"].loc[train_test["bathrooms"] > ulimit] print(outliers) # **构造新特征** # 1. price_bathrooms:单位bathroom的价格 # 2. price_bedrooms:单位bedroom的价格 train_test['price_bathrooms'] = (train_test["price"]) / (train_test["bathrooms"] + 1.0) train_test['price_bedrooms'] = (train_test["price"] / (train_test["bedrooms"]) + 1.0) # **构造新特征** # 1. room_diff: bathroom房间数 - bedrooms房间数 # 2. room_num: bathroom房间数 - bedroom房间数 train_test["room_diff"] = train_test["bathrooms"] - train_test["bedrooms"] train_test["room_num"] = train_test["bedrooms"] + train_test["bathrooms"] plt.figure(figsize=(8,4)) sns.countplot(train_test.bathrooms); plt.ylabel('Number of Occurrences', fontsize=12) plt.xlabel('bathrooms', fontsize=12) plt.show() # #### 创建日期 # + train_test['Date'] = pd.to_datetime(train_test['created']) train_test['Year'] = train_test['Date'].dt.year train_test['Month'] = train_test['Date'].dt.month train_test['Day'] = train_test['Date'].dt.day train_test['Wday'] = train_test['Date'].dt.dayofweek train_test['Yday'] = train_test['Date'].dt.dayofyear train_test['hour'] = train_test['Date'].dt.hour train_test = train_test.drop(['Date', 'created'], axis=1) # - # #### description # count of words present in description colum train_test["num_description_words"] = train_test["description"].apply(lambda x: len(x.split(" "))) train_test = train_test.drop(['description'] , axis = 1) # #### manager_id # # 将manager分为几个等级 top 1%, 2%, 5, 10, 15, 20, 25, 30, 50 # + def getTopXLimit(X, counted_feature_data): ''' X: the top number counted_feature_data: counted single feature data functionality: return the top Xth limit of relevant feature ''' top_limit = np.percentile(counted_feature_data.values, 100 - X) return top_limit def getTopX(X, counted_feature_data): ''' X: top number feature_data : counted feature data ''' return counted_feature_data[counted_feature_data.values >= getTopXLimit(X, counted_feature_data)] def storeTopX(X : int, source : dict, feature_name : str, to_data : dict, to_feature : str, isReturn: bool = False): ''' X: top number source: source of data feature_name: name of feature to_data: <class 'dict'>, to_feature: name of stored feature return: to_data['top_X_' + to_feature] ''' feature_counts = source[feature_name].value_counts(); to_data[('top_{}_' + to_feature).format(X)] = source[feature_name].apply(lambda x: 1 if x in feature_counts.index.values[feature_counts.values >= getTopXLimit(X, feature_counts)] else 0) if isReturn: return to_data[('top_{}_' + to_feature).format(X)] else: return None # + print(getTopX(1, train_test['manager_id'].value_counts())) tops = [1, 2, 5, 10, 15, 20, 30, 50] for i in tops: storeTopX(i,train_test, 'manager_id', train_test, 'manager_id', False) print("Stored top {} of manager id relevant to the number of rental inqueries".format(i)) print(train_test['top_1_manager_id'].head()) # - # #### building_id # # 类似manager_id处理 # + print(getTopX(1, train_test['building_id'].value_counts())) tops = [1, 2, 5, 10, 15, 20, 30, 50] for i in tops: storeTopX(i,train_test, 'building_id', train_test, 'building_id', False) print("Stored top {} of building id relevant to the number of rental inqueries".format(i)) print(train_test.head(10)) # - # #### photos # + train_test['photos_count'] = train_test['photos'].apply(lambda x: len(x)) train_test.drop(['photos'] , axis = 1 , inplace = True) print(train_test['photos_count'].head(10)) # - # #### latitude,longtitude # # 聚类降维编码(#用训练数据训练,对训练数据和测试数据都做变换)到中心的距离(论坛上讨论到曼哈顿中心的距离更好) # + # Clustering ntrain = train.shape[0] train_location = train_test.loc[:ntrain - 1, ['latitude', 'longitude']] test_location = train_test.loc[ntrain:, ['latitude', 'longitude']] kmeans_cluster = KMeans(n_clusters=20) res = kmeans_cluster.fit(train_location) res = kmeans_cluster.predict( pd.concat((train_location, test_location), axis=0).reset_index(drop=True)) train_test['cenroid'] = res # L1 distance center = [ train_location['latitude'].mean(), train_location['longitude'].mean()] train_test['distance'] = abs(train_test['latitude'] - center[0]) + abs(train_test['longitude'] - center[1]) # - print(train_test['distance'].head()) print(train_test['cenroid'].head()) # #### display_address train_test['display_address'] = train_test['display_address'].apply(lambda x: x.lower().strip()) print(train_test['display_address'].head()) # #### street_address train_test['street_address'] = train_test['street_address'].apply(lambda x: x.lower().strip()) print(train_test['street_address'].head()) # #### 类别型特征 # # LableEncode # + #categoricals = [x for x in train_test.columns if train_test[x].dtype == 'object'] categoricals = ['building_id', 'manager_id', 'display_address', 'street_address'] print(train_test.loc[:5, categoricals]) for feat in categoricals: lbl = LabelEncoder() lbl.fit(list(train_test[feat].values)) train_test[feat] = lbl.transform(list(train_test[feat].values)) # - train_test.loc[:5, categoricals] # 定义**高基数类别型特征编码函数** (manager_id, building_id, display_address,street_address ) 对这些特征进行**均值编码**(该特征值在每个类别的概率,即原来的一维特征变成了C-1维特征,C为标签类别数目) from MeanEncoder import MeanEncoder # + me = MeanEncoder(categoricals) #trian #import pdb #pdb.set_trace() train_new = train_test.iloc[:ntrain, :] train_new_cat = me.fit_transform(train_new, y_train) #test test_new = train_test.iloc[ntrain:, :] test_new_cat = me.transform(test_new) # - print(train_new_cat.head(1).T) # #### features # 描述特征文字长度 特征中单词的词频,相当于以数据集features中出现的词语为字典的one-hot编码(虽然是词频,但在这个任务中每个单词) train_test['features'] # + train_test['features_count'] = train_test['features'].apply(lambda x: len(x)) train_test['features2'] = train_test['features'] train_test['features2'] = train_test['features2'].apply(lambda x: ' '.join(x)) c_vect = CountVectorizer(stop_words='english', max_features=300, ngram_range=(1, 1)) c_vect_sparse = c_vect.fit_transform(train_test['features2']) c_vect_sparse_cols = c_vect.get_feature_names() train_test.drop(['features', 'features2'], axis=1, inplace=True) #hstack作为特征处理的最后一部,先将其他所有特征都转换成数值型特征才能处理 train_test_sparse = sparse.hstack([train_test, c_vect_sparse]).tocsr() # - train_test['features_count'] train_test_sparse c_vect_sparse_cols # #### 特征处理结果存为文件 # + #存为csv格式方便用excel查看 train_test_new = pd.DataFrame(train_test_sparse.toarray()) X_train = train_test_new.iloc[:ntrain, :] X_test = train_test_new.iloc[ntrain:, :] train_new = pd.concat((X_train, y_train), axis=1).reset_index(drop=True) train_new.to_csv(dpath + 'RentListingInquries_FE_train.csv', index=False) X_test.to_csv(dpath + 'RentListingInquries_FE_test.csv', index=False) # + from scipy.io import mmwrite X_train_sparse = train_test_sparse[:ntrain, :] X_test_sparse = train_test_sparse[ntrain:, :] train_sparse = sparse.hstack([X_train_sparse, sparse.csr_matrix(y_train).T]).tocsr() mmwrite(dpath + 'RentListingInquries_FE_train.txt',train_sparse) mmwrite(dpath + 'RentListingInquries_FE_test.txt',X_test_sparse) #存为libsvm稀疏格式,直接调用XGBoost的话用稀疏格式更高效 #from sklearn.datasets import dump_svmlight_file #dump_svmlight_file(, y_train, dpath + 'RentListingInquries_FE_train.txt',X_train_sparse) #dump_svmlight_file(X_test_sparse, dpath + 'RentListingInquries_FE_test.txt') # + train_test_new = pd.DataFrame(train_test_sparse.toarray()) X_train = train_test_new.iloc[:ntrain, :] X_test = train_test_new.iloc[ntrain:, :] train_new = pd.concat((X_train, y_train), axis=1)
29,261
/MMP12_experiments.ipynb
481ed991b4789c9f8a0ecad8e9abfed06cef7498
[]
no_license
rnaimehaom/scaffold-constrained-generation
https://github.com/rnaimehaom/scaffold-constrained-generation
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # IMPORT THE LIBRARIES NEEDED # !pip install python_speech_features # + from python_speech_features import mfcc import scipy.io.wavfile as wav import numpy as np from tempfile import TemporaryFile import os import pickle import random import operator # - import math import numpy as np # FUNCTION TO PERFORM ACTUAL DISTANCE CALCULATION BETWEEN FEATURE VECTORS def distance(instance1 , instance2 , k ): distance =0 mm1 = instance1[0] cm1 = instance1[1] mm2 = instance2[0] cm2 = instance2[1] distance = np.trace(np.dot(np.linalg.inv(cm2), cm1)) distance+=(np.dot(np.dot((mm2-mm1).transpose() , np.linalg.inv(cm2)) , mm2-mm1 )) distance+= np.log(np.linalg.det(cm2)) - np.log(np.linalg.det(cm1)) distance-= k return distance # DEFINING A FUNCTION THAT WILL EVALUATE THE MODEL def getAccuracy(testSet, predictions): correct = 0 for x in range (len(testSet)): if testSet[x][-1]==predictions[x]: correct+=1 return 1.0*correct/len(testSet) # DEFINING A FUNCTION TO GET NEIGHBOURS def getNeighbors(trainingSet, instance, k): distances = [] for x in range (len(trainingSet)): dist = distance(trainingSet[x], instance, k )+ distance(instance, trainingSet[x], k) distances.append((trainingSet[x][2], dist)) distances.sort(key=operator.itemgetter(1)) neighbors = [] for x in range(k): neighbors.append(distances[x][0]) return neighbors # CLASSIFYING / IDENTIFYING THE CLASS OF THE NEIGHBOURS def nearestClass(neighbors): classVote = {} for x in range(len(neighbors)): response = neighbors[x] if response in classVote: classVote[response]+=1 else: classVote[response]=1 sorter = sorted(classVote.items(), key = operator.itemgetter(1), reverse=True) return sorter[0][0] # DIRECTORY THAT HOLDS THE DATA SET directory = 'C:\\Users\\Roshini\\Desktop\\GROUP PROJECT\\Data\\genres\\' f= open("my.dat" ,'wb') i=0 # + for folder in os.listdir(directory): i+=1 if i==11 : break for file in os.listdir(directory+'/'+folder): (rate,sig) = wav.read(directory+folder+'/'+file) mfcc_feat = mfcc(sig,rate ,winlen=0.020, appendEnergy = False) covariance = np.cov(np.matrix.transpose(mfcc_feat)) mean_matrix = mfcc_feat.mean(0) feature = (mean_matrix , covariance , i) pickle.dump(feature , f) f.close() # - # SPLIT THE DATASET INTO TRAINING AND TESTING SETS RESPECTIVELY # + dataset = [] def loadDataset(filename , split , trSet , teSet): with open("my.dat" , 'rb') as f: while True: try: dataset.append(pickle.load(f)) except EOFError: f.close() break for x in range(len(dataset)): if random.random() <split : trSet.append(dataset[x]) else: teSet.append(dataset[x]) trainingSet = [] testSet = [] loadDataset("my.dat" , 0.66, trainingSet, testSet) # - # MAKING PREDICTIONS USING KNN # + leng = len(testSet) predictions = [] for x in range (leng): predictions.append(nearestClass(getNeighbors(trainingSet ,testSet[x] , 5))) accuracy1 = getAccuracy(testSet , predictions) print(accuracy1) s = 0 scores = [] smarts = Chem.MolToSmarts(Chem.MolFromSmiles('C(NS(=O)(=O)c1ccccc1)C(=O)O')) clf = joblib.load("data/MMP12/final_activity_model.pkl") for path in paths: with open(path) as f: content = f.readlines() smiles_1 = [x.strip().split()[0] for x in content[1:]] scores_1 = [x.strip().split()[1] for x in content[1:]] scores.extend(scores_1) mols = [Chem.MolFromSmiles(s) for s in smiles_1] has_substruct = [Chem.AddHs(mol).HasSubstructMatch(Chem.MolFromSmarts(smarts)) if mol is not None else 0 for mol in mols] activities = [clf.predict(np.array(AllChem.GetMorganFingerprintAsBitVect(mol, 4)).reshape(1, -1))[0] if mol is not None else 0 for mol in mols] # Get activity and substruct match for i in range(len(mols)): total += 1 if float(scores_1[i])>0.999: good += 1 elif has_substruct[i]>0.999: substructs += 1 elif activities[i]>0.999: actives += 1 else: failures += 1 return good/total, substructs/total, actives/total, failures/total # - good_custom, substructs_custom, actives_custom, failures_custom = return_results(["data/results/Scaffold_constrained_RNN_" + str(i) + "/memory" for i in range(9)]) good_bench, substructs_bench, actives_bench, failures_bench = return_results(["data/results/RNN_" + str(i) + "/memory" for i in range(9)]) # + import matplotlib.pyplot as plt import matplotlib matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) labels = ['Active, right scaffold', 'Right scaffold, not active', 'Active, without scaffold', 'Not active, without scaffold'] font = {'family' : 'normal', 'size' : 20} matplotlib.rc('font', **font) x = np.arange(len(labels)) # the label locations width = 0.35 fig, ax = plt.subplots(figsize=(25,25)) fig.set_size_inches(w=17, h=7) rects1 = ax.bar(x - width/2, [good_custom, substructs_custom, actives_custom, failures_custom], width, label='Scaffold-constrained generator') rects2 = ax.bar(x + width/2, [good_bench, substructs_bench, actives_bench, failures_bench], width, label='SMILES based RNN') #ax.set_xlabel('Criteria met') # ax.set_ylabel('% of molecules', font) plt.ylabel('Percentage of molecules', fontsize=23) ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend(prop={'size': 20}) plt.savefig('MMP_12.pgf') # - # # Performance of the classification model on the test set clf = joblib.load("data/MMP12/final_activity_model.pkl") with open('data/MMP12/test_set.smi') as f: content = f.readlines() smiles = [x.strip() for x in content] test_fps = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(s), 4) for s in smiles] df = pd.read_csv('data/MMP12/mmp12.csv') smiles = df["Smiles"] full_datatest_fps = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(s), 4) for s in smiles] pIC50 = df["pIC50_MMP12"] y_test = [] for fp in test_fps: for i, query_fp in enumerate(full_datatest_fps): if DataStructs.TanimotoSimilarity(query_fp, fp)==1: try: y_test.append(float(pIC50[i])) except: # Then it's "inactive" y_test.append(4) y_test = np.array(y_test) # + y_pred = clf.predict(np.array(test_fps)) print('Mean squared error: %.2f' % mean_squared_error(y_test, y_pred)) # The coefficient of determination: 1 is perfect prediction print('Coefficient of determination: %.2f' % r2_score(y_test, y_pred)) # Plot outputs plt.scatter(y_test, y_test, color='black') plt.scatter(y_test, y_pred, color='blue', linewidth=3) # -
7,359
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[]
no_license
krunalkumar-degamdiya/The-Spark-Foundation-Internship
https://github.com/krunalkumar-degamdiya/The-Spark-Foundation-Internship
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import os import geopandas import json import topojson from IPython.display import SVG, display from shapely import geometry # %matplotlib inline # - # ### natural earth low resolution data = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) data.plot() data.head() # apply topolgy where vertices are snapped to a grid before applying topology (quantization) tj_data = topojson.join(data) tj_data # write computed topology to file tj_path = '../tests/files_topojson/naturalearth_lowres.topojson' with open(tj_path, 'w') as fp: json.dump(tj_data, fp) # compare file sizes of the geojson and topjson file gj_path = '../tests/files_geojson/naturalearth_lowres.geojson' tj_kb = os.path.getsize(tj_path)/1000 gj_kb = os.path.getsize(gj_path)/1000 print('topojson naturalearth_loweres: {}kb\ngeojson naturalearth_loweres: {}kb'.format(tj_kb, gj_kb)) # read the saved topojson file into geopandas and see that it works! data_tj = geopandas.read_file(tj_path) data_tj.plot() data_tj.head() # + # for gdf_row in data_tj.iterrows(): # print(gdf_row[1]['name']) # g1_svg = gdf_row[1].geometry._repr_svg_() # display(SVG(g1_svg)) # - # %%prun -l 10 # present timing of applying the whole topology tj_data = topojson.topology(data, snap_vertices=True, gridsize_to_snap=1e6) # %%prun -l 10 # present timing split out in the different subtasks ex = topojson.extract(data) jo = topojson.join(ex, quant_factor=1e4) cu = topojson.cut(jo) de = topojson.dedup(cu) ha = topojson.hashmap(de)
1,793
/week4_approx/practice_approx_qlearning.ipynb
414fb9f5cd499e63208e4a5037d6cd3ccfc077c8
[]
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ArezooAalipanah/RL_Examples
https://github.com/ArezooAalipanah/RL_Examples
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # The following additional libraries are needed to run this # notebook. Note that running on Colab is experimental, please report a Github # issue if you have any problem. # !pip install d2l==1.0.0 # !pip install -U mxnet-cu112==1.9.1 # + [markdown] origin_pos=1 # # Attention Scoring Functions # :label:`sec_attention-scoring-functions` # # # In :numref:`sec_attention-pooling`, # we used a number of different distance-based kernels, including a Gaussian kernel to model # interactions between queries and keys. As it turns out, distance functions are slightly more expensive to compute than dot products. As such, # with the softmax operation to ensure nonnegative attention weights, # much of the work has gone into *attention scoring functions* $a$ in :eqref:`eq_softmax_attention` and :numref:`fig_attention_output` that are simpler to compute. # # ![Computing the output of attention pooling as a weighted average of values, where weights are computed with the attention scoring function $\mathit{a}$ and the softmax operation.](../img/attention-output.svg) # :label:`fig_attention_output` # # + origin_pos=2 tab=["mxnet"] import math from mxnet import np, npx from mxnet.gluon import nn from d2l import mxnet as d2l npx.set_np() # + [markdown] origin_pos=6 # ## [**Dot Product Attention**] # # # Let's review the attention function (without exponentiation) from the Gaussian kernel for a moment: # # $$ # a(\mathbf{q}, \mathbf{k}_i) = -\frac{1}{2} \|\mathbf{q} - \mathbf{k}_i\|^2 = \mathbf{q}^\top \mathbf{k}_i -\frac{1}{2} \|\mathbf{k}_i\|^2 -\frac{1}{2} \|\mathbf{q}\|^2. # $$ # # First, note that the final term depends on $\mathbf{q}$ only. As such it is identical for all $(\mathbf{q}, \mathbf{k}_i)$ pairs. Normalizing the attention weights to $1$, as is done in :eqref:`eq_softmax_attention`, ensures that this term disappears entirely. Second, note that both batch and layer normalization (to be discussed later) lead to activations that have well-bounded, and often constant, norms $\|\mathbf{k}_i\|$. This is the case, for instance, whenever the keys $\mathbf{k}_i$ were generated by a layer norm. As such, we can drop it from the definition of $a$ without any major change in the outcome. # # Last, we need to keep the order of magnitude of the arguments in the exponential function under control. Assume that all the elements of the query $\mathbf{q} \in \mathbb{R}^d$ and the key $\mathbf{k}_i \in \mathbb{R}^d$ are independent and identically drawn random variables with zero mean and unit variance. The dot product between both vectors has zero mean and a variance of $d$. To ensure that the variance of the dot product still remains $1$ regardless of vector length, we use the *scaled dot product attention* scoring function. That is, we rescale the dot product by $1/\sqrt{d}$. We thus arrive at the first commonly used attention function that is used, e.g., in Transformers :cite:`Vaswani.Shazeer.Parmar.ea.2017`: # # $$ a(\mathbf{q}, \mathbf{k}_i) = \mathbf{q}^\top \mathbf{k}_i / \sqrt{d}.$$ # :eqlabel:`eq_dot_product_attention` # # Note that attention weights $\alpha$ still need normalizing. We can simplify this further via :eqref:`eq_softmax_attention` by using the softmax operation: # # $$\alpha(\mathbf{q}, \mathbf{k}_i) = \mathrm{softmax}(a(\mathbf{q}, \mathbf{k}_i)) = \frac{\exp(\mathbf{q}^\top \mathbf{k}_i / \sqrt{d})}{\sum_{j=1} \exp(\mathbf{q}^\top \mathbf{k}_j / \sqrt{d})}.$$ # :eqlabel:`eq_attn-scoring-alpha` # # As it turns out, all popular attention mechanisms use the softmax, hence we will limit ourselves to that in the remainder of this chapter. # # ## Convenience Functions # # We need a few functions to make the attention mechanism efficient to deploy. This includes tools for dealing with strings of variable lengths (common for natural language processing) and tools for efficient evaluation on minibatches (batch matrix multiplication). # # # ### [**Masked Softmax Operation**] # # One of the most popular applications of the attention mechanism is to sequence models. Hence we need to be able to deal with sequences of different lengths. In some cases, such sequences may end up in the same minibatch, necessitating padding with dummy tokens for shorter sequences (see :numref:`sec_machine_translation` for an example). These special tokens do not carry meaning. For instance, assume that we have the following three sentences: # # ``` # Dive into Deep Learning # Learn to code <blank> # Hello world <blank> <blank> # ``` # # Since we do not want blanks in our attention model we simply need to limit $\sum_{i=1}^n \alpha(\mathbf{q}, \mathbf{k}_i) \mathbf{v}_i$ to $\sum_{i=1}^l \alpha(\mathbf{q}, \mathbf{k}_i) \mathbf{v}_i$ for however long, $l \leq n$, the actual sentence is. Since it is such a common problem, it has a name: the *masked softmax operation*. # # Let's implement it. Actually, the implementation cheats ever so slightly by setting the values of $\mathbf{v}_i$, for $i > l$, to zero. Moreover, it sets the attention weights to a large negative number, such as $-10^{6}$, in order to make their contribution to gradients and values vanish in practice. This is done since linear algebra kernels and operators are heavily optimized for GPUs and it is faster to be slightly wasteful in computation rather than to have code with conditional (if then else) statements. # # + origin_pos=7 tab=["mxnet"] def masked_softmax(X, valid_lens): #@save """Perform softmax operation by masking elements on the last axis.""" # X: 3D tensor, valid_lens: 1D or 2D tensor if valid_lens is None: return npx.softmax(X) else: shape = X.shape if valid_lens.ndim == 1: valid_lens = valid_lens.repeat(shape[1]) else: valid_lens = valid_lens.reshape(-1) # On the last axis, replace masked elements with a very large negative # value, whose exponentiation outputs 0 X = npx.sequence_mask(X.reshape(-1, shape[-1]), valid_lens, True, value=-1e6, axis=1) return npx.softmax(X).reshape(shape) # + [markdown] origin_pos=11 # To [**illustrate how this function works**], # consider a minibatch of two examples of size $2 \times 4$, # where their valid lengths are $2$ and $3$, respectively. # As a result of the masked softmax operation, # values beyond the valid lengths for each pair of vectors are all masked as zero. # # + origin_pos=12 tab=["mxnet"] masked_softmax(np.random.uniform(size=(2, 2, 4)), np.array([2, 3])) # + [markdown] origin_pos=16 # If we need more fine-grained control to specify the valid length for each of the two vectors of every example, we simply use a two-dimensional tensor of valid lengths. This yields: # # + origin_pos=17 tab=["mxnet"] masked_softmax(np.random.uniform(size=(2, 2, 4)), np.array([[1, 3], [2, 4]])) # + [markdown] origin_pos=21 # ### Batch Matrix Multiplication # :label:`subsec_batch_dot` # # Another commonly used operation is to multiply batches of matrices by one another. This comes in handy when we have minibatches of queries, keys, and values. More specifically, assume that # # $$\mathbf{Q} = [\mathbf{Q}_1, \mathbf{Q}_2, \ldots, \mathbf{Q}_n] \in \mathbb{R}^{n \times a \times b}, \\ # \mathbf{K} = [\mathbf{K}_1, \mathbf{K}_2, \ldots, \mathbf{K}_n] \in \mathbb{R}^{n \times b \times c}. # $$ # # Then the batch matrix multiplication (BMM) computes the elementwise product # # $$\textrm{BMM}(\mathbf{Q}, \mathbf{K}) = [\mathbf{Q}_1 \mathbf{K}_1, \mathbf{Q}_2 \mathbf{K}_2, \ldots, \mathbf{Q}_n \mathbf{K}_n] \in \mathbb{R}^{n \times a \times c}.$$ # :eqlabel:`eq_batch-matrix-mul` # # Let's see this in action in a deep learning framework. # # + origin_pos=22 tab=["mxnet"] Q = np.ones((2, 3, 4)) K = np.ones((2, 4, 6)) d2l.check_shape(npx.batch_dot(Q, K), (2, 3, 6)) # + [markdown] origin_pos=26 # ## [**Scaled Dot Product Attention**] # # Let's return to the dot product attention introduced in :eqref:`eq_dot_product_attention`. # In general, it requires that both the query and the key # have the same vector length, say $d$, even though this can be addressed easily by replacing # $\mathbf{q}^\top \mathbf{k}$ with $\mathbf{q}^\top \mathbf{M} \mathbf{k}$ where $\mathbf{M}$ is a matrix suitably chosen for translating between both spaces. For now assume that the dimensions match. # # In practice, we often think of minibatches for efficiency, # such as computing attention for $n$ queries and $m$ key-value pairs, # where queries and keys are of length $d$ # and values are of length $v$. The scaled dot product attention # of queries $\mathbf Q\in\mathbb R^{n\times d}$, # keys $\mathbf K\in\mathbb R^{m\times d}$, # and values $\mathbf V\in\mathbb R^{m\times v}$ # thus can be written as # # $$ \mathrm{softmax}\left(\frac{\mathbf Q \mathbf K^\top }{\sqrt{d}}\right) \mathbf V \in \mathbb{R}^{n\times v}.$$ # :eqlabel:`eq_softmax_QK_V` # # Note that when applying this to a minibatch, we need the batch matrix multiplication introduced in :eqref:`eq_batch-matrix-mul`. In the following implementation of the scaled dot product attention, # we use dropout for model regularization. # # + origin_pos=27 tab=["mxnet"] class DotProductAttention(nn.Block): #@save """Scaled dot product attention.""" def __init__(self, dropout): super().__init__() self.dropout = nn.Dropout(dropout) # Shape of queries: (batch_size, no. of queries, d) # Shape of keys: (batch_size, no. of key-value pairs, d) # Shape of values: (batch_size, no. of key-value pairs, value dimension) # Shape of valid_lens: (batch_size,) or (batch_size, no. of queries) def forward(self, queries, keys, values, valid_lens=None): d = queries.shape[-1] # Set transpose_b=True to swap the last two dimensions of keys scores = npx.batch_dot(queries, keys, transpose_b=True) / math.sqrt(d) self.attention_weights = masked_softmax(scores, valid_lens) return npx.batch_dot(self.dropout(self.attention_weights), values) # + [markdown] origin_pos=31 # To [**illustrate how the `DotProductAttention` class works**], # we use the same keys, values, and valid lengths from the earlier toy example for additive attention. For the purpose of our example we assume that we have a minibatch size of $2$, a total of $10$ keys and values, and that the dimensionality of the values is $4$. Lastly, we assume that the valid length per observation is $2$ and $6$ respectively. Given that, we expect the output to be a $2 \times 1 \times 4$ tensor, i.e., one row per example of the minibatch. # # + origin_pos=32 tab=["mxnet"] queries = np.random.normal(0, 1, (2, 1, 2)) keys = np.random.normal(0, 1, (2, 10, 2)) values = np.random.normal(0, 1, (2, 10, 4)) valid_lens = np.array([2, 6]) attention = DotProductAttention(dropout=0.5) attention.initialize() d2l.check_shape(attention(queries, keys, values, valid_lens), (2, 1, 4)) # + [markdown] origin_pos=36 # Let's check whether the attention weights actually vanish for anything beyond the second and sixth column respectively (because of setting the valid length to $2$ and $6$). # # + origin_pos=37 tab=["mxnet"] d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)), xlabel='Keys', ylabel='Queries') # + [markdown] origin_pos=39 # ## [**Additive Attention**] # :label:`subsec_additive-attention` # # When queries $\mathbf{q}$ and keys $\mathbf{k}$ are vectors of different dimension, # we can either use a matrix to address the mismatch via $\mathbf{q}^\top \mathbf{M} \mathbf{k}$, or we can use additive attention # as the scoring function. Another benefit is that, as its name indicates, the attention is additive. This can lead to some minor computational savings. # Given a query $\mathbf{q} \in \mathbb{R}^q$ # and a key $\mathbf{k} \in \mathbb{R}^k$, # the *additive attention* scoring function :cite:`Bahdanau.Cho.Bengio.2014` is given by # # $$a(\mathbf q, \mathbf k) = \mathbf w_v^\top \textrm{tanh}(\mathbf W_q\mathbf q + \mathbf W_k \mathbf k) \in \mathbb{R},$$ # :eqlabel:`eq_additive-attn` # # where $\mathbf W_q\in\mathbb R^{h\times q}$, $\mathbf W_k\in\mathbb R^{h\times k}$, # and $\mathbf w_v\in\mathbb R^{h}$ are the learnable parameters. This term is then fed into a softmax to ensure both nonnegativity and normalization. # An equivalent interpretation of :eqref:`eq_additive-attn` is that the query and key are concatenated # and fed into an MLP with a single hidden layer. # Using $\tanh$ as the activation function and disabling bias terms, # we implement additive attention as follows: # # + origin_pos=40 tab=["mxnet"] class AdditiveAttention(nn.Block): #@save """Additive attention.""" def __init__(self, num_hiddens, dropout, **kwargs): super(AdditiveAttention, self).__init__(**kwargs) # Use flatten=False to only transform the last axis so that the # shapes for the other axes are kept the same self.W_k = nn.Dense(num_hiddens, use_bias=False, flatten=False) self.W_q = nn.Dense(num_hiddens, use_bias=False, flatten=False) self.w_v = nn.Dense(1, use_bias=False, flatten=False) self.dropout = nn.Dropout(dropout) def forward(self, queries, keys, values, valid_lens): queries, keys = self.W_q(queries), self.W_k(keys) # After dimension expansion, shape of queries: (batch_size, no. of # queries, 1, num_hiddens) and shape of keys: (batch_size, 1, # no. of key-value pairs, num_hiddens). Sum them up with # broadcasting features = np.expand_dims(queries, axis=2) + np.expand_dims( keys, axis=1) features = np.tanh(features) # There is only one output of self.w_v, so we remove the last # one-dimensional entry from the shape. Shape of scores: # (batch_size, no. of queries, no. of key-value pairs) scores = np.squeeze(self.w_v(features), axis=-1) self.attention_weights = masked_softmax(scores, valid_lens) # Shape of values: (batch_size, no. of key-value pairs, value # dimension) return npx.batch_dot(self.dropout(self.attention_weights), values) # + [markdown] origin_pos=44 # Let's [**see how `AdditiveAttention` works**]. In our toy example we pick queries, keys and values of size # $(2, 1, 20)$, $(2, 10, 2)$ and $(2, 10, 4)$, respectively. This is identical to our choice for `DotProductAttention`, except that now the queries are $20$-dimensional. Likewise, we pick $(2, 6)$ as the valid lengths for the sequences in the minibatch. # # + origin_pos=45 tab=["mxnet"] queries = np.random.normal(0, 1, (2, 1, 20)) attention = AdditiveAttention(num_hiddens=8, dropout=0.1) attention.initialize() d2l.check_shape(attention(queries, keys, values, valid_lens), (2, 1, 4)) # + [markdown] origin_pos=49 # When reviewing the attention function we see a behavior that is qualitatively quite similar to that of `DotProductAttention`. That is, only terms within the chosen valid length $(2, 6)$ are nonzero. # # + origin_pos=50 tab=["mxnet"] d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)), xlabel='Keys', ylabel='Queries') # + [markdown] origin_pos=52 # ## Summary # # In this section we introduced the two key attention scoring functions: dot product and additive attention. They are effective tools for aggregating across sequences of variable length. In particular, the dot product attention is the mainstay of modern Transformer architectures. When queries and keys are vectors of different lengths, # we can use the additive attention scoring function instead. Optimizing these layers is one of the key areas of advance in recent years. For instance, [NVIDIA's Transformer Library](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html) and Megatron :cite:`shoeybi2019megatron` crucially rely on efficient variants of the attention mechanism. We will dive into this in quite a bit more detail as we review Transformers in later sections. # # ## Exercises # # 1. Implement distance-based attention by modifying the `DotProductAttention` code. Note that you only need the squared norms of the keys $\|\mathbf{k}_i\|^2$ for an efficient implementation. # 1. Modify the dot product attention to allow for queries and keys of different dimensionalities by employing a matrix to adjust dimensions. # 1. How does the computational cost scale with the dimensionality of the keys, queries, values, and their number? What about the memory bandwidth requirements? # # + [markdown] origin_pos=53 tab=["mxnet"] # [Discussions](https://discuss.d2l.ai/t/346) #
16,930
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aucan/Turkish-Reading-Comprehension-Question-Answering-Dataset
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import json with open('../../data/final_dataset/final_dev_dataset_v2.json') as f: data1 = json.load(f), data1 text = "İstanbul'un Fethinin hemen ardından II. Mehmed şehrin onarımına başladı. Amacı Doğu Roma’yı yıkmak değil onu Osmanlı yapısı içinde diriltmekti.Kuracağı imparatorluk bir İslâm devleti olmakla birlikte Doğu Roma gibi kozmopolit bir yapıya sahip olacaktı.Fatih, Rum Ortodoks Patrikhanesi, Ermeni Patrikhanesi ve Yahudi hahambaşı bulunmasına izin verdi. 6 Ocak 1454’te Yorgo Skolaris'i yeni Ortodoks patriği olarak atadı.Ayasofya camiye çevrildiğinden Patrikliğe resmî makam yeri olarak Havariyun Kilisesi verildi. Şehirdeki Yahudilerin hahambaşı olarak Moşe Kapsali atadı.1461 yılında ise Bursa Psikoposu Hovakim İstanbul Ermeni Patriği olarak atandı.II. Mehmed Theodosius Forumu’nun olduğu yerde ilk sarayının inşasını başlattı. Daha sonraki yıllarda ise Sarayburnu’nda Topkapı Sarayı’nı inşa ettirdi." len(text) with open('data/1792-1922/test_data.json') as f: data2 = json.load(f), data2
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/functions.ipynb
a6518d4715b614c85fbf1bb23756da0a10d926d1
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Rachel-Veenstra/AGRON935-Class-Notes
https://github.com/Rachel-Veenstra/AGRON935-Class-Notes
0
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # FUN functions # ##### Step 1 - create function # ##### Step 2 - call/use function ## Defining a function def squarednum(x): a = x**2 print(a) squarednum(2) ## calling the function c = squarednum(2) print(c) # + ## WORKSPACE ## example - leaving and coming back with the answer # - def numsquared(x): a = x**2 return a ## stops process and stores/shows value numsquared(2) for i in range(0,10): print(numsquared(i)) # + ## FEB 21 - WRITE A FUNCTION THAT CALCULATES AND RETURNS THE AREA AND VOLUME OF A CONE # Inputs - radius and height of cone # Volume = pi*r**2*h/3 # Area = base area import math def cone(R,H): """Calculates the area and colume of a cone Inputs: radius in cm height in cm Outputs: cone volume in cm^3 cone base area in cm^2 Author: RV Date: 21-Feb-2019 """ area = (math.pi)*(R**2) ## area in cm^2 vol = (area)*H/3 ## volume in cm^3 print("Your cone's base area is " + str(area) +", and it's volume is " + str(vol) + ".") return area, vol # - cone(2,5) cone(H=5,R=2) ## can change order of variables if called as original names values = cone(2,5) print(type(values)) values[0], values[1] # + ## Function to compute the sum of all the integers between 1 - 100 import math def fun100(): x = sum(range(1,101)) return x # - fun100() def funsum(a,b): y = sum(range(a,(b+1))) return y funsum(0,100)
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/notebooks/20220107_jaccard_sim_of_nbhd_with_dda_boxplot.ipynb
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2022-05-30T00:26:51
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# --- # jupyter: # jupytext: # text_representation: # extension: .r # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: R # language: R # name: ir # --- # This notebook was another forray trying to dig into the similarity of neighborhoods across disease states. # # The goal was to determine if there are disease specific sequences; i.e. determine whether sequences that are more abundant in CD are only present in CD. # # This notebook didn't really end up addressing that question...but I kept it around anyway :) setwd("..") library(readr) library(ggplot2) library(dplyr) library(tidyr) # ### read in metadata metadata <- read_tsv("inputs/working_metadata.tsv", show_col_types = F) %>% select(library_name, study_accession, diagnosis) %>% distinct() %>% mutate(diagnosis = factor(diagnosis, levels = c("nonIBD", "CD", "UC"))) acc_to_species <- read_csv("outputs/genbank/gather_vita_vars_gtdb_shared_assemblies.x.genbank.lineages.csv", col_names = c("accession", "kingdom", "phylum", "class", "order", "family", "genus", "species"), show_col_types = F) %>% select(accession, species) %>% mutate(accession = gsub("_genomic.fna.gz", "", accession)) # ### sourmash compare acc_string <- "GCF_008121495.1" comp <- read_csv(paste0("outputs/sgc_genome_queries_vs_pangenome_corncob_sequences_comp/", acc_string, "_CD_decreased_contig_comp.csv"), show_col_types = F) colnames(comp) <- gsub(paste0("_", acc_string), "", colnames(comp)) colnames(comp) <- gsub("outputs/sgc_pangenome_catlases_corncob_sequences/", "", colnames(comp)) colnames(comp) <- gsub("_contigs.fa", "", colnames(comp)) comp$library_name <- colnames(comp) comp_dec <- comp %>% pivot_longer(cols = -library_name, names_to = "comp_lib_name", values_to = "jaccard") %>% distinct() %>% filter(comp_lib_name == paste0(acc_string, "_", "CD_decreased")) %>% left_join(metadata, by = "library_name") %>% filter(!is.na(diagnosis)) %>% mutate(abundance = "decreased") comp <- read_csv(paste0("outputs/sgc_genome_queries_vs_pangenome_corncob_sequences_comp/", acc_string, "_CD_increased_contig_comp.csv"), show_col_types = F) colnames(comp) <- gsub(paste0("_", acc_string), "", colnames(comp)) colnames(comp) <- gsub("outputs/sgc_pangenome_catlases_corncob_sequences/", "", colnames(comp)) colnames(comp) <- gsub("_contigs.fa", "", colnames(comp)) comp$library_name <- colnames(comp) comp_inc <- comp %>% pivot_longer(cols = -library_name, names_to = "comp_lib_name", values_to = "jaccard") %>% distinct() %>% filter(comp_lib_name == paste0(acc_string, "_", "CD_increased")) %>% left_join(metadata, by = "library_name") %>% filter(!is.na(diagnosis))%>% mutate(abundance = "increased") all_comp <- bind_rows(comp_inc, comp_dec) %>% mutate(abundance = factor(abundance, levels = c("increased", "decreased"))) ggplot(all_comp, aes(x = diagnosis, y = jaccard, fill = diagnosis)) + geom_boxplot(alpha = .6) + facet_wrap(~abundance) + scale_fill_manual(values = c("black", "orange", "steelblue"), name = "Diagnosis") + theme_classic() + labs(y = "Jaccard similarity")
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/project3/04.FanZhu_source/CODE&DATA/TransitionPath/Sapporo-Hakkodate/Split/Sapporo-Hakodate-Split.ipynb
1f156b4264319fcfa3ae799d590f3e61da0ef730
[]
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yuany-pku/2017_CSIC5011
https://github.com/yuany-pku/2017_CSIC5011
5
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda root] # language: python # name: conda-root-py # --- import pandas as pd import numpy as np from discreteMarkovChain import markovChain # ## Reading Data: Score and Adjancy Matrix adj_mat_data = pd.read_csv('JRPASS_split.csv',header=0,index_col=0,nrows=19) adj_mat = adj_mat_data.values adj_mat_data score_data = pd.read_csv('Score_split.csv',header=0,index_col=0) score_data # Here we use split strategy to solve passing by the airport problem. Score of Splitting node is a hyper-parameter, which could be tuned according to, for example, the stationary distribution. Here we set both of the splitting node score to be 500. score = np.max(score_data.values[[0,2,4]],axis=0) N = score.shape[0] pd.DataFrame(score.reshape((1,-1)), columns=city, index=['score']) city = adj_mat_data.columns print(city) # ## Transition Matrix tran_mat = np.matmul(adj_mat, np.diag(score)) deg = np.sum(tran_mat,axis=1) tran_mat = np.matmul(np.diag(1/deg), tran_mat) df = pd.DataFrame(tran_mat, index=city, columns=city) df.to_csv('Tran_mat_split.csv') df # ## Stationary Distribution # Solve a linear equation $\pi * P = \pi$. Here we use package ``discreteMarkovChain`` mc = markovChain(P=tran_mat) mc.computePi('linear') pd.DataFrame(mc.pi.reshape((1,-1)), columns=city, index=['Prob']) # ## Committor function # Solve a (discrete) Dirichlet boundary problem for committor function, here we should let the boundary be the first col and last col. a1 = np.zeros(N) a1[0] = 1 a2 = np.zeros(N) a2[-1] = 1 a = np.vstack((tran_mat[1:-1,0], (tran_mat[1:-1,1:-1]-np.identity(N-2)).T, tran_mat[1:-1,-1])).T a = np.vstack((a1,a,a2)) b = np.zeros(N) b[0] = 0 b[-1] = 1 q = np.linalg.solve(a,b) pd.DataFrame(q.reshape(1,-1), columns=city, index=['commitor']) # ## Effective Flux # Solve the effective flux on each edge (x,y) # Firstly, calculate reactive current, which describes the ``reactive flow passing by x and y``. # Then, effective flux is basically the max(J(xy)-J(yx),0) J_dir = np.zeros((N,N)) for x in range(N): for y in range(N): J_dir[x,y] = mc.pi[x] * (1-q[x]) * tran_mat[x,y] * q[y] J_eff = np.maximum(J_dir - J_dir.T, 0) print(J_eff[0]) np.max(J_eff) # ## Transition Current of a Node # Compute the transition flux through each node x$\in$V. flux = np.zeros(N) flux[0] = np.sum(J_eff[0]) flux[-1] = np.sum(J_eff[:,-1]) for x in np.arange(1,N-1): flux[x] = np.sum(J_eff[x,]) #print(J_eff[x,].sum() - J_eff[:,x].sum()) print(flux) pd.DataFrame( np.reshape( flux / np.sum(flux[1:-1]), (1,-1)), index=['Current'], columns=city) # ## Graph Force Layout nodes = [] for index, name in enumerate(city): if index == 0 or index == N-1: nodes.append({ 'id':name, 'group':'red', 'radius':np.mean(flux[1:-1])/2+np.max(flux[1:-1])/2 }) else: nodes.append({ 'id':name, 'group':'blue', 'radius':flux[index] }) nodes mean_0 = np.mean(J_eff[0][J_eff[0]!=0]) mean_n = np.mean(J_eff[:,-1][J_eff[:,-1]!=0]) mean = np.percentile(J_eff[1:-1,1:-1][J_eff[1:-1,1:-1]!=0], q=65) J_eff_copy = J_eff.copy() J_eff_copy[0] *= mean/mean_0 J_eff_copy[:,-1] *= mean/mean_n cut_thresh = np.percentile(J_eff[1:-1,1:-1][J_eff[1:-1,1:-1]!=0],q=75) # stress 1/5 def f(index1, index2, dis, cut_thresh=cut_thresh): if index1 == 0 or index1 == N-1 or index2 == 0 or index2==N-1: return 'red' elif dis > cut_thresh: return 'green' else: return 'gray' links = [] for index1, a in enumerate(adj_mat): for index2, b in enumerate(a): if index1>index2: if J_dir[index1,index2] > J_dir[index2,index1]: links.append({ 'source':city[index1], 'target':city[index2], 'value':J_eff_copy[index1,index2], 'group': f(index1, index2, J_eff[index1, index2]) }) elif J_dir[index2,index1] > J_dir[index1,index2]: links.append({ 'source':city[index2], 'target':city[index1], 'value':J_eff_copy[index2,index1], 'group': f(index1, index2, J_eff[index2, index1]) }) graph = {'nodes':nodes, 'links':links } import json with open('Sapporo-Hakodate_TP_Sp.json', 'w') as outfile: json.dump(graph, outfile)
4,643
/Conditioner.ipynb
444ea30e9539c83f987b265455886d47ed732895
[]
no_license
alxkorn/PhygeText
https://github.com/alxkorn/PhygeText
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Homework 5: Machine Reading # ## 1. Random QA # 1) What is the probability $p_{k,n}$ that our random QA system will output an answer with $k$ tokens when given a context paragraph with $n$ token as input? # $$p_{k,n} = $$ # 2) For a fixed $n$ of $100$, produce a plot of $p_{k,n}$ and $\hat{p}_{k,n}$ vs $k$. Design a monte-carlo experiment to estimate the values for $\hat{p}_{k,n}$. import numpy as np import matplotlib.pyplot as plt def p_k_n(k, n): #implement answer from part 1 here p_k_n = 0.5 return p_k_n def monte_carlo_p_k_n(k, n, T = int(1e5)): #T is the number of times you run experiment p_k_n_hat = 1 return p_k_n_hat k_array = np.arange(1, 100, 5) p_k_n_hat = [monte_carlo_p_k_n(k, n=100, T = int(1e5)) for k in k_array] p_k_n = [p_k_n(k, n=100) for k in k_array] plt.plot(k_array, p_k_n_hat, label = r'$\hat{p_{k,n}}$') plt.plot(k_array, p_k_n, label = r'$p_{k, n}$') plt.legend() # 3) Calculate the expected length of the answer given by your random QA system. i,e write an expression for $L_n = E[K]$ (It's fine to leave it as a summation). # $$ L_n = $$ # 4) Plot $\hat{L_n}$ estimated through monte-carlo simulations and $L_n$ for $n = 5, 10, 25, 50, 100, 250$ and $500$. # + def L_n(n): #implement answer from above l_n = 5 return l_n def monte_carlo_L_n(n, T = int(1e5)): #T is the number of times you run experiment l_n = 1 return l_n # - n_array = [5, 10, 25, 50, 100, 250, 500] l_n_hat = [monte_carlo_L_n(n, T = int(1e5)) for n in n_array] l_n = [L_n( n) for n in n_array] plt.plot(n_array, l_n_hat, '-o', label = r'$\hat{L_n}$') plt.plot(n_array, l_n, '-*', label = r'$L_n$') plt.legend() # 5) Calculate the probability, $p_n$ that Random QA system outputs the correct answer to your question. # $$p_n = $$ # 6) In SQuAD 2.0 data set, the answer for your question can either lie within the context paragraph or there could be no answer within the given paragraph. Let $\alpha$ represent the fraction of questions for which the answer \textbf{does not} lie within the paragraph. def estimate_alpha(list_of_answers): return alpha # + ## load dataset and call function to find alpha # + ## implement random QA model ## load dataset ## find F1 and EM on Dev Set # - # ##### F1 Score on Dev Set: # ##### EM on Dev Set: # ## RNN Based Model # ### 2.1.1 Diagram of Baseline from IPython.display import Image Image('https://img.mukewang.com/5af3eb2400015bd813980728.png') # ### 2.1.2 Performance of Baseline # + ### code to load your model and evaluate on the dev set ### model = def evaluate_on_dev_set(model, devloader): return f1_score, em_score # - # ##### F1 Score on Dev Set: # ##### EM on Dev Set: # ## Improving the Baseline # ### 2.2.1 Diagram of your architecture Image('https://img.mukewang.com/5af3eb2400015bd813980728.png') # ### 2.2.2 Performance of your Architecture # + ## load your saved model # evaluate_on_dev_set(your_model, dev_set) # - # ##### F1 Score on Dev Set: # ##### EM on Dev Set: # ## 3 Fine Tuning Bert # + ## load your saved model # evaluate_on_dev_set(your_bert_model, dev_set) # - # ##### F1 Score on Dev Set: # ##### EM on Dev Set: # ## 4 Analysis # Example 1 # # Example 2 # # Example 3
3,535
/DAV Assignment.ipynb
249de23203c234542a80e5430a25e2227c6dfa51
[]
no_license
swaraj70/FIFA-19-DATA-ANALYTICS-AND-VISUALIZATION
https://github.com/swaraj70/FIFA-19-DATA-ANALYTICS-AND-VISUALIZATION
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Reference data accuracy assessment by Radiant Earth # # Radiant Earth is conducting an accuracy assessment of DE Africa cropmask reference data using the airbus high-res satellite archive. This notebook produces a confusion matrix between DE AFrica's labels and Radiant Earth's labels. # # Inputs will be: # # 1. `<AEZ-region_RE_sample_validation.geojson>` : The results from collecting training data in the CEO tool # # Output will be: # 1. A `confusion error matrix` containing Overall, Producer's, and User's accuracy, along with the F1 score. # # *** import pandas as pd import numpy as np import seaborn as sn import geopandas as gpd import matplotlib.pyplot as plt from sklearn.metrics import f1_score # ## Analysis Parameters folder = 'data/training_validation/collect_earth/central/' gjson = 'data/training_validation/collect_earth/central/Central_region_RE_sample_validated.geojson' # ### Load the dataset #ground truth shapefile df = gpd.read_file(gjson) df.head() # ### Clean up dataframe # #rename columns df = df.rename(columns={'Class':'Prediction', 'Validation_Class':'Actual'}) df.head() # *** # ### Reclassify prediction & actual columns # # 1 = crop, # 0 = non-crop # + df['Prediction'] = np.where(df['Prediction']=='non-crop', 0, df['Prediction']) df['Prediction'] = np.where(df['Prediction']=='crop', 1, df['Prediction']) df['Actual'] = np.where(df['Actual']=='non-crop', 0, df['Actual']) df['Actual'] = np.where(df['Actual']=='crop', 1, df['Actual']) df.head() # - # ### Generate a confusion matrix with all classes # + confusion_matrix = pd.crosstab(df['Actual'], df['Prediction'], rownames=['Actual'], colnames=['Prediction'], margins=True) confusion_matrix # - # ### Reclassify into a binary assessment # + counts = df.groupby('Actual').count() print("Total number of samples: " + str(len(df))) print("Number of 'mixed' samples: "+ str(counts[counts.index=='mixed']['Prediction'].values[0])) # print("Number of 'N/A' samples: "+ str(counts[counts.index=='N/A']['Prediction'].values[0])) print("Dropping 'mixed' and 'N/A' samples") df = df.drop(df[df['Actual']=='mixed'].index) df = df.drop(df[df['Actual']=='N/A'].index) # - # --- # # ### Recreate confusion matrix # + confusion_matrix = pd.crosstab(df['Actual'], df['Prediction'], rownames=['Actual'], colnames=['Prediction'], margins=True) confusion_matrix # - # ### Calculate User's and Producer's Accuracy # `Producer's Accuracy` confusion_matrix["Producer's"] = [confusion_matrix.loc[0, 0] / confusion_matrix.loc[0, 'All'] * 100, confusion_matrix.loc[1, 1] / confusion_matrix.loc[1, 'All'] * 100, np.nan] # `User's Accuracy` # + users_accuracy = pd.Series([confusion_matrix[0][0] / confusion_matrix[0]['All'] * 100, confusion_matrix[1][1] / confusion_matrix[1]['All'] * 100] ).rename("User's") confusion_matrix = confusion_matrix.append(users_accuracy) # - # `Overall Accuracy` confusion_matrix.loc["User's","Producer's"] = (confusion_matrix.loc[0, 0] + confusion_matrix.loc[1, 1]) / confusion_matrix.loc['All', 'All'] * 100 # `F1 Score` # # The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall), and is calculated as: # # $$ # \begin{aligned} # \text{Fscore} = 2 \times \frac{\text{UA} \times \text{PA}}{\text{UA} + \text{PA}}. # \end{aligned} # $$ # # Where UA = Users Accuracy, and PA = Producer's Accuracy # + fscore = pd.Series([(2*(confusion_matrix.loc["User's", 0]*confusion_matrix.loc[0, "Producer's"]) / (confusion_matrix.loc["User's", 0]+confusion_matrix.loc[0, "Producer's"])) / 100, f1_score(df['Actual'].astype(np.int8), df['Prediction'].astype(np.int8), average='binary')] ).rename("F-score") confusion_matrix = confusion_matrix.append(fscore) # - # ### Tidy Confusion Matrix # # * Limit decimal places, # * Add readable class names # * Remove non-sensical values # round numbers confusion_matrix = confusion_matrix.round(decimals=2) # rename booleans to class names confusion_matrix = confusion_matrix.rename(columns={0:'Non-crop', 1:'Crop', 'All':'Total'}, index={0:'Non-crop', 1:'Crop', 'All':'Total'}) #remove the nonsensical values in the table confusion_matrix.loc["User's", 'Total'] = '--' confusion_matrix.loc['Total', "Producer's"] = '--' confusion_matrix.loc["F-score", 'Total'] = '--' confusion_matrix.loc["F-score", "Producer's"] = '--' confusion_matrix # ### Export csv # + # confusion_matrix.to_csv(folder+ 'radiant_earth_reference_data_accuracy_results.csv') # - pend_dict_others) #others_df['total_payments'] = others_df['value_reported'] df_gemsjade = pd.concat([df_gemsjade, others_df]) df_gemsjade # - df_gemsjade['name_of_revenue_stream'] = df_gemsjade['name_of_revenue_stream'].replace({'Other significant payments (&gt; 50,000 USD)': 'Other significant payments (> 50,000 USD)'}) company_totals = df_gemsjade.pivot_table(index=['Company_name_cl'], aggfunc='sum')['value_reported'] company_totals = company_totals.to_frame() company_totals.rename(columns={'value_reported': 'total_payments'}, inplace=True) company_totals.reset_index(level=0, inplace=True) company_totals.sort_values(by=['total_payments'], ascending = False, inplace=True) company_totals df_gemsjade = pd.merge(df_gemsjade, company_totals, on='Company_name_cl') # ## Remove negative payments for Sankey df_gemsjade = df_gemsjade[df_gemsjade["value_reported"] > 0] df_gemsjade = df_gemsjade.sort_values(by=['total_payments'], ascending=False) df_gemsjade.drop(['Unnamed: 0'], axis=1) df_gemsjade df_gemsjade_summary = df_gemsjade[df_gemsjade['Company_name_cl'] != 'Companies not in EITI Reconciliation'] df_gemsjade_summary = df_gemsjade_summary.groupby(['name_of_revenue_stream','paid_to','target_type','type']).sum().reset_index() df_gemsjade_summary['Company_name_cl'] = 'Companies in EITI Reconciliation' df_gemsjade_summary = df_gemsjade[df_gemsjade['Company_name_cl'] == 'Companies not in EITI Reconciliation'] \ .append(df_gemsjade_summary) df_gemsjade_summary # ## Prepare Source-Target-Value dataframe # + links_companies = pd.DataFrame(columns=['source','target','value','type']) # + to_append = df_gemsjade.groupby(['name_of_revenue_stream','paid_to'],as_index=False)['type','value_reported','total_payments'].sum() #to_append["target"] = "Myanmar Gems Enterprise" to_append.rename(columns = {'name_of_revenue_stream':'source', 'value_reported' : 'value', 'paid_to': 'target'}, inplace = True) to_append = to_append.sort_values(by=['value'], ascending = False) to_append['target_type'] = 'entity' links_companies = pd.concat([links_companies,to_append]) print(to_append['value'].sum()) links_companies # + ## Page 239 of 2015-16 Report. Appendix 8: SOEs reconciliation sheets append_dict_transfers = [{'source': 'Myanmar Gems Enterprise', 'type': 'entity', 'target': 'Corporate Income Tax (Inter-Government)', 'value': 53788313000 }, {'source': 'Myanmar Gems Enterprise', 'type': 'entity', 'target': 'Commercial Tax (Inter-Government)', 'value': 15000000 }, {'source': 'Myanmar Gems Enterprise', 'type': 'entity', 'target': 'Production Royalties (Inter-Government)', 'value': 17249087176 }, {'source': 'Myanmar Gems Enterprise', 'type': 'entity', 'target': 'State Contribution (Inter-Government)', 'value': 46833942000 }, {'source': 'Corporate Income Tax (Inter-Government)', 'target_type': 'entity', 'target': 'Internal Revenue Department', 'value': 53788313000 }, {'source': 'Commercial Tax (Inter-Government)', 'target_type': 'entity', 'target': 'Internal Revenue Department', 'value': 15000000 }, {'source': 'Production Royalties (Inter-Government)', 'target_type': 'entity', 'target': 'Department of Mines', 'value': 17249087176 }, {'source': 'State Contribution (Inter-Government)', 'target_type': 'entity', 'target': 'Ministry of Planning and Finance', 'value': 46833942000 }, {'source': 'Myanmar Gems Enterprise', 'type': 'entity', 'target': 'Other Accounts', 'value': 107705106000 }, {'source': 'Other Accounts', 'target_type': 'entity', 'target': 'Ministry of Planning and Finance', 'value': 107705106000 }, {'source': 'Internal Revenue Department', 'type': 'entity', 'target_type': 'entity', 'target': 'Ministry of Planning and Finance', 'value': 393194500968 }] append_dict_transfers_df = pd.DataFrame(append_dict_transfers) links_summary = pd.concat([links_companies, append_dict_transfers_df]) links_govt = append_dict_transfers_df #links = pd.concat([links, append_dict_transfers_df]) # + to_append = df_gemsjade.groupby(['name_of_revenue_stream','Company_name_cl','type'],as_index=False) \ ['value_reported','total_payments'] \ .agg({'value_reported':sum,'total_payments':'first'}) to_append.rename(columns = {'Company_name_cl':'source','name_of_revenue_stream':'target', 'value_reported' : 'value'}, inplace = True) to_append = to_append.sort_values(by=['total_payments'], ascending = False) links_companies = pd.concat([links_companies,to_append]) print(to_append['value'].sum()) #links to_append # + to_append = df_gemsjade_summary.groupby(['name_of_revenue_stream','Company_name_cl','type'],as_index=False) \ ['value_reported','total_payments'] \ .agg({'value_reported':sum,'total_payments':'first'}) to_append.rename(columns = {'Company_name_cl':'source','name_of_revenue_stream':'target', 'value_reported' : 'value'}, inplace = True) to_append = to_append.sort_values(by=['total_payments'], ascending = False) links_summary = pd.concat([links_summary,to_append]) links_summary # + def prep_nodes_links(links): unique_source = links['source'].unique() unique_targets = links['target'].unique() unique_source = pd.merge(pd.DataFrame(unique_source), links, left_on=0, right_on='source', how='left') unique_source = unique_source.filter([0,'type']) unique_targets = pd.merge(pd.DataFrame(unique_targets), links, left_on=0, right_on='target', how='left') unique_targets = unique_targets.filter([0,'target_type']) unique_targets.rename(columns = {'target_type':'type'}, inplace = True) unique_list = pd.concat([unique_source[0], unique_targets[0]]).unique() unique_list = pd.merge(pd.DataFrame(unique_list), \ pd.concat([unique_source, unique_targets]), left_on=0, right_on=0, how='left') unique_list.drop_duplicates(subset=0, keep='first', inplace=True) replace_dict = {k: v for v, k in enumerate(unique_list[0])} unique_list return [unique_list,replace_dict] #unique_list = pd.concat([links['source'], links['target']]).unique() #replace_dict = {k: v for v, k in enumerate(unique_list)} # - [unique_list_summary,replace_dict_summary] = prep_nodes_links(links_summary) [unique_list_companies,replace_dict_companies] = prep_nodes_links(links_companies) [unique_list_govt,replace_dict_govt] = prep_nodes_links(links_govt) def write_nodes_links(filename,unique_list,replace_dict,links): links_replaced = links.replace({"source": replace_dict,"target": replace_dict}) nodes = pd.DataFrame(unique_list) nodes.rename(columns = {0:'name'}, inplace = True) nodes_json= pd.DataFrame(nodes).to_json(orient='records') links_json= pd.DataFrame(links_replaced).to_json(orient='records') data = { 'links' : json.loads(links_json), 'nodes' : json.loads(nodes_json) } data_json = json.dumps(data) data_json = data_json.replace("\\","") #print(data_json) #with open('sankey_data.json', 'w') as outfile: # json.dump(data_json, outfile) text_file = open(filename + ".json", "w") text_file.write(data_json) text_file.close() write_nodes_links("sankey_data_2015-16_summary",unique_list_summary,replace_dict_summary,links_summary) write_nodes_links("sankey_data_2015-16_companies",unique_list_companies,replace_dict_companies,links_companies) write_nodes_links("sankey_data_2015-16_govt",unique_list_govt,replace_dict_govt,links_govt)
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/.ipynb_checkpoints/test_ML_sandbox-checkpoint.ipynb
085801b67c1a2212742474a5b5136f2895f3a7b0
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sunnyCUD/one_camera_knee_angle
https://github.com/sunnyCUD/one_camera_knee_angle
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- jobs = [] with open("n_jobs.txt") as f: s = f.readline() while True: s = f.readline() if s is None or s is "": break w, l = int(s.split(" ")[0]), int(s.split(" ")[1]) jobs.append((w, l)) # ### N_JOBS # Your task in this problem is to run the greedy algorithm that # schedules jobs in decreasing order of the difference (weight - length). # Recall from lecture that this algorithm is not always optimal. # *IMPORTANT*: if two jobs have equal difference (weight - length), # you should schedule the job with higher weight first. # Beware: if you break ties in a different way, you are likely to get the wrong answer. You should report the sum of weighted completion times of the resulting schedule --- a positive integer --- in the box below. # # non_opt_jobs = [] for j in jobs: non_opt_jobs.append((j[0], j[1], j[0] - j[1])) non_opt_jobs.sort(key = lambda x: (x[2], x[0]), reverse=True) comp_time = 0 length = 0 for j in non_opt_jobs: length += j[1] comp_time += j[0] * length print(f" the weighted completion time is {comp_time}") # ### N_JOBS_OPTIMAL # # Your task now is to run the greedy algorithm that schedules jobs (optimally) in decreasing order of the ratio (weight/length). # In this algorithm, it does not matter how you break ties. # You should report the sum of weighted completion times of the resulting schedule --- a positive integer --- in the box below. opt_jobs = [] for j in jobs: opt_jobs.append((j[0], j[1], j[0] / j[1])) opt_jobs.sort(key = lambda x: (x[2], x[0]), reverse=True) comp_time = 0 length = 0 for j in opt_jobs: length += j[1] comp_time += j[0] * length print(f" the weighted completion time is {comp_time}") # ### PRIM's MST # # Your task is to run Prim's minimum spanning tree algorithm on this graph. You should report the overall cost of a minimum spanning tree --- an integer, which may or may not be negative --- in the box below. # # IMPLEMENTATION NOTES: This graph is small enough that the straightforward O(mn) time implementation of Prim's algorithm should work fine. # # OPTIONAL: For those of you seeking an additional challenge, try implementing a heap-based version. The simpler approach, which should already give you a healthy speed-up, is to maintain relevant edges in a heap (with keys = edge costs). The superior approach stores the unprocessed vertices in the heap, as described in lecture. Note this requires a heap that supports deletions, and you'll probably need to maintain some kind of mapping between vertices and their positions in the heap. from sortedcontainers import SortedDict import random import sys graph = None with open("prim_edges.txt") as f: graph = {int(x): [] for x in range(1, int(f.readline().split(" ")[0]) + 1)} while True: s = f.readline() if s is None or s is "": break e1, e2, l = int(s.split(" ")[0]), int(s.split(" ")[1]), int(s.split(" ")[2]) graph[e1].append((e2,l)) graph[e2].append((e1,l)) class PrimDict(dict): def __setitem__(self, key, value): if self.get(key, sys.maxsize) > value: dict.__setitem__(self, key, value) visited = set() distances = PrimDict() cost = 0 next_node = random.randint(0, len(graph.keys()) - 1) visited.add(next_node) while visited != graph.keys(): for node in graph[next_node]: if node[0] not in visited: distances[node[0]] = node[1] while visited != graph.keys(): smallest_item = min(distances.items(), key = lambda x: x[1]) next_node = smallest_item[0] if next_node not in visited: visited.add(next_node) cost += smallest_item[1] del distances[next_node] break print(f" overall cost is {cost}")
4,061
/Lists_in Python_Day1.ipynb
7a719cc1c38cb9c1c2101ab1dda7731916c89860
[]
no_license
Sahzadah/PythonFiles
https://github.com/Sahzadah/PythonFiles
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + _cell_guid="3caaab12-4abb-4aed-b2b5-638146f15c6d" _uuid="b574f83f-bdff-46f8-b5a8-aa119b074ffc" jupyter={"outputs_hidden": false} papermill={"duration": 4.845353, "end_time": "2021-09-10T05:28:45.734303", "exception": false, "start_time": "2021-09-10T05:28:40.888950", "status": "completed"} import math import matplotlib.pyplot as plt import numpy as np import shutil from time import time import torch from copy import copy from glob import glob from PIL import Image, ImageFile from torch import nn from torch import optim from torch.autograd import Variable from torch.utils.data import random_split, DataLoader from torchvision import datasets, transforms, models from tqdm import tqdm import recgn_utils # utility script # + _cell_guid="710ff97d-5e7e-42fb-bbe1-2cf8adfaccad" _uuid="29035e2f-897e-4da2-9aa4-6e3f69252a97" jupyter={"outputs_hidden": false} papermill={"duration": 0.07208, "end_time": "2021-09-10T05:28:45.822675", "exception": false, "start_time": "2021-09-10T05:28:45.750595", "status": "completed"} # Verifica se CUDA está disponível gpu_on = torch.cuda.is_available() if not gpu_on: print('Use a CPU. CUDA não está disponível...') else: print('Use a GPU. CUDA está disponível...') # + _cell_guid="d0664a9c-63d9-43f7-ba13-b630c3c505b5" _uuid="49c4a59d-990f-4e91-9817-e4ace4eb5006" jupyter={"outputs_hidden": false} papermill={"duration": 0.024969, "end_time": "2021-09-10T05:28:45.863978", "exception": false, "start_time": "2021-09-10T05:28:45.839009", "status": "completed"} # Configure alguns parametros: # Path raiz do dataset rcgn_dir = '../input/dog-breed-recognition-v3/dogs/recognition' # Numero de classes a serem selecionadas para enroll (de 1 a 20) num_classes_enroll = 5 # Numero medio de imagens selecionadas para enroll, em cada classe num_img_enroll = 11 # Total de imagens utilizadas no enroll total_img_enroll = num_classes_enroll * num_img_enroll # Tamanho do dataset batch_size = num_classes_enroll #TBC # Numero de workers num_workers = 0 # Numero de epocas num_epochs = 25 # Metadados e Modelo treinado com fine-tune, na Parte-1 checkpoint_path = "../input/modelp1v9ep15nllloss/model_epoch_15_acc_84.4318_loss_0.5199.pth" # + jupyter={"outputs_hidden": true} papermill={"duration": 0.625949, "end_time": "2021-09-10T05:28:46.506204", "exception": false, "start_time": "2021-09-10T05:28:45.880255", "status": "completed"} # Paths contendo imagens originais para treino e teste data_dir = rcgn_dir + '/enroll' test_dir = rcgn_dir + '/test' # Plota quantidade ordenada de imagens por classe, para verficar se estao desbalanceadas _, _ = recgn_utils.check_class(data_dir) # + papermill={"duration": 0.027632, "end_time": "2021-09-10T05:28:46.551671", "exception": false, "start_time": "2021-09-10T05:28:46.524039", "status": "completed"} # Lista nomes de todas as classes full_class_names = [item.split('/')[-2] for item in sorted(glob(data_dir + "/*/"))] print(f'> Lista das classes disponiveis para enroll = {full_class_names}\n') # Seleciona subset de classes para enroll, e.g. classes de 1 a 5 partial_class_names = full_class_names[0:num_classes_enroll] print(f'> Lista das classes selecionadas para enroll = {partial_class_names}') # + papermill={"duration": 0.026681, "end_time": "2021-09-10T05:28:46.596563", "exception": false, "start_time": "2021-09-10T05:28:46.569882", "status": "completed"} # Seleciona as classes para treinamento/validacao no enroll # Copia arquivos apenas das classes selecionadas def sel_class(class_names, from_path, to_path): print(f'Copiando {len(class_names)} classes de {from_path}/enroll/ para {to_path}/enroll/') print(f'Copiando {len(class_names)} classes de {from_path}/test/ para {to_path}/test/') for name in class_names: old_path_train = (from_path + '/enroll/' + name) old_path_test = (from_path + '/test/' + name) new_path_train = (to_path + '/enroll/' + name) new_path_test = (to_path + '/test/' + name) shutil.copytree(old_path_train, new_path_train) shutil.copytree(old_path_test, new_path_test) # + papermill={"duration": 5.614787, "end_time": "2021-09-10T05:28:52.228377", "exception": false, "start_time": "2021-09-10T05:28:46.613590", "status": "completed"} # Define diretorios para transferencia de imagens new_rcgn_dir = '/kaggle/working/rcgn_sample' # Esvazia diretorio de destino (i.e. apaga e recria) # !rm -rf {new_rcgn_dir} # !mkdir {new_rcgn_dir} # Realiza transferencia das classes selecionadas, para novo diretorio de desino from_path = rcgn_dir to_path = new_rcgn_dir sel_class(partial_class_names, from_path, to_path) # Lista novo diretorio de imagens # !ls {new_rcgn_dir} # + papermill={"duration": 0.028428, "end_time": "2021-09-10T05:28:52.275021", "exception": false, "start_time": "2021-09-10T05:28:52.246593", "status": "completed"} # NOVOS paths contendo imagens para treino (enroll) e teste data_dir = new_rcgn_dir + '/enroll' test_dir = new_rcgn_dir + '/test' # Lista nomes das classes selecionadas partial_class_names = [item.split('/')[-2] for item in sorted(glob(data_dir + "/*/"))] print(f'> Lista das classes selecionadas para enroll = {partial_class_names}\n') # Calcula numero de classes num_classes_enroll = len(partial_class_names) print(f'> Numero de classes selecionadas = {num_classes_enroll}') # + _cell_guid="da8c9060-33e8-433f-a803-876bcfeed4a1" _uuid="060147b4-9e26-4107-8748-0e6ef7279774" papermill={"duration": 0.033633, "end_time": "2021-09-10T05:28:52.327822", "exception": false, "start_time": "2021-09-10T05:28:52.294189", "status": "completed"} # Cria datasets de imagens de treino e teste ds_train = datasets.ImageFolder(data_dir) ds_test = datasets.ImageFolder(test_dir) # Calcula total de imagens total_img = len(ds_train) print(f'> Numero de total de imagens disponiveis para enroll = {total_img}') # + _cell_guid="97f1429b-0543-4f32-8361-16d38b3fed3c" _uuid="18bd37ea-3178-4289-8576-f45c2139beba" jupyter={"outputs_hidden": false} papermill={"duration": 0.044642, "end_time": "2021-09-10T05:28:52.391229", "exception": false, "start_time": "2021-09-10T05:28:52.346587", "status": "completed"} # Define tamanho dos datasets de acordo com parametros iniciais # Isto eh: total_img_enroll = num_classes_enroll * num_img_enroll train_size = math.floor(num_img_enroll * 0.9) * num_classes_enroll valid_size = math.ceil(num_img_enroll * 0.1) * num_classes_enroll rest_size = total_img - train_size - valid_size train_set, val_set, _ = random_split(ds_train, [train_size, valid_size, rest_size], torch.Generator().manual_seed(2147483647)) print(f'Numero final de imagens de treinamento: {len(train_set)}') print(f'Numero final de imagens de validacao: {len(val_set)}') print(f'Numero final de imagens para enroll = {train_size + valid_size}') # + _cell_guid="d7a9da13-07cd-484e-91d3-696ff90b4ae2" _uuid="b20b67be-8f7a-477f-b0f0-7dbd7346e9ac" jupyter={"outputs_hidden": false} papermill={"duration": 0.035458, "end_time": "2021-09-10T05:28:52.445803", "exception": false, "start_time": "2021-09-10T05:28:52.410345", "status": "completed"} # Define valores mean e std para normalizar as imagens # TODO: Valores baseados no ImageNet. Idealmente calcular o mean e std do dataset original img_mean = np.array((0.485, 0.456, 0.406)) img_std = np.array((0.229, 0.224, 0.225)) # Define e aplica transformações nos datasets de treinamento, validação e teste train_set.dataset = copy(ds_train) train_set.dataset.transform = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomRotation(10), transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(img_mean, img_std)]) val_set.dataset.transform = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(img_mean, img_std)]) test_transforms = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(img_mean, img_std)]) test_set = datasets.ImageFolder(test_dir, transform=test_transforms) print(f'Numero final de imagens de testes:', len(test_set)) # Cria conjunto de loaders train_loader = DataLoader(train_set, batch_size=batch_size, num_workers=num_workers, shuffle=True) valid_loader = DataLoader(val_set, batch_size=batch_size, num_workers=num_workers, shuffle=True) test_loader = DataLoader(test_set, batch_size=batch_size, num_workers=num_workers, shuffle=True) loaders = {'train': train_loader, 'valid': valid_loader, 'test': test_loader} # + _cell_guid="ab120cb5-2ead-4afb-aab9-8d11d386cfe7" _uuid="5e83ad2c-ffd9-40dc-a691-eea546ccb6b4" jupyter={"outputs_hidden": false} papermill={"duration": 0.421651, "end_time": "2021-09-10T05:28:52.886345", "exception": false, "start_time": "2021-09-10T05:28:52.464694", "status": "completed"} # Exibe algumas imagens do loader com correspondentes labels meanm = np.mean(img_mean) stdm = np.mean(img_std) recgn_utils.sample_img_show(train_loader, partial_class_names, meanm, stdm) # + papermill={"duration": 0.035669, "end_time": "2021-09-10T05:28:52.948538", "exception": false, "start_time": "2021-09-10T05:28:52.912869", "status": "completed"} # Funcao que carrega modelo treinado com fine-tune, na Parte-1 def load_model(checkpoint_path): chpt_dict = torch.load(checkpoint_path, map_location=torch.device('cpu')) chpt_out_features = 100 # Recria model class model = models.resnet152(pretrained=True) classifier = nn.Sequential(nn.Linear(model.fc.in_features, 512), nn.ReLU(), nn.Linear(512, 256), nn.ReLU(), nn.Dropout(0.5), nn.Linear(256, chpt_out_features), nn.LogSoftmax(dim=1)) model.fc = classifier model.load_state_dict(chpt_dict['model_state_dict']) return model # + papermill={"duration": 12.225649, "end_time": "2021-09-10T05:29:05.200572", "exception": false, "start_time": "2021-09-10T05:28:52.974923", "status": "completed"} # Carrega modelo treinado com fine-tune, na Parte-1 model = load_model(checkpoint_path) print(model.fc) # + papermill={"duration": 5.440076, "end_time": "2021-09-10T05:29:10.669569", "exception": false, "start_time": "2021-09-10T05:29:05.229493", "status": "completed"} # Cria novo classificador # Congela camadas para treinamento (feature extraction) for param in model.parameters(): param.requires_grad = False classifier = nn.Sequential(nn.Linear(2048, 512), # model.fc.in_features = 2048 nn.ReLU(), nn.Linear(512, 256), nn.ReLU(), nn.Dropout(0.5), nn.Linear(256, num_classes_enroll), nn.LogSoftmax(dim=1)) model.fc = classifier # Define loss function (categorical cross-entropy) # https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html criterion = nn.NLLLoss() # Define otimizador de treinamento e diferentes taxas de aprendizado ao longo da rede # https://pytorch.org/docs/stable/generated/torch.optim.SGD.html optimizer = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9) if gpu_on: model.cuda() # + _cell_guid="29a0fcd4-84ac-4465-8ff9-7be71e5554b4" _uuid="fec29ae4-8e12-48db-8bc5-9e381ff0e064" jupyter={"outputs_hidden": false} papermill={"duration": 55.511081, "end_time": "2021-09-10T05:30:06.210717", "exception": false, "start_time": "2021-09-10T05:29:10.699636", "status": "completed"} # Treina modelo start = time() # Considera 3 condicoes de parada: # 1) Valid loss medio crescente # 2) Numero de epocas = num_epochs # 3) Tempo maximo = total_img_enroll - 1 max_time = total_img_enroll - 1 model = recgn_utils.train_model(model, criterion, optimizer, loaders, num_epochs, gpu_on, max_time) end = time() print(f'Tempo total (aprox.) = {end - start} segundos') print(f'Tempo medio por imagem (aprox.) = {(end-start)/total_img} segundos') # + _cell_guid="f0c41353-9281-40af-9122-2e21a2208f39" _uuid="2e15d460-ff39-4313-98fd-751e52e4e675" jupyter={"outputs_hidden": false} papermill={"duration": 31.371689, "end_time": "2021-09-10T05:30:39.346183", "exception": false, "start_time": "2021-09-10T05:30:07.974494", "status": "completed"} # Testa modelo treinado com loader de testes prob_pass, prob_fail = recgn_utils.test_model(model, criterion, test_loader, gpu_on) # + _cell_guid="31e7dd36-19ca-4d78-bb6b-d55ac455476e" _uuid="2d4ff006-af72-40f5-b718-52fa3847e342" jupyter={"outputs_hidden": false} papermill={"duration": 0.568605, "end_time": "2021-09-10T05:30:40.045645", "exception": false, "start_time": "2021-09-10T05:30:39.477040", "status": "completed"} # Plota distribuição de probabilidades nos cassos de pass e fail do teste plt.hist(prob_fail, bins = np.arange(0,1.05,0.05)) plt.hist(prob_pass, bins = np.arange(0,1.05,0.05), alpha = 0.7) labels= ["Fail","Pass"] plt.legend(labels) plt.xlabel('Probability') plt.ylabel('Frequency') plt.title('Max outputs') # + _cell_guid="7bc667bf-0ef1-4960-be06-f023e470d9cc" _uuid="213f6709-ea53-4e23-b902-47eb8b41cef7" jupyter={"outputs_hidden": false} papermill={"duration": 0.565477, "end_time": "2021-09-10T05:30:40.807650", "exception": false, "start_time": "2021-09-10T05:30:40.242173", "status": "completed"} # Seleciona uma foto aleatoria e testa o modelo enroll_dir = './rcgn_sample/test/*/*' enroll_data = np.array(glob(enroll_dir)) img_path = np.random.choice(enroll_data, 1)[0] recgn_utils.imshow(img_path) print(f'Foto selecionada aleatoriamente em:{img_path}') pred_breed, pred_prob = recgn_utils.predict_breed_dog(model, partial_class_names, img_mean, img_std, img_path, gpu_on) print(f'Probabilidade de {pred_prob*100:.2f}% de ser um {pred_breed}')
14,640
/Programming for Geographical Information Analysis Advanced Skills - Assessment Two.ipynb
72f1cf8d8f7db582496f86aa005604fed1c78700
[ "MIT" ]
permissive
annabelelizabethwhipp/Programming-for-Geographical-Information-Analysis-Advanced-Skills-Assessment-2
https://github.com/annabelelizabethwhipp/Programming-for-Geographical-Information-Analysis-Advanced-Skills-Assessment-2
0
1
null
2019-04-01T12:29:48
2019-04-01T12:28:07
Jupyter Notebook
Jupyter Notebook
false
false
.py
615,057
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Time Series Analysis # This project aims to analyse time series from a Wi-Fi sensor located in the town of Otley, West Yorkshire. For the purpose of this project data from one sensor has been utilised. The project utilises a number of techniques, including: # # - Visualising time series data # # # - Calculating the moving average based on a 30 day period # # # - First order differencing # # # - Anomaly detection # # # - ARIMA modelling # # # - Stepwise modelling # # # The purpose of the project is to utilise techniques which provide an insight into the temporal fluctuations within the dataset. # # # The Wi-Fi sensor data represents footfall and is for the year 2016, covering 1st Janauary - 31st December. The data was originally at an hourly level but was aggregated to a daily level. # # Please note that the data must be in date order before proceeding # - Importing the packages # + # Import packages import sklearn import numpy as np import pandas as pd import seaborn as sns import scipy.stats as scs import statsmodels.api as sm import matplotlib.pyplot as plt import statsmodels.tsa.api as smt import statsmodels.formula.api as smf # %matplotlib inline sns.set() from pandas import Series from itertools import product from tqdm import tqdm_notebook from scipy.optimize import minimize from sklearn.metrics import r2_score from pmdarima.arima import auto_arima from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_absolute_error from dateutil.relativedelta import relativedelta from sklearn.model_selection import TimeSeriesSplit from statsmodels.tsa.arima_model import ARIMAResults # - # # Import the dataset with .read_csv() and check the first 5 rows with .head() # # - We can see that there are 2 columns of data: date and count # # # - Date is the timestamp from the day, month and year the data was collected and count is the footfall recorded by all the cameras combined on each day # + # reading in the data which is saved as a CSV file and giving it the name 'df' df = pd.read_csv('FootfallData.csv') # printing the first 5 rows of the dataframe df.head() # - # ## Using the .info() method to check the data types, number of rows, etc # retrieving information about the dataframe df.info() # # Wrangling the data # - The columns of the dataframe are renamed so that they have no whitespaces # # - To do this a list of what the columns are called is reassigned to df.columns # + # the names of the column headings are specified as 'date' and 'count' df.columns = ['date', 'count'] """ the first 5 rows are printed in order to check the headings are displayed correctly """ df.head() # - # - The date column is turned into a DateTime data type and is made the index of the dataframe # + # the 'date' column of the dataframe is converted into a datetime data type df.date = pd.to_datetime(df.date) # the date is set as the index of the dataframe df.set_index('date', inplace=True) # - # printing the first 5 values of the dataframe df.head() # # Exploratory Data Analysis (EDA) # - Plotting the data as a time series # # - Arguments can be specified such as figsize, linewidth and fontsize # # - A label can be applied to the x-axis and the font size of the label can be specified # + """ plotting the data frame, specifying the size of the figure, line width and font size """ df.plot(figsize=(20,10), linewidth=3, fontsize=20) """" adding a label to the x-axis of the plot with the name 'Time' and specifying that it uses font size 20 """ plt.xlabel('Time', fontsize=20); # - # - Above is a plot of the data in which we can see the fluctuations in footfall across the period of the year. Generally, monthly fluctuations can be identified as at the end of one month/the beginning of another there is a drop in footfall. The exception is for the month of February in which there appears to be two significant peaks, one towards towards the second week fo the month, and another which occurs near the end of the month. From this plot there is one extreme value which can be identified, which takes places on the 29th June. This coincides with a popular annual cycle race in Otley town centres and attracts a large number of visitors. It is challenging to establish any pattern which occurs across the course of the year from this plot. # # Trends and Seasonality in Time Series Data # # - Identifying trends in time series data # # # - There are several ways to identify trends in time series data # # # - One way is to take the rolling average # # # - This means that for each time point you take the average of the points either side of it # # # - The number of points is specified by a window size which needs to be selected # # + """ specifying the column of the dataframe from which we want to calculate the rolling average """ count = df[['count']] """ here we calulate the rolling mean with a window length of 30 this means that a monthly rolling mean is created the size of the figure is then specified """ rolling_periods = [30] for p in rolling_periods: count.rolling(window=p).mean().plot(figsize=(20,10), linewidth=3, fontsize=20) plt.show() # - # Here we can see that there is a distinct trend in the data with increases occuring from April onwards until a peak is reached in mid-October. Figures start to fall once again after the first week in December. There is a significant increase which occurs during the final week of February and remains at over 800 counts until the last week of March in which there is a rapid decrease. Rolling averages can be extremely useful as they smooth out trends which appear in the plot of the time series data. General trends over the period of 2016 can be identified and the findings can be utilised by both the private and the public sector. # # Seasonal Patterns in Time Series Data # Seasonal components of time series data can be analysed by removing the trend from a time series so that seasonsality can be investigated more easily. # # # One way to remove the trend is called differencing, where you look at the difference between successive date points. This is called first order differencing. This method is demonstrated below: # #### First order differencing # The diff() and plot() methods are utilised to compute and plot the first order difference of the counts """ here we are calculating the difference between two counts for different time points then plotting the values of those differences a value of 0 would mean that there was no difference between a count and the count for the previous day """ count.diff().plot(figsize=(20,10), linewidth=3, fontsize=20) plt.xlabel('Year', fontsize=20); # - This method measures the difference between counts at each time point, for example, the difference between the count on the 1st of January and the 2nd of January. Negative values occur when there is a decrease between time points. Positive values occur when there is an increase in counts in between data points. # # # - First order differencing is useful for turning the time series into a stationary time series # # # - Stationary time series are useful because many time series forecasting methods are based on the assumption that the time series is approximately stationary # - First order differencing is useful for turning the time series into a stationary time series # # - Stationary time series are useful because many time series forecasting methods are based on the assumption that the time series is approximately stationary # # Below the first difference ordering values are printed: # + """ printing the values of count.diff (the difference between the data for two time points) """ x = count.diff() # making a dataframe called 'stationary' with the data 'x' stationary = pd.DataFrame(data = x) # showing the dataframe 'stationary' stationary # - # First order differencing is a useful tool for a number of reasons. Firstly, it makes the data stationary which can be useful for a range of time series analysis techniques. Additionally, we are able to see the changes between days which can aid the detection of trends, especially if we want to investigate specific events. # # Anomaly detection # - Anomaly detection detects data points within a dataset that do not fit well with the rest of the data # # # - Below a simple anomaly detection system is created using the moving average # creating a function def plotMovingAverage(series, window, plot_intervals=True, scale=1.96, plot_anomalies=True): """ series - dataframe with timeseries window - rolling window size plot_intervals - show confidence intervals plot_anomalies - show anomalies """ # specifying the moving average also referred to as the rolling mean rolling_mean = series.rolling(window=window).mean() # plotting the figure plt.figure(figsize=(15,5)) # plotting the figure title plt.title("Moving average\n window size = {}".format(window)) # plotting the rolling mean plt.plot(rolling_mean, "g", label="Rolling mean trend") # Plot confidence intervals for smoothed values (the moving average) if plot_intervals: mae = mean_absolute_error(series[window:], rolling_mean[window:]) deviation = np.std(series[window:] - rolling_mean[window:]) lower_bond = rolling_mean - (mae + scale * deviation) upper_bond = rolling_mean + (mae + scale * deviation) plt.plot(upper_bond, "r--", label="Upper Bond / Lower Bond") plt.plot(lower_bond, "r--") # Having the intervals, find abnormal values if plot_anomalies: anomalies = pd.DataFrame(index=series.index, columns=series.columns) anomalies[series<lower_bond] = series[series<lower_bond] anomalies[series>upper_bond] = series[series>upper_bond] plt.plot(anomalies, "ro", markersize=10) # plotting the labels, legend and the grid markings plt.plot(series[window:], label="Counts") plt.legend(loc="upper left") plt.grid(True) # this dectects if we have a 50% change in footfall values count.iloc[-50] = count.iloc[-50] * 0.5 """ plotting the moving average specifying a window size of 30 a window size of 30 was chosen to reflect the monthly patterns which occur within the dataset the number '30' represents the number of days within the month """ plotMovingAverage(count, 30) # - 6 anomalies were identified # - The model did not just capture changes between months due to seasonality, therefore it is likely that there may be underlying reasosns for these anomalies. # - The 29th of June is highlighted as a significant peak, this coincides with the annual cycling race which takes place in Otley town centre. # - There are some dates with very low counts and some of 0, which suggests issues with the Wi-Fi sensors on these dates # # ARIMA modelling # ARIMA models are a form of statistical models commonly utilised for analyzing and forecasting time series data # ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average # - AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations. # # # - I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary. # # # - MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations. # # There are 3 integers used as parameters within ARIMA models: p, d and q. These paramaters account for seasonality, trend and noise within datasets. # # - p: auto-regressive element # # - d: integrated part of the model # # - q: moving average element # + # wrapper around run time error of ARIMA class def __getnewargs__(self): return ((self.endog),(self.k_lags, self.k_diff, self.k_ma)) ARIMA.__getnewargs__ = __getnewargs__ # load data series = Series.from_csv('FootfallData.csv', header=0) # prepare data X = series.values X = X.astype('float32') """ fit model the three values following order represent P, D and Q which are the model parameters the model parameters can be tweaked to change the results """ model = ARIMA(X, order=(2,1,3)) model_fit = model.fit() # save the model model_fit.save('model.pkl') # load the model loaded = ARIMAResults.load('model.pkl') # - # - Below the results of the ARIMA model are printed # # - It summarises coefficient values, z score and p- values # printing a stastical summary of the fit of the ARIMA model print(model_fit.summary()) # The model summary provides a lot of information regarding the ARIMA model. The table in the middle is the coefficients table where the values listes under the heading coef are the weights of each term. # # The coefficient column highlights the weight (importance) of each feature and how each value impacts upon the time series. The coefficient value for the moving average was nearly-1, thus significant. # # The P> column shows the P values. The P values tell us the significance of each feature weight. The MA (moving average)and AR (autoregression) have a P value which is less than 0.05 therefore they should be kept in the model # # ### Plotting the residual errors # The residual errors can be plotted to ensure that there aren't any patterns # + # plot residual errors and the kernel density estimation of the residuals residuals = pd.DataFrame(model_fit.resid) # plot the residuals residuals.plot() # plot the kernel density estimates residuals.plot(kind='kde') # show the plots plt.show() # print summary statistics for the residuals print(residuals.describe()) # - # - The mean of the residuals is close to 0 but as it is not 0 there is still room for improvement in the model # # - The results are distributed normally # ## The same process is now repeated utilising different P, D and Q paramters # + # load data series = Series.from_csv('FootfallData.csv', header=0) # prepare data X = series.values X = X.astype('float32') """ fit model the three values following order represent P, D and Q which are the model parameters the model parameters can be tweaked to change the results """ model = ARIMA(X, order=(2,1,4)) model_fit = model.fit() # save the model model_fit.save('model.pkl') # load the model loaded = ARIMAResults.load('model.pkl') # - # printing a stastical summary of the fit of the ARIMA model print(model_fit.summary()) # + # plot residual errors and the kernel density estimation of the residuals residuals = pd.DataFrame(model_fit.resid) # plot the residuals residuals.plot() # plot the kernel density estimates residuals.plot(kind='kde') # show the plots plt.show() # print summary statistics for the residuals print(residuals.describe()) # - # The new parameters produce a model with a lower AIC and more of the variables have a significant P-value # The mean of the residuals is slightly higher # # Creating a stepwise model # Stepwise models are a method of fitting regression model # # The choice of the predictive variables is carried out by an automatic procedure # # here we are setting up the parameters of the stepwise model stepwise_model = auto_arima(df, start_p=1, start_q=1, # m=7 relates to weekly fluctuations max_p=7, max_q=4, m=7, start_P=0, seasonal=True, trace=True, # don't show warnings suppress_warnings=True, stepwise=True) # only uses stepwise models """ print the AIC values of the stepwise model the lower the value of the AIC, the better the model the AIC of the model with the lowest AIC is printed after the fit name the AIC takes into account the goodness of fit and the simplicity of the model """ print(stepwise_model.aic()) # + # specifying the data which will be included in the train set train = df.loc['2016-01-01':'2016-10-31'] # specifying the data which will be included in the test set test = df.loc['2016-11-01':'2016-12-31'] # - # fit the stepwise model using the train dataframe stepwise_model.fit(train) # print the length of the test dataset len(test) # name a variable called future forecast and assign it to the 61 predicted values future_forecast = stepwise_model.predict(n_periods=61) # print the dataframe future forecast print(future_forecast) # + """ name a variable called future_forecast and create a dataframe which has a column called prediction """ future_forecast = pd.DataFrame(future_forecast,index = test.index,columns=['Prediction']) # link the test data frame with the future_forecast dataframe output_data = pd.concat([test,future_forecast],axis=1) # print the dataframe 'output_data' print(output_data) # + # plot the figure plt.figure() # plot the output_data output_data.plot() # show the plot plt.show() # - # Here we can see that for the month of November the timestep model is able to predict the counts relatively successfully. The temporal spacing of the flucutations in the counts are also predicted for December, however the model clearly does not capture realistic counts. Given the dataset which has been read in, we would not expect figures for December to be accuractely predicted. The impact of Christmas is significant on the counts of footfall, with the number of counts significantly less than in other months.
17,890
/5.12 plot_historical_data.ipynb
3f4e750d28e5737e1febc316fc80aff6fc3f3730
[]
no_license
bjkim2004/crawling2
https://github.com/bjkim2004/crawling2
1
0
null
null
null
null
Jupyter Notebook
false
false
.py
34,163
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- class Node: def __init__(self, key): self.key = key self.left = None self.right = None def insert(self, newKey): newNode = Node(newKey) if(self.key<newNode.key): if(self.right==None): self.right = newNode else: self.right.insert(newKey) else: if(self.left==None): self.left = newNode else: self.left.insert(newKey) #inorder def inorder(self): if(self.left): self.left.inorder() print(self.key) if(self.right): self.right.inorder() #postorder def postorder(self): if(self.left): self.left.inorder() if(self.right): self.right.inorder() print(self.key) #preorder def preorder(self): print(self.key) if(self.left): self.left.inorder() if(self.right): self.right.inorder() # + root = Node(10) root.insert(20) root.insert(30) root.insert(5) root.insert(3) print('inorder:\n') root.inorder() print('postorder:\n') root.postorder() print('preorder:\n') root.preorder() # -
1,549
/Data_analysis_NLP/arxiv_cs_papers_classification.ipynb
5d15509eeef16c7d5c11fb24e6fe275f2c46d02d
[ "MIT" ]
permissive
SaeedPourjafar/ws_2021
https://github.com/SaeedPourjafar/ws_2021
0
0
null
2021-10-14T13:39:22
2021-05-07T10:51:02
HTML
Jupyter Notebook
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="ntfPYT-QdJ8r" colab_type="text" # # Установка PySpark # # + id="ZhqMg1goc8GT" colab_type="code" colab={} # !apt-get install openjdk-8-jdk-headless -qq > /dev/null # + id="xepI4mP2dY8h" colab_type="code" colab={} # !wget -q www-us.apache.org/dist/spark/spark-2.4.5/spark-2.4.5-bin-hadoop2.7.tgz # + id="yfgFEFUFddyh" colab_type="code" outputId="0e05f5ec-7045-49fa-abc2-43c7381ad003" colab={"base_uri": "https://localhost:8080/", "height": 1000} # !tar -xvf spark-2.4.5-bin-hadoop2.7.tgz # + id="vd7Z3YWdd0ka" colab_type="code" colab={} # !pip install -q findspark # + id="1StB7X4Zdj4D" colab_type="code" colab={} import os os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64" os.environ["SPARK_HOME"] = "/content/spark-2.4.5-bin-hadoop2.7" # + id="uBEcPr4Mdq26" colab_type="code" colab={} import findspark findspark.init() from pyspark.sql import SparkSession spark = SparkSession.builder.master("local[*]").getOrCreate() # + [markdown] id="I4L9P9nheCsW" colab_type="text" # # Загрузка данных из CSV # + id="mVFvolGYwg-2" colab_type="code" outputId="5aaaf385-1883-4771-fbc1-816078d2edf2" colab={"base_uri": "https://localhost:8080/", "height": 34} data = spark.read.csv('iris2.csv', inferSchema=True, header=True) data # + id="JeOUHOs-whIW" colab_type="code" outputId="bcf54c0c-443b-4e98-f0a1-f3dc9b887910" colab={"base_uri": "https://localhost:8080/", "height": 102} data.take(5) # + id="zjQyCdpMefBQ" colab_type="code" outputId="9e263570-d8c2-4608-fd1a-9f73f4bb04a8" colab={"base_uri": "https://localhost:8080/", "height": 34} data.count() # + [markdown] id="roRdH8LIesna" colab_type="text" # ## Для каждого типа цветка определите максимальное, минимальное и среднее значение 4-х параметров # + id="lCm0S6akewQn" colab_type="code" outputId="56e611ea-283c-47e5-9680-f132d181baca" colab={"base_uri": "https://localhost:8080/", "height": 102} data.dtypes # + id="MGmZPpd-wzUT" colab_type="code" outputId="8abc0963-0fa2-427d-a9ad-6b91c731c573" colab={"base_uri": "https://localhost:8080/", "height": 153} variety_types = data.dropDuplicates(['variety']) variety_types.show() # + id="gyr7WBhv2kQD" colab_type="code" outputId="39d338ec-5261-4640-c417-a3e68d196aed" colab={"base_uri": "https://localhost:8080/", "height": 187} data_Virginica = data.where(data['variety'] == 'Virginica') data_Virginica.describe().show() # + id="c5rs6sa13ZkZ" colab_type="code" outputId="1c659dfc-98bd-4ea9-d7cb-d16955468d79" colab={"base_uri": "https://localhost:8080/", "height": 187} data_Setosa = data.where(data['variety'] == 'Setosa') data_Setosa.describe().show() # + id="4kbdc42C3tke" colab_type="code" outputId="27c02d89-a567-4efb-843d-098060204fc3" colab={"base_uri": "https://localhost:8080/", "height": 187} data_Versicolor = data.where(data['variety'] == 'Versicolor') data_Versicolor.describe().show() # + [markdown] id="SLKrPsdw4Wgm" colab_type="text" # ## Визуализируйте точечный график (plt.scatter) по каждой паре параметров # + id="cpbj5Vrc4YMW" colab_type="code" colab={} import pandas as pd import matplotlib.pyplot as plt # %matplotlib inline # + id="D3z-GofPw5PQ" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 748} outputId="b1b69a0d-580d-4354-c9fe-b694a1409e8c" import seaborn as sns dataset = data.toPandas() g = sns.pairplot(dataset) # + [markdown] id="ksQgtwVhx5gH" colab_type="text" # ## Попробуйте отделить какой-нибудь тип цветка от всех остальных (сформулируйте правило на основе диаграммы - ЕСЛИ ЗНАЧЕНИЕ ПАРАМЕТРА X БОЛЬШЕ/МЕНЬШЕ Y, ТО ЦВЕТОК СКОРЕЕ ВСЕГО ОТНОСИТСЯ/НЕ ОТНОСИТСЯ К ТИПУ Z) # # + id="pygGxAQIx97H" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 748} outputId="980f53fd-66fd-4b5c-c451-b9f9510530f4" fig = sns.pairplot(data=dataset, hue='variety') plt.show() # + [markdown] id="bzrH55k-zN2N" colab_type="text" # ## Сделайте отдельную колонку для своего предсказания # + id="1Bw5XaXpzQHi" colab_type="code" colab={} from pyspark.sql.functions import udf from pyspark.sql.types import * # + id="9dLDCSHd1TeB" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 459} outputId="9b0407e7-5318-4bf8-84ce-5a4d026bc14e" datatest = data datatest.show() # + id="clYPR1qw1r58" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 459} outputId="c9e2c4ab-abb3-4d11-a1f8-1dbb7da74363" def valueToCategory(value1, value2, value3, value4): if (5.8 >= value1 >= 4.3) and (4.4 >= value2 >= 2.3) and (1.9 >= value3 >= 1.0) and (0.6 >= value4 >= 0.1): return 'Setosa' elif (7.0 >= value1 >= 4.9) and (3.4 >= value2 >= 2.0) and (5.1 >= value3 >= 3.0) and (1.8 >= value4 >= 1.0): return 'Versicolor' elif (7.9 >= value1 >= 4.9) and (3.8 >= value2 >= 2.2) and (6.9 >= value3 >= 4.5) and (2.5 >= value4 >= 1.4): return 'Virginica' else: return 'n/a' udfValueToCategory = udf(valueToCategory, StringType()) df_with = datatest.withColumn("category", udfValueToCategory("sepal_length", "sepal_width", "petal_length","petal_width")) df_with.show() # + [markdown] id="D7hiVs47-QTS" colab_type="text" # ## Оцените качество (сколько раз Вы угадали с ответом и сколько раз не угадали) # + id="dI9WOeu6-cWg" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 459} outputId="5f34e288-61b7-473a-f573-58c446a14117" def quality(value1, value2): if value1 == value2 : return 1 elif value1 != value2: return 0 else: return 'n/a' udfQuality = udf(quality, IntegerType()) datatest_with = df_with.withColumn("quality", udfQuality("variety", "category")) datatest_with.show() # + id="IJ2VCkHr_n_t" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 207} outputId="cef018cd-9e33-4f7b-8918-f74e5a071ea7" datatest_with.describe().show() # + id="dw0i9WsLARKt" colab_type="code" colab={} # точность моей модели составляет 95%
6,076
/assignment2-wells.ipynb
c37a052c3e9c9472c63e69c7b5189caaf702a287
[]
no_license
run-cmw/data-mining-practice
https://github.com/run-cmw/data-mining-practice
1
0
null
null
null
null
Jupyter Notebook
false
false
.py
396,004
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Exploring and Preprocessing Data with Pandas and Scikit-Learn # ## 1 Iris Dataset # Load Iris dataset import pandas as pd url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" # Name is a class, not a feature df = pd.read_csv(url, header=None, names=["Sepal Length", "Sepal Width", "Petal Length", "Petal Width", "Name"]) # ## 1.1 Summary Statistics # # Print the first 5 elements of your DataFrame using the command head(). How many features # are there and what are their types (e.g., numeric, nominal)? df.head() # - There are 4 features ('Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width') # - All 4 of the features are numeric (cm measurements) # Compute and display summary statistics for each numeric feature available in the dataset. # These must include the minimum value, maximum value, mean, standard deviation, count, and # 25:50:75% percentiles. df.describe() # ## 1.2 Data Visualization # # Histograms: To illustrate the feature distributions, create a histogram for each numeric feature in the dataset. You may plot each histogram individually or combine them all into a single # plot. When generating histograms for this assignment, use the default number of bins. # + sepal_length = df['Sepal Length'] sepal_length_hist = sepal_length.hist(color='r', alpha=0.6) sepal_width = df['Sepal Width'] sepal__width_hist = sepal_width.hist(color='orange', alpha=0.5) petal_length = df['Petal Length'] petal__length_hist = petal_length.hist(color='yellow', alpha=0.6) petal_width = df['Petal Width'] petal_width_hist = petal_width.hist(color='g', alpha=0.4) # - sepal_length = df['Sepal Length'] sepal_length_hist = sepal_length.hist(color='r', alpha=0.5) sepal_width = df['Sepal Width'] sepal__width_hist = sepal_width.hist(color='orange', alpha=0.5) petal_length = df['Petal Length'] petal__length_hist = petal_length.hist(color='yellow', alpha=0.5) petal_width = df['Petal Width'] petal_width_hist = petal_width.hist(color='g', alpha=0.5) # Box Plots: To further assess the data, create a box plot for each numeric feature in the # dataset. All of the box plots will be combined into a single plot. box = df.boxplot(grid=False, return_type='axes') # ## 2 Ames, Iowa Housing Data # Load Ames Housing dataset import pandas as pd pd.set_option("display.max_columns", 100) url = "https://raw.githubusercontent.com/cs6220/cs6220.spring2019/master/data/AmesHousing.txt" df = pd.read_csv(url, sep="\t") # ## 2.1 Imputation # # Identify and impute the features with missing values: # # - How many features have missing values? import numpy as np df # Get the non-null count for each of the 82 columns, and convert the values to a list non_nulls_list = df.count().values.tolist() # Since there are 2930 rows, having 2930 non_nulls mean there are no nulls. # So filter list based on non-null values less than 2930. filtered_list = [i for i in non_nulls_list if i < 2930] # The length of the filtered list is the number of features with null/missing values. len(filtered_list) # - Fill each missing nominal feature value with the string “Missing”. # First select only nominal features, then use fillna() (otherwise fillna() will alter numeric features) nominal_cols = df.select_dtypes(exclude=np.number) nominal_cols.fillna('Missing') # - Interpolate each missing numeric feature value using linear interpolation. # First select only numeric features numeric_cols = df.select_dtypes(np.number) numeric_cols # Drop non-feature numeric cols numeric_cols = numeric_cols.drop(['Order', 'PID', 'SalePrice'], axis=1) numeric_cols # Then interpolate numeric_cols = numeric_cols.interpolate(method='linear') numeric_cols # ## 2.2. Standardization # # - Standardize the imputed feature data so that the values of each numeric feature are standard normally distributed (i.e., each feature is Gaussian with zero mean and unit variance). # + from sklearn import preprocessing numeric_cols_scaled = preprocessing.scale(numeric_cols) numeric_cols_scaled # - # - Visualize the results using box plots. How do the plots differ from box plots made before feature standardization? Which feature has the outlier furthest from the mean before and after standardization? # + # Scaled Numeric Plot import matplotlib.pyplot as plt fig1, ax1 = plt.subplots(figsize=(20, 20)) x1 = numeric_cols_scaled ax1.set_title('Scaled Numeric Plot') ax1.boxplot(x1, patch_artist=True); # Kate, is there a way to not show all of the text output before the graph? # Answer (from Kate's README feedback): To suppress text output with plots, put a semicolon after the command that # draws the plot. In your case for 2.2, you would just have: ax1.boxplot(x1, patch_artist=True); # - # - Restating: How do the plots differ from box plots made before feature standardization? Which feature has the outlier furthest from the mean before and after standardization? # # There were no box plots made before feature standardization, so I did that below. These scaled plots differ in that the medians are so similar. I didn't even need to drop features to have a decent visualization, whereas with the boxplots for unstandardized features are not easy to read - even after dropping the features with larger values. # # Misc Val (label 34 above) has the outlier furtherst from the mean after standardization. Before standardization, it appears that Lot Area has the outlier furthest from the mean. # Non-scaled Numeric Plot numeric_cols.boxplot(grid=False, figsize=(35, 30)) # Identify features with large values giants = numeric_cols.columns[numeric_cols.min() > 50].values giants # Remove identified features for a clearer plot numeric_cols_no_giants = numeric_cols.drop(giants, axis=1) numeric_cols_no_giants.boxplot(grid=False, figsize=(35, 20)) # ## 2.3 Feature Selection # # - To get an idea of their relative importance, estimate the mutual information between the numeric features and the class column, ‘SalePrice’. # + from sklearn.feature_selection import mutual_info_regression, SelectKBest X = numeric_cols_scaled y = df['SalePrice'] mi = mutual_info_regression(X, y) # mi /= np.max(mi) # needed? mi # - # - What are the top 5 numeric features ranked by mutual information? Note that features with a higher estimated mutual information are considered more informative. # Get the sorted values' index positions for the mutial info regression above mi_sorted = mi.argsort() mi_sorted # Get the top 5 index positions top_5 = mi_sorted[::-1][:5] top_5 # + # Get the feature list names feature_list = list(numeric_cols.columns) top_count = 5 counter = 0 # Print the top 5 (according to index position) feature list names while counter < top_count: print(feature_list[top5[counter]]) counter += 1 # - # - How do you expect the values for the top-ranked feature to affect the sales price (i.e., would you expect the sales price to increase when its values go up or down)? Why? # I expect increasing values for the top-ranked feature (Pool Area) to cause the sales price to also increase. Since pools often increase a home's value, it stands to reason that a larger pool will increase the sales price even more - especially when it is the top-ranked feature. # + # Kate's feedback: K best features not right, although mutual info results look good. # Check out the mutual info function SelectKBest() # Tried: SelectKBest(mi, k=5).fit(X, y) # Result -> TypeError: The score function should be a callable, ... (<class 'numpy.ndarray'>) was passed. # Kate suggestion: Do you get the same error if you put your code in this form? selector = SelectKBest(mi, k=5) selector.fit(X, y)
7,904
/main22_but_bad_prediction_acc.ipynb
b45b36f283a86ec9ed08e926cd04ec1cd5f877a5
[]
no_license
kgoldra/capstone_Xplisit
https://github.com/kgoldra/capstone_Xplisit
0
0
null
2021-05-06T10:48:46
2021-05-06T03:03:56
null
Jupyter Notebook
false
false
.py
9,014,427
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/kgoldra/capstone_Xplisit/blob/main/main22_but_bad_prediction_acc.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="-tE649_CgBoO" import os import zipfile import random import tensorflow as tf import shutil import keras_preprocessing from keras_preprocessing import image from keras_preprocessing.image import ImageDataGenerator from tensorflow.keras.optimizers import RMSprop from shutil import copyfile from os import getcwd import matplotlib.pyplot as plt import matplotlib.image as mpimg import cv2 import numpy as np from sklearn.utils import class_weight from google.colab import files from keras.models import load_model # + id="LzB_ebqpCHu4" try: shutil.rmtree('/content/Data') hutil.rmtree('/content/nail diseases') except: pass # + id="abqQQjekYrbj" path_nails = f"/content/nail diseases.zip" local_zip = path_nails zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('/content/') zip_ref.close() # + [markdown] id="dVlSGWoQwziY" # # + id="dcRZQER1PFLM" ORIGINDIR = "/content/nail diseases" DATADIR = "/content/Data" TRAINORTEST = ["training", "testing"] CATEGORIES = ["aloperia areata", "beau's lines", "bluish nail", "clubbing", "darier's disease", "eczema", "koilonychia", "leukonychia", "lindsay's nails", "muehrck-e's lines", "normal", "onycholycis", "pale nail", "red lunula", "splinter hemmorrage", "terry's nail", "white nail", "yellow nails"] # + colab={"base_uri": "https://localhost:8080/"} id="HLaMuqujPNky" outputId="a4f0aea1-945f-4825-dd35-8631f1d7aee2" counter = 0 for trainortest in TRAINORTEST: path = os.path.join(DATADIR, trainortest) for category in CATEGORIES: counter += 1 originpath = os.path.join(ORIGINDIR, category) path2 = os.path.join(path, category) if(counter) <= 18: print(category, "Datasets Has: ", len(os.listdir(originpath)) ,"Data") os.makedirs(path2) pass # + id="iAMe8DqK2RaH" def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE): files = [] for filename in os.listdir(SOURCE): file = SOURCE + filename if os.path.getsize(file) > 0: files.append(filename) else: print(filename + " is zero length, so ignoring.") training_sets = int(len(files) * SPLIT_SIZE) testing_sets = int(len(files) - training_sets) randomed = random.sample(files,len(files)) training_files = randomed[0:training_sets] testing_files = randomed[:testing_sets] for filename in training_files: source = SOURCE + filename destination = TRAINING + filename copyfile(source, destination) for filename in testing_files: source = SOURCE + filename destination = TESTING + filename copyfile(source, destination) # + id="bzM_lhjz2Z7-" normal_DIR = "/content/nail diseases/normal/" TRAINING_normal_DIR = "/content/Data/training/normal/" TESTING_normal_DIR = "/content/Data/testing/normal/" aloperia_DIR = "/content/nail diseases/aloperia areata/" TRAINING_aloperia_DIR = "/content/Data/training/aloperia areata/" TESTING_aloperia_DIR = "/content/Data/testing/aloperia areata/" beau_DIR = "/content/nail diseases/beau's lines/" TRAINING_beau_DIR = "/content/Data/training/beau's lines/" TESTING_beau_DIR = "/content/Data/testing/beau's lines/" bluish_DIR = "/content/nail diseases/bluish nail/" TRAINING_bluish_DIR = "/content/Data/training/bluish nail/" TESTING_bluish_DIR = "/content/Data/testing/bluish nail/" clubbing_DIR = "/content/nail diseases/clubbing/" TRAINING_clubbing_DIR = "/content/Data/training/clubbing/" TESTING_clubbing_DIR = "/content/Data/testing/clubbing/" Darier_DIR = "/content/nail diseases/darier's disease/" TRAINING_Darier_DIR = "/content/Data/training/darier's disease/" TESTING_Darier_DIR = "/content/Data/testing/darier's disease/" eczema_DIR = "/content/nail diseases/eczema/" TRAINING_eczema_DIR = "/content/Data/training/eczema/" TESTING_eczema_DIR = "/content/Data/testing/eczema/" koilonychia_DIR = "/content/nail diseases/koilonychia/" TRAINING_koilonychia_DIR = "/content/Data/training/koilonychia/" TESTING_koilonychia_DIR = "/content/Data/testing/koilonychia/" leukonychia_DIR = "/content/nail diseases/leukonychia/" TRAINING_leukonychia_DIR = "/content/Data/training/leukonychia/" TESTING_leukonychia_DIR = "/content/Data/testing/leukonychia/" lindsay_DIR = "/content/nail diseases/lindsay's nails/" TRAINING_lindsay_DIR = "/content/Data/training/lindsay's nails/" TESTING_lindsay_DIR = "/content/Data/testing/lindsay's nails/" Muehrck_DIR = "/content/nail diseases/muehrck-e's lines/" TRAINING_Muehrck_DIR = "/content/Data/training/muehrck-e's lines/" TESTING_Muehrck_DIR = "/content/Data/testing/muehrck-e's lines/" onycholycis_DIR = "/content/nail diseases/onycholycis/" TRAINING_onycholycis_DIR = "/content/Data/training/onycholycis/" TESTING_onycholycis_DIR = "/content/Data/testing/onycholycis/" pale_nail_DIR = "/content/nail diseases/pale nail/" TRAINING_pale_nail_DIR = "/content/Data/training/pale nail/" TESTING_pale_nail_DIR = "/content/Data/testing/pale nail/" red_lunula_DIR = "/content/nail diseases/red lunula/" TRAINING_red_lunula_DIR = "/content/Data/training/red lunula/" TESTING_red_lunula_DIR = "/content/Data/testing/red lunula/" splinter_hemmorrage_DIR = "/content/nail diseases/splinter hemmorrage/" TRAINING_splinter_hemmorrage_DIR = "/content/Data/training/splinter hemmorrage/" TESTING_splinter_hemmorrage_DIR = "/content/Data/testing/splinter hemmorrage/" terry_DIR = "/content/nail diseases/terry's nail/" TRAINING_terry_DIR = "/content/Data/training/terry's nail/" TESTING_terry_DIR = "/content/Data/testing/terry's nail/" white_DIR = "/content/nail diseases/white nail/" TRAINING_white_DIR = "/content/Data/training/white nail/" TESTING_white_DIR = "/content/Data/testing/white nail/" yellow_DIR = "/content/nail diseases/yellow nails/" TRAINING_yellow_DIR = "/content/Data/training/yellow nails/" TESTING_yellow_DIR = "/content/Data/testing/yellow nails/" split_size = .70 split_data(Darier_DIR, TRAINING_Darier_DIR, TESTING_Darier_DIR, split_size) split_data(Muehrck_DIR, TRAINING_Muehrck_DIR, TESTING_Muehrck_DIR, split_size) split_data(aloperia_DIR, TRAINING_aloperia_DIR, TESTING_aloperia_DIR, split_size) split_data(beau_DIR, TRAINING_beau_DIR, TESTING_beau_DIR, split_size) split_data(bluish_DIR, TRAINING_bluish_DIR, TESTING_bluish_DIR, split_size) split_data(clubbing_DIR, TRAINING_clubbing_DIR, TESTING_clubbing_DIR, split_size) split_data(eczema_DIR, TRAINING_eczema_DIR, TESTING_eczema_DIR, split_size) split_data(koilonychia_DIR, TRAINING_koilonychia_DIR, TESTING_koilonychia_DIR, split_size) split_data(leukonychia_DIR, TRAINING_leukonychia_DIR, TESTING_leukonychia_DIR, split_size) split_data(lindsay_DIR, TRAINING_lindsay_DIR, TESTING_lindsay_DIR, split_size) split_data(onycholycis_DIR, TRAINING_onycholycis_DIR, TESTING_onycholycis_DIR, split_size) split_data(pale_nail_DIR, TRAINING_pale_nail_DIR, TESTING_pale_nail_DIR, split_size) split_data(red_lunula_DIR, TRAINING_red_lunula_DIR, TESTING_red_lunula_DIR, split_size) split_data(splinter_hemmorrage_DIR, TRAINING_splinter_hemmorrage_DIR, TESTING_splinter_hemmorrage_DIR, split_size) split_data(terry_DIR, TRAINING_terry_DIR, TESTING_terry_DIR, split_size) split_data(white_DIR, TRAINING_white_DIR, TESTING_white_DIR, split_size) split_data(yellow_DIR, TRAINING_yellow_DIR, TESTING_yellow_DIR, split_size) split_data(normal_DIR, TRAINING_normal_DIR, TESTING_normal_DIR, split_size) # + colab={"base_uri": "https://localhost:8080/"} id="R4g2NKzq7-F9" outputId="07fd1b99-8790-4791-d710-876f8b70fadf" print(len(os.listdir("/content/Data/testing/normal"))) print(len(os.listdir("/content/Data/training/normal"))) # + id="Yn07ozaavH30" IMGSIZE = 150 # + id="D3WZthQUt4H6" training_data = [] def create_training_data(): path = os.path.join(DATADIR, "training") for category in CATEGORIES: path2 = os.path.join(path, category) class_label = CATEGORIES.index(category) for img in os.listdir(path2): img_array = cv2.imread(os.path.join(path2,img)) new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE)) training_data.append([new_array, class_label]) create_training_data() # + id="92jlht5vt7H2" testing_data = [] def create_testing_data(): path = os.path.join(DATADIR, "testing") for category in CATEGORIES: path2 = os.path.join(path, category) class_label = CATEGORIES.index(category) for img in os.listdir(path2): img_array = cv2.imread(os.path.join(path2,img)) new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE)) testing_data.append([new_array, class_label]) create_testing_data() # + id="K2lNZgnJdrsX" X = [] x_label = [] y = [] y_label = [] for features, label in training_data: X.append(features) x_label.append(label) for features, label in testing_data: y.append(features) y_label.append(label) # + id="I2_f4KYn-0pu" weight = class_weight.compute_class_weight('balanced', np.unique(np.ravel(y)), np.ravel(y)) # + id="mB0PApMnEBoB" colab={"base_uri": "https://localhost:8080/"} outputId="28bd4d89-20a5-4b55-b641-d07ad1e09a24" weights = {i : weight[i] for i in range(18)} print (weights) # + id="g0mjP69v_BEi" colab={"base_uri": "https://localhost:8080/"} outputId="76efdec8-9ac0-4861-92c3-b70d00f35785" X = np.array(X).reshape(-1, IMGSIZE,IMGSIZE, 3).astype('float') y = np.array(y).reshape(-1, IMGSIZE,IMGSIZE, 3).astype('float') x_label = np.array(x_label).astype('float') y_label = np.array(y_label).astype('float') print(X.shape) print(y.shape) print(x_label.shape) print(y_label.shape) # + id="MfuUqwnBWA5g" training_datagen = ImageDataGenerator( rescale = 1.0/255., rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') validation_datagen = ImageDataGenerator( rescale = 1./255. ) # + id="wYq8BSEKV9IJ" model = tf.keras.models.Sequential([ # Note the input shape is the desired size of the image 150x150 with 3 bytes color # This is the first convolution tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape= (150,150,3)), tf.keras.layers.MaxPooling2D(2, 2), # The second convolution tf.keras.layers.Conv2D(32, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The third convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The fourth convolution tf.keras.layers.Conv2D(128, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # Flatten the results to feed into a DNN tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), # 512 neuron hidden layer tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(12, activation='softmax') ]) # + id="x8plq71fzgCA" train_generator = training_datagen.flow( X, x_label, batch_size = 32 ) validation_generator = validation_datagen.flow( y, y_label, batch_size = 32 ) # + id="ZRRWx2w6ZVzd" class myCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if(logs.get('accuracy')>=95): print("\nReached 95% Accuracy so cancelling training!") self.model.stop_training = True # + id="VmI3558OG7u8" from keras.callbacks import Callback,ModelCheckpoint from keras.models import Sequential,load_model from keras.layers import Dense, Dropout from keras.wrappers.scikit_learn import KerasClassifier import keras.backend as K # + id="9CyZAFvCG-Li" def get_f1(y_true, y_pred): #taken from old keras source code true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) recall = true_positives / (possible_positives + K.epsilon()) f1_val = 2*(precision*recall)/(precision+recall+K.epsilon()) return f1_val # + id="r4HmNly5g4Um" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="21d49edd-276f-4254-b0dc-8ea3e078fea0" callbacks=myCallback() model.summary() model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'rmsprop', metrics=['accuracy',get_f1]) history = model.fit( train_generator, steps_per_epoch = 500/32, validation_data = validation_generator, validation_steps = 226/32, epochs = 500, callbacks = [callbacks], class_weight=weights ) model.save('model.h5') files.download('model.h5') # + [markdown] id="h7dUWqKrHxUq" # # + id="bEhqLe6_Kyrq" colab={"base_uri": "https://localhost:8080/", "height": 573} outputId="f358b5ac-7a0c-462d-9942-3b6c7a79b4bf" def plot_train_history(history1): # Summarize history for accuracy plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # Summarize history for loss plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() plot_train_history(history) # + id="_FUzGPWZivmd" model.save_weights("model.h5") # + id="gRDq9yYnh0ju" def preparation(filepath): img_array = cv2.imread(filepath) new_array = cv2.resize(img_array, (IMGSIZE, IMGSIZE)) return new_array.reshape(-1, IMGSIZE, IMGSIZE, 3) # + id="hFjbaUs4pVo2" colab={"resources": {"http://localhost:8080/nbextensions/google.colab/files.js": {"data": "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"ok": true, "headers": [["content-type", "application/javascript"]], "status": 200, "status_text": ""}}, "base_uri": "https://localhost:8080/", "height": 1000} outputId="bef6dfa1-9fd1-4bf2-b187-b96e7babe817" from google.colab import files from keras.preprocessing import image uploaded = files.upload() # + id="B5rPUXW--LRZ" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="8da0b043-397c-4350-ce81-d7eabc9b2bdf" for fn in uploaded.keys(): predictions = model.predict([preparation(fn)]) img = image.load_img(fn, target_size=(150, 150)) img = np.array(img) img = img/255. xy = img xy = np.expand_dims(img, axis=0) imgplot = plt.imshow(img) plt.show() print(fn) print(predictions) print(xy.shape) # + id="7qbn9JOx5vKW" class_names = CATEGORIES test_label = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] # + id="8gB9s-Q35UYY" def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array[i], true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([]) plt.imshow(img, cmap=plt.cm.binary) predicted_label = np.argmax(predictions_array) color = 'blue' plt.xlabel("{} {:2.0f}%".format(class_names[predicted_label], 100*np.max(predictions_array), class_names[true_label]), color=color) # + id="C8qZQ0j-y0Uk" def plot_value_array(i, predictions_array, true_label): predictions_array, true_label = predictions_array[i], true_label[i] plt.grid(False) plt.xticks([]) plt.yticks([]) thisplot = plt.bar(range(18), predictions_array, color="#777777") plt.ylim([0, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color('red') # + id="VkF6pENvyC2G" colab={"base_uri": "https://localhost:8080/", "height": 203} outputId="8e71733c-57c9-4795-d28f-faf271d82107" i = 0 plt.figure(figsize=(6,3)) plt.subplot(1,2,1) plot_image(i, predictions, test_label, xy) plt.subplot(1,2,2) plot_value_array(i, predictions, test_label) plt.show() # + id="HkQgFFGl4ywl" import tensorflow as tf # Load the model. new_model= tf.keras.models.load_model(filepath="nailss.h5") # Convert the model. converter = tf.lite.TFLiteConverter.from_keras_model(new_model) tflite_model = converter.convert() # Save the TF Lite model. with tf.io.gfile.GFile('nailss.tflite', 'wb') as f: f.write(tflite_model) # + [markdown] id="oe3uXaKqNSf0" # # **Don't Run** # + id="_7XsVGZZNUIH" from keras import backend as K def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = (true_positives / (possible_positives + K.epsilon())) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = (true_positives / (predicted_positives + K.epsilon())) return precision def f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon()))
24,712
/Blap14_1_ha.ipynb
c41f5a5016911094609b7be9ccc14dfe9de909d6
[]
no_license
P-R-McWhirter/BLAP_spectra_analysis
https://github.com/P-R-McWhirter/BLAP_spectra_analysis
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
12,343
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # Import what we need for the script. import numpy as np from matplotlib import pyplot as plt # %matplotlib inline from EqW import * from tqdm import tqdm_notebook as tqdm # + # Create a function which generates a gaussian. def gaussian(x, mu, sig, pwr): return pwr * (np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))) # + # Define a continuum fit function. def region_around_line(w, flux, cont, pf = 0): '''cut out and normalize flux around a line Parameters ---------- w : 1 dim np.ndarray array of wanvelenghts flux : np.ndarray of shape (N, len(w)) array of flux values for different spectra in the series cont : list of lists wavelengths for continuum normalization [[low1,up1],[low2, up2]] that described two areas on both sides of the line ''' #index is true in the region where we fit the polynomial indcont = ((w > cont[0][0]) & (w < cont[0][1])) |((w > cont[1][0]) & (w < cont[1][1])) #index of the region we want to return indrange = (w > cont[0][0]) & (w < cont[1][1]) fluxmean = np.mean(flux[:,np.where(indcont)]) # make a flux array of shape # (nuber of spectra, number of pointsin indrange) f = np.zeros((flux.shape[0], indrange.sum())) for i in range(flux.shape[0]): # fit polynom of second order to the continuum region linecoeff = np.polyfit(w[indcont], flux[i, indcont], pf) # devide the flux by the polynom and put the result in our # new flux array f[i,:] = flux[i,indrange]/np.polyval(linecoeff, w[indrange]) if fluxmean < 0: f = -f return w[indrange], f # + # Define error functions for the optimisation of the gaussian fit. Penalise fits far from the Ha line using regularisation (check if this is appropriate). def error(data, flux, wavelength): mu, sig, pwr = data if sig < 1.8 or sig > 25: return np.inf fit = gaussian(wavelength, mu, sig, pwr) return np.sum(np.power(flux - fit, 2.)) + 0.01 * np.power(mu - 6563, 2.) def error2(data, flux, wavelength): mu, sig, pwr = data if sig < 1.8 or sig > 25: return np.inf fit = gaussian(wavelength, mu, sig, pwr) return np.power(flux - fit, 2.) # + # Import the scipy.optimize.minimize function from scipy.optimize import minimize # + # Import SpectRes package to rebin the gaussian into the spectrum wavelength bins whilst conserving flux. from spectres import spectres # + # Define a new error function using SpectRes for the optimisation. def reerr(data, w, f, gauw): mu, sig, pwr = data if sig < 1.8 or sig > 25: return np.inf res_fluxes = spectres(w, gauw, gaussian(gauw, mu, sig, pwr)) return np.sum(np.power(f - res_fluxes, 2.)) + 0.1 * np.power(mu - 6563, 2.) # - def halinefit(file, rang, quiet = False, cfit = 0): flux = np.load(file) wavelength = np.load('wavelength.npy') wha, fha = region_around_line(wavelength, np.reshape(flux, (1, np.size(flux))), rang, pf = cfit) fha = np.reshape(fha, np.size(wha)) if not quiet: plt.plot(wavelength, flux) plt.xlim((rang[0][0]-10,rang[1][1]+10)) plt.ylim((-0.4e-17,0.2e-17)) plt.show() x0 = np.array((6563, 10, -5)) gauw = np.linspace(rang[0][0]-10, rang[1][1]+10, 1000) res = minimize(reerr, x0, args=(wha, fha, gauw), method='Nelder-Mead', tol=1e-6) if not quiet: plt.plot(wha, fha) plt.plot(gauw, gaussian(gauw, res.x[0], res.x[1], res.x[2])) res_spec = spectres(wha, gauw, gaussian(gauw, res.x[0], res.x[1], res.x[2])) if not quiet: plt.show() cont = fha - res_spec if not quiet: plt.plot(wha, cont) plt.show() ew = (np.sum(gaussian(gauw, res.x[0], res.x[1], res.x[2]))/res.x[2])*(gauw[1]-gauw[0]) snr = np.abs(res.x[2]) / np.std(cont) if not quiet: print(res.x) print(np.std(cont)) print(snr) quans = np.quantile(cont, [0.05, 0.95]) return ew, snr, quans[0], quans[1], res.x[0] # + np.random.seed(10) ews = np.zeros(1000) snrs = np.zeros(1000) conts_low = np.zeros(1000) conts_high = np.zeros(1000) wls = np.zeros(1000) for i in tqdm(range(1000)): a1 = 0 a2 = 0 while np.abs(a2 - a1) < 20: a1 = np.random.uniform(low = 6000, high = 6550) a2 = np.random.uniform(low = 6000, high = 6550) if a2 < a1: h = a1 a1 = a2 a2 = h b1 = 0 b2 = 0 while np.abs(b2 - b1) < 20: b1 = np.random.uniform(low = 6600, high = 7200) b2 = np.random.uniform(low = 6600, high = 7200) if b2 < b1: h = b1 b1 = b2 b2 = h rang = [[a1, a2],[b1, b2]] ews[i], snrs[i], conts_low[i], conts_high[i], wls[i] = halinefit('blap14_group1_mean_subtracted.npy', rang, quiet = True) # - quans_ews = np.quantile(ews, [0.05, 0.5, 0.95]) quans_ews np.std(ews[(ews > quans_ews[0]) & (ews < quans_ews[2])]) quans_snrs = np.quantile(snrs, [0.05, 0.5, 0.95]) quans_snrs np.std(snrs[(snrs > quans_snrs[0]) & (snrs < quans_snrs[2])]) quans_conts_low = np.quantile(conts_low, [0.05, 0.5, 0.95]) quans_conts_low np.std(conts_low[(conts_low > quans_conts_low[0]) & (conts_low < quans_conts_low[2])]) quans_conts_high = np.quantile(conts_high, [0.05, 0.5, 0.95]) quans_conts_high np.std(conts_high[(conts_high > quans_conts_high[0]) & (conts_high < quans_conts_high[2])]) quans_wls = np.quantile(wls, [0.05, 0.5, 0.95]) quans_wls np.std(wls[(wls > quans_wls[0]) & (wls < quans_wls[2])])
5,925
/Foundations of ballot polling.ipynb
c98f7580ebc561661f24bdd61ff1b3579d00edb1
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umbernhard/rlamath
https://github.com/umbernhard/rlamath
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42,065
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.pylab import rcParams rcParams['figure.figsize'] =15,9 from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.arima_model import ARIMA import statsmodels.api as sm from sklearn.metrics import mean_squared_error df = pd.read_csv('Queen_1year_Raw.csv') df.tail() df.head(20) df.shape df.info() df['start_time'] = pd.to_datetime(df.ArrivalTime).astype('datetime64[s]') df['end_time'] = pd.to_datetime(df.DepartureTime).astype('datetime64[s]') df['start_from_fc']= df.start_time.dt.floor('5min') df['end_from_fc'] = df.end_time.dt.ceil('5min') df.head() df.dtypes n = (df.end_from_fc - df.start_from_fc) # n,(n.dt.total_seconds())/60 df['diff'] = (n.dt.total_seconds())/60 df.head() df['slots'] = df['diff']/5 df['slots']=df['slots'].astype('int64') df['bins'] = df.slots.map( lambda x: np.arange(1,x+1,1) if x > 1.0 else np.arange(1,x+1)) df['start'] = df.start_time.dt.date df['start'] = pd.to_datetime(df.start,format='%Y\%m\%d') df.tail() def explode(df, lst_cols, fill_value=''): if lst_cols and not isinstance(lst_cols, list): lst_cols = [lst_cols] idx_cols = df.columns.difference(lst_cols) lens = df[lst_cols[0]].str.len() if (lens > 0).all(): return pd.DataFrame({ col:np.repeat(df[col].values, lens) for col in idx_cols }).assign(**{col:np.concatenate(df[col].values) for col in lst_cols}) \ .loc[:, df.columns] else: return pd.DataFrame({ col:np.repeat(df[col].values, lens) for col in idx_cols }).assign(**{col:np.concatenate(df[col].values) for col in lst_cols}) \ .append(df.loc[lens==0, idx_cols]).fillna(fill_value) \ .loc[:, df.columns] new_df=explode(df, ['bins'], fill_value='') new_df.loc[new_df['bins']==""] new_df['bins'] = (new_df.start_from_fc + pd.to_timedelta(5*(new_df['bins']), unit='m')) new_df['bins1'] = new_df.bins - pd.to_timedelta(5, unit='m') new_df.groupby(['bins1','bins']).count()[['start_from_fc']] df_5min = new_df.groupby(['bins1','bins']).count()[['start_from_fc']].add_suffix('_Count').reset_index() df_5min.rename(columns={'bins1':'start_time','bins':'end_time' ,'start_from_fc_Count':'no_of_cars'} ,inplace=True) df_5min.tail(50) df_all_sensors_1stweek = df_5min.groupby([df_5min.start_time.dt.dayofweek])\ .sum()[['no_of_cars']].add_suffix('_Count').reset_index() df_all_sensors_1stweek.set_index('start_time').plot() df_all_sensors_1stweek df_5min = pd.read_csv('queen.csv') df_5min.columns df_5min['start_time'] = pd.to_datetime(df_5min.start_time).astype('datetime64[s]') df_all_sensors_1stweek = df_5min.groupby([df_5min.start_time.dt.dayofweek]).sum()[['no_of_cars']].add_suffix('_Count').reset_index() df_all_sensors_1stweek.set_index('start_time').plot() df_all_sensors_1stweek df_5min.dtypes df_5min.loc[(df_5min['start_time']>='2017-08-03') & (df_5min['start_time']<='2017-08-10')].groupby([df_5min.start_time.dt.date]).count()[['no_of_cars']] df_5min.drop(columns={'Unnamed: 0'},inplace=True) df_5min = df_5min.loc[(df_5min['start_time']<='2017-08-27') & (df_5min['start_time']>='2017-08-03')] df_tst_5min= df_5min[['start_time','no_of_cars']] df_tst_5min = df_tst_5min.set_index('start_time') df_tst_5min.head() df_tst_5min.plot(figsize=(15,9)) indexedDataset_logScale = np.log(df_tst_5min) indexedDataset_logScale.plot(figsize=(15,9)) def test_stationarity(timeseries): movingAverage = timeseries.rolling(window=50).mean() movingSTD = timeseries.rolling(window=50).std() orig = plt.plot(timeseries, color='blue', label='Original') mean = plt.plot(movingAverage, color='red', label='Rolling Mean') std = plt.plot(movingSTD, color='black', label='Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean & Standard Deviation') plt.show(block=False) print('Results of Dickey Fuller Test:') dftest = adfuller(timeseries['no_of_cars'], autolag='AIC') dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used']) for key,value in dftest[4].items(): dfoutput['Critical Value (%s)'%key] = value print(dfoutput) test_stationarity(indexedDataset_logScale) from statsmodels.graphics.tsaplots import plot_acf fig=plt.figure(figsize=(13,8)) ax1=fig.add_subplot(211) plot_acf(indexedDataset_logScale,lags=50,ax=ax1) from statsmodels.graphics.tsaplots import plot_pacf fig=plt.figure(figsize=(13,8)) ax1=fig.add_subplot(211) plot_pacf(indexedDataset_logScale,lags=50,ax=ax1) # + model = ARIMA(indexedDataset_logScale, order=(8,0,0)) results_AR = model.fit(disp=-1) plt.plot(indexedDataset_logScale, color='green',label='Actual Values') plt.plot(results_AR.fittedvalues, color='red', label='Predicted Values') plt.legend(loc='best') plt.title('RSS: %.4f'%sum((results_AR.fittedvalues - indexedDataset_logScale['no_of_cars'])**2)) print('Plotting AR model') print("Lag: ", results_AR.aic, results_AR.bic, results_AR.hqic) # - arma_model_2 = sm.tsa.ARMA(indexedDataset_logScale, (2, 0)).fit(disp=False) arma_model_3 = sm.tsa.ARMA(indexedDataset_logScale, (3, 0)).fit(disp=False) arma_model_5 = sm.tsa.ARMA(indexedDataset_logScale, (5, 0)).fit(disp=False) arma_model_7 = sm.tsa.ARMA(indexedDataset_logScale, (7, 0)).fit(disp=False) arma_model_10 = sm.tsa.ARMA(indexedDataset_logScale, (10, 0)).fit(disp=False) print("Lag 2: ", arma_model_2.aic, arma_model_2.bic, arma_model_2.hqic) print("Lag 3: ", arma_model_3.aic, arma_model_3.bic, arma_model_3.hqic) print("Lag 5: ", arma_model_5.aic, arma_model_5.bic, arma_model_5.hqic) print("Lag 7: ", arma_model_7.aic, arma_model_7.bic, arma_model_7.hqic) print("Lag 10: ", arma_model_10.aic, arma_model_10.bic, arma_model_10.hqic) from pyramid.arima import auto_arima # !pip install pyramid-arima stepwise_model = auto_arima(df_tst_5min, start_p=1, start_q=1, max_p=3, max_q=3, m=12, start_P=0, seasonal=True, d=1, D=1, trace=True, error_action='ignore', suppress_warnings=True, stepwise=True) print(stepwise_model.aic()) X = indexedDataset_logScale.values X1= df_tst_5min.values size = int(len(X) * 0.66) train, test = X[0:size], X[size:len(X)] size1 = int(len(X1) * 0.66) train1, test1 = X1[0:size1], X1[size1:len(X1)] print(train.shape) print(test.shape) history = [x for x in train1] predictions = list() for t in range(len(test1)): model = ARIMA(history, order=(4,1,0)) model_fit = model.fit(disp=0) output = model_fit.forecast() yhat = output[0] predictions.append(yhat) obs = test1[t] history.append(obs) # print('predicted=%f, expected=%f' % (yhat, obs)) error = (mean_squared_error(test1, predictions)) print('Test MSE: %.3f' % error) # plot plt.plot(test1, color='green',label='Actual Values') plt.plot(predictions, color='red', label='Predicted Values',alpha=0.6) plt.legend(loc='best') plt.title('Actual vs Predicted Values') plt.show(block=False) n = sm.tsa.SARIMAX(test1, order=(10,1,1), seasonal_order=(10,1,1,150)).fit(disp=False) print(n.aic) j = n.predict(0,2465) plt.plot(test1) plt.plot(j) plt.plot(figsize=(25,12)) df_tst_5min.head()
7,712
/binder/atom_rules.ipynb
a7bfa6cfac7549c99357ff2649e7bc93b67d062f
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LoLab-VU/pyvipr
https://github.com/LoLab-VU/pyvipr
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ShuZhe_022 % [d]2021-01-22; [xmxp](); [n]爷爷的金子; [l]绘本; [t]Grandpa's Gold; [by]Kerry Saadien-Raad; Elsabé Milandri; Mathilde de Blois; [p/s]bookdash; [ebook](https://bookdash.org/books/grandpas-gold-by-kerry-saadien-raad-elsabe-milandri-and-mathilde-de-blois/); # # --- # ![01](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_01.jpg) # # --- # # ### 爷爷的金子 # # Kerry Saadien-Raad | Elsabé Milandri | Mathilde de Blois # # --- # # 动物宝宝们在谈论他们的爷爷. # # ![05](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_05.jpg) # # --- # # 长颈鹿宝宝说(giraffe), 我的爷爷可以建造插入天空的高塔, 他曾经为国王建造了一栋摩天大楼(skyscraper). # # ![06](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_06.jpg) # # --- # # 猎豹宝宝(Cheetah)说, 我的爷爷可以钓到大海里任意的鱼, 他曾经钓了一只鲸鱼(whale), 并把它放到了自己的浴缸(bath). # # ![07](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_07.jpg) # # --- # # 山羊宝宝(Mountain Goat)说, 我的爷爷可以爬到天上的云朵里去, 他只用了四个小时就爬上了世界上最高的山(the tallest mountain in the world). # # ![08](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_08.jpg) # # --- # # 大象宝宝(Elephant)说, 我的爷爷可以烹饪盛宴(cook a feast), 他曾经一个人(all by himself)就为总统的(president's)生日派对(birthday party)供应了所有的伙食. # # ![09](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_09.jpg) # # --- # # 猴子宝宝(Monkey)说, 好吧, 我的爷爷既不会造房子(build), 也不会爬山(climb), 也不会做饭(cook), 但是他有金子! 他把金子藏在了自己的嘴里, 每天晚上的时候还会把金子泡在(soak)玻璃杯中的水里. # # ![10](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_10.jpg) # # --- # # 动物宝宝们都不相信猴子宝宝所说的话, 现场陷入了一阵混乱. 猴子宝宝说, 好吧, 如果你们不相信我的话(if you dont belive me), 就跟着我去看看好啦(come and see). # # ![11](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_11.jpg) # # --- # # 于是, 动物宝宝们一起相约去看猴子宝宝的爷爷了. # # ![12](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_12.jpg) # # --- # # 猴子说, 你们看到了吗(You see)? 他还能把它们拿出来呢! # # ![13](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_13.jpg) # # --- # # 动物宝宝们大喊着, 这不可能! # # ![14](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_14.jpg) # # --- # # 猴子爷爷"啊~"了一句, 便把假牙拿了出来. 还说, 我这里还有足够的... # # ![15](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_15.jpg) # # --- # # 给每个人的金子... # # ![16](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_16.jpg) # # --- # # 哈~ 原来只是一副假牙(denture)啊! # # ![18](https://bookdash.org/wp-content/uploads/2015/09/grandpas-gold-pdf-ebook-20150919_Page_18.jpg) ange(len(targets)), targets] return -out.sum()/len(out) # + id="XxuHQJUbX_qr" executionInfo={"status": "ok", "timestamp": 1623739011808, "user_tz": -330, "elapsed": 25, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def log_softmax_crossentropy_with_logits(logits,target): out = np.zeros_like(logits) out[np.arange(len(logits)),target] = 1 softmax = np.exp(logits) / np.exp(logits).sum(axis=-1,keepdims=True) return (- out + softmax) / logits.shape[0] # + id="WMo5G_2tYC_J" executionInfo={"status": "ok", "timestamp": 1623739011809, "user_tz": -330, "elapsed": 25, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def forward(context_idxs, theta): m = embeddings[context_idxs].reshape(1, -1) n = linear(m, theta) o = log_softmax(n) return m, n, o # + id="6zHc5qNFYGxe" executionInfo={"status": "ok", "timestamp": 1623739011810, "user_tz": -330, "elapsed": 25, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def backward(preds, theta, target_idxs): m, n, o = preds dlog = log_softmax_crossentropy_with_logits(n, target_idxs) dw = m.T.dot(dlog) return dw # + id="lydbVt9iYJyk" executionInfo={"status": "ok", "timestamp": 1623739011813, "user_tz": -330, "elapsed": 27, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def optimize(theta, grad, lr=0.03): theta -= grad * lr return theta # + id="s1CGhuV1YNyu" executionInfo={"status": "ok", "timestamp": 1623739012499, "user_tz": -330, "elapsed": 39, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} theta = np.random.uniform(-1, 1, (2 * context_size * embed_dim, vocab_size)) # + id="ht7FhmzJYRjC" executionInfo={"status": "ok", "timestamp": 1623739012501, "user_tz": -330, "elapsed": 37, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} epoch_losses = {} for epoch in range(80): losses = [] for context, target in data: context_idxs = np.array([word_to_ix[w] for w in context]) preds = forward(context_idxs, theta) target_idxs = np.array([word_to_ix[target]]) loss = NLLLoss(preds[-1], target_idxs) losses.append(loss) grad = backward(preds, theta, target_idxs) theta = optimize(theta, grad, lr=0.03) epoch_losses[epoch] = losses # + colab={"base_uri": "https://localhost:8080/", "height": 328} id="-_TpJjTkYVht" executionInfo={"status": "ok", "timestamp": 1623739012505, "user_tz": -330, "elapsed": 38, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} outputId="25a3db32-0380-4dd2-bbf6-e179c411e230" ix = np.arange(0,80) fig = plt.figure() fig.suptitle('Epoch/Losses', fontsize=20) plt.plot(ix,[epoch_losses[i][0] for i in ix]) plt.xlabel('Epochs', fontsize=12) plt.ylabel('Losses', fontsize=12) # + id="EzU29aPPYaL5" executionInfo={"status": "ok", "timestamp": 1623739012508, "user_tz": -330, "elapsed": 33, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def predict(words): context_idxs = np.array([word_to_ix[w] for w in words]) preds = forward(context_idxs, theta) word = ix_to_word[np.argmax(preds[-1])] return word # + colab={"base_uri": "https://localhost:8080/", "height": 35} id="DObq7FJCYekO" executionInfo={"status": "ok", "timestamp": 1623739012510, "user_tz": -330, "elapsed": 32, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} outputId="77acb0ca-bb0c-44f1-e06f-ed4e342f5c85" predict(['we', 'are', 'to', 'study']) # + id="fmgdZ_LzYh2m" executionInfo={"status": "ok", "timestamp": 1623739012512, "user_tz": -330, "elapsed": 29, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} def accuracy(): wrong = 0 for context, target in data: if(predict(context) != target): wrong += 1 return (1 - (wrong / len(data))) # + colab={"base_uri": "https://localhost:8080/"} id="-diBISHTYlea" executionInfo={"status": "ok", "timestamp": 1623739012513, "user_tz": -330, "elapsed": 29, "user": {"displayName": "20WH1DB008 VALUGUBELLY ANUPAMA", "photoUrl": "", "userId": "02251695447687599737"}} outputId="b6f7f82c-4317-4858-c237-69f1b2722f3b" accuracy()
7,736
/conv1d_demo.ipynb
43e29a446a52ada3b5463c01410680020e13066e
[]
no_license
guanguanboy/TestPytorch
https://github.com/guanguanboy/TestPytorch
0
0
null
null
null
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Jupyter Notebook
false
false
.py
4,079
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # 深入分析一维卷积nn.Conv1d # # - Applies a 1D convolution over an input signal composed of several input planes # # - 因为一维卷积是在最后维度上扫的 # # Shape: # - Input: :math:`(N, C_{in}, L_{in})` # - Output: :math:`(N, C_{out}, L_{out})` where # # .. math:: # L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation} # \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor # # Lout = (Lin + 2*padding - kernel_size)/stride + 1 # # - https://www.jianshu.com/p/45a26d278473 # - https://blog.csdn.net/sunny_xsc1994/article/details/82969867 # # + import torch import torch.nn as nn m = nn.Conv1d(16, 33, 3, stride=2) input = torch.randn(20, 16, 50) output = m(input) print(output.shape) #torch.Size([20, 33, 24]) # - conv1 = nn.Conv1d(in_channels=256, out_channels=100, kernel_size=2) input = torch.randn(32, 35, 256) input = input.permute(0, 2, 1) output = conv1(input) print(output.shape) #torch.Size([32, 100, 34]) # - https://zhuanlan.zhihu.com/p/95058866 m = nn.Conv1d(3,2,2) input = torch.randn(4,3,5) print(input) output = m(input) print(output) for i in range(4): nx = x + dx[i] ny = y + dy[i] if nx < 0 or nx >= n or ny < 0 or ny >= n: continue if graph[nx][ny] == 0: continue if not visited[nx][ny]: dfs(graph, visited, (nx, ny)) n = int(input()) graph = [] visited =[[False] * n for _ in range(n)] for _ in range(n): a = input() graph.append(list(map(int, a))) result = [] for i in range(n): for j in range(n): res = 0 if not visited[i][j] and graph[i][j] != 0: dfs(graph, visited, (i, j)) result.append(res) result.sort() print(len(result)) for i in range(len(result)): print(result[i]) # - # ## 문제 1012. 유기농 배추 (O) # + ## import sys # input = sys.stdin.readline dx = [-1, 1, 0, 0] dy = [0, 0, -1 , 1] def dfs(graph, x, y): graph[x][y] = 0 for i in range(4): nx = x + dx[i] ny = y + dy[i] if nx < 0 or nx >= m or ny < 0 or ny >= n: continue if graph[nx][ny] == 0: continue dfs(graph, nx, ny) for i in range(int(input())): m, n, k = map(int, input().split()) graph = [[0] * n for _ in range(m)] result = 0 for _ in range(k): x, y = map(int, input().split()) graph[x][y] = 1 # print(graph) for i in range(m): for j in range(n): if graph[i][j] == 1: dfs(graph, i, j) result += 1 print(result) # - # ## 문제 2606. 바이러스 (O) # + def dfs(graph, start, result, visited): result.append(start) visited[start] = True for i in graph[start]: if not visited[i]: dfs(graph, i, result, visited) n = int(input()) k = int(input()) start = 1 visited = [False] * (n+1) graph = [[] for _ in range(n+1)] result = [] for _ in range(k): a, b = map(int, input().split()) graph[a].append(b) graph[b].append(a) dfs(graph, start, result, visited) print(len(result)-1) # - # ## 문제 1987. 알파벳 (x, 시간초과) # + # 예시 답안, alpha리스트를 통해 중복검사 dx = [-1, 1, 0, 0] dy = [0, 0, -1, 1] r, c = map(int, input().split()) graph = [list(map(lambda x: ord(x) - 65, input().rstrip())) for _ in range(r)] alpha = [0] * 26 def dfs(start, cnt): global result result = max(result, cnt) x, y = start for i in range(4): nx = x + dx[i] ny = y + dy[i] if 0 <= nx < r and 0 <= ny < c and alpha[graph[nx][ny]] == 0: alpha[graph[nx][ny]] = 1 dfs((nx, ny), cnt + 1) alpha[graph[nx][ny]] = 0 result = 1 alpha[graph[0][0]] = 1 dfs((0,0), 1) print(result) # - # ## 문제 11724. 연결 요소의 개수(O) # + def dfs(graph, node): visited[node] = True for i in graph[node]: if not visited[i]: dfs(graph, i) n, m = map(int, input().split()) graph = [[] for _ in range(n+1)] visited = [False] * (n+1) for _ in range(m): a, b = map(int, input().split()) graph[a].append(b) graph[b].append(a) result = 0 for i in range(1, n+1): if not visited[i]: dfs(graph, i) result += 1 print(result)
4,568
/diagno/DrDiagno.ipynb
ac62994df4670823aea643b03f3df60124ad41a1
[]
no_license
GanapathyPT/Dr.Diagno_Backend
https://github.com/GanapathyPT/Dr.Diagno_Backend
0
1
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null
null
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Jupyter Notebook
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245,249
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix, classification_report from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from catboost import CatBoostClassifier import joblib import pickle df = pd.read_csv("dataset.csv") pd.set_option("display.max_columns", None) df df.isnull().sum() cols = [i for i in df.iloc[:,1:].columns] tmp = pd.melt(df.reset_index() ,id_vars = ['index'], value_vars = cols ) tmp['found'] = 1 tmp.head(40) newdf = pd.pivot_table(tmp, values = 'found', index = 'index', columns = 'value') newdf.insert(0,'label',df['Disease']) newdf = newdf.fillna(0) newdf X = newdf.drop('label',axis=1) y = newdf['label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0, stratify=y) rfc = CatBoostClassifier(iterations=2000, eval_metric = "AUC") rfc.fit(X_train, y_train) y_pred = rfc.predict(X_test) print("Confustion Matrix:", confusion_matrix(y_test,y_pred)) print("Accuracy:", accuracy_score(y_test,y_pred)*100) print(classification_report(y_test,y_pred)) pd.DataFrame(y_pred, y_test) lst = [] sym_dict = {} for i in newdf.columns: sym_dict[i]=0 lst.append(i) print(sym_dict) newdf.columns y_pred = rfc.predict_proba(X_test) y_pred newdf["label"].unique() a = np.zeros((1, 131)) a # + y_pred = rfc.predict_proba(X_tes) y_pred1 = rfc.predict(X_tes) # - y_pred y_pred1 joblib.dump(rfc, "rf.pkl") X_tes = [[1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]] model = pickle.load(open("D:\AI Proj\My Own Projects\RainPrediction\models\cat.pkl", "rb")) model.predict(X_tes) du.com/index.php?title=webapi/guide/webservice-geocoding address = area url = 'http://api.map.baidu.com/geocoder/v2/?address=' + address + '&output=json&ak=' + ak json_data = requests.get(url = url).json() coor_loc = json_data['result']['location'] return coor_loc except: return "nocoor" # 不换ak多半都是 'nocoor' print(df.loc[0,'area']) area2coor(df.loc[0,'area']) # - # ## 经纬度 # 上面先测试下,能拿到经纬度后,创建经纬度列 # %%time df['coor_loc'] = df.area.apply(area2coor) df.coor_loc # 确定`'coor_loc'`列是字典形式后,就可以直接从字典中拿到经度和维度了。 coor1 = df.coor_loc.values.tolist() type(coor1),coor1 # ## 拆分经度纬度 # 选出非'nocoor'的数据,再分别拿到经度和纬度,然后就可以导出数据,以便后面在BDP里操作。 df_coor = df[df['coor_loc'] != 'nocoor'] df_coor['lng'] = df_coor['coor_loc'].apply(lambda x: x['lng']) # 经度 df_coor['lat'] = df_coor['coor_loc'].apply(lambda x: x['lat']) # 纬度 df_coor[['lng','lat']] # ## 保存数据 df_coor.to_csv('Sina_Finance_Comments_All_20180811_toBDP.csv', encoding='utf-8', line_terminator='\r\n') # ## 动态热力图 # 古柳以前也用过 BDP,所以这回拿到数据后,就想着间隔近一年的时间后重新绘制动态热力图,虽则早已生疏了,但以前机智的写过一篇“使用手册”:[(送福利)BDP绘制微博转发动态热力图](https://zhuanlan.zhihu.com/p/29557747),于是按照文中步骤很快就重新捡回并制作出来了。 # # 具体步骤就不截图演示了,更详细的步骤请参考上面给出的文章,内含爬取的微博转发数据集,可供把玩(用Gephi一则热门微博的14层转发网络图谱:[《Gephi绘制微博转发图谱:以“@老婆孩子在天堂”为例》](https://zhuanlan.zhihu.com/p/29557827)) # <img src='https://pic1.zhimg.com/80/v2-46ee907f5ef96db00aa12456728620d8_hd.jpg'> # <img src='https://pic1.zhimg.com/v2-244681d95cdd619de6d7939a339ba6b5_r.jpg'> # # 此处仅记录大致操作步骤如下: # - 网上搜索:[BDP个人版](https://me.bdp.cn/home.html),注册账号以便使用; # - 点击“数据源”,点击“立即添加”,点击“CSV上传”,按照跳出的页面,上传本地对应的CSV文件,“逗号”分割,确定后,等待上传成功后,就能看到数据,此处将相应的时间列,设定为日期,否则后面动态展示时可能会出错。点击下一步,改不改文件名,目录,随意,之后下一步,完成数据上传; # - 点击菜单栏右上角“新建图表”,选择“经纬度地图”后确定; # - 经度选择上传的CSV数据里的“lng”列,纬度选择“lat”列,坐标系选择为百度地图; # - 将工作表中文件拖曳到图层里,就能在地图上加载出数据,非常简单地拿到了地图; # # 更改设置参数,以便录制 GIF 时展示效果更佳: # - 热力半径:8像素 # - 时间粒度:按时 # - 时间间隔:2小时 / 1小时 # - 自定义速度:FPS:8 / 12 # # 可根据数据量、数据展示的效果、以及自身的要求自行修改。最后就拿到了文章评论的动态热力图,还是蛮酷的。 # # <img src='images/heat-map-BDP-1h-11FPS.gif'>
4,652
/opt_jafroc.ipynb
1089ce4abd6f8feddd8b1a20fca2e8db1b627b38
[]
no_license
pyo-lee/Anlaysis_Lunit
https://github.com/pyo-lee/Anlaysis_Lunit
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
26,914
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %load_ext autoreload # %autoreload 2 import sys r_path_data = "../src/utils/descriptive_engine/" sys.path.append(r_path_data) from descriptives import * import time # + import os import sys r_path_data = "../src/models/kmeans/" sys.path.append(r_path_data) from kmeans import * from create_interactive_chart import * r_path_data = "../src/utils/load_data/" sys.path.append(r_path_data) from load_dataframes import * # - r_path_data = "../src/models/sequence_analysis/data/clustering_results/cluster_results_Jul_Aug_10000_sample.csv" df = pd.read_csv(r_path_data) r_path = "../src/utils/geolocation/" sys.path.append(r_path) from preprocessing import * str_to_list(df) df_medoid = df[df['medoids']==1] df_medoid df_medoid=df_medoid.reset_index(drop=True) df_medoid r_path = "../src/utils/read_shapefiles/" sys.path.append(r_path) from read_files_2 import * df = read_shapefiles_in(path_to_shapefile='../src/utils/read_shapefiles/', file_name='shape_files_path.json', version='municipalities',only_tusc=False, apply_crs=True) dict_loc = dict(zip(df['PRO_COM'].values, df['COMUNE'].values)) loc = list(df_medoid['locations_list'])[1] loc.pop() loc.pop(0) loc [dict_loc[int(i)] for i in loc] # + r_path = "../src/utils/read_shapefiles/" sys.path.append(r_path) from read_files_2 import * def get_com_names(path_to_shapefile, file_name, version, only_tusc=False, apply_crs=True): """ Returns a dictionary with keys as PRO_COM and values as COMUNE names """ df = read_shapefiles_in(path_to_shapefile, file_name, version, only_tusc=False, apply_crs=True) dict_loc = dict(zip(df['PRO_COM'].values, df['COMUNE'].values)) return dict_loc # - dict_loc = get_com_names(path_to_shapefile='../src/utils/read_shapefiles/', file_name='shape_files_path.json', version='municipalities',only_tusc=False, apply_crs=True) def pro_com_to_comune(row): """ Returns comune name based on pro_com """ loc = list(row['locations_list']) # loc.pop() # loc.pop(0) ### removes coutry code return [dict_loc[int(i)] for i in loc] # + r_path = "../src/utils/geolocation/" sys.path.append(r_path) from preprocessing import * r_path_data = "../results/sequence_analysis/Netherlands_summer/cluster_results_Netherlands_summer_0d_to_30d_WDaligned_FALSE_win_8_wCtryTRUE_N_30000_CONSTANT_LCS_NClus_4.csv" def get_medoid_comunes(r_path_data, dict_loc): """ Returns a DataFrame which includes a column that contains comune names of medoid trajectories """ df = pd.read_csv(r_path_data) str_to_list(df) df_medoid = df[df['medoids']==1] df_medoid = df_medoid.reset_index(drop=True) df_medoid['comune']=df_medoid.apply(pro_com_to_comune,1) return df_medoid # - df_medoid = get_medoid_comunes(r_path_data, dict_loc) df_medoid['comune']=df_medoid.apply(pro_com_to_comune,1) df_medoid df_medoid def trip_list(df_row): # trip = list(map(int,df_row['locations_list'].split(', '))) trip = df_row['locations_list'] times = list(map(int,df_row['times'].split(', '))) #times = df_row['times'].split(', ').tolist() times.append(int((pd.to_datetime(df_row['en_time']) - pd.to_datetime(df_row['st_time'])).seconds/60 - sum(times))) df_trip = pd.DataFrame(data={'pro_com': trip,'times':times}) return times def trip_list(df_row): #trip = list(map(int,df_row['locations'].split(', '))) trip = df_row['locations_list'] times = list(map(int,df_row['times'].split(', '))) times.append(int((pd.to_datetime(df_row['en_time']) - pd.to_datetime(df_row['st_time'])).seconds/60 - sum(times))) df_trip = pd.DataFrame(data={'pro_com': trip,'times':times}) return df_trip df = trip_list(df_medoid.loc[0,])#.to_dict('list') dict(zip(df['pro_com'].values, df['times'].values)) trip_list(df_medoid.loc[0,]).to_dict df_medoid.apply() df_medoid len(df_medoid) def get_N_of_clusters_in_traj(df_medoid): N = len(df_medoid) return N def join_medoid_customer_features(traj_result, username, season, country): """ Returns a dataframe with trajectory clustering results and customer features joined for the medoids Params: traj_result: dataframe with trajectory clustering result: customer_nr,column called cluster username: username to access aws season: season for clustering used country: country used for clustering (note: there is NO option for all) """ user_features=get_k_means_data(username,season, country).set_index("customer_nr") features_with_trajectory=user_features[['hrs_in_italy']].join(traj_result.set_index('customer_nr'), how = "inner") return features_with_trajectory result = join_medoid_customer_features(df_medoid, 'ywang99587','summer','Netherlands') def get_time_spent_in_italy(df_medoid): """ Helper funcion, which calculates how many days customers spend in Italy result: clustering result with customer features """ df_medoid['days_in_italy'] = np.round(pd.DataFrame(result[['hrs_in_italy']])/24) return df_medoid get_time_spent_in_italy(result) def start_and_end_trip(row): comunes = row['comune'] start = comunes[0] end = comunes[-1] return (start,end) def add_start_end(df_medoid): df_medoid['st_end_comune'] = df_medoid.apply(start_and_end_trip,1) return df_medoid add_start_end(df_medoid) def num_locs_visited_total(df_medoid): for i in range(len(df_medoid)): comunes = df_medoid.loc[i,'comune'] df_medoid.loc[i,'num_comunes_visited'] = len(set(comunes)) return df_medoid num_locs_visited_total(df_medoid) comunes = df_medoid.loc[1,'comune'] comunes set(comunes) start_and_end_trip(df_medoid.loc[1,])[1] # # K-means # + import json with open('../pipeline/config_location_k_means.json') as f: params = json.load(f) username = "ywang99587" season = "pre-summer" country = "all" nc = 4 #names = params["names"] colors = params["colors"] mapbox_access_token = "pk.eyJ1IjoidmFzYXJoZWx5aW8iLCJhIjoiY2prYjV2djh0M2R3NDNxbWw3dTFqdGZvbyJ9.stZ2MjMsogAYJ9fMb-lrsg" # - print(season,country) result1=get_cluster_results(username,season, country, features, nc) names=calculate_cluster_size(result1, 'label').index type(names[0]) df_reg_tus=read_tusc("../src/utils/read_shapefiles/") df_reg_tus.crs names[int(i)]) clusters=calculate_cluster_size(result1, 'label') clusters clus = result1['label'].value_counts().index clus seq = pd.Series([0,1,2,3], index=clus) seq.index.get_loc(int(1.0)) type(clus[0]) x=result1[result1['label']==clus[0]][['avg_lat', 'avg_lon', 'label']] x.head() x.index names[int(clus[0])] cluster_names=calculate_cluster_size(result1, 'label').index cluster_names list(zip(range(0,len(clusters)), cluster_names[:len(clusters)])) clusters.ratio.iloc[2] # + #get_cluster_country_distr(result1, 'label') # - # result1 = result1.sample(10000, replace=False) names = ['City Hoppers','Coast lovers','Explorers','Countrysiders'] f=plot_kmeans(result1, names, colors, country, season, mapbox_access_token) from plotly.offline import download_plotlyjs, init_notebook_mode, iplot, plot #from plotly.offline import download_plotlyjs, init_notebook_mode, iplot init_notebook_mode(connected=True) iplot(f) get_kmeans_description(result1, season, country, "label", nc, 5, names) season='pre-summer' country='all' nc=4 n=5 result=get_cluster_results('ovasarhelyi',season, country, features, nc=4) names=calculate_cluster_size(result, 'label').index f=plot_kmeans(result, names, colors, country, season, mapbox_access_token)#iplot(f) get_kmeans_description(result_samp, season, country, "label", nc, 5, names) get_kmeans_description(result_samp, season, country, "label", nc, 5, names) # # Trajectories path_to_result='../src/models/sequence_analysis/data/clustering_results/' d=pd.read_csv(path_to_result+'cluster_results_hungary_winter.csv') traj_result=d country = 'hungary' season = 'winter' var = 'cluster' names=calculate_cluster_size(traj_result, 'cluster').index get_trajectory_description(traj_result, username, season, country, var, names,print_it=True) 4))) #interest_list = ['u2/t2'] print(interest_list) with open('jafroc_respiratory(opt_resp).txt', 'w') as csvfile: for interest_dir in interest_list: print(interest_dir) gt_masks = [] human_masks = [] for index, file_name in enumerate(mapping_cases): if file_name.split('-')[0] == 'B': hospital_name = 'brmh' elif file_name.split('-')[0] == 'K': hospital_name = 'kyuh' elif file_name.split('-')[0] == 'G': hospital_name = 'gugh' else: raise ValueError('invalid hospital name') json_root_path = 'D:/lunit/data/review_result_20200705/{}-A1/{}/respiratory'.format(hospital_name.upper(), hospital_name) json_file = os.path.join(json_root_path, (file_name+'.dcm.json')) # heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name) with open(json_file, "r") as f: data = json.load(f) # handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR') # pixel_array = handler.pixels # pixel_array = (pixel_array * 255).astype(np.uint8) height, width = data['height'], data['width'] pixel_array = np.zeros((height,width)) # mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax'] # mca_list = ['Nodule / Mass'] mca_list = ['Consolidation'] # mca_list = ['Pneumothorax'] gt_masks.append(get_gt_final_mask(data)) human_root_path = 'D:/lunit/data/cxr_opt_respiratory' human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json' human_json_full = os.path.join(human_root_path, interest_dir, 'with_AI', human_json_name) with open(human_json_full, "r") as f: human_data = json.load(f) human_masks.append(get_human_output(pixel_array, human_data)) new_shape = (512, 512) human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks] gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks] resized_human_outputs = [] for index, human_output in enumerate(human_outputs): resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape)) jafroc_value = metric.jafroc(resized_human_outputs, gt_masks) print("jafroc:\t{:.3f}".format(jafroc_value), file=csvfile) print("jafroc:\t{:.3f}".format(jafroc_value)) jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123) print(jaf_ci, file=csvfile) print(jaf_ci) csvfile.close() # + mapping_df = pd.read_csv('./data/brmh_2_mapping_table_healthcheck.csv') mapping_cases = mapping_df['case_no'].tolist() interest_list = [] for i in range(9): interest_list.append('u{}_u{}'.format(str(i+2),str(i+11))) #interest_list = ['u2/t2'] print(interest_list) with open('jafroc_respiratory(opt_health).txt', 'w') as csvfile: for interest_dir in interest_list: print(interest_dir) gt_masks = [] human_masks = [] for index, file_name in enumerate(mapping_cases): if file_name.split('-')[0] == 'B': hospital_name = 'brmh' elif file_name.split('-')[0] == 'K': hospital_name = 'kyuh' elif file_name.split('-')[0] == 'G': hospital_name = 'gugh' else: raise ValueError('invalid hospital name') json_root_path = 'D:/lunit/data/review_result_20200705/{}-A2/{}/healthcheck'.format(hospital_name.upper(), hospital_name) json_file = os.path.join(json_root_path, (file_name+'.dcm.json')) # heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name) with open(json_file, "r") as f: data = json.load(f) # handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR') # pixel_array = handler.pixels # pixel_array = (pixel_array * 255).astype(np.uint8) height, width = data['height'], data['width'] pixel_array = np.zeros((height,width)) # mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax'] # mca_list = ['Nodule / Mass'] mca_list = ['Consolidation'] # mca_list = ['Pneumothorax'] gt_masks.append(get_gt_final_mask(data)) human_root_path = 'D:/lunit/data/cxr_opt_healthcheck' human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json' human_json_full = os.path.join(human_root_path, interest_dir, 'without_AI', human_json_name) with open(human_json_full, "r") as f: human_data = json.load(f) human_masks.append(get_human_output(pixel_array, human_data)) new_shape = (512, 512) human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks] gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks] resized_human_outputs = [] for index, human_output in enumerate(human_outputs): resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape)) jafroc_value = metric.jafroc(resized_human_outputs, gt_masks) print("jafroc:\t{:.3f}".format(jafroc_value), file=csvfile) print("jafroc:\t{:.3f}".format(jafroc_value)) jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123) print(jaf_ci, file=csvfile) print(jaf_ci) csvfile.close() # + mapping_df = pd.read_csv('./data/brmh_2_mapping_table_healthcheck.csv') mapping_cases = mapping_df['case_no'].tolist() interest_list = [] for i in range(9): interest_list.append('u{}_u{}'.format(str(i+2),str(i+11))) #interest_list = ['u2/t2'] print(interest_list) with open('jafroc_respiratory(opt_health).txt', 'w') as csvfile: for interest_dir in interest_list: print(interest_dir) gt_masks = [] human_masks = [] for index, file_name in enumerate(mapping_cases): if file_name.split('-')[0] == 'B': hospital_name = 'brmh' elif file_name.split('-')[0] == 'K': hospital_name = 'kyuh' elif file_name.split('-')[0] == 'G': hospital_name = 'gugh' else: raise ValueError('invalid hospital name') json_root_path = 'D:/lunit/data/review_result_20200705/{}-A2/{}/healthcheck'.format(hospital_name.upper(), hospital_name) json_file = os.path.join(json_root_path, (file_name+'.dcm.json')) # heatmap_root_path = '/storage2/ctr/original/cxr/external_validation/BRMH-GIL-KONYANG/{}/respiratory'.format(hospital_name) with open(json_file, "r") as f: data = json.load(f) # handler = dicom_handler.get_handler(os.path.join(heatmap_root_path, (file_name+'.dcm')), modality='CXR') # pixel_array = handler.pixels # pixel_array = (pixel_array * 255).astype(np.uint8) height, width = data['height'], data['width'] pixel_array = np.zeros((height,width)) # mca_list = ['Nodule / Mass', 'Consolidation', 'Pneumothorax'] # mca_list = ['Nodule / Mass'] mca_list = ['Consolidation'] # mca_list = ['Pneumothorax'] gt_masks.append(get_gt_final_mask(data)) human_root_path = 'D:/lunit/data/cxr_opt_healthcheck' human_json_name = str(mapping_df['seq'].tolist()[index]) + '.json' human_json_full = os.path.join(human_root_path, interest_dir, 'with_AI', human_json_name) with open(human_json_full, "r") as f: human_data = json.load(f) human_masks.append(get_human_output(pixel_array, human_data)) new_shape = (512, 512) human_outputs = [cv2.resize(np.asarray(human_mask), new_shape, interpolation=cv2.INTER_NEAREST) for human_mask in human_masks] gt_masks = [gt_mask.astype(bool) for gt_mask in gt_masks] resized_human_outputs = [] for index, human_output in enumerate(human_outputs): resized_human_outputs.append(np.resize(human_output, gt_masks[index].shape)) jafroc_value = metric.jafroc(resized_human_outputs, gt_masks) print("jafroc:\t{:.3f}".format(jafroc_value), file=csvfile) print("jafroc:\t{:.3f}".format(jafroc_value)) jaf_ci = metric.bootstrap_jafroc_ci(resized_human_outputs, gt_masks, n_bootstraps=100, alpha=0.05, rng_seed=123) print(jaf_ci, file=csvfile) print(jaf_ci) csvfile.close() # -
17,562
/HW12014.ipynb
793435a979b9def4bce8762ae7cc7d637f95ad21
[]
no_license
erikrichardlarson/HarvardCS109-HW
https://github.com/erikrichardlarson/HarvardCS109-HW
0
0
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Jupyter Notebook
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683,006
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf import matplotlib.pyplot as plt import numpy as np # + celsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype = float) farenheit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype = float) for i,c in enumerate(celsius_q): print('{} градусов цельсия = {} градусов Фаренгейта'.format(c, farenheit_a[i])) # - l0 = tf.keras.layers.Dense(units=1, input_shape=[1]) model = tf.keras.Sequential([l0]) model.compile(loss = 'mean_squared_error', optimizer = tf.keras.optimizers.Adam(0.1)) history = model.fit(celsius_q, farenheit_a, epochs = 500, verbose = False) plt.xlabel('Epochs') plt.ylabel('Loss') plt.plot(history.history['loss']); print(model.predict([100.0])) print('это значения переменных слоя:{}'.format(l0.get_weights()) ) l0= tf.keras.layers.Dense(units=4, input_shape=[1]) l1= tf.keras.layers.Dense(units=4) l2= tf.keras.layers.Dense(units=1) model = tf.keras.Sequential([l0, l1, l2]) model.compile(loss = 'mean_squared_error', optimizer = tf.keras.optimizers.Adam(0.1)) model.fit(celsius_q, farenheit_a, epochs = 500, verbose = False) print('значения внутренних переменных слоя1 {}'.format(l0.get_weights())) print('значения внутренних переменных слоя2 {}'.format(l1.get_weights())) print('значения внутренних переменных слоя3 {}'.format(l2.get_weights()))
1,662
/30_교육과정_실습파일/courses/machine_learning/deepdive/08_image/.ipynb_checkpoints/mnist_models_review_01-checkpoint.ipynb
b7f73b3a729216b92197a2281e6499dfe1aeb434
[]
no_license
younseun/google-asl-study
https://github.com/younseun/google-asl-study
1
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import warnings from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor # %matplotlib inline # # Load dataset # import dataset train = pd.read_csv('train.csv') test = pd.read_csv('test.csv') submit = pd.read_csv('gender_submission.csv') # # Data Description # Variable Definition Key # survival Survival 0 = No, 1 = Yes # pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd # sex Sex # Age Age in years # sibsp # of siblings / spouses aboard the Titanic # parch # of parents / children aboard the Titanic # ticket Ticket number # fare Passenger fare # cabin Cabin number # embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton # # Find the missing values # # Age and Cabin are missing values in train and test set train.info() train.isnull().mean() test.info() test.isnull().mean() # # Statistics of dataset train.describe() test.describe() # # Combining train and test set # # Before preprocessing, we conbine train and test set.This step will let us more convenience. # Beacause we preprocess the missing values just once. # # After combining train and test set, we have to reset our index or index order will be mess. data = train.append(test) data.reset_index(inplace=True, drop=True) # # Starting Analysis data # # Using the seaborn to visualize data's relation between Survived. # # Pclass vs Survived # # Sex vs Survived # # Embarked vs Survived # # Age vs Survived # # Fare vs Survived # # parch vs Survived # # SibSp vs Survived # # Family_Size vs Survived sns.countplot(data['Survived']) # Pclass vs Survived sns.countplot(data['Pclass'], hue=data['Survived']) # Sex vs Survived sns.countplot(data['Sex'], hue=data['Survived']) # Embarked vs Survived sns.countplot(data['Embarked'], hue=data['Survived']) # age vs survived g = sns.FacetGrid(data, col='Survived') g.map(sns.distplot, 'Age') # Fare vs survived g = sns.FacetGrid(data, col='Survived') g.map(sns.distplot, 'Fare') # parch(parents / children aboard the Titanic) vs survived g = sns.FacetGrid(data, col='Survived') g.map(sns.distplot, 'Parch') # SibSp(of siblings / spouses aboard) vs survived g = sns.FacetGrid(data, col='Survived') g.map(sns.distplot, 'SibSp') # create new columns data 'Family_Size' which combine with 'Parch' and 'SibSp' data['Family_Size'] = data['Parch'] + data['SibSp'] # Family_Size vs survived g = sns.FacetGrid(data, col='Survived') g.map(sns.distplot, 'Family_Size') # # Preprocess # get the title(Ms..., Miss..., etc...) data['Title1'] = data['Name'].str.split(", ", expand=True)[1] data['Name'].str.split(", ", expand=True).head(3) data['Title1'].head(3) data['Title1'] = data['Title1'].str.split(".", expand=True)[0] data['Title1'].head(3) data['Title1'].unique() pd.crosstab(data['Title1'], data['Sex']).T.style.background_gradient(cmap='summer_r') pd.crosstab(data['Title1'], data['Survived']).T.style.background_gradient(cmap='summer_r') data.groupby(['Title1'])['Age'].mean() # replace few title to common data['Title2'] = data['Title1'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','the Countess','Jonkheer','Col','Rev','Capt','Sir','Don','Dona'], ['Miss','Mrs','Miss','Mr','Mr','Mrs','Mrs','Mr','Mr','Mr','Mr','Mr','Mr','Mrs']) data['Title2'].unique() pd.crosstab(data['Title2'], data['Sex']).T.style.background_gradient(cmap='summer_r') pd.crosstab(data['Title2'], data['Survived']).T.style.background_gradient(cmap='summer_r') data['Ticket_info'] = data['Ticket'].apply(lambda x : x.replace(".","").replace("/","").strip().split(' ')[0] if not x.isdigit() else 'X') data['Ticket_info'].unique() data['Embarked'] = data['Embarked'].fillna('S') data['Fare'] = data['Fare'].fillna(data['Fare'].mean()) data['Cabin'].head(5) data['Cabin'] = data['Cabin'].apply(lambda x : str(x)[0] if not pd.isnull(x) else 'NoCabin') data['Cabin'].unique() sns.countplot(data['Cabin'], hue=data['Survived']) # convert categorical values to int for fitting classifier later. data['Sex'] = data['Sex'].astype('category').cat.codes data['Embarked'] = data['Embarked'].astype('category').cat.codes data['Pclass'] = data['Pclass'].astype('category').cat.codes data['Title1'] = data['Title1'].astype('category').cat.codes data['Title2'] = data['Title2'].astype('category').cat.codes data['Cabin'] = data['Cabin'].astype('category').cat.codes data['Ticket_info'] = data['Ticket_info'].astype('category').cat.codes # + dataAgeNull = data[data["Age"].isnull()] dataAgeNotNull = data[data["Age"].notnull()] remove_outlier = dataAgeNotNull[(np.abs(dataAgeNotNull["Fare"]-dataAgeNotNull["Fare"].mean())>(4*dataAgeNotNull["Fare"].std()))| (np.abs(dataAgeNotNull["Family_Size"]-dataAgeNotNull["Family_Size"].mean())>(4*dataAgeNotNull["Family_Size"].std())) ] rfModel_age = RandomForestRegressor(n_estimators=2000,random_state=42) ageColumns = ['Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title1', 'Title2','Cabin','Ticket_info'] rfModel_age.fit(remove_outlier[ageColumns], remove_outlier["Age"]) ageNullValues = rfModel_age.predict(X= dataAgeNull[ageColumns]) dataAgeNull.loc[:,"Age"] = ageNullValues data = dataAgeNull.append(dataAgeNotNull) data.reset_index(inplace=True, drop=True) # - dataTrain = data[pd.notnull(data['Survived'])].sort_values(by=["PassengerId"]) dataTest = data[~pd.notnull(data['Survived'])].sort_values(by=["PassengerId"]) dataTrain.columns dataTrain = dataTrain[['Survived', 'Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']] dataTest = dataTest[['Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']] dataTrain.head(3) # using SVM to predict from sklearn.svm import SVC classifier = SVC(kernel='rbf') classifier.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0]) rf_res = classifier.predict(dataTest) submit['Survived'] = rf_res submit['Survived'] = submit['Survived'].astype(int) submit.to_csv('submit.csv', index= False) # + # using RandomForest to predict rf = RandomForestClassifier(criterion='gini', n_estimators=1000, min_samples_split=12, min_samples_leaf=1, oob_score=True, random_state=1, n_jobs=-1) rf.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0]) print("%.4f" % rf.oob_score_) # - tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file EVAL_INTERVAL = 60 mnist = input_data.read_data_sets("mnist/data", one_hot = True, reshape = False) train_input_fn = tf.estimator.inputs.numpy_input_fn( x = {"image": mnist.train.images}, y = mnist.train.labels, batch_size = 100, num_epochs = None, shuffle = True, queue_capacity = 5000 ) eval_input_fn = tf.estimator.inputs.numpy_input_fn( x = {"image": mnist.test.images}, y = mnist.test.labels, batch_size = 100, num_epochs = 1, shuffle = False, queue_capacity = 5000 ) estimator = tf.estimator.Estimator( model_fn = image_classifier, model_dir = output_dir, params = hparams) train_spec = tf.estimator.TrainSpec( input_fn = train_input_fn, max_steps = hparams["train_steps"]) exporter = tf.estimator.LatestExporter(name = "exporter", serving_input_receiver_fn = serving_input_fn) eval_spec = tf.estimator.EvalSpec( input_fn = eval_input_fn, steps = None, exporters = exporter) tf.estimator.train_and_evaluate(estimator = estimator, train_spec = train_spec, eval_spec = eval_spec) output_dir="/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/08_image/mnist_trained_review" hparams={'train_batch_size': 100, 'model':'dnn', 'output_dir': '/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/08_image/mnist_trained', 'job_dir': 'junk', 'ksize2': 5, 'nfil1': 10, 'dprob': 0.25, 'train_steps': 100, 'ksize1': 5, 'batch_norm': False, 'nfil2': 20, 'learning_rate': 0.01} train_and_evaluate(output_dir, hparams) # + language="bash" # python3 task_review_01.py \ # --output_dir=${PWD}/mnist_trained \ # --train_steps=100 \ # --learning_rate=0.01
8,748
/apicalls_scraping.ipynb
73b52941cc24faea3be027d4646b913a8dc0bb1d
[]
no_license
JohnTheTripper/MovieProjectFIDS
https://github.com/JohnTheTripper/MovieProjectFIDS
1
0
null
2020-03-06T16:00:24
2020-03-06T15:54:16
Jupyter Notebook
Jupyter Notebook
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124,518
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:lendingclub] # language: python # name: conda-env-lendingclub-py # --- # %load_ext autoreload # %autoreload 2 # + import os import pickle import sys import numpy as np import pandas as pd import seaborn as sns # testing from pandas.testing import assert_frame_equal from tqdm import tqdm import j_utils.munging as mg from lendingclub.lc_utils import gen_datasets from lendingclub import config pd.options.display.max_columns = 999 pd.options.display.max_rows = 60 pd.options.display.max_seq_items = None # - # # Make the train script # input should be model type (should try to accept list) # bundled with the parameters for each type of model # # should return/create the saved model and anything necessary for data processing # for the model # + # %%writefile ../../lendingclub/modeling/08_train.py import os import sys import argparse import pickle import joblib import numpy as np import pandas as pd from sklearn.linear_model import LogisticRegression from catboost import CatBoostRegressor, CatBoostClassifier from lendingclub import config, utils import j_utils.munging as mg def prepare_data(model_n, data, proc=None, ds_type='train'): ''' returns the processed data for a model, which could be different between model types e.g. can handle categoricals or not. additionally returns a tuple of anything necessary to process valid/test data in the same manner ds_type must be 'train', 'valid', or 'test' ''' assert ds_type in ['train', 'valid', 'test'], print('ds_type invalid') if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']: return data, None # elif model_n == 'logistic_regr': else: if ds_type == 'train': temp = mg.train_proc(data) procced = temp[0] return procced, temp[1:] elif ds_type in ['test', 'valid']: assert proc, print('must pass data processing artifacts') temp = mg.val_test_proc(data, *proc) return temp def train_model(model_n, X_train, y_train, X_valid=None, y_valid=None): ''' Fit model and return model ''' if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']: return 42 elif model_n == 'logistic_regr': lr_model = LogisticRegression(class_weight='balanced') lr_model.fit(X_train, y_train) return lr_model elif model_n == 'catboost_regr': # basic params for regressor params = { 'iterations': 100000, 'one_hot_max_size': 10, # 'learning_rate': 0.01, # 'has_time': True, 'depth': 7, 'l2_leaf_reg': .5, 'random_strength': 5, 'loss_function': 'RMSE', 'eval_metric': 'RMSE',#'Recall', 'random_seed': 42, 'use_best_model': True, 'task_type': 'GPU', # 'boosting_type': 'Ordered', # 'loss_function': 'Log', 'custom_metric': ['MAE', 'RMSE', 'MAPE', 'Quantile'], 'od_type': 'Iter', 'od_wait': 300, } obj_cols = X_train.select_dtypes(['object', 'datetime']).columns categorical_features_indices = [X_train.columns.get_loc(col) for col in obj_cols] catboost_regr = CatBoostRegressor(**params) catboost_regr.fit(X_train, y_train, cat_features=categorical_features_indices, eval_set=(X_valid, y_valid,), logging_level='Verbose', plot=True) # return catboost_regr elif model_n == 'catboost_clf': # basic params params = { 'iterations': 100000, 'one_hot_max_size': 10, 'learning_rate': 0.01, 'depth': 7, 'l2_leaf_reg': .5, 'random_strength': 5, # 'has_time': True, 'eval_metric': 'Logloss',#'Recall', 'random_seed': 42, 'logging_level': 'Silent', 'use_best_model': True, 'task_type': 'GPU', # 'boosting_type': 'Ordered', # 'loss_function': 'Log', 'custom_metric': ['F1', 'Precision', 'Recall', 'Accuracy', 'AUC'], 'od_type': 'Iter', 'od_wait': 300, } # get categorical feature indices for catboost obj_cols = X_train.select_dtypes(['object', 'datetime']).columns categorical_features_indices = [X_train.columns.get_loc(col) for col in obj_cols] catboost_clf = CatBoostClassifier(**params) catboost_clf.fit(X_train, y_train, cat_features=categorical_features_indices, eval_set=(X_valid, y_valid,), logging_level='Verbose', plot=True) # return catboost_clf def export_models(m, model_n): if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']: with open(os.path.join(config.modeling_dir, '{0}_model.pkl'.format(model_n)), 'wb') as file: pickle.dump(m, file) elif model_n == 'logistic_regr': joblib.dump(m,os.path.join(config.modeling_dir, '{0}_model.pkl'.format(model_n))) elif model_n in ['catboost_clf', 'catboost_regr']: m.save_model(os.path.join(config.modeling_dir, '{0}_model.cb'.format(model_n))) def export_data_processing(proc_arti, model_n): if model_n in ['baseline', 'A', 'B', 'C', 'D', 'E', 'F', 'G']: with open(os.path.join(config.modeling_dir, '{0}_model_proc_arti.pkl'.format(model_n)), 'wb') as file: pickle.dump(proc_arti, file) elif model_n in ['logistic_regr', 'catboost_clf', 'catboost_regr']: joblib.dump(proc_arti, os.path.join(config.modeling_dir, '{0}_model_proc_arti.pkl'.format(model_n))) parser = argparse.ArgumentParser() parser.add_argument('--model', '-m', help='specify model(s) to train') if not len(sys.argv) > 1: models = ['logistic_regr'] # , 'A', 'B', 'C', 'D', 'E', 'F', 'G' args = parser.parse_args() if args.model: models = args.model.split() # models = ['logistic_regr'] if not os.path.isdir(config.modeling_dir): os.makedirs(config.modeling_dir) tr_val_base_data, tr_val_eval_data, _ = utils.load_dataset(ds_type='train') # ensure ordering is correct for time series split tr_val_base_data, tr_val_eval_data = mg.sort_train_eval(tr_val_base_data, tr_val_eval_data, 'id', 'issue_d') for model_n in models: print('training {0}'.format(model_n)) # do 3 steps of TS cross validation, with valid size at 5% (20 splits) tscv = mg.time_series_data_split(tr_val_eval_data, 'issue_d', 20, 1) for tr_idx, val_idx in tscv: # split out validation from train_data if model_n in ['logistic_regr', 'catboost_clf']: y_train = tr_val_eval_data.loc[tr_idx, 'target_loose'] y_valid = tr_val_eval_data.loc[val_idx, 'target_loose'] else: y_train = tr_val_eval_data.loc[tr_idx, '0.07'] y_valid = tr_val_eval_data.loc[val_idx, '0.07'] X_train = tr_val_base_data.loc[tr_idx] X_valid = tr_val_base_data.loc[val_idx] X_train, proc_arti = prepare_data(model_n, X_train, ds_type='train') X_valid = prepare_data(model_n, X_valid, proc = proc_arti, ds_type='valid') m = train_model(model_n, X_train, y_train, X_valid, y_valid) #save stuff export_models(m, model_n) export_data_processing(proc_arti, model_n) # - m = train_model(model_n, X_train, y_train) export_models(m, model_n) export_data_processing(proc_arti, model_n) # # copied code to incorporate into train script import pandas as pd import os from lendingclub import config import pickle dpath = config.data_dir # from lendingclub.lc_utils import gen_datasets from j_utils import munging as mg from sklearn.model_selection import train_test_split # ls {dpath} # + base_loan_info = pd.read_feather(os.path.join(dpath, 'base_loan_info.fth')) eval_loan_info = pd.read_feather(os.path.join(dpath, 'eval_loan_info.fth')) with open(os.path.join(dpath, 'train_test_ids.pkl'), 'rb') as f: train_test_ids = pickle.load(f) use_ids = train_test_ids['train'] print(base_loan_info.shape, eval_loan_info.shape, len(use_ids)) tv_base_loan_info = base_loan_info.query('id in @use_ids') tv_eval_loan_info = eval_loan_info.query('id in @use_ids') # - print tt_base_loan_info = pd.read_feather(os.path.join(dpath, 'train_testable_base_loan_info.fth')) tt_eval_loan_info = pd.read_feather(os.path.join(dpath, 'train_testable_eval_loan_info.fth')) print(tt_base_loan_info.shape, tt_eval_loan_info.shape) X_train, X_valid, y_train, y_valid = train_test_split(train_loan_info, train_eval_loan_info['target_strict'], test_size=.05) # fastai style processing X_train, all_train_colnames, max_dict, min_dict, new_null_colnames, fill_dict, cats_dict, norm_dict = mg.train_proc(X_train) X_valid = mg.val_test_proc(X_valid, all_train_colnames, max_dict, min_dict, fill_dict, cats_dict, norm_dict)
9,128
/learn/pandas/Exercise_ Summary Functions and Maps.ipynb
68682be7481ab585782647bd8e62e4f85815611b
[]
no_license
sidorkinandrew/kaggle
https://github.com/sidorkinandrew/kaggle
0
0
null
null
null
null
Jupyter Notebook
false
false
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11,996
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## 第4章朴素贝叶斯法-习题 # # ### 习题4.1 # &emsp;&emsp;用极大似然估计法推出朴素贝叶斯法中的概率估计公式(4.8)及公式 (4.9)。 # **解答:** # **第1步:**证明公式(4.8):$\displaystyle P(Y=c_k) = \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k)}{N}$ # 由于朴素贝叶斯法假设$Y$是定义在输出空间$\mathcal{Y}$上的随机变量,因此可以定义$P(Y=c_k)$概率为$p$。 # 令$\displaystyle m=\sum_{i=1}^NI(y_i=c_k)$,得出似然函数:$$L(p)=f_D(y_1,y_2,\cdots,y_n|\theta)=\binom{N}{m}p^m(1-p)^{(N-m)}$$使用微分求极值,两边同时对$p$求微分:$$\begin{aligned} # 0 &= \binom{N}{m}\left[mp^{(m-1)}(1-p)^{(N-m)}-(N-m)p^m(1-p)^{(N-m-1)}\right] \\ # & = \binom{N}{m}\left[p^{(m-1)}(1-p)^{(N-m-1)}(m-Np)\right] # \end{aligned}$$可求解得到$\displaystyle p=0,p=1,p=\frac{m}{N}$ # 显然$\displaystyle P(Y=c_k)=p=\frac{m}{N}=\frac{\displaystyle \sum_{i=1}^N I(y_i=c_k)}{N}$,公式(4.8)得证。 # # ---- # # **第2步:**证明公式(4.9):$\displaystyle P(X^{(j)}=a_{jl}|Y=c_k) = \frac{\displaystyle \sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)}{\displaystyle \sum_{i=1}^N I(y_i=c_k)}$ # 令$P(X^{(j)}=a_{jl}|Y=c_k)=p$,令$\displaystyle m=\sum_{i=1}^N I(y_i=c_k), q=\sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)$,得出似然函数:$$L(p)=\binom{m}{q}p^q(i-p)^{m-q}$$使用微分求极值,两边同时对$p$求微分:$$\begin{aligned} # 0 &= \binom{m}{q}\left[qp^{(q-1)}(1-p)^{(m-q)}-(m-q)p^q(1-p)^{(m-q-1)}\right] \\ # & = \binom{m}{q}\left[p^{(q-1)}(1-p)^{(m-q-1)}(q-mp)\right] # \end{aligned}$$可求解得到$\displaystyle p=0,p=1,p=\frac{q}{m}$ # 显然$\displaystyle P(X^{(j)}=a_{jl}|Y=c_k)=p=\frac{q}{m}=\frac{\displaystyle \sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k)}{\displaystyle \sum_{i=1}^N I(y_i=c_k)}$,公式(4.9)得证。 # ### 习题4.2 # &emsp;&emsp;用贝叶斯估计法推出朴素贝叶斯法中的慨率估计公式(4.10)及公式(4.11) # **解答:** # **第1步:**证明公式(4.11):$\displaystyle P(Y=c_k) = \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k) + \lambda}{N+K \lambda}$ # 加入先验概率,在没有任何信息的情况下,可以假设先验概率为均匀概率(即每个事件的概率是相同的)。 # 可得$\displaystyle p=\frac{1}{K} \Leftrightarrow pK-1=0\quad(1)$ # 根据习题4.1得出先验概率的极大似然估计是$\displaystyle pN - \sum_{i=1}^N I(y_i=c_k) = 0\quad(2)$ # 存在参数$\lambda$使得$(1) \cdot \lambda + (2) = 0$ # 所以有$$\lambda(pK-1) + pN - \sum_{i=1}^N I(y_i=c_k) = 0$$可得$\displaystyle P(Y=c_k) = \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k) + \lambda}{N+K \lambda}$,公式(4.11)得证。 # # ---- # # **第2步:**证明公式(4.10):$\displaystyle P_{\lambda}(X^{(j)}=a_{jl} | Y = c_k) = \frac{\displaystyle \sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k) + \lambda}{\displaystyle \sum_{i=1}^N I(y_i=c_k) + S_j \lambda}$ # 根据第1步,可同理得到$$ # P(Y=c_k, x^{(j)}=a_{j l})=\frac{\displaystyle \sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\lambda}{N+K S_j \lambda}$$ # $$\begin{aligned} # P(x^{(j)}=a_{jl} | Y=c_k) # &= \frac{P(Y=c_k, x^{(j)}=a_{j l})}{P(y_i=c_k)} \\ # &= \frac{\displaystyle \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\lambda}{N+K S_j \lambda}}{\displaystyle \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k) + \lambda}{N+K \lambda}} \\ # &= (\lambda可以任意取值,于是取\lambda = S_j \lambda) \\ # &= \frac{\displaystyle \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\lambda}{N+K S_j \lambda}}{\displaystyle \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k) + \lambda}{N+K S_j \lambda}} \\ # &= \frac{\displaystyle \sum_{i=1}^N I(y_i=c_k, x_i^{(j)}=a_{jl})+\lambda}{\displaystyle \sum_{i=1}^N I(y_i=c_k) + \lambda} (其中\lambda = S_j \lambda)\\ # &= \frac{\displaystyle \sum_{i=1}^N I(x_i^{(j)}=a_{jl},y_i=c_k) + \lambda}{\displaystyle \sum_{i=1}^N I(y_i=c_k) + S_j \lambda} # \end{aligned} $$ # 公式(4.11)得证。
3,591
/Murales-0.9.ipynb
ffd973a78cf2ae0cad00c59196a1412fe9678da9
[ "MIT" ]
permissive
ygingras/mp-84-atelier
https://github.com/ygingras/mp-84-atelier
0
2
MIT
2021-03-13T17:00:05
2021-03-13T03:11:16
Jupyter Notebook
Jupyter Notebook
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.py
5,876
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # # ASTRONOMY WITH THE 21-CM LINE; SOME MICROWAVE ELECTRONICS # # 4.2. Software—Some Useful Python Procedures for Time Conversion print_function import os, sys, h5py import numpy as np import matplotlib.pyplot as plt import seaborn as sb import tensorflow as tf import scipy import sys sys.path.append('../../..') import mutagenesisfunctions as mf from deepomics import neuralnetwork as nn from deepomics import utils, fit, visualize, saliency from Bio import AlignIO import time as time import pandas as pd #--------------------------------------------------------------------------------------------------------------------------------- '''DEFINE LOOP''' trials = ['med']#['small', 'med', 'large'] exp = 'toyhp' #for both the data folder and the params folder exp_data = 'data_%s'%(exp) for t in trials: #--------------------------------------------------------------------------------------------------------------------------------- '''OPEN DATA''' starttime = time.time() #Open data from h5py filename = '%s_50k_%s.hdf5'%(exp, t) data_path = os.path.join('../..', exp_data, filename) with h5py.File(data_path, 'r') as dataset: X_data = np.array(dataset['X_data']) Y_data = np.array(dataset['Y_data']) numdata, seqlen, dims = X_data.shape X_data = np.expand_dims(X_data, axis=2) # get validation and test set from training set test_frac = 0.3 valid_frac = 0.1 N = numdata split_1 = int(N*(1-valid_frac-test_frac)) split_2 = int(N*(1-test_frac)) shuffle = np.random.permutation(N) #set up dictionaries train = {'inputs': X_data[shuffle[:split_1]], 'targets': Y_data[shuffle[:split_1]]} valid = {'inputs': X_data[shuffle[split_1:split_2]], 'targets': Y_data[shuffle[split_1:split_2]]} test = {'inputs': X_data[shuffle[split_2:]], 'targets': Y_data[shuffle[split_2:]]} print ('Data extraction and dict construction completed in: ' + mf.sectotime(time.time() - starttime)) #--------------------------------------------------------------------------------------------------------------------------------- '''SAVE PATHS AND PARAMETERS''' params_results = '../../results' modelarch = 'resbind' trial = t modelsavename = '%s_%s'%(modelarch, trial) '''BUILD NEURAL NETWORK''' def cnn_model(input_shape, output_shape): # create model layer1 = {'layer': 'input', #41 'input_shape': input_shape } layer2 = {'layer': 'conv1d', 'num_filters': 96, 'filter_size': input_shape[1]-29, 'norm': 'batch', 'activation': 'relu', 'dropout': 0.3, 'padding': 'VALID', } layer3 = {'layer': 'conv1d_residual', 'filter_size': 5, 'function': 'relu', 'dropout_block': 0.1, 'dropout': 0.3, 'mean_pool': 10, } layer4 = {'layer': 'dense', # input, conv1d, dense, conv1d_residual, dense_residual, conv1d_transpose, # concat, embedding, variational_normal, variational_softmax, + more 'num_units': 196, 'norm': 'batch', # if removed, automatically adds bias instead 'activation': 'relu', # or leaky_relu, prelu, sigmoid, tanh, etc 'dropout': 0.5, # if removed, default is no dropout } layer5 = {'layer': 'dense', 'num_units': output_shape[1], 'activation': 'sigmoid' } model_layers = [layer1, layer2, layer3, layer4, layer5] # optimization parameters optimization = {"objective": "binary", "optimizer": "adam", "learning_rate": 0.0003, "l2": 1e-5, #"label_smoothing": 0.05, #"l1": 1e-6, } return model_layers, optimization tf.reset_default_graph() # get shapes of inputs and targets input_shape = list(train['inputs'].shape) input_shape[0] = None output_shape = train['targets'].shape # load model parameters model_layers, optimization = cnn_model(input_shape, output_shape) # build neural network class nnmodel = nn.NeuralNet(seed=247) nnmodel.build_layers(model_layers, optimization) # compile neural trainer save_path = os.path.join(params_results, exp) param_path = os.path.join(save_path, modelsavename) nntrainer = nn.NeuralTrainer(nnmodel, save='best', file_path=param_path) #--------------------------------------------------------------------------------------------------------------------------------- sess = utils.initialize_session() '''TEST''' if TEST: # set best parameters nntrainer.set_best_parameters(sess) # test model loss, mean_vals, std_vals = nntrainer.test_model(sess, test, name='test') if WRITE: metricsline = '%s,%s,%s,%s,%s,%s,%s'%(exp, modelarch, trial, loss, mean_vals[0], mean_vals[1], mean_vals[2]) fd = open('test_metrics.csv', 'a') fd.write(metricsline+'\n') fd.close() '''SORT ACTIVATIONS''' nntrainer.set_best_parameters(sess) predictionsoutput = nntrainer.get_activations(sess, test, layer='output') plot_index = np.argsort(predictionsoutput[:,0])[::-1] #--------------------------------------------------------------------------------------------------------------------------------- '''FIRST ORDER MUTAGENESIS''' if FOM: num_plots = range(1) for ii in num_plots: X = np.expand_dims(test['inputs'][plot_index[ii]], axis=0) mf.fom_heatmap(X, layer='dense_1_bias', alphabet='rna', nntrainer=nntrainer, sess=sess, figsize=(15,1.5)) plt.close() #--------------------------------------------------------------------------------------------------------------------------------- # -
6,409
/notebookexample/bike-sharing-in-the-bay-area.ipynb
5c1fc73f7b3b1ac2221a7389006dd6e49e089f85
[ "MIT" ]
permissive
leestott/Dockercodespace
https://github.com/leestott/Dockercodespace
1
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605,209
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # ___ # # Linear Regression - USA Housing # # By Himani Desai # # # ## Check out the data # import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # %matplotlib inline USAhousing = pd.read_csv('USA_Housing.csv') USAhousing.head() USAhousing.info() USAhousing.describe() USAhousing.columns # # EDA # sns.pairplot(USAhousing) sns.distplot(USAhousing['Price']) sns.heatmap(USAhousing.corr()) # ## Training a Linear Regression Model # # # ### X and y arrays X = USAhousing[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms', 'Avg. Area Number of Bedrooms', 'Area Population']] y = USAhousing['Price'] # ## Train Test Split # # from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101) # ## Creating and Training the Model from sklearn.linear_model import LinearRegression lm = LinearRegression() lm.fit(X_train,y_train) # ## Model Evaluation # # # print the intercept print(lm.intercept_) coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient']) coeff_df # ## Predictions from our Model # # predictions = lm.predict(X_test) plt.scatter(y_test,predictions) # **Residual Histogram** sns.distplot((y_test-predictions),bins=50); # ## Regression Evaluation Metrics # # # from sklearn import metrics print('MAE:', metrics.mean_absolute_error(y_test, predictions)) print('MSE:', metrics.mean_squared_error(y_test, predictions)) print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions))) ch. commute.hist('Duration', bins=60, unit='Second') # ### Exploring the Data with `group` and `pivot` ### # # We can use `group` to identify the most highly used Start Station: starts = commute.group('Start Station').sort('count', descending=True) starts # The largest number of trips started at the Caltrain Station on Townsend and 4th in San Francisco. People take the train into the city, and then use a shared bike to get to their next destination. # The `group` method can also be used to classify the rentals by both Start Station and End Station. commute.group(['Start Station', 'End Station']) # Fifty-four trips both started and ended at the station on 2nd at Folsom. A much large number (437) were between 2nd at Folsom and 2nd at Townsend. # # The `pivot` method does the same classification but displays its results in a contingency table that shows all possible combinations of Start and End Stations, even though some of them didn't correspond to any trips. Remember that the first argument of a `pivot` statement specifies the column labels of the pivot table; the second argument labels the rows. # # There is a train station as well as a Bay Area Rapid Transit (BART) station near Beale at Market, explaining the high number of trips that start and end there. commute.pivot('Start Station', 'End Station') # We can also use `pivot` to find the shortest time of the rides between Start and End Stations. Here `pivot` has been given `Duration` as the optional `values` argument, and `min` as the function which to perform on the values in each cell. commute.pivot('Start Station', 'End Station', 'Duration', min) # Someone had a very quick trip (271 seconds, or about 4.5 minutes) from 2nd at Folsom to Beale at Market, about five blocks away. There are no bike trips between the 2nd Avenue stations and Adobe on Almaden, because the latter is in a different city. # ### Drawing Maps ### # The table `stations` contains geographical information about each bike station, including latitude, longitude, and a "landmark" which is the name of the city where the station is located. stations = Table.read_table(path_data + 'station.csv') stations # We can draw a map of where the stations are located, using `Marker.map_table`. The function operates on a table, whose columns are (in order) latitude, longitude, and an optional identifier for each point. Marker.map_table(stations.select('lat', 'long', 'name')) # The map is created using [OpenStreetMap](http://www.openstreetmap.org/#map=5/51.500/-0.100), which is an open online mapping system that you can use just as you would use Google Maps or any other online map. Zoom in to San Francisco to see how the stations are distributed. Click on a marker to see which station it is. # You can also represent points on a map by colored circles. Here is such a map of the San Francisco bike stations. sf = stations.where('landmark', are.equal_to('San Francisco')) sf_map_data = sf.select('lat', 'long', 'name') Circle.map_table(sf_map_data, color='green', radius=200) # ### More Informative Maps: An Application of `join` ### # The bike stations are located in five different cities in the Bay Area. To distinguish the points by using a different color for each city, let's start by using group to identify all the cities and assign each one a color. cities = stations.group('landmark').relabeled('landmark', 'city') cities colors = cities.with_column('color', make_array('blue', 'red', 'green', 'orange', 'purple')) colors # Now we can join `stations` and `colors` by `landmark`, and then select the columns we need to draw a map. joined = stations.join('landmark', colors, 'city') colored = joined.select('lat', 'long', 'name', 'color') Marker.map_table(colored) # Now the markers have five different colors for the five different cities. # To see where most of the bike rentals originate, let's identify the start stations: starts = commute.group('Start Station').sort('count', descending=True) starts # We can include the geographical data needed to map these stations, by first joining `starts` with `stations`: station_starts = stations.join('name', starts, 'Start Station') station_starts # Now we extract just the data needed for drawing our map, adding a color and an area to each station. The area is 1000 times the count of the number of rentals starting at each station, where the constant 1000 was chosen so that the circles would appear at an appropriate scale on the map. starts_map_data = station_starts.select('lat', 'long', 'name').with_columns( 'color', 'blue', 'area', station_starts.column('count') * 1000 ) starts_map_data.show(3) Circle.map_table(starts_map_data) # That huge blob in San Francisco shows that the eastern section of the city is the unrivaled capital of bike rentals in the Bay Area. telechargement-enseignants-maj-2018) # # # <span style='color:blue'>**Question 3**</span> : # Tester le programme **Ouvrir** suivant, et expliquer son fonctionnement dans la cellule texte qui se trouve en dessous du programme **Ouvrir**. On précisera bien le rôle du slice **line[0:-1]**. # Programme Ouvrir file = open("sujet1.txt", "r") lines = file.readlines() file.close() texte="" for line in lines: texte=texte+line[0:-1] print(texte) # Donner la réponse à la question 3, dans la cellule texte ci-après. # + active="" # # - # ### c) Utilisation du programme de Boyer-Moore # <span style='color:blue'>**Question 4**</span> : # en utilisant les programmes **Boyer-Moore 2** et **Ouvrir**, écrire en dessous le programme de **Boyer-Moore 3** qui vous permettra de dire quels sont les personnes parmi les sujets 1 à 4 qui seront successibles de contracter la maladie de Huntington dans les années à venir. # Boyer-Moore 3 ... # Donner la réponse à la question 4, dans la cellule texte ci-après. # + active="" # # - # ## 3) Aspect historique, et mise en perspective # ### a) Qui sont Boyer et Moore ? # Robert Stephen Boyer et J Strother Moore (La lettre J est son prénom et n'est pas une abréviation) ont inventé l'algorithme de recherche de chaînes de Boyer–Moore, un algorithme de recherche de chaînes particulièrement efficace, en 1977. # # # * Robert Stephen Boyer : professeur retraité d'informatique, de mathématiques et de philosophie à l'Université du Texas à Austin # # ![image2](image2.jpg) # # * J Strother Moore : professeur en informatique à l'université du Texas à Austin. # # ![image3](image3.jpg) # # ### b) 85 algorithmes de recherche textuelle # Voici un lien qui Recense plus de 85 algorithmes différents de recherche textuelle, les plus célèbres datant des années 1970, mais plus de la moitié ont moins de 10 ans : # # [Lien vers le site](http://monge.univ-mlv.fr/~lecroq/string/)
8,601
/Modulo01/Desafio03/Desafio03.ipynb
1aa4756edf94fe8dd2d0764145463654663ed639
[]
no_license
thiagoiori/Bootcamp_DS
https://github.com/thiagoiori/Bootcamp_DS
0
0
null
null
null
null
Jupyter Notebook
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.py
1,662,975
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/thiagoiori/Bootcamp_DS/blob/main/Modulo01/Desafio03/Desafio03.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="L6sYFqbTNwb2" import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker import numpy as np pd.options.display.float_format = "{:.2f}".format # + id="0HN2AC9rK4SW" dados = pd.read_csv("https://raw.githubusercontent.com/thiagoiori/Bootcamp_DS/main/Modulo01/Desafio02/A201526189_28_143_208.csv", sep=";", encoding="ISO-8859-1", skiprows=3, skipfooter=12, engine="python", decimal=",") # + [markdown] id="WW5d6vm1s1wD" # Fatiando o dataframe (slice) # + colab={"base_uri": "https://localhost:8080/"} id="huU-0hsNtfzL" outputId="8a162045-1cb6-4ff7-e755-9317d80fe8ec" colunas_usaveis = dados.mean().index.tolist() colunas_usaveis.insert(0, "Unidade da Federação") colunas_usaveis # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="wj51fyPmuANf" outputId="e93b49fb-0ff1-4d07-e553-a8a595bf2894" dados[colunas_usaveis] # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="qhNjfamfulEA" outputId="a6fd3b74-37a9-4e2c-d3d5-233ed9ddc9b0" usaveis = dados[colunas_usaveis] usaveis = usaveis.set_index("Unidade da Federação") usaveis # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="newS4EBExkTy" outputId="edf92829-a51e-4d97-86c6-d212cd953d3e" usaveis.plot(figsize=(12, 9)) # + colab={"base_uri": "https://localhost:8080/", "height": 489} id="8wwKreqYylKc" outputId="14d3ffae-403b-4e1d-b548-db6bf7334421" usaveis.T.plot(figsize=(12, 6)) # + id="HzWitdiQy8gs" usaveis = usaveis.drop("Total", axis=1) # + colab={"base_uri": "https://localhost:8080/", "height": 489} id="zJnUQ6P5zPaU" outputId="b488985f-545c-439f-cf9b-69a6e50e3903" usaveis.T.plot(figsize=(12, 6)) # + colab={"base_uri": "https://localhost:8080/", "height": 403} id="Qu7mQZFYD-26" outputId="25aa0076-0c68-4946-f130-abdd9585b1df" usaveis["Total"] = usaveis.sum(axis=1) ordenado_por_gasto = usaveis.sort_values("Total", ascending=False) ordenado_por_gasto = ordenado_por_gasto.drop("Total", axis=1) ordenado_por_gasto.head(5).T.plot(figsize=(12, 6)) # + colab={"base_uri": "https://localhost:8080/", "height": 403} id="5vc_ccKMIMnV" outputId="6e2ba96a-b42a-4b6f-f874-d711c0ecba2c" colunas_interessadas = ordenado_por_gasto.columns[6:] ordenado_por_gasto = ordenado_por_gasto[colunas_interessadas] ordenado_por_gasto.head().T.plot(figsize=(12,6)) # + colab={"base_uri": "https://localhost:8080/"} id="tlUfmKRyQiZm" outputId="4020dd7c-ff1f-4692-d493-c8401c482c31" usaveis.index # + colab={"base_uri": "https://localhost:8080/", "height": 356} id="aw6Efss1QkNV" outputId="6fbb3199-9e98-4946-87e9-4a3ce8bfbf00" usaveis["Total"] = usaveis.sum(axis=1) usaveis.head() # + colab={"base_uri": "https://localhost:8080/", "height": 436} id="VqKZwQUMzyb9" outputId="a1e13b82-d92e-4aef-ca16-aad19c958501" usaveis.sample(n=7) # + id="MqWgsx6lAa-4" # + [markdown] id="HVYWYwwrPXlA" # # Desafios # # Desafio 01: Escolher uma palete de cores mais adequada do matplotlib. # # Desafio 02: Adicionar uma coluna mostrando a região (Norte, Nodeste, Sul, Suldeste e Centro-Oeste) de cada estado. # # Desafio 03: Formatar o gráfico de custos por mês dos 5 estados, deixando ele agradável (Bonitão, segundo o Gui) # # Desafio 04: Adicione o seu estado aos 5 estados plotados anteriormente # # Desafio 05: Buscar os casos de dengue no Brasil (época de maior número de casos e regiões mais atingidas) e se os picos de alguns estados em fevereiro e verão de modo geral, pode ser reflexos dos casos de dengue # # Desafio 06: Plotar o gráfico dos custos apenas dos estados da região sudeste e verificar se os picos de 2013/Fev teve comportamento similar em todos os demais estados da região # # Desafio 07: Adicionar seu estado escolhido novamente, deixe o gráfico informativo e tire conclusões sobre seus estados comparando com os demais. Tire suas conclusões e compartilhe com a gente. # + [markdown] id="Ou4jeTsrseZB" # ## Desafio 01 # Escolher uma palete de cores mais adequada do matplotlib. # + colab={"base_uri": "https://localhost:8080/", "height": 0} id="HIvXNZUHsW49" outputId="9cef2641-04be-4958-f51f-32f2d0ef6649" ax = ordenado_por_gasto.T.plot(figsize=(12,6), color="blue") ax.legend(loc="right", bbox_to_anchor=(.8, 0.2, 0.5, 0.5)) leg = ax.get_legend() colors = ['#FF6633', '#FFB399', '#FF33FF', '#FFFF99', '#00B3E6', '#E6B333', '#3366E6', '#999966', '#99FF99', '#B34D4D', '#80B300', '#809900', '#E6B3B3', '#6680B3', '#66991A', '#FF99E6', '#CCFF1A', '#FF1A66', '#E6331A', '#33FFCC', '#FF6633', '#B366CC', '#4D8000', '#B33300', '#CC80CC', '#66664D'] for idx, color in enumerate(colors): ax.get_lines()[idx].set_color(color) leg.legendHandles[idx].set_color(color) # + [markdown] id="I6y5s4GtJga4" # ## Desafio 02 # Adicionar uma coluna mostrando a região (Norte, Nodeste, Sul, Suldeste e Centro-Oeste) de cada estado # + colab={"base_uri": "https://localhost:8080/", "height": 408} id="s9WUf9GjJmY6" outputId="ac71aed3-7875-4c1b-f245-f88b0d552293" condition = [(ordenado_por_gasto.index.str[:1] == "1"), (ordenado_por_gasto.index.str[:1] == "2"), (ordenado_por_gasto.index.str[:1] == "3"), (ordenado_por_gasto.index.str[:1] == "4"), (ordenado_por_gasto.index.str[:1] == "5")] values = ["Norte", "Nordeste", "Sudeste", "Sul", "Centro-Oeste"] ordenado_por_gasto["Região"] = np.select(condition, values) ordenado_por_gasto.head() # + id="VYuNyI93Lqn5" # + [markdown] id="ZBAtcimjqZg6" # ## Desafio 03 # Formatar o gráfico de custos por mês dos 5 estados, deixando ele agradável (Bonitão, segundo o Gui) # + colab={"base_uri": "https://localhost:8080/", "height": 390} id="nmzwafPAqiFY" outputId="af53529e-62a8-4b75-8ec6-27424053b182" ordenado_por_gasto = ordenado_por_gasto[colunas_interessadas] ax = ordenado_por_gasto.head().T.plot(figsize=(12,6)) ax.legend(loc="right", bbox_to_anchor=(.75, .2, 0.5, 0.5)) ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:,.2f}")) plt.title("Top 5 maiores gastos") plt.grid(axis="y") plt.ylabel("Gasto em R$") plt.show() # + [markdown] id="CgXk9yy30sbA" # ## Desafio 04 # Adicione o seu estado aos 5 estados plotados anteriormente # + colab={"base_uri": "https://localhost:8080/", "height": 390} id="1dKbZgxB0w8L" outputId="8864fec0-107f-4a5b-a67e-1c189b25e45e" ordenado_por_gasto = ordenado_por_gasto[colunas_interessadas] maiores_6_gastos = ordenado_por_gasto.head() maiores_6_gastos = maiores_6_gastos.append(ordenado_por_gasto.loc["23 Ceará"]) ax = maiores_6_gastos.T.plot(figsize=(12,6)) ax.legend(loc="right", bbox_to_anchor=(.75, .2, 0.5, 0.5)) ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:,.2f}")) plt.title("Top 6 maiores gastos") plt.grid(axis="y") plt.ylabel("Gasto em R$") plt.show() # + [markdown] id="Q1fbidcy7Sd3" # ## Desafio 05 # Buscar os casos de dengue no Brasil (época de maior número de casos e regiões mais atingidas) e se os picos de alguns estados em fevereiro e verão de modo geral, pode ser reflexos dos casos de dengue # + id="sR8Bk4_52v6k" # dados = pd.read_csv("https://raw.githubusercontent.com/thiagoiori/Bootcamp_DS/main/Modulo01/Desafio02/A201526189_28_143_208.csv", # sep=";", # encoding="ISO-8859-1", # skiprows=3, # skipfooter=12, # engine="python", # decimal=",") # + [markdown] id="R_dg73RYe6XN" # ## Desafio 06 # Plotar o gráfico dos custos apenas dos estados da região sudeste e verificar se os picos de 2013/Fev teve comportamento similar em todos os demais estados da região # + colab={"base_uri": "https://localhost:8080/", "height": 390} id="9QFu6zJee_8E" outputId="e4bbcd78-e995-4f25-d8d9-d1520be5f9e3" condition = [(ordenado_por_gasto.index.str[:1] == "1"), (ordenado_por_gasto.index.str[:1] == "2"), (ordenado_por_gasto.index.str[:1] == "3"), (ordenado_por_gasto.index.str[:1] == "4"), (ordenado_por_gasto.index.str[:1] == "5")] values = ["Norte", "Nordeste", "Sudeste", "Sul", "Centro-Oeste"] ordenado_por_gasto["Região"] = np.select(condition, values) ordenado_por_gasto_SE = ordenado_por_gasto.loc[ordenado_por_gasto["Região"] == "Sudeste"] ordenado_por_gasto_SE = ordenado_por_gasto_SE[colunas_interessadas] ax = ordenado_por_gasto_SE.T.plot(figsize=(12,6)) ax.legend(loc="right", bbox_to_anchor=(.75, .2, 0.5, 0.5)) ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:,.2f}")) plt.title("Gastos na saúde da Região SE") plt.grid(axis="y") plt.ylabel("Gasto em R$") plt.show() # + [markdown] id="Sfjuh2kJdDid" # ## Desafio 07 # Adicionar seu estado escolhido novamente, deixe o gráfico informativo e tire conclusões sobre seus estados comparando com os demais. Tire suas conclusões e compartilhe com a gente. # + colab={"base_uri": "https://localhost:8080/", "height": 390} id="poGIbNDHdNyD" outputId="0a8b7aeb-d54b-4ab0-873f-f27ec6c423d9" condition = [(ordenado_por_gasto.index.str[:1] == "1"), (ordenado_por_gasto.index.str[:1] == "2"), (ordenado_por_gasto.index.str[:1] == "3"), (ordenado_por_gasto.index.str[:1] == "4"), (ordenado_por_gasto.index.str[:1] == "5")] values = ["Norte", "Nordeste", "Sudeste", "Sul", "Centro-Oeste"] ordenado_por_gasto["Região"] = np.select(condition, values) ordenado_por_gasto_CO = ordenado_por_gasto.loc[ordenado_por_gasto["Região"] == "Sul"] ordenado_por_gasto_CO = ordenado_por_gasto_CO[colunas_interessadas] ordenado_por_gasto_CO = ordenado_por_gasto_CO.append(ordenado_por_gasto.loc["35 São Paulo"][colunas_interessadas]) ordenado_por_gasto_CO ax = ordenado_por_gasto_CO.T.plot(figsize=(12,6)) ax.legend(loc="right", bbox_to_anchor=(.75, .2, 0.5, 0.5)) ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:,.2f}")) plt.title("Gastos na saúde da Região Sul x SP") plt.grid(axis="y") plt.ylabel("Gasto em R$") plt.show() # + id="SxLgh1KUeWzD"
10,568
/assignment2/assignment2:1.ipynb
c8de5156e6880991bc375b0f6bb845c41d04d470
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JavaStudentAlex/ML_DM_assignments
https://github.com/JavaStudentAlex/ML_DM_assignments
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38,422
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + pycharm={"name": "#%%\n", "is_executing": false} import csv import numpy as np from matplotlib import pyplot as plt # + pycharm={"name": "#%%\n", "is_executing": false} #read dataset from file dataset = [] with open("assignment02.csv") as csvfile: reader = csv.reader(csvfile) for word in csvfile: color,radius,weight,class_name = word.strip().split(",") if color == "Color": continue new_fruit = (color, float(radius), float(weight), class_name) dataset.append(new_fruit) dataset = np.array(dataset,dtype="object") print("Data is read") dataset # + pycharm={"name": "#%%\n", "is_executing": false} fruit_markers = {"Apple":"+", "Pear":"x", "Lemon":"o"} def plot_scatter_radius_weight(data, class_name): raws_to_plot = np.where(data[:,3] == class_name) matrix_to_plot = data[raws_to_plot] plt.scatter(matrix_to_plot[:,1],matrix_to_plot[:,2], c="black", marker=fruit_markers[class_name]) for fruit_name in fruit_markers.keys(): plot_scatter_radius_weight(dataset,fruit_name) plt.grid() plt.xlabel("Radius [cm]") plt.ylabel("Weight [grams]") print("Scatter plot is built") plt.show() # + pycharm={"name": "#%%\n", "is_executing": false} fruit_bar_markers = {"Apple":"/", "Pear" : "o", "Lemon": "\\",} colors = ("Green", "Yellow", "Red") length_between_colors = 5 def plot_bar_fruit_color_frequency(data, fruit): color_graph_pos = [] bar_length_measurements = [] all_fruit = data[np.where(data[:, 3] == fruit)] for color_index in range(len(colors)): color_fruit = all_fruit[np.where(all_fruit[:,0] == colors[color_index])] frout_color_bar_position = list(fruit_bar_markers.keys()).index(fruit) if len(color_fruit) != 0 else 0 current_color_graph_pos = frout_color_bar_position + color_index*length_between_colors color_graph_pos.append(current_color_graph_pos) bar_length_measurements.append(len(color_fruit)) plt.bar(color_graph_pos, bar_length_measurements, color="grey",width=1, hatch=fruit_bar_markers[fruit], label=fruit) return color_graph_pos positions = [] for fruit in fruit_bar_markers.keys(): current_bar_pos = plot_bar_fruit_color_frequency(dataset, fruit) positions.append(current_bar_pos) ticks = np.median(positions, axis=0) axes = plt.gca() axes.set_xticks(ticks) axes.set_xticklabels(colors) axes.legend() plt.ylabel("Frequency") print("Bar plot is built") plt.show() # + pycharm={"name": "#%%\n", "is_executing": false} def float_not_equal(num1, num2): float_scale = pow(10, -11) return not abs(num1-num2) < float_scale def rows_equal(row_a, row_b): if row_a[0] != row_b[0] or row_a[3] != row_b[3]: return False if float_not_equal(row_a[1], row_b[1]) or float_not_equal(row_a[2], row_b[2]): return False return True duplicates = [] for i in range(len(dataset)): for j in range(i+1, len(dataset[i])): if rows_equal(dataset[i], dataset[j]): duplicates.append(j) dataset = np.delete(dataset, duplicates, axis=0) print("Duplicates are removed") dataset # + pycharm={"name": "#%%\n", "is_executing": false} zero_indecies = np.where(dataset == 0.0) rows, cols = zero_indecies for i in range(len(rows)): x = rows[i] y = cols[i] array_for_mean = dataset[:,y] val = np.mean(np.delete(array_for_mean, x)) dataset[x,y] = val print("Zero values replaced by mean") dataset # + pycharm={"name": "#%%\n", "is_executing": false} colors_numeration = {"Green" : 0, "Red" : 1, "Yellow" : 2} for k,v in colors_numeration.items(): dataset[dataset == k] = v print("String values instead of class value are replaced for numbers") dataset # + pycharm={"name": "#%%\n", "is_executing": false} min_val = np.min(dataset[:,:3], axis=0) max_val = np.max(dataset[:,:3], axis=0) dataset[:, :3] = (dataset[:, :3] - min_val)/(max_val - min_val) print("Matrix normalized through the min max criteria") dataset
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/_build/jupyter_execute/FFG/0.ipynb
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StraightDraw/Geometry
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- import torch import torchvision import torch.nn as nn import torch.nn.functional as F print(f'torch version: {torch.__version__}') print(f'torchvision version: {torchvision.__version__}') import numpy as np import os import time import wandb import IPython.display from IPython.display import display from PIL import Image import matplotlib.pyplot as plt import matplotlib matplotlib.style.use('ggplot') # %matplotlib inline wandb.login() data_root = os.path.expanduser('~/ml_datasets/') path_to_dataset = os.path.join(data_root, 'cifar10') # ## Define the parameters config_dict = { 'batch_size': 32, 'num_epochs': 20, 'learning_rate': 0.003, 'base_lr': 0.001, 'max_lr': 0.05 } # ## Define the data # + transform = torchvision.transforms.Compose( [torchvision.transforms.ToTensor()] ) batch_size = config_dict['batch_size'] # data for training train_dataset = torchvision.datasets.CIFAR10( path_to_dataset, transform=transform, download=True, train=True, ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, shuffle=True ) # data for testing test_dataset = torchvision.datasets.CIFAR10( path_to_dataset, transform=transform, download=True, train=False, ) test_dataloader = torch.utils.data.DataLoader( test_dataset, batch_size=batch_size, shuffle=False ) # - classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') images, labels = next(iter(train_dataloader)) type(images), images.shape, type(labels), labels.shape # ## Define a convolutional neural network class LeNet5(nn.Module): def __init__(self): super(LeNet5, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5) # output shape: (N, 6, 28, 28) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # output shape: (N, C=6, 14, 14) self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5) # output shape: (N, 16, 10, 10) # self.pool will also be applied after self.conv2, the second pooling makes the output shape: (N, 16, 5, 5) self.fc1 = nn.Linear(in_features=16 * 5 * 5, out_features=120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = torch.flatten(x, start_dim=1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x lenet5 = LeNet5() logits = lenet5(images) print(f'input shape: {images.shape}, output shape: {logits.shape}') # ## Define objects to keep track of metrics during training # + class Mean(object): """ Vanilla version of tf.keras.metrics.Mean """ def __init__(self): self.value = 0. self.n = 0 def __call__(self, value): self.value += value self.n += 1 def reset_state(self): self.value = 0. self.n = 0 def result(self): return self.value / float(self.n) class SparseCategoricalAccuracy(object): """ Vanilla version of tf.keras.metrics.SparseCategoricalAccuracy """ def __init__(self): self.hits = 0 self.total = 0 def __call__(self, logits, targets): self.hits += (torch.argmax(logits, dim=1) == targets).sum().item() self.total += logits.size(0) def reset_state(self): self.hits = 0 self.total = 0 def result(self): return float(self.hits) / float(self.total) # - # ## Define an training loop def make_model(config_dict): model = LeNet5() optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) loss_func = torch.nn.CrossEntropyLoss() scheduler = torch.optim.lr_scheduler.CyclicLR( optimizer, base_lr=config_dict['base_lr'], max_lr=config_dict['max_lr'], mode='triangular2' ) return model, optimizer, scheduler, loss_func def run_one_epoch(dataloader, model, optimizer, scheduler, loss_func, training=True): loss_metric = Mean() acc_metric = SparseCategoricalAccuracy() for images, labels in dataloader: if training: optimizer.zero_grad() logits = model(images) batch_loss = loss_func(logits, labels) batch_loss.backward() optimizer.step() scheduler.step() else: with torch.no_grad(): logits = model(images) batch_loss = loss_func(logits, labels) loss_metric(batch_loss) acc_metric(logits, labels) return loss_metric.result(), acc_metric.result() def train_model(train_dataloader, test_dataloader, model, optimizer, scheduler, loss_func, config_dict): wandb.watch(model, loss_func, log="all", log_freq=1) for epoch_idx in range(config_dict['num_epochs']): model.train() train_loss, train_acc = run_one_epoch(train_dataloader, model, optimizer, scheduler, loss_func, True) model.eval() test_loss, test_acc = run_one_epoch(test_dataloader, model, None, None, loss_func, False) wandb.log( {'epoch': epoch_idx, 'train_loss': train_loss, 'train_acc': train_acc, 'test_loss': test_loss, 'test_acc': test_acc, 'lr': scheduler.get_last_lr()[0] }, step=epoch_idx ) if (epoch_idx+1) % 5 == 0: print(f'finished epoch {epoch_idx}, train_loss: {train_loss:.3f}, train_acc: {train_acc:.3f}, test_loss: {test_loss:.3f}, test_acc: {test_acc:.3f}, last_lr: {scheduler.get_last_lr()[0]:.5f}') def run(train_dataloader, test_dataloader, config_dict, path_to_model=None): with wandb.init(project='CIFAR10-LeNet5-PyTorch', config=config_dict): model, optimizer, scheduler, loss_func = make_model(config_dict) train_model(train_dataloader, test_dataloader, model, optimizer, scheduler, loss_func, config_dict) if path_to_model is not None: torch.save(model.state_dict(), path_to_model) loaded_model = LeNet5() loaded_mode.load_state_dict(torch.load(path_to_model)) run(train_dataloader, test_dataloader, config_dict) IPython.display.Image(filename='LeNet5_CIFAR10_WandB_dashboard.png') #, width=100, height=100) ce) x_target = x[:size[1]] x = self.convs[i]((x, x_target), edge_index) if i != self.num_layers - 1: x = self.bns[i](x) x = F.relu(x) x = F.dropout(x, p=self.dropout, training=self.training) # Append the node embeddings to xs xs.append(x.cpu()) # Concat all embeddings into one tensor x_all = torch.cat(xs, dim=0) return x_all # + [markdown] id="7cfm7K3wRqqY" # ## Training and Testing # # Now lets implement the training and testing functions. # # In both training and testing, we need to sample batch from the dataloader. # # Each batch in the `NeighborSampler` dataloader holds three elements: # * `batch_size`: The batch size specified in the dataloader. # * `n_id`: All nodes (in index format) used in the adjacency matrices. # * `adjs`: The three-element tuples. # + id="-JN0-_QCRn8N" def train(model, data, train_loader, train_idx, optimizer, loss_fn, mode="batch"): model.train() total_loss = 0 if mode == "batch": for batch_size, n_id, adjs in train_loader: # Move all adj sparse tensors to GPU adjs = [adj.to(device) for adj in adjs] optimizer.zero_grad() # Index on the node features out = model(data.x[n_id], adjs) train_label = data.y[n_id[:batch_size]].squeeze(-1) loss = loss_fn(out, train_label) loss.backward() optimizer.step() total_loss += loss.item() else: optimizer.zero_grad() out = model(data.x, data.adj_t, mode=mode)[train_idx] train_label = data.y.squeeze(1)[train_idx] loss = loss_fn(out, train_label) loss.backward() optimizer.step() total_loss = loss.item() return total_loss @torch.no_grad() def test(model, data, all_loader, split_idx, evaluator, mode="batch"): model.eval() if mode == "batch": out = model.inference(data.x, all_loader) else: out = model(data.x, data.adj_t, mode="all") y_true = data.y.cpu() y_pred = out.argmax(dim=-1, keepdim=True) train_acc = evaluator.eval({ 'y_true': y_true[split_idx['train']], 'y_pred': y_pred[split_idx['train']], })['acc'] valid_acc = evaluator.eval({ 'y_true': y_true[split_idx['valid']], 'y_pred': y_pred[split_idx['valid']], })['acc'] test_acc = evaluator.eval({ 'y_true': y_true[split_idx['test']], 'y_pred': y_pred[split_idx['test']], })['acc'] return train_acc, valid_acc, test_acc # + [markdown] id="AiehZ8OiR2q9" # ## Mini-batch Training # + id="zFaI2eCARy0v" args = { 'device': device, 'num_layers': 2, 'hidden_dim': 128, 'dropout': 0.5, 'lr': 0.01, 'epochs': 100, } batch_model = SAGE(data.num_features, args['hidden_dim'], dataset.num_classes, args['num_layers'], args['dropout']).to(device) batch_model.reset_parameters() optimizer = torch.optim.Adam(batch_model.parameters(), lr=args['lr']) loss_fn = F.nll_loss best_batch_model = None best_valid_acc = 0 batch_results = [] for epoch in range(1, 1 + args["epochs"]): loss = train(batch_model, data, train_loader, train_idx, optimizer, loss_fn, mode="batch") result = test(batch_model, data, all_loader, split_idx, evaluator, mode="batch") batch_results.append(result) train_acc, valid_acc, test_acc = result if valid_acc > best_valid_acc: best_valid_acc = valid_acc best_batch_model = copy.deepcopy(batch_model) print(f'Epoch: {epoch:02d}, ' f'Loss: {loss:.4f}, ' f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * valid_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') best_result = test(best_batch_model, data, all_loader, split_idx, evaluator, mode="batch") train_acc, valid_acc, test_acc = best_result print(f'Best model: ' f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * valid_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="-OyqW-1pSMLW" # ## Full-batch Training # + id="mU5eAviTSFMO" # Use the same parameters for a full-batch training args = { 'device': device, 'num_layers': 2, 'hidden_dim': 128, 'dropout': 0.5, 'lr': 0.01, 'epochs': 100, } all_model = SAGE(data.num_features, args['hidden_dim'], dataset.num_classes, args['num_layers'], args['dropout']).to(device) all_model.reset_parameters() optimizer = torch.optim.Adam(all_model.parameters(), lr=args['lr']) loss_fn = F.nll_loss best_all_model = None best_valid_acc = 0 all_results = [] for epoch in range(1, 1 + args["epochs"]): loss = train(all_model, data, train_loader, train_idx, optimizer, loss_fn, mode="all") result = test(all_model, data, all_loader, split_idx, evaluator, mode="all") all_results.append(result) train_acc, valid_acc, test_acc = result if valid_acc > best_valid_acc: best_valid_acc = valid_acc best_all_model = copy.deepcopy(all_model) print(f'Epoch: {epoch:02d}, ' f'Loss: {loss:.4f}, ' f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * valid_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') best_result = test(best_all_model, data, all_loader, split_idx, evaluator, mode="all") train_acc, valid_acc, test_acc = best_result print(f'Best model: ' f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * valid_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="NrECcOQQSZo1" # ## Visualization # + id="sh_qvSG1SV63" import numpy as np from matplotlib import pyplot as plt batch_results = np.array(batch_results) all_results = np.array(all_results) x = np.arange(1, 101) plt.figure(figsize=(9, 7)) plt.plot(x, batch_results[:, 1], label="Batch Validation") plt.plot(x, batch_results[:, 2], label="Batch Test") plt.plot(x, all_results[:, 1], label="All Validation") plt.plot(x, all_results[:, 2], label="All Test") plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend() plt.show() # + [markdown] id="WFb2OAvOSn_O" # # 2 Neighbor Sampling with Different Ratios # # Now we will implement a simplified version of Neighbor Sampling by using DeepSNAP and NetworkX, and train models with different neighborhood sampling ratios. # # To make the experiments faster, we will use the Cora graph here. # + [markdown] id="P9U0F7bnSz9u" # ## Setup # + id="PUF4on-fSxcq" import copy import torch import random import numpy as np import networkx as nx import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import SAGEConv from torch.utils.data import DataLoader from torch_geometric.datasets import Planetoid from torch.nn import Sequential, Linear, ReLU from deepsnap.dataset import GraphDataset from deepsnap.graph import Graph pyg_dataset = Planetoid('./tmp', "Cora") # + [markdown] id="qw6k-KdFTEYw" # ## GNN Model # + id="PvUlNi2TS09i" class GNN(torch.nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, args): super(GNN, self).__init__() self.dropout = args['dropout'] self.num_layers = args['num_layers'] self.convs = nn.ModuleList() self.bns = nn.ModuleList() self.convs.append(SAGEConv(input_dim, hidden_dim)) self.bns.append(nn.BatchNorm1d(hidden_dim)) for l in range(self.num_layers - 2): self.convs.append(SAGEConv(hidden_dim, hidden_dim)) self.bns.append(nn.BatchNorm1d(hidden_dim)) self.convs.append(SAGEConv(hidden_dim, hidden_dim)) self.post_mp = nn.Linear(hidden_dim, output_dim) def forward(self, data, mode="batch"): if mode == "batch": edge_indices, x = data for i in range(len(self.convs) - 1): edge_index = edge_indices[i] x = self.convs[i](x, edge_index) x = self.bns[i](x) x = F.relu(x) x = F.dropout(x, p=self.dropout, training=self.training) x = self.convs[-1](x, edge_indices[len(self.convs) - 1]) else: x, edge_index = data.node_feature, data.edge_index for i in range(len(self.convs) - 1): x = self.convs[i](x, edge_index) x = self.bns[i](x) x = F.relu(x) x = F.dropout(x, p=self.dropout, training=self.training) x = self.convs[-1](x, edge_index) x = self.post_mp(x) x = F.log_softmax(x, dim=1) return x # + [markdown] id="Ulp1A3evcJ-I" # ## Neighbor Sampling # # Here we implement functions that will sample neighbors by using DeepSNAP and NetworkX. # # Notice that node classification task on Cora is a semi-supervised classification task, here we keep all the labeled training nodes (140 nodes) by setting the last ratio to 1. # + id="LI4qHkE4cQOh" def sample_neighbors(nodes, G, ratio, all_nodes): # This fuction takes a set of nodes, a NetworkX graph G and neighbor sampling ratio. # It will return sampled neighbors (unioned with input nodes) and edges between neighbors = set() edges = [] for node in nodes: neighbors_list = list(nx.neighbors(G, node)) # We only sample the (ratio * number of neighbors) neighbors num = int(len(neighbors_list) * ratio) if num > 0: # Random shuffle the neighbors random.shuffle(neighbors_list) neighbors_list = neighbors_list[:num] for neighbor in neighbors_list: # Add neighbors neighbors.add(neighbor) edges.append((neighbor, node)) return neighbors, neighbors.union(all_nodes), edges def nodes_to_tensor(nodes): # This function transform a set of nodes to node index tensor node_label_index = torch.tensor(list(nodes), dtype=torch.long) return node_label_index def edges_to_tensor(edges): # This function transform a set of edges to edge index tensor edge_index = torch.tensor(list(edges), dtype=torch.long) edge_index = torch.cat([edge_index, torch.flip(edge_index, [1])], dim=0) edge_index = edge_index.permute(1, 0) return edge_index def relable(nodes, labeled_nodes, edges_list): # Relable the nodes, labeled_nodes and edges_list relabled_edges_list = [] sorted_nodes = sorted(nodes) node_mapping = {node : i for i, node in enumerate(sorted_nodes)} for orig_edges in edges_list: relabeled_edges = [] for edge in orig_edges: relabeled_edges.append((node_mapping[edge[0]], node_mapping[edge[1]])) relabled_edges_list.append(relabeled_edges) relabeled_labeled_nodes = [node_mapping[node] for node in labeled_nodes] relabeled_nodes = [node_mapping[node] for node in nodes] return relabled_edges_list, relabeled_nodes, relabeled_labeled_nodes def neighbor_sampling(graph, K=2, ratios=(0.1, 0.1, 0.1)): # This function takes a DeepSNAP graph, K the number of GNN layers, and neighbor # sampling ratios for each layer. This function returns relabeled node feature, # edge indices and node_label_index assert K + 1 == len(ratios) labeled_nodes = graph.node_label_index.tolist() random.shuffle(labeled_nodes) num = int(len(labeled_nodes) * ratios[-1]) if num > 0: labeled_nodes = labeled_nodes[:num] nodes_list = [set(labeled_nodes)] edges_list = [] all_nodes = labeled_nodes for k in range(K): # Get nodes and edges from the previous layer nodes, all_nodes, edges = \ sample_neighbors(nodes_list[-1], graph.G, ratios[len(ratios) - k - 2], all_nodes) nodes_list.append(nodes) edges_list.append(edges) # Reverse the lists nodes_list.reverse() edges_list.reverse() relabled_edges_list, relabeled_all_nodes, relabeled_labeled_nodes = \ relable(all_nodes, labeled_nodes, edges_list) node_index = nodes_to_tensor(relabeled_all_nodes) # All node features that will be used node_feature = graph.node_feature[node_index] edge_indices = [edges_to_tensor(edges) for edges in relabled_edges_list] node_label_index = nodes_to_tensor(relabeled_labeled_nodes) log = "Sampled {} nodes, {} edges, {} labeled nodes" print(log.format(node_feature.shape[0], edge_indices[0].shape[1] // 2, node_label_index.shape[0])) return node_feature, edge_indices, node_label_index # + [markdown] id="ooy6Hcf7TIhI" # ## Training and Testing # + id="iSmZhpzPTGPY" def train(train_graphs, val_graphs, args, model, optimizer, mode="batch"): best_val = 0 best_model = None accs = [] graph_train = train_graphs[0] graph_train.to(args['device']) for epoch in range(1, 1 + args['epochs']): model.train() optimizer.zero_grad() if mode == "batch": node_feature, edge_indices, node_label_index = neighbor_sampling(graph_train, args['num_layers'], args['ratios']) node_feature = node_feature.to(args['device']) node_label_index = node_label_index.to(args['device']) for i in range(len(edge_indices)): edge_indices[i] = edge_indices[i].to(args['device']) pred = model([edge_indices, node_feature]) pred = pred[node_label_index] label = graph_train.node_label[node_label_index] elif mode == "community": graph = random.choice(train_graphs) graph = graph.to(args['device']) pred = model(graph, mode="all") pred = pred[graph.node_label_index] label = graph.node_label[graph.node_label_index] else: pred = model(graph_train, mode="all") label = graph_train.node_label pred = pred[graph_train.node_label_index] loss = F.nll_loss(pred, label) loss.backward() optimizer.step() train_acc, val_acc, test_acc = test(val_graphs, model) accs.append((train_acc, val_acc, test_acc)) if val_acc > best_val: best_val = val_acc best_model = copy.deepcopy(model) print(f'Epoch: {epoch:02d}, ' f'Loss: {loss:.4f}, ' f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') return best_model, accs def test(graphs, model): model.eval() accs = [] for graph in graphs: graph = graph.to(args['device']) pred = model(graph, mode="all") label = graph.node_label pred = pred[graph.node_label_index].max(1)[1] acc = pred.eq(label).sum().item() acc /= len(label) accs.append(acc) return accs # + id="HV7i0v0ETKzf" args = { 'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'), 'dropout': 0.5, 'num_layers': 2, 'hidden_size': 64, 'lr': 0.005, 'epochs': 50, 'ratios': (0.8, 0.8, 1), } # + [markdown] id="rLpRYKbnTQnj" # ## Full-Batch Training # + id="pMGGjbJBTOo1" graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) graphs = [graph_train, graph_val, graph_test] all_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode="all") train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="JWkGiwB6Thr4" # ## Sampling with Ratios 0.8 # + id="yWusJ9u3Tfhv" args['ratios'] = (0.8, 0.8, 1) graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) graphs = [graph_train, graph_val, graph_test] batch_best_model, batch_accs = train(graphs, graphs, args, model, optimizer) train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="w_FjkNHDT4c6" # ## Sampling with Ratios 0.3 # + id="booJ6DASTjO4" # Change the ratio to 0.3 args['ratios'] = (0.3, 0.3, 1) graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) graphs = [graph_train, graph_val, graph_test] batch_best_model, batch_accs_1 = train(graphs, graphs, args, model, optimizer) train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], batch_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="EePAvNlGUM2K" # ## Visualization # + id="7etNAkXAT55d" import numpy as np from matplotlib import pyplot as plt batch_results = np.array(batch_accs) batch_results_1 = np.array(batch_accs_1) all_results = np.array(all_accs) x = np.arange(1, 51) plt.figure(figsize=(9, 7)) plt.plot(x, batch_results[:, 0], label="Batch 0.8 Train") plt.plot(x, batch_results[:, 1], label="Batch 0.8 Validation") plt.plot(x, batch_results[:, 2], label="Batch 0.8 Test") plt.plot(x, batch_results_1[:, 0], label="Batch 0.3 Train") plt.plot(x, batch_results_1[:, 1], label="Batch 0.3 Validation") plt.plot(x, batch_results_1[:, 2], label="Batch 0.3 Test") plt.plot(x, all_results[:, 0], label="All Train") plt.plot(x, all_results[:, 1], label="All Validation") plt.plot(x, all_results[:, 2], label="All Test") plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend() plt.show() # + [markdown] id="bkA7-0groq7q" # Here all accuracies are evaluated on the full-batch mode. # + [markdown] id="Iee0U8KGURc8" # # 3 Cluster Sampling # # Instead of the neighbor sampling, we can use another approach, subgraph (cluster) sampling, to scale up GNN. This approach is proposed in Cluster-GCN ([Chiang et al. (2019)](https://arxiv.org/abs/1905.07953)). # # In this section, we will implement vanilla Cluster-GCN and experiment with 3 different community partition algorithms. # # Notice that this section requires you have run the `Setup`, `GNN Model` and `Training and Testing` cells of the last section. # + [markdown] id="_BXjP79gUYir" # ## Setup # + id="UGQ_VKp8UOEm" import copy import torch import random import numpy as np import networkx as nx import torch.nn as nn import torch.nn.functional as F import community as community_louvain from torch_geometric.nn import SAGEConv from torch.utils.data import DataLoader from torch_geometric.datasets import Planetoid from torch.nn import Sequential, Linear, ReLU from deepsnap.dataset import GraphDataset from deepsnap.graph import Graph pyg_dataset = Planetoid('./tmp', "Cora") # + id="bzMatyCSUaB6" args = { 'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'), 'dropout': 0.5, 'num_layers': 2, 'hidden_size': 64, 'lr': 0.005, 'epochs': 150, } # + [markdown] id="ekV-sokSUeLc" # ## Partition the Graph into Clusters # # Here we use following three community detection / partition algorithms to partition the graph into different clusters: # * [Kernighan–Lin algorithm (bisection)](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection.html) # * [Clauset-Newman-Moore greedy modularity maximization](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html#networkx.algorithms.community.modularity_max.greedy_modularity_communities) # * [Louvain algorithm](https://python-louvain.readthedocs.io/en/latest/api.html) # # # To make the training more stable, we discard the cluster that has less than 10 nodes. # # Let's first define these algorithms as DeepSNAP transformation on a graph. # + id="N8XeT005UcKh" def preprocess(G, node_label_index, method="louvain"): graphs = [] labeled_nodes = set(node_label_index.tolist()) if method == "louvain": community_mapping = community_louvain.best_partition(G, resolution=10) communities = {} for node in community_mapping: comm = community_mapping[node] if comm in communities: communities[comm].add(node) else: communities[comm] = set([node]) communities = communities.values() elif method == "bisection": communities = nx.algorithms.community.kernighan_lin_bisection(G) elif method == "greedy": communities = nx.algorithms.community.greedy_modularity_communities(G) for community in communities: nodes = set(community) subgraph = G.subgraph(nodes) # Make sure each subgraph has more than 10 nodes if subgraph.number_of_nodes() > 10: node_mapping = {node : i for i, node in enumerate(subgraph.nodes())} subgraph = nx.relabel_nodes(subgraph, node_mapping) # Get the id of the training set labeled node in the new graph train_label_index = [] for node in labeled_nodes: if node in node_mapping: # Append relabeled labeled node index train_label_index.append(node_mapping[node]) # Make sure the subgraph contains at least one training set labeled node if len(train_label_index) > 0: dg = Graph(subgraph) # Update node_label_index dg.node_label_index = torch.tensor(train_label_index, dtype=torch.long) graphs.append(dg) return graphs # + [markdown] id="7CYEamCAU-TJ" # ## Louvain Preprocess # + id="-TrC6ybWU7eO" graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] graphs = preprocess(graph_train.G, graph_train.node_label_index, method="louvain") print("Partition the graph in to {} communities".format(len(graphs))) avg_num_nodes = 0 avg_num_edges = 0 for graph in graphs: avg_num_nodes += graph.num_nodes avg_num_edges += graph.num_edges avg_num_nodes = int(avg_num_nodes / len(graphs)) avg_num_edges = int(avg_num_edges / len(graphs)) print("Each community has {} nodes in average".format(avg_num_nodes)) print("Each community has {} edges in average".format(avg_num_edges)) # + [markdown] id="O03uXIuGVIgJ" # ## Louvain Training # + id="iSbGf5ADVFQq" model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) louvain_best_model, louvain_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode="community") train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], louvain_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="6CvTf0ANVO9U" # ## Bisection Preprocess # + id="HkV0zlhgVJ7u" graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] graphs = preprocess(graph_train.G, graph_train.node_label_index, method="bisection") print("Partition the graph in to {} communities".format(len(graphs))) avg_num_nodes = 0 avg_num_edges = 0 for graph in graphs: avg_num_nodes += graph.num_nodes avg_num_edges += graph.num_edges avg_num_nodes = int(avg_num_nodes / len(graphs)) avg_num_edges = int(avg_num_edges / len(graphs)) print("Each community has {} nodes in average".format(avg_num_nodes)) print("Each community has {} edges in average".format(avg_num_edges)) # + [markdown] id="IqMCvP8wVVms" # ## Bisection Training # + id="k1wgFg1bVRGY" model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) bisection_best_model, bisection_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode="community") train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], bisection_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="5PROPwoOVcJy" # ## Greedy Preprocess # + id="h3DVamWqVT92" graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] graphs = preprocess(graph_train.G, graph_train.node_label_index, method="greedy") print("Partition the graph in to {} communities".format(len(graphs))) avg_num_nodes = 0 avg_num_edges = 0 for graph in graphs: avg_num_nodes += graph.num_nodes avg_num_edges += graph.num_edges avg_num_nodes = int(avg_num_nodes / len(graphs)) avg_num_edges = int(avg_num_edges / len(graphs)) print("Each community has {} nodes in average".format(avg_num_nodes)) print("Each community has {} edges in average".format(avg_num_edges)) # + [markdown] id="93pR_-kxVgma" # ## Greedy Training # + id="lQgQY-jPVd_U" model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) greedy_best_model, greedy_accs = train(graphs, [graph_train, graph_val, graph_test], args, model, optimizer, mode="community") train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], greedy_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="D5edKKT6Vk1C" # ## Full-Batch Training # + id="N5tIXxC8ViFD" graphs_train, graphs_val, graphs_test = \ GraphDataset.pyg_to_graphs(pyg_dataset, verbose=True, fixed_split=True) graph_train = graphs_train[0] graph_val = graphs_val[0] graph_test = graphs_test[0] model = GNN(graph_train.num_node_features, args['hidden_size'], graph_train.num_node_labels, args).to(args['device']) optimizer = torch.optim.Adam(model.parameters(), lr=args['lr']) graphs = [graph_train, graph_val, graph_test] all_best_model, all_accs = train(graphs, graphs, args, model, optimizer, mode="all") train_acc, val_acc, test_acc = test([graph_train, graph_val, graph_test], all_best_model) print('Best model:', f'Train: {100 * train_acc:.2f}%, ' f'Valid: {100 * val_acc:.2f}% ' f'Test: {100 * test_acc:.2f}%') # + [markdown] id="6RpuETv7Vpx0" # ## Visualization # + id="PMK33kY5VmF5" import numpy as np from matplotlib import pyplot as plt louvain_results = np.array(louvain_accs) bisection_results = np.array(bisection_accs) greedy_results = np.array(greedy_accs) all_results = np.array(all_accs) x = np.arange(1, 151) plt.figure(figsize=(9, 7)) plt.plot(x, louvain_results[:, 1], label="Louvain Validation") plt.plot(x, louvain_results[:, 2], label="Louvain Test") plt.plot(x, bisection_results[:, 1], label="Bisection Validation") plt.plot(x, bisection_results[:, 2], label="Bisection Test") plt.plot(x, greedy_results[:, 1], label="Greedy Validation") plt.plot(x, greedy_results[:, 2], label="Greedy Test") plt.plot(x, all_results[:, 1], label="All Validation") plt.plot(x, all_results[:, 2], label="All Test") plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend() plt.show()
35,533
/IBM_Data_Engineering/Connecting to Db2 database.ipynb
694ec3bc15458043a02aae683a691420dc582efa
[]
no_license
amoldsdev/IBM-Data-Engineering-
https://github.com/amoldsdev/IBM-Data-Engineering-
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
11,579
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="bJdXH59FyqUo" # Connectin to Db2 database through Python # + colab={"base_uri": "https://localhost:8080/"} id="Mbv-C7E6yt7Y" executionInfo={"status": "ok", "timestamp": 1620054875549, "user_tz": 180, "elapsed": 29198, "user": {"displayName": "Luciano Muratore", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64", "userId": "00398105948454054445"}} outputId="0bbfc4dc-3700-4306-a6d8-238c6beebd68" pip install ibm_db # + id="B3lloVWQzEiB" executionInfo={"status": "ok", "timestamp": 1620054927750, "user_tz": 180, "elapsed": 986, "user": {"displayName": "Luciano Muratore", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64", "userId": "00398105948454054445"}} import ibm_db # + id="eoCqmp7PzGxd" executionInfo={"status": "ok", "timestamp": 1620055016942, "user_tz": 180, "elapsed": 932, "user": {"displayName": "Luciano Muratore", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64", "userId": "00398105948454054445"}} #Placeholder values dsn_hostname = "" dsn_uid = "" dsn_pwd = "" dsn_driver = "{IBM DB2 ODBC DRIVER}" dsn_database = "BLUDB" dsn_port = "50000" dsn_protocol = "TCPIP" # + colab={"base_uri": "https://localhost:8080/"} id="KQ78vo9CzMFG" executionInfo={"status": "ok", "timestamp": 1620055019380, "user_tz": 180, "elapsed": 940, "user": {"displayName": "Luciano Muratore", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64", "userId": "00398105948454054445"}} outputId="8c813ef8-3260-412f-c834-c3ca47b4becb" #Create the dsn connection string dsn = ( "DRIVER={0};" "DATABASE={1};" "HOSTNAME={2};" "PORT={3};" "PROTOCOL={4};" "UID={5};" "PWD={6};").format(dsn_driver, dsn_database, dsn_hostname, dsn_port, dsn_protocol, dsn_uid, dsn_pwd) #print the connection string to check correct values are specified print(dsn) # + colab={"base_uri": "https://localhost:8080/"} id="Kep6cHeAzeSP" executionInfo={"status": "ok", "timestamp": 1620055033802, "user_tz": 180, "elapsed": 1498, "user": {"displayName": "Luciano Muratore", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwKvrx0yTOMIfgg9Zs_ckw6Cpo2LsJBNL1LRJ_Fg=s64", "userId": "00398105948454054445"}} outputId="704f476d-f83e-467d-f84f-0ca2bcd213c8" #Create the database connection try: conn=ibm_db.connect(dsn,"","") print("Connected to database: ", dsn_database,"as user: ",dsn_uid, "on host: ",dsn_hostname) except: print("Unable to connect: ", ibm_db.conn_errormsg()) # + id="lAtZwQKFzhAC" #Retrieve Metadata for the Database Server server = ibm_db.server_info(conn) print ("DBMS_NAME: ", server.DBMS_NAME) print ("DBMS_VER: ", server.DBMS_VER) print ("DB_NAME: ", server.DB_NAME) # + id="hNppDQ1tzkMK" #Retrieve Metadata for the Database Client / Driver client = ibm_db.client_info(conn) print ("DRIVER_NAME: ", client.DRIVER_NAME) print ("DRIVER_VER: ", client.DRIVER_VER) print ("DATA_SOURCE_NAME: ", client.DATA_SOURCE_NAME) print ("DRIVER_ODBC_VER: ", client.DRIVER_ODBC_VER) print ("ODBC_VER: ", client.ODBC_VER) print ("ODBC_SQL_CONFORMANCE: ", client.ODBC_SQL_CONFORMANCE) print ("APPL_CODEPAGE: ", client.APPL_CODEPAGE) print ("CONN_CODEPAGE: ", client.CONN_CODEPAGE) # + id="1RkPaQHhzpFt" #close connection ibm_db.close(conn)
3,734
/evento_sjc.ipynb
a35551ad71d6c783dddcc62815de2a8c344606ff
[]
no_license
jeference/analise_vendaval_sjc
https://github.com/jeference/analise_vendaval_sjc
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
2,426,965
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="y2pvpjUuwKFg" # # Análises do Evento de vendaval no município de São José dos Campos em 09 de Março de 2021 # + [markdown] id="rzvokE7KwdLZ" # Eventos extremos podem ocorrer em diferentes grandezas, como chuvas, ventos e nível de rios. Tais extremos podem deflagar desastres naturais/socioambientais - processos complexos que envolvem construções sociais e gatilhos naturais. O evento meteorológico extremo que ocorreu no dia 09 de março de 2021 em São José dos Campos, interior de São Paulo, pode ser classificado como um Vendaval. Mesmo não havendo registros de inundações ou deslizamentos/movimentos de massa nessa data na cidade, o vendaval gerou diversos impactos aos cidadãoes joseenses, especialmente pelas quedas de árvores e galhos e pela interrupção do fornecimento de energia elétrica - essa é a justificativa para o trabalho aqui apresentado. # + [markdown] id="XoSseXAcx59O" # ## Análises Meteorológicas # + [markdown] id="szjrgJjwyRJt" # Fala do Meteorologista e Pesquisador Giovanni Dollif sobre o processo meteorológico: https://g1.globo.com/sp/vale-do-paraiba-regiao/noticia/2021/03/10/meteorologista-aponta-que-sao-jose-dos-campos-foi-atingida-por-tornado.ghtml # # Registra-se, ainda, que a taxa de precipitação chegou a 7 mm em 10 min, com acumulado da ordem de 50 mm para todo o evento em alguns pontos da cidade (referência pelo pluviômetro do Cemaden situado no Parque Tecnológico de São José dos Campos). O total esperado para o mês de março na cidade é de aproximadamente 150 mm. A título de comparação, no evento de 06 de março de 2015, a taxa de precipitação chegou a 15 mm em 10 minutos, acumulando 59 mm em um intervalo de 1 hora (Santos et al., 2015). # + [markdown] id="_mPbV4gx_TPU" # ### Nível de chuva # + [markdown] id="lcpIkB4MRDea" # #### Importando as bibliotecas # # + id="RVok9M7asYYg" import pandas as pd import matplotlib.pyplot as plt from datetime import timedelta # + [markdown] id="YiRWb0wsROkT" # #### Lendo os dados de nível de chuva [mm] no dia do evento # + id="aSiNWOMGPeBo" df = pd.read_csv("data.csv", sep=';', index_col=False) # Lendo o CSV df.columns=df.columns.str.replace('\t','') # Ta separado por ; mas tem uns tabs aleatórios, aqui vai limpar os tabs do nome das colunas df['valorMedida']=df['valorMedida'].str.replace('\t','.').astype(float) # tirando os tabs dos valores e transformando em inteiro df['latitude']=df['latitude'].str.replace('\t','.') # mesma coisa pra latitude df['longitude']=df['longitude'].str.replace('\t','.') # e longitude converting = timedelta(hours = 3) # convertendo de utc -3h pra hora local df["datahora"] = pd.to_datetime(df["datahora"]) - converting # transformando a coluna de data e hora no tipo datetime # + [markdown] id="f2fPKycT-PCO" # #### Dividindo os dados só para o horário do ocorrido # + id="ilT2Q7g0-PiH" start_time = pd.to_datetime("2021-03-09 17:00:00") # definindo o começo dos dados finish_time = pd.to_datetime("2021-03-10 21:00:00") # definindo o fim dos dados df = df[df['datahora'] > start_time ] df = df[df['datahora'] < finish_time ] estacoes = set(df['nomeEstacao']) # + [markdown] id="HivbkPEO-UfU" # #### Plot para cada estação meteorológica # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="W1hLUpNG-T66" outputId="cb7474a1-64a7-479d-b909-5a566ebe7468" for estacao in estacoes: # pra percorrer todas as estações únicas cod = set(df[df['nomeEstacao'] == estacao]["codEstacao"]) # guardando o código lat = set(df[df['nomeEstacao'] == estacao]["latitude"]) # a latitude long = set(df[df['nomeEstacao'] == estacao]["longitude"]) # a longitude title = f"Estação: {estacao}, Codigo: {list(cod)[0]} \nlat/long: {list(lat)[0]},{list(long)[0]}" # pra colocar no título de cada uma df[df["nomeEstacao"] == estacao].plot(x = 'datahora', y = "valorMedida", title = title, grid = True) # fazendo o gráfico com um slicing pra cada estação # + [markdown] id="BtLhRYWE-Y6Q" # # # # #### Plot para todas as estações meteorológicas juntas # + colab={"base_uri": "https://localhost:8080/", "height": 310} id="Wev4CeVZ-YZo" outputId="15391473-90bf-4d15-b1be-15fce1ec732b" # agora gráfico com todas as estações plt.figure() # Criando a figura plt.title("All stations") # Titulo plt.grid() for i in estacoes: plt.plot(df[df['nomeEstacao'] == i]["datahora"], df[df['nomeEstacao'] == i]["valorMedida"], label = i) # Fazendo o plot plt.xticks(rotation=45) # Rodando os ticks pra não ficar atropelando plt.legend(bbox_to_anchor=(1.05, 1)) # mudando a legenda pra fora do gráfico plt.show() # mostrando o gráfico # + [markdown] id="XU1cg_Al9LOX" # ### Formação de nuvens # # + [markdown] id="wRrBbc20-NEO" # A animação de imagens de satélite mostra a rápida formação de nuvens carregadas (convecção profunda), na região e horário onde foram registradas as descargas elétricas na atmosfera, o vento no aeroporto, a chuva e os estragos na cidade. A forma da convecção nas imagens não mostra um formato claro, mas há semelhança com o padrão de uma linha de instabilidade (https://www.sciencedirect.com/topics/earth-and-planetary-sciences/squall-line, https://www.nssl.noaa.gov/education/svrwx101/thunderstorms/types/ ) . São necessárias análises mais detalhadas, possivelmente de dados de Radar Meteorológico (preferencialmente de Banda X), para uma classificação mais confiável. # # + [markdown] id="L923aSop-3bQ" # ### Ocorrências de raios # + [markdown] id="L2sHBcJ6_3nn" # A figura (abaixo) de acumulados de descargas elétricas na atmosfera revelam uma significativa concentração de registros dentro do município de São José dos Campos entre 18h e 20h. # # + [markdown] id="KAR3BRV3AGm4" # ![raios_vp_20210309.gif](data:image/gif;base64,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) # + [markdown] id="htjcTlRaAiYN" # ## Análise da mobilidade urbana nas regiões mais afetadas pelo vendaval # + [markdown] id="4hzOh4BEAn78" # O setor de transportes e mobilidade está entre os mais afetados por eventos meteorológicos intensos. No evento de 09 de março de 2021 em São José dos Campos/SP não foi diferente. # # Interessado nas características temporais, espaciais e espaço-temporais da mobilidade urbana em São José dos Campos/SP? Aqui está um artigo científico novinho em folha sobre o tema: # https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248126 # # # + [markdown] id="uavKI8MlCpyM" # ### Origem # + [markdown] id="3-qWypwkBW9U" # A Figura abaixo mostra um mapa com o número de pessoas que fizeram viagem com início entre 18h e 20h com origem nas Zonas de Tráfego (ZTs) 14 (Jd. Aquarius e Colinas) e 32 (Jd. Alvorada e Jd. das Indústrias). Nota-se que há um grande número de pessoas que se deslocam dentro da própria zona. Desconsiderando esses deslocamentos intrazonas, as ZTs 28, 34, 29 e 30 são as que recebem maior fluxo de pessoas com origem nas ZTs 14 e 32. # # + [markdown] id="FejdGpFqBeMS" # ![mapaOrigens.jpeg](data:image/jpeg;base64,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# + [markdown] id="4IQ-hV45Ctxr" # ### Destino # + [markdown] id="3k7u9OHkB6UV" # A Figura abaixo, por sua vez, traz um mapa com o número de pessoas que fizeram viagem com início entre 18h e 20h com destino às Zonas de Tráfego (ZTs) 14 (Jd. Aquarius e Colinas) e 32 (Jd. Alvorada e Jd. das Indústrias). Nota-se que há um grande número de pessoas que se deslocam dentro das próprias zonas analisadas. Desconsiderando esses deslocamentos intrazonas, as ZTs 1 e 6 são as que possuem maior fluxo de pessoas com destino às ZTs 14 e 32, seguidas pelas ZTs 31, 7, 25 e 13 respectivamente. Os dados de mobilidade urbana analisados referem-se à Pesquisa Origem-Destino realizada em 2011. # # + [markdown] id="A-KlK96HCOwb" # 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) # + [markdown] id="Sur3xY4uxfZ4" # ## Análise dos impactos do evento sob a perspectiva de redes complexas # + [markdown] id="mjIEE5Li0aUM" # A partir de dados do sistema viário de São José dos Campos/SP, obtidos através da plataforma OpenStreetMaps, foi construída uma rede de arruamento através da aplicação gis4graph (https://github.com/aurelienne/gis4graph). # O grafo (objeto computacional que representa a rede), conta com 1184 ruas e foi construído com base no sistema viário da região Oeste do município, devido ao alto impacto gerado na região em consequência do vendaval. A imagem abaixo mostra o sistema viário do município # # + [markdown] id="B7lLQnEWu0NK" # ![WhatsApp Image 2021-03-11 at 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) # + [markdown] id="7xkWDWd3C3qq" # ### Beneficios da abordagem de redes # + [markdown] id="40O7E5qy02X-" # Através de uma abordagem de redes é possível obter medidas que indiquem quais ruas, quando interditadas, geram um maior impacto na eficiência geral da mobilidade na cidade. Este tipo de análise é especialmente útil em casos de eventos intensos como o do dia 09 de Março, onde diversas vias públicas tornam-se inacessíveis, e devido a escassez de recursos é necessário priorizar a restauração do acesso às vias mais importantes, algo que nem sempre é algo evidente. Deste modo, métricas como o índice de vulnerabilidade podem ser úteis como suporte à tomada de decisões sobre quais vias devem ser priorizadas. # # + [markdown] id="cG83B0pINKXn" # ### Cálculo do Índice de Vulnerabilidade # + [markdown] id="AWX2hE72MXUr" # A eficiência $e_{ij}$ na comunicação entre os nós $i$ e $j$ é inversamente proporcional ao comprimento do seu menor caminho, i.e., $e_{ij} \sim {1}/{d_{ij}}$. Vamos considerar um grafo $G = (V, L)$, onde $V$ é o conjunto de $|V|= N$ nós e $L$ é o conjunto de $|L| = M$ arestas. Dado que $E$ é a eficiencia global, e dado que $V_{k}$ é a vulnerabilidade associada ao vértice (ou aresta) $k$ do grafo $G$, a vulnerabilidade $V_{k}$ associada a um elemento $k$ será dada por # $V_{k}$ = $\frac{E - E_{k}^{\star}}{E}$, onde $E_{k}^{\star}$ é a eficiência do grafo após a desconexeão do elemento $k$ . # + [markdown] id="b25qUJQ4DCfy" # ### Mapa de Vulnerabilidade # + [markdown] id="HeSUMXjiHnqG" # A Figura abaixo mostra a aplicação do índice de vulnerabilidade topológico na região Oeste do município, indicando que é possível haver uma perda de eficiência de até 3% na mobilidade da rede em caso da interdição de determinadas vias. Dentre as mais vulneráveis, além da Rodovia Presidente Dutra, estão inclusas tanto avenidas, como a Av. São João e Av. Possidônio José de Freitas, quanto ruas, como as R. Emílio Marelo e R. Carlos Marcondes. # + [markdown] id="GQmX07U3u3qa" # ![WhatsApp Image 2021-03-11 at 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[]
no_license
alexanderlwkuhn/Keras-CNN-image-classification
https://github.com/alexanderlwkuhn/Keras-CNN-image-classification
0
0
null
null
null
null
Jupyter Notebook
false
false
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13,644
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl mpl.rcParams['figure.dpi'] = 100 t_min, t_max = 0.0, 2.0 intervals = 25 dt = (t_max - t_min) / intervals Nt = intervals+1 time=np.linspace(t_min, t_max, Nt) theta = np.pi/6 v0 = 8.0 gx, gy = 0.0, -10.0 ax, ay = np.ones(Nt)*gx, np.ones(Nt)*gy vx, vy = np.zeros(Nt), np.zeros(Nt) vx[0], vy[0] = v0*np.cos(theta), v0*np.sin(theta) for i in range(Nt-1): vx[i+1] = vx[i] + ax[i]*dt vy[i+1] = vy[i] + ay[i]*dt # + px, py = np.zeros(Nt), np.zeros(Nt) vx_avg = 0.5*(vx[1:] + vx[:Nt-1]) vy_avg = 0.5*(vy[1:] + vy[:Nt-1]) for i in range(Nt-1): px[i+1] = px[i] + vx_avg[i]*dt py[i+1] = py[i] + vy_avg[i]*dt # - px_real = v0*np.cos(theta)*time py_real = 0.5*ay*time**2 + v0*np.sin(theta)*time plt.plot(px_real, py_real, 'o', label='real trajectory') plt.plot(px, py, label='centered trajectory') plt.legend() plt.xlabel('x') plt.ylabel('y') plt.gca().set_aspect('equal') # - 如果速度的numerical是正好符合的,那么位移的numerical就会偏差; # - 如果位移的numerical是正好符合的, 那么速度的numerical就会偏差;
1,319
/Google Search Analyser.ipynb
b6fe2421acd0b48bba9e99bcf6e2f06c9a0881d4
[]
no_license
aath0/Google-Searches-Analyzer
https://github.com/aath0/Google-Searches-Analyzer
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Importing & analyzing google search history data! # # # In this notebook you can analyze your google search history data. First, you have to request your data from google, with instructions that you can find [here](https://goo-searches-analyzer.herokuapp.com/). # # The goal is to get your data in a more usable format that what google provides when you're requesting it and be able to do some quantitative analyses yourself. And of course to get some nice plots! # # All of the plots that are generated from this notebook will be saved in your Open Humans home folder, so check that out once you're done. # # # ### Step 0: Import your data from Open Humans! # Unless you know what you're doing I'd recommend not changing this part: # + import zipfile, requests, os, json, tempfile from bs4 import BeautifulSoup response = requests.get("https://www.openhumans.org/api/direct-sharing/project/exchange-member/?access_token={}".format(os.environ.get('OH_ACCESS_TOKEN'))) user = json.loads(response.content) for entry in user['data']: if entry['source'] == "direct-sharing-151": # unique project id (you can find that in your OH website) google_data_url = entry['download_url'] break google_data_url tf = tempfile.NamedTemporaryFile() print('downloading file') tf.write(requests.get(google_data_url).content)# write a temporary file with the download request tf.flush() zf = zipfile.ZipFile(tf.name) print('reading index') with zf.open('Takeout/My Activity/Search/MyActivity.html', 'r') as f: soup = BeautifulSoup(f) # - # ### 1. Retrieve your google searches from the messy .html file that you got from Google # Here we're simply retrieving the search queries that you made to google, and the dates when you made them # + divs = soup.find_all('div', class_="content-cell mdl-cell mdl-cell--6-col mdl-typography--body-1") search_queries = [] dates = [] for element in divs: if str(element.contents[0]).startswith('Searched'): search_queries.append(element.contents[1].text.split()) dates.append(element.contents[-1]) # - # ### 2. Keep only the essential search terms # # Here we'll be removing stop words (common words, such as "the" / "they", etc). The goal is to keep only the most infomrative search terms, in the form of a list ('filtered_words'). # + import nltk nltk.download('stopwords') from nltk.tokenize import word_tokenize from nltk.corpus import stopwords stop_words = set(stopwords.words('english')) filtered_words = [[word for word in single_search if word not in stopwords.words('english')] for single_search in search_queries] # - # ### 3. Do some more formatting # # Here we are extracting information about the date and time when you did your searches. Also, we're breaking down each search into unique search terms ('single_words') # + from datetime import datetime, time formated_dates = [datetime.strptime(sdate, "%b %d, %Y, %I:%M:%S %p") for sdate in dates] formated_days = [sdate.isoweekday() for sdate in formated_dates] single_words = [] single_dates = [] single_days = [] for search,timepoint,day in zip(filtered_words, formated_dates, formated_days): for word in search: single_words.append(word.replace("\"", "").replace("“", "").replace('\'', '').replace('‘', '')) single_dates.append(timepoint) single_days.append(day) # - # ### 4. Time to plot something! # # Now we're ready to combine everything into a dataframe and do our very first plot! We'll be plotting the frequency of searches for the top 10 most searched terms. You can change that by modifying 'items2plot' # # Not very surprisingly, my efforts to learn python clearly show here! Also, it wouldn't be too hard to guess that I'm into neuroscience, programming experiments with [psychopy](http://www.psychopy.org/) and mainly working with electroencephalography data and [mne](https://www.martinos.org/mne/stable/index.html). # + import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt import pylab, os # combine everything in a dataframe: searches_df = pd.DataFrame({'Searches' : single_words, 'Dates' : single_dates, 'Days': single_days}, columns=['Searches','Dates','Days']) searches_df_week = searches_df.loc[searches_df['Days'] <= 5] searches_df_weekend = searches_df.loc[searches_df['Days'] > 5] items2plot = 10 # number of top terms we want to plot. cpalette = sns.color_palette("Set3", items2plot) searches_counted = searches_df.groupby('Searches').count().sort_values(by=['Dates'],ascending=False).reset_index() dictPalette = {} for counter,search_term in enumerate(list(searches_counted[:items2plot]['Searches'])): dictPalette[search_term] = cpalette[counter] # and the actual plot: # %matplotlib inline fig=plt.figure(figsize=(14, 3), dpi= 150) sns.set(font_scale = 2) sns.barplot(y='Dates', x='Searches', data=searches_counted[:items2plot], palette=cpalette) plt.xticks(rotation=90) plt.ylabel('# searches') plt.xlabel('Search terms') # save figure: out_f = os.getcwd() pylab.savefig(out_f + '/TopSearches.png', bbox_inches='tight') # - # ### 5. How do my searches evolve over the past few months? # # Now it's time to use all the date & time information that we've been extracting. We're still keeping the top 10 search terms that we extracted in step 4, but now we're looking how each of them changes over time. # # In my case I unfortunately only had google search data from November 2017. My python queries peak in January 2018, and after that they keep a rather low but steady pace. Maybe I did learn something after all! # + def export_year_month(x): return datetime.strptime(datetime.strftime(x, '%Y-%m'),'%Y-%m') searches_df['year_month'] = searches_df['Dates'].apply(export_year_month) searches_by_month = searches_df.groupby(['Searches','year_month']).count().reset_index() top_queries = searches_by_month[searches_by_month['Searches'].isin(list(searches_counted[:items2plot]['Searches']))] # %matplotlib inline plt.style.use('seaborn-whitegrid') fig = plt.figure(figsize=(14, 3), dpi=150) counter = 0 for name, group in top_queries[top_queries['year_month']>datetime.strptime('2017-10-01', '%Y-%m-%d')].groupby('Searches'): plt.plot(group.year_month, group.Dates, marker = 'o', ms=12, label = name, color = dictPalette[name]) counter +=1 plt.xlabel('Year-Month') plt.ylabel('# searches') plt.legend(loc='upper left', bbox_to_anchor=(1., 1.1), ncol=1, fancybox=True, shadow=True) pylab.savefig(out_f + '/TopSearches_By_Month.png') # - # ### 6. How do my searches change throughout the day? # # Going back to the time information that we extracted, you can now group the top search terms by the hour of the day when you searched for them. # # I'd say that I'm an python evening kind of person... # + def export_hour(x): return int(datetime.strftime(x, '%H')) searches_df['Hour'] = searches_df['Dates'].apply(export_hour) searches_by_hour = searches_df.groupby(['Searches','Hour']).count().reset_index() top_queries_hour = searches_by_hour[searches_by_hour['Searches'].isin(list(searches_counted[:items2plot]['Searches']))] # %matplotlib inline plt.style.use('seaborn-whitegrid') fig = plt.figure(figsize=(14, 3), dpi = 300) counter = 0 for name, group in top_queries_hour.groupby('Searches'): plt.plot(group.Hour, group.Dates, marker = 'o', ms=12, label = name, color = dictPalette[name]) counter +=1 plt.legend(loc='upper left', bbox_to_anchor=(1., 1.1), ncol=1, fancybox=True, shadow=True) plt.ylim(0,75) plt.xlim(0,23) plt.xlabel('Hour') plt.ylabel('# searches') pylab.savefig(out_f + '/TopSearches_ByHour.png', bbox_inches='tight') # - # ### 7. Zooming into specific search terms # # Here you can choose specific terms from your searches and see how these evolve over time of the day. You can add your own search terms in the first line of this cell ('my_favourite_searches'). For this we normalize each time course by the total number of searches for a specific term. # # In my case, after figuring out that I'm doing most of my python searches in the evening, I wanted to see whether my R or matlab schedules are any different. It looks like I'm mostly searching for help with R earlier in the day compared to python or matlab... # + # focus on these search terms only: my_favourite_searches = ['python', 'matlab','R'] searches_by_programming = searches_df.groupby(['Searches','Hour']).count().reset_index() top_queries_programming = searches_by_hour[searches_by_programming['Searches'].isin(my_favourite_searches)] # %matplotlib inline plt.style.use('seaborn-whitegrid') fig = plt.figure(figsize=(14, 3), dpi = 300) counter = 0 for name, group in top_queries_programming.groupby('Searches'): plt.plot(group.Hour, group.Dates/pd.Series.max(group.Dates), marker = 'o', ms=12, label = name, color = dictPalette[name]) counter +=1 plt.legend(loc='upper left', bbox_to_anchor=(1., 1.1), ncol=1, fancybox=True, shadow=True) plt.ylim(0,1.1) plt.xlabel('Hour') plt.ylabel('search ratio') pylab.savefig(out_f + '/TopSearches_ByProgramming.png', bbox_inches='tight') # - # ### 8. Google searches as a network # # Now you can visualize your searches within a network. Here you can see whether some of your search terms tend to co-occur with others, and in which frequency. The first step for this analysis is to create a graph object containing all search terms. # + import networkx as nx import itertools G = nx.MultiGraph() L=2 # for each unique search term add a node: G.add_nodes_from(list(searches_counted['Searches'])) # Prepare queries by stripping them from unwanted characters: filtered_words_stripped = [] for search in filtered_words: filtered_words_stripped.append([word.replace("\"", "").replace("“", "").replace('\'', '').replace('‘', '') for word in search]) # for each stripped query add an edge: for queries in filtered_words_stripped: for subset in itertools.combinations(queries, L): G.add_edges_from([subset]) # Transofrm to a simple graph, and compute the rate of occurence as weights: Gf = nx.Graph() for u,v,data in G.edges(data=True): w = 1.0 if Gf.has_edge(u,v): Gf[u][v]['weight'] += w else: Gf.add_edge(u, v, weight=w) # - # ### 9. How are my search terms connected? # # Time to visualize the connections among the top search terms! For display we're only keeping searches that appear more than 40 times, and are connected with at least 5 other search terms. # + cutoff = 40 # minimum number of times a search term has to appear cutoff_edge = 5 # minimum number of connections top_terms = list(searches_counted[searches_counted['Dates']>cutoff]['Searches']) # filter edges by top occurences & top weights: top = [edge for edge in Gf.edges(data=True) if (((edge[0] in top_terms) or (edge[1] in top_terms)) and edge[2]['weight'] > cutoff_edge)] Gf_plot = nx.Graph(top) # compute size of nodes, proportional to word occurence: nodesize = [int(searches_counted[searches_counted['Searches'] == node]['Dates'])*10 for node in Gf_plot.nodes] fsize = 18 # font size edges,weights = zip(*nx.get_edge_attributes(Gf_plot,'weight').items()) weights=np.array(list(weights))*100 # edge weights (for color) fig, ax = plt.subplots(figsize=(14, 14)) nx.draw_networkx(Gf_plot, nx.spring_layout(Gf_plot, 0.5), with_labels=True, edge_color=weights, edge_cmap = plt.get_cmap('pink'),font_size = fsize,node_size = nodesize, alpha = 0.7) # OR: nx.draw(G, pos) plt.axis('off') pylab.savefig(out_f + '/Network.png', bbox_inches='tight') # - # ### 10. How is my top search term connected to other searches? # # Finally, you can see how some of your most searched terms are connected to other searches. For this we're filtering our graph object for a very high cutoff (i.e. rate of search term occurence) and a rather low number of co-occuring connections (cutoff_edge). # # For my queries, it shows the terms that I've been searching together with Python! # + cutoff = 400 cutoff_edge = 1 top_terms = list(searches_counted[searches_counted['Dates']>cutoff]['Searches']) # filter edges by top occurences & top weights: top = [edge for edge in Gf.edges(data=True) if (((edge[0] in top_terms) or (edge[1] in top_terms)) and edge[2]['weight'] > cutoff_edge)] Gf_plot = nx.Graph(top) # compute size of nodes, proportional to word occurence: nodesize = [int(searches_counted[searches_counted['Searches'] == node]['Dates'])*10 for node in Gf_plot.nodes] fsize = 18 # font size edges,weights = zip(*nx.get_edge_attributes(Gf_plot,'weight').items()) weights=np.array(list(weights))*100 # edge weights (for color) fig, ax = plt.subplots(figsize=(14, 14)) nx.draw_networkx(Gf_plot, nx.spring_layout(Gf_plot, 0.5), with_labels=True, edge_color=weights, edge_cmap = plt.get_cmap('pink'),font_size = fsize,node_size = nodesize, alpha = 0.7) # OR: nx.draw(G, pos) plt.axis('off') pylab.savefig(out_f + '/Network_OneNode.png', bbox_inches='tight') # - tim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4) #scheduler = StepLR(optimizer, step_size=70, gamma=0.1) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50,70,75,80], gamma=0.1) criterion = nn.CrossEntropyLoss() # - # Implement validation def train(epoch): model.train() #writer = SummaryWriter() for batch_idx, (data, target) in enumerate(train_loader): if use_cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) optimizer.zero_grad() output = model(data) correct = 0 pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.data.view_as(pred)).sum() loss = criterion(output, target) loss.backward() accuracy = 100. * (correct.cpu().numpy()/ len(output)) optimizer.step() if batch_idx % 5*show_step == 0: # if batch_idx % 2*show_step == 0: # print(model.layers[1].conv1D.weight.shape) # print(model.layers[1].conv1D.weight[0:2][0:2]) print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, Accuracy: {:.2f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item(), accuracy)) # f1=open("Cifar10_INFO.txt","a+") # f1.write("\n"+'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, Accuracy: {:.2f}'.format( # epoch, batch_idx * len(data), len(train_loader.dataset), # 100. * batch_idx / len(train_loader), loss.item(), accuracy)) # f1.close() #writer.add_scalar('Loss/Loss', loss.item(), epoch) #writer.add_scalar('Accuracy/Accuracy', accuracy, epoch) scheduler.step() # + def validate(epoch): model.eval() #writer = SummaryWriter() valid_loss = 0 correct = 0 for data, target in valid_loader: if use_cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) output = model(data) valid_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.data.view_as(pred)).sum() valid_loss /= len(valid_idx) accuracy = 100. * correct.cpu().numpy() / len(valid_idx) print('\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( valid_loss, correct, len(valid_idx), 100. * correct / len(valid_idx))) # f1=open("Cifar10_INFO.txt","a+") # f1.write('\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( # valid_loss, correct, len(valid_idx), # 100. * correct / len(valid_idx))) # f1.close() #writer.add_scalar('Loss/Validation_Loss', valid_loss, epoch) #writer.add_scalar('Accuracy/Validation_Accuracy', accuracy, epoch) return valid_loss, accuracy # + # Fix best model def test(epoch): model.eval() test_loss = 0 correct = 0 for data, target in test_loader: if use_cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) output = model(data) test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.data.view_as(pred)).cpu().sum() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct.cpu().numpy() / len(test_loader.dataset))) # f1=open("Cifar10_INFO.txt","a+") # f1.write('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( # test_loss, correct, len(test_loader.dataset), # 100. * correct.cpu().numpy() / len(test_loader.dataset))) # f1.close() # - def save_best(loss, accuracy, best_loss, best_acc): if best_loss == None: best_loss = loss best_acc = accuracy file = 'saved_models/best_save_model.p' torch.save(model.state_dict(), file) elif loss < best_loss and accuracy > best_acc: best_loss = loss best_acc = accuracy file = 'saved_models/best_save_model.p' torch.save(model.state_dict(), file) return best_loss, best_acc # + # Fantastic logger for tensorboard and pytorch, # run tensorboard by opening a new terminal and run "tensorboard --logdir runs" # open tensorboard at http://localhost:6006/ from tensorboardX import SummaryWriter best_loss = None best_acc = None import time SINCE=time.time() for epoch in range(max_epoch): train(epoch) loss, accuracy = validate(epoch) best_loss, best_acc = save_best(loss, accuracy, best_loss, best_acc) NOW=time.time() DURINGS=NOW-SINCE SINCE=NOW print("the time of this epoch:[{} s]".format(DURINGS)) if epoch>=10 and (epoch-10)%2==0: test(epoch) # writer = SummaryWriter() # writer.export_scalars_to_json("./all_scalars.json") # writer.close() #---------------------------- Test ------------------------------ test(epoch) # - # # Step 3: Test test(epoch) # ## 第一次 scale 位于[0,1] # ![](http://op4a94iq8.bkt.clouddn.com/18-7-14/70206949.jpg) # + # 查看训练过程的信息 import matplotlib.pyplot as plt def parse(in_file,flag): num=-1 ys=list() xs=list() losses=list() with open(in_file,"r") as reader: for aLine in reader: #print(aLine) res=[e for e in aLine.strip('\n').split(" ")] if res[0]=="Train" and flag=="Train": num=num+1 ys.append(float(res[-1])) xs.append(int(num)) losses.append(float(res[-3].split(',')[0])) if res[0]=="Validation" and flag=="Validation": num=num+1 xs.append(int(num)) tmp=[float(e) for e in res[-2].split('/')] ys.append(100*float(tmp[0]/tmp[1])) losses.append(float(res[-4].split(',')[0])) plt.figure(1) plt.plot(xs,ys,'ro') plt.figure(2) plt.plot(xs, losses, 'ro') plt.show() def main(): in_file="D://INFO.txt" # 显示训练阶段的正确率和Loss信息 parse(in_file,"Train") # "Validation" # 显示验证阶段的正确率和Loss信息 #parse(in_file,"Validation") # "Validation" if __name__=="__main__": main() # + # 查看训练过程的信息 import matplotlib.pyplot as plt def parse(in_file,flag): num=-1 ys=list() xs=list() losses=list() with open(in_file,"r") as reader: for aLine in reader: #print(aLine) res=[e for e in aLine.strip('\n').split(" ")] if res[0]=="Train" and flag=="Train": num=num+1 ys.append(float(res[-1])) xs.append(int(num)) losses.append(float(res[-3].split(',')[0])) if res[0]=="Validation" and flag=="Validation": num=num+1 xs.append(int(num)) tmp=[float(e) for e in res[-2].split('/')] ys.append(100*float(tmp[0]/tmp[1])) losses.append(float(res[-4].split(',')[0])) plt.figure(1) plt.plot(xs,ys,'r-') plt.figure(2) plt.plot(xs, losses, 'r-') plt.show() def main(): in_file="D://INFO.txt" # 显示训练阶段的正确率和Loss信息 parse(in_file,"Train") # "Validation" # 显示验证阶段的正确率和Loss信息 parse(in_file,"Validation") # "Validation" if __name__=="__main__": main() # -
21,145
/R for beginer 4 Graphics with R.ipynb
679d5c21f9a370769e168477de5faa003fb21efe
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# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # text_representation: # extension: .r # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: R # language: R # name: ir # --- # ## Basic # + # demo(graphics) # + # barplot(rnorm(1000, 0.3, 10)) # + # qqplot(rnorm(1000, 0, 10), 1:1000) # - dnorm(20, 0.3, 10) pnorm(20, 0.3, 10) qnorm(0.6, 0.3, 10) quantile(rnorm(1000, 0.3, 10), 0.6) # + # demo(persp) # + # ?device # - dev.list(); dev.cur(); dev.set(3) dev.off(3) dev.off() # + # x11() # + # split.screen(c(1,2)) # + # screen(2) # - layout(matrix(1:4, 2, 2)) # + # layout.show(4) # + # m <- matrix(c(1:3, 3), 2, 2) # + # layout(m) # + # m <- matrix(1:4, 2, 2) # layout(m, widths=c(1,3), # height=c(3,1)) # layout.show(4) # - x <- rep(1,100); y = rexp(100) # + # plot(x, y) # + # iris[,4:5] # + # sunflowerplot(iris[,4:5],col="gold",seg.col="gold") # + # ?sunflowerplot # + # x <- c(1,1,1,3,2,2,2,4,4,4); y<- c(3,3,3,2,2,2,2,3,3,3) # sunflowerplot(x, y) # + # ?coplot # + # quakes # + # coplot(lat ~ long | depth, data = quakes) # given.depth <- co.intervals(quakes$depth, number = 4, overlap = .1) # coplot(lat ~ long | depth, data = quakes, given.v = given.depth, rows = 1) # + # require(graphics) # with(ToothGrowth, { # interaction.plot(dose, supp, len, fixed = TRUE) # dose <- ordered(dose) # interaction.plot(dose, supp, len, fixed = TRUE, col = 2:3, leg.bty = "o") # interaction.plot(dose, supp, len, fixed = TRUE, col = 2:3, type = "p") # }) # + # (-4:5)^2 # + # matplot((-4:5)^2, main = "Quadratic") # almost identical to plot(*) # + # sines <- outer(1:20, 1:4, function(x, y) sin(x / 20 * pi * y)) # matplot(sines, pch = 1:4, type = "o", col = rainbow(ncol(sines))) # + # x <- 0:50/50 # matplot(x, outer(x, 1:8, function(x, k) sin(k*pi * x)), # ylim = c(-2,2), type = "plobcsSh", # main= "matplot(,type = \"plobcsSh\" )") # + # lends <- c("round","butt","square") # matplot(matrix(1:12, 4), type="c", lty=1:3, lwd=10, lend=lends) # text(cbind(2.5, 2*c(1,3,5)-.4), lends, col= 1:3, cex = 1.5) # + # table(iris$Species) # is data.frame with 'Species' factor # iS <- iris$Species == "setosa" # iV <- iris$Species == "versicolor" # op <- par(bg = "bisque") # matplot(c(1, 8), c(0, 4.5), type = "n", xlab = "Length", ylab = "Width", # main = "Petal and Sepal Dimensions in Iris Blossoms") # matpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = c(1,2), col = c(2,4)) # matpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = "vV", col = c(2,4)) # legend(1, 4, c(" Setosa Petals", " Setosa Sepals", # "Versicolor Petals", "Versicolor Sepals"), # pch = "sSvV", col = rep(c(2,4), 2)) # + # dotchart(VADeaths, main = "Death Rates in Virginia - 1940") # op <- par(xaxs = "i") # 0 -- 100% # dotchart(t(VADeaths), xlim = c(0,100), # main = "Death Rates in Virginia - 1940") # par(op) # + # ## Use the Berkeley admission data as in Friendly (1995). # x <- aperm(UCBAdmissions, c(2, 1, 3)) # dimnames(x)[[2]] <- c("Yes", "No") # names(dimnames(x)) <- c("Sex", "Admit?", "Department") # stats::ftable(x) # + # pairs(iris) # + # x <- -6:16 # op <- par(mfrow = c(2, 2)) # contour(outer(x, x), method = "edge", vfont = c("sans serif", "plain")) # z <- outer(x, sqrt(abs(x)), FUN = "/") # image(x, x, z) # contour(x, x, z, col = "pink", add = TRUE, method = "edge", # vfont = c("sans serif", "plain")) # contour(x, x, z, ylim = c(1, 6), method = "simple", labcex = 1, # xlab = quote(x[1]), ylab = quote(x[2])) # contour(x, x, z, ylim = c(-6, 6), nlev = 20, lty = 2, method = "simple", # main = "20 levels; \"simple\" labelling method") # par(op) # + # z[1:6, 7]=-10; z[8:23, 7]=10 # + # persp(x,x,z) # star(z) # + # require(stats) # for rnorm # plot(-4:4, -4:4, type = "o") # setting up coord. system # points(rnorm(200), rnorm(200), col = "red") # points(rnorm(100)/2, rnorm(100)/2, col = "blue", cex = 1.5) # op <- par(bg = "light blue") # x <- seq(0, 2*pi, len = 51) # # something "between type='b' and type='o'": # plot(x, sin(x), type = "o", pch = 21, bg = par("bg"), col = "blue", cex = .6, # main = 'plot(..., type="o", pch=21, bg=par("bg"))') # par(op) # + # ?ar # + # plot(-1:1, -1:1, type = "n", xlab = "Re", ylab = "Im") # K <- 16; text(exp(1i * 2 * pi * (1:K) / K), col = 2) #complex number represents coordinats # text(x, y, expression(p == over(1, 1+e^-(beta*x+alpha)))) ## The following two examples use latin1 characters: these may not ## appear correctly (or be omitted entirely). # plot(1:10, 1:10, main = "text(...) examples\n~~~~~~~~~~~~~~", # sub = "R is GNU ©, but not ® ...") # mtext("«Latin-1 accented chars»: éè øØ å<Å æ<Æ", side = 3) # points(c(6,2), c(2,1), pch = 3, cex = 4, col = "red") # text(6, 2, "the text is CENTERED around (x,y) = (6,2) by default", # cex = .8) # Rsquared = 0.911 ^ 2 # par(adj=0.2) # text(3,8,as.expression(substitute(italic(R)^2==r, list(r=round(Rsquared, 3))))) # - # ## A practical example # x <- rnorm(10); y<-rnorm(10) # plot(x, y, xlab="Ten random values", ylab="Ten other values", # xlim=c(-2, 2), ylim=c(-2, 2), pch=22, col="red", # bg="yellow", bty="l", tcl=0.4, #tcl stands for - symble of tick; bty stands for cordinator shape :"l", "7", "c", "u", or "]" # main="How to customize a plot with R", las=1, cex=1.5) # + # opar <- par() # par(bg="lightyellow", col.axis = "blue", mar=c(4,4,2.5,0.25))#mar stands for margin # plot(x,y, xlab="Ten random values", ylab="Ten other values", # xlim=c(-2, 2), ylim=c(-2, 2), pch = 22, col="red", bg="yellow", cex=1.5, bty="u", tcl=-.25, las=1) #las stands for axis lable direction # title("How to customize a plot with R (bis)", font.main=3, adj=0.5) # par(opar) # + # ?mtext # + # opar<-par() # par(bg="lightgray", mar=c(2.5,1.5,2.5,0.25)) # plot(x, y, type="n", xlab ="", ylab="", xlim=c(-2, 2), ylim=c(-2,2), xaxt="n", yaxt="n") # rect(-3, -3, 3, 3, col="cornsilk") # points(x,y, pch=10, col="red", cex=2) # axis(side=1, c(-2, 0, 2), tcl=-0.2, labels=FALSE)#labels=c("A", "B", "C"). # axis(side=2, -1:1, tcl=-0.2, labels=FALSE) # title("How to customize a plot with R (ter)", # font.main=4, adj=1, cex.main=1 ) # mtext("Ten random values", side=1, line=1, at=1, cex=0.9, font=3) # mtext("Ten other values", side = 3, line=1, at=-1.8, cex=0.9, font=3) #default 3 # mtext(c(-2, 0, 2), side=1, las=1, at=c(-2, 0, 2), line=0.3, col="blue", cex=0.9) # mtext(-1:1, side=2, las=1, at=-1:1, line=0.2, col="blue", cex=0.9) # par(opar) # - # ## lattice and gride library(lattice) n<-seq(5, 45, 5) x<-rnorm(sum(n)) # factor convert val to category value. only need to define a distinct values list of the same size of distinct number of org vec y <- factor(rep(n, n), labels=paste("n=", n)) #rep n$i n$i times # + # ?densityplot # + # densityplot(~ x| y, panel = function(x, ...) { # panel.densityplot(x, col="DarkOliveGreen", ...) # panel.mathdensity(dmath=dnorm, args=list(mean=mean(x), sd=sd(x)), # col ="darkblue") # }) # + # data(quakes) # mini <- min(quakes$depth) # maxi <- max(quakes$depth) # int <- ceiling((maxi - mini)/9) # inf <- seq(mini, maxi, int) # quakes$depth.cat <- factor(floor(((quakes$depth- mini) / int)), labels=paste(inf, inf + int, sep = "-")) # xyplot(lat ~ long | depth.cat, data = quakes) # - data(iris) # + # xyplot(Petal.Length ~ Petal.Width, data = iris, groups = Species, panel = panel.superpose, type= c("p", "smooth"), span=.75, # auto.key = list(x=0.15, y=0.85)) #superpose get all plot displayed in the same plot; span:smoothness; auto.key: add label # + # coplot(Sepal.Length~Sepal.Width | Species, data=iris) # + # splom(~iris[1:4], group=Species,data=iris, xlab="", panel=panel.superpose, auto.key=list(columns=3)) # + # pairs(iris) # + # splom(~iris[1:3] ,groups = Species, data = iris,panel = panel.superpose) # + # splom(~iris[1:3] | Species, data = iris, pscales = 0, #pscales=0 remove all ticks marks # varnames = c("Sepal\nLength", "Sepal\nWidth", "Petal\nLength")) # + # parallel(~iris[,1:4]| Species, data=iris, layout=c(3,1))
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rlagywns0213/codeit_DataScience_study
https://github.com/rlagywns0213/codeit_DataScience_study
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # # Example: Compare MIPS of RISC-V Instruction Set Simulators # Typically MLonMCU would be used to benchmark TinyML workloads on real wardware or simulators. However it's flexibility also allows some interesting experiments not directly related to Embedded ML. In the following it the performance of some RISC-V ISA Simulators is compared using the MLonMCU command line or Python API. # ## Supported components # **Models:** Any (`sine_model` used below) # # **Frontends:** Any (`tflite` used below) # # **Frameworks/Backends:** Any (`tvmaotplus` used below) # # **Platforms/Targets:** `etiss_pulpino`, `spike`, `ovpsim` (`etiss_pulpino` and `spike` used below) # ## Prerequisites # Set up MLonmCU as usual, i.e. initialize an environment and install all required dependencies. Feel free to use the following minimal `environment.yml.j2` template: # ```yaml # --- # home: "{{ home_dir }}" # logging: # level: DEBUG # to_file: false # rotate: false # cleanup: # auto: true # keep: 10 # paths: # deps: deps # logs: logs # results: results # plugins: plugins # temp: temp # models: # - "{{ home_dir }}/models" # - "{{ config_dir }}/models" # repos: # tvm: # url: "https://github.com/apache/tvm.git" # ref: de6d8067754d746d88262c530b5241b5577b9aae # etiss: # url: "https://github.com/tum-ei-eda/etiss.git" # ref: 4d2d26fb1fdb17e1da3a397c35d6f8877bf3ceab # spike: # url: "https://github.com/riscv-software-src/riscv-isa-sim.git" # ref: 0bc176b3fca43560b9e8586cdbc41cfde073e17a # spikepk: # url: "https://github.com/riscv-software-src/riscv-pk.git" # ref: 7e9b671c0415dfd7b562ac934feb9380075d4aa2 # mlif: # url: "https://github.com/tum-ei-eda/mlonmcu-sw.git" # ref: 4b9a32659f7c5340e8de26a0b8c4135ca67d64ac # frameworks: # default: tvm # tvm: # enabled: true # backends: # default: tvmaot # tvmaot: # enabled: true # features: [] # features: [] # frontends: # tflite: # enabled: true # features: [] # toolchains: # gcc: true # platforms: # mlif: # enabled: true # features: [] # targets: # default: spike # spike: # enabled: true # features: [] # etiss_pulpino: # enabled: true # features: [] # ``` # Do not forget to set your `MLONMCU_HOME` environment variable first if not using the default location! # ## Usage # If supported by the defined target, the measured MIPS (of the Simulation) is part of the report printed/returned my MLonMCU. The following shows you how to get rid of unwanted further information and how to increase the accuracy of the MIPS value. # ### A) Command Line Interface # Let's start with an example benchmark of two models using 2 different RISC-V simulators: # !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike # The MIPS value can be found in the column next to the Cycles (which are in this case actually counting instructions). However there is a lot of further information we want to filter out next. This can be achieved using the `filter_cols` subprocess. # !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike --postprocess filter_cols --config filter_cols.keep="Model,Target,MIPS" # That looks much more clean! However the numbers seem quite low, especially for the smaller `toycar` (MLPerfTiny Anomaly Detection) model. Let's see if the MIPS will increase when running more than a single inference. We are using the `benchmark` feature for this. # # *Hint*: Since we are now running our benchmarks 60 times more often, the following cell will likely need a few minutes to execute. # !mlonmcu flow run resnet toycar --backend tvmaot --target etiss_pulpino --target spike --postprocess config2cols --postprocess filter_cols --config filter_cols.keep="Model,Target,MIPS,config_benchmark.num_runs" --feature benchmark --config-gen benchmark.num_runs=1 --config-gen benchmark.num_runs=10 --config-gen benchmark.num_runs=50 # This look more promising. This experiment shows MIPS measurements might not be accurate for short-running simulations. Also spike seems to be more than twice as fast compared to ETISS. # ### B) Python Scripting # Some imports # + from tempfile import TemporaryDirectory from pathlib import Path import pandas as pd from mlonmcu.context.context import MlonMcuContext from mlonmcu.session.run import RunStage # - # Benchmark Configuration FRONTEND = "tflite" MODELS = ["resnet", "toycar"] BACKEND = "tvmaot" PLATFORM = "mlif" TARGETS = ["etiss_pulpino", "spike"] POSTPROCESSES = ["config2cols", "filter_cols"] FEATURES = ["benchmark"] CONFIG = { "filter_cols.keep": ["Model", "Target", "MIPS", "config_benchmark.num_runs"] } # Initialize and run a single benchmark with MlonMcuContext() as context: session = context.create_session() for model in MODELS: for target in TARGETS: def helper(session, num=0): cfg = CONFIG.copy() cfg["benchmark.num_runs"] = num run = session.create_run(config=cfg) run.add_frontend_by_name(FRONTEND, context=context) run.add_features_by_name(FEATURES, context=context) run.add_model_by_name(model, context=context) run.add_backend_by_name(BACKEND, context=context) run.add_platform_by_name(PLATFORM, context=context) run.add_target_by_name(target, context=context) run.add_postprocesses_by_name(POSTPROCESSES) for num in [1, 10]: # Removed 50 to cut down runtime helper(session, num) session.process_runs(context=context) report = session.get_reports() report.df entiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. # # ***The architecture of a ConvNet is analogous to that of the con # conectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex*** # # 2. ***How Convutional Neural Networl work?*** # # https://e2eml.school/how_convolutional_neural_networks_work.html # https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/ # # Please go trough the above links to get better understanding at the working of the CNN, # # ![image.png](attachment:image.png) def build_model(): model = Sequential() model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1))) model.add(BatchNormalization()) model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu')) model.add(MaxPool2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu')) model.add(BatchNormalization()) model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu')) model.add(MaxPool2D(pool_size=(2,2), strides=(2,2))) model.add(Dropout(0.25)) model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', activation ='relu')) model.add(BatchNormalization()) model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', activation ='relu')) model.add(MaxPool2D(pool_size=(2,2), strides=(2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation = "relu")) model.add(Dropout(0.5)) model.add(Dense(10, activation = "softmax")) return model # # How I built my model? # # Here I've created a function build_model, # **Defining Cnn's Architecture** # Most simply, we can compare an architecture with a building. It consists of walls, windows, doors, et cetera – and together these form the building. Explaining what a neural network architecture is benefits from this analogy. Put simply, it is a collection of components that is put in a particular order. The components themselves may be repeated and also may form blocks of components. Together, these components form a neural network: in this case, a CNN to be precise. # # So the first step was to decide the model type as Seuential,**A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor**. # So basically,In a sequential layer there is one input and one output, and then the output is fed into another layer(can be seen, in the picture later) # # The next step is to define the layers of single Network or Architechture, # 1. Convulutional layer:- # What a Convutional layer does, it basically perform a element-wise operation with filters(used for eedge detetction) as shown here: # ![image.png](attachment:image.png) # # 2. Batch Normalisation:- # To increase the stability of a neural network, batch normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation. # batch normalization allows each layer of a network to learn by itself a little bit more independently of other layers. # 3. Another Convutional layer:- # It perform the same action as the previous layer # 4. Max Pooling layer:- # Sometimes when the images are too large, we would need to reduce the number of trainable parameters. It is then desired to periodically introduce pooling layers between subsequent convolution layers. Pooling is done for the sole purpose of reducing the spatial size of the image. # ![image.png](attachment:image.png) # # # 5. Dropout Layer:- # Dropout is a technique used to improve over-fit on neural networks, Basically during training half of neurons on a particular layer will be deactivated. This improve generalization because force your layer to learn with different neurons the same "concept". During the prediction phase the dropout is deactivated. # # Then we'll repeat the same layer three times # *All the layers other than output layer will have ReLu activation In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node or output for that input. The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero*. # # **Output layer** # # 6. Flatten layer:- # In this lyaer we are literally going to flatten our pooled feature map into a column like in the image below. # ![image.png](attachment:image.png) # # 7. Dense layer:- # The dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer.A densely connected layer provides learning features from all the combinations of the features of the previous layer # # **For the fully connected layer the activation function is Softmax: which is used for multiclass classifiaction** model= build_model() # # Compiling the model # model.compile is used to compile the model the loss is Categoriacal crossentopy since we are doing multiclass classification, one can use Binary crossentropy for binary classification, # The opitimizer is Adam,they basically optimize loss and make trainning better and fast to read more # https://algorithmia.com/blog/introduction-to-optimizers, # https://towardsdatascience.com/how-to-train-neural-network-faster-with-optimizers-d297730b3713 # please go through above links model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy']) model.summary() # # plotting model # Everything that I've explained earlier can be understood in a better way with help of this from keras.utils.vis_utils import plot_model plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) # # Data Augmentation: # Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks. # ![image.png](attachment:image.png) datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180) zoom_range = 0.1, # Randomly zoom image width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) height_shift_range=0.1, # randomly shift images vertically (fraction of total height) horizontal_flip=False, # randomly flip images vertical_flip=False)# randomly flip images datagen.fit(X_train) batch_size=64 # # Fitting the model:- # We are using model.fit_generator that takes the augmented data, I've defined epochs(number of times we will go through the model) as 20 (the larger the number of epochs the better the accuracy). # For the sake of notebook purpose I've taken it as 20, if you"ll fork it change it to 50/60 hist = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=64), steps_per_epoch=len(X_train)//batch_size, epochs=20, #Increase this when not on Kaggle kernel verbose=1, #1 for ETA, 0 for silent validation_data=(X_val[:400,:], Y_val[:400,:])) final_loss, final_acc = model.evaluate(X_val, Y_val, verbose=0) print("Final loss: {0:.4f}, final accuracy: {1:.4f}".format(final_loss, final_acc)) # # Evaluating Model:- # This is the plot of model training and we can clearly see the decreasing loss and increasing accuracy plt.plot(hist.history['loss'], color='b') plt.plot(hist.history['val_loss'], color='r') plt.show() plt.plot(hist.history['accuracy'], color='b') plt.plot(hist.history['val_accuracy'], color='r') plt.show() # Confusion Matrix for better understanding of True positive and Negative y_hat_val = model.predict(X_val) y_pred = np.argmax(y_hat_val, axis=1) y_true = np.argmax(Y_val, axis=1) cm = confusion_matrix(y_true, y_pred) print(cm) # # Creating Prediction y_hat = model.predict(test, batch_size=64) y_pred = np.argmax(y_hat,axis=1) # **This is the notebook I've created for learning purpose, I've missed out following thing (for keeping it short and not giving too much information in one notebook)** # 1. Hyperparameter tunning :- # Can done with the help of GridSearchCV, it helps you select the best parameters for your model, # 2. Callbacks:- # Callback is a technique that prevents overfitting, Earlystopping and model checkpoint are few examples of callbacks # # ***If you liked this notebook and it helped you in learnig something please upvote, # It has taken a large amount of time, and an upvote will motivate me to make more such content, and give back to the community. # Thanks for reading. # Feedbacks and Suggestions are welcomed.****
15,670
/proj_3_test.ipynb
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[]
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braiyen/MagnetizationStatistics
https://github.com/braiyen/MagnetizationStatistics
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import numpy as np import matplotlib.pyplot as plt # %matplotlib inline # - def weighted_die(num_steps): probabilities = np.array([3,3,1,1,1,1]) states = np.zeros(6) current = np.random.randint(1,7) for n in range(num_steps): proposed = np.random.randint(1,7) r = np.random.random_sample() if current != proposed: p_accept = np.minimum(1, (probabilities[proposed-1]/probabilities[current-1])) if r < p_accept: current = proposed states[current-1] += 1 earnings = np.sum(states[:2]) - np.sum(states[2:]) return states, earnings def random_S(L): S = np.random.rand(L,L) for i in range(L): for j in range(L): if S[i,j] > .5: S[i,j] = 1 else: S[i,j] = -1 return S def two_dim_ising(L, temp, num_steps): N = L**2 U = np.zeros(num_steps+1) M = np.zeros(num_steps+1) X = np.zeros(num_steps+1) C = np.zeros(num_steps+1) S = random_S(L) E = energy(S) s = net_spin(S) E_ave = E E_square_ave = E**2 s_ave = s s_square_ave = s**2 U[0] = E_ave/N M[0] = s_ave/N X[0] = (1/(N*temp))*(s_square_ave - s_ave**2) C[0] = (1/(N*temp**2))*(E_square_ave - E_ave**2) for n in range(num_steps): i = np.random.randint(0, L) j = np.random.randint(0, L) delta = delta_E(S, i, j) if delta <= 0: S[i,j] *= -1 E += delta if S[i,j] < 0: s -= 2 else: s += 2 else: r = np.random.random_sample() if r < np.exp(-1*delta/temp): S[i,j] *= -1 E += delta if S[i,j] < 0: s -= 2 else: s += 2 E_ave = E_ave + (1/(1+n))*(E - E_ave) s_ave = s_ave + (1/(1+n))*(s - s_ave) E_square_ave = E_square_ave + (1/(1+n))*(E**2 - E_square_ave) s_square_ave = s_square_ave + (1/(1+n))*(s**2 - s_square_ave) U[n+1] = E_ave/N M[n+1] = s_ave/N X[n+1] = (1/(N*temp))*(s_square_ave - s_ave**2) C[n+1] = (1/(N*temp**2))*(E_square_ave - E_ave**2) return S, U, M, X, C def two_dim_ising_mag(S, L, temp, num_steps, sample_steps): N = L**2 E = energy(S) E_ave = E #Run algorithm until close to equillibrium for n in range(num_steps): i = np.random.randint(0, L) j = np.random.randint(0, L) delta = delta_E(S, i, j) if delta <= 0: S[i,j] *= -1 E += delta else: r = np.random.random_sample() if r < np.exp(-1*delta/temp): S[i,j] *= -1 E += delta E_ave = E_ave + (1/(1+n))*(E - E_ave) E = energy(S) s = net_spin(S) s_ave = s #Run algorithm and sample spins for n in range(sample_steps): i = np.random.randint(0, L) j = np.random.randint(0, L) delta = delta_E(S, i, j) if delta <= 0: S[i,j] *= -1 E += delta if S[i,j] < 0: s -= 2 else: s += 2 else: r = np.random.random_sample() if r < np.exp(-1*delta/temp): S[i,j] *= -1 E += delta if S[i,j] < 0: s -= 2 else: s += 2 E_ave = E_ave + (1/(1+n))*(E - E_ave) s_ave = s_ave + (1/(1+n))*(s - s_ave) U = E_ave/N M = s_ave/N return S, U, M def delta_E(S, i, j): total = 0 aux = np.block([[S,S,S], [S,S,S], [S,S,S]]) i += np.shape(S)[0] j += np.shape(S)[0] total += aux[i,j]*aux[i+1,j] total += aux[i,j]*aux[i-1,j] total += aux[i,j]*aux[i,j+1] total += aux[i,j]*aux[i,j-1] return total*2 def energy(S): total = 0 aux = np.block([[S,S,S], [S,S,S], [S,S,S]]) for i in range(np.shape(S)[0]): n = i + np.shape(S)[0] for j in range(np.shape(S)[0]): m = j + np.shape(S)[0] total += aux[n,m]*aux[n+1,m] total += aux[n,m]*aux[n-1,m] total += aux[n,m]*aux[n,m+1] total += aux[n,m]*aux[n,m-1] return -1*total/2 def net_spin(S): n = np.shape(S)[0] total = 0 for i in range(n): for j in range(n): total += S[i,j] return total def onsager(T): if np.any(T >= 2.2692): return 0 else: return (1-(np.sinh(2/T))**(-4))**(1/8) def graph_spins(S): L = np.shape(S)[0] for j in range(0, L): for i in range(0, L): if S[i,j] == 1: plt.plot(i, L-j-1, marker='s', color='white') else: plt.plot(i, L-j-1, marker='s', color='black') print("Spin up: White") print("Spin down: Black") # + num_steps = 1000000 L = 16 T = 10 S, U, M, X, C = two_dim_ising(L, T, num_steps) x_axis = np.linspace(0, num_steps/L**2, num_steps+1) print("Magnetization converging to:", M[num_steps], "with system size:", L) plt.plot(x_axis, M) print("Onsager's result:", onsager(T)) # - print("Internal Energy converging to:", U[num_steps], "with system size:", L) plt.plot(x_axis, U) # + num_steps = 2000000 L = 32 T = 10 S, U, M, X, C = two_dim_ising(L, T, num_steps) x_axis = np.linspace(0, num_steps/L**2, num_steps+1) print("Magnetization converging to:", M[num_steps], "with system size:", L) plt.plot(x_axis, M) print("Onsager's result:", onsager(T)) # - print("Internal Energy converging to:", U[num_steps], "with system size:", L) plt.plot(x_axis, U) print("Magnetic Susceptability converging to:", X[num_steps], "with system size:", L) plt.plot(x_axis, X) print("Heat Capacity converging to:", C[num_steps], "with system size:", L) plt.plot(x_axis, C) sample_steps = 100 L = np.array([8,16,32,64]) T = np.append([20,10],np.flip(np.arange(.1,9.9,.1),0)) M = np.zeros(np.size(T)) for i in range(np.size(L)): num_steps = 100000*L[i] S = random_S(L[i]) for j in range(np.size(T)): if j > 0: num_steps = 1000 n = 1 S, U, M[j] = two_dim_ising_mag(S, L[i], T[j], num_steps, sample_steps) plt.plot(T, M) plt.plot([2.2692,20],[0,0], 'k-') plt.plot([2.2962,2.2692],[onsager(2.2961), 0], 'k-') t = np.arange(0.1,2.2692,0.1) plt.plot(t, onsager(t), color='black') plt.xlabel('Temperature') plt.ylabel('Magnetization') num_steps = 100000 L = 10 T = .1 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S) num_steps = 5000000 L = 256 T = 10 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S) num_steps = 5000000 L = 256 L = 256 T = 8 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S) num_steps = 5000000 L = 256 T = 2.3 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S) num_steps = 5000000 L = 256 L = 256 T = 4.0 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S) num_steps = 5000000 L = 256 L = 256 T = 1.8 S, U, M, X, C = two_dim_ising(L, T, num_steps) graph_spins(S)
7,611
/TitanicSurvivalExploration/Titanic_Survival_Exploration.ipynb
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pydevhacker/MachineLearningNanoDegreeUdacity
https://github.com/pydevhacker/MachineLearningNanoDegreeUdacity
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # Solution_1 : class Solution(object): def grayCode(self, n): """ :type n: int :rtype: List[int] """ grays = dict() grays[0] = ['0'] grays[1] = ['0', '1'] for i in range(2, n + 1): n_gray = [] for pre in grays[i - 1]: n_gray.append('0' + pre) for pre in grays[i - 1][::-1]: n_gray.append('1' + pre) grays[i] = n_gray return map(lambda x: int(x, 2), grays[n]) 'f', '', 'kernel') tf.flags.DEFINE_integer("batch_size", "10", "batch size for training") tf.flags.DEFINE_string("logs_dir", "/data/logs", "path to logs directory") tf.flags.DEFINE_string("data_dir", "/data/", "path to dataset") tf.flags.DEFINE_float("learning_rate", "1e-4", "Learning rate for Adam Optimizer") tf.flags.DEFINE_float("beta1", "0.9", "Beta 1 value to use in Adam Optimizer") tf.flags.DEFINE_string("model_dir", "/data/imagenet-vgg-verydeep-19.mat", "Path to vgg model mat") tf.flags.DEFINE_bool('debug', "False", "Debug mode: True/ False") tf.flags.DEFINE_string('mode', "train", "Mode train/ test") FLAGS = flags.FLAGS MODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat' IMAGE_SIZE = 128 MAX_ITERATION = 6000 restore_model = False # # FLAGS.debug # + def vgg_net(weights, image): # load the pre-trained VGG19 , https://arxiv.org/pdf/1409.1556.pdf layers = ( # 'conv1_1', 'relu1_1', # skip conv1_1 of VGG 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4' ) net = {} current = image for i, name in enumerate(layers): kind = name[:4] if kind == 'conv': kernels, bias = weights[i + 2][0][0][0][0] # matconvnet: weights are [width, height, in_channels, out_channels] # tensorflow: weights are [height, width, in_channels, out_channels] kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w") bias = utils.get_variable(bias.reshape(-1), name=name + "_b") current = utils.conv2d_basic(current, kernels, bias) elif kind == 'relu': current = tf.nn.relu(current, name=name) if FLAGS.debug: utils.add_activation_summary(current) elif kind == 'pool': current = utils.avg_pool_2x2(current) net[name] = current return net def HyperColumns(images, train_phase): print("setting up vgg initialized conv layers ...") model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL) weights = np.squeeze(model_data['layers']) with tf.variable_scope("HyperColumns") as scope: # VGG takes in 3channel (RGB) images. # In order to input 1-channel (gray) image, # define a new filter that takes in gray color image and map it into 64 channels so as to fit VGG conv1_2 W0 = utils.weight_variable([3, 3, 1, 64], name="W0") b0 = utils.bias_variable([64], name="b0") conv0 = utils.conv2d_basic(images, W0, b0) hrelu0 = tf.nn.relu(conv0, name="relu") image_net = vgg_net(weights, hrelu0) # HyperColumns # https://arxiv.org/abs/1411.5752 relu1_2 = image_net["relu1_2"] layer_relu1_2 = tf.image.resize_bilinear(relu1_2, (IMAGE_SIZE, IMAGE_SIZE)) relu2_1 = image_net["relu2_1"] layer_relu2_1 = tf.image.resize_bilinear(relu2_1, (IMAGE_SIZE, IMAGE_SIZE)) relu2_2 = image_net["relu2_2"] layer_relu2_2 = tf.image.resize_bilinear(relu2_2, (IMAGE_SIZE, IMAGE_SIZE)) relu3_1 = image_net["relu3_1"] layer_relu3_1 = tf.image.resize_bilinear(relu3_1, (IMAGE_SIZE, IMAGE_SIZE)) relu3_2 = image_net["relu3_2"] layer_relu3_2 = tf.image.resize_bilinear(relu3_2, (IMAGE_SIZE, IMAGE_SIZE)) relu3_3 = image_net["relu3_3"] layer_relu3_3 = tf.image.resize_bilinear(relu3_3, (IMAGE_SIZE, IMAGE_SIZE)) relu3_4 = image_net["relu3_4"] layer_relu3_4 = tf.image.resize_bilinear(relu3_4, (IMAGE_SIZE, IMAGE_SIZE)) relu4_1 = image_net["relu4_1"] layer_relu4_1 = tf.image.resize_bilinear(relu4_1, (IMAGE_SIZE, IMAGE_SIZE)) relu4_2 = image_net["relu4_2"] layer_relu4_2 = tf.image.resize_bilinear(relu4_2, (IMAGE_SIZE, IMAGE_SIZE)) relu4_3 = image_net["relu4_3"] layer_relu4_3 = tf.image.resize_bilinear(relu4_3, (IMAGE_SIZE, IMAGE_SIZE)) relu4_4 = image_net["relu4_4"] layer_relu4_4 = tf.image.resize_bilinear(relu4_4, (IMAGE_SIZE, IMAGE_SIZE)) relu5_1 = image_net["relu5_1"] layer_relu5_1 = tf.image.resize_bilinear(relu5_1, (IMAGE_SIZE, IMAGE_SIZE)) relu5_2 = image_net["relu5_2"] layer_relu5_2 = tf.image.resize_bilinear(relu5_2, (IMAGE_SIZE, IMAGE_SIZE)) relu5_3 = image_net["relu5_3"] layer_relu5_3 = tf.image.resize_bilinear(relu5_3, (IMAGE_SIZE, IMAGE_SIZE)) relu5_4 = image_net["relu5_4"] layer_relu5_4 = tf.image.resize_bilinear(relu5_4, (IMAGE_SIZE, IMAGE_SIZE)) HyperColumns = tf.concat([layer_relu1_2, \ layer_relu2_1, layer_relu2_2, \ layer_relu3_1, layer_relu3_2, layer_relu3_3, layer_relu3_4, \ layer_relu4_1, layer_relu4_2, layer_relu4_3, layer_relu4_4, \ layer_relu5_1, layer_relu5_2, layer_relu5_3, layer_relu5_4 \ ] ,3) wc1 = utils.weight_variable([1, 1, 5440, 2], name="wc1") wc1_biase = utils.bias_variable([2], name="wc1_biase") pred_AB_conv = tf.nn.conv2d(HyperColumns, wc1, [1, 1, 1, 1], padding='SAME') pred_AB = tf.nn.bias_add(pred_AB_conv, wc1_biase) return tf.concat(values=[images, pred_AB], axis=3, name="pred_image") def train(loss, var_list): optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1) grads = optimizer.compute_gradients(loss, var_list=var_list) for grad, var in grads: utils.add_gradient_summary(grad, var) return optimizer.apply_gradients(grads) # + print("Setting up network...") train_phase = tf.placeholder(tf.bool, name="train_phase") images = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='L_images') lab_images = tf.placeholder(tf.float32, shape=[None, None, None, 3], name="LAB_images") pred_image = HyperColumns(images, train_phase) gen_loss_mse = tf.reduce_mean(2 * tf.nn.l2_loss(pred_image - lab_images)) / (IMAGE_SIZE * IMAGE_SIZE * 100 * 100) tf.summary.scalar("HyperColumns_loss_MSE", gen_loss_mse) train_variables = tf.trainable_variables() for v in train_variables: utils.add_to_regularization_and_summary(var=v) train_op = train(gen_loss_mse, train_variables) # - print("Reading image dataset...") train_images, testing_images, validation_images = flowers.read_dataset(FLAGS.data_dir) image_options = {"resize": True, "resize_size": IMAGE_SIZE, "color": "LAB"} batch_reader_train = dataset.BatchDatset(train_images, image_options) batch_reader_validate = dataset.BatchDatset(validation_images, image_options) batch_reader_testing = dataset.BatchDatset(testing_images, image_options) # + print("Setting up session") sess = tf.Session() summary_op = tf.summary.merge_all() saver = tf.train.Saver() train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph) validate_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validate') sess.run(tf.global_variables_initializer()) # - if restore_model == True: ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) print("Model restored...") FLAGS.mode = 'test' # + check_variables_trainable = False if check_variables_trainable == True : print('printing out the trainable variables...') variables_names = [v.name for v in tf.trainable_variables()] values = sess.run(variables_names) for k, v in zip(variables_names, values): print ("Variable: ", k) print ("Shape: ", v.shape) mse_train_list = [] if FLAGS.mode == 'train': for itr in xrange(MAX_ITERATION): l_image, color_images = batch_reader_train.next_batch(FLAGS.batch_size) feed_dict = {images: l_image, lab_images: color_images, train_phase: True} if itr % 10 == 0: mse, summary_str = sess.run([gen_loss_mse, summary_op], feed_dict=feed_dict) mse_train_list.append(mse) train_writer.add_summary(summary_str, itr) print("Step: %d, MSE: %g" % (itr, mse)) if itr % 100 == 0: saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr) pred = sess.run(pred_image, feed_dict=feed_dict) idx = np.random.randint(0, FLAGS.batch_size) save_dir = os.path.join(FLAGS.logs_dir, "image_checkpoints") utils.save_image(color_images[idx], save_dir, "gt" + str(itr // 100)) utils.save_image(pred[idx].astype(np.float64), save_dir, "pred" + str(itr // 100)) print("%s --> Model saved" % datetime.datetime.now()) sess.run(train_op, feed_dict=feed_dict) if itr % 10000 == 0: FLAGS.learning_rate /= 2 elif FLAGS.mode == "test": count = 10 l_image, color_images = batch_reader_testing.get_N_images(count) feed_dict = {images: l_image, lab_images: color_images, train_phase: False} save_dir = os.path.join(FLAGS.logs_dir, "image_pred") pred = sess.run(pred_image, feed_dict=feed_dict) for itr in range(count): utils.save_image(color_images[itr], save_dir, "gt" + str(itr)) utils.save_image(pred[itr].astype(np.float64), save_dir, "pred" + str(itr)) print("--- Images saved on test run ---") # - plot_train_loss = True if plot_train_loss == True: plt.semilogy(mse_train_list[0:MAX_ITERATION], '-ro', label="train loss") # train loss plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.) plt.xlabel('iteration index') plt.ylabel('loss') plt.show() # Adding the feature **Age** as a condition in conjunction with **Sex** improves the accuracy by a small margin more than with simply using the feature **Sex** alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions. # **Pclass**, **Sex**, **Age**, **SibSp**, and **Parch** are some suggested features to try. # # Use the `survival_stats` function below to to examine various survival statistics. # **Hint:** To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: `["Sex == 'male'", "Age < 18"]` survival_stats(data, outcomes, 'Age', ["Sex == 'male'", "Age < 18"]) survival_stats(data, outcomes, 'Pclass', ["Sex == 'male'", "Age < 18"]) # After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction. # Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model. # **Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_2`. # + def predictions_3(data): """ Model with multiple features. Makes a prediction with an accuracy of at least 80%. """ predictions = [] for _, passenger in data.iterrows(): # Remove the 'pass' statement below # and write your prediction conditions here if passenger['Sex'] == 'female': predictions.append(1) elif passenger['Age'] < 10 and passenger['Pclass'] != 3: predictions.append(1) else: predictions.append(0) # Return our predictions return pd.Series(predictions) # Make the predictions predictions = predictions_3(data) # - print(accuracy_score(outcomes, predictions)) # **Answer**: Predictions have an accuracy of 79.91%. survival_stats(data, outcomes, 'Pclass', ["Sex == 'female'" ]) survival_stats(data, outcomes, 'Parch', ["Sex == 'female'"]) survival_stats(data, outcomes, 'Parch', ["Sex == 'male'", "Age < 18"]) survival_stats(data, outcomes, 'SibSp', ["Sex == 'female'"]) survival_stats(data, outcomes, 'SibSp', ["Sex == 'male'", "Age < 18"]) # + def predictions_4(data): """ Model with multiple features. Makes a prediction with an accuracy of at least 80%. """ predictions = [] for _, passenger in data.iterrows(): # Remove the 'pass' statement below # and write your prediction conditions here if passenger['Sex'] == 'female' and passenger['SibSp'] < 3 and passenger['Parch'] < 4: predictions.append(1) elif passenger['Sex'] == 'male' and passenger['Age'] < 10 and passenger['Pclass'] != 3 : predictions.append(1) else: predictions.append(0) # Return our predictions return pd.Series(predictions) # Make the predictions predictions = predictions_4(data) # - print(accuracy_score(outcomes, predictions)) # ### Question 4 # *Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions?* # **Hint:** Run the code cell below to see the accuracy of your predictions. # Steps taken to implement final prediciton model: # - If passenger is Female and we predict every female passanger chances of survial to true # - we can predict with accuracy of 78.68 % # - For Male passengers if their age is less than 10 years, there are high chances of survival # - accuracy goes up to 79.35 % # - If Male passenger is not from 'Pclass' 3, Passengers from lower class have low chances of survial # - accuracy increases to 79.91 % # - If Female passenger has less than 3 siblings, Female passengers having more siblings are less likely to survive. # - accuracy increases to 80.92 % # - If Female passenger has less than 4 children or parents is more likely to survive. # - accuracy goes up to 81.48 % print(accuracy_score(outcomes, predictions)) # **Answer**: Predictions have an accuracy of 81.48%. # # Conclusion # # Congratulations on what you've accomplished here! You should now have an algorithm for predicting whether or not a person survived the Titanic disaster, based on their features. In fact, what you have done here is a manual implementation of a simple machine learning model, the _decision tree_. In a decision tree, we split the data into smaller groups, one feature at a time. Each of these splits will result in groups that are more homogeneous than the original group, so that our predictions become more accurate. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. [This link](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) provides another introduction into machine learning using a decision tree. # # A decision tree is just one of many algorithms that fall into the category of _supervised learning_. In this Nanodegree, you'll learn about supervised learning techniques first. In supervised learning, we concern ourselves with using features of data to predict or model things with objective outcome labels. That is, each of our datapoints has a true outcome value, whether that be a category label like survival in the Titanic dataset, or a continuous value like predicting the price of a house. # # ### Question 5 # *Can you think of an example of where supervised learning can be applied?* # **Hint:** Be sure to note the outcome variable to be predicted and at least two features that might be useful for making the predictions. # **Answer**: # - Handwriting recognition: # - Outcome : Input image corresponds to which alphabatic letter. # - Features : pixel intensity, edges # - House Price Prediction: # - Outcome : predicted house price # - Features : Area in sq ft, number of bed room, location of house. # - Predicting Estimated time of autonomus vehice: # - Outcome : Expected time to reach destination # - Features : Weather condition, traffic, red lights # > **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to # **File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Long Short Term Memory # Credits: # # - [A Gentle Introduction to Long Short-Term Memory Networks by the Experts](https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/) # - [The 10 Neural Network Architectures Machine Learning Researchers Need To Learn # ](https://medium.com/cracking-the-data-science-interview/a-gentle-introduction-to-neural-networks-for-machine-learning-d5f3f8987786) # ## Overview # LSTMs are a type of RNNs which were created to solve the vanishing gradient problem. It does this by introducing gates and an explicitly defined memory cell. The memory cell stores the previous values and holds onto it unless a “forget gate” tells the cell to forget those values. # ## How do LSTMs work? #
1,027
/Face detection/Face detection.ipynb
0ff03377f066cc11990661d878d86054aecac5db
[]
no_license
Sadhana-acharya/rep
https://github.com/Sadhana-acharya/rep
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
1,401
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Assignment 10, Part 1: Tidy Data Practice # Use this notebook to follow along with the tasks in the `AO6-Matplotlib_Part2.ipynb` notebook. # # ## Instructions # For each task, use the cell below to write and test your code. You may add additional cells for any task as needed or desired. # ## Task 1a: Setup # # Import the following packages: # + `pandas` as `pd` # + `numpy` as `np` # + `matplotlib.pyplot` as `plt` # # Activate the `%matplotlib inline` magic. import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # %matplotlib inline # ## Task 2a: Understand the data # Execute the following code to display the sample data frame: # Create the data rows and columns. data = [['John Smith', None, 2], ['Jane Doe', 16, 11], ['Mary Johnson', 3, 1]] # Create the list of labels for the data frame. headers = ['', 'Treatment_A', 'Treatement_B'] # Create the data frame. pd.DataFrame(data, columns=headers) # Using the table above, answer the following: # # What are the variables? # + active="" # Treatment A, Treatment B and the name of individuals # - # What are the observations? # + active="" # the numerical values of both the treatments # - # What is the observable unit? # + active="" # # - # Are the variables columns? # + active="" # no, they are both in rows and columns # - # Are the observations rows? # + active="" # no, it has one variable of individual's names # - # ## Task 2b: Explain causes of untidyness # # Execute the following code to display the sample data frame: data = [['Agnostic',27,34,60,81,76,137], ['Atheist',12,27,37,52,35,70], ['Buddhist',27,21,30,34,33,58], ['Catholic',418,617,732,670,638,1116], ['Don\'t know/refused',15,14,15,11,10,35], ['Evangelical Prot',575,869,1064,982,881,1486], ['Hindu',1,9,7,9,11,34], ['Historically Black Prot',228,244,236,238,197,223], ['Jehovah\'s Witness',20,27,24,24,21,30], ['Jewish',19,19,25,25,30,95]] headers = ['religion','<$10k','$10-20k','$20-30k','$30-40k','$40-50k','$50-75k'] religion = pd.DataFrame(data, columns=headers) religion # Explain why the data above is untidy? # + active="" # Because the variable labels are not only restricted to the column names and they fall into the rows as well. for a tidy data, rows should only have the observations. # - # What are the variables? # + active="" # religion # <$10k # $10-20k # $20-30k # $30-40k # $40-50k # $50-75k # - # What are the observations? # + active="" # the numerical value for each variable # - # ## Task 2c: Explain causes of untidyness # # Execute the following code to display the sample data frame: data = [['AD', 2000, 0, 0, 1, 0, 0, 0, 0, None, None], ['AE', 2000, 2, 4, 4, 6, 5, 12, 10, None, 3], ['AF', 2000, 52, 228, 183, 149, 129, 94, 80, None, 93], ['AG', 2000, 0, 0, 0, 0, 0, 0, 1, None, 1], ['AL', 2000, 2, 19, 21, 14, 24, 19, 16, None, 3], ['AM', 2000, 2, 152, 130, 131, 63, 26, 21, None, 1], ['AN', 2000, 0, 0, 1, 2, 0, 0, 0, None, 0], ['AO', 2000, 186, 999, 1003, 912, 482, 312, 194, None, 247], ['AR', 2000, 97, 278, 594, 402, 419, 368, 330, None, 121], ['AS', 2000, None, None, None, None, 1, 1, None, None, None]] headers = ['country', 'year', 'm014', 'm1524', 'm2534', 'm3544', 'm4554', 'm5564', 'm65', 'mu', 'f014'] demographics = pd.DataFrame(data, columns=headers) demographics # Using the dataset above: # # Explain why the data above is untidy? # + active="" # because the variable labels of country and year are occupying the rows as well which they should not for a data to be tidy. Also, the sex variable is not in a separate column # - # What are the variables? # + active="" # sex, age group, country and year. # - # What are the observations? # + active="" # from column 2 onwards are the observations # - # ## Task 3a: Melt data, use case #1 # # Using the `pd.melt` function, melt the demographics data introduced in section 2. Be sure to: # - Set the colum headers correctly. # - Order by country # - Print the first 10 lines of the resulting melted dataset. # # ***Note*** The demographics dataset is provided in Task 2c above # ## Task 3b: Practice with a new dataset # # Download the [PI_DataSet.txt](https://hivdb.stanford.edu/download/GenoPhenoDatasets/PI_DataSet.txt) file from [HIV Drug Resistance Database](https://hivdb.stanford.edu/pages/genopheno.dataset.html). Store the file in the same directory as the practice notebook for this assignment. # # ***Note***: Choose the file labeled “10935 phenotype results from 1808 isolates” # # Here is the meaning of data columns: # - SeqID: a numeric identifier for a unique HIV isolate protease sequence. Note: disruption of the protease inhibits HIV’s ability to reproduce. # - The Next 8 columns are identifiers for unique protease inhibitor class drugs. # - The values in these columns are the fold resistance over wild type (the HIV strain susceptible to all drugs). # - Fold change is the ratio of the drug concentration needed to inhibit the isolate. # - The latter columns, with P as a prefix, are the positions of the amino acids in the protease. # - '-' indicates consensus. # - '.' indicates no sequence. # - '#' indicates an insertion. # - '~' indicates a deletion;. # - '*' indicates a stop codon # - a letter indicates one letter Amino Acid substitution. # - two and more amino acid codes indicates a mixture.  # # Import this dataset into your notebook, view the top few rows of the data and respond to these questions: # # What are the variables? # + active="" # # - # What are the observations? # + active="" # # - # What are the values? # ## Task 3c: Practice with a new dataset Part 2 # # Use the data retreived from task 3b, generate a data frame containing a Tidy’ed set of values for drug concentration fold change. BE sure to: # # - Set the column names as ‘SeqID’, ‘Drug’ and ‘Fold_change’. # - Order the data frame first by sequence ID and then by Drup name # - Reset the row indexes # - Display the first 10 elements.
6,431
/kannada_NN_Kaggle_entry.ipynb
8407956b06c69e3c9a50fda40438e83c0c19c5ed
[]
no_license
pfvbell/kannada_neuralnetwork_kaggle
https://github.com/pfvbell/kannada_neuralnetwork_kaggle
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
14,335
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + colab={"base_uri": "https://localhost:8080/"} id="L8J3PscjK6IJ" outputId="a0db4aff-5493-4e01-c5a5-564683dffa17" #Otwieranie pliku file = open('kowalecki_175ic_txt.txt') print(file) # + id="yNnpWlrDLhxN" #Otwieranie pliku oraz zamknięcie go w razie pojawienia się błędu reader = open('kowalecki_175ic_txt.txt') try: reader.read() finally: reader.close() # + id="i_f-QV-9MJnk" #Uruchomienie pliku. Zamknięcie następuje po wyjściu z 'with' with open('kowalecki_175ic_txt.txt') as reader: reader.read() # + colab={"base_uri": "https://localhost:8080/"} id="rs3VebYYOWik" outputId="d9781deb-6064-4e29-9517-8b28f0fa8bcb" #Typy plików print(type(open('kowalecki_175ic_txt.txt', 'r'))) print(type(open('kowalecki_175ic_txt.txt', 'rb'))) print(type(open('kowalecki_175ic_txt.txt', 'rb', buffering=0))) # + colab={"base_uri": "https://localhost:8080/"} id="_g96BlUdPmpC" outputId="f4aa141f-9114-443e-9aed-7856397a7f7a" #Czytanie plików with open('kowalecki_175ic_txt.txt', 'r') as reader: print(reader.read()) # + colab={"base_uri": "https://localhost:8080/"} id="FnI2x8Z0QgSt" outputId="ca7088cf-a1f6-4915-886f-55e1d2c57428" #Czytanie po 5 bajtów z pliku with open('kowalecki_175ic_txt.txt', 'r') as reader: print(reader.readline(5)) print(reader.readline(5)) print(reader.readline(5)) # + colab={"base_uri": "https://localhost:8080/"} id="66sn6tKiQ9wH" outputId="d444b408-1030-405d-b03f-ebab31b8f14d" #Zwrócenie tekstu jako listę f = open('kowalecki_175ic_txt.txt') f.readlines() # + colab={"base_uri": "https://localhost:8080/"} id="wY6f_zr_RQIp" outputId="d70b36ff-fe36-475c-e460-eb972394fa9a" #Iterowanie po każdej linii w pliku na 3 różne sposoby with open('kowalecki_175ic_txt.txt', 'r') as reader: line = reader.readline() while line != '': print(line, end='') line = reader.readline() with open('kowalecki_175ic_txt.txt', 'r') as reader: for line in reader.readlines(): print(line, end='') with open('kowalecki_175ic_txt.txt', 'r') as reader: for line in reader: print(line, end='') # + colab={"base_uri": "https://localhost:8080/"} id="VhkVk5iRSeKN" outputId="ca8a483c-bc23-4ec5-ee6e-c612e97d7044" #Zapis do pliku with open('kowalecki_175ic_txt.txt', 'r') as reader: read_only_text = reader.readlines() with open('kowalecki_175ic_write.txt', 'w') as writer: for text in reversed(read_only_text): writer.write(text) with open('kowalecki_175ic_write.txt', 'r') as reader: print(reader.read()) # + colab={"base_uri": "https://localhost:8080/"} id="yY3i0FadTmr3" outputId="5dca4b5f-9d61-4c9e-8c3d-0fdf382862c1" #Praca z bajtami - wczytanie pliku with open('kowalecki_175ic_write.txt', 'rb') as reader: print(reader.readline()) # + colab={"base_uri": "https://localhost:8080/"} id="Ta1ZDJizUnku" outputId="fea18754-4238-406f-81bf-182ecde30285" #Wczytanie obrazu i wyświetlenie jego danych with open('test.jpg', 'rb') as byte_reader: print(byte_reader.read(1)) print(byte_reader.read(3)) print(byte_reader.read(2)) print(byte_reader.read(1)) print(byte_reader.read(1)) 고 말했다.</span> </p> ''' re.findall("(?<=<span>).+(?=</span>)", article) new_list = [] for text in re.findall("<span>.+</span>", article): # print(text) # print(text.replace("<span>", "").replace("</span>", "")) new_list.append(text[6:-7]) new_list phones = """ park 010-1234-1234 kim 02-3450-3459 lee 00000000 """ # + for phone in phones.split("n"): if phone: num = re.search(r"[a-zA-Z]+\s\d{3}-\d{3,4}-\d{4}", phone).group() print("이름 : ", num.split()[0]) print("전화번호 : ", num.split()[1]) # - print(re.search(r"([a-zA-Z]+)\s(\d{3}-\d{3,4}-\d{4})", phone).group(1)) print(re.findall(r"([a-zA-Z]+)\s(\d{3}-\d{3,4}-\d{4})", phone)) text = "Paris in the the spring" text2 = "Paris in in the spring" re.search(r'(\b\w+)\s+\1', text2) re.sub(r'(foo)(bar)', r'\g<1>123\g<2>456', 'foobar') #foo123bar import requests from bs4 import BeautifulSoup url = 'https://search.musinsa.com/category/001' response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser") item_list = soup.select("div.list-box li.li_box") for item in item_list: print(item) import requests from bs4 import BeautifulSoup response = requests.get("https://scrapying-study.firebaseapp.com/04/") print(response.text) import json response = requests.get("https://jsonplaceholder.typicode.com/posts") data = json.loads(response.text) data[0]['title'] # + headers = { 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36' } # "accept": text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9 # "accept-encoding": gzip, deflate, br # "accept-language": ko-KR,ko;q=0.9 # "cache-control": max-age=0 # "cookie": JSESSIONID=D000B102B9D3882A4D4FF0D5D05548D1; NNB=IRMRKG3QFXSF6; NRTK=ag#all_gr#1_ma#-2_si#0_en#0_sp#0; nx_ssl=2; page_uid=U+e7clprvhGssnIZSmossssssGo-245854 # "referer": https://news.naver.com/main/list.nhn?mode=LPOD&mid=sec&oid=003 # sec-fetch-dest: document # sec-fetch-mode: navigate # sec-fetch-site: same-origin # sec-fetch-user: ?1 # upgrade-insecure-requests: 1 # user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 response = requests.get("https://news.naver.com/main/read.nhn?mode=LPOD&mid=sec&oid=003&aid=0010260737", headers=headers) print(response.text) # -
5,824
/notebooks/.ipynb_checkpoints/S5PL2-checkpoint.ipynb
00b2c9a1fc38b005ad2ffbaa75d9e4bff9eb561b
[]
no_license
MachineLearningJournalClub/EOChallenge
https://github.com/MachineLearningJournalClub/EOChallenge
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
7,615
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:python-hawaii] # language: python # name: conda-env-python-hawaii-py # --- import math math.sin(math.radians(30)) angles = [0, 1, 30, 45, 180] math.sin(angles) angles angles/2 angles*3 import requests url = 'http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/hawaii-TAVG-Trend.txt' print(url) response = requests.get(url) response.status_code print(response.text) with open('hawaii-temperature-data.txt', 'w') as open_file: open_file.write(response.text)
710
/tv/tvl2dcn_den.ipynb
c48e86ff030239bfe941aee8a93de2b8d039075f
[ "BSD-3-Clause" ]
permissive
bwohlberg/sporco-notebooks
https://github.com/bwohlberg/sporco-notebooks
18
4
null
null
null
null
Jupyter Notebook
false
false
.py
513,741
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # --- # Greyscale ℓ2-TV Denoising # ========================= # # This example demonstrates the use of class [tvl2.TVL2Deconv](http://sporco.rtfd.org/en/latest/modules/sporco.admm.tvl2.html#sporco.admm.tvl2.TVL2Deconv) for removing Gaussian white noise from a greyscale image using Total Variation regularization with an ℓ2 data fidelity term (ℓ2-TV denoising). (This class is primarily intended for deconvolution problems, but can be applied to denoising problems by choosing an impulse filter as the blurring kernel.) # + from __future__ import print_function from builtins import input import numpy as np from sporco.admm import tvl2 from sporco import util from sporco import metric from sporco import plot plot.config_notebook_plotting() # - # Load reference image. img = util.ExampleImages().image('monarch.png', scaled=True, idxexp=np.s_[:,160:672], gray=True) # Construct test image corrupted Gaussian white noise with a 0.05 standard deviation. np.random.seed(12345) imgn = img + np.random.normal(0.0, 0.05, img.shape) # Set regularization parameter and options for ℓ2-TV deconvolution solver. The regularization parameter used here has been manually selected for good performance. lmbda = 0.04 opt = tvl2.TVL2Deconv.Options({'Verbose': True, 'MaxMainIter': 200, 'gEvalY': False}) # Create solver object and solve, returning the the denoised image ``imgr``. b = tvl2.TVL2Deconv(np.ones((1,1)), imgn, lmbda, opt) imgr = b.solve() # Display solve time and denoising performance. print("TVL2Deconv solve time: %5.2f s" % b.timer.elapsed('solve')) print("Noisy image PSNR: %5.2f dB" % metric.psnr(img, imgn)) print("Denoised image PSNR: %5.2f dB" % metric.psnr(img, imgr)) # Display reference, corrupted, and denoised images. fig = plot.figure(figsize=(20, 5)) plot.subplot(1, 3, 1) plot.imview(img, title='Reference', fig=fig) plot.subplot(1, 3, 2) plot.imview(imgn, title='Corrupted', fig=fig) plot.subplot(1, 3, 3) plot.imview(imgr, title=r'Restored ($\ell_2$-TV)', fig=fig) fig.show() # Get iterations statistics from solver object and plot functional value, ADMM primary and dual residuals, and automatically adjusted ADMM penalty parameter against the iteration number. its = b.getitstat() fig = plot.figure(figsize=(20, 5)) plot.subplot(1, 3, 1) plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig) plot.subplot(1, 3, 2) plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T, ptyp='semilogy', xlbl='Iterations', ylbl='Residual', lgnd=['Primal', 'Dual'], fig=fig) plot.subplot(1, 3, 3) plot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig) fig.show() & 3 & & 5 & & 8 & & 13 \\ # & & & & & & & & & & & & & & \\ # 21 & & 34 & & 55 & & 89 & & 144 & & 233 & & 377 & & 610 \\ # & & & & & & & & & & & & & & \\ # 987 & & 1597 & & 2584 & & 4181 & & 6765 & & 10946 & & 17711 & & 28657 # \\ # & & & & & & & & & & & & & & \\ # 46368 & & 75025 & & 121393 & & 196418 & & 317811 & & 614229 & & 832040 # & & 1346269 \\ # & & & & & & & & & & & & & & \\ # 2178309 & & 3524578 & & 5702887 & & 9227465 & & 14930352 & & 24157817 & # & 39088169 & & 63245986% # \end{array}$$ # # **Exercise 2:** Write a code that computes the first N Fibonacci numbers, saves them into an array, and displays them on the screen. # **WAIT** -- before you read the next cell, try to do Exercise 2! # + N = 20 # Set the size of the list we will compute F=[0,1] # The first two numbers in the list for i in range(2, N): F.append(F[i-1]+F[i-2]) # append the next item on the list print('First',N,'Fibonacci numbers:',F) # - # **For fun,** we can make a little widget to control how many numbers to print out. # + from ipywidgets import interact def printFib(N=10): F=[0,1] # The first two numbers in the list for i in range(2, N): F.append(F[i-1]+F[i-2]) # append the next item on the list print(F) interact(printFib, N=(10,100,10)); # - # By moving the slider above, print out the first 100 Fibonacci numbers # # As we can see, this sequence grows pretty fast. The Fibonacci numbers seem # to have one more digit after about every five terms in the sequence. # ## How fast does it grow? # # One of the ways to study the growth of a sequence is to look at ratios between consecutive terms. We look at ratios of pairs of numbers in the Fibonacci sequence. # # The first few values are # \begin{eqnarray} # F_2/F_1 &=& 1 \\ # F_3/F_2 &=& 2/1 = 2 \\ # F_4/F_3 &=& 3/2 = 1.5 \\ # F_5/F_4 &=& 5/3 = 1.666... \\ # F_6/F_5 &=& 8/5 = 1.6 \\ # F_7/F_6 &=& 13/8 = 1.625 # \end{eqnarray} # # So the ratios are levelling out somewhere around 1.6. We observe that $1.6^5 \approx 10$, which is why after every five terms in the Fibonacci sequence, we get another digit. This tells us we have roughly **exponential growth,** where $F_n$ grows about as quickly as the exponential function $(1.6)^n$. # # We can check this computation in Python. We use $ ** $ to take a power, as in the following cell. (1.6)**5 # ## The Golden Ratio # # We can print out a bunch of these ratios, and plot them, just to see that they do. The easiest way to do this is with a bit of Python code. Perhaps you can try this yourself. # # **Exercise 3** Write some code that computes the first N ratios $F_{n+1}/F_n$, save them it into an array, and displays them on the screen. # **WAIT!** Don't read any further until you try the exercises. # + # %matplotlib inline from matplotlib.pyplot import * N = 20 F = [0,1] R = [] for i in range(2, N): F.append(F[i-1]+F[i-2]) # append the next item on the list R.append(F[i]/F[i-1]) figure(figsize=(10,6)); plot(R,'o') title('The first '+str(N-2)+' Ratios $F_{n+1}/F_n$') xlabel('$n$') ylabel('$Ratio$'); print('The first', N-2, 'ratios are:',R) # - # We see the numbers are levelling out at the value 1.6108034... This number may be familiar to you. It is called the **Golden Ratio.** # # We can compute the exact value by observing the ratios satisfy a nice algebraic equation: # $$ # \frac{F_{n+2}}{F_{n+1}}=\frac{F_{n+1}+F_{n}}{F_{n+1}}=1+\frac{F_{n}}{F_{n+1}}=1+\frac{1}{\frac{F_{n+1}}{F_{n}}}, # $$ # or more simply # $$\frac{F_{n+2}}{F_{n+1}}=1+\frac{1}{\frac{F_{n+1}}{F_{n}}}.$$ # # As $n$ gets larger and larger, the ratios $F_{n+2}/F_{n+1}$ and $F_{n+1}/F_{n}$ tend toward a final value, say $x$. This value must then solve the equation # $$x=1+\frac{1}{x}.$$ # # We rewrite this as a quadratic equation # $$x^2=x+1$$ # which we solve from the quadratic formula # $$ x= \frac{1 \pm \sqrt{1+4}}{2} = \frac{1 \pm \sqrt{5}}{2}.$$ # It is the positive solution $x= \frac{1 + \sqrt{5}}{2} = 1.6108034...$ which is called the Golden Ratio. # # # The **Golden ratio** comes up in art, geometry, and Greek mythology as a perfect ratio that is pleasing to the eye (and to the gods). # # For instance, the rectangle shown below is said to have the dimensions of the Golden ratio, because the big rectangle has the same shape as the smaller rectangle inside. Mathematically, we have the ratios of lengths # $$ \frac{a+b}{a} = \frac{a}{b}.$$ # # ![Golden ratio rectangle](images/Golden2.png) # # Writing $x = \frac{a}{b}$, the above equation simplifies to # $$ 1 + \frac{1}{x} = x,$$ # which is the same quadratic equation we saw for the limit of ratios of Fibonacci numbers. # For more information about the Golden ratio see # https://en.wikipedia.org/wiki/Golden_ratio # ## A Formula for the Fibonacci Sequence $F_n$ # Let's give the Golden ratio a special name. In honour of the ancient Greeks who used it so much, we call it `phi:' # $$ \varphi = \frac{1 + \sqrt{5}}{2}. $$ # We'll call the other quadratic root 'psi:' # $$ \psi = \frac{1 - \sqrt{5}}{2}. $$ # This number $\psi$ is called the **conjugate** of $\varphi$ because it looks the same, except for the negative sign in front of the $\sqrt{5}$. # # Here's something **amazing.** It turns out that we have a remarkable formula for the Fibonnaci numbers, in terms of these two Greek numbers. The formula says # $$F_n = \frac{\varphi^n - \psi^n}{\sqrt{5}}.$$ # # #### Wow! # # Seems amazing. And it is handy because now we can compute, say, the thousandth term in the sequence, $F_{1000}$ directly, without having to compute all the other terms that come before. # # But, whenever someone gives you a formula, you should check it! # # **Exercise 4:** Write a piece of code to show that the formula above, with $\varphi,\psi$ does produce, say, the first 20 Fibonnaci numbers. # # **WAIT!** Don't go on until you try writing a program yourself, to compute the Fibonacci numbers using only powers of $\varphi, \psi$. # + ## SOLUTION (don't peak!) from numpy import * ## We need this to define square roots phi = (1 + sqrt(5))/2 psi = (1 - sqrt(5))/2 for n in range(20): print( (phi**n - psi**n)/sqrt(5) ) # - # Looking at that computer output, it does seem to give Fibonacci numbers, with a bit of numerical error. # # ## Checking the Math # # Doing math, though, we like exact answers and we want to know why. So WHY does this formula $(\phi^n - \psi^n)/\sqrt{5}$ give Fibonacci numbers? # # Well, we can check, step by step. # # For $n=0$, the formula gives # $$\frac{\varphi^0 - \psi^0}{\sqrt{5}} = \frac{1-1}{\sqrt{5}} = 0,$$ which is $F[0]$, the first Fibonacci number. # # For $n=1$, the formula gives # $$\frac{\varphi^1 - \psi^1}{\sqrt{5}} = # \frac{\frac{1 + \sqrt{5}}{2} - \frac{1 -\sqrt{5}}{2} }{\sqrt{5}} = \frac{\sqrt{5}}{\sqrt{5}} = 1,$$ which is $F[1]$, the next Fibonacci number. # # For $n=2$, it looks harder because we get the squares $\varphi^2, \psi^2$ in the formula. But then remember that both $\varphi$ and $\psi$ solve the quadratic $x^2 = x+1$, so we know $\varphi^2 = \phi +1$ and $\psi^2 = \psi +1$. So we can write # $$\frac{\phi^2 - \psi^2}{\sqrt{5}} = \frac{\phi + 1 - \psi -1}{\sqrt{5}} = \frac{\phi - \psi }{\sqrt{5}} = 1,$$ # since we already calculated this in the $n=1$ step. So this really is $F[2]=1$. # # For $n=3,4,5,\ldots$ again it might seem like it will be hard because of the higher powers. But multiplying the formulas $\varphi^2 = \varphi +1$ and $\psi^2 = \psi +1$ by powers of $\phi$ and $\psi$, we get # # $$\begin{eqnarray*} # \varphi^2 &=& \varphi +1,\quad \varphi^3 = \varphi^2+\varphi # ,\quad \varphi^4=\varphi^3+\varphi^2,\qquad \dots \qquad % # \varphi^{n+2}=\varphi^{n+1}+{\varphi}^n,\quad \text{and} \\ # \psi^2 &=&\psi +1,\quad \psi^3=\psi^2+\psi ,\quad \psi^4=\psi^3+\psi^2,\qquad # \dots \qquad \psi^{n+2}=\psi^{n+1}+\psi^n. # \end{eqnarray*}$$ # # So, assuming we know the generating formula already for $n$ and $n+1$ we can write the next term as # $$\frac{\varphi^{n+2} - \psi^{n+2}}{\sqrt{5}} = \frac{\varphi^{n+1} +\varphi^n - \psi^{n+1} - \psi^n}{\sqrt{5}} # = \frac{\varphi^{n+1} - \psi^{n+1}}{\sqrt{5}} + \frac{\varphi^{n} - \psi^{n}}{\sqrt{5}} = F[n+1] + F[n] = F[n+2].$$ # # So we do get $\frac{\varphi^{n+2} - \psi^{n+2}}{\sqrt{5}} = F[n+2]$, and the formula holds for all numbers n. # # This method of verifying the formula for all n, based on previous values of n, is an example of **mathematical induction.** # ## Why did this work? # # Well, from the Golden ratio, we have the formula $\varphi^2 = \varphi + 1$, which then gives the formula $\varphi^{n+2} = \varphi^{n+1} + \varphi^n$. This looks a lot like the Fibonacci formula $$F[n+2] = F[n+1] + F[n].$$ Same powers of $\psi$. # # If we take ANY linear combination of powers of $\varphi, \psi$, such as # $$f(n) = 3\varphi^n + 4\psi^n,$$ # we will get a sequence that behaves like the Fibonacci sequence, with $f(n+2) = f(n+1) + f(n).$ To get the 'right' Fibonacci sequence, we just have to replace the 3 and 4 with the right coefficients. # ## From sequences to functions # # Wouldn't it be fun to extend Fibonacci numbers to a function, defined for all numbers $x$? # # The problems is the function # $$F[x] = \frac{\varphi^x - \psi^x}{\sqrt{5}}$$ # is not defined for values of $x$ other than integers. # # The issue is the term $\psi^{x}=\left( \frac{1-\sqrt{5}}{2}\right) ^{x}$, which is the power of a negative number. # We don't really know how to define that. For instance, what is the square root of a negative number? # # To # overcome this technical difficulty, we write # # $$\psi ^{x}=\left( -\left( -\psi \right) \right) ^{x}=\left( -\left( \frac{% # \sqrt{5}-1}{2}\right) \right) ^{x}=\left( -1\right) ^{x}\left( \frac{\sqrt{5}% # -1}{2}\right) ^{x}. $$ # # Now the factor $\left( \frac{\sqrt{5}-1}{2} \right) ^{x}$ make sense since # the number inside the brackets is positive. We have localized the problem into the powers of $-1$ for the term $\left( # -1\right) ^{x}$. We would like to replace this term by a # continuous function $m(x)$ such that it takes the values $\pm1$ on the integers. That is, # # $$m(n) =1\quad \text{if }n\text{ is even }\quad\text{and}\quad m(n) =-1\quad \text{if }n\text{ is odd.} $$ # # The cosine function works. That is # # $$m\left( x\right) =\cos \left( \pi x\right) \qquad \text{does the job.} $$ # That is: # $$\cos \left( n\pi \right) =1\quad \text{if }n\text{ is even}\quad\text{ and}\quad % # \cos \left( n\pi \right) =-1\quad \text{if }n\text{ is odd.}$$ # # Why this is a **good** choice would lead us to complex numbers and more! # Hence, we obtain the following closed formula for our function $F[x]:$ # # $$\begin{eqnarray*} # F[x] &=&\frac{{\varphi }^{x}-\left( -1\right) ^{x}\left( -\psi # \right) ^{x}}{{\varphi -\psi }}=\frac{1}{\sqrt{5}}\left( {\varphi }% # ^{x}-\left( -1\right) ^{x}\left( -\psi \right) ^{x}\right) \\ # &=&\frac{1}{\sqrt{5}}\left( \left( \frac{1+\sqrt{5}}{2}\right) ^{x}-\cos # \left( \pi x\right) \left( \frac{\sqrt{5}-1}{2}\right) ^{x}\right) . # \end{eqnarray*}$$ # # Let's plot this function, and the Fibonacci sequence. # # ## A plot of the continuous Fibonacci function # + # %matplotlib inline from numpy import * from matplotlib.pyplot import * phi=(1+5**(1/2))/2 psi=(5**(1/2)-1)/2 x = arange(0,10) y = (pow(phi,x) - cos(pi*x)*pow(psi,x))/sqrt(5) xx = linspace(0,10) yy = (pow(phi,xx) - cos(pi*xx)*pow(psi,xx))/sqrt(5) figure(figsize=(10,6)); plot(x,y,'o',xx,yy); title('The continuous Fibonacci function') xlabel('$x$') ylabel('$Fib(x)$'); # - # ## A plot with negative values # # Well, with this general definition, we can even include negative numbers for $x$ in the function. # # Let's plot this too. # + # %matplotlib inline from numpy import * from matplotlib.pyplot import * phi=(1+5**(1/2))/2 psi=(5**(1/2)-1)/2 x = arange(-10,10) y = (pow(phi,x) - cos(pi*x)*pow(psi,x))/sqrt(5) xx = linspace(-10,10,200) yy = (pow(phi,xx) - cos(pi*xx)*pow(psi,xx))/sqrt(5) figure(figsize=(10,6)); plot(x,y,'o',xx,yy); title('The Fibonacci function, extended to negative values') xlabel('$x$') ylabel('$Fib(x)$'); # - # So we see we can even get negative Fibonacci numbers! # ## The Golden Ratio and Continued Fractions # We have found that the Golden ratio ${\varphi =}\frac{{1+}\sqrt{5}}{2}$ # satisfies the identity # # $$ # {\varphi =1+}\frac{1}{{\varphi }}. # $$ # # Substituting for ${\varphi }$ on the denominator in the right, we obtain # # $$ # {\varphi =1+}\frac{1}{{1+}\frac{1}{{\varphi }}}. # $$ # # Substituting again for ${\varphi }$ on the denominator in the right, we # obtain # # $$ # {\varphi =1+}\frac{1}{{1+}\dfrac{1}{{1+}\frac{1}{{\varphi }}}}. # $$ # # Repeating this again, # # $$ # {\varphi =1+}\frac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\frac{1}{{\varphi }}}}}% # .$$ # # And again, # # $$ # {\varphi =1+}\frac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\frac{1}{% # {\varphi }}}}}}. # $$ # # And again, # # $$ # {\varphi =1+}\frac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1% # }{{1+}\frac{1}{{\varphi }}}}}}}. # $$ # # We see that this process can be $\textit{continued indefinitely}$. This results # in an $\textit{infinite expansion of a fraction}$. These type of expressions are known as # $\textbf{continued fractions}$: # # $$ # {\varphi =1+}\frac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1% # }{{1+}\dfrac{1}{{1+}\dfrac{1}{1+\dfrac{1}{{\vdots }}}}}}}}}. # $$ # # We can approximate continued fractions with the finite fractions obtained by # stopping the development at some point. In our case, we obtain the # approximates # # $$ # 1,~1+1,~1+\frac{1}{1+1},~1+\frac{1}{1+\dfrac{1}{1+1}},~1+\frac{1}{1+\dfrac{1% # }{1+\dfrac{1}{1+1}}},~1+\frac{1}{1+\dfrac{1}{1+\dfrac{1}{1+\dfrac{1}{1+1}}}}% # ,\dots # $$ # # Explicitly, these approximates are # # $$ # 1,~2,~\frac{3}{2},~\frac{5}{3},~\frac{8}{5},~\frac{13}{8},\dots # $$ # # This looks like it is just the sequence of ratios $F_{n+1}/F_n$ we saw above! How can we prove this is the case for all $n$? # # We know that the sequence $R_{n} = F_{n+1}/F_n$ satisfies the recursive relation. # # $$ # R_{n}=\frac{F_{n+1}}{F_{n}}=1+\frac{F_{n-1}}{F_{n}}=1+\frac{1}{R_{n-1}}% # ,\qquad \text{with}\qquad R_{1}=1. # $$ # # Then, we can generate all the terms in the sequence $R_{n}$ by staring with $% # R_{1}=1$, and then using the relation $R_{n+1}=1+\frac{1}{R_{n}}:$ # # $$ # \begin{eqnarray*} # R_{1} &=&1 \\ # R_{2} &=&1+\frac{1}{R_{1}}=1+\frac{1}{1}=2 \\ # R_{3} &=&1+\frac{1}{R_{2}}=1+\frac{1}{1+R_{1}}=1+\frac{1}{1+1} \\ # R_{4} &=&1+\frac{1}{R_{3}}=1+\frac{1}{1+\frac{1}{1+1}} \\ # R_{5} &=&1+\frac{1}{R_{4}}=1+\frac{1}{1+\frac{1}{1+\frac{1}{1+1}}} \\ # &&\vdots # \end{eqnarray*} # $$ # # This confirms that both the sequence of rations $R_{n}$ and the sequence of # approximations to the continuous fraction of ${\varphi }$ are the same # sequence. $\square $ # # In general, continued fractions are expressions of the form # # $$ # a_{0}+\frac{1}{a_{1}+\dfrac{1}{a_{2}+\dfrac{1}{a_{3}+\dots }}} # $$ # # where $a_{0}$ is an integer and $a_{1},a_{2},a_{3},\dots $ are positive # integers. These type of fractions are abbreviated by the notation # # $$ # \left[ a_{0};a_{1},a_{2},a_{3},\dots \right] =a_{0}+\frac{1}{a_{1}+\dfrac{1}{% # a_{2}+\dfrac{1}{a_{3}+\dots }}}. # $$ # # For example # # $$ # \begin{eqnarray*} # \left[ 1;1,1,2\right] &=&1+\frac{1}{1+\dfrac{1}{1+\dfrac{1}{1+1}}}=\frac{8}{% # 5} \\ # && \\ # \left[ 1;1,1,1,1,\dots \right] &=&{1+}\frac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{% # {1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{{1+}\dfrac{1}{1+\dfrac{1}{{\vdots }% # }}}}}}}}={\varphi } # \end{eqnarray*} # $$ # # For more information of continued fractions, see # https://en.wikipedia.org/wiki/Continued_fraction # # # ## Conclusion # # ### What have we learned? # # - a **sequence** is an ordered list of numbers, which may go on forever. # - the **Fibonacci sequence** 0,1,1,2,3,5,8,13,... is a famous list of numbers, well-studied since antiquity. # - each number in this sequence is the sum of the two coming before it in the sequence. # - the sequence grows fast, increasing by a **factor** of about **10** for every **five** terms. # - the **ratio** of pairs of Fibonacci numbers converges to the **Golden ratio,** known since the ancient Greeks as the number # $$\varphi = \frac{1 + \sqrt{5}}{2} \approx 1.6108.$$ # - the Fibonacci numbers can be computed directly as the difference of powers of $\varphi$ and its **conjugate,** $\psi = \frac{1 - \sqrt{5}}{2}.$ This is sometimes faster than computing the whole list of Fibonnaci numbers. # - this formula with powers of $\varphi, \psi$ is verified using **induction.** # - The Fibonacci numbers can be **extended** to a **continuous function** $Fib(x)$, defined for all real numbers $x$ (including negatives). It **oscillates** (wiggles) on the negative x-axis. # - The **Golden Ratio** can also be expressed a **continued fraction,** which is an infinite expansion of fractions with sub-fraction terms. Many interesting numbers come from interesting continued fraction forms. # [![Callysto.ca License](https://github.com/callysto/curriculum-notebooks/blob/master/callysto-notebook-banner-bottom.jpg?raw=true)](https://github.com/callysto/curriculum-notebooks/blob/master/LICENSE.md)
20,265
/kaggle/great/dog_breed.ipynb
04f9e0a12d20c26cfa7b05131f3a1ba34da76a85
[]
no_license
pirate-turtle/deep-learning
https://github.com/pirate-turtle/deep-learning
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
2,647,262
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session # + _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0" from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D,ZeroPadding2D,Input from keras.callbacks import ModelCheckpoint,EarlyStopping import matplotlib.pyplot as plt import os import tensorflow as tf from keras.losses import categorical_crossentropy from keras.preprocessing.image import img_to_array,load_img,ImageDataGenerator from keras.utils import to_categorical # - from keras.layers import BatchNormalization labels = pd.read_csv('../input/dog-breed-identification/labels.csv') img_path = '../input/dog-breed-identification/train/' labels = labels.assign(img_path = lambda x : img_path + x['id']+'.jpg') labels # + fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(15, 15), subplot_kw={'xticks': [], 'yticks': []}) for i, ax in enumerate(axes.flat): ax.imshow(plt.imread(labels.img_path[i])) ax.set_title(labels.breed[i]) plt.tight_layout() plt.show() # - labels = labels.assign(img_path = lambda x : img_path + x['id']+'.jpg') labels.breed.value_counts() labelplot = pd.value_counts(labels['breed'],ascending=True).plot(kind='barh',fontsize="40",title="Class Distribution",figsize=(50,100)) # + # image size check from PIL import Image img_size = [] for image in labels['img_path']: im=Image.open(image) img_size.append(im.size) img_size.sort(reverse=True) img_size # - #Top 20 breed top_20=list(labels.breed.value_counts()[0:20].index) top_20 data=labels[labels.breed.isin(top_20)] data img_pixel=np.array([img_to_array(load_img(img, target_size=(256, 256))) for img in data['img_path'].values.tolist()]) img_label=data.breed img_label=pd.get_dummies(data.breed) img_label.head() X=img_pixel y=img_label.values print(X.shape) print(y.shape) from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2) print(X_train.shape) print(y_train.shape) print(X_test.shape) print(y_test.shape) # + train_datagen = ImageDataGenerator( rotation_range=30, width_shift_range=0.2, height_shift_range=0.2, rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') test_datagen=ImageDataGenerator(rescale=1./255) # - training_set=train_datagen.flow(X_train,y=y_train,batch_size=32) testing_set=test_datagen.flow(X_test,y=y_test,batch_size=32) # + # CNN model model=Sequential() model.add(ZeroPadding2D((1,1),input_shape=(256,256,3))) model.add(Conv2D(32,kernel_size=(3,3),activation='relu')) model.add(BatchNormalization()) model.add(ZeroPadding2D(padding=(1,1))) model.add(Conv2D(32,kernel_size=(3,3),activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2))) model.add(Flatten()) model.add(Dense(64,activation='relu')) model.add(Dropout(0.2)) model.add(Dense(20,activation='softmax')) model.compile(loss=categorical_crossentropy,optimizer='adam',metrics=['accuracy']) model.summary() # - history=model.fit_generator(training_set, steps_per_epoch = 16, validation_data = testing_set, validation_steps = 4, epochs = 50, verbose = 1) # + from tensorflow.keras.applications import VGG16 transfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3)) # - transfer_model.trainable = False transfer_model.summary() finetune_model = Sequential() finetune_model.add(transfer_model) finetune_model.add(Flatten()) finetune_model.add(Dense(64, activation='relu')) finetune_model.add(Dense(20, activation='softmax')) finetune_model.compile(loss=categorical_crossentropy,optimizer='adam',metrics=['accuracy']) finetune_model.summary() history=finetune_model.fit_generator(training_set, steps_per_epoch = 16, validation_data = testing_set, validation_steps = 4, epochs = 50, verbose = 1) # + ##
5,465
/7/captioning.ipynb
1c99e98ece1dd1d8e8c6ecf2ccf9cfcf94b68a07
[]
no_license
onaga1958/ml-hw
https://github.com/onaga1958/ml-hw
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
187,615
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %load_ext autoreload # %autoreload 2 from QueEstaPasando import clean_data import pandas as pd from QueEstaPasando import trainer_ml pd.set_option('max_colwidth', 800) data = pd.read_csv("../QueEstaPasando/data/total.csv", index_col=0) data.head() is_pos = data.loc[:, 'score'] == 'pos' df_pos = data.loc[is_pos] df_pos.head(80) df_pos.head(28) df_pos1 = pd.DataFrame(df_pos['text']) df_pos1.columns = ['text'] df_pos1['text'] = df_pos['text'] df_pos1['created_at'] = df_pos['created_at'] df_pos1.head(35) t_ml = trainer_ml.Trainer_ML() t_ml.predict_model(df_pos1) df= pd.DataFrame() df['created_at'] =['2021-04-27T07:26:38.000Z'] df['text'] = ["@MercantilBanco gracias por la información. Resuelto el problema. Gracias por vuestra atención."] t_ml.predict_model(df) a.org/learn/machine-learning-big-data-apache-spark/discussions/all # # Please make sure to follow the guidelines before asking a question: # # https://github.com/IBM/skillsnetwork/wiki/FAQ#im-feeling-lost-and-confused-please-help-me # # # If running outside Watson Studio, this should work as well. In case you are running in an Apache Spark context outside Watson Studio, please remove the Apache Spark setup in the first notebook cells. # + from IPython.display import Markdown, display def printmd(string): display(Markdown('# <span style="color:red">'+string+'</span>')) if ('sc' in locals() or 'sc' in globals()): printmd('<<<<<!!!!! It seems that you are running in a IBM Watson Studio Apache Spark Notebook. Please run it in an IBM Watson Studio Default Runtime (without Apache Spark) !!!!!>>>>>') # - # !pip install pyspark==2.4.5 try: from pyspark import SparkContext, SparkConf from pyspark.sql import SparkSession except ImportError as e: printmd('<<<<<!!!!! Please restart your kernel after installing Apache Spark !!!!!>>>>>') # + sc = SparkContext.getOrCreate(SparkConf().setMaster("local[*]")) spark = SparkSession \ .builder \ .getOrCreate() # - def assignment1(sc): rdd = sc.parallelize(list(range(100))) return rdd.count() print(assignment1(sc)) # !rm -f rklib.py # !wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py # Please provide your email address and obtain a submission token on the grader’s submission page in coursera, then execute the cell # + from rklib import submit import json key = "R1eDmiHNEei9kxIYdin0mA" part = "fnFg7" email = "[email protected]" token = "WPdAV3tVjXKg7Wwe" #you can obtain it from the grader page on Coursera (have a look here if you need more information https://youtu.be/GcDo0Rwe06U?t=276) submit(email, token, key, part, [part], json.dumps(assignment1(sc))) # - ig,ax=plt.subplots(1,1,figsize=(9,5)) sns.kdeplot(df_train[df_train['Survived']==1]['Age'], ax=ax) sns.kdeplot(df_train[df_train['Survived']==0]['Age'], ax=ax) plt.legend(['Survived == 1', 'Survived == 0']) plt.show() # + plt.figure(figsize=(8, 6)) df_train['Age'][df_train['Pclass'] == 1].plot(kind='kde') df_train['Age'][df_train['Pclass'] == 2].plot(kind='kde') df_train['Age'][df_train['Pclass'] == 3].plot(kind='kde') plt.xlabel('Age') plt.title('Age Distribution within classes') plt.legend(['1st Class', '2nd Class', '3rd Class']) # - cummulate_survival_ratio = [] for i in range(1,80): cummulate_survival_ratio.append(df_train[df_train['Age']<i]['Survived'].sum() / len(df_train[df_train['Age']<i]['Survived'])) plt.figure(figsize=(7,7)) plt.plot(cummulate_survival_ratio) plt.title('Survival rate change depending on range of Age', y=1.02) plt.ylabel('Survival rate') plt.xlabel('Range of Age(0~x)') plt.show() f,ax=plt.subplots(1,2,figsize=(18,8)) sns.violinplot("Pclass","Age",hue="Survived", data=df_train, scale='count', split=True,ax=ax[0]) ax[0].set_title('Pclass and Age vs Survived') ax[0].set_yticks(range(0,110,10)) sns.violinplot("Sex","Age", hue="Survived",data=df_train, scale='count', split=True, ax=ax[1]) ax[1].set_title('Sex and Age vs Survived') ax[1].set_yticks(range(0,110,10)) plt.show() f, ax = plt.subplots(1, 1, figsize=(7, 7)) df_train[['Embarked', 'Survived']].groupby(['Embarked'], as_index=True).mean().sort_values(by='Survived', ascending=False).plot.bar(ax=ax) f,ax=plt.subplots(2,2,figsize=(20,15)) sns.countplot('Embarked',data=df_train,ax=ax[0,0]) ax[0,0].set_title('(1) No. Of Passengers Boarded') sns.countplot('Embarked', hue='Sex', data=df_train, ax=ax[0,1]) ax[0,1].set_title('(2) Male-Female Split for Embarked') sns.countplot('Embarked', hue='Survived',data=df_train, ax=ax[1,0]) ax[1,0].set_title('(3) Embarked vs Survived') sns.countplot('Embarked', hue='Pclass', data=df_train, ax=ax[1,1]) ax[1,1].set_title('(4) Embarked vs Pclass') plt.subplots_adjust(wspace=0.2, hspace=0.5) plt.show() df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch'] + 1 df_test['FamilySize'] = df_test['SibSp'] + df_test['Parch'] + 1 print("Maximum size of Family: ", df_train['FamilySize'].max()) print("Minimum size of Family: ", df_train['FamilySize'].min()) # + f,ax=plt.subplots(1,3,figsize=(40,10)) sns.countplot('FamilySize', data=df_train, ax=ax[0]) ax[0].set_title('(1) No. Of Passengers Boarded', y=1.02) sns.countplot('FamilySize',hue='Survived',data=df_train, ax=ax[1]) ax[1].set_title('(2) Survived countplot depending on FamilySize', y=1.02) df_train[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=True).mean().sort_values(by='Survived', ascending=False).plot.bar(ax=ax[2]) ax[2].set_title('(3) Survived rate depending on FamilySize', y=1.02) plt.subplots_adjust(wspace=0.2, hspace=0.5) plt.show() # - fig, ax = plt.subplots(1,1,figsize=(8,8)) g= sns.distplot(df_train['Fare'], color='b', label='Skewness : {:.2f}'.format(df_train['Fare'].skew()), ax=ax) g = g.legend(loc='best') df_test.loc[df_test.Fare.isnull(),'Fare'] = df_test['Fare'].mean() df_train['Fare'] = df_train['Fare'].map(lambda i: np.log(i) if i >0 else 0) df_test['Fare'] = df_test['Fare'].map(lambda i: np.log(i) if i >0 else 0) fig, ax = plt.subplots(1,1,figsize=(8,8)) g= sns.distplot(df_train['Fare'], color='b', label='Skewness : {:.2f}'.format(df_train['Fare'].skew()), ax=ax) g = g.legend(loc='best') # + deletable=true editable=true print("each image code is a 1000-unit vector:", img_codes.shape) print(img_codes[0, :10]) print('\n\n') print("for each image there are 5-7 descriptions, e.g.:\n") print(captions[0], sep='\n') # + deletable=true editable=true #split descriptions into tokens for img_i in range(len(captions)): for caption_i in range(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] # + deletable=true editable=true # Build a Vocabulary word_counts = Counter() for image in captions: for caption in image: word_counts.update(caption[1:-1]) vocab = ['#UNK#', '#START#', '#END#'] vocab += [k for k, v in word_counts.items() if v >= 5] n_tokens = len(vocab) assert 10000 <= n_tokens <= 10500 word_to_index = {w: i for i, w in enumerate(vocab)} # + deletable=true editable=true PAD_ix = -1 UNK_ix = vocab.index('#UNK#') def as_matrix(sequences, max_len=None): max_len = max_len or max(map(len, sequences)) matrix = np.zeros((len(sequences), max_len), dtype='int32') + PAD_ix for i, seq in enumerate(sequences): row_ix = [word_to_index.get(word, UNK_ix) for word in seq[:max_len]] matrix[i, :len(row_ix)] = row_ix return matrix # + deletable=true editable=true captions = np.array(captions) def generate_batch(images, captions, batch_size, max_caption_len=None): #sample random numbers for image/caption indicies random_image_ix = np.random.randint(0, len(images), size=batch_size) #get images batch_images = images[random_image_ix] #5-7 captions for each image captions_for_batch_images = captions[random_image_ix] #pick 1 from 5-7 captions for each image batch_captions = list(map(choice, captions_for_batch_images)) #convert to matrix batch_captions_ix = as_matrix(batch_captions, max_len=max_caption_len) return batch_images, batch_captions_ix # + [markdown] deletable=true editable=true # ### Mah Neural Network # + deletable=true editable=true # network shapes. CNN_FEATURE_SIZE = img_codes.shape[1] EMBED_SIZE = 256 # Didn't make a valuable deffernce LSTM_UNITS = 512 # Didn't make a valuable deffernce # Input Variable sentences = T.imatrix() # [batch_size x time] of word ids image_vectors = T.matrix() # [batch size x unit] of CNN image features sentence_mask = T.neq(sentences, PAD_ix) # network inputs l_words = InputLayer((None, None), sentences) l_mask = InputLayer((None, None), sentence_mask) # embeddings for words l_word_embeddings = EmbeddingLayer(l_words, input_size=n_tokens, output_size=EMBED_SIZE) # input layer for image features l_image_features = InputLayer((None, CNN_FEATURE_SIZE), image_vectors) l_image_features_small = DenseLayer(l_image_features, num_units=LSTM_UNITS, W=lasagne.init.HeNormal(gain='relu')) l_image_features_small = DropoutLayer(l_image_features_small) decoder = LSTMLayer(l_word_embeddings, num_units=LSTM_UNITS, cell_init=l_image_features_small, mask_input=l_mask, grad_clipping=1e25) # try different values. With huge numbers results were a bit better # apply whatever comes next to each tick of each example in a batch. Equivalent to 2 reshapes broadcast_decoder_ticks = BroadcastLayer(decoder, (0, 1)) predicted_probabilities_each_tick = DenseLayer(broadcast_decoder_ticks, n_tokens, nonlinearity=lasagne.nonlinearities.softmax) # un-broadcast back into (batch,tick,probabilities) predicted_probabilities = UnbroadcastLayer(predicted_probabilities_each_tick, broadcast_layer=broadcast_decoder_ticks) next_word_probas = get_output(predicted_probabilities) reference_answers = sentences[:, 1:] output_mask = sentence_mask[:, 1:] loss = lasagne.objectives.categorical_crossentropy( next_word_probas[:, :-1].reshape((-1, n_tokens)), reference_answers.reshape((-1,)) ).reshape(reference_answers.shape) loss = (loss * output_mask).sum() / output_mask.sum() weights = lasagne.layers.get_all_params(predicted_probabilities, trainable=True) updates = lasagne.updates.adam(loss, weights) train_step = theano.function(inputs=[image_vectors, sentences], outputs=loss, updates=updates) val_step = theano.function(inputs=[image_vectors, sentences], outputs=loss) # + [markdown] deletable=true editable=true # ### Main loop # * We recommend you to periodically evaluate the network using the next "apply trained model" block # * its safe to interrupt training, run a few examples and start training again # + deletable=true editable=true batch_size = 32 # just for pass memory limit n_epochs = 100 # adjust me n_batches_per_epoch = int(len(img_codes) / batch_size) # to transform short epochs to long with little spam n_validation_batches = 5 # how many batches are used for validation after each epoch # + deletable=true editable=true for epoch in range(n_epochs): train_loss = 0 for _ in range(n_batches_per_epoch): train_loss += train_step(*generate_batch(img_codes, captions, batch_size)) train_loss /= n_batches_per_epoch val_loss = 0 for _ in range(n_validation_batches): val_loss += val_step(*generate_batch(img_codes, captions, batch_size)) val_loss /= n_validation_batches print('\nEpoch: {}, train loss: {}, val loss: {}'.format(epoch + 1, train_loss, val_loss)) # + [markdown] deletable=true editable=true # ### apply trained model # + deletable=true editable=true #the same kind you did last week, but a bit smaller from pretrained_lenet import build_model, preprocess, MEAN_VALUES # build googlenet lenet = build_model() #load weights lenet_weights = pickle.load(open('data/blvc_googlenet.pkl', 'rb'), encoding='bytes')[b'param values'] set_all_param_values(lenet["prob"], lenet_weights) #compile get_features cnn_input_var = lenet['input'].input_var cnn_feature_layer = lenet['loss3/classifier'] get_cnn_features = theano.function([cnn_input_var], lasagne.layers.get_output(cnn_feature_layer)) # + deletable=true editable=true from matplotlib import pyplot as plt # %matplotlib inline #sample image img = plt.imread('data/Dog-and-Cat.jpg') img = preprocess(img) # + deletable=true editable=true # deprocess and show, one line :) from pretrained_lenet import MEAN_VALUES plt.imshow(np.transpose((img[0] + MEAN_VALUES)[::-1],[1,2,0]).astype('uint8')) # + [markdown] deletable=true editable=true # ## Generate caption # + deletable=true editable=true last_word_probas_det = get_output(predicted_probabilities, deterministic=False)[:, -1] get_probs = theano.function([image_vectors, sentences], last_word_probas_det) #this is exactly the generation function from week5 classwork, #except now we condition on image features instead of words def generate_caption(image, caption_prefix = ("START",), t=1, sample=True, max_len=100): image_features = get_cnn_features(image) caption = list(caption_prefix) for _ in range(max_len): next_word_probs = get_probs(image_features, as_matrix([caption])).ravel() #apply temperature next_word_probs = next_word_probs**t / np.sum(next_word_probs**t) if sample: next_word = np.random.choice(vocab, p=next_word_probs) else: next_word = vocab[np.argmax(next_word_probs)] caption.append(next_word) if next_word=="#END#": break return caption # + deletable=true editable=true for i in range(50): print(' '.join(generate_caption(img, t=1.)[1:-1])) # - # Bad results, don't know why :( # I tried different variants of EMBED_SIZE, LSTM_UNITS, learning rate scheduling, grad_cliping..., check architecture, don't know what's wrong. # + [markdown] deletable=true editable=true # # + [markdown] deletable=true editable=true # Конец вывода после долгой тренеровки (другой запуск): # # Epoch: 495, train loss: 2.21678551197052, val loss: 2.194071388244629 # # Epoch: 496, train loss: 2.21208044052124, val loss: 2.1524736404418947 # # Epoch: 497, train loss: 2.213805365562439, val loss: 2.269657611846924 # # Epoch: 498, train loss: 2.2279205179214476, val loss: 2.2288233280181884 # # Epoch: 499, train loss: 2.188794708251953, val loss: 2.2174277305603027 # # Epoch: 500, train loss: 2.202223610877991, val loss: 2.2282715320587156 # Finish :) # # a base suspended in a tight tank # edible cat peers over flowers out of the window # two little girls brush their head # two small brown and white # behind a white and black and white dog watching a red ball # seal looking ahead # brown and dog and a small skull # wet and head of water and birds # grey and white small kitten lays on a blue and blue comforter # eating a pan of pizza # dog allows a tree to look like they #UNK# are vitamin and photographs # close to of a brown and white bird # chargers flying light # hogs setting in the back are in front of the trees # close up sitting around while another woman brushes her teeth while holding a sheep #UNK# # some big brown old ewe # tall dog saying him to keep its ring # this head in gray and white of its fur toothbrush suit is traveling down a green carpet # fluffy brown and white cat sits on the street # drink that has a small child laying on each # dog #UNK# treat # close up of a lamp # brown and white and black and white photo of a ball chasing a feather # and small dog # greek thing in the clear nest # strewn eating pink bag # furry rent by head # with two fuzzy spotted blue and white near each other in the middle of grassy area with a green pattern # grey and gold teddy bear sitting in a tree # enjoying a match # + [markdown] deletable=true editable=true # # Bonus Part # - Use ResNet Instead of GoogLeNet # - Use W2V as embedding # - Use Attention :) # + [markdown] deletable=true editable=true # # Pass Assignment https://goo.gl/forms/2qqVtfepn0t1aDgh1
16,671
/week11_homework (Feature engineering).ipynb
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # **Неделя 11. feature engineering** # ## **Домашняя работа** # Импортируем необходимые модули # + import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # %matplotlib inline from catboost import CatBoostRegressor # нужно установить библиотеку catboost from sklearn.metrics import mean_absolute_error import warnings warnings.filterwarnings('ignore') RANDOM_STATE = 42 # - # Загрузим датасет California Housing dataset from sklearn.datasets import fetch_california_housing california_housing = fetch_california_housing() data = pd.DataFrame(california_housing.data, columns=california_housing.feature_names) data['target'] = california_housing.target # **Описание датасета:** # Датасет содержит информацию о жилых районах штата Калифорния. 20640 Записей, 8 признаков. # # **Описание признаков:** # 1. *MedInc* - медианный доход домохозяйства # 2. *HouseAge* - медианный возраст домов # 3. *AveRooms* - среднее число комнат # 4. *AveBedrms* - среднее число спален # 5. *Population* - число людей, проживающих в районе # 6. *AveOccup* - среднее число людей, проживающих в доме # 7. *Latitude* - широта, геогр. координаты # 8. *Longitude* - долгота, геогр. координаты # # **Целевая переменная:** Медианная стоимость дома в районе, единица измерения $10 тыс. data.head() # В этой домашней работе вам предстоит реализовывать различные гипотезы и тестировать их. # # Метрика качества - *mean_absolute_error*. # # Разбиение на обучающую и тестовую выборку случайное. # # Cтратегия кросс-валидации - ShuffledSplit. # # **Во всех следующих заданиях нужно использовать модель, полученную с помощью *create_model()*.** def create_model(): """ create instance of CatBoostRegressor model """ return CatBoostRegressor(random_state=RANDOM_STATE, logging_level='Silent', iterations=60) # разделим выборку на обучающую и тестовую часть # + from sklearn.model_selection import train_test_split train, test = train_test_split(data, test_size = 0.2, random_state = RANDOM_STATE, shuffle=True) print('train size: {}'.format(train.shape[0])) print('test size: {}'.format(test.shape[0])) # - # #### **Задание 1.** # Попробуем обучить модель на датасете без дополнительных признаков и преобразований. Оцените качество модели на кросс-валидации. Модель нужно создать с помощью функции *create_model()*. В форме укажите значение метрики. # # Разделить выборку на фолды нужно с помошью sklearn.model_selection.KFold c параметрами: # 1. n_splits = 5 # 2. shuffle = True # 3. random_state = RANDOM_STATE # # Для проведения кросс-валидации можно использовать функции cross_val_score или cross_val_predict, в которые нужно передать созданный KFold. Можете попробовать сделать кросс-валидацию с помощью самого объекта KFold. # # Метрика - mean_absolute_error. Кросс-валидацию необходимо проводить на train части датасета. # # *Не забудьте исключить target.* # + from sklearn.model_selection import KFold, cross_val_score, cross_val_predict model = create_model() kfold = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)# your code here predictions = cross_val_predict(model, train.drop('target', axis=1), train['target'],cv=kfold) # your code here score = cross_val_score(model, train.drop('target', axis=1), train['target'], scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here print(score) # - round(-score, 2) # ------------------------ # В датасете есть интересные признаки - координаты районов. Координаты часто являются очень полезными, но не в исходном виде. Посмотрим, есть ли зависимость между target и расположением района. # # Нарисуйте диаграмму рассеивания, в которой ось x - широта, y - долгота, окрасьте точки по значению target. #your code here plt.figure(figsize=(10,8)) plt.scatter(data['Longitude'], data['Latitude'], c=data['target'], s=15) plt.xlabel('Latitude', fontsize=16) plt.ylabel('Longitude', fontsize=16) # #### **Задание 2.** # # Если сопоставить диаграмму с картой штата Калифорния, то будет видно, что цены выше у крупных городов и на побережье, попробуем добавить такой признак. Посчитайте расстояние от района до следующих крупных городов: # 1. Лос-Анджелес (34.05, -118.24) # 2. Сан-Хосе (37.33, -121.88) # 3. Сан-Франциско (37.77, -122.41) # 4. Сакраменто (38.58, -121.49) # # Расстояние от каждого города до района должно быть отдельным признаком. # # Добавьте новые признаки в датасет, оцените качество на кросс-валидации как в 1 задании. В форме укажите значение метрики. # + #your code here import geopy.distance as distance cities = { 'distance_LA' : (34.05, -118.24), 'distance_SJ' : (37.33, -121.88), 'distance_SF' : (37.77, -122.41), 'distance_SA' : (38.58, -121.49) } for city in cities.keys(): vals = [] for coord1 in zip(data['Latitude'], data['Longitude']): vals.append(distance.vincenty(coord1, cities[city]).km) data[city] = vals # - data.head() model2 = create_model() score2 = cross_val_score(model2, data.drop('target', axis=1), data['target'], scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here print(round(-score2,2)) # ------------------------ # #### **Задание 3.** # # Можно пойти дальше и добавить еще городов или других объектов, до которых можно посчитать расстояние, но тогда число признаков может значительно вырасти, что почти всегда приводит к плохим последствиям. Преобразуем расстояния до городов в новый категориальный признак - самый близкий город. # # Постройте такой признак, удалите признаки расстояний до городов и оцените качество на кросс-валидации как в 1 задании. В форме укажите значение метрики. # # Названия городов можно закодировать с помощью sklearn.preprocessing.LabelEncoder. # + #your code here from sklearn.preprocessing import LabelEncoder cols = ['distance_LA', 'distance_SJ', 'distance_SF', 'distance_SA'] city = [] for i in range(data.shape[0]): city.append(cols[np.argsort(data[cols].iloc[i].values)[0]]) data['Nearest_city'] = city le = LabelEncoder() data['Nearest_city'] = le.fit_transform(data['Nearest_city']) # - data.head(10) model3 = create_model() score3 = cross_val_score(model3, data.drop(cols+['target'], axis=1), data['target'], scoring='neg_mean_absolute_error', cv=kfold).mean() #your code here print(round(-score3,2)) # ------------------------ # #### **Задание 4.** # # Попробуем добавить более сложный признак. Номер кластера, в который попадает район. Постройте такой признак, оцените качество с новым признаком на кросс-валидации. В форме укажите значение метрики. # # Постройте кластеризацию на всех признаках с помощью алгоритма KMeans со следующими параметрами: # 1. n_clusters = 20 # 2. random_state = RANDOM_STATE # # Перед кластеризацией данные необходимо отмасштабировать с помощью StandartScaler. # Масштабирование и вычисление параметров кластеризации необходимо производить на train фолдах, а применять их к train и test фолдам. # # Используйте все рассчитанные ранее признаки. from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler # + kfold = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE) train, test = train_test_split(data, test_size = 0.2, random_state = RANDOM_STATE, shuffle=True) tmp = 0 scores_on_folds = [] for train_ids, test_ids in kfold.split(train): #kfold будет итеративно генерировать id train и test фолдов #разделяем выборку на train и test фолды train_folds = train.iloc[train_ids] test_fold = train.iloc[test_ids] #создаем скейлер, алгоритм кластеризации и модель scaler = StandardScaler() kmeans = KMeans(n_clusters=20, random_state=RANDOM_STATE) model = create_model() #your code here scaled_train = scaler.fit_transform(train_folds.drop('target', axis=1)) scaled_test = scaler.transform(test_fold.drop('target', axis=1)) kmeans_train = kmeans.fit_transform(scaled_train) kmeans_test = kmeans.transform(scaled_test) tmp = kmeans_train model.fit(kmeans_train, train_folds.target) predictions = model.predict(kmeans_test) score = mean_absolute_error(test_fold.target, predictions) scores_on_folds.append(score) print('score_by_fold: {}'.format(scores_on_folds)) print('cross-validation score: {}'.format(np.mean(scores_on_folds))) # - round(np.mean(scores_on_folds),2) # ------------------------ # #### **Задание 5.** # # Оценивать качество на тестовой выборке стоит только в самом конце, не важно генерируем мы новые признаки или подбираем гиперпараметры модели. Если часто смотреть на метрики на тестовой выборке и делать по ним выводы, то можно переобучиться под тест. Оценка качества на тесте получится недостоверной, по ней нельзя сделать вывод о работе модели с реальными данными. # # 1. Оцените качество модели на кросс-валидации на train на всех построенных в домашней работе признаках. # 2. Рассчитайте признаки из домашней работы для train и test выборок. # 3. Оцените качество на test, обучившись на train. # # Сравните качество на кросс-валидации и test. В форме укажите разницу между значением метрики на train и cv (train_score - cv_core). # # Используйте все рассчитанные раннее признаки. # + #your code here model5 = create_model() model5.fit(train.drop('target', axis=1), train['target']) preds = model5.predict(test.drop('target', axis=1)) mae = mean_absolute_error(test['target'], preds) # - round(mae-np.mean(scores_on_folds),2) # ------------------------
9,796
/Tensorflow Basics - Lesson 1.ipynb
8114a573c4be46dabc78d938861c0d4265964998
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no_license
hushenglang/tensorflow_basics
https://github.com/hushenglang/tensorflow_basics
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Jupyter Notebook
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + colab={"base_uri": "https://localhost:8080/"} id="8W8zcTKkUyi1" outputId="2b07c19f-f57f-4a84-e71c-7381021679b7" # Python ≥3.5 is required import sys assert sys.version_info >= (3, 5) # Scikit-Learn ≥0.20 is required import sklearn assert sklearn.__version__ >= "0.20" try: # # %tensorflow_version only exists in Colab. # %tensorflow_version 2.x IS_COLAB = True except Exception: IS_COLAB = False # TensorFlow ≥2.0 is required import tensorflow as tf from tensorflow import keras assert tf.__version__ >= "2.0" if not tf.config.list_physical_devices('GPU'): print("No GPU was detected. LSTMs and CNNs can be very slow without a GPU.") if IS_COLAB: print("Go to Runtime > Change runtime and select a GPU hardware accelerator.") # Common imports import numpy as np import os from pathlib import Path # to make this notebook's output stable across runs np.random.seed(42) tf.random.set_seed(42) # To plot pretty figures # %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "." CHAPTER_ID = "rnn" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) os.makedirs(IMAGES_PATH, exist_ok=True) def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) # + [markdown] id="1w6k1tuexBRf" # ## Download the dataset # + colab={"base_uri": "https://localhost:8080/"} id="uJqDiZvKwypM" outputId="b6906af9-a573-4806-bcfb-a91f768585df" DOWNLOAD_ROOT = "https://github.com/ageron/handson-ml2/raw/master/datasets/jsb_chorales/" FILENAME = "jsb_chorales.tgz" filepath = keras.utils.get_file(FILENAME, DOWNLOAD_ROOT + FILENAME, cache_subdir="datasets/jsb_chorales", extract=True) # + id="wg_IxDD8xFu8" jsb_chorales_dir = Path(filepath).parent train_files = sorted(jsb_chorales_dir.glob("train/chorale_*.csv")) valid_files = sorted(jsb_chorales_dir.glob("valid/chorale_*.csv")) test_files = sorted(jsb_chorales_dir.glob("test/chorale_*.csv")) # + id="em_CndBmxauM" import pandas as pd def load_chorales(filepaths): return [pd.read_csv(filepath).values.tolist() for filepath in filepaths] train_chorales = load_chorales(train_files) valid_chorales = load_chorales(valid_files) test_chorales = load_chorales(test_files) # + colab={"base_uri": "https://localhost:8080/"} id="poTYdQ1ayB7X" outputId="92f8ac2b-0740-432c-ab63-4447a72c2310" train_chorales[0] # + [markdown] id="q4hpXC3ayZxA" # Notes range from 36 (C1 = C on octave 1) to 81 (A5 = A on octave 5), plus 0 for silence: # + id="L9B9fHdYyGxC" notes = set() for chorales in (train_chorales, valid_chorales, test_chorales): for chorale in chorales: for chord in chorale: notes |= set(chord) n_notes = len(notes) min_note = min(notes - {0}) max_note = max(notes) assert min_note == 36 assert max_note == 81 # + [markdown] id="-aMK2we624a-" # writing a few functions to listen to these chorales : # + id="SQL68r3FydnR" from IPython.display import Audio def notes_to_frequencies(notes): # Frequency doubles when you go up one octave; there are 12 semi-tones # per octave; Note A on octave 4 is 440 Hz, and it is note number 69. return 2 ** ((np.array(notes) - 69) / 12) * 440 def frequencies_to_samples(frequencies, tempo, sample_rate): note_duration = 60 / tempo # the tempo is measured in beats per minutes # To reduce click sound at every beat, we round the frequencies to try to # get the samples close to zero at the end of each note. frequencies = np.round(note_duration * frequencies) / note_duration n_samples = int(note_duration * sample_rate) time = np.linspace(0, note_duration, n_samples) sine_waves = np.sin(2 * np.pi * frequencies.reshape(-1, 1) * time) # Removing all notes with frequencies ≤ 9 Hz (includes note 0 = silence) sine_waves *= (frequencies > 9.).reshape(-1, 1) return sine_waves.reshape(-1) def chords_to_samples(chords, tempo, sample_rate): freqs = notes_to_frequencies(chords) freqs = np.r_[freqs, freqs[-1:]] # make last note a bit longer merged = np.mean([frequencies_to_samples(melody, tempo, sample_rate) for melody in freqs.T], axis=0) n_fade_out_samples = sample_rate * 60 // tempo # fade out last note fade_out = np.linspace(1., 0., n_fade_out_samples)**2 merged[-n_fade_out_samples:] *= fade_out return merged def play_chords(chords, tempo=160, amplitude=0.1, sample_rate=44100, filepath=None): samples = amplitude * chords_to_samples(chords, tempo, sample_rate) if filepath: from scipy.io import wavfile samples = (2**15 * samples).astype(np.int16) wavfile.write(filepath, sample_rate, samples) return display(Audio(filepath)) else: return display(Audio(samples, rate=sample_rate)) # + [markdown] id="ghy-DcIF3yt2" # Now let's listen to a few chorales: # + colab={"base_uri": "https://localhost:8080/", "height": 191} id="Q2My9je93lK3" outputId="524b94c9-e132-49ca-81d5-962f18fe2f28" for index in range(3): play_chords(train_chorales[index]) # + id="AdYzWcte35jK" def create_target(batch): X = batch[:, :-1] Y = batch[:, 1:] # predict next note in each arpegio, at each step return X, Y def preprocess(window): window = tf.where(window == 0, window, window - min_note + 1) # shift values return tf.reshape(window, [-1]) # convert to arpegio def bach_dataset(chorales, batch_size=32, shuffle_buffer_size=None, window_size=32, window_shift=16, cache=True): def batch_window(window): return window.batch(window_size + 1) def to_windows(chorale): dataset = tf.data.Dataset.from_tensor_slices(chorale) dataset = dataset.window(window_size + 1, window_shift, drop_remainder=True) return dataset.flat_map(batch_window) chorales = tf.ragged.constant(chorales, ragged_rank=1) dataset = tf.data.Dataset.from_tensor_slices(chorales) dataset = dataset.flat_map(to_windows).map(preprocess) if cache: dataset = dataset.cache() if shuffle_buffer_size: dataset = dataset.shuffle(shuffle_buffer_size) dataset = dataset.batch(batch_size) dataset = dataset.map(create_target) return dataset.prefetch(1) # + [markdown] id="qoMWXPHhM4dO" # creating the training set, the validation set and the test set: # + id="NYvIQiH6MePb" train_set = bach_dataset(train_chorales, shuffle_buffer_size=1000) valid_set = bach_dataset(valid_chorales) test_set = bach_dataset(test_chorales) # + [markdown] id="Oj7C2B7GPUqo" # ## building the model # + colab={"base_uri": "https://localhost:8080/"} id="Vsueh4ehM-CW" outputId="6a597ea3-70c1-48f9-b9ea-e95b749cddf4" n_embedding_dims = 5 model = keras.models.Sequential([ keras.layers.Embedding(input_dim=n_notes, output_dim=n_embedding_dims, input_shape=[None]), keras.layers.Conv1D(32, kernel_size=2, padding="causal", activation="relu"), keras.layers.BatchNormalization(), keras.layers.Conv1D(48, kernel_size=2, padding="causal", activation="relu", dilation_rate=2), keras.layers.BatchNormalization(), keras.layers.Conv1D(64, kernel_size=2, padding="causal", activation="relu", dilation_rate=4), keras.layers.BatchNormalization(), keras.layers.Conv1D(96, kernel_size=2, padding="causal", activation="relu", dilation_rate=8), keras.layers.BatchNormalization(), keras.layers.LSTM(256, return_sequences=True), keras.layers.Dense(n_notes, activation="softmax") ]) model.summary() # + colab={"base_uri": "https://localhost:8080/"} id="ougFu4UBPXO8" outputId="bc57553c-36da-456c-b50d-18f2bf68c6cf" optimizer = keras.optimizers.Nadam(lr=1e-3) model.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]) model.fit(train_set, epochs=20, validation_data=valid_set) # + colab={"base_uri": "https://localhost:8080/"} id="ctIauHk7Pxuw" outputId="2b8a13ca-19f4-42c6-c186-1b127e2a6551" model.save("my_bach_model.h5") model.evaluate(test_set) # + id="jFyDUQygQDAb" def generate_chorale(model, seed_chords, length): arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64)) arpegio = tf.reshape(arpegio, [1, -1]) for chord in range(length): for note in range(4): next_note = model.predict_classes(arpegio)[:1, -1:] arpegio = tf.concat([arpegio, next_note], axis=1) arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1) return tf.reshape(arpegio, shape=[-1, 4]) # + [markdown] id="sQ1hPIaxREvf" # test this function using the first 8 chords of one of the test chorales # + colab={"base_uri": "https://localhost:8080/", "height": 75} id="ESCKLsYgQqio" outputId="0995d176-4066-4498-8811-11b744ffdb07" seed_chords = test_chorales[2][:8] play_chords(seed_chords, amplitude=0.2) # + [markdown] id="UKB7y8LYRTUt" # generate 56 more chords, for a total of 64 chords # + colab={"base_uri": "https://localhost:8080/", "height": 146} id="wzkljWHsRHoO" outputId="b655df1e-b5e5-421b-8c03-f4dafa92c91c" new_chorale = generate_chorale(model, seed_chords, 56) play_chords(new_chorale) # + [markdown] id="iFNLCqdcXHb5" # This approach has one major flaw: it is often too conservative. Indeed, the model will not take any risk, it will always choose the note with the highest score, and since repeating the previous note generally sounds good enough, it's the least risky option, so the algorithm will tend to make notes last longer and longer. Pretty boring. Plus, if you run the model multiple times, it will always generate the same melody. # # So let's spice things up a bit! Instead of always picking the note with the highest score, we will pick the next note randomly, according to the predicted probabilities. For example, if the model predicts a C3 with 75% probability, and a G3 with a 25% probability, then we will pick one of these two notes randomly, with these probabilities. We will also add a temperature parameter that will control how "hot" (i.e., daring) we want the system to feel. A high temperature will bring the predicted probabilities closer together, reducing the probability of the likely notes and increasing the probability of the unlikely ones. # + id="TK4qJdM5RWmI" def generate_chorale_v2(model, seed_chords, length, temperature=1): arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64)) arpegio = tf.reshape(arpegio, [1, -1]) for chord in range(length): for note in range(4): next_note_probas = model.predict(arpegio)[0, -1:] rescaled_logits = tf.math.log(next_note_probas) / temperature next_note = tf.random.categorical(rescaled_logits, num_samples=1) arpegio = tf.concat([arpegio, next_note], axis=1) arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1) return tf.reshape(arpegio, shape=[-1, 4]) # + [markdown] id="rmiWO43VXe27" # generating 3 chorales using this new function: one cold, one medium, and one hot # + colab={"base_uri": "https://localhost:8080/", "height": 75} id="M3P47zLfXXm3" outputId="20f0a567-5318-4124-dd59-1ba58f9b7135" new_chorale_v2_cold = generate_chorale_v2(model, seed_chords, 56, temperature=0.8) play_chords(new_chorale_v2_cold, filepath="bach_cold.wav") # + colab={"base_uri": "https://localhost:8080/", "height": 75} id="U68lpGKxXidx" outputId="a3e47904-6ac8-4f75-d4a1-7dcd91f63504" new_chorale_v2_medium = generate_chorale_v2(model, seed_chords, 56, temperature=1.0) play_chords(new_chorale_v2_medium, filepath="bach_medium.wav") # + colab={"base_uri": "https://localhost:8080/", "height": 75} id="FZHuI-WxXmV8" outputId="6e59faba-1a82-433a-9872-f90482b4200f" new_chorale_v2_hot = generate_chorale_v2(model, seed_chords, 56, temperature=1.5) play_chords(new_chorale_v2_hot, filepath="bach_hot.wav") # + id="Qe9B3aPeXwcf"
12,494
/Regressão Linear/Regressão Linear com gradiente descendente.ipynb
6a6f7bdf93a50b948c531e209db16a1cbe359e79
[]
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eder0782/Tutoriais-de-AM
https://github.com/eder0782/Tutoriais-de-AM
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Jupyter Notebook
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350,266
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Regressão Linear com gradiente descendente # # Métodos iterativos de otimização são usadas em aprendizado de máquina em toda a parte. Aqui, nós vamos olhar o método de gradiente descendente, o método mais popular para treinar redes neurais artificiais. Em se tratando de uma simples regressão linear, o método de gradiente descendente só é recomendado quando temos dados com muitas dimensões. Nesse caso, a inversão da matriz $\pmb{X}^T\pmb{X}$ começa a demorar muito e resolver regressão linear pela fórmula analítica $\pmb{\hat{w}} = (\pmb{X}^T \pmb{X})^{-1} \pmb{X}^T \pmb{y}$ não vale mais a pena. # # Nós também veremos um pouco de regimes de aprendizado online e em lotes (*mini-batch learning*) e discutiremos como esses regimes podem ser usados para aprender utilizando bases gigantescas que não são possíveis de carregar de uma só vez para o RAM do computador (e.g. bases com +/- 20 GB). # # Para melhor entendimento do algoritmo de otimização, é mais interessante começar usando-o em um problema mais simples, então vamos introduzir o algoritmo com um problema de regressão linear simples, com apenas uma variável na matriz de dados $\pmb{X}$. # # # ## Pré-requisitos # # É preciso ter um conhecimento básico de Python, incluindo o mínimo de Python orientado à objetos. Caso não saiba programar, os cursos de [Introdução à Ciência da Computação](https://br.udacity.com/course/intro-to-computer-science--cs101/) e [Fundamentos de Programação com Python](https://br.udacity.com/course/programming-foundations-with-python--ud036/) fornecem uma base suficiente sobre programação em Python e Python orientado à objetos, respectivamente. Além disso, é necessário ter conhecimento das bibliotecas de manipulação de dados Pandas e Numpy. Alguns bons tutoriais são o [Mini-curso 1](https://br.udacity.com/course/machine-learning-for-trading--ud501/) do curso de Aprendizado de Máquina para Negociação, o site [pythonprogramming.net](https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/) ou o primeiro curso do DataCamp em [Python](https://www.datacamp.com/getting-started?step=2&track=python). # # Para entender o desenvolvimento do algoritmo de regressão linear é preciso ter o conhecimento de introdução à álgebra linear. Na UnB, a primeira parte do curso de Economia Quantitativa 1 já cobre o conteúdo necessário. Caso queira relembrar ou aprender esse conteúdo, o curso online do MIT de [Introdução à Álgebra Linear](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8) fornece uma boa base sobre a matemática que será desenvolvida nos algoritmos de aprendizado de máquina. # # Conhecimento de cálculo e principalmente otimização é fundamental para o entendimento dos algoritmos de aprendizado de máquina, que muitas vezes são encarados explicitamente como problemas de otimização. Uma noção de cálculo multivariado também ajudará na compreensão dos algoritmos, visto que muitas vezes otimizaremos em várias direções. # # # ## Intuição e explicação matemática # # Vamos utilizar um exemplo de regressão linear bastante simples, com apenas uma variável dependente e uma independente. A relação entre elas pode ser expressa na equação $\pmb{y} = b + \pmb{x} w + \pmb{\epsilon}$ e nós queremos achar os valores $\hat{b}$ e $\hat{w}$ que minimizam a norma do vetor $\pmb{\epsilon}$, isto é, minimizamos a soma dos quadrados dos resíduos. # # A ideia pro trás dos métodos iterativos de otimização é bastante simples: nós começamos com algum chute razoável para os valores de $\hat{b}$ e $\hat{w}$ e vamos atualizando-os na direção certa até que chegamos no valor mínimo da nossa função custo, nesse caso, $||\pmb{\hat{\epsilon}}||^2$. # # Matematicamente, nós temos que perceber que a nossa função custo, $||\pmb{\hat{\epsilon}}||^2$, é uma função de $\hat{b}$ e $\hat{w}$: # # $$L(\hat{b}, \hat{w})=||\pmb{\hat{\epsilon}}||^2 = \sum{\epsilon}^2 \\= # \sum{(\hat{y}} - y)^2 \\= # \sum{(\hat{b} + x\hat{w}} - y)^2$$ # # E que, portanto, podemos minimizá-la nesses dois parâmetros usando cálculo multivariado. Essa função custo - especifica de regressão linear - é uma função convexa, o que quer dizer que ela o único ponto de mínimo que ela tem é um mínimo global. Em outras palavras, a função custo pode ser vista como uma tigela, e o gradiente dessa função nós apontará a direção de descida mais ingrime nas direções $\hat{b}$ e $\hat{w}$, de forma que possamos chegar ao fundo da tigela, onde está o ponto de menor custo. Para implementar o gradiente descendente, basta atualizar **simultaneamente** os valores de $\hat{b}$ e $\hat{w}$, subtraindo deles as respectivas derivadas parciais da função custo vezes uma taxa de aprendizado $\alpha$ (o sinal $:=$ significa atualizar): # # $$\hat{b} := \hat{b} - \alpha \frac{\partial}{\partial \hat{b}}L(\hat{b}, \hat{w})$$ # # $$\hat{w} := \hat{w} - \alpha \frac{\partial}{\partial \hat{w}}L(\hat{b}, \hat{w})$$ # # Ou, no caso específico da nossa função custo de soma dos erros quadrados: # # $$\hat{b} := \hat{b} - \alpha \frac{1}{2} \sum{(\hat{b} + \hat{w} x - y)} $$ # # $$\hat{w} := \hat{w} - \alpha \frac{1}{2} \sum{((\hat{b} + \hat{w} x - y) x)} $$ # # Se quisermos simplificar, podemos retirar da fórmula $\frac{1}{2}$ que não fará diferença, uma vez que as derivadas já estão sendo multiplicadas por uma constante $\alpha$. Se quisermos simplificar a notação mais ainda, podemos utilizar a de vetores: # # $$\pmb{\hat{w}} := \pmb{\hat{w}} - \alpha \nabla(L)) $$ # # Em que $\pmb{\hat{w}}$ é o vetor dos parâmetros da regressão linear, incluindo o intercepto $\hat{b}$. Note que esse última regra de atualização é geral para qualquer número de dimensões que tenham nossos dados. # # E pronto. É só isso. Simples assim! # # ## Visualizando gradiente descendente # # Para entender melhor como funciona o algoritmo de gradiente descendente, vamos simular alguns dados com uma relação conhecida, de forma que possamos ver gradiente descendente em ação. Nós vamos trabalhar com uma regressão linear bem simples, com apenas dois parâmetros para aprender: o intercepto $\hat{b}$ e a inclinação com respeito a única variável, $\hat{w}$. # # Particularmente, vamos gerar dados x e y de forma que $y = 5 + 3x + \epsilon$, em que $\epsilon$ é algum erro aleatório. Nós sabemos que os valores ótimos de $\hat{w}$ e $\hat{b}$ seriam então 3 e 2, respectivamente, então poderemos vêr quão perto deles chegarão os parâmetros aprendidos por gradiente descendente. # # Visualmente, se plotarmos os pares (x,y) teremos um gráfico como o abaixo. A nossa esperança é que a técnica de gradiente descendente consiga achar uma reta que melhor se encaixa nestes dados. # + import pandas as pd import numpy as np np.random.seed(0) from matplotlib import pyplot as plt dados = pd.DataFrame() dados['x'] = np.linspace(-10,10,100) dados['y'] = 5 + 3*dados['x'] + np.random.normal(0,3,100) plt.scatter(dados['x'], dados['y']) plt.axhline(y=0, linewidth=2, color = 'k') plt.axvline(x=0, linewidth=2, color = 'k') plt.show() # - # Antes de implementar a regressão linear por gradiente descendente, é uma boa visualizar como é a nossa função custo quando plotada nas duas dimensões dos parâmetros $\hat{b}$ e $\hat{w}$ que queremos aprender: # + from IPython import display from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter # %matplotlib notebook # define a função custo def L(y, y_hat): return ((y-y_hat) ** 2).sum() # define valores de b_hat e w_hat b_hat, w_hat = np.linspace(-10,20,40), np.linspace(0,6,40) # acha o custo para cada combinação de b_hat e w_hat loss = np.array([L(dados['y'], i + j * dados['x']) for i in b_hat for j in w_hat]).reshape(40,40) b_hat, w_hat = np.meshgrid(b_hat, w_hat) # combina os b_hat e w_hat em uma grade # faz o gráfico em 3d fig = plt.figure() ax = fig.gca(projection='3d') ax.set_zticks([]) ax.set_xlabel('$\hat{b}$') ax.set_ylabel('$\hat{w}$') ax.set_zlabel('Custo', rotation=90) surf = ax.plot_surface(b_hat, w_hat, loss, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0) plt.show() # - # Como eu disse, a função de custo parece uma tigela. Se você olhar bem, vai perceber como o ponto de mínimo da tigela está onde $\hat{b}=5$ e $\hat{w}=3$. O gradiente dessa função é simplesmente um vetor que dá a inclinação dessa tigela em cada ponto: # # $$\nabla(L)=\Bigg[\frac{\partial L}{\partial \hat{b}}, \frac{\partial L}{\partial \hat{w}} \Bigg]$$ # # Se nós seguirmos na direção oposta do gradiente, então chegaremos no ponto de mínimo. Podemos traçar uma analogia com uma bolinha de gude sendo solta em uma tigela: a bolinha descerá na direção mais inclinada e eventualmente parará no ponto mais baixo da tigela. Há uma importante diferença, no entanto. Quando falamos de uma bolinha de gude desliando para o fundo de uma tigela, podemos visualizar a bolinha começando com uma pequena velocidade e acelerando ao longo do trajeto. Com gradiente descendente ocorre o oposto: inicialmente, os parâmetros $\hat{b}$ e $\hat{w}$ caminham rapidamente em direção ao ponto de mínimo e, quanto mais se aproximam dele, passam a caminhar cada vez mais devagar. # # Mas por que isso acontece? Pense em como a cada iteração os parâmetros $\hat{b}$ e $\hat{w}$ dão um passo em direção ao mínimo. O tamanho desse passo será o valor do gradiente naquele ponto multiplicado pela constante $\alpha$. Olhe de novo para o gráfico acima e note que quanto mais próximos estamos do ponto de mínimo, menor a inclinação da função custo, **OU SEJA** menor o gradiente, **OU SEJA**, menor o passo dado em direção ao mínimo. # # Essa característica do método de gradiente descendente é ao mesmo tempo boa e ruim. É ruim pois atrasa o processo de aprendizado quando chegamos próximo do mínimo, mas é boa porque nos permite uma exploração mais minuciosa da superfície de custo em torno do ponto de mínimo. Dessa forma, podemos localizá-lo com mais precisão. Isso talvez não pareça muito importante nesse caso super simples de regressão linear com apenas dois parâmetros para aprender, mas quando estamos lidando com aprendizado de redes neurais com milhares de parâmetros e uma função custo não convexa você vai entender porque é importante essa exploração minuciosa do espaço da função custo. # # Tendo dito tudo isso, vamos agora implementar a regressão linear com gradiente descendente. Note como abaixo nós nos restringimos ao caso simples para que possamos visualizar o processo de aprendizado. Algumas pequenas mudanças são necessárias no caso de uma regressão linear com vários parâmetros para aprender. # + class linear_regr(object): def __init__(self, learning_rate=0.0001, training_iters=1000, show_learning=False): self.learning_rate = learning_rate self.training_iters = training_iters self.show_learning = show_learning def fit(self, X_train, y_train, plot=False): # formata os dados if len(X_train.values.shape) < 2: X = X_train.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) # para plotar o aprendizado (é preciso conhecer a equação geradora) if self.show_learning: assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)] self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)] # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável) self.w_hat = np.random.normal(0,5, size = X[0].shape) loss = [] # para plotar o aprendizado for _ in range(self.training_iters): gradient = np.zeros(self.w_hat.shape) # inicia o gradiente # atualiza o gradiente com informação de todos os pontos for point, yi in zip(X, y_train): gradient += (point * self.w_hat - yi) * point gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado # atualiza os parâmetros self.w_hat -= gradient l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo loss.append(l) # armazeno o custo para gráfico if self.show_learning: # mostra o estado atual do aprendizado self._show_state(X_train, y_train, loss) def predict(self, X_test): # formata os dados if len(X_test.values.shape) < 2: X = X_test.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) return np.dot(X, self.w_hat) def _show_state(self, X_train, y_train, loss): # visualiza o processo de aprendizado lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w # scatter plot plt.subplot(221) plt.scatter(X_train, y_train, s= 10) plt.plot(X_train, self.predict(X_train), c='r') plt.title('$y = b + w x$') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # loss plt.subplot(222) plt.plot(range(len(loss)), loss) plt.title('Custo') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # b_loss plt.subplot(223) plt.plot( np.linspace(-10,20,20), self.b_loss) plt.scatter(self.w_hat[0], lb, c = 'k') plt.title('Custo em $\hat{b}$') plt.tick_params(labelleft=False) plt.grid(True) # w_loss plt.subplot(224) plt.plot(np.linspace(0,6,20), self.w1_loss) plt.scatter(self.w_hat[1], lw, c = 'k') plt.title('Custo em $\hat{w}$') plt.grid(True) plt.tick_params(labelleft=False) plt.tight_layout() display.display(plt.gcf()) display.clear_output(wait=True) plt.clf() # limpa a imagem do gráfico regr = linear_regr(learning_rate=0.0005, training_iters=30, show_learning=True) regr.fit(dados['x'], dados['y']) # - # Nos gráficos acima, pode ver como evolui a posição da reta que queremos encaixar nos dados (gráfico 1), o custo (gráfico 2), o parâmetro $\hat{b}$ (gráfico 3) e o parâmetro $\hat{w}$ (gráfico 4). Note como com apenas 30 iterações de treino nós conseguimos que os parâmetros aprendidos chegassem muito perto dos valores de mínimo. Nós podemos dizer com confiança que nosso algoritmo de gradiente descendente foi um sucesso! # # # ## Hiper-parâmetros # # O algoritmo de otimização iterativa por gradiente descendente é talvez o algoritmo de aprendizado de máquina mais importante que você vai aprender: ele é extremamente poderoso, relativamente rápido e funciona nos mais diversos cenários. No entanto, tudo isso vêm a um preço e nesse caso são os hiper-parâmetros. # # Diferentemente dos parâmetros $\pmb{\hat{w}}$ aprendidos durante o treinamento de uma regressão linear (ou de uma rede neural, como veremos mais para frente), os hiper-parâmetros **nãos** são aprendidos pela máquina durante o treinamento e devem ser ajustados manualmente. No caso da nossa regressão linear por gradiente descendente, podemos distinguir três hiper-parâmetros: # * A taxa de aprendizado # * O número de iterações de treino # * Os valores iniciais de $\pmb{\hat{w}}$ # # No caso de regressão linear, como a função custo é convexa, não importa muito onde começamos em termos de $\pmb{\hat{w}}$. **Se os outros dois hiper-parâmetros** forem ajustados corretamente chegaremos no mínimo independentemente do ponto de partido. Então, aqui nós não vamos dar muita atenção aos valores iniciais de $\pmb{\hat{w}}$ (note como na nossa implementação eles nem sequer foram feitos para serem ajustados e são simplesmente pequenos valores aleatórios). # # Agora, os dois primeiros hiper-parâmetros são muito importantes e o sucesso ou fracasso do aprendizado depende severamente de conseguirmos ajustá-los corretamente. A **taxa de aprendizado** é definitivamente o mais importante de todos, então vamos gastar um certo tempo discutindo como ela influencia no aprendizado e como ajustá-la bem. # # A taxa de aprendizado define o tamanho dos passos que daremos em direção ao mínimo em cada iteração. Se esses passos forem muito pequenos, é quase garantido que chegaremos ao ponto de mínimo da função, mas para isso talvez precisaremos de muitas iterações de treino, tornando o algoritmo desnecessariamente lento. # # Por outro lado, se colocarmos uma taxa de aprendizado muito alta, pode acontecer de sermos catapultados para cima da função custo e irmos cada vez mais longe do mínimo, resultando em uma falha completa de aprendizado. Isso acontecerá quando o passo que dermos for tão grande que pulará o ponto de mínimo e chegará em um ponto na função custo mais alto do que o de onde saímos. Nesse novo ponto, o gradiente será ainda maior, aumentando mais ainda o passo seguinte e nos arremessando ainda mais longe do ponto de mínimo a cada iteração. # # <img src="./img/lrate.jpeg", width=500> # # Podemos ver que a taxa de aprendizado não deve ser nem tão grande, nem tão pequena. Uma sugestão de ajustamento desse hiper-parâmetro é começar com 0.01 e explorar os pontos em volta dez vezes maior/menor (isto é, 0.1 e 0.001). Na maioria dos casos, uma boa taxa de aprendizado será algum dos seguintes valores: 1, 0.1, 0.01, 0.001, 0.0001, 0.00001. # # Com uma boa taxa de aprendizado, selecionar o **número de iterações de treino** é uma tarefa fácil. Mesmo assim, recomenda-se plotar o valor da função custo a cada iteração de treino, assim como fizemos no gráfico 2 acima. Dessa forma você poderá ver se a função custo já chegou em uma região em que o seu valor não diminui ou diminui pouco a cada iteração. # # No nosso caso, o gráfico da função custo a cada iteração é bastante suave, mas pode acontecer de haver tanto iterações em que o custo cai quando iterações em que o custo sobe. Se esse é o caso e a função custo flutua muito a cada iteração, recomenda-se baixar a taxa de aprendizado. Se a função custo desce suavemente e constantemente, mas muito devagar, recomenda-se aumentar a taxa de aprendizado. # # ## Problemas no aprendizado # # Lembre-se de como a função custo da regressão linear é uma tigela? Se fizermos secções horizontais nessa tigela teremos um mapa topográfico da superfície de custo, assim como no ótimo desenho abaixo feito por mim. # # <img src="./img/custo1.jpeg", width=400> # # Sabemos que a otimização por gradiente descendente dará passos na direção mais inclinada, ou seja, na direção perpendicular as curvas de nível, assim como desenhado acima. Se as curvas de nível forem círculos perfeitos (como os que eu tentei desenhar), gradiente descendente só dará passos em direção ao ponto de mínimo e convergirá rapidamente. Por outro lado, se as curvas de nível da superfície de custo forem elipses alongadas, o tempo de convergência dependerá fortemente da inicialização dos nossos parâmetros. # # <img src="./img/custo2.jpeg", width=400> # # Por exemplo, se começarmos nossa descida no ponto 2 da imagem acima, a direção perpendicular à curva de nível aponta diretamente para o ponto de mínimo e não teremos maiores problemas durante o aprendizado. Mas se começarmos em um ponto como o 1 da imagem acima, a direção perpendicular à curva de nível aponta numa direção quase 90 graus da direção ao ponto de mínimo. Como consequência, daremos muitos passos em zig-zag e a convergência demorará muito mais. # # Esse formato de elipse da função custo surge quando as variáveis dos nossos dados estão em escalas muito diferentes. Assim, uma solução simples para esse problema é deixar todos as variáveis na mesma escala. Uma forma de realizar isso é, para cada variável, subtrair a média e dividir pelo desvio padrão (normalização). # # # ## Gradiente descendente estocástico: aprendizado em mini-lotes # # No exemplo simples que estamos usando, simulamos apenas 100 dados. Mas imagine agora que você deseja trabalhar com dados de algum censo, em que teremos observações na ordem de dezenas de milhões. Em primeiro lugar, você provavelmente não teria memória RAM suficiente para carregar todos os dados de uma vez, mas vamos supor que isso não seja um problema e você consiga implementar facilmente um procedimento que carrega os dados por partes. Você então inicia os parâmetros da regressão linear e agora precisaria percorrer todos os milhões de dados para computar o gradiente e dar **apenas um** passo da otimização. Em outras palavas, **cada passo** da otimização por gradiente descendente demora linearmente mais conforme mais dados temos. Isso é muito ineficiente e há uma forma muito mais rápida de realizar essa otimização. # # Em primeiro lugar, considere se os seus dados tem alguma redundância, isto é, se você embaralhasse todas as observações, uma parte dos dados seria parecida com a outra? Se sim, então nós não precisamos percorrer todos os dados para computar o gradiente e podemos conseguir uma aproximação dele apenas olhando alguns exemplos dos dados. Essa é a ideia central por trás da técnica de gradiente descendente estocástico (G.D.E.). # # Para possibilitar que a otimização por gradiente descendente continue rápida mesmo com milhões de dados, nós vamos alterá-la da seguinte forma: # # 1. primeiro, embaralhamos os nossos dados de forma que se retirássemos diferentes sub-amostras deles, elas não defeririam muito. # 2. segundo lugar, ao invés de computar o gradiente usando todos os dados, nós vamos fazer uma estimação dele usando apenas alguns dados - digamos um lote de 5 observações. Nós então atualizaremos os parâmetros com base nessa estimação do gradiente. Na atualização seguinte, nós repetiremos esse processo, mas agora estimando o gradiente com o próximo lote de dados, e assim por diante. # # Você pode estar pensando que utilizar apenas 5 observações para estimar um gradiente nos dará uma estimativa bem ruim e você tem razão. Na verdade, essa estimativa é tão ruim que muitas vezes o gradiente estimado nos levará em uma direção errada e custo *aumentará*. No entanto, na média, o gradiente estimado nos levará na direção correta. Em resumo, com GDE precisaremos de mais iterações de treino para chegar próximo do mínimo, mas cada iteração demorará muito (muuuito muuutio) menos tempo e o aprendizado como um todo será mais rápido. A rigor, se gradiente descendente com todos os dados demora linearmente mais conforme mais dados temos, com GDE o tempo de treino é **CONSTANTE** e **não** aumenta com o a quantidade de dados! Isso porque pode acontecer de nem sequer precisarmos ver todas as observações para chegar a uma região razoável na função de custo. # # Mais ainda, como não precisamos de todos os dados de uma vez para o processo de treinamento, podemos utilizar essa técnicas para aprendizado de máquina com em base de dados gigantescas, maiores até do que nosso computador suportaria trazer para a memória de curto prazo de uma só vez. # # Ao utilizar GDE introduzimos mais um hiper-parâmetro que terá que ser ajustado manualmente: o tamanho do lote. É importante entender como esse hiper-parâmetro funciona para saber como ajustá-lo bem. Em geral, lotes maiores significam passos mais precisos em direção ao mínimo, mas ao mesmo tempo significa passos mais demorados. # # Um outro detalhe que vale a pena mencionar é que GDE normalmente não converge, mas fica vagando em alguma região próxima ao ponto de mínimo. Na prática, isso não é um problema, pois nessa região o custo já é baixo o suficiente. De qualquer forma, é uma boa visualizar um exemplo do tipo de trajeto que GDE percorrerá numa superfície de custo: # # <img src="./img/gde.jpeg", width=400> # # E finalmente, nosso implementação de GDE. Para notar o aumento de velocidade, é necessário desligar a visualização. # + np.random.seed(23) class linear_regr(object): def __init__(self, learning_rate=0.0001, training_iters=30, batch_size=10, show_learning=False): self.learning_rate = learning_rate self.training_iters = training_iters self.batch_size = batch_size self.show_learning = show_learning def fit(self, X_train, y_train, plot=False): # formata os dados if len(X_train.values.shape) < 2: X = X_train.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) # para plotar o aprendizado (é preciso conhecer a equação geradora) if self.show_learning: assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)] self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)] # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável) self.w_hat = np.random.normal(0,5, size = X[0].shape) loss = [] # para plotar o aprendizado for i in range(self.training_iters): # cria os mini-lotes offset = (i * self.batch_size) % (y_train.shape[0] - self.batch_size) batch_X = X[offset:(offset + self.batch_size), :] batch_y = y_train[offset:(offset + self.batch_size)] gradient = np.zeros(self.w_hat.shape) # inicia o gradiente # atualiza o gradiente com informação dos pontos do lote for point, yi in zip(batch_X, batch_y): gradient += (point * self.w_hat - yi) * point gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado # atualiza os parâmetros self.w_hat -= gradient l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo loss.append(l) # armazeno o custo para gráfico if self.show_learning: # mostra o estado atual do aprendizado self._show_state(X_train, y_train, loss) def predict(self, X_test): # formata os dados if len(X_test.values.shape) < 2: X = X_test.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) return np.dot(X, self.w_hat) def _show_state(self, X_train, y_train, loss): # visualiza o processo de aprendizado lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w # scatter plot plt.subplot(221) plt.scatter(X_train, y_train, s= 10) plt.plot(X_train, self.predict(X_train), c='r') plt.title('$y = b + w x$') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # loss plt.subplot(222) plt.plot(range(len(loss)), loss) plt.title('Custo') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # b_loss plt.subplot(223) plt.plot( np.linspace(-10,20,20), self.b_loss) plt.scatter(self.w_hat[0], lb, c = 'k') plt.title('Custo em $\hat{b}$') plt.tick_params(labelleft=False) plt.grid(True) # w_loss plt.subplot(224) plt.plot(np.linspace(0,6,20), self.w1_loss) plt.scatter(self.w_hat[1], lw, c = 'k') plt.title('Custo em $\hat{w}$') plt.grid(True) plt.tick_params(labelleft=False) plt.tight_layout() display.display(plt.gcf()) display.clear_output(wait=True) plt.clf() # limpa a imagem do gráfico regr = linear_regr(learning_rate=0.0003, training_iters=40, show_learning=True) regr.fit(dados['x'], dados['y']) # - # ## Explorando melhoras: acelerando GDE # # GDE sozinho já é um método bastante popular para treinar modelos de aprendizado de máquina. Mesmo assim, várias extensões e variações foram propostas com o intuito de diminuir as flutuações na função custo ou acelerar o processo de treinamento. Aqui, vamos explorar apenas uma delas, mas saiba que muitas outras existem. # # Como já dissemos, a diferença fundamental entre o método de gradiente descendente e o processo de uma bolinha de gude descendo em uma cuia é que a bolinha acumula momento, acelerando conforme desce. Em outras palavras, quando a direção de descida é a mesma, a bolinha aumenta a velocidade. Isso é definitivamente uma propriedade que gostaríamos de ter no nosso processo de aprendizado por GDE: se estamos indo na direção certa, é uma boa ideia acelerar! # # Não é tão difícil modificar GDE para incorporar momento. Para isso, basta sabermos a velocidade passada da bolinha e atualizá-la conforme o processo de descida. Além disso, nós agora vamos atualizar os parâmetros conforme a velocidade ao invés de utilizar apenas o gradiente. Eis a nova regra de atualização dos parâmetros: # # $$\pmb{v_t} := \gamma \pmb{v_{t-1}} + \alpha \nabla(L)) $$ # # $$\pmb{\hat{w}} := \pmb{\hat{w}} - \pmb{v_t}$$ # # Na primeira linha, nós atualizamos a velocidade. O termo $\gamma v_{t-1}$ funciona como um atrito ou resistência do ar, diminuindo a velocidade em uma porcentagem $1-\gamma$ da velocidade anterior. $\gamma$ é mais um hiper-parâmetro que precisa ser ajustado manualmente. O termo seguinte, $\alpha \nabla(L))$, incorpora a informação da inclinação descida. # # E por fim, nossa implementação. # + np.random.seed(23) class linear_regr(object): def __init__(self, learning_rate=0.0001, training_iters=1000, gamma=0.9, batch_size=10, show_learning=False): self.learning_rate = learning_rate self.training_iters = training_iters self.gamma = gamma self.batch_size = batch_size self.show_learning = show_learning def fit(self, X_train, y_train, plot=False): # formata os dados if len(X_train.values.shape) < 2: X = X_train.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) # para plotar o aprendizado (é preciso conhecer a equação geradora) if self.show_learning: assert X.shape[1] <= 2 # só é possível viazualizar 2 parâmetros self.w1_loss = [L(y_train, 5 + i * X_train) for i in np.linspace(0,6,20)] self.b_loss = [L(y_train, i + 3 * X_train) for i in np.linspace(-10,20,20)] # inicia os parâmetros com pequenos valores aleatórios (nosso chute razoável) self.w_hat = np.random.normal(0,5, size = X[0].shape) velocidade = np.zeros(self.w_hat.shape) # inicia a velocidade loss = [] # para plotar o aprendizado for i in range(self.training_iters): # cria os mini-lotes offset = (i * self.batch_size) % (y_train.shape[0] - self.batch_size) batch_X = X[offset:(offset + self.batch_size), :] batch_y = y_train[offset:(offset + self.batch_size)] gradient = np.zeros(self.w_hat.shape) # inicia o gradiente # atualiza o gradiente com informação de todos os pontos for point, yi in zip(batch_X, batch_y): gradient += (point * self.w_hat - yi) * point gradient *= self.learning_rate # multiplica o gradiente pela taxa de aprendizado velocidade = (velocidade * self.gamma) + gradient # atualiza a velocidade # atualiza os parâmetros self.w_hat -= velocidade l = ((y_train - self.predict(X_train)) ** 2).sum() # calcula o custo loss.append(l) # armazeno o custo para gráfico if self.show_learning: # mostra o estado atual do aprendizado self._show_state(X_train, y_train, loss) def predict(self, X_test): # formata os dados if len(X_test.values.shape) < 2: X = X_test.values.reshape(-1,1) X = np.insert(X, 0, 1, 1) return np.dot(X, self.w_hat) def _show_state(self, X_train, y_train, loss): # visualiza o processo de aprendizado lb = L(y_train, self.w_hat[0] + 3 * X_train) # calcula o custo na direção b lw = L(y_train, 5 + self.w_hat[1] * X_train) # calcula o custo na direção w # scatter plot plt.subplot(221) plt.scatter(X_train, y_train, s= 10) plt.plot(X_train, self.predict(X_train), c='r') plt.title('$y = b + w x$') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # loss plt.subplot(222) plt.plot(range(len(loss)), loss) plt.title('Custo') plt.tick_params(labelsize=9, labelleft=False, labelbottom = False) plt.grid(True) # b_loss plt.subplot(223) plt.plot( np.linspace(-10,20,20), self.b_loss) plt.scatter(self.w_hat[0], lb, c = 'k') plt.title('Custo em $\hat{b}$') plt.tick_params(labelleft=False) plt.grid(True) # w_loss plt.subplot(224) plt.plot(np.linspace(0,6,20), self.w1_loss) plt.scatter(self.w_hat[1], lw, c = 'k') plt.title('Custo em $\hat{w}$') plt.grid(True) plt.tick_params(labelleft=False) plt.tight_layout() display.display(plt.gcf()) display.clear_output(wait=True) plt.clf() # limpa a imagem do gráfico regr = linear_regr(learning_rate=0.0001, training_iters=30, gamma = 0.9, show_learning=True) regr.fit(dados['x'], dados['y']) # - # ## Usando gradiente descendente na prática # # Se você prestou atenção até aqui, sabe que para implementar gradiente descendente precisamos da derivada parcial da função custo com relação aos parâmetros que queremos otimizar. No nosso exemplo de regressão linear simples, isso foi bem fácil de calcular, mas nem sempre isso será o caso. Felizmente para nós, na prática, as bibliotecas de programação especializadas em otimização já calculam essas derivadas automaticamente para nós. Mais ainda, nelas, gradiente descendente e suas variações já vem implementados! # # Para mostrar como utilizar gradiente descendente na prática vamos utilizar uma biblioteca de aprendizado de máquina desenvolvida pelo Google e agora aberta ao público: [TensorFlow](https://www.tensorflow.org/). Veja como em poucas linhas podemos implementar a técnica de gradiente descendente para resolver nosso exemplo de regressão linear. Note também como podemos rodar muito mais iterações rapidamente: # # + import tensorflow as tf import numpy as np x, y = dados['x'].values, dados['y'].values # dados # Monta a estrutura tf # valores iniciais shape W_hat = tf.Variable(tf.random_normal([1], 0, 5)) b_hat = tf.Variable(tf.zeros([1])) # modelo y_hat = W_hat * x + b_hat # Função custo loss = tf.reduce_mean(tf.square(y_hat - y)) # otimizador e passo no treinamento optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss) sess = tf.Session() # para rodar a estrutura sess.run(tf.global_variables_initializer()) # inicia variáveis for step in range(200): sess.run(optimizer) w_final, b_final = sess.run([W_hat, b_hat]) print('Após treinamento, w_hat = %.2f e w_hat = %.2f' % (w_final[0], b_final[0])) sess.close() # - # ## Ligações externas # # Dada a sua importância, há muitas fontes excelentes para aprender sobre gradiente descendente: # # * Os vídeos do curso online de Neural Networks for Machine Learning, da universidade de Torono, são provavelmente a melhor fonte para estudar gradiente descendente. Todos os vídeos [desta secção do cusro](https://www.youtube.com/playlist?list=PLnnr1O8OWc6bAAkp43m0jNF_DEqwWp2o2) são excelentes para bastante sobre gradiente descendente e suas extensões. # # * Os vídeos [1](https://www.youtube.com/watch?v=LN0PLnDpGN4&index=5&t=598s&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0), [2](https://www.youtube.com/watch?v=kWq2k1gPyBs&index=6&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0) e [3](https://www.youtube.com/watch?v=7LqYTTwuu0k&list=PLnnr1O8OWc6ajN_fNcSUz9k5gF_E9huF0&index=7) sobre gradiente descendente com regressão linear de uma variável (do curso de Machine Learning com o professor Ng) cobrem a maioria do conteúdo que vimos aqui com bastante visualização e de maneira intuitiva. Além disso, o vídeo [4](https://www.youtube.com/watch?v=UfNU3Vhv5CA&t=627s) do mesmo curso mostra bem a intuição de GDE # # * Os vídeos [1](https://www.youtube.com/watch?v=hMLUgM6kTp8&index=20&list=PLAwxTw4SYaPn_OWPFT9ulXLuQrImzHfOV) e [2](https://www.youtube.com/watch?v=s6jC7Wc9iMI&index=21&list=PLAwxTw4SYaPn_OWPFT9ulXLuQrImzHfOV) do custo de Deep Learning do Google resumem bem GDE, dão dicas de como acelerar o aprendizado e ainda falam sobre a extensão do algoritmo com momento - embora um pouco diferente da nossa. # # # #
37,524
/notebooks/Hands_on_2_competition_Titanic_predict_survival.ipynb
1134d9bcf81232fdc62bf0d3a259e0996ff12614
[]
no_license
alexmasselot/crea-introduction-to-programmation
https://github.com/alexmasselot/crea-introduction-to-programmation
0
0
null
2017-12-08T21:09:35
2017-12-08T07:49:32
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6,442
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # Your purpose is now to predict if one passenger has survived based on what we know (his age, gender...) # + import pandas as pd import numpy as np # Load the data as a dataframe df = pd.read_csv('./../data/titanic_train.csv') # - Y_truth = list(1.0*df['Survived']) def compute_accuracy(Y, Y_pred): """ This function is used below to compute the score associated to a prediction. Y should be a list with the result Y_pred should be a prediction of this list This function will return 1.0 if you correctly predict the survival state of all passengers. """ assert type(Y) is list assert type(Y_pred) is list assert len(Y) == len(Y_pred) n_success = [(1 if round(y_pred) == y else 0) for (y, y_pred) in zip(Y, Y_pred)] return 1.0*sum(n_success)/len(Y) df # ## Making simple predictors # # We have 891 passengers. For each of them, we are going to attribute: # # * 0 if we predict they are going to die # * 1 if we predict they are going to survive # # The goal is then to compare our prediction against the real results to see how accurate we are. # # We are 100% accurate when we predict the correct outcome for everyone, and 0% when we predict for none. # # The ideal goal is to build the best **predicition model** based on all the available passenger data (age, class, gender...) # # ### What if we predict everyone dies? # # In this case we simply attribute a *0* to everyone. # # The output shows *~62%*, because *62%* of passengers actually lost their lives. Y_pred_everyone_dies = [0.0]*len(df) # This will return 0.616, as asserting that everybody # died is true for 62% of the population. compute_accuracy(Y_truth, Y_pred_everyone_dies) # ### What about using the Pclass information? # # We saw in the data exploration a bias with the ticket class. The first class passengers were more likely to survive. # # Let's therefore build a finer model, predicting a favorable outcome for the first class. # # The output now shws a better outcome, of %68%"! # # + #We build a Y_pred_with_class list, and for each passenger, we set 0 or 1 into it Y_pred_with_class = [] for passenger in df.to_dict(orient='records'): if passenger['Pclass'] == 1: yp = 1 else: yp = 0 # we then add the passenger prediction to the whole list Y_pred_with_class.append(yp) #we can now check how relevant it is compute_accuracy(Y_truth, Y_pred_with_class) # - # ## You turn!!! # ### make a prediction based on gender # + Y_pred_with_gender = [] for passenger in df.to_dict(orient='records'): ####### insert your condition here, based on the same template as above ####### end of your code Y_pred_with_gender.append(yp) #we can now check how relevant it is compute_accuracy(Y_truth, Y_pred_with_gender) # - # ### Be more imaginative: create your own model! # ### This is a contest # # The idea is maybe to combine various factors, in the form, for example # # ####### insert your condition here, based on the same template as above # if passenger['Pclass']>=2: # if(passenger['embarked'] == 'S': # yp=1 # else: # yp=0 # else: # if(passenger['Sex'] == 'female': # yp=0 # else: # yp=1 # # ####### end of your code # # + Y_pred_my_model_1 = [] for passenger in df.to_dict(orient='records'): ####### insert your condition here, based on the same template as above ####### end of your code Y_pred_my_model_1.append(yp) #we can now check how relevant it is compute_accuracy(Y_truth, Y_pred_my_model_1) # - # You can of course copy/paste the code above, create new cell to test other models. #
3,981
/Thompson Sampling/.ipynb_checkpoints/Thompson Sampling-checkpoint.ipynb
7e66bad4164d6f55912fdcd23aa7a2aee254f437
[]
no_license
mohamed-amine-guerras/Reinforcement-Learning
https://github.com/mohamed-amine-guerras/Reinforcement-Learning
1
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ### N-Repeated Element in Size 2N Array # In a array A of size 2N, there are N+1 unique elements, and exactly one of these elements is repeated N times. # # Return the element repeated N times. # ##### Example 1: # Input: [1, 2, 3, 3] # # Output: 3 # ##### Example 2: # Input: [2, 1, 2, 5, 3, 2] # # Output: 2 # ##### Example 3: # Input: [5, 1, 5, 2, 5, 3, 5, 4] # # Output: 5 # ### Brief analysis # sum is an easy way def repeatedNTimes(A: list) -> int: return int((sum(A) - sum(set(A))) / (len(A) / 2 - 1)) from IPython.display import Image Image(filename="/Users/xlyue/Documents/leetcode practice/1559408450984.jpg",width=400,height=400) om_beta ad = i ads_selected.append(ad) reward = data.values[n,ad] if (reward == 0): numbers_of_rewards_0[ad] += 1 else: numbers_of_rewards_1[ad] += 1 total_reward += reward total_reward # ## Plot the results plt.hist(ads_selected) plt.title('Histogramme of Ads selections') plt.xlabel('Ads') plt.ylabel('Number of times each ad is selected') plt.show()
1,322
/RNN/Untitled.ipynb
e1ddc3b0b37ee41985640745b784bdb28c9e718e
[]
no_license
ftfarias/data-science_notebooks
https://github.com/ftfarias/data-science_notebooks
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:tf] # language: python # name: conda-env-tf-py # --- # + from __future__ import absolute_import from __future__ import division from __future__ import print_function import random import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.models.rnn.translate import data_utils class Seq2SeqModel(object): """Sequence-to-sequence model with attention and for multiple buckets. This class implements a multi-layer recurrent neural network as encoder, and an attention-based decoder. This is the same as the model described in this paper: http://arxiv.org/abs/1412.7449 - please look there for details, or into the seq2seq library for complete model implementation. This class also allows to use GRU cells in addition to LSTM cells, and sampled softmax to handle large output vocabulary size. A single-layer version of this model, but with bi-directional encoder, was presented in http://arxiv.org/abs/1409.0473 and sampled softmax is described in Section 3 of the following paper. http://arxiv.org/abs/1412.2007 """ def __init__(self, source_vocab_size, target_vocab_size, buckets, size, num_layers, max_gradient_norm, batch_size, learning_rate, learning_rate_decay_factor, use_lstm=False, num_samples=512, forward_only=False, dtype=tf.float32): """Create the model. Args: source_vocab_size: size of the source vocabulary. target_vocab_size: size of the target vocabulary. buckets: a list of pairs (I, O), where I specifies maximum input length that will be processed in that bucket, and O specifies maximum output length. Training instances that have inputs longer than I or outputs longer than O will be pushed to the next bucket and padded accordingly. We assume that the list is sorted, e.g., [(2, 4), (8, 16)]. size: number of units in each layer of the model. num_layers: number of layers in the model. max_gradient_norm: gradients will be clipped to maximally this norm. batch_size: the size of the batches used during training; the model construction is independent of batch_size, so it can be changed after initialization if this is convenient, e.g., for decoding. learning_rate: learning rate to start with. learning_rate_decay_factor: decay learning rate by this much when needed. use_lstm: if true, we use LSTM cells instead of GRU cells. num_samples: number of samples for sampled softmax. forward_only: if set, we do not construct the backward pass in the model. dtype: the data type to use to store internal variables. """ self.source_vocab_size = source_vocab_size self.target_vocab_size = target_vocab_size self.buckets = buckets self.batch_size = batch_size self.learning_rate = tf.Variable( float(learning_rate), trainable=False, dtype=dtype) self.learning_rate_decay_op = self.learning_rate.assign( self.learning_rate * learning_rate_decay_factor) self.global_step = tf.Variable(0, trainable=False) # If we use sampled softmax, we need an output projection. output_projection = None softmax_loss_function = None # Sampled softmax only makes sense if we sample less than vocabulary size. if num_samples > 0 and num_samples < self.target_vocab_size: w_t = tf.get_variable("proj_w", [self.target_vocab_size, size], dtype=dtype) w = tf.transpose(w_t) b = tf.get_variable("proj_b", [self.target_vocab_size], dtype=dtype) output_projection = (w, b) def sampled_loss(inputs, labels): labels = tf.reshape(labels, [-1, 1]) # We need to compute the sampled_softmax_loss using 32bit floats to # avoid numerical instabilities. local_w_t = tf.cast(w_t, tf.float32) local_b = tf.cast(b, tf.float32) local_inputs = tf.cast(inputs, tf.float32) return tf.cast( tf.nn.sampled_softmax_loss(local_w_t, local_b, local_inputs, labels, num_samples, self.target_vocab_size), dtype) softmax_loss_function = sampled_loss # Create the internal multi-layer cell for our RNN. single_cell = tf.nn.rnn_cell.GRUCell(size) if use_lstm: single_cell = tf.nn.rnn_cell.BasicLSTMCell(size) cell = single_cell if num_layers > 1: cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers) # The seq2seq function: we use embedding for the input and attention. def seq2seq_f(encoder_inputs, decoder_inputs, do_decode): return tf.nn.seq2seq.embedding_attention_seq2seq( encoder_inputs, decoder_inputs, cell, num_encoder_symbols=source_vocab_size, num_decoder_symbols=target_vocab_size, embedding_size=size, output_projection=output_projection, feed_previous=do_decode, dtype=dtype) # Feeds for inputs. self.encoder_inputs = [] self.decoder_inputs = [] self.target_weights = [] for i in xrange(buckets[-1][0]): # Last bucket is the biggest one. self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[batch_size], name="encoder{0}".format(i))) for i in xrange(buckets[-1][1] + 1): self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[batch_size], name="decoder{0}".format(i))) self.target_weights.append(tf.placeholder(dtype, shape=[batch_size], name="weight{0}".format(i))) # Our targets are decoder inputs shifted by one. targets = [self.decoder_inputs[i + 1] for i in xrange(len(self.decoder_inputs) - 1)] # Training outputs and losses. if forward_only: self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets( self.encoder_inputs, self.decoder_inputs, targets, self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True), softmax_loss_function=softmax_loss_function) # If we use output projection, we need to project outputs for decoding. if output_projection is not None: for b in xrange(len(buckets)): self.outputs[b] = [ tf.matmul(output, output_projection[0]) + output_projection[1] for output in self.outputs[b] ] else: self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets( self.encoder_inputs, self.decoder_inputs, targets, self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, False), softmax_loss_function=softmax_loss_function) # Gradients and SGD update operation for training the model. params = tf.trainable_variables() if not forward_only: self.gradient_norms = [] self.updates = [] opt = tf.train.GradientDescentOptimizer(self.learning_rate) for b in xrange(len(buckets)): gradients = tf.gradients(self.losses[b], params) clipped_gradients, norm = tf.clip_by_global_norm(gradients, max_gradient_norm) self.gradient_norms.append(norm) self.updates.append(opt.apply_gradients( zip(clipped_gradients, params), global_step=self.global_step)) self.saver = tf.train.Saver(tf.all_variables()) def step(self, session, encoder_inputs, decoder_inputs, target_weights, bucket_id, forward_only): """Run a step of the model feeding the given inputs. Args: session: tensorflow session to use. encoder_inputs: list of numpy int vectors to feed as encoder inputs. decoder_inputs: list of numpy int vectors to feed as decoder inputs. target_weights: list of numpy float vectors to feed as target weights. bucket_id: which bucket of the model to use. forward_only: whether to do the backward step or only forward. Returns: A triple consisting of gradient norm (or None if we did not do backward), average perplexity, and the outputs. Raises: ValueError: if length of encoder_inputs, decoder_inputs, or target_weights disagrees with bucket size for the specified bucket_id. """ # Check if the sizes match. encoder_size, decoder_size = self.buckets[bucket_id] if len(encoder_inputs) != encoder_size: raise ValueError("Encoder length must be equal to the one in bucket," " %d != %d." % (len(encoder_inputs), encoder_size)) if len(decoder_inputs) != decoder_size: raise ValueError("Decoder length must be equal to the one in bucket," " %d != %d." % (len(decoder_inputs), decoder_size)) if len(target_weights) != decoder_size: raise ValueError("Weights length must be equal to the one in bucket," " %d != %d." % (len(target_weights), decoder_size)) # Input feed: encoder inputs, decoder inputs, target_weights, as provided. input_feed = {} for l in xrange(encoder_size): input_feed[self.encoder_inputs[l].name] = encoder_inputs[l] for l in xrange(decoder_size): input_feed[self.decoder_inputs[l].name] = decoder_inputs[l] input_feed[self.target_weights[l].name] = target_weights[l] # Since our targets are decoder inputs shifted by one, we need one more. last_target = self.decoder_inputs[decoder_size].name input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32) # Output feed: depends on whether we do a backward step or not. if not forward_only: output_feed = [self.updates[bucket_id], # Update Op that does SGD. self.gradient_norms[bucket_id], # Gradient norm. self.losses[bucket_id]] # Loss for this batch. else: output_feed = [self.losses[bucket_id]] # Loss for this batch. for l in xrange(decoder_size): # Output logits. output_feed.append(self.outputs[bucket_id][l]) outputs = session.run(output_feed, input_feed) if not forward_only: return outputs[1], outputs[2], None # Gradient norm, loss, no outputs. else: return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs. def get_batch(self, data, bucket_id): """Get a random batch of data from the specified bucket, prepare for step. To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding. Args: data: a tuple of size len(self.buckets) in which each element contains lists of pairs of input and output data that we use to create a batch. bucket_id: integer, which bucket to get the batch for. Returns: The triple (encoder_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(...) later. """ encoder_size, decoder_size = self.buckets[bucket_id] encoder_inputs, decoder_inputs = [], [] # Get a random batch of encoder and decoder inputs from data, # pad them if needed, reverse encoder inputs and add GO to decoder. for _ in xrange(self.batch_size): encoder_input, decoder_input = random.choice(data[bucket_id]) # Encoder inputs are padded and then reversed. encoder_pad = [data_utils.PAD_ID] * (encoder_size - len(encoder_input)) encoder_inputs.append(list(reversed(encoder_input + encoder_pad))) # Decoder inputs get an extra "GO" symbol, and are padded then. decoder_pad_size = decoder_size - len(decoder_input) - 1 decoder_inputs.append([data_utils.GO_ID] + decoder_input + [data_utils.PAD_ID] * decoder_pad_size) # Now we create batch-major vectors from the data selected above. batch_encoder_inputs, batch_decoder_inputs, batch_weights = [], [], [] # Batch encoder inputs are just re-indexed encoder_inputs. for length_idx in xrange(encoder_size): batch_encoder_inputs.append( np.array([encoder_inputs[batch_idx][length_idx] for batch_idx in xrange(self.batch_size)], dtype=np.int32)) # Batch decoder inputs are re-indexed decoder_inputs, we create weights. for length_idx in xrange(decoder_size): batch_decoder_inputs.append( np.array([decoder_inputs[batch_idx][length_idx] for batch_idx in xrange(self.batch_size)], dtype=np.int32)) # Create target_weights to be 0 for targets that are padding. batch_weight = np.ones(self.batch_size, dtype=np.float32) for batch_idx in xrange(self.batch_size): # We set weight to 0 if the corresponding target is a PAD symbol. # The corresponding target is decoder_input shifted by 1 forward. if length_idx < decoder_size - 1: target = decoder_inputs[batch_idx][length_idx + 1] if length_idx == decoder_size - 1 or target == data_utils.PAD_ID: batch_weight[batch_idx] = 0.0 batch_weights.append(batch_weight) return batch_encoder_inputs, batch_decoder_inputs, batch_weights asonably far from all the points (as determined by the value of $\sigma$). # # For each feature $(x_i, y_i)$ in our dataset, we can calculate the similarity to each feature via the selected kernel: # # $$f_i = \left[\begin{align} # k(x_i, &x_0) \\ # k(x_i, &x_1) \\ # k(x_i, &x_2) \\ # \vdots & \\ # k(x_i, &x_n) # \end{align}\right]$$ # # notice that, under the Gaussian kernel at least, there will be one element $k(x_i, x_i)$ that evaluates to 1. # # To use the SVM, we use this $f \in \mathbb{R}^{n+1}$ to calculate the inner product $\theta^{\prime} f$ and predict $y_i=1$ if $\theta^{\prime} f_i \ge 0$. We obtain the parameters for $\theta$ by minimizing: # # $$\min_{\theta} \left[ C \sum_{i=1}^n y_i k_1(\theta^{\prime} f_i) + (1-y_i) k_0(\theta^{\prime} f_i) \right] + \frac{1}{2}\sum_{j=1}^k \theta^2_j$$ # # ### Regularization and soft margins # # There remains a choice to be made for the values of the SVM parameters. Recall $C$, which corresponds to the inverse of the regularization parameter in a lasso model. This choice of $C$ involves a **bias-variance tradeoff**: # # * large C = low bias, high variance # * small C = high bias, low variance # # In a support vector machine, regularization results in a **soft margin** that allows some points to cross the optimal decision boundary (resulting in misclassifiction for those points). As C gets larger, the more stable the margin becomes, since it is allowing more points to determine the margin. # # We can think of C as a "budget" for permitting points to exceed the margin. We can tune C to determine the optimal hyperplane. # # Similarly, if we are using the Gaussian kernel, we must choose a value for $\sigma^2$. When $\sigma^2$ is large, then features are considered similar over greater distances, resulting in a smoother decision boundary, while for smaller $\sigma^2$, similarity falls off quickly with distance. # # * large $\sigma^2$ = high bias, low variance # * small $\sigma^2$ = low bias, high variance # ### Linear kernel # # The simplest choice of kernel is to use no kernel at all, but rather to simply use the **linear combination** of the features themselves as the kernel. Hence, # # $$y = \left\{ \begin{aligned} 1 &\text{, if } \theta^{\prime} x \ge 0\\ # 0 &\text{ otherwise}\end{aligned}\right.$$ # # This approach is useful when there are a *large number of features*, but the *size of the dataset is small*. In this case, a simple linear decision boundary may be appropriate given that there is relatively little data. If the reverse is true, where there are a small number of features and plenty of data, a Gaussian kernel may be more appropriate, as it allows for a more complex decision boundary. # ## Multi-class Classification # # In the exposition above, we have addressed binary classification problems. The SVM can be generalized to multi-class classification. This involves training $K$ binary classifiers, where each of $k=1,\ldots,K$ classes is trained against the remaining classes pooled into a single group ("all-versus-one"). Then for each point, we select the class for which the inner product $\theta_k^{\prime} x$ is largest. # ## Data Preprocessing # # It is important with many kernels to **scale** the features prior to using them in a SVM. This is because features which are numerically large relative to the others will tend to dominate the norm. So that each feature is able to contribute equally to the selection of the decision boundary, we want them all to have approximately the same range. # # In general, standardization of datasets is a common pratice for statistical learning algorithms. We often ignore the shape of the data distribution and simply center it on the mean, then scale it by dividing by their standard deviation (unless the feature is constant). This is important because the objective function in several learning algorithms (*e.g.* the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features. # # Scikit-learn's `preprocessing` module provides a `scale` function to perform this operation on a single array-like dataset: # + from sklearn import preprocessing X = np.array([[ 1., -1., 2.], [ 2., 0., 0.], [ 0., 1., -1.]]) X_scaled = preprocessing.scale(X) X_scaled # - # Scaled data has zero mean and unit variance: X_scaled.mean(0) X_scaled.std(0) # The `preprocessing` module also provides a utility class called `StandardScaler` that allows for the computation of the mean and standard deviation on a training set. This allows one to later *reapply* the same transformation on validation and test sets. scaler = preprocessing.StandardScaler().fit(X) scaler scaler.mean_ scaler.scale_ scaler.transform(X) # So then, for new data, we can simply apply the `scaler` object's `transform` method: scaler.transform([[-1., 1., 0.]]) # Optionally, one can disable either centering or scaling by passing `with_mean=False` or `with_std=False`, respectively. # ### Range scaling # # An alternative standardization is scaling features to lie between a given minimum and maximum value (typically between zero and one). This is often the case where we want robustness to very small standard deviations of features or we want to preserve zero entries in sparse data. # # The `MinMaxScaler` provides this scaling. # + min_max_scaler = preprocessing.MinMaxScaler() min_max_scaler.fit_transform(X) # - # The same instance of the transformer can then be applied to some new test data, which results in the same scaling and shifting operations: X_test = np.array([[ -3., -1., 4.]]) min_max_scaler.transform(X_test) # ### Normalization # # Normalization is the process of scaling individual samples to have unit norm. This is useful if you plan to use a quadratic function such as the dot-product or any other kernel to quantify the similarity of any pair of samples. # # The function `normalize` performs this operation on a single array-like dataset, either using the l1 or l2 norms: preprocessing.normalize(X, norm='l2') preprocessing.normalize(X, norm='l1') # As with scaling, there is also a `Normalizer` class that can be used to establish normalization with respect to a training set. normalizer = preprocessing.Normalizer().fit(X) normalizer.transform(X_test) # ### Categorical feature encoding # # Often features are not given as continuous values, but rather as categorical classes. For example, variables may be defined as `["male", "female"]`, `["Europe", "US", "Asia"]`, `["Disease A", "Disease B", "Disease C"]`. Such features can be efficiently coded as integers, for instance `["male", "US", "Disease B"]` could be expressed as `[0, 1, 1]`. # # Unfortunately, an integer representation can not be used directly with estimators in scikit-learn, because these expect *continuous* input, and would therefore interpret the categories as being ordered, which for the above examples, would be inappropriate. # # One approach is to use a "one-of-K" or "one-hot" encoding, which is implemented in `OneHotEncoder`. This estimator transforms a categorical feature with `m` possible values into `m` binary features, with only one active. enc = preprocessing.OneHotEncoder() enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) enc.transform([[0, 1, 3]]).toarray() # By default, the cardinality of each feature is inferred automatically from the dataset; this can be manually overriden using the `n_values` argument. # `LabelBinarizer` is a utility class to help create a label indicator matrix from a list of multi-class labels: lb = preprocessing.LabelBinarizer() lb.fit([1, 2, 6, 4, 2]) lb.classes_ lb.transform((1,4)) # For multiple labels per instance, use MultiLabelBinarizer: lb = preprocessing.MultiLabelBinarizer() lb.fit_transform([(1, 2), (3,)]) lb.classes_ # `LabelEncoder` is a utility class to help normalize labels such that they contain only consecutive values between 0 and `n_classes-1`. le = preprocessing.LabelEncoder() le.fit([1,2,2,6]) le.classes_ le.transform([1, 1, 2, 6]) le.inverse_transform([0, 0, 1, 2]) # ## Missing Data Imputation # # Missing data is a common problem in most real-world scientific datasets. While the best way for dealing with missing data will always be preventing their occurrence in the first place, it usually can't be helped, particularly when data are collected passively or voluntarily, or when data collection and recording is distributed among several people. There are a variety of ways for dealing with missing data, from the very naïve to the very sophisticated, and unfortunately the more common approaches tend to be *ad hoc* and will usually do more harm than good. # # It turns out that more robust methods for imputation are not as difficult to implement as they first appear to be. Two of the best ones are Bayesian imputation and multiple imputation. In this section, we will use **multiple imputation** to account for missing data in a regression analysis. # As a motivating example, we will use a dataset of educational outcomes for children with hearing impairment. Here, we are interested in determining factors that are associated with better or poorer learning outcomes. # # ![hearing aid](images/hearing_aid.jpg) # # There is a suite of available predictors, including: # # * gender (`male`) # * number of siblings in the household (`siblings`) # * index of family involvement (`family_inv`) # * whether the primary household language is not English (`non_english`) # * presence of a previous disability (`prev_disab`) # * non-white race (`non_white`) # * age at the time of testing (in months, `age_test`) # * whether hearing loss is not severe (`non_severe_hl`) # * whether the subject's mother obtained a high school diploma or better (`mother_hs`) # * whether the hearing impairment was identified by 3 months of age (`early_ident`). test_scores = pd.read_csv('../data/test_scores.csv', index_col=0) test_scores.head() # For three variables in the dataset, there are incomplete records. test_scores.isnull().sum(0) # ### Strategies for dealing with missing data # # The easiest (and worst) way to deal with missing data is to **ignore it**. That is, simply run the analysis, missing values and all, hoping for the best. If your software is any good, this approach will simply not work; the algorithm will try to operate on data that includes missing values, and propagate them, resulting in statistics and estimates that cannot be calculated, which will typically raise errors. If your software is poor, it will make some assumption or decision about the missing values, and proceed to generate results conditional on the assumption, which creates problems that may never be detected because no indication was given to any potential problem. # # The next easiest (worst) approach to analyzing data with missing values is to conduct list-wise deletion, by deleting the records that have missing values. This is called **complete case analysis**, because only records that are complete get retained for the analysis. The degree to which complete case analysis is undesirable depends on the mechanism by which data have become missing. # ### Types of Missingness # # - **Missing completely at random (MCAR)**: When data are MCAR, missing cases are, on average, identical to non-missing cases, with respect to the model. Ignoring the missingness will reduce the power of the analysis, but will not bias inference. # - **Missing at random (MAR)**: Missing data depends (usually probabilistically) on measured values, and hence can be modeled by variables observed in the data set. Accounting for the values which “cause” the missing data will produce unbiased results in an analysis. # - **Missing not at random(MNAR)**: Missing data depend on unmeasured or unknown variables. There is no information available to account for the missingness. # # The very best-case scenario for using complete case analysis, which corresponds to MCAR missingness, results in a **loss of power** due to the reduction in sample size. The analysis will lose the information contained in the non-missing elements of a partially-missing record. When data are not missing completely at random, inferences from complete case analysis may be **biased** due to systematic differences between missing and non-missing records that affects the estimates of key parameters. # # One alternative to complete case analysis is to simply fill (*impute*) the missing values with a reasonable guess a the true value, such as the mean, median or modal value of the fully-observed records. This imputation, while not recovering any information regarding the missing value itself for use in the analysis, does provide a mechanism for including the non-missing values in the analysis, thereby making use of all available information. # The `Imputer` class in scikit-learn provides methods for imputing missing values, either using the mean, the median or the most frequent value of the row or column in which the missing values are located. This class also allows for different missing value encodings. # # For example, we can replace missing entries encoded as `np.nan` using the mean value of the columns (axis 0) that contain the missing values: # + from sklearn.preprocessing import Imputer imp = Imputer(missing_values='NaN', strategy='mean', axis=0) # - imp.fit([[1, 2], [np.nan, 3], [7, 6]]) X = [[np.nan, 1], [6, np.nan], [3, 6]] imp.transform(X) # In our educational outcomes dataset, we are probably better served using mode imputation: mode_imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0) mode_imp.fit(test_scores) mode_imp.transform(test_scores)[:3] # Of course, in Python it is often easier to impute data using Pandas `DataFrame` method `fillna`. test_scores.siblings.mean() siblings_imputed = test_scores.siblings.fillna(test_scores.siblings.mean()) # This approach may be reasonable under the MCAR assumption, but may induce bias under a MAR scenario, whereby missing values may **differ systematically** relative to non-missing values, making the particular summary statistic used for imputation *biased* as a mean/median/modal value for the missing values. # # Beyond this, the use of a single imputed value to stand in place of the actual missing value glosses over the **uncertainty** associated with this guess at the true value. Any subsequent analysis procedure (*e.g.* regression analysis) will behave as if the imputed value were observed, despite the fact that we are actually unsure of the actual value for the missing variable. The practical consequence of this is that the variance of any estimates resulting from the imputed dataset will be **artificially reduced**. # ## Multiple Imputation # # One robust alternative to addressing missing data is **multiple imputation** (Schaffer 1999, van Buuren 2012). It produces unbiased parameter estimates, while simultaneously accounting for the uncertainty associated with imputing missing values. It is conceptually and mechanistically straightforward, and produces complete datasets that may be analyzed using any statistical methodology or software one chooses, as if the data had no missing values to begin with. # # Multiple imputation generates imputed values based on a **regression model**. This regression model will help us generate reasonable values, particularly if data are MAR, since it uses information in the dataset that may be informative in predicting what the true value may be. Ideally, we want predictor variables that are **correlated** with the missing variable, and with the mechanism of missingness, if any. For example, one might be able to use test scores from one subject to predict missing test scores from another; or, the probability of income reporting to be missing may vary systematically according to the education level of the individual. # To see if there is any potential information among the variables in our dataset to use for imputation, it is helpful to calculate the pairwise correlation between all the variables. Since we have discrete variables in our data, the [Spearman rank correlation coefficient](http://www.wikiwand.com/en/Spearman%27s_rank_correlation_coefficient) is appropriate. test_scores.dropna().corr(method='spearman') # We will try to impute missing values the mother's high school education indicator variable, which takes values of 0 for no high school diploma, or 1 for high school diploma or greater. The appropriate model to predict binary variables is a **logistic regression**. We will use the scikit-learn implementation, `LogisticRegression`. from sklearn.linear_model import LogisticRegression # To keep things simple, we will only use variables that are themselves complete to build the predictive model, hence our subset of predictors will exclude family involvement score (`family_inv`) and previous disability (`prev_disab`). impute_subset = test_scores.drop(labels=['family_inv','prev_disab','score'], axis=1) # Next, we scale the predictor variables to range from 0 to 1, to improve the performance of the regression model. y = impute_subset.pop('mother_hs').values X = preprocessing.StandardScaler().fit_transform(impute_subset.astype(float)) # Next, we create a `LogisticRegression` model, and fit it using the non-missing observations. # + missing = np.isnan(y) mod = LogisticRegression() mod.fit(X[~missing], y[~missing]) # - mother_hs_pred = mod.predict(X[missing]) mother_hs_pred # These values can then be inserted in place of the missing values, and an analysis can be performed on the entire dataset. # # However, this is still just a single imputation for each missing value, and hence glosses over the uncertainty associated with the derivation of the imputes. Multiple imputation proceeds by **imputing several values**, to generate several complete datasets and performing the same analysis on all of them. With a set of estimates in hand, an *average* estimate of model parameters can be obtained that more adequately accounts for the uncertainty, hopefully providing more robust inference than from a single impute. # # There are a variety of ways to generate multiple imputations. Here, we will exploit **regularization** in order to do this. The `LogisticRegression` class from scikit-learn provides facilities for regularization using either L2 (resulting in ridge regression) or L1 (resulting in LASSO regression) penalties. The degree of regularization in either case is controlled by the `C` parameter, whereby large values of `C` give more freedom to the model, while smaller values of `C` constrain the model more. We can use a selection of `C` values to obtain a range of predictions from variants of the same model. For example: mod2 = LogisticRegression(C=1, penalty='l1') mod2.fit(X[~missing], y[~missing]) mod2.predict(X[missing]) mod3 = LogisticRegression(C=0.4, penalty='l1') mod3.fit(X[~missing], y[~missing]) mod3.predict(X[missing]) # Surprisingly few imputations are required to acheive reasonable estimates, with 3-10 usually sufficient. We will use 3. # + mother_hs_imp = [] for C in 0.1, 0.4, 2: mod = LogisticRegression(C=C, penalty='l1') mod.fit(X[~missing], y[~missing]) imputed = mod.predict(X[missing]) mother_hs_imp.append(imputed) # - mother_hs_imp # ## SVM using `scikit-learn` # # The scikit-learn machine learning package for Python includes a nice implementation of support vector machines. from sklearn import svm # Let's begin with a fun enological example. Your textbook includes a dataset `wine.dat` that is the result of chemical analyses of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. (The response variable is incorrectly labeled `region`; it should be the grape from which the wine was derived). We might be able to correctly classify a given wine based on its chemical profile. # # To illustrate the characteristics of the SVM, we will select two attributes, which will make things easy to visualize. # + wine = pd.read_table("../data/wine.dat", sep='\s+') attributes = ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline'] grape = wine.pop('region') y = grape.values wine.columns = attributes X = wine[['Alcohol', 'Proline']].values svc = svm.SVC(kernel='linear') svc.fit(X, y) # - wine.head() # It is easiest to display the model fit graphically, by evaluating the model over a grid of points. # + from matplotlib.colors import ListedColormap # Create color maps for 3-class classification problem, as with iris cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) def plot_estimator(estimator, X, y, ax=None): try: X, y = X.values, y.values except AttributeError: pass if ax is None: _, ax = plt.subplots() estimator.fit(X, y) x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1 y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1 xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100)) Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) ax.pcolormesh(xx, yy, Z, cmap=cmap_light) # Plot also the training points ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) ax.axis('tight') ax.axis('off') plt.tight_layout() # - # %matplotlib inline plot_estimator(svc, X, y) # The SVM gets its name from the samples in the dataset from each class that lie closest to the other class. These training samples are called **support vectors** because changing their position in *p*-dimensional space would change the location of the decision boundary. # # In scikits-learn, the indices of the support vectors for each class can be found in the `support_vectors_` attribute of the `SVC` object. Here is a 2 class problem using only classes 1 and 2 in the wine dataset. # # The support vectors are circled. # + # Extract classes 1 and 2 X, y = X[np.in1d(y, [1, 2])], y[np.in1d(y, [1, 2])] plt.figure() plot_estimator(svc, X, y) plt.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=120, facecolors='none', edgecolors='w', linewidths=2, zorder=10) # - # Clearly, these classes are not linearly separable. # # As we learned, regularization is tuned via the $C$ parameter. In practice, a large $C$ value means that the number of support vectors is small (less regularization), while a small $C$ implies many support vectors (more regularization). scikit-learn sets a default value of $C=1$. # + def plot_regularized(power, ax): svc = svm.SVC(kernel='linear', C=10**power) plot_estimator(svc, X, y, ax=ax) ax.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=80, facecolors='none', edgecolors='w', linewidths=2, zorder=10) ax.set_title('Power={}'.format(power)) fig, axes = plt.subplots(2, 3, figsize=(12,10)) for power, ax in zip(range(-2, 4), axes.ravel()): plot_regularized(power, ax) # - # We can choose from a suite of available kernels (`linear`, `poly`, `rbf`, `sigmoid`, `precomputed`) or a custom kernel can be passed as a function. Note that the radial basis function (`rbf`) kernel is just a Gaussian kernel, but with parameter $\gamma=1/\sigma^2$. # + def plot_poly_svc(degree=3, ax=None): svc_poly = svm.SVC(kernel='poly', degree=degree) plot_estimator(svc_poly, X, y, ax=ax) ax.scatter(svc_poly.support_vectors_[:, 0], svc_poly.support_vectors_[:, 1], s=80, facecolors='none', linewidths=2, zorder=10) ax.set_title('Polynomial degree {}'.format(degree)) fig, axes = plt.subplots(2, 3, figsize=(12,10)) for deg, ax in zip(range(1, 7), axes.ravel()): plot_poly_svc(deg, ax) # + def plot_rbf_svc(power=1, ax=None): svc_rbf = svm.SVC(kernel='rbf', gamma=10**power) plot_estimator(svc_rbf, X, y, ax=ax) ax.scatter(svc_rbf.support_vectors_[:, 0], svc_rbf.support_vectors_[:, 1], s=80, facecolors='none', linewidths=2, zorder=10) ax.set_title('$\gamma=10^{%i}$' % power) fig, axes = plt.subplots(2, 3, figsize=(12,10)) for pow, ax in zip(range(-3, 3), axes.ravel()): plot_rbf_svc(pow, ax) # - # Of course, the radial basis function (RBF) kernel is very flexible and performs best for this dataset. However, it is easy to get carried away tuning to a training dataset--we don't really believe the resulting decision boundary, do we? # ## Cross-validation # # In order to make objective choices for either kernels or hyperparameter values, we can apply the cross-validation methods outlined in last week's lecture. Every estimator class in `scikit-learn` exposes a `score` method that can judge the quality of the fit (or the prediction) on new data. # # The `score(x,y)` method for the `SVC` class returns the *mean accuracy* of the predictions from `x` with respect to `y`, for the fitted SVM. svc_lin = svm.SVC(kernel='linear', C=2) svc_lin.fit(X, y) svc_lin.score(X, y) svc_poly = svm.SVC(kernel='poly', degree=3) svc_poly.fit(X, y) svc_poly.score(X, y) svc_rbf = svm.SVC(kernel='rbf', gamma=1e-2) svc_rbf.fit(X, y) svc_rbf.score(X, y) # Each estimator in `scikit-learn` has a default estimator score method, which is an evaluation criterion for the problem they are designed to solve. For the `SVC` class, this is the **mean accuracy**, as shown above. # # Alternately, if we use cross-validation, you can specify one of a set of built-in scoring metrics. For classifiers such as support vector machines, these include: # # **accuracy** # : `sklearn.metrics.accuracy_score` # # **average_precision** # : `sklearn.metrics.average_precision_score` # # **f1** # : `sklearn.metrics.f1_score` # # **precision** # : `sklearn.metrics.precision_score` # # **recall** # : `sklearn.metrics.recall_score` # # **roc_auc** # : `sklearn.metrics.roc_auc_score` # # Regression models can use appropriate metrics, like `mean_squared_error` or `r2`. # # Finally, one can specify arbitrary loss functions to be used for assessment. The `metrics` module implements functions assessing prediction errors for specific purposes. def custom_loss(observed, predicted): diff = np.abs(observed - predicted).max() return np.log(1 + diff) from sklearn.metrics import make_scorer custom_scorer = make_scorer(custom_loss, greater_is_better=False) # Implementing cross-validation on our wine SVC is straightforward: # + from sklearn import model_selection X_train, X_test, y_train, y_test = model_selection.train_test_split( wine.values, grape.values, test_size=0.4, random_state=0) # - X_train.shape, y_train.shape X_test.shape, y_test.shape f = svm.SVC(kernel='linear', C=1) f.fit(X_train, y_train) f.score(X_test, y_test) # The following example demonstrates how to estimate the accuracy of a linear kernel support vector machine on the wine dataset by splitting the data, fitting a model and computing the score 5 consecutive times (with different splits each time): scores = model_selection.cross_val_score(f, wine.values, grape.values, cv=5) scores print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) # Furthermore, we can customize the scoring method by specifying the `scoring` parameter: model_selection.cross_val_score(f, wine.values, grape.values, cv=5, scoring='f1_weighted') # The module `sklearn.metric` also exposes a set of simple functions measuring prediction error given observations and prediction, such as the confusion matrix: # + from sklearn.metrics import confusion_matrix svc_poly = svm.SVC(kernel='poly', degree=3).fit(X_train, y_train) confusion_matrix(y_test, svc_poly.predict(X_test)) # - # ## Exercise: Titanic survival # # Try to estimate a reasonable support vector classfier for the fate of passengers on the Titanic (`../data/titanic.xls`). Specifically, see if you can correctly classify the survivors based on the covariates available in the dataset. # # As an extension, use multiple imputation to allow for the inclusion of age into the analysis, and see if it makes a difference in the results. # !conda install -y xlrd titanic = pd.read_excel("../data/titanic.xls", "titanic") titanic.head() # + # Write answer here # - # ## References # # - [Coursera's Machine Learning course](https://www.coursera.org/course/ml) by Stanford's Andrew Ng # - [`scikit-learn` User's Guide](http://scikit-learn.org/stable/modules/svm.html) SVM section # - [Scikit-learn tutorials for the Scipy 2013 conference](https://github.com/jakevdp/sklearn_scipy2013) by Jake Vanderplas
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Laboratorio 9 # <p>Para este laboratorio, hemos decidido usar un dataset con los tweets que realizaron las personas frente a Apple</p> import nltk import re nltk.download('punkt') # ## Lectura del archivo de entrada import pandas as pd df = pd.read_csv("Apple-Twitter-Sentiment-DFE.csv", header=0,encoding = 'utf_8') df.sample(5) # ## Limpieza de columnas # Eliminamos las columnas que no aportan informacion en cuanto al sentimiento dentro del texto. Estas columnas son las fechas, identificadores y estados. df2 = df.drop(["_unit_id","_unit_state","date","id","query","_last_judgment_at"],axis=1) df2.sample(5) # ## Separacion de datos # Separamos la columna "sentiment" que es el dato final que queremos lograr. Además dividimos los datos de forma que el 80% de los datos servirá para entrenar y el 20% restante servirá para probar. # + X_all = df2.drop(['sentiment'],axis=1) y_all = df2['sentiment'] from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score num_test = 0 X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test) # - import numpy as np titles =np.array(X_train["text"]) print(X_train["text"]) print(titles[1]) import re text = " ".join(titles) titles # Eliminación de URLs # + from collections import Counter for i in range(len(titles)): #URLs_dict = Counter( re.findall(r"(https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s]{2,}|www\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s]{2,}|https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9]\.[^\s]{2,}|www\.[a-zA-Z0-9]\.[^\s]{2,})",i) ) #URLs = list(URLs_dict.keys()) titles[i] = re.sub(r"(https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s]{2,}|www\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s]{2,}|https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9]\.[^\s]{2,}|www\.[a-zA-Z0-9]\.[^\s]{2,})","", titles[i]) titles # - # Se obtienen todas las siglas presentes en los textos, ya sean un conjunto de 2 o más letras mayúsculas o separadas # por punto. # + from collections import Counter my_dict = Counter( re.findall(r"[A-Z][A-Z]+",text) ) siglas = list(my_dict.keys()) my_dict2 = Counter(re.findall(r"([A-Z]\.([A-Z]\.)+)",text)) siglas2 = list(my_dict2.keys()) sig =[] for i in siglas2: sig.append(i[0]) siglas = sig + siglas siglas # - # Como todos los tweets son de idioma inglés se puede quitar las palabras que no aportan (stopwords) y se puede aplicar lemmatization y stem, se intentan conservar las mayúsculas, pues, en un tweet, se usan las mayúsculas para remarcar una idea, normalmente expresa incomodidad u odio. import nltk nltk.download('stopwords') stopwords_eng = nltk.corpus.stopwords.words('english') print(stopwords_eng) stopwords_MAY_eng = list((" ".join([token.upper() for token in stopwords_eng])).split()) stopwords_MAY_eng # + for i in range(len(titles)): titles[i] = " ".join([token for token in titles[i].split() if (token not in stopwords_eng and token not in stopwords_MAY_eng )]) #len(text.split()) - len(text2.split()) text2 = " ".join([token for token in text.split() if (token not in stopwords_eng and token not in stopwords_MAY_eng )]) titles # + print("Sin stop words: ") my_dicc_No_StopWords = Counter(text2.split()) print("cant tokens: ",len(text2.split())) print("tamanho vocabulario: ",len(my_dicc_No_StopWords)) my_dicc_No_StopWords.most_common(10) # + porter = nltk.PorterStemmer() import copy tweets_st =[] for i in range(len(titles)): copia1 = copy.deepcopy(titles[i]) spliteado = copia1.split() tweets_stem_split = [] for i in spliteado: if(i not in siglas): tweets_stem_split.append(porter.stem(i)) else: tweets_stem_split.append(i) tweets_stem = " ".join([token for token in tweets_stem_split]) tweets_st.append(tweets_stem) tweets_st # + #print("Stem: ") #my_dicc_Stem = Counter(tweets_st.split()) #print("cant tokens: ",len(tweets_st.split())) #print("tamanho vocabulario: ",len(my_dicc_Stem)) #my_dicc_Stem.most_common(10) # - nltk.download('wordnet') wnl = nltk.WordNetLemmatizer() # + tweets_lemma_split = [] tweets_le =[] for i in range(len(titles)): copia2 = copy.deepcopy(titles[i]) spliteado2 = copia2.split() tweets_lemma_split = [] for j in spliteado2: if(j not in siglas): tweets_lemma_split.append(wnl.lemmatize(j)) else: tweets_lemma_split.append(j) tweets_lemma = " ".join([token for token in tweets_lemma_split]) tweets_le.append(tweets_lemma) tweets_le # - def clean_tokens(text): return text.split() # ## Análisis de la Representación Vectorial # + from sklearn.model_selection import cross_val_score from sklearn.svm import LinearSVC from sklearn.linear_model import SGDClassifier from sklearn.linear_model import Perceptron from sklearn.linear_model import PassiveAggressiveClassifier from sklearn.naive_bayes import BernoulliNB, MultinomialNB from sklearn.neighbors import KNeighborsClassifier from sklearn.neighbors import NearestCentroid from sklearn.ensemble import RandomForestClassifier # - def run_model(clf, X, y): scores = cross_val_score(clf, X, y, cv=5) print("%s accuracy: %0.2f (+/- %0.2f)" % \ (str(clf.__class__).split('.')[-1].replace('>','').replace("'",''), scores.mean(), scores.std() * 2)) def run_models(X, y): run_model(LinearSVC(), X, y) run_model(SGDClassifier(), X, y) run_model(Perceptron(), X, y) run_model(PassiveAggressiveClassifier(), X, y) run_model(BernoulliNB(), X, y) run_model(MultinomialNB(), X, y) run_model(KNeighborsClassifier(), X, y) run_model(NearestCentroid(), X, y) run_model(RandomForestClassifier(n_estimators=100, max_depth=10), X, y) # ### BAG OF WORDS text=titles Documentos = copy.deepcopy(text) Documentos from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() # Se transforma el texto en una matriz de tXd, donde se indica si el token t está en el documento d txd_matrix = vectorizer.fit_transform(Documentos) # Se transforma la matriz txd para que sea binaria txd_matrix = CountVectorizer(binary=True,tokenizer=clean_tokens,stop_words='english').fit_transform(Documentos) txd_matrix.shape run_models(txd_matrix, y_train) # ### TFIDF VECTORIZER # Una matriz de métricas tfidf para cada documento del dataset DocumentosTFIDF = copy.deepcopy(text) from sklearn.feature_extraction.text import TfidfVectorizer # Transformamos la matriz de solo tf (tf_matrix) y la matriz tfidf tf_matrix = TfidfVectorizer(use_idf=False).fit_transform(DocumentosTFIDF) tfidf_matrix = TfidfVectorizer().fit_transform(DocumentosTFIDF) DocumentosTFIDF # + #print(tf_matrix[3107]) # - print(tfidf_matrix) #nltk.download() text1=copy.deepcopy(text) for i in range(len(text1)): text1[i]=text1[i].split() from collections import Counter import numpy as np # + #Obtener la matriz TF-IDF de los tweets from sklearn.feature_extraction.text import TfidfVectorizer X_1 = TfidfVectorizer(tokenizer=clean_tokens, stop_words='english').fit_transform(DocumentosTFIDF) print(X_1) # - print(X_1.shape,y_train.shape) # + run_models(X_1, y_train) #clf = LinearSVC(random_state = 0) #clf.fit(X_1,y_train) #prueba = ["a"] * X_1.shape[1] #prueba[0] = "I loved this amazing version" #print(prueba) #X_5 = TfidfVectorizer(tokenizer=clean_tokens, stop_words='english').fit_transform(prueba) #X_5 #clf.predict(X_5) # - # ## Utilizando Word Vectors de Spacy # !pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz --user import spacy nlp = spacy.load('en_core_web_sm') doc = nlp(u'This is a sentence.') # !pip3 install --upgrade gensim --user from gensim.models import word2vec sentences = word2vec.Word2Vec(sentences=) print(sentences)
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# # [Amin M. Boulouma Blog](https://amboulouma.com) # # ## Advanced Python - Algorithms in Python #1 # # - Help the creator channel reach 20k subscribers. He will keep uploading quality content for you: [Amin M. Boulouma Channel](https://www.youtube.com/channel/UCOZbokHO727qeStxeYSKMUQ?sub_confirmation=1) # - Those algorithms are best understood following the course: [Python Basics Tutorial](https://youtu.be/ne4Xsoe5Br8) # <!--?title Stars and bars --> # # Stars and bars # # Stars and bars is a mathematical technique for solving certain combinatorial problems. # It occurs whenever you want to count the number of ways to group identical objects. # # ## Theorem # # The number of ways to put $n$ identical objects into $k$ labeled boxes is # $$\binom{n + k - 1}{n}.$$ # # The proof involves turning the objects into stars and separating the boxes using bars (therefore the name). # E.g. we can represent with $\bigstar | \bigstar \bigstar |~| \bigstar \bigstar$ the following situation: # in the first box is one object, in the second box are two objects, the third one is empty and in the last box are two objects. # This is one way of dividing 5 objects into 4 boxes. # # It should be pretty obvious, that every partition can be represented using $n$ stars and $k - 1$ bars and every stars and bars permutation using $n$ stars and $k - 1$ bars represents one partition. # Therefore the number of ways to divide $n$ identical objects into $k$ labeled boxes is the same number as there are permutations of $n$ stars and $k - 1$ bars. # The [Binomial Coefficient](./combinatorics/binomial-coefficients.html) gives us the desired formula. # # ## Number of non-negative integer sums # # This problem is a direct application of the theorem. # # You want to count the number of solution of the equation # $$x_1 + x_2 + \dots + x_k = n$$ # with $x_i \ge 0$. # # Again we can represent a solution using stars and bars. # E.g. the solution $1 + 3 + 0 = 4$ for $n = 4$, $k = 3$ can be represented using $\bigstar | \bigstar \bigstar \bigstar |$. # # It is easy to see, that this is exactly the stars an bars theorem. # Therefore the solution is $\binom{n + k - 1}{n}$. # # ## Number of lower-bound integer sums # # This can easily be extended to integer sums with different lower bounds. # I.e. we want to count the number of solutions for the equation # $$x_1 + x_2 + \dots + x_k = n$$ # with $x_i \ge a_i$. # # After substituting $x_i' := x_i - a_i$ we receive the modified equation # $$(x_1' + a_i) + (x_2' + a_i) + \dots + (x_k' + a_k) = n$$ # $$\Leftrightarrow ~ ~ x_1' + x_2' + \dots + x_k' = n - a_1 - a_2 - \dots - a_k$$ # with $x_i' \ge 0$. # So we have reduced the problem to the simpler case with $x_i' \ge 0$ and again can apply the stars and bars theorem.
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # ## This notebook contains e2e analysis used to allocate tolerances for each $\color{red}{\text{Global Zernike Aberration}}$ mode for a segmented telescope. import os import time from shutil import copy from astropy.io import fits import astropy.units as u import hcipy import numpy as np import pastis.util as util from pastis.config import CONFIG_PASTIS from pastis.simulators.luvoir_imaging import LuvoirA_APLC from pastis.simulators.generic_segmented_telescopes import SegmentedAPLC import matplotlib.pyplot as plt import pandas as pd from scipy.interpolate import griddata import exoscene.image import exoscene.star import exoscene.planet from exoscene.planet import Planet from astropy.io import fits as pf from matplotlib.colors import TwoSlopeNorm import matplotlib.gridspec as gridspec from pastis.analytical_pastis.temporal_analysis import req_closedloop_calc_batch coronagraph_design = 'small' nb_seg = CONFIG_PASTIS.getint('LUVOIR', 'nb_subapertures') nm_aber = CONFIG_PASTIS.getfloat('LUVOIR', 'calibration_aberration') * 1e-9 sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling') # + data_dir = "<create your own directory where you want to save the data>" repo_dir = "<path-to-PASTIS-repo>" overall_dir = util.create_data_path(data_dir, telescope='luvoir_'+coronagraph_design) resDir = os.path.join(overall_dir, 'matrix_numerical') os.makedirs(resDir, exist_ok=True) # - optics_input = os.path.join(util.find_repo_location(), CONFIG_PASTIS.get('LUVOIR', 'optics_path_in_repo')) luvoir = LuvoirA_APLC(optics_input, coronagraph_design, sampling) max_LO = 20 luvoir.create_global_zernike_mirror(max_LO) n_LO = luvoir.zernike_mirror.num_actuators LO_modes = np.zeros(n_LO) luvoir.zernike_mirror.actuators = LO_modes luvoir.zernike_mirror.flatten() unaberrated_coro_psf, ref = luvoir.calc_psf(ref=True, display_intermediate=True, norm_one_photon=True) norm = np.max(ref) dh_intensity = (unaberrated_coro_psf / norm) * luvoir.dh_mask contrast_floor = np.mean(dh_intensity[np.where(luvoir.dh_mask != 0)]) print(f'norm: {norm}',f'constrast floor: {contrast_floor}') nonaberrated_coro_psf, ref, efield = luvoir.calc_psf(ref=True, display_intermediate=False, return_intermediate='efield',norm_one_photon=True) Efield_ref = nonaberrated_coro_psf.electric_field # + # LO_modes = np.zeros(n_LO) # LO_modes[3] = 100*(nm_aber)/2 # luvoir.zernike_mirror.actuators = LO_modes # aberrated_coro_psf, ref2 = luvoir.calc_psf(ref=True, display_intermediate=True) # dh_intensity_aberrated = (aberrated_coro_psf/ norm) * luvoir.dh_mask # aberrated_contrast = np.mean(dh_intensity_aberrated[np.where(luvoir.dh_mask != 0)]) # print(f'contrast floor: {aberrated_contrast}') # + print('Generating the E-fields for low order zernike modes in science plane') print(f'Calibration aberration used: {nm_aber} m') start_time = time.time() focus_fieldS = [] focus_fieldS_Re = [] focus_fieldS_Im = [] for i in range(1, n_LO): print(f'Working on global zernike mode: {i}') # Apply calibration aberration to used mode LO_modes = np.zeros(n_LO) LO_modes[i] = (nm_aber)/2 luvoir.zernike_mirror.actuators = LO_modes # Calculate coronagraphic E-field and add to lists aberrated_coro_psf, inter = luvoir.calc_psf(display_intermediate=False, return_intermediate='efield',norm_one_photon=True) focus_field1 = aberrated_coro_psf focus_fieldS.append(focus_field1) focus_fieldS_Re.append(focus_field1.real) focus_fieldS_Im.append(focus_field1.imag) # - focus_fieldS[0] mat_LO = np.zeros([n_LO-1, n_LO-1]) for i in range(0, n_LO-1): for j in range(0, n_LO-1): test = np.real((focus_fieldS[i].electric_field -Efield_ref) * np.conj(focus_fieldS[j].electric_field-Efield_ref)) dh_test = (test / norm) * luvoir.dh_mask contrast = np.mean(dh_test[np.where(luvoir.dh_mask != 0)]) mat_LO[i, j] = contrast mat_LO.shape # + from matplotlib.colors import LinearSegmentedColormap plt.figure(figsize=(10,8)) plt.imshow((mat_LO)) plt.title(r"PASTIS matrix $M$ for global zernike", fontsize=20) plt.xlabel("Mode Index",fontsize=20) plt.ylabel("Mode Index",fontsize=20) plt.tick_params(labelsize=15) cbar = plt.colorbar(fraction=0.046, pad=0.04) cbar.set_label(r"in units of $1/{nm^2}$",fontsize =15) plt.tight_layout() # + filename_matrix1 = 'PASTISmatrix_n_LO_' + str(n_LO) hcipy.write_fits(mat_LO, os.path.join(resDir, filename_matrix1 + '.fits')) print('Matrix saved to:', os.path.join(resDir, filename_matrix1 + '.fits','\n')) filename_matrix2 = 'EFIELD_Re_matrix_n_LO_' + str(n_LO) hcipy.write_fits(focus_fieldS_Re, os.path.join(resDir, filename_matrix2 + '.fits')) print('Efield Real saved to:', os.path.join(resDir, filename_matrix2 + '.fits', '\n')) filename_matrix3 = 'EFIELD_Im_matrix_n_LO_' + str(n_LO) hcipy.write_fits(focus_fieldS_Im, os.path.join(resDir, filename_matrix3 + '.fits')) print('Efield Imag saved to:', os.path.join(resDir, filename_matrix3 + '.fits','\n')) # - evals, evecs = np.linalg.eig(mat_LO) sorted_evals = np.sort(evals) sorted_indices = np.argsort(evals) sorted_evecs = evecs[:, sorted_indices] c_target_log = -11 c_target = 10**(c_target_log) n_repeat = 20 mu_map_LO = np.sqrt(((c_target) / (n_LO-1)) / (np.diag(mat_LO))) # + z_pup_downsample = CONFIG_PASTIS.getfloat('numerical', 'z_pup_downsample') N_pup_z = int(luvoir.pupil_grid.shape[0] / z_pup_downsample) #N_pup_z = 100,used to define out-of-band efield grid_zernike = hcipy.field.make_pupil_grid(N_pup_z, diameter=luvoir.diam) npup = int(np.sqrt(luvoir.pupil_grid.x.shape[0])) nimg = int(np.sqrt(luvoir.focal_det.x.shape[0])) # Getting the flux together sptype = 'A0V' Vmag = 5.0 minlam = 500 maxlam = 600 dark_current = 0 CIC = 0 star_flux = exoscene.star.bpgs_spectype_to_photonrate(spectype=sptype, Vmag=Vmag, minlam=minlam, maxlam=maxlam) #ph/s/m^2 Nph = star_flux.value*15**2*np.sum(luvoir.apodizer**2) / npup**2 # - luvoir.zernike_mirror.flatten() nonaberrated_coro_psf ,refshit ,inter_ref = luvoir.calc_psf(ref=True, display_intermediate=False, return_intermediate='efield',norm_one_photon=True) Efield_ref = nonaberrated_coro_psf.electric_field luvoir.zernike_mirror.flatten() defocus_ref2 = luvoir.calc_out_of_band_wfs(norm_one_photon=True) #returns wavefront on obwfs detector defocus_ref2_sub_real = hcipy.field.subsample_field(defocus_ref2.real, z_pup_downsample, grid_zernike, statistic='mean') defocus_ref2_sub_imag = hcipy.field.subsample_field(defocus_ref2.imag, z_pup_downsample, grid_zernike, statistic='mean') Efield_ref_OBWFS = (defocus_ref2_sub_real + 1j*defocus_ref2_sub_imag) * z_pup_downsample # + nyquist_sampling = 2. # Actual grid for LUVOIR images grid_test = hcipy.make_focal_grid( luvoir.sampling, luvoir.imlamD, pupil_diameter=luvoir.diam, focal_length=1, reference_wavelength=luvoir.wvln, ) # Actual grid for LUVOIR images that are nyquist sampled grid_det_subsample = hcipy.make_focal_grid( nyquist_sampling, np.floor(luvoir.imlamD), pupil_diameter=luvoir.diam, focal_length=1, reference_wavelength=luvoir.wvln, ) n_nyquist = int(np.sqrt(grid_det_subsample.x.shape[0])) # + design = 'small' dh_outer_nyquist = hcipy.circular_aperture(2 * luvoir.apod_dict[design]['owa'] * luvoir.lam_over_d)(grid_det_subsample) dh_inner_nyquist = hcipy.circular_aperture(2 * luvoir.apod_dict[design]['iwa'] * luvoir.lam_over_d)(grid_det_subsample) dh_mask_nyquist = (dh_outer_nyquist - dh_inner_nyquist).astype('bool') dh_size = len(np.where(luvoir.dh_mask != 0)[0]) dh_size_nyquist = len(np.where(dh_mask_nyquist != 0)[0]) dh_index = np.where(luvoir.dh_mask != 0)[0] dh_index_nyquist = np.where(dh_mask_nyquist != 0)[0] # - E0_OBWFS = np.zeros([N_pup_z*N_pup_z,1,2]) E0_OBWFS[:,0,0] = Efield_ref_OBWFS.real E0_OBWFS[:,0,1] = Efield_ref_OBWFS.imag E0_coron = np.zeros([nimg*nimg,1,2]) E0_coron[:,0,0] = Efield_ref.real E0_coron[:,0,1] = Efield_ref.imag filename_matrix2 = 'EFIELD_Re_matrix_n_LO_' + str(n_LO) + '.fits' G_zernike_real = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix2)) filename_matrix3 = 'EFIELD_Im_matrix_n_LO_' + str(n_LO) + '.fits' G_zernike_imag = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix3)) G_coron_zernike= np.zeros([nimg*nimg,2,n_LO-1]) for pp in range(0, n_LO-1): G_coron_zernike[:,0,pp] = G_zernike_real[pp] - Efield_ref.real G_coron_zernike[:,1,pp] = G_zernike_imag[pp] - Efield_ref.imag # + start_time = time.time() focus_fieldS = [] focus_fieldS_Re = [] focus_fieldS_Im = [] for i in range(1, n_LO): #print(f'Working on "defocus" zernike mode, segment: {i}') # Apply calibration aberration to used mode LO_modes = np.zeros(n_LO) #sm_mode[6*i-3] = (nm_aber)/2 LO_modes[i] = (nm_aber)/2 luvoir.zernike_mirror.actuators = LO_modes zernike_meas = luvoir.calc_out_of_band_wfs(norm_one_photon=True) zernike_meas_sub_real = hcipy.field.subsample_field(zernike_meas.real, z_pup_downsample, grid_zernike, statistic='mean') zernike_meas_sub_imag = hcipy.field.subsample_field(zernike_meas.imag, z_pup_downsample, grid_zernike, statistic='mean') focus_field1 = zernike_meas_sub_real + 1j * zernike_meas_sub_imag focus_fieldS.append(focus_field1) focus_fieldS_Re.append(focus_field1.real) focus_fieldS_Im.append(focus_field1.imag) # + filename_matrix = 'EFIELD_OBWFS_Re_matrix_num_LO_' + str(n_LO) hcipy.write_fits(focus_fieldS_Re, os.path.join(resDir, filename_matrix + '.fits')) print('Efield Real saved to:', os.path.join(resDir, filename_matrix + '.fits')) filename_matrix = 'EFIELD_OBWFS_Im_matrix_num_LO_' + str(n_LO) hcipy.write_fits(focus_fieldS_Im, os.path.join(resDir, filename_matrix + '.fits')) print('Efield Imag saved to:', os.path.join(resDir, filename_matrix + '.fits')) # - filename_matrix = 'EFIELD_OBWFS_Re_matrix_num_LO_' + str(n_LO)+'.fits' G_OBWFS_real = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix)) filename_matrix = 'EFIELD_OBWFS_Im_matrix_num_LO_' + str(n_LO)+'.fits' G_OBWFS_imag = fits.getdata(os.path.join(overall_dir, 'matrix_numerical', filename_matrix)) G_OBWFS= np.zeros([N_pup_z*N_pup_z,2,n_LO-1]) for pp in range(0, n_LO-1): G_OBWFS[:,0,pp] = G_OBWFS_real[pp]*z_pup_downsample - Efield_ref_OBWFS.real G_OBWFS[:,1,pp] = G_OBWFS_imag[pp]*z_pup_downsample - Efield_ref_OBWFS.imag # + flux = Nph Q_LO = np.diag(np.asarray(mu_map_LO**2)) Ntimes = 20 TimeMinus = -2 TimePlus = 5.5 #3.5 Nwavescale = 8 Nflux = 3 res = np.zeros([Ntimes, Nwavescale, Nflux, 1]) result_wf_test =[] #i=-1 for wavescale in range (1,15,2): #i=i+1 print('Harris modes with batch OBWFS and noise %f'% wavescale, "i",i) niter = 10 timer1 = time.time() StarMag = 0.0 #j=-1 for tscale in np.logspace(TimeMinus, TimePlus, Ntimes): j=j+1 Starfactor = 10**(-StarMag/2.5) print(tscale) tmp0 = req_closedloop_calc_batch(G_coron_zernike, G_OBWFS, E0_coron, E0_OBWFS, dark_current+CIC/tscale, dark_current+CIC/tscale, tscale, flux*Starfactor,0.0001*wavescale**2*Q_LO, niter, luvoir.dh_mask, norm) tmp1 = tmp0['averaged_hist'] n_tmp1 = len(tmp1) result_wf_test.append(tmp1[n_tmp1-1]) # + delta_wf = [] for wavescale in range (1,15,2): wf = 1e3*np.sqrt(0.0001*wavescale**2) delta_wf.append(wf) texp = np.logspace(TimeMinus, TimePlus, Ntimes) font = {'family': 'serif','color' : 'black','weight': 'normal','size' : 20} plt.figure(figsize =(15,10)) plt.title('Target contrast = %s, Vmag= %s'%(c_target, Vmag),fontdict=font) plt.plot(texp,result_wf_test[0:20]-contrast_floor, label=r'$\Delta_{wf}= %d\ pm$'%(delta_wf[0])) plt.plot(texp,result_wf_test[20:40]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[0])) plt.plot(texp,result_wf_test[40:60]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[2])) plt.plot(texp,result_wf_test[60:80]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[3])) plt.plot(texp,result_wf_test[80:100]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[4])) plt.plot(texp,result_wf_test[100:120]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[5])) plt.plot(texp,result_wf_test[120:140]-contrast_floor, label=r'$\Delta_{wf}=%d\ pm$'%(delta_wf[6])) plt.xlabel("$t_{WFS}$ in secs",fontsize=20) plt.ylabel("$\Delta$ contrast",fontsize=20) plt.yscale('log') plt.xscale('log') plt.legend(loc = 'upper center',fontsize=20) plt.tick_params(top=False, bottom=True, left=True, right=True,labelleft=True, labelbottom=True, labelsize=20) plt.tick_params(axis='both',which='major',length=10, width=2) plt.tick_params(axis='both',which='minor',length=6, width=2) plt.grid() plt.show() # - delta_wf[1]
13,268
/I-140_Premium_RFE_Yes.ipynb
19001496d6bf4f8497e81f4feecac34b5a8ff60c
[]
no_license
Kim-SeongCheol/i-140
https://github.com/Kim-SeongCheol/i-140
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
418,894
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:tensorflow] # language: python # name: conda-env-tensorflow-py # --- # + from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import datetime, time sns.set_style('whitegrid') # %matplotlib inline # - # <h2> 데이터 불러오기 </h2> # # input = pd.read_csv('input_data.csv', encoding = 'cp1252') input.shape input.info() input.describe() # <h2> Ouput data 정리 </h2> # 1. nan 데이터 삭제 # 2. Total Processing Time 데이터 integer형으로 변경 # 3. 600보다 큰 데이터 삭제 # 4. 총 기간 일 자별 -> 월별 class_output 데이터 생성 # 5. Approval/Denial Data와 USCIS Received Data 간의 차이 -> delta_output # # # input = input.dropna(subset=['Total Processing Time', 'Approval/Denial Date', 'USCIS Received Date']) input.shape # + input['output'] = input['Total Processing Time'].apply(lambda x: int(x.split()[0])) input = input.drop(input[input.output > 600].index) input.shape # - input.columns today = datetime.date(2018, 4, 10) input['AF_DATE'] = input['Application Filed'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input['month_ago'] = today - input['AF_DATE'] input['month_ago'] = input['month_ago'].apply(lambda x: int(x.days)) input['month_ago'] = input['month_ago'].apply(lambda x: int(x / 30)) # <h2> 연도별 데이터 사용 </h2> # Approval/Denial Date 기준 # <br> # 2015년 이후 input['Approval/Denial Date'].isnull().sum() input['year'] = input['Approval/Denial Date'].apply(lambda x: int(x.split('/')[2])) #input = input.drop(input[input['year'] < 2013].index) input.shape input[['year', 'output']].groupby(['year'], as_index=False).mean().sort_values(by='output', ascending=False) input[['USCIS Received Date', 'Approval/Denial Date']][0:10] input['USCIS Received Date'] = input['USCIS Received Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input['Approval/Denial Date'] = input['Approval/Denial Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input['USCIS_Approval_Delta'] = input['Approval/Denial Date'] - input['USCIS Received Date'] input['USCIS_Approval_Delta'] = input['USCIS_Approval_Delta'].apply(lambda x: int(x.days)) sns.distplot(input['USCIS_Approval_Delta']) # <h2> 불필요 Columns 삭제 </h2> input = input.drop(['Applicant Type', 'Service Center','Approval/Denial Date', 'USCIS Received Date', 'Reason for RFE','Application Status','USCIS Notice Date', 'USCIS Receipt Number','Most Recent LUD', 'Days Elapsed', 'Notes', 'State', 'Case Added to Tracker', 'Last Updated', 'year','Total Processing Time', 'AF_DATE' ], axis = 1) input.shape input.sample(10) # <h2> Nationality 데이터 정리 </h2> # # Chinal, India 제외한 데이터 Others로 변경 len(input[input.Nationality == 'China']) len(input[input.Nationality == 'India']) len(input[(input.Nationality != 'China') & (input.Nationality != 'India')]) input.ix[(input.Nationality != 'India') & (input.Nationality != 'China'), 'Nationality'] = 'Others' input[['Nationality', 'output']].groupby(['Nationality'], as_index=False).mean().sort_values(by='output', ascending=False) nationality_one_hot = pd.get_dummies(input['Nationality']) input = input.drop('Nationality', axis = 1) input = input.join(nationality_one_hot) # <h2> Category 데이터 정리 </h2> # # 1. EB4, EB5, nan 삭제 # <br> # 2. 카테고리 데이터 -> one hot encoding # set(input.Category) input = input.dropna(subset=['Category']) input.shape sns.countplot(input.Category) input = input.drop(input[input.Category == 'EB5'].index) input = input.drop(input[input.Category == 'EB4'].index) input.shape sns.countplot(input.Category) input[['Category', 'output']].groupby(['Category'], as_index=False).mean().sort_values(by='output', ascending=False) category_one_hot = pd.get_dummies(input['Category']) input = input.drop('Category', axis = 1) input = input.join(category_one_hot) # <h2> I-140 /486 Filing, Processing Type, RFE Received? 데이터 정리 </h2> # 1. Binary Categorical 데이터 이므로 binary data 로 변경(0,1) # 2. RFE received? 데이터는 nan값이 많으므로 yes, no nan에 대해서 2,1,0 넣기? input["I-140/485 Filing"].isnull().sum() input["Processing Type"].isnull().sum() # + input = input.dropna(subset = ['Processing Type']) input.shape # - input.ix[input['I-140/485 Filing'] == 'concurrent', 'I-140/485_Filing'] = int(1) input.ix[input['I-140/485 Filing'] == 'non-concurrent', 'I-140/485_Filing'] = int(0) input['Application Filed'].isnull().sum() input['AF_Month'] = input['Application Filed'].apply(lambda x: int(x.split('/')[0])) input[['AF_Month', 'output']].groupby(['AF_Month'], as_index=False).mean().sort_values(by='output', ascending=False) input['AF_Month'] = input['AF_Month'].apply(lambda x: 1 if x > 6 else 0) input['Priority Date'].isnull().sum() input = input.dropna(subset = ['Priority Date']) input.shape input['Priority Date'] = input['Priority Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input['Application Filed'] = input['Application Filed'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input['AF_PD_Delta'] = input['Application Filed'] - input['Priority Date'] input['AF_PD_Delta'] = input['AF_PD_Delta'].apply(lambda x: int(x.days)) input.ix[input['AF_PD_Delta'] < 0, 'AF_PD_Delta'] = 0 input.ix[input['AF_PD_Delta'] > 500, 'AF_PD_Delta'] = 500 input['AF_PD_Delta'] = input['AF_PD_Delta'].apply(lambda x: int(x/50)) input[['AF_PD_Delta', 'output']].groupby(['AF_PD_Delta'], as_index=False).mean().sort_values(by='output', ascending=False) sns.countplot(input.AF_PD_Delta) input_premium = input[input['Processing Type'] == 'premium'] input_premium.shape input_premium_yes = input_premium[input_premium['RFE Received?'] == 'yes'] input_premium_yes.shape input_premium_yes = input_premium_yes.drop(['I-140/485 Filing', 'Processing Type', 'RFE Received?'], axis = 1) input_premium_noanswer = input_premium[input_premium['RFE Received?'].isnull()] input_premium_noanswer = input_premium_noanswer.drop(['I-140/485 Filing', 'Processing Type', 'RFE Received?'], axis = 1) input_premium_noanswer.shape additional_input_premium_yes = input_premium_noanswer[input_premium_noanswer['USCIS_Approval_Delta'] > 30] additional_input_premium_yes.shape input_premium_yes = pd.concat([input_premium_yes, additional_input_premium_yes]) input_premium_yes.shape # <h2> RFE_AF_Delta 데이터 만들기 </h2> # RFE Received Date과 Application Filed Date의 날짜 차이<br> # regression 에서 좋은 성능을 내게 해주므로 Normalize만 시키고 continuous데이터 그대로 사용 # # # RFE Received? 가 NaN인 데이터중 USCIS Received Date과 Approval/Denial Date과의 차이가 30일 이상인데이터 추가 했지만, # 그럴 경우 데이터 대부분이 RFE Received Date 가 nan이라 영향 주지 않음. input_premium_yes['RFE Received Date'].isnull().sum() input_premium_yes = input_premium_yes.dropna(subset = ['RFE Received Date']) input_premium_yes['RFE Received Date'] = input_premium_yes['RFE Received Date'].apply(lambda x: datetime.date(int(x.split('/')[2]), int(x.split('/')[0]), int(x.split('/')[1]))) input_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE Received Date'] - input_premium_yes['Application Filed'] input_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE_AF_Delta'].apply(lambda x: int(x.days)) input_premium_yes.ix[input_premium_yes['RFE_AF_Delta'] < 0, 'RFE_AF_Delta'] = 0 input_premium_yes.ix[input_premium_yes['RFE_AF_Delta'] > 365, 'RFE_AF_Delta'] = 365 input_premium_yes['RFE_AF_Delta'] = input_premium_yes['RFE_AF_Delta'].apply(lambda x: x/365) sns.distplot(input_premium_yes.RFE_AF_Delta) input_premium_yes['RFE_AF_Delta'].corr(input_premium_yes['output']) input_premium_yes = input_premium_yes.drop(['Priority Date', 'Application Filed', 'RFE Received Date', 'RFE Replied Date','USCIS_Approval_Delta'], axis = 1) # <h2> Classification output </h2> sns.distplot(input_premium_yes.output) input_premium_yes = input_premium_yes.drop(input_premium_yes[input_premium_yes.output > 150].index) input_premium_yes['class_output'] = input_premium_yes['output'].apply(lambda x: int(x / 30)) input_premium_yes.shape # <h2> 데이터 스플릿 </h2> # + import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.ensemble import GradientBoostingRegressor from sklearn.metrics import mean_squared_error import matplotlib import itertools from sklearn.svm import SVC, LinearSVC from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression, LinearRegression from sklearn.neural_network import MLPClassifier, MLPRegressor from sklearn.metrics import accuracy_score import scipy # - # 1) Classification input_premium_yes_dic = {} period_list = [25, 31, 37, 43, 49, 55] # 2년 , 2.5년, 3년, 3.5년 for period in period_list: input_premium_yes_dic[period] = input_premium_yes[input_premium_yes['month_ago'] < period] print("period %d shape : %s" %(period-1, input_premium_yes_dic[period].shape)) col_train = list(input_premium_yes.columns) col_train.remove('output') col_train.remove('class_output') col_train.remove('month_ago') FEATURES = col_train LABEL = "class_output" top_1_acc = [] top_2_acc = [] for period in period_list: # Training set and Prediction set with the features to predict X = input_premium_yes_dic[period][FEATURES] y = input_premium_yes_dic[period].class_output acc_top1 = 0 acc_top2 = 0 for i in range(0,4): x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25) y_train = pd.DataFrame(y_train, columns = [LABEL]) y_test = pd.DataFrame(y_test, columns = [LABEL]) # Top- 1 Accuracy logreg = LogisticRegression() logreg.fit(x_train, y_train) y_pred = logreg.predict(x_test) acc_top1 = acc_top1 + round(logreg.score(x_test, y_test) * 100, 2) probs = logreg.predict_proba(x_test) best_n = np.argsort(probs, axis=1) correct_top2 = 0 for i in range(0, y_test.shape[0]): if y_test.values[i][0] in list(best_n[i][-2:][::-1]): correct_top2 += 1 acc_top2 = acc_top2 + (correct_top2 / y_test.shape[0] * 100) acc_top1 = acc_top1 / 4 print("Logistic Regression %d month ago top-1 acc : %f" % (period - 1,acc_top1)) top_1_acc.append(acc_top1) acc_top2 = acc_top2 / 4 print("Logistic Regression %d month ago top-2 acc : %f" % (period - 1,acc_top2)) top_2_acc.append(acc_top2) print("-" *60) fig, ax = plt.subplots() x = np.linspace(-4, 4, 150) ax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o') ax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o') ax.legend() plt.title("Logistic Regression Classification") plt.xlabel("months ago") plt.ylabel("Accuracy") plt.show() FEATURES = col_train LABEL = "class_output" top_1_acc = [] top_2_acc = [] for period in period_list: # Training set and Prediction set with the features to predict X = input_premium_yes_dic[period][FEATURES] y = input_premium_yes_dic[period].class_output # Train and Test x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25) y_train = pd.DataFrame(y_train, columns = [LABEL]) y_test = pd.DataFrame(y_test, columns = [LABEL]) acc_top1 = 0 acc_top2 = 0 for i in range(0,4): x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25) y_train = pd.DataFrame(y_train, columns = [LABEL]) y_test = pd.DataFrame(y_test, columns = [LABEL]) # Top- 1 Accuracy mlp = MLPClassifier(solver='lbfgs', alpha=5e-5, hidden_layer_sizes=(200, 150, 100, 50), random_state=1) mlp.fit(x_train, y_train) y_pred = logreg.predict(x_test) acc_top1 = acc_top1 + round(logreg.score(x_test, y_test) * 100, 2) probs = logreg.predict_proba(x_test) best_n = np.argsort(probs, axis=1) correct_top2 = 0 for i in range(0, y_test.shape[0]): if y_test.values[i][0] in list(best_n[i][-2:][::-1]): correct_top2 += 1 acc_top2 = acc_top2 + correct_top2 / y_test.shape[0] * 100 acc_top1 = acc_top1 / 4 print("Neural Network %d month ago top-1 acc : %f" % (period - 1,acc_top1)) top_1_acc.append(acc_top1) acc_top2 = acc_top2 / 4 print("Neural Network %d month ago top-2 acc : %f" % (period - 1,acc_top2)) top_2_acc.append(acc_top2) print("-" *60) fig, ax = plt.subplots() x = np.linspace(-4, 4, 150) ax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o') ax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o') ax.legend() plt.title("Neural Network Classification") plt.xlabel("months ago") plt.ylabel("Accuracy") plt.show() # 2) Regression def rsquared(x, y): """ Return R^2 where x and y are array-like.""" slope, intercept, r_value, p_value, std_err = scipy.stats.mstats.linregress(x, y) return r_value**2 # + rsq_list = [] top_1_acc = [] top_2_acc = [] y_test_dic = {} y_pred_dic = {} for period in period_list: FEATURES = col_train LABEL = "output" # Columns for tensorflow feature_cols = [tf.contrib.layers.real_valued_column(k) for k in FEATURES] # Training set and Prediction set with the features to predict X = input_premium_yes_dic[period][FEATURES] y = input_premium_yes_dic[period].output # Train and Test rsq = 0 acc_top1 = 0 acc_top2 = 0 for i in range(0,4): x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25) y_train = pd.DataFrame(y_train, columns = [LABEL]) y_test = pd.DataFrame(y_test, columns = [LABEL]) regressor = LinearRegression() regressor.fit(x_train, y_train) y_pred = regressor.predict(x_test) rsq = rsq + rsquared(y_test, y_pred) diff = np.array(y_test).reshape(-1,) - y_pred.reshape(-1,) acc_top1 = acc_top1 + ((-15 < diff) & (diff < 15)).sum() / x_test.shape[0] * 100 acc_top2 = acc_top2 + ((-30 < diff) & (diff < 30)).sum() / x_test.shape[0] * 100 y_test_dic[period] = y_test y_pred_dic[period] = y_pred rsq = rsq / 4 print("Logistic Regression %d month ago rsq : %f" % (period -1, rsq)) rsq_list.append(rsq) acc_top1 = acc_top1 / 4 print("Logistic Regression %d month ago top-1 acc : %f" % (period - 1,acc_top1)) top_1_acc.append(acc_top1) acc_top2 = acc_top2 / 4 print("Logistic Regression %d month ago top-2 acc : %f" % (period - 1,acc_top2)) top_2_acc.append(acc_top2) print("-" *60) # - fig, ax = plt.subplots() x = np.linspace(-4, 4, 150) ax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o') ax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o') ax.legend() plt.title("Logisctic Regression") plt.xlabel("months ago") plt.ylabel("Accuracy") plt.show() # + i = 1 plt.figure(figsize = (15,10)) for period in period_list: y_test = y_test_dic[period] y_pred = y_pred_dic[period] plt.subplot(320 + i) i = i + 1 plt.scatter(y_test, y_pred) plt.xlabel('Test Data') plt.ylabel('Predicted Data') plt.title("%d month ago" % (period -1)) plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--') plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.3, wspace=0.2) plt.show() # + rsq_list = [] top_1_acc = [] top_2_acc = [] y_test_dic = {} y_pred_dic = {} for period in period_list: FEATURES = col_train LABEL = "output" # Columns for tensorflow feature_cols = [tf.contrib.layers.real_valued_column(k) for k in FEATURES] # Training set and Prediction set with the features to predict X = input_premium_yes_dic[period][FEATURES] y = input_premium_yes_dic[period].output # Train and Test rsq = 0 acc_top1 = 0 acc_top2 = 0 for i in range(0,4): x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25) y_train = pd.DataFrame(y_train, columns = [LABEL]) y_test = pd.DataFrame(y_test, columns = [LABEL]) regressor = MLPRegressor(hidden_layer_sizes=(100, 50, 25), activation='relu', solver='lbfgs', learning_rate='adaptive', max_iter=2000, alpha=5e-5) regressor.fit(x_train, y_train) y_pred = regressor.predict(x_test) rsq = rsq + rsquared(y_test, y_pred) diff = np.array(y_test).reshape(-1,) - y_pred.reshape(-1,) acc_top1 = acc_top1 + ((-15 < diff) & (diff < 15)).sum() / x_test.shape[0] * 100 acc_top2 = acc_top2 + ((-30 < diff) & (diff < 30)).sum() / x_test.shape[0] * 100 y_test_dic[period] = y_test y_pred_dic[period] = y_pred rsq = rsq / 4 print("Logistic Regression %d month ago rsq : %f" % (period -1, rsq)) rsq_list.append(rsq) acc_top1 = acc_top1 / 4 print("Logistic Regression %d month ago top-1 acc : %f" % (period - 1,acc_top1)) top_1_acc.append(acc_top1) acc_top2 = acc_top2 / 4 print("Logistic Regression %d month ago top-2 acc : %f" % (period - 1,acc_top2)) top_2_acc.append(acc_top2) print("-" *60) # - fig, ax = plt.subplots() x = np.linspace(-4, 4, 150) ax.plot(period_list, top_1_acc, linewidth=2, alpha=0.6, label='top-1 acc', marker='o') ax.plot(period_list, top_2_acc, linewidth=2, alpha=0.6, label='top-2 acc', marker='o') ax.legend() plt.title("Neural Network Regression") plt.xlabel("months ago") plt.ylabel("Accuracy") plt.show() # + i = 1 plt.figure(figsize = (15,10)) for period in period_list: y_test = y_test_dic[period] y_pred = y_pred_dic[period] plt.subplot(320 + i) i = i + 1 plt.scatter(y_test, y_pred) plt.xlabel('Test Data') plt.ylabel('Predicted Data') plt.title("%d month ago" % (period -1)) plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--') plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.3, wspace=0.2) plt.show()
18,602
/inverse-problems/2018/code/L06-iterative.ipynb
667b372f787c1e22628e488408baa05ab94e5ffd
[ "MIT" ]
permissive
omaclaren/open-learning-material
https://github.com/omaclaren/open-learning-material
27
4
null
null
null
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.py
7,061,914
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ### Iterative regularisation # # Simple Landweber Iteration scheme illustrated on deconvolution problem. import numpy as np from IPython.display import set_matplotlib_formats set_matplotlib_formats('png', 'pdf') #forward mapping for convolution def create_fmap_con(n=4,d=1): weights = np.zeros(d) weights[0:n] = 1./n A = np.zeros([d,d]) for i in range(0,d): A[i,:] = np.roll(weights,i-int(n/2)) return A t = np.linspace(0,4*np.pi,1000) x = np.sin(t) plt.plot(t,x,'r--') plt.show() #create forward mapping for convolution with greater width of smoothing A = create_fmap_con(n=int(len(x)/4+1),d=len(x)) y = np.dot(A,x) plt.plot(t,y,'k') plt.plot(t,x,'r--') plt.show() #add almost undetectable noise #y_noisy = y+np.random.normal(0,0.001,size=len(y)) y_noisy = y+np.random.normal(0,0.01,size=len(y)) plt.plot(t,y_noisy,'k') plt.plot(t,y,'r--') plt.show() #invert noisy plt.plot(t,x,'r--') plt.plot(t,np.dot(np.linalg.inv(A),y_noisy),'k') plt.show() # #### Iterative approach from scipy.sparse.linalg import svds U, s, VT = svds(A, k=1) s1 = s print(s1) niter = 1000 xs = np.zeros((niter,len(x))) for i in range(0,niter-1): #update rule xs[i+1,:] = xs[i,:] + A.T@(y_noisy-A@xs[i,:]) niter = 1000 xs = np.zeros((niter,len(x))) x_norms = np.zeros(niter) data_norms = np.zeros(niter) for i in range(0,niter-1): #calc norms x_norms[i] = np.linalg.norm(xs[i,:],2) #x_norms_1[i] = np.linalg.norm(xs[i,:],1) data_norms[i] = np.linalg.norm(y_noisy - np.dot(A,xs[i,:])) #update rule xs[i+1,:] = xs[i,:] + A.T@(y_noisy-A@xs[i,:]) plt.plot(t,xs[0,:],'r') plt.plot(t,xs[1,:],'b') plt.plot(t,xs[2,:],'g') plt.plot(t,xs[3,:],'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs[6,:],'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs[10,:],'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs.T,'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs[29,:],'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs[299,:],'k') plt.plot(t,x,'r--') plt.show() plt.plot(t,xs[999,:],'k') plt.plot(t,x,'r--') plt.show() n = 30 plt.plot(data_norms[0:n]**2,linewidth=5) plt.ylabel(r'$||Ax-y||^2$',fontsize=14) plt.xlabel(r'iteration',fontsize=14) plt.show() n = 30 plt.plot(np.arange(5,n),data_norms[5:n]**2,linewidth=5) plt.ylabel(r'$||Ax-y||^2$',fontsize=14) plt.xlabel(r'iteration',fontsize=14) plt.show() #n = 30 n = 100 plt.plot(np.arange(15,n),data_norms[15:n]**2,linewidth=5) plt.ylabel(r'$||Ax-y||^2$',fontsize=14) plt.xlabel(r'iteration',fontsize=14) plt.show() n = 999 plt.plot(x_norms[0:n]**2,data_norms[0:n]**2,'-+',markersize=10,linewidth=2) plt.ylabel(r'$||Ax-y||^2$',fontsize=14) plt.xlabel(r'$||x||^2$',fontsize=14) plt.show()
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/nbody_simulation/.ipynb_checkpoints/cosmic_web-checkpoint.ipynb
b419113e38be3ff7b4b1de3b80adf7029b7f0bac
[]
no_license
pitt1321/IndrasNet
https://github.com/pitt1321/IndrasNet
0
2
null
2015-12-09T07:20:59
2015-11-06T15:18:26
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.py
215,405
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %matplotlib inline import numpy from matplotlib import pyplot as plt from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.size'] = 16 from matplotlib import animation from JSAnimation.IPython_display import display_animation # + #Parameters nx = 81 dx = 0.25 dt = 0.0002 gamma = 1.4 nt = int(0.01/dt)+1 x=numpy.linspace(-10,10,nx) # + #Define rho with the initial conditions def rho_initial(nx): rho = numpy.zeros(nx) rho[0:(nx-1)/2] = 1 #kg/m^3 rho[(nx-1)/2:nx] = 0.125 #kg/m^3 return rho rho_initial = rho_initial(nx) #print(rho) # + #Define u with the initial conditions #def u_initial(): u_initial = numpy.zeros(nx) # return u #u = u_initial() #print(u) # + #Define p with the initial conditions def p_initial(nx): p = numpy.zeros(nx) p[0:(nx-1)/2] = 100*1000 #N/m^2 p[(nx-1)/2:nx] = 10*1000 #N/m^2 return p p_initial = p_initial(nx) #print(p) # - def getE(p, rho, u): e = p/((gamma-1)*rho) e_t = e + (1/2*u**2) return e_t e_t_initial = getE(p_initial, rho_initial, u_initial) #print(e_t_initial) rho_initial.shape def getU_vector(rho, u, e_t): u_vector = numpy.zeros((nx, 3)) u_vector[:, 0] = rho u_vector[:, 1] = rho*u u_vector[:, 2] = rho*e_t return u_vector u_vector_initial = getU_vector(rho_initial, u_initial, e_t_initial) # + #u_vector_initial[:,1]/rho_initial==u_initial # - def computeF(u_vector): f_vector = numpy.zeros((nx, 3)) f_vector[:, 0] = u_vector[:, 1] f_vector[:, 1] = u_vector[:, 1]**2/u_vector[:, 0] + (gamma-1)*(u_vector[:, 2] - 0.5*u_vector[:, 1]**2/u_vector[:, 0]) f_vector[:, 2] = (u_vector[:, 2] + (gamma-1)*(u_vector[:, 2] - 0.5*u_vector[:, 1]**2/u_vector[:, 0]))*(u_vector[:, 1]/u_vector[:, 0]) return f_vector def richtmyer (u, nt, dt, dx): un = numpy.zeros((nt, nx, 3)) un[:,:,:] = u.copy() ustar = u.copy() for t in range(1, nt): #predictor f = computeF(u) ustar[:-1,:] = 0.5*(u[1:,:] + u[:-1,:]) - dt/(2*dx)*(f[1:,:] - f[:-1,:]) #corrector fstar = computeF(ustar) un[t,1:-1,:] = u[1:-1,:] - dt/dx*(fstar[1:-1,:] - fstar[:-2,:]) u = un[t,:,:].copy() return un a = richtmyer(u_vector_initial, nt, dt, dx) print(numpy.shape(a)) numpy.where(x==2.5) # + #find final velocity v_final=a[nt-1,:,1]/a[nt-1,:,0] print("the velocity at x = 2.5 is:") print(v_final[50]) #nx=50 # + #find final density rho_final = a[nt-1,:,0] print("the density at x = 2.5 is:") print(rho_final[50]) # + #find final pressure p_final = (gamma-1)*(a[nt-1, :, 2]-0.5*(a[nt-1, :, 1])**2/a[nt-1,:,0]) print("the pressure at x = 2.5 is:") print(p_final[50]) # + #diplay animation fig = plt.figure(); ax = plt.axes(xlim=(-10,10),ylim=(0,2),xlabel=('Distance'),ylabel=('Traffic density')); line, = ax.plot([],[],color='#003366', lw=2); def animate(data): x = numpy.linspace(-10,10,nx) y = a[data, :, 0] line.set_data(x,y) return line, anim = animation.FuncAnimation(fig, animate, frames=nt-1, interval=50) display_animation(anim, default_mode='once') # - = RAmax: z = data[i][0] ra = data[i][1] dec = data[i][2] Mly = 3.26 * (3000.0*z - 607.8 * z**2 - 156.3 * z**3 + 138.3*z**4)/0.71 x = Mly*np.cos(ra*np.pi/180) y = Mly*np.sin(ra*np.pi/180) galaxies.append([x,y]) return np.array(galaxies) galaxies = galaxy(data_cf, RAmin=0, RAmax=60) def randGalaxy(galaxies, zmin,zmax,RAmin,RAmax): """ Creates a random galaxy distribution to match data distribution ----------------------------------------------------------------- Inputs: galaxies: a numpy array of [x,y] positions of galaxies- output of galaxy function zmin/max: the minimum/maximum red-shift of the data set (float). RAmin/max: the min/max right ascension of the data set (float). Output: numpy array of [x,y] coordinates of random galaxies in desired range """ #random z numbers in given range: zrand = zmax*np.sqrt(np.random.uniform(0,1,len(galaxies))) #random ra numbers in given range: rarand = np.random.uniform(RAmin,RAmax,len(galaxies)) #known conversion from redshift to distance from Earth Mly = 3.26 * (3000.0*zrand - 607.8 * zrand**2 - 156.3 * zrand**3 + 138.3*zrand**4)/0.71 #x,y coordinates of randomly distributed galaxies: xrand = Mly*np.cos(rarand*np.pi/180) yrand = Mly*np.sin(rarand*np.pi/180) rgalaxies=zip(xrand,yrand) return np.array(rgalaxies) rgalaxies = randGalaxy(galaxies, zmin=zmin, zmax=zmax, RAmin=0, RAmax=60) def galaxyPairFinder(galaxies,randomGalaxies): """ Finds the distance between all pairs of galaxies from both the data and the random distribution. ------------------------------------------------- Inputs: galaxies -- numpy array of [x,y] positions of galaxies from SDSS data (output of galaxy function) randomGalaxies -- numpy array of [x,y] positions of random galaxies (output of randGalaxy function) """ d = [] dr= [] for i in range(0,len(galaxies)): for j in range(0,len(galaxies)): if j > i: d.append(np.sqrt((galaxies[i][0] - galaxies[j][0])**2 + (galaxies[i][1] - galaxies[j][1])**2)) #want every unique pair of real data dr.append(np.sqrt((galaxies[i][0] - randomGalaxies[j][0])**2 +(galaxies[i][1] - randomGalaxies[j][1])**2)) #every possible pair of random galaxies with real galaxy data, do not have to worry about uniqueness return np.array([d,dr]) gpairs = galaxyPairFinder(galaxies, rgalaxies) def twoPointCorr(galaxyPairs,Nbins): """ Calculates the two-point correlation function of a set of galaxies, using already created pairs of distances ------------------------------------------------- Inputs: galaxyPairs -- numpy array of distances between real and random galaxies [d, dr] (output of galaxyPairFinder function) Nbins -- desired number of bins to use Output: numpy array of bins and xi values [r, xi] """ #find min/max distances between random galaxies rmin = min(galaxyPairs[1]) rmax = max(galaxyPairs[1]) r = np.linspace(rmin,rmax,Nbins) #set up bins #create empty arrays to build using bins ddr = [] rrr = [] for i in range(0, len(r)-1): counterD=0.0 counterR=0.0 for w in galaxyPairs[0]: #for each value of distances between real galaxies, determine whether it belongs in current bin "w" if w>=r[i] and w<r[i+1]: counterD+=1.0 for k in galaxyPairs[1]: #repeat process of bin finding for random galaxies if k>= r[i] and k<r[i+1]: counterR+=1.0 rrr.append(counterR) ddr.append(counterD) ddr = np.array(ddr) rrr = np.array(rrr) #calculate xi xi = (float(len(galaxyPairs[1]))/(float(len(galaxyPairs[0]))))*(ddr/rrr) - 1.0 return np.array(zip(r,xi)) tpcr = twoPointCorr(gpairs, 100) # **3. Creating a Power Spectrum** # def powerSpectrum(galaxyPairs,Nbins,twoPoint): """ Finds the power spectrum of input data Inputs: galaxyPairs -- numpy array of distances between real and random galaxies [d, dr] (output of galaxyPairFinder function) Nbins -- desired number of bins to use twoPoint -- output of twoPointCorr function Output: numpy array of [frequency, positive real fft values] """ #find min and max of the distance between galaxies to create bins: rmin = min(galaxyPairs[0])*(1/(0.70*3.26)) #convert Mly -> Mpc rmax = max(galaxyPairs[0])*(1/(0.70*3.26)) signal = twoPoint[:,1] #get values corresponding to positive frequencies fourier = np.fft.fft(signal) n = signal.size waveLength = (rmax - rmin)/Nbins freq =np.linspace(0,(n/2+1)*2.*(1/waveLength),n/2+1) fourier_pos = fourier[:n/2+1] return np.array([freq,fourier_pos.real]) # **Combining all functions of 2 / 3 ** def largeScaleStructure(data,RAmin,RAmax,Nbins): """ Combines functions related to the large scale structure and plots: 1. Galaxy data in used field from SDSS 2. Distribution of random galaxies 3. Two-point correlation function 4. Fourier Transform of Two-Point Correlation Function (Power Spectrum) Inputs: data -- data to use, taken from SDSS RAmin/max -- desired minimum and maximum of RA range Nbins -- amount of bins to use for two-point correlation function """ zmin = min(data[:,0]) zmax = max(data[:,0]) galaxies = galaxy(data,RAmin,RAmax) randomGalaxies = randGalaxy(galaxies,zmin,zmax,RAmin,RAmax) galaxyPairs = galaxyPairFinder(galaxies,randomGalaxies) twoPoint = twoPointCorr(galaxyPairs,Nbins) powerSpectra = powerSpectrum(galaxyPairs,Nbins,twoPoint) #plot galaxies from sdss: plt.scatter(galaxies[:,0],galaxies[:,1],s=2) plt.title('Galaxy Distribution: %f <z< %f' % (zmin, zmax)) plt.xlabel('Distance from Earth [Mly]') plt.ylabel('Distance from Earth [Mly]') plt.savefig('realdata.pdf') plt.show() #plot random galaxies: plt.clf() plt.scatter(randomGalaxies[:,0],randomGalaxies[:,1]) plt.title('Random Galaxy Distrubition') plt.xlabel('Distance from Earth [Mly]') plt.ylabel('Distance from Earth [Mly]') plt.savefig('randomdata.pdf') plt.show() #plot two-point correlation function: plt.clf() plt.scatter(twoPoint[:,0]*(1/(0.70*3.26)),twoPoint[:,1]) #convert Mly -> Mpc #plt.xlim(0,500000) #plt.ylim(-2,2) plt.title('Two-Point Correlation Function') plt.xlabel(r'$f [h^{-1}Mpc]$') plt.ylabel(r'$\xi(r)$') #plt.yscale('log') plt.savefig('tpcf.pdf') plt.show() #plot power spectrum plt.clf() plt.scatter(powerSpectra[0],powerSpectra[1]) plt.title('Power Spectrum') plt.xlabel(r'$f [h{Mpc}^{-1}]$') plt.ylabel(r'$F(\xi(r))$') plt.xlim(0,36) plt.ylim(-20,40) plt.savefig('power_spectrum.pdf') plt.show() largeScaleStructure(data_cf, RAmin=0, RAmax=30, Nbins=100)
10,707
/3D_representations_on_simulated_rf_matrices_dataset.ipynb
896fc8c31c668780975c2bb41c0b1c94b4683867
[]
no_license
TahiriNadia/ML_DL_Classification_Trees
https://github.com/TahiriNadia/ML_DL_Classification_Trees
0
0
null
null
null
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Jupyter Notebook
false
false
.py
156,555
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/svdcvt/math_python_hse/blob/master/fall-2021/homeworks/homework-3-1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + [markdown] id="DftcBgmBCYUX" # Домашнее задание №3, часть 1: Анализ данных. Pandas + Визуализации. # + id="OPEWrRsrCR7a" # + id="sPefesl5ChCg" # + id="LEN7daAlChGM" # + [markdown] id="w-CSGTpUCiAl" # Черновик. opular synthetic dataset called `iris` for this notebook. # # The way it usually goes in sklearn is that you make a classifier, train it, and then use this trained model to make predictions. # # ``` # clf = sklearn.SomeClassifier() # clf.fit(training_features, training_answers) # predictions = clf.predict(prediction_features) # ``` from sklearn.datasets import load_iris from sklearn import tree iris = load_iris() clf = tree.DecisionTreeClassifier() clf = clf.fit(iris.data, iris.target) import graphviz dot_data = tree.export_graphviz(clf, out_file=None) graph = graphviz.Source(dot_data) graph dot_data = tree.export_graphviz(clf, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names, filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph clf.predict(iris.data[:1, :]) clf.predict_proba(iris.data[:1, :]) # Exercise for you! # # 1. Try doing the same stuff with a Random Forest and Adaboost # 2. Read about cross validation here: http://scikit-learn.org/stable/modules/cross_validation.html. Try figure out the best depth for the decision tree using cross validation. d +[None]): with open(file, 'r', encoding = 'utf-8') as fin: for line in fin.readlines(): encoded_line = encode(line,subword) for word in encoded_line.split(' '): word_dict.add(word.lower()) print('word nums : %d' % (len(word_dict))) # + # with open(embed_file,encoding='ISO-8859-1') as f: # with open(embed_file+'.u8','w+',encoding='utf-8') as f2: # for line in f.readlines(): # try: # uft_str = line.encode("iso-8859-1").decode('utf-8') # f2.write(uft_str) # except : # pass # + # 加载embedding import numpy as np def read_vectors(path, topn): # read top n word vectors, i.e. top is 10000 lines_num, dim = 0, 0 vectors = {} iw = [] wi = {} with open(path,encoding='utf-8') as f: first_line = True for line in f: if first_line: first_line = False dim = int(line.strip().split()[1]) continue lines_num += 1 tokens = line.rstrip().split(' ') if tokens[0] in word_dict: vectors[tokens[0]] = np.asarray([float(x) for x in tokens[1:]]) vectors[tokens[0]] = vectors[tokens[0]] iw.append(tokens[0]) if topn != 0 and lines_num >= topn: break for i, w in enumerate(iw): wi[w] = i return vectors, iw, wi, dim vectors = read_vectors(embed_file,0) print(len(vectors[0])) # - # + # 计算一个文件的embedding random_embedding = dict() stop_words = set(['!','?','。','.',',']) def get_embeddings(file,subword=None): embeddings = [] valids = [] count = 0 with open(file, encoding='utf-8') as fin: for line in fin.readlines(): encoded_line = encode(line,subword) tmp = np.zeros([300]) count = 0 for word in encoded_line.split(' '): if word in vectors[0]: tmp += vectors[0][word] count += 1 else: if word in random_embedding: noisy = random_embedding[word] else: noisy = np.random.normal(size=[300]) random_embedding[word] = noisy tmp+=noisy count += 1 if count > 0: tmp = tmp / sum(np.sqrt(tmp*tmp)) valids.append(1) else: valids.append(0) embeddings.append(tmp) return embeddings,valids ref_embeddings = get_embeddings(ref_file,None) def distances(embedA,embedB,validA,validB): res = [] for a,b,c,d in zip(embedA,embedB,validA,validB): dis = (sum(a*b)) / (np.sqrt(sum(a*a))* np.sqrt(sum(b*b))+1e-10) res.append(dis) return sum(res) / len(res) embeddings = [] final_names = eval_files + [ref_file] valids = [] for file,subword in zip(eval_files + [ref_file], eval_subword +[None]): print(file) embedding,valid = get_embeddings(file,subword) embeddings.append(embedding) valids.append(valid) matrix = [] for i in range(len(embeddings)-1,len(embeddings)): row = [] for j in range(0,len(embeddings)): diss = distances(embeddings[i],embeddings[j],valids[i],valids[j]) row.append(diss) matrix.append(row) print(matrix) # + # # 计算一个文件的embedding Gready # def get_embeddings(file,subword=None): # embeddings = [] # valids = [] # count = 0 # with open(file, encoding='utf-8') as fin: # for line in fin.readlines(): # encoded_line = encode(line,subword) # tmp = [] # count = 0 # for word in encoded_line.split(' '): # if word in vectors[0]: # tmp.append(vectors[0][word]) # count += 1 # else: # noisy = np.random.uniform(size=[300]) # noisy = noisy/sum(np.sqrt(noisy*noisy)) # tmp.append(noisy) # count += 1 # if count > 0: # valids.append(1) # else: # valids.append(0) # embeddings.append(tmp) # return embeddings,valids # ref_embeddings = get_embeddings(ref_file,None) # def distances(embedA,embedB,validA,validB): # res = [] # for a,b,c,d in zip(embedA,embedB,validA,validB): # if c*d == 0: # print('error') # for r in a: # score1 = -1 # for r1 in b: # dis = (sum(r*r1)+1e-10) / (np.sqrt(sum(r*r))* np.sqrt(sum(r1*r1))+1e-10) # score1 = max(dis,score1) # for r in b: # score2 = -1 # for r1 in a: # dis = (sum(r*r1)+1e-10) / (np.sqrt(sum(r*r))* np.sqrt(sum(r1*r1))+1e-10) # score2 = max(dis,score2) # res.append(score1+score2) # return sum(res) / len(res) / 2 # embeddings = [] # final_names = eval_files + [ref_file] # valids = [] # for file,subword in zip(eval_files + [ref_file], eval_subword +[None]): # print(file) # embedding,valid = get_embeddings(file,subword) # embeddings.append(embedding) # valids.append(valid) # matrix = [] # for i in range(len(embeddings)-1,len(embeddings)): # row = [] # for j in range(0,len(embeddings)): # diss = distances(embeddings[i],embeddings[j],valids[i],valids[j]) # row.append(diss) # matrix.append(row) # print(matrix) # + # - print(matrix) 0-4200-8a5d-27ec4c842e13" model_2.summary() # + id="d_KSwJrV6sLR" # model_2.optimizer = optimizer train_cubes = train_cubes.cache() val_cubes=val_cubes.cache() # + colab={"base_uri": "https://localhost:8080/"} id="BeKp0bIFGUL-" outputId="cf95f7b6-c98e-4357-bd18-65be6ff339ff" epochs=10 history=model_2.fit(train_cubes, validation_data =val_cubes, epochs=epochs, # steps_per_epoch=split//batch_size, # validation_steps = int(length - split)//batch_size ) # + colab={"base_uri": "https://localhost:8080/", "height": 336} id="OyyA_uZJrQeu" outputId="b39897cc-4c6b-4972-8a17-49ce214727a5" import matplotlib.pyplot as plt plt.style.use('grayscale') loss = history.history['loss'] val_loss = history.history['val_loss'] accuracy = history.history['accuracy'] val_accuracy = history.history['val_accuracy'] epoch_scale = range(1,len(loss)+1) fig = plt.figure(figsize=(15,5)) ax=fig.add_subplot(1,2,1,) ax.set_title('a) Loss') ax.plot(epoch_scale, loss,'*-') ax.plot(epoch_scale, val_loss, '*-') ax.legend(["training", "validation"]) # ax.axes.margins(0) ax1=fig.add_subplot(1,2,2) ax1.set_title('b) Accuracy') ax1.plot(epoch_scale, accuracy, 'o-') ax1.plot(epoch_scale, val_accuracy, '*-') ax1.legend(["training", "validation"]) fig.show() # + colab={"base_uri": "https://localhost:8080/"} id="CEDB4W9gGOaY" outputId="031a4257-46ee-4675-e1fa-a9c50a6c5e10" initial_epoch=epochs epochs=epochs+10 history=model_2.fit(train_cubes, validation_data =val_cubes, epochs=epochs, initial_epoch = initial_epoch, # steps_per_epoch=split//batch_size, # validation_steps = int(length - split)//batch_size ) # + id="QZu9Rr2zuWBv" loss=[*loss, *history.history['loss']] val_loss =[*val_loss, *history.history['val_loss']] accuracy =[*accuracy, *history.history['accuracy']] val_accuracy =[*val_accuracy, *history.history['val_accuracy']] epoch_scale = range(1,len(loss)+1) # + colab={"base_uri": "https://localhost:8080/", "height": 336} id="KkzbYCYq7y5F" outputId="0d5443a9-ca62-4193-fbd7-c502450b75de" fig = plt.figure(figsize=(15,5)) ax=fig.add_subplot(1,2,1,) ax.set_title('a) Loss') ax.plot(epoch_scale, loss,'*-') ax.plot(epoch_scale, val_loss, '*-') ax.legend(["training", "validation"]) # ax.axes.margins(0) ax1=fig.add_subplot(1,2,2) ax1.set_title('b) Accuracy') ax1.plot(epoch_scale, accuracy, 'o-') ax1.plot(epoch_scale, val_accuracy, '*-') ax1.legend(["training", "validation"]) fig.show() # + [markdown] id="jZ_J4yaxgYyx" # for 20 epochs the same result is getting # so 10 epochs is enaugh # + [markdown] id="XgYxakD9MMdr" # We got 98% accuracy for our 3D images of RF-Matrices, and these images is invariant to the number of trees in the matrices or order of entry of trees to the matrix eather # + [markdown] id="dN405zd6nTyy" # ##Confusion matrix # + id="p8fpkqi1nXag" ds = tf.data.Dataset.from_tensor_slices((test_x,test_y)) ds = ds.map(to_3DCube) test_cubes = ds.map(lambda ind,val,l:[tf.SparseTensor(ind,tf.squeeze(val),shape_3D),l]) test_cubes= test_cubes.map(lambda c,l:[tf.expand_dims(tf.sparse.to_dense(c,default_value=0,validate_indices=False), axis=-1),l]) test_array = np.array(list(test_cubes.as_numpy_iterator()),dtype=object) tst_x = np.stack(test_array[:,0]) tst_y = np.stack(test_array[:,1]) # + id="KUquE58Bu54j" # Use the model to predict the labels test_predictions = model_2.predict(tst_x,steps=tst_x.shape[0]) test_y_pred = np.argmax(test_predictions, axis=1) test_y_true = np.argmax(tst_y, axis=1) # + colab={"base_uri": "https://localhost:8080/"} id="DW49EsZ2nclT" outputId="68443929-6690-4921-e486-4bd4ca40dcfd" from sklearn.metrics import confusion_matrix confMat=confusion_matrix( test_y_pred,test_y_true) print( np.sum(np.diag(confMat))/len(test_y_true)) # + colab={"base_uri": "https://localhost:8080/", "height": 585} id="IkCdkpU7njJ3" outputId="aa9da859-31fe-4a9a-f6c1-e4f376242195" import itertools import io from tensorflow.image import decode_png def plot_confusion_matrix(cm, class_names): """ Returns a matplotlib figure containing the plotted confusion matrix. Args: cm (array, shape = [n, n]): a confusion matrix of integer classes class_names (array, shape = [n]): String names of the integer classes """ figure = plt.figure(figsize=(8, 8)) plt.imshow(cm, interpolation='nearest', cmap=plt.cm.gray_r) plt.title("Confusion matrix") plt.colorbar() tick_marks = np.arange(len(class_names)) plt.xticks(tick_marks, class_names, rotation=90) plt.yticks(tick_marks, class_names) # Normalize the confusion matrix. cm = np.around(cm.astype(np.float) / cm.sum(axis=1)[:, np.newaxis], decimals=2) # Use white text if squares are dark; otherwise black. threshold = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): color = "white" if cm[i, j] > threshold else "black" plt.text(j, i, cm[i, j], horizontalalignment="center", color=color) plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # return figure plot_confusion_matrix(confMat,class_names=('one','two','three','four','five'))
12,900
/churn_model.ipynb
123d68c0d3e5d02f40f83a6b0b423d95d7a3028e
[]
no_license
tejasbangera/Churn-Prediction-Model
https://github.com/tejasbangera/Churn-Prediction-Model
0
0
null
null
null
null
Jupyter Notebook
false
false
.py
245,986
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error import numpy as np breast_cancer = load_breast_cancer() features = breast_cancer.data target = breast_cancer.target train_features, test_features, train_target, test_target = train_test_split(features, target, test_size=0.33) dtr = DecisionTreeClassifier() # - simulate_blocks = 100000 # + #dtr.fit(train_features, train_target) # - model_path = "breast-cancer.model" # Save the model import pickle # with open(model_path, 'wb') as handle: # pickle.dump(dtr, handle) with open(model_path, 'rb') as handle: dtr = pickle.load(handle) # + from sklearn.tree import export_graphviz from IPython.display import Image import pydotplus dot_data = export_graphviz(dtr) graph = pydotplus.graph_from_dot_data(dot_data) Image(graph.create_png()) # - from hummingbird.ml import convert, load hb_model = convert(dtr, 'pytorch') # + features = np.tile(features, (simulate_blocks, 1)) # - # %%timeit -r 3 # Run predictions on CPU hb_model.predict(features) # Run predictions on GPU hb_model.to('cuda') # %%timeit -r 3 hb_model.predict(features) onary = {'Name': names_list, 'Expertise': expertise_list, 'Stars': stars_list, 'Rating_Count': rating_count_list} # ### Dictionary to Pandas Dataframe df = pd.DataFrame.from_dict (dent_dictionary) # dataframe before cleaning df # ### Clean the Data df['Stars'] = df['Stars'].apply(lambda x: x.replace('star rating', '')) # dataframe after cleaning df df['Expertise'] = df['Expertise'].apply(lambda x: x.replace('\n', ',')) df # ### Save Data in Excel df.to_excel('yelp_cleaned_data.xlsx', index= False)
2,087
/code/fmri_experiment/main_fmri.ipynb
eac12ef5a3307b73cf11d9f8595d1908228159a2
[]
no_license
kingjr/b2b
https://github.com/kingjr/b2b
0
0
null
null
null
null
Jupyter Notebook
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false
.py
7,448,451
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import pandas as pd from sklearn.model_selection import train_test_split from helperFunctions import * import numpy as np class Model: def __init__(self): self.model = None self.target = None self.features = None self.data = None self.testX = None self.testY = None self.trainX = None self.trainY = None def _readDataset(self, filename): self.data = pd.read_csv(filename) def _dropNulls(self): self.data.drop(["education"], axis = 1, inplace = True) #dropping this improved accuracy self.data.dropna(inplace = True) def _saveProcessedData(self): self.features = self.data.drop("TenYearCHD", axis = 1) self.target = self.data.TenYearCHD self.data.to_csv("../data/processedData.csv") def _trainTestSplit(self): self.trainX, self.testX, self.trainY, self.testY = train_test_split(self.features, self.target, test_size=0.2) def preProcessing(self, filename): self._readDataset(filename) self._dropNulls() self.data.reset_index(drop = True) self._saveProcessedData() self._trainTestSplit() #display(self.data.corr()) # - model = Model() model.preProcessing("../data/framingham.csv") model.data.TenYearCHD.hist() print() model.data.hist("age", "TenYearCHD") model.data.hist("male", "TenYearCHD") model.data.hist("male") model.data.corr("spearman") es, or likewise, a high number of features. # # Here I will use the Exhaustive feature selection algorithm from mlxtend in a classification (Paribas) and regression (House Price) dataset. # 참고 자료 - http://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/ # + import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # %matplotlib inline import warnings from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.metrics import roc_auc_score from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS # - warnings.filterwarnings(action='ignore') # + file_path = '/Users/wontaek/Documents/Lecture_dataset/BNP_Paribas_Cardif_claims/train.csv' data = pd.read_csv(file_path, nrows=50000) data.shape # In practice, feature selection should be done after data pre-processing, # so ideally, all the categorical variables are encoded into numbers, # and then you can assess how deterministic they are of the target # here for simplicity I will use only numerical variables # select numerical columns: numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] numerical_vars = list(data.select_dtypes(include=numerics).columns) data = data[numerical_vars] data.shape # separate train and test sets X_train, X_test, y_train, y_test = train_test_split( data.drop(labels=['target', 'ID'], axis=1), data['target'], test_size=0.3, random_state=0) X_train.shape, X_test.shape # find and remove correlated features # in order to reduce the feature space a bit # so that the algorithm takes shorter def correlation(dataset, threshold): col_corr = set() # Set of all the names of correlated columns corr_matrix = dataset.corr() for i in range(len(corr_matrix.columns)): for j in range(i): if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value colname = corr_matrix.columns[i] # getting the name of column col_corr.add(colname) return col_corr corr_features = correlation(X_train, 0.8) print('correlated features: ', len(set(corr_features)) ) # removed correlated features X_train.drop(labels=corr_features, axis=1, inplace=True) X_test.drop(labels=corr_features, axis=1, inplace=True) X_train.shape, X_test.shape # - X_train.columns[0:10] # 조합을 만들어서 feature를 선택하는 방법이다. # - 최소 조합의 개수와 최대 조합의 개수를 선택해서 하는 방식 # - feature가 많을 수록 경우의 수도 많으니 오래 걸린다. # + # exhaustive feature selection # I indicate that I want to select 10 features from # the total, and that I want to select those features # based on the optimal roc_auc # in order to shorter search time for the demonstration # i will ask the algorithm to try all possible 1,2,3 and 4 # feature combinations from a dataset of 4 features # if you have access to a multicore or distributed computer # system you can try more greedy searches efs1 = EFS(RandomForestClassifier(n_jobs=4, random_state=0), min_features=1, max_features=5, scoring='roc_auc', print_progress=True, cv=2) efs1 = efs1.fit(np.array(X_train[X_train.columns[0:5]].fillna(0)), y_train) # - def run_randomForests(X_train, X_test, y_train, y_test): rf = RandomForestClassifier(n_estimators=200, random_state=39, max_depth=4) rf.fit(X_train, y_train) print('Train set') pred = rf.predict_proba(X_train) print('Random Forests roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1]))) print('Test set') pred = rf.predict_proba(X_test) print('Random Forests roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1]))) efs1.subsets_ efs1.best_idx_ selected_feat= X_train.columns[list(efs1.best_idx_)] selected_feat # + # evaluate performance of classifier using selected features run_randomForests(X_train[selected_feat].fillna(0), X_test[selected_feat].fillna(0), y_train, y_test) # - # regression도 동일하게 진행한다. in], X[train], self.alphas, self.independent_alphas) self.G_.append(G) # Fit encoder H = ols.fit(X[test], Y[test] @ G.T).coef_ self.H_.append(H) # Aggregate ensembling self.G_ = np.mean(self.G_, 0) self.H_ = np.mean(self.H_, 0) self.E_ = np.diag(self.H_) return self def score(self, X, Y, scoring=None, multioutput='raw_values'): scoring = self.scoring if scoring is None else scoring if multioutput != 'raw_values': raise NotImplementedError # Transform with decoder YG = Y @ self.G_.T # Make standard and knocked-out encoders predictions XE = X @ np.diag(self.E_).T # Compute R for each column X return rn_score(YG, XE, scoring=scoring, multioutput='raw_values') def ridge_cv(X, Y, alphas, independent_alphas=False, Uv=None): """ Similar to sklearn RidgeCV but (1) can optimize a different alpha for each column of Y (2) return leave-one-out Y_hat """ if isinstance(alphas, (float, int)): alphas = np.array([alphas, ], np.float64) if Y.ndim == 1: Y = Y[:, None] n, n_x = X.shape n, n_y = Y.shape # Decompose X if Uv is None: U, s, _ = linalg.svd(X, full_matrices=0) v = s**2 else: U, v = Uv UY = U.T @ Y # For each alpha, solve leave-one-out error coefs cv_duals = np.zeros((len(alphas), n, n_y)) cv_errors = np.zeros((len(alphas), n, n_y)) for alpha_idx, alpha in enumerate(alphas): # Solve w = ((v + alpha) ** -1) - alpha ** -1 c = U @ np.diag(w) @ UY + alpha ** -1 * Y cv_duals[alpha_idx] = c # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T)) G_diag = (w * U ** 2).sum(axis=-1) + alpha ** -1 error = c / G_diag[:, np.newaxis] cv_errors[alpha_idx] = error # identify best alpha for each column of Y independently if independent_alphas: best_alphas = (cv_errors ** 2).mean(axis=1).argmin(axis=0) duals = np.transpose([cv_duals[b, :, i] for i, b in enumerate(best_alphas)]) cv_errors = np.transpose([cv_errors[b, :, i] for i, b in enumerate(best_alphas)]) else: _cv_errors = cv_errors.reshape(len(alphas), -1) best_alphas = (_cv_errors ** 2).mean(axis=1).argmin(axis=0) duals = cv_duals[best_alphas] cv_errors = cv_errors[best_alphas] coefs = duals.T @ X Y_hat = Y - cv_errors return coefs, best_alphas, Y_hat class Forward(): def __init__(self, alphas=alphas, independent_alphas=True, scoring='r', multioutput='uniform_average'): self.alphas = alphas self.independent_alphas = independent_alphas self.scoring = scoring self.multioutput = multioutput self.__name__ = 'Forward' def fit(self, X, Y): # Fit encoder self.H_, H_alpha, _ = ridge_cv(X, Y, self.alphas, self.independent_alphas) self.E_ = np.sum(self.H_**2, 0) return self def score(self, X, Y, scoring=None, multioutput=None): scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput # Make standard and knocked-out encoders predictions XH = X @ self.H_.T # Compute R for each column of Y return rn_score(Y, XH, scoring=scoring, multioutput=multioutput) def predict(self, X): return X @ self.H_.T class Backward(): def __init__(self, alphas=alphas, independent_alphas=True, scoring='r'): self.alphas = alphas self.independent_alphas = independent_alphas self.scoring = scoring self.__name__ = 'Backward' def fit(self, X, Y): # Fit encoder self.H_, H_alpha, _ = ridge_cv(Y, X, self.alphas, self.independent_alphas) self.E_ = np.sum(self.H_**2, 1) return self def score(self, X, Y, scoring=None, multioutput='raw_values'): scoring = self.scoring if scoring is None else scoring if multioutput != 'raw_values': raise NotImplementedError # Make standard and knocked-out encoders predictions YH = Y @ self.H_.T # Compute R for each column of Y return rn_score(X, YH, scoring=scoring, multioutput=multioutput) def predict(self, X): return 0 def canonical_correlation(model, X, Y, scoring, multioutput): """Score in canonical space""" # check valid model for xy in 'xy': for var in ('mean', 'std', 'rotations'): assert hasattr(model, '%s_%s_' % (xy, var)) assert model.x_rotations_.shape[1] == model.y_rotations_.shape[1] # check valid data if Y.ndim == 1: Y = Y[:, None] if X.ndim == 1: X = X[:, None] assert len(X) == len(Y) # Project to canonical space X = X - model.x_mean_ X /= model.x_std_ X = np.nan_to_num(X, 0) XA = X @ model.x_rotations_ Y = Y - model.y_mean_ Y /= model.y_std_ Y = np.nan_to_num(Y, 0) YB = Y @ model.y_rotations_ return rn_score(XA, YB, scoring=scoring, multioutput=multioutput) def validate_number_components(n, X, Y): n_max = min(X.shape[1], Y.shape[1]) if n == -1: n = n_max elif n >= 0. and n < 1.: n = int(np.floor(n_max * n)) n = 1 if n == 0 else n assert n == int(n) and n > 0 and n <= n_max return int(n) class GridPLS(BaseEstimator, RegressorMixin): """Optimize n_components by minimizing Y_pred error""" def __init__(self, n_components=components, cv=5, scoring='r', multioutput='uniform_average', tol=1e-15): self.n_components = n_components self.cv = cv self.scoring = scoring self.multioutput = multioutput self.__name__ = 'GridPLS' def fit(self, X, Y): N = self.n_components if not isinstance(N, (list, np.ndarray)): N = [N, ] components = np.unique([validate_number_components(n, X, Y) for n in N]) # Optimize n_components on Y prediction! if len(components) > 1: models = GridSearchCV(SkPLS(), dict(n_components=components)) best = models.fit(X, Y).best_estimator_ self.n_components_ = best.n_components x_valid = range(X.shape[1]) y_valid = range(Y.shape[1]) else: best = PLS(n_components=components[0], scoring=self.scoring, multioutput=self.multioutput) best.fit(X, Y) self.n_components_ = best.n_components_ x_valid = best.x_valid_ y_valid = best.y_valid_ self.x_mean_ = np.zeros(X.shape[1]) self.x_std_ = np.zeros(X.shape[1]) self.x_rotations_ = np.zeros((X.shape[1], self.n_components_)) self.y_mean_ = np.zeros(Y.shape[1]) self.y_std_ = np.zeros(Y.shape[1]) self.y_rotations_ = np.zeros((Y.shape[1], self.n_components_)) self.x_mean_[x_valid] = best.x_mean_ self.x_std_[x_valid] = best.x_std_ self.x_rotations_[x_valid, :] = best.x_rotations_ self.y_mean_[y_valid] = best.y_mean_ self.y_std_[y_valid] = best.y_std_ self.y_rotations_[y_valid, :] = best.y_rotations_ self.E_ = np.sum(self.x_rotations_**2, 1) return self def score(self, X, Y, scoring=None, multioutput=None): scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput return canonical_correlation(self, X, Y, scoring, multioutput) def transform(self, X): return self.best.transform(X[:, self.x_valid_]) def fit_transform(self, X, Y): return self.fit(X, Y).best.transform(X) class GridCCA(BaseEstimator, RegressorMixin): """Optimize n_components by minimizing Y_pred error""" def __init__(self, n_components=components, cv=5, scoring='r', multioutput='uniform_average', tol=1e-15): self.n_components = n_components self.cv = cv self.scoring = scoring self.multioutput = multioutput self.__name__ = 'GridCCA' def fit(self, X, Y): N = self.n_components if not isinstance(N, (list, np.ndarray)): N = [N, ] components = np.unique([validate_number_components(n, X, Y) for n in N]) # Optimize n_components on Y prediction! if len(components) > 1: models = GridSearchCV(SkCCA(), dict(n_components=components)) best = models.fit(X, Y).best_estimator_ self.n_components_ = best.n_components x_valid = range(X.shape[1]) y_valid = range(Y.shape[1]) else: best = CCA(n_components=components[0], scoring=self.scoring, multioutput=self.multioutput) best.fit(X, Y) self.n_components_ = best.n_components_ x_valid = best.x_valid_ y_valid = best.y_valid_ self.x_mean_ = np.zeros(X.shape[1]) self.x_std_ = np.zeros(X.shape[1]) self.x_rotations_ = np.zeros((X.shape[1], self.n_components_)) self.y_mean_ = np.zeros(Y.shape[1]) self.y_std_ = np.zeros(Y.shape[1]) self.y_rotations_ = np.zeros((Y.shape[1], self.n_components_)) self.x_mean_[x_valid] = best.x_mean_ self.x_std_[x_valid] = best.x_std_ self.x_rotations_[x_valid, :] = best.x_rotations_ self.y_mean_[y_valid] = best.y_mean_ self.y_std_[y_valid] = best.y_std_ self.y_rotations_[y_valid, :] = best.y_rotations_ self.E_ = np.sum(self.x_rotations_**2, 1) return self def score(self, X, Y, scoring=None, multioutput=None): scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput return canonical_correlation(self, X, Y, scoring, multioutput) def transform(self, X): return self.best.transform(X[:, self.x_valid_]) def fit_transform(self, X, Y): return self.fit(X, Y).best.transform(X) class GridRegCCA(BaseEstimator, RegressorMixin): def __init__(self, alphas=np.logspace(-4, 4., 20), cv=5, n_components=[-1, ], scoring='r', multioutput='uniform_average', tol=1e-15): self.alphas = alphas self.cv = cv self.scoring = scoring self.n_components = n_components self.multioutput = multioutput self.__name__ = 'GridRegCCA' def fit(self, X, Y): self.x_valid_ = np.where(X.std(0) > 0)[0] self.y_valid_ = np.where(Y.std(0) > 0)[0] X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] N = self.n_components if not isinstance(N, (list, np.ndarray)): N = [N, ] components = np.unique([validate_number_components(n, X, Y) for n in N]) grid = {'alpha': self.alphas, 'n_components': components} # Optimize n_components on Y prediction! if np.prod(list(map(np.shape, grid.values()))) > 1: models = GridSearchCV(RegCCA(scoring=self.scoring, multioutput=self.multioutput), grid) best = models.fit(X, Y).best_estimator_ else: best = RegCCA(alpha=grid['alpha'][0], n_components=components[0], scoring=self.scoring, multioutput=self.multioutput) best.fit(X, Y) self.n_components_ = best.n_components self.alpha_ = best.alpha self.x_mean_ = best.x_mean_ self.x_std_ = best.x_std_ self.x_rotations_ = best.x_rotations_ self.y_mean_ = best.y_mean_ self.y_std_ = best.y_std_ self.y_rotations_ = best.y_rotations_ self.E_ = np.sum(self.x_rotations_**2, 1) return self def score(self, X, Y, scoring=None, multioutput=None): X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput return canonical_correlation(self, X, Y, scoring, multioutput) def transform(self, X): return self.best.transform(X[:, self.x_valid_]) def fit_transform(self, X, Y): return self.fit(X, Y).best.transform(X) class CCA(SkCCA): """overwrite scikit-learn CCA to get scoring function in canonical space""" def __init__(self, n_components=-1, scoring='r', multioutput='uniform_average', tol=1e-15): self.scoring = scoring self.multioutput = multioutput self.__name__ = 'CCA' super().__init__(n_components=n_components, tol=tol) def fit(self, X, Y): self.x_valid_ = np.where(X.std(0) > 0)[0] self.y_valid_ = np.where(Y.std(0) > 0)[0] X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] N = self.n_components self.n_components = validate_number_components(N, X, Y) super().fit(X, Y) self.n_components_ = self.n_components self.n_components = N self.E_ = np.sum(self.x_rotations_**2, 1) return self def score(self, X, Y, scoring=None, multioutput=None): X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput return canonical_correlation(self, X, Y, scoring, multioutput) def transform(self, X): return super().transform(X[:, self.x_valid_]) def fit_transform(self, X, Y): return self.fit(X, Y).transform(X) class PLS(SkPLS): """overwrite scikit-learn PLSRegression to get scoring function in canonical space""" def __init__(self, n_components=-1, scoring='r', multioutput='uniform_average', tol=1e-15): self.scoring = scoring self.multioutput = multioutput self.__name__ = 'PLS' super().__init__(n_components=n_components, tol=tol) def fit(self, X, Y): self.x_valid_ = np.where(X.std(0) > 0)[0] self.y_valid_ = np.where(Y.std(0) > 0)[0] X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] N = self.n_components self.n_components = validate_number_components(N, X, Y) super().fit(X, Y) self.n_components_ = self.n_components self.n_components = N self.E_ = np.sum(self.x_rotations_**2, 1) return self def score(self, X, Y, scoring=None, multioutput=None): X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] scoring = self.scoring if scoring is None else scoring multioutput = self.multioutput if multioutput is None else multioutput return canonical_correlation(self, X, Y, scoring, multioutput) def transform(self, X): return super().transform(X[:, self.x_valid_]) def fit_transform(self, X, Y): return self.fit(X, Y).transform(X) class RegCCA(CCA): """Wrapper to get sklearn API for Regularized CCA """ def __init__(self, alpha=0., n_components=-1, scoring='r', multioutput='uniform_average', tol=1e-15): self.alpha = alpha self.n_components = n_components assert (n_components > 0) or (n_components == -1) self.tol = tol self.scoring = scoring self.multioutput = multioutput self.__name__ = 'RegCCA' def fit(self, X, Y): self.x_valid_ = np.where(X.std(0) > 0)[0] self.y_valid_ = np.where(Y.std(0) > 0)[0] X = X[:, self.x_valid_] Y = Y[:, self.y_valid_] # Set truncation dx, dy = X.shape[1], Y.shape[1] dz_max = min(dx, dy) dz = dz_max if self.n_components == -1 else self.n_components dz = min(dz, dz_max) self.n_components_ = dz self.x_rotations_ = np.zeros((dx, dz)) self.y_rotations_ = np.zeros((dy, dz)) # Preprocess self.x_std_ = X.std(0) self.y_std_ = Y.std(0) self.x_mean_ = X.mean(0) self.y_mean_ = Y.mean(0) X = (X - self.x_mean_) / self.x_std_ Y = (Y - self.y_mean_) / self.y_std_ # compute cca comps = self._compute_kcca([X, Y], reg=self.alpha, numCC=dz) self.x_rotations_ = comps[0] self.y_rotations_ = comps[1] self.E_ = np.sum(self.x_rotations_**2, 1) return self def _compute_kcca(self, data, reg=0., numCC=None): """Adapted from https://github.com/gallantlab/pyrcca Copyright (c) 2015, The Regents of the University of California (Regents). All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for educational, research, and not-for-profit purposes, without fee and without a signed licensing agreement, is hereby granted, provided that the above copyright notice, this paragraph and the following two paragraphs appear in all copies, modifications, and distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643-7201, for commercial licensing opportunities. Created by Natalia Bilenko, University of California, Berkeley. """ kernel = [d.T for d in data] nDs = len(kernel) nFs = [k.shape[0] for k in kernel] numCC = min([k.shape[1] for k in kernel]) if numCC is None else numCC # Get the auto- and cross-covariance matrices crosscovs = [np.dot(ki, kj.T) for ki in kernel for kj in kernel] # Allocate left-hand side (LH) and right-hand side (RH): LH = np.zeros((sum(nFs), sum(nFs))) RH = np.zeros((sum(nFs), sum(nFs))) # Fill the left and right sides of the eigenvalue problem for i in range(nDs): RH[sum(nFs[:i]): sum(nFs[:i+1]), sum(nFs[:i]): sum(nFs[:i+1])] = (crosscovs[i * (nDs + 1)] + reg * np.eye(nFs[i])) for j in range(nDs): if i != j: LH[sum(nFs[:j]): sum(nFs[:j+1]), sum(nFs[:i]): sum(nFs[:i+1])] = crosscovs[nDs * j + i] LH = (LH + LH.T) / 2. RH = (RH + RH.T) / 2. maxCC = LH.shape[0] try: r, Vs = linalg.eigh(LH, RH, eigvals=(maxCC - numCC, maxCC - 1)) except linalg.LinAlgError: # noqa r = np.zeros(numCC) Vs = np.zeros((sum(nFs), numCC)) r[np.isnan(r)] = 0 rindex = np.argsort(r)[::-1] comp = [] Vs = Vs[:, rindex] for i in range(nDs): comp.append(Vs[sum(nFs[:i]):sum(nFs[:i + 1]), :numCC]) return comp def score_knockout(model, X, Y, XY_train=None, scoring='r', fix_grid=True): assert isinstance(model, (CCA, PLS, GridCCA, GridPLS, RegCCA, GridRegCCA, B2B, Forward, Backward)) assert len(X) == len(Y) assert scoring in ('r', 'r2') is_b2b = isinstance(model, B2B) dim_x = X.shape[1] # Compute standard scores score_full = model.score(X, Y, scoring=scoring, multioutput='raw_values') score_delta = np.zeros(dim_x) # Compute knock out scores for f in range(dim_x): # Setup knockout matrix knockout = np.eye(dim_x) knockout[f] = 0 model_ = model # refit the model if XY_train is not None: X_train, Y_train = XY_train model_ = deepcopy(model) if isinstance(model, (GridPLS, GridCCA, GridRegCCA)) and fix_grid: n = model.n_components_ model_.n_components = -1 if n == X.shape[1] else n if is_b2b: model_.fit_H(X_train @ knockout, Y_train) else: model_.fit(X_train @ knockout, Y_train) # Score score_ko = model_.score(X @ knockout, Y, scoring=scoring, multioutput='raw_values') # Aggregate predicted dimensions if is_b2b: score_delta[f] = score_full[f] - score_ko[f] elif len(score_full) != len(score_ko): print('Different dims!') score_delta[f] = score_full.mean() - score_ko.mean() else: score_delta[f] = (score_full - score_ko).mean() return score_delta class Manova(): def __init__(self, statistics='F Value'): self.statistics = statistics def fit(self, X, Y): from statsmodels.multivariate.manova import MANOVA model = MANOVA(Y, X) manova_results = model.mv_test().summary_frame.reset_index() idx = manova_results.Statistic == "Wilks' lambda" self.coef_ = np.array([v for v in manova_results.loc[idx, 'F Value'].values]) class Composite(): def __init__(self, models): self.models = models def fit(self, X, Y): self.models_ = list() for y, model in zip(Y.T, self.models): self.models_.append(model.fit(X, y)) return self def predict(self, X): Y_pred = [model.predict(X) for model in self.models_] return np.transpose(Y_pred) def get_model(model_name, n_features, n_voxels): max_comp = min(n_voxels, n_features) comp_sweep = np.unique(np.floor(np.linspace(1, max_comp, 20))).astype(int) alpha_sweep = np.logspace(-4, 4, 20) models = dict( B2B=B2B(alpha_sweep), B2B_ensemble=B2B(alpha_sweep, ensemble=20), UnbiasedB2B=UnbiasedB2B(alpha_sweep, ensemble=KFold(3, shuffle=False)), # FIXME use b2b H=LinearRegregrssion UnbiasedB2B_ensemble=UnbiasedB2B(alpha_sweep, ensemble=20), # FIXME use b2b H=LinearRegregrssion Backward=Backward(alpha_sweep), Forward=Forward(alpha_sweep), CCA=GridCCA(comp_sweep), RegCCA=GridRegCCA(alpha_sweep, n_components=[max_comp]), PLS=GridPLS(comp_sweep), B2B_CCALinearSVR=B2B(alpha_sweep, ensemble=20, G=make_pipeline(CCA(4), Composite([LinearSVR() for i in range(4)]))), ) return models[model_name] # if __name__ == '__main__': # X = np.zeros((10, 100)) # Y = np.zeros((10, 200)) # assert validate_number_components(0, X, Y) == 1 # assert validate_number_components(1, X, Y) == 1 # assert validate_number_components(.5, X, Y) == 50 # def make_data(): # n = 1000 # dx = 4 # dy = 5 # X = np.random.randn(n, dx) # E = np.eye(dx) # E[2:] = 0 # N = np.random.randn(n, dx) # N2 = np.random.randn(n, dy) / 10. # F = np.random.randn(dx, dy) # Y = (X @ E + N) @ F + N2 # train, test = range(0, n, 2), range(1, n, 2) # return X, Y, train, test # X, Y, train, test = make_data() # models = (B2B, Forward, Backward, CCA, RegCCA, PLS, # GridCCA, GridPLS, GridRegCCA) # canonicals = (CCA, RegCCA, PLS, GridCCA, GridPLS, GridRegCCA) # for scoring in ('r', 'r2'): # for mo in ('uniform_average', 'variance_weighted'): # params = dict(scoring=scoring, multioutput=mo) # for model in models: # if model in (B2B, Backward): # model = model(scoring=scoring) # else: # model = model(scoring=scoring, multioutput=mo) # model.fit(X[train], Y[train]) # assert len(model.E_) == X.shape[1] # score = model.score(X[test], Y[test], multioutput='raw_values') # if isinstance(model, canonicals): # assert len(score) == model.n_components_ # elif isinstance(model, (B2B, Backward)): # assert len(score) == X.shape[1] # elif isinstance(model, Forward): # assert len(score) == Y.shape[1] # for fix_grid in (False, True): # # assert np.mean(score) > .3 # score_delta = score_knockout(model, X[test], Y[test], # scoring=scoring, # fix_grid=fix_grid) # assert len(score_delta) == X.shape[1] # print(model.__name__, score_delta) # score_delta = score_knockout(model, X[test], Y[test], # (X[train], Y[train]), # scoring=scoring, # fix_grid=fix_grid) # assert len(score_delta) == X.shape[1] # print(model.__name__, score_delta) # for model in canonicals: # for n_components in (-1, 1, 2): # if model in (CCA, RegCCA, PLS): # m = model(n_components) # else: # m = model([n_components, ]) # m.fit(X[train], Y[train]) # assert len(m.E_) == X.shape[1] # score = m.score(X[test], Y[test], multioutput='raw_values') # assert len(score) == m.n_components_ # print(model.__name__, score_delta) # + import os import pathlib import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy.stats import pearsonr, norm, wilcoxon from sklearn.model_selection import cross_val_predict, cross_val_score from sklearn.linear_model import LinearRegression, RidgeCV from sklearn.model_selection import KFold, ShuffleSplit from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn import neighbors from sklearn.metrics import make_scorer import nibabel as nib import nilearn from nilearn import surface from nilearn import plotting from nilearn.decoding.searchlight import search_light from nistats.design_matrix import make_first_level_design_matrix as make_design from nistats.first_level_model import run_glm, FirstLevelModel from nistats.contrasts import compute_contrast from nistats.hemodynamic_models import compute_regressor from wordfreq import zipf_frequency import spacy # - # # Paths # + deriv_path = pathlib.Path('/private/home/jeanremi/project/mous_fmri/fmriprep/') data_path = pathlib.Path('/private/home/jeanremi/data/mous/raw/') func_suffix = '_task-visual_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' func_fname = str(deriv_path / '%s' / 'func'/ ('%s' + func_suffix)) confounds_suffix = '_task-visual_desc-confounds_regressors.tsv' confounds_fname = str(deriv_path / '%s' / 'func'/ ('%s' + confounds_suffix)) events_fname = str(data_path / '%s' / 'func' / ('%s' + '_task-visual_events.tsv')) # - def read_events(event_fname): # Read MRI events events = pd.read_csv(event_fname, sep='\t') # Add context: sentence or word list? contexts = dict(WOORDEN='word_list', ZINNEN='sentence') for key, value in contexts.items(): sel = events.value.str.contains(key) events.loc[sel, 'context'] = value events.loc[sel, 'condition'] = value # Clean up MRI event mess sel = ~events.context.isna() start = 0 context = 'init' for idx, row in events.loc[sel].iterrows(): events.loc[start:idx, 'context'] = context start = idx context = row.context events.loc[start:, 'context'] = context # Add event condition: word, blank, inter stimulus interval etc conditions = (('50', 'pulse'), ('blank', 'blank'), ('ISI', 'isi')) for key, value in conditions: sel = events.value == key events.loc[sel, 'condition'] = value events.loc[events.value.str.contains('FIX '), 'condition'] = 'fix' # Extract words from file sel = events.condition.isna() words = events.loc[sel, 'value'].apply(lambda s: s.strip('0123456789 ')) events.loc[sel, 'word'] = words # Remove empty words sel = (events.word.astype(str).apply(len) == 0) & (events.condition.isna()) events.loc[sel, 'word'] = pd.np.nan events.loc[sel, 'condition'] = 'blank' events.loc[~events.word.isna(), 'condition'] = 'word' # Define sequence events.loc[events.word=='QUESTION', 'condition'] = 'question' events.loc[events.word=='QUESTION', 'word'] = np.nan words = events.query('condition=="word"') events['sequence'] = np.cumsum(events.condition=='fix') for s, words in events.query('condition=="word"').groupby('sequence'): events.loc[words.index, 'word_position'] = range(len(words)) # Fix bids events['trial_type'] = events['type'] return events # # GLM: one Subject # + # subject = 'sub-V1002' # img = nib.load(func_fname % (subject, subject)) # events = read_events(events_fname % (subject, subject)) # confounds = pd.read_csv(confounds_fname % (subject, subject), sep='\t', index_col=None) # confounds = confounds.fillna(method='ffill').fillna(method='bfill') # model = FirstLevelModel(t_r=2., n_jobs=12) # model.fit(img, events, confounds) # p001_unc = norm.isf(0.001) # zmap = model.compute_contrast('Picture-Response') # plotting.plot_glass_brain(zmap, # colorbar=False, # threshold=p001_unc, # plot_abs=False) # - # # Across subjects # + from nilearn.decoding.searchlight import GroupIterator from joblib import Parallel, delayed import warnings def searchlight_multiscore(X, Y, estimator, A, cv=3, scorer=None, groups=None, n_jobs=1, verbose=1, direction='decod'): """Same as nilearn but acces multidimensional scoring scorer is a callable""" group_iter = GroupIterator(A.shape[0], n_jobs) parallel = Parallel(n_jobs=n_jobs, verbose=verbose) iter_func = delayed(_group_iter_searchlight) scores = parallel(iter_func(A.rows[idx], estimator, X, Y, groups, scorer, cv, direction) for idx in group_iter) return np.concatenate(scores, axis=0) def _group_iter_searchlight(A, estimator, X, Y, groups, scorer, cv, direction): if isinstance(cv, int): cv = KFold(cv) all_scores = list() for row in A: if direction == 'decod': x, y = X[:, row], Y else: x, y = X, Y[:, row] if x.std(0).sum()==0 or y.std(0).sum()==0: all_scores.append(None) continue try: scores = list() for train, test in cv.split(x): estimator.fit(x[train], y[train]) if scorer == 'knockout': score = score_knockout(estimator, x[test], y[test]) elif isinstance(scorer, str): score = getattr(estimator, scorer) elif scorer is not None: y_pred = estimator.predict(x[test]) score = scorer(y[test], y_pred) else: score = estimator.score(x[test], y[test]) scores.append(score) except: all_scores.append(None) continue all_scores.append(np.mean(scores, 0)) # deal with missing data shapes = [np.shape(s) for s in all_scores if s is not None] assert len(set(shapes)) == 1 for i, scores in enumerate(all_scores): if scores is None: all_scores[i] = np.zeros(shapes[0]) * np.nan return np.array(all_scores) # + def correlate(X, Y): if X.ndim == 1: X = X[:, None] if Y.ndim == 1: Y = Y[:, None] X = X - X.mean(0) Y = Y - Y.mean(0) SX2 = (X**2).sum(0)**.5 SY2 = (Y**2).sum(0)**.5 SXY = (X * Y).sum(0) return SXY / (SX2 * SY2) def scale(X): shape = X.shape if len(shape) == 1: X = X[:, None] X = X - np.nanmean(X, 0, keepdims=True) std = np.nanstd(X, 0, keepdims=True) non_zero = np.where(std>0) X[:, non_zero] /= std[non_zero] X[np.isnan(X)] = 0 X[~np.isfinite(X)] = 0 return X.reshape(shape) def convolve_events(events, frame_times, hrf_model='glover'): # Define potential causal factors events = events.query('condition=="word"') word_freq = events.word.apply(lambda w: zipf_frequency(w, 'nl')) word_length = events.word.apply(len) nlp = spacy.load("nl_core_news_sm") pos = events.word.apply(lambda w: nlp(w)[0].pos_ in ('VERB', 'ADJ', 'NOUN', 'ADV')) regressors = dict( #word=np.ones(len(events)), word_length=scale(word_length.values), word_freq=scale(word_freq.values), word_function=scale(pos.values), dummy=scale(word_length.values)+scale(word_freq.values)+np.random.randn(len(events)), ) # Convolve then with standard HRF reg_signals = list() reg_names = list() for name, values in regressors.items(): signal, name_ = compute_regressor( np.c_[events.onset, np.ones(len(events)), values].T, hrf_model=hrf_model, frame_times=frame_times, oversampling=16) reg_signals.append(signal) reg_names.extend([name + n.split('cond')[1] for n in name_]) reg_signals = np.concatenate(reg_signals, 1) return reg_signals, reg_names # - def main(subject, remove_confounds=False): # Read Bold files = [f for f in os.listdir(deriv_path / subject / 'func') if f.endswith('-preproc_bold.nii.gz') and '_task-visual_space-MNI152' in f] assert len(files) img = nib.load(str(deriv_path / subject / 'func' / files[0])) # Read events and convolve with HRF response tr = 2. frame_times = np.arange(img.shape[-1]) * tr events = read_events(events_fname % (subject, subject)) reg_signals, reg_names = convolve_events(events, frame_times, hrf_model='glover') # Prepare linear modeling estimator = make_pipeline(StandardScaler(), RidgeCV(np.logspace(-2, 8, 20))) cv = KFold(5, shuffle=False) # Loop across hemisphere fsaverage = nilearn.datasets.fetch_surf_fsaverage() out = dict() for hemi in ('left', ): # 'right' # Volume to surface mesh = fsaverage['pial_%s' % hemi] radius = 8. bold = surface.vol_to_surf(img, mesh, radius=radius).T bold = scale(bold) mesh = fsaverage['infl_%s' % hemi] coords, _ = surface.load_surf_mesh(mesh) radius = 8. nn = neighbors.NearestNeighbors(radius=radius) A = nn.fit(coords).radius_neighbors_graph(coords).tolil() nn = neighbors.NearestNeighbors(radius=2.) A_small = nn.fit(coords).radius_neighbors_graph(coords).tolil() # Remove confounds variable from bold signal if remove_confounds: keys = ['csf', 'white_matter'] keys.extend(['trans_' + x for x in 'xyz']) keys.extend(['rot_' + x for x in 'xyz']) keys.extend(['trans_%s_derivative1' % x for x in 'xyz']) keys.extend(['rot_%s_derivative1' % x for x in 'xyz']) confounds = pd.read_csv(confounds_fname % (subject, subject), sep='\t', index_col=None) confounds = confounds[keys].fillna(method='ffill').fillna(method='bfill') confounds = scale(confounds.values) bold_pred = cross_val_predict(estimator, X=confounds, y=bold, cv=cv, n_jobs=-1) bold = scale(bold - bold_pred) # Get manova small scores = searchlight_multiscore(X=scale(reg_signals), Y=bold, estimator=Manova(), A=A_small, cv=cv, scorer='coef_', direction='encod', n_jobs=-1) for score, name in zip(scores.T, reg_names): out['_'.join(('Manova_small', hemi, name))] = score # Get manova scores = searchlight_multiscore(X=scale(reg_signals), Y=bold, estimator=Manova(), A=A, cv=cv, scorer='coef_', direction='encod', n_jobs=-1) for score, name in zip(scores.T, reg_names): out['_'.join(('Manova', hemi, name))] = score # Get encoding coefficients betas = LinearRegression().fit(X=reg_signals, y=bold).coef_ for beta, name in zip(betas.T, reg_names): out['betas_%s_%s' % (hemi, name)] = beta # Get Encoding scores for idx, name in enumerate(reg_names): scores = list() for train, test in cv.split(bold): estimator.fit(reg_signals[train, idx][:, None], bold[train]) bold_pred = estimator.predict(reg_signals[test, idx][:, None]) r = correlate(bold[test], bold_pred) scores.append(r) out['encod_%s_%s' % (hemi, name)] = np.mean(scores, 0) # Get decoding scores scores = searchlight_multiscore(X=bold, Y=scale(reg_signals), estimator=estimator, A=A, cv=cv, scorer=correlate, n_jobs=-1) for score, name in zip(scores.T, reg_names): out['decod_%s_%s' % (hemi, name)] = score # B2b unbiased E_hat model = get_model('UnbiasedB2B', reg_signals.shape[1], A.sum(1).min()) model.ensemble = cv scores = searchlight_multiscore(X=scale(reg_signals), Y=bold, estimator=model, A=A, cv=cv, scorer='E_', direction='encod', n_jobs=-1) for score, name in zip(scores.T, reg_names): out['_'.join(('UnbiasedB2B_betas', hemi, name))] = score # paper models models = ('Forward', 'Backward', 'PLS', 'RegCCA', #'CCA', 'B2B', 'B2B_ensemble',#'UnbiasedB2B', 'B2B_CCALinearSVR', ) for model_name in models: print(model_name) model = get_model(model_name, reg_signals.shape[1], A.sum(1).min()) scores = searchlight_multiscore(X=scale(reg_signals), Y=bold, estimator=model, A=A, cv=cv, scorer='knockout', direction='encod', n_jobs=-1) for score, name in zip(scores.T, reg_names): out['_'.join((model_name, hemi, name, 'knockout'))] = score if model_name not in ('Forward', 'Backward'): continue scores = searchlight_multiscore(X=scale(reg_signals), Y=bold, estimator=model, A=A, cv=cv, scorer=None, direction='encod', n_jobs=-1) # FIXME: for all but B2B, there is only one full score if scores.ndim == 1: scores = np.concatenate([scores[:, None]] * reg_signals.shape[1], axis=1) for score, name in zip(scores.T, reg_names): out['_'.join((model_name, hemi, name))] = score return out # + # subject = 'sub-V1001' # out = mini(subject, False) # + # fsaverage = nilearn.datasets.fetch_surf_fsaverage() # hemi = 'left' # var = 'word_freq' # plotting.plot_surf_stat_map( # stat_map=out['_'.join(('encod', hemi, var))], # surf_mesh=fsaverage['infl_%s' % hemi], # hemi=hemi, # cmap=cmap, # ); # + from submitit import AutoExecutor subjects = sorted([f.split('.html')[0] for f in os.listdir('fmriprep') if f[:3] == 'sub' and f.endswith('.html')]) subjects = [s for s in subjects if 'sub-V' in s] executor = AutoExecutor('b2b_results/') executor.update_parameters(timeout_min=60*10, slurm_partition='learnfair,scavenge,uninterrupted', slurm_constraint='pascal', mem_gb=128, slurm_cpus_per_task=10, gpus_per_node=1) jobs = executor.map_array(main, subjects) # - results = pd.DataFrame([j.results()[0] for j in jobs if j.state=='COMPLETED']) # + def add_colorbar(figure, axes, vmax, cmap, threshold=None): from matplotlib.colors import Normalize, LinearSegmentedColormap from matplotlib.cm import ScalarMappable from matplotlib.colorbar import make_axes vmin = -vmax our_cmap = plt.get_cmap(cmap) norm = Normalize(vmin=vmin, vmax=vmax) nb_ticks = 5 ticks = np.linspace(vmin, vmax, nb_ticks) bounds = np.linspace(vmin, vmax, our_cmap.N) if threshold is not None: cmaplist = [our_cmap(i) for i in range(our_cmap.N)] # set colors to grey for absolute values < threshold istart = int(norm(-threshold, clip=True) * (our_cmap.N - 1)) istop = int(norm(threshold, clip=True) * (our_cmap.N - 1)) for i in range(istart, istop): cmaplist[i] = (0.5, 0.5, 0.5, 1.) our_cmap = LinearSegmentedColormap.from_list( 'Custom cmap', cmaplist, our_cmap.N) # we need to create a proxy mappable proxy_mappable = ScalarMappable(cmap=our_cmap, norm=norm) # proxy_mappable.set_array(surf_map_faces) cax, kw = make_axes(axes, location='right', fraction=.1, shrink=.6, pad=.0) cbar = figure.colorbar( proxy_mappable, cax=cax, ticks=ticks, boundaries=bounds, spacing='proportional', format='%.2g', orientation='vertical') # + def plot(data, hemi='left', threshold=None, cmap='cold_hot', **kwargs): fig, ax = plt.subplots(1, figsize=[4.5, 5], subplot_kw={'projection': '3d'}, dpi=150) coords, verts = surface.load_surf_mesh(fsaverage['infl_%s' % hemi]) coords2 = coords[:, [2, 1, 0]] coords2[:, 2] *= -1 coords2[:, 2] -= 120 xlim, ylim, zlim = zip(np.r_[coords, coords2].min(0), np.r_[coords, coords2].max(0)) opt = dict(threshold=threshold, view='lateral', cmap=cmap, colorbar=False, bg_map=fsaverage['sulc_%s' % hemi]) for k, v in kwargs.items(): opt[k] = v for view in (coords2, coords): plotting.plot_surf_stat_map(stat_map=data, surf_mesh=(view, verts), axes=ax, **opt) ax.set_xlim(*xlim) ax.set_ylim(ylim[0]*1.1, ylim[1]*1.1) ax.set_zlim(*zlim) # desaturate cortex from matplotlib.collections import PolyCollection for poly in ax.get_children(): from mpl_toolkits.mplot3d.art3d import Poly3DCollection if not isinstance(poly, Poly3DCollection): continue facecolors = poly._facecolors[:, :3] idx = np.where(np.apply_along_axis(lambda r: len(set(r)), 1, facecolors)==1)[0] poly._facecolors[idx, :3] /= 3. poly._facecolors[idx, :3] += .2 # add colorbar vmax = np.nanmax(data) if 'vmax' not in kwargs.keys() else kwargs['vmax'] add_colorbar(fig, ax, vmax, cmap=cmap, threshold=threshold) return fig def fig_to_img(fig): from matplotlib.backends.backend_agg import FigureCanvasAgg canvas = FigureCanvasAgg(fig) canvas.draw() width, height = fig.get_size_inches() * fig.get_dpi() img = np.fromstring(canvas.tostring_rgb(), dtype='uint8') return img.reshape(int(height), int(width), 3) def crop(img): white = np.mean(img, 2) != 255 first_row = np.where(white.sum(1))[0][0] - 10 last_row = np.where(white[::-1].sum(1))[0][0] - 10 first_column = np.where(white.sum(0))[0][0] - 10 return img[first_row:-last_row][:, first_column:] # - from matplotlib.colors import LinearSegmentedColormap import colorcet black = 10 colors = np.r_[colorcet.cm.fire(np.linspace(.95, 0, 128-black//2))[:, [2, 1, 0, 3]], np.c_[np.zeros((black, 3)), np.ones((black, 1))], colorcet.cm.fire(np.linspace(0, .95, 128-black//2))] luminosity = (1 - np.clip(np.linspace(-5, 5, len(colors))**2, 0, 1)) ** 2 colors[:, :3] = np.clip(colors[:, :3] + luminosity[:, None]/2., 0, 1) cmap = LinearSegmentedColormap.from_list('ice_fire', colors) # plt.matshow(np.random.randn(100, 100), cmap=cmap, vmin=-4, vmax=4) # plt.colorbar() fsaverage = nilearn.datasets.fetch_surf_fsaverage() # + features = ('word_length', 'word_freq', 'word_function', 'dummy') analyses = ['Forward', 'PLS', 'RegCCA', 'B2B'] analyses += ['B2B_CCALinearSVR',]# 'B2B_CCASVR'] imgs = dict() for feature in features: print(feature) for analysis in analyses: hemi = 'left' key = '_'.join((analysis, hemi, feature, 'knockout')) X = np.array([d for d in results[key].values]) X = np.nan_to_num(X) # p-values valid = np.nanstd(X, 0)>0 p = np.ones(X.shape[1]) _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) # only display r gain mean = np.nanmean(X, 0) values = mean * ((mean>0) * (p < .01)) fig = plot(values, threshold=.0001, cmap=cmap, vmax=.05 if feature != 'word' else 1.) imgs[key] = crop(fig_to_img(fig)) fig = plot(-np.log10(p), threshold=2, vmax=15, cmap=cmap) imgs[key + '_pval'] = crop(fig_to_img(fig)) # - features = ('word_length', 'word_freq', 'word_function', 'dummy') hemi = 'left' for pval in [False, True]: fig = plt.figure(figsize=[7, 9], constrained_layout=False) gs = fig.add_gridspec(ncols=len(features)*2+1, nrows=len(analyses), wspace=0) axes = [[gs[r, (c*2):(c+1)*2] for c in range(len(features))] for r in range(len(analyses))] for analysis, axs in zip(analyses, axes): for feature, ax in zip(features, axs): ax = fig.add_subplot(ax) key = '_'.join((analysis, hemi, feature, 'knockout')) if pval: key += '_pval' img = imgs[key] colorbar = img[:, int(img.shape[1]*3.7/5):, :] img = img[:, :int(img.shape[1]*3.1/5), :] ax.imshow(img) for s in ('top', 'right', 'bottom', 'left'): ax.spines[s].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) if feature == features[0]: ylabel = analysis if ylabel == 'B2B_CCALinearSVR': ylabel = '$B2B_{SVM}$' ax.set_ylabel(ylabel) if analysis == analyses[0]: ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')])) ax = fig.add_subplot(gs[:, -1]) ax.imshow(colorbar) for s in ('top', 'right', 'bottom', 'left'): ax.spines[s].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) if pval: ax.set_ylabel('$-log_{10}(p)$', labelpad=-10) else: ax.set_ylabel('$\Delta R$', labelpad=-10) ax.yaxis.set_label_position("right") if not pval: fig.savefig('fmri_delta_r.pdf', dpi=150, facecolor='white') fig.savefig('fmri_delta_r.png', dpi=150, facecolor='white') else: fig.savefig('fmri_pvals.pdf', dpi=150, facecolor='white') fig.savefig('fmri_pvals.png', dpi=150, facecolor='white') # # ROI rois = dict() for hemi in ('left',): #'right' analysis = 'Forward' key = '_'.join((analysis, hemi, feature)) X = np.array([d for d in results[key].values]) X = np.nan_to_num(X) # p-values valid = np.nanstd(X, 0)>0 p = np.ones(X.shape[1]) _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) rois[hemi] = p<.001 # plot fig = plot(np.nanmean(X, 0) * (p<.001), threshold=0.001, vmax=.04, cmap=cmap, hemi=hemi) roi_img = crop(fig_to_img(fig)) features = ('word_length', 'word_freq', 'word_function', 'dummy') models = ('Forward', 'PLS', 'RegCCA', 'B2B', 'B2B_CCALinearSVR') summary = list() for feature in features: for model in models: scores = list() for hemi in ('left', ): key = '_'.join((model, hemi, feature, 'knockout')) scores.append([s[rois[hemi]] for s in results[key].values]) scores = np.nanmean(np.concatenate(scores, axis=1), axis=1) for i, score in enumerate(scores): summary.append(dict(subject=i, r=score, model=model, feature=feature)) summary = pd.DataFrame(summary) # + fig, axes = plt.subplots(1, len(features)+1, #sharex=True, sharey=True, figsize=[7, 2], facecolor='white') axes[0].set_visible(False) ax = fig.add_subplot(256) ax.text(0, 2, 'Fwd', color='C0', horizontalalignment='center', fontsize=12) ax.text(1, 2, 'PLS', color='C1', horizontalalignment='center', fontsize=12) ax.text(0, 0, 'CCA', color='C2', horizontalalignment='center', fontsize=12) ax.text(1, 0, 'B2B', color='C3', horizontalalignment='center', fontsize=12) ax.text(.5, -2, '$B2B_{SVM}$', color='C4', horizontalalignment='center', fontsize=12) ax.set_ylim(-2.1, 4) ax.set_xlim(-1, 2) ax.axis('off') ax = fig.add_subplot(251) ax.imshow(roi_img[9:249, 10:304]) ax.axis('off') ax.set_title('ROI') for feature, ax in zip(features, axes[1:]): d = summary.query('feature==@feature') sns.stripplot(x='model', y='r', data=d, jitter=.3, s=2, ax=ax) # legend clean up ax.axhline(0,color='k', ls=':') ax.legend().set_visible(False) ylim = .1 ax.set_ylim(-.03, ylim) yticks = np.around(np.arange(-.03, .101, .01), 2) ax.set_yticks(yticks) if ax == axes[1]: ax.set_ylabel('ΔR', labelpad=-10).set_rotation(0) ax.set_yticklabels(np.around(yticks, 2)) ax.set_yticklabels(['%.2f' % f if f in (0., .1) else '' for f in yticks]) else: ax.set_ylabel('') ax.set_yticklabels([]) ax.set_xticks([]) ax.set_xlabel('') ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')])) ax.set_xlabel('Models') ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['bottom'].set_visible(False) # statistical comparison if feature == 'dummy': pass space = np.ptp(ax.get_ylim()) / 30 k = 0 for idx, m1 in enumerate(models): r = list() _, p = wilcoxon(d.query('model == @m1').r.values) # print('%s: %s: %.4f' % (feature, m1, p)) for jdx, m2 in enumerate(models): if m1 == m2: continue if m2 != 'B2B': # _CCALinearSVR continue r = list() d.query('model==@m1').r.values for _, subject in d.query('model in [@m1, @m2]').groupby('subject'): if len(subject)==2: r.append([subject.query('model==@m1').r.values[0], subject.query('model==@m2').r.values[0]]) r1, r2 = np.transpose(r) u, p = wilcoxon(r1, r2) print('%s: %s versus %s: %.4f' % (feature, m1, m2, p)) if p < .05: # and 'B2B_CCALinearSVR' == m2: k += 1 y = .8 * ylim + k * space if np.median(r1) > np.median(r2): color = 'C%i' % idx else: color = 'C%i' % jdx ax.plot(np.linspace(0, 4, 5)[[idx, jdx]], [y, y], color=color, lw=1.) if k: ax.text(1.5, .085, '*', color='C3', fontsize=20, horizontalalignment='center') ax.set_ylim(-.03, .101) fig.tight_layout(h_pad=0, w_pad=-1) fig.savefig('fmri_strip.pdf', dpi=150, facecolor='white') fig.savefig('fmri_strip.png', dpi=150, facecolor='white') # - # # Controls betas manova decod etc # # manova # + # results_manova = pd.DataFrame([j.results()[0] for j in jobs_manova if j.state=='COMPLETED']) # results_manova = pd.DataFrame([j.results()[0] for j in jobs_manova_small if j.state=='COMPLETED']) # + # analysis = 'Manova_small' # hemi = 'left' # key = '_'.join((analysis, hemi, 'dummy')) # for feature in ('word_length', 'word_freq', 'word_function', 'dummy'): # key = '_'.join((analysis, hemi, feature)) # X = np.array([d for d in results[key].values]) # X = np.nan_to_num(X) # # p-values # valid = np.nanstd(X, 0)>0 # p = np.ones(X.shape[1]) # _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) # # plot # fig = plot(-np.log10(p), threshold=3, vmax=13, # cmap=cmap, hemi=hemi) # imgs[key] = crop(fig_to_img(fig)) # - features = ['word_length', 'word_freq', 'word_function', 'dummy'] for feature in features: print(feature) for analysis in ('decod', 'encod', 'betas', 'Manova', 'UnbiasedB2B_betas'): hemi = 'left' key = '_'.join((analysis, hemi, feature)) X = np.array([d for d in results[key].values]) X = np.nan_to_num(X) vmax = .12 if analysis in ('decod', 'encod', 'Forward', 'Backward'): fig = plot(np.clip(np.nanmean(X, 0), -vmax, vmax), threshold=0.008, vmax=vmax, cmap=cmap) imgs[key] = crop(fig_to_img(fig)) # p-values valid = np.nanstd(X, 0)>0 p = np.ones(X.shape[1]) _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) fig = plot(np.clip(-np.log10(p), 0, 13), threshold=3, vmax=13, cmap=cmap) imgs[key + '_pval'] = crop(fig_to_img(fig)) # + analysis = 'Manova' hemi = 'left' key = '_'.join((analysis, hemi, 'dummy')) dummy = np.array([d for d in results[key].values]) for feature in ('word_length', 'word_freq', 'word_function'): key = '_'.join((analysis, hemi, feature)) X = np.array([d for d in results[key].values]) X = np.nan_to_num(X - dummy) # p-values valid = np.nanstd(X, 0)>0 p = np.ones(X.shape[1]) _, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) # plot fig = plot(np.clip(-np.log10(p), 0, 13), threshold=3, vmax=13, cmap=cmap) imgs['dummy_vs_' + key + '_pval'] = crop(fig_to_img(fig)) # + analyses = ('decod', 'betas', 'UnbiasedB2B_betas', 'Manova', 'dummy_vs_Manova') analysis_names = dict( Backward='Decode\nR', decod='Decode\nR', encod='Encod 1 Feature\nR', betas='Forward\n$\hat H$', Manova='Manova\n$F$', UnbiasedB2B_betas='Unbiased B2B\n$\hat S$', #B2B='B2B\n$\Delta R$', dummy_vs_Manova='Manova\n$F - F_{dummy}$', ) features = ['word_length', 'word_freq', 'word_function', 'dummy'] fig = plt.figure(figsize=[8, 12], constrained_layout=False) gs = fig.add_gridspec(ncols=2*len(features)+1, nrows=len(analyses), wspace=0) axes = iter([[gs[r, (c*2):(c+1)*2] for c in range(len(features))] for r in range(len(analyses))]) c_axes = [fig.add_subplot(gs[:1, -1]), fig.add_subplot(gs[2:4, -1])] group_analyses = ( ('decod', ), ('betas', 'UnbiasedB2B_betas', 'Manova', 'dummy_vs_Manova'), ) for group, analyses_ in enumerate(group_analyses): for analysis in analyses_: axs = next(axes) for feature, ax in zip(features, axs): if analysis == 'dummy_vs_Manova' and feature == 'dummy': continue ax = fig.add_subplot(ax) key = '_'.join((analysis, hemi, feature)) if analysis == 'B2B': key += '_knockout' if group == 1: key += '_pval' cmap_label = '$-log_{10}(p)$' else: cmap_label = '$R$' img = imgs[key] colorbar = img[:, int(img.shape[1]*3.7/5):, :] img = img[:, :int(img.shape[1]*3.1/5), :] ax.imshow(img) for s in ('top', 'right', 'bottom', 'left'): ax.spines[s].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) if feature == features[0]: ax.set_ylabel(analysis_names[analysis]) if analysis == analyses_[0]: ax.set_title(' '.join([w[0].upper() + w[1:] for w in feature.split('_')])) ax = c_axes[group] ax.imshow(colorbar) for s in ('top', 'right', 'bottom', 'left'): ax.spines[s].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) ax.set_ylabel(cmap_label, labelpad=-10) ax.yaxis.set_label_position("right") fig.tight_layout(w_pad=0, h_pad=.01) fig.savefig('fmri_controls.pdf', dpi=150, facecolor='white') fig.savefig('fmri_controls.png', dpi=150, facecolor='white') # - # # all vertices # + features = ['word_length', 'word_freq', 'dummy'] analyses = ['Forward', 'PLS', 'RegCCA', 'CCA', 'B2B'] summary = list() for feature in features: for analysis in analyses: scores = list() for hemi in ('left', 'right'): key = '_'.join((analysis, hemi, feature, 'knockout')) scores.append(np.array([d for d in results[key].values])) scores = np.concatenate(scores, axis=1) scores = np.nanmean(scores, axis=1) for subject, s in enumerate(scores): summary.append(dict(feature=feature, analysis=analysis, score=s, subject=subject)) summary = pd.DataFrame(summary) # - summary = list() for feature in features: for analysis in analyses: scores = list() for hemi in ('left', 'right'): key = '_'.join((analysis, hemi, feature, 'knockout')) scores.append(np.array([d for d in results[key].values])) scores = np.concatenate(scores, axis=1) scores = np.nanmean(scores, axis=0) for vertex, s in enumerate(scores): summary.append(dict(feature=feature, analysis=analysis, score=s, vertex=vertex)) summary = pd.DataFrame(summary) summary = list() for feature in features: print(feature) for analysis in analyses: scores = list() for hemi in ('left', 'right'): key = '_'.join((analysis, hemi, feature, 'knockout')) scores.append(np.array([d for d in results[key].values])) scores = np.concatenate(scores, axis=1) p_vals = np.ones(scores.shape[1]) for idx, s in enumerate(scores.T): valid = ~np.isnan(s) if not sum(valid): continue _, p_vals[idx] = wilcoxon(s[valid]) for vertex, p in enumerate(p_vals): summary.append(dict(feature=feature, analysis=analysis, p=p, vertex=vertex)) summary = pd.DataFrame(summary) import seaborn as sns # + fig, axes = plt.subplots(len(features), 3, sharey=True, sharex=True, figsize=[6, 6]) for feature, axs in zip(features, axes): for c, (analysis, ax) in enumerate(zip(('Forward', 'PLS', 'CCA'), axs)): y = -np.log10(summary.query('analysis=="B2B" and feature==@feature').p) x = -np.log10(summary.query('analysis==@analysis and feature==@feature').p) ax.scatter(x, y, s=.05, color='C%i' % c) ax.plot([0, 15], [0, 15], 'k', lw=.5) ax.set_xlim([0, 15]) ax.set_ylim([0, 15]) ax.set_aspect('equal') if feature == features[0]: ax.set_title(analysis) elif feature == features[-1]: ax.set_xlabel('$\Delta$ R') if c == 0: ax.set_ylabel('B2B $\Delta$ R') fig.tight_layout() # + fig, axes = plt.subplots(len(features), 3, sharey=True, sharex=True, figsize=[6, 6]) for feature, axs in zip(features, axes): for c, (analysis, ax) in enumerate(zip(('Forward', 'PLS', 'CCA'), axs)): y = summary.query('analysis=="B2B" and feature==@feature').score x = summary.query('analysis==@analysis and feature==@feature').score ax.scatter(x, y, s=.05, color='C%i' % c) ax.plot([-1, 1], [-1, 1], 'k', lw=.5) ax.set_xlim([-.02, .12]) ax.axhline(0, color='k', lw=.5) ax.axvline(0, color='k', lw=.5) ax.set_ylim([-.02, .12]) ax.axhline(0, color='k', lw=.5) ax.axvline(0, color='k', lw=.5) ax.set_aspect('equal') if feature == features[0]: ax.set_title(analysis) elif feature == features[-1]: ax.set_xlabel('$\Delta$ R') if c == 0: ax.set_ylabel('B2B $\Delta$ R') fig.tight_layout() # - # # Old Averages # + def plot(left, right, views=3, threshold=None, **kwargs): if views == 3: fig, axes = plt.subplots(1, 3, subplot_kw={'projection': '3d'}, figsize=[11, 3]) axes = dict(left=axes[:2], right=axes[[2, 1]]) else: fig, axes = plt.subplots(1, 2, subplot_kw={'projection': '3d'}, figsize=[7, 3]) axes = dict(left=axes[:2], right=[None, axes[1]]) for hemi, data in dict(left=left, right=right).items(): coords, verts = surface.load_surf_mesh(fsaverage['infl_%s' % hemi]) side = -1 if hemi == 'left' else 1 coords[:, 0] += side * coords[:, 0].max() opt = dict(threshold=threshold, colorbar=False, surf_mesh=(coords, verts), bg_map=fsaverage['sulc_%s' % hemi]) for k, v in kwargs.items(): opt[k] = v if axes[hemi] is not None: plotting.plot_surf_stat_map(stat_map=data, hemi=hemi, axes=axes[hemi][0], **opt) axes[hemi][0].set_xlim(-83, 83) axes[hemi][0].set_ylim(-108, 108) axes[hemi][0].set_zlim(-73, 73) plotting.plot_surf_stat_map(stat_map=data, view='posterior', axes=axes[hemi][1], **opt) axes[hemi][1].set_xlim(-83, 83) axes[hemi][1].set_ylim(-108, 108) axes[hemi][1].set_zlim(-73, 73) fig.tight_layout(w_pad=0) return fig def get_pvals(analysis, key, hemis=('left', 'right')): p_vals = list() for hemi in hemis: X = np.array([d for d in results['_'.join((analysis, hemi, key))].values]) valid = X.std(0)>0 p = np.ones(X.shape[1]) r, p[valid] = np.transpose(list(map(wilcoxon, X.T[valid]))) p_vals.append(-np.log10(p)) return p_vals # - # # check fmriprep SLURM # ls /private/home/jeanremi/data/mous/freesurfer -halt # + import os import pandas as pd failed = [f for f in os.listdir('jobs') if f.endswith('.err')] df = list() for job in failed: with open('jobs/' + job) as f: err = '\n'.join(f.readlines()) if 'fMRIPrep finished without errors' in err: success = True err_type = '' else: success = False if 'DUE TO TIME LIMIT' in err: err_type = 'time' elif err == '': err_type = 'unknown' elif 'fmriprep: error: argument' in err: err_type = 'not subject' with open('jobs/' + job.replace('.err', '.out')) as f: out = '\n'.join(f.readlines()) try: start = out.split('200509-')[1].split(',')[0] stop = out.split('200509-')[-2].split(',')[0] except IndexError: start, stop = None, None task_subject = out.split('task ')[1].split('\n')[0] try: task, subject = task_subject.split() except ValueError: task = task_subject subject = None df.append(dict(job=job, success=success, err_type=err_type, task=task, subject=subject, start=start, stop=stop)) df = pd.DataFrame(df) df # - print(df.query('not success')) print(len(df.query('success')))
72,285
/Mod_2/hypothesis_testing/hypothesis_testing.ipynb
0381aac2b98263fcdae16110f3127c52a4652378
[ "MIT" ]
permissive
chibz3/nyc-mhtn-ds-051120-lectures
https://github.com/chibz3/nyc-mhtn-ds-051120-lectures
2
0
null
2020-05-11T20:07:17
2020-05-11T19:59:54
null
Jupyter Notebook
false
false
.py
41,123
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] slideshow={"slide_type": "slide"} # # Hypothesis testing # - # ## Intuition for Hypothesis Testing Example # # Cristian has recently claimed that his lucky quarter is actually distinctly different than every other kind of quarter. Due to the unique weight distribution from the quarter's design there is actually a greater chance for the quarter to land tails than other fair coins. # # Do we believe him? # # I sure don't. But lets be good data scientists and put this claim to the test. # # Let's flip the coin once and if it comes up tails then I'll change my mind. # # Would you change your mind? # # How many tails would I have to flip in order to convince you that this coin actual isn't fair? How many to know for sure that it isn't fair? # # What is a reasonable threshold to set? # imports from scipy import stats import numpy as np import matplotlib.pyplot as plt # ### High Level Hypothesis Testing # 1. Start with a Scientific Question (yes/no) # 2. Take the skeptical stance (Null hypothesis) # 3. State the complement (Alternative) # 4. Create a model of the situation Assuming the Null Hypothesis is True! # 5. Decide how surprised you would need to be in order to change your mind # ## Definitions # + [markdown] slideshow={"slide_type": "slide"} # **What is statistical hypothesis testing?** # # When we perform experiments, we typically do not have access to all the members of a population, and need to take **samples** of measurements to make inferences about the population. # # A statistical hypothesis test is a method for testing a hypothesis about a parameter in a population using data measured in a sample. # # We test a hypothesis by determining the chance of obtaining a sample statistic if the null hypothesis regarding the population parameter is true. # # > The goal of hypothesis testing is to make a decision about the value of a population parameter based on sample data. # # # + [markdown] slideshow={"slide_type": "slide"} # **Why do we care about hypothesis testing?** # # Scenarios: # * Chemistry - do inputs from two different barley fields produce different yields? # * Astrophysics - do star systems with near-orbiting gas giants have hotter stars? # * Economics - demography, surveys, etc. # * Medicine - BMI vs. Hypertension, etc. # * Business - which ad is more effective given engagement? # + [markdown] slideshow={"slide_type": "notes"} # **Intuition** # # Suppose you have a large dataset for a population. The data is normally distributed with mean 0 and standard deviation 1. # # Along comes a new sample with a sample mean of 2.9. # # > The idea behind hypothesis testing is a desire to quantify our belief as to whether our sample of observations came from the same population as the original dataset. # # According to the empirical (68–95–99.7) rule for normal distributions there is only roughly a 0.003 chance that the sample came from the same population, because it is roughly 3 standard deviations above the mean. # # <img src="images/normal_sd_new.png" width="500"> # # To formalize this intuition, we define an threshold value for deciding whether we believe that the sample is from the same underlying population or not. This threshold is $\alpha$, the **significance threshold**. # # This serves as the foundation for hypothesis testing where we will reject or fail to reject the null hypothesis. # # + [markdown] slideshow={"slide_type": "slide"} # # Hypothesis testing # # Regardless of the type of statistical hypothesis test you're performing, there are five main steps to executing them: # # 1. Set up a null and alternative hypothesis # # 2. Choose a significance level $\alpha$ (or use the one assigned). # # 3. Determine the critical test statistic value or p-value. **(Find the rejection region for the null hypothesis.)** # # 4. Calculate the value of the test statistic. # # 5. Compare the test statistic value to the critical test statistic value to reject the null hypothesis or not. # + [markdown] slideshow={"slide_type": "subslide"} # <img src="images/hypothesis_test.png" width="500"> # + [markdown] slideshow={"slide_type": "notes"} # **Decision Rule**: # # The decision rule tells us when we can reject the null hypothesis. # # It depends on 3 factors: # 1. The alternative hypothesis # * Is this an upper-tailed, lower-tailed, or two-tailed test? # 2. The test statistic # 3. The level of significance $\alpha$. # # # Upper-tailed test (right-tailed test): # * The null hypothesis is rejected if the test statistic is greater than the critical value. # # Lower-tailed test (left-tailed test): # * The null hypothesis is rejected if the test statistic is smaller than the critical value. # # Two-tailed test: # * The null hypothesis is rejected if the test statistic is either larger than an upper critical value or smaller than a lower critical value. # + [markdown] slideshow={"slide_type": "notes"} # # Language of Hypothesis testing # # **Significance Level $\alpha$** # # The significance level $\alpha$ is the threshold at which you're okay with rejecting the null hypothesis. It is the probability of rejecting the null hypothesis when it is true. # # The most commonly used $\alpha$ in science is $\alpha = 0.05$. When you set $\alpha = 0.05$, you're saying "I'm okay with rejecting the null hypothesis if there is less than a 5% chance that the results I am seeing are actually due to randomness". # # **p-values** # # The p-value is the probability of observing a test statistic at least as large as the one observed, by random chance, assuming that the null hypothesis is true. # # If $p \lt \alpha$, we reject the null hypothesis. # # If $p \geq \alpha$, we fail to reject the null hypothesis. # # > **We do not accept the alternative hypothesis, we only reject or fail to reject the null hypothesis in favor of the alternative.** # # # **What if the experiment we perform fails to reject the null hypothesis?** # # * We do not throw out failed experiments! # * We say "this methodology, with this data, does not produce significant results" # * Maybe we need more data! # - # ## Type 1 Errors (False Positives) and Type 2 Errors (False Negatives) # Most tests for the presence of some factor are imperfect. And in fact most tests are imperfect in two ways: They will sometimes fail to predict the presence of that factor when it is after all present, and they will sometimes predict the presence of that factor when in fact it is not. Clearly, the lower these error rates are, the better, but it is not uncommon for these rates to be between 1% and 5%, and sometimes they are even higher than that. (Of course, if they're higher than 50%, then we're better off just flipping a coin to run our test!) # # Predicting the presence of some factor (i.e. counter to the null hypothesis) when in fact it is not there (i.e. the null hypothesis is true) is called a "false positive". Failing to predict the presence of some factor (i.e. in accord with the null hypothesis) when in fact it is there (i.e. the null hypothesis is false) is called a "false negative". # # # How does changing our alpha value change the rate of type 1 and type 2 errors? # + [markdown] slideshow={"slide_type": "slide"} # # Let's continue our discussion of hypothesis tests with an example. # + [markdown] slideshow={"slide_type": "slide"} # Suppose that African elephants have weights distributed normally around a mean of 9000 lbs with a standard deviation of 900 lbs. _Pachyderm Adventures_ has recently measured the weights of **35** Gabonese elephants and has calculated their average weight at 8637 lbs. # # Is the average weight of Gabonese elephants different that the average weight of African elephants? Use significance level $\alpha = 0.05$. # # **What are the null and alternative hypotheses? What is the significance level of the test?** # + [markdown] slideshow={"slide_type": "notes"} # * Null hypothesis # * The average weight of Gabonese elephants is the same as the average weight of African elephants. # # * Alternative hypothesis # * The average weight of Gabonese elephants is different than the average weight of African elephants. # # The significance level of our test is $\alpha = 0.05$. # + [markdown] slideshow={"slide_type": "slide"} # **What should be our test statistic? Are we running an upper, lower, or two-tailed test? Why?** # + [markdown] slideshow={"slide_type": "notes"} # Since we know the population standard deviation, the size of our sample is greater than 30, and we are comparing the sample mean to the population mean, we are going to run a one-sample z-test. # # Since we want to know if the sample mean is **different** from the population mean, we are running a two-tailed test. # + [markdown] slideshow={"slide_type": "slide"} # **What's the value of the critical test statistic that we should use for our test?** # + slideshow={"slide_type": "notes"} # critical z-statistic alpha = 0.05 # point percent function is the inverse of the cumulative density function which can be understood as the quantile stats.norm.ppf(alpha/2), stats.norm.ppf(1-alpha/2) # + [markdown] slideshow={"slide_type": "notes"} # > Since we are performing a two-tailed one-sample z-test and $\alpha = 0.05$, if the z-score we compute is greater than 1.96 or smaller than -1.96, then we can reject the null hypothesis at significance level 0.05 in favor of the alternative hypothesis. # + [markdown] slideshow={"slide_type": "slide"} # **Perform the test.** # # Compute the relevant test statistic for the sample. # + [markdown] slideshow={"slide_type": "notes"} # Compute the z-statistic for the sample. # # $$\text{z-statistic} = \frac{\bar{x} - \mu}{\sigma/\sqrt{n}}, $$ where $\bar x$ is the sample mean, $\mu$ is the population mean, $\sigma$ is the population standard deviation, and $n$ is the sample size. # + slideshow={"slide_type": "notes"} n = 35 sigma = 900 x_bar = 8637 mu = 9000 se = sigma/np.sqrt(n) z = (x_bar - mu)/se print(z) # + [markdown] slideshow={"slide_type": "slide"} # **Make a decision: do we reject the null hypothesis or not?** # + [markdown] slideshow={"slide_type": "notes"} # > z = -2.39 is smaller than -1.96, thus we can reject the null hypothesis in favor of the alternative hypothesis at significance level $\alpha = 0.05$. # - - - # # Another way of getting to same answer: # + slideshow={"slide_type": "notes"} stats.norm.cdf(z) # + [markdown] slideshow={"slide_type": "notes"} # > The area of the tail corresponding to this z-score is 0.0085. This is below 0.025. Thus we reject the null hypothesis in favor of the alternative at significance level $\alpha = 0.05$. # + [markdown] slideshow={"slide_type": "slide"} # **Would we be able to reject the null hypothesis if our significance threshold was $\alpha = 0.01$?** # + [markdown] slideshow={"slide_type": "notes"} # The area of the tail corresponding to the calculated z-statistic z = -2.386 is `stats.norm.cdf(z) = 0.0085`. # # > Since the area of the tail corresponding to the z-score we obtained is 0.0085, which is greater than 0.005, we fail to reject the null hypothesis in favor of the alternative at a significance level of $\alpha = 0.01$. # + slideshow={"slide_type": "notes"} # critical z-statistic alpha = 0.01 stats.norm.ppf(alpha/2), stats.norm.ppf(1-alpha/2) # + [markdown] slideshow={"slide_type": "notes"} # > Alternatively, since we are performing a two-tailed one-sample z-test and $\alpha = 0.01$, if the z-score we compute is greater than 2.58 or smaller than -2.58, then we can reject the null hypothesis at significance level 0.05 in favor of the alternative hypothesis. # # >Since the calculated z-statistic is -2.386, we fail to reject the null hypothesis in favor of the alternative at a significance level of $\alpha = 0.01$. # + [markdown] slideshow={"slide_type": "slide"} # # z-tests vs t-tests # # According to the **Central Limit Theorem**, the sampling distribution of a statistic, like the sample mean, will follow a normal distribution _as long as the sample size is sufficiently large_. # # __What if we don't have large sample sizes?__ # # When we do not know the population standard deviation or we have a small sample size, the sampling distribution of the sample statistic will follow a t-distribution. # * Smaller sample sizes have larger variance, and t-distributions account for that by having heavier tails than the normal distribution. # * t-distributions are parameterized by degrees of freedom, fewer degrees of freedom fatter tails. Also converges to a normal distribution as dof >> 0 # + [markdown] slideshow={"slide_type": "slide"} # # One-sample z-tests and one-sample t-tests # # One-sample z-tests and one-sample t-tests are hypothesis tests for the population mean $\mu$. # # How do we know whether we need to use a z-test or a t-test? # # <img src="images/z_or_t_test.png" width="500"> # # + [markdown] slideshow={"slide_type": "slide"} # **When we perform a hypothesis test for the population mean, we want to know how likely it is to obtain the test statistic for the sample mean given the null hypothesis that the sample mean and population mean are not different.** # # The test statistic for the sample mean summarizes our sample observations. How do test statistics differ for one-sample z-tests and t-tests? # # A t-test is like a modified z-test. # # * Penalize for small sample size: "degrees of freedom" # # * Use sample standard deviation $s$ to estimate the population standard deviation $\sigma$. # # <img src="images/img5.png" width="500"> # # # + [markdown] slideshow={"slide_type": "notes"} # A one-sample t-test estimates the population mean (one parameter). A sample with size $n$ provides $n$ pieces of information, or degrees of freedom, for estimating the population mean and its variability. # # One degree of freedom is used to estimate the mean, the remaining $n-1$ degrees of freedom are used to estimate variability. # # >The one-sample t-test for samples of size $n$ has $n-1$ degrees of freedom. # + [markdown] slideshow={"slide_type": "slide"} # <img src="images/img4.png" width="500"> # # + [markdown] slideshow={"slide_type": "notes"} # ## One-sample z-test # # * For large enough sample sizes $n$ with known population standard deviation $\sigma$, the test statistic of the sample mean $\bar x$ is given by the **z-statistic**, # $$Z = \frac{\bar{x} - \mu}{\sigma/\sqrt{n}}$$ where $\mu$ is the population mean. # # * Our hypothesis test tries to answer the question of how likely we are to observe a z-statistic as extreme as our sample's given the null hypothesis that the sample and the population have the same mean, given a significance threshold of $\alpha$. This is a one-sample z-test. # + [markdown] slideshow={"slide_type": "notes"} # ## One-sample t-test # # * For small sample sizes or samples with unknown population standard deviation, the test statistic of the sample mean is given by the **t-statistic**, # $$ t = \frac{\bar{x} - \mu}{s/\sqrt{n}} $$ Here, $s$ is the sample standard deviation, which is used to estimate the population standard deviation, and $\mu$ is the population mean. # # * Our hypothesis test tries to answer the question of how likely we are to observe a t-statistic as extreme as our sample's given the null hypothesis that the sample and population have the same mean, given a significance threshold of $\alpha$. This is a one-sample t-test. # + [markdown] slideshow={"slide_type": "notes"} # ## Compare and contrast z-tests and t-tests. # In both cases, it is assumed that the samples are normally distributed. # # A t-test is like a modified z-test: # 1. Penalize for small sample size; use "degrees of freedom" # 2. Use the _sample_ standard deviation $s$ to estimate the population standard deviation $\sigma$. # # T-distributions have more probability in the tails. As the sample size increases, this decreases and the t distribution more closely resembles the z, or standard normal, distribution. By sample size n = 1000 they are virtually indistinguishable from each other. # + [markdown] slideshow={"slide_type": "slide"} # ## Here's an example: # # A coffee shop relocates from Manhattan to Brooklyn and wants to make sure that all lattes are consistent before and after their move. They buy a new machine and hire a new barista. In Manhattan, lattes are made with 4 oz of espresso. A random sample of 25 lattes made in their new store in Brooklyn shows a mean of 4.6 oz and standard deviation of 0.22 oz. Are their lattes different now that they've relocated to Brooklyn? # # **What's the null and alternative hypothesis to test in this case? What kind of test should we run? Why?** # + [markdown] slideshow={"slide_type": "notes"} # > $H_0$: Lattes are the same. # # > $H_1$: Lattes are different. # # >> Should run a one-sample t-test. Unknown population standard deviation. Small sample size. # + [markdown] slideshow={"slide_type": "slide"} # ## Two-sample t-tests # # Sometimes, we are interested in determining whether two population means are equal. In this case, we use two-sample t-tests. # # There are two types of two-sample t-tests: **paired** and **independent** (unpaired) tests. # # What's the difference? # # **Paired tests**: How is a sample affected by a certain treatment? The individuals in the sample remain the same and you compare how they change after treatment. # # **Independent tests**: When we compare two different, unrelated samples to each other, we use an independent (or unpaired) two-sample t-test. # + [markdown] slideshow={"slide_type": "notes"} # The test statistic for an unpaired two-sample t-test is slightly different than the test statistic for the one-sample t-test. # # Assuming equal variances, the test statistic for a two-sample t-test is given by: # # $$ t = \frac{\bar{x_1} - \bar{x_2}}{\sqrt{s^2 \left( \frac{1}{n_1} + \frac{1}{n_2} \right)}}$$ # # where $s^2$ is the pooled sample variance, # # $$ s^2 = \frac{\sum_{i=1}^{n_1} \left(x_i - \bar{x_1}\right)^2 + \sum_{j=1}^{n_2} \left(x_j - \bar{x_2}\right)^2 }{n_1 + n_2 - 2} $$ # # Here, $n_1$ is the sample size of sample 1 and $n_2$ is the sample size of sample 2. # # An independent two-sample t-test for samples of size $n_1$ and $n_2$ has $(n_1 + n_2 - 2)$ degrees of freedom. # + [markdown] slideshow={"slide_type": "slide"} # ## Sample problem: Unpaired two-sample t-test # # You measure the delivery times of ten different restaurants in two different neighborhoods, A and B. You want to know if restaurants in the different neighborhoods have the same delivery times. It's okay to assume both samples have equal variances. # # ``` python # delivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8] # delivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3] # ``` # + [markdown] slideshow={"slide_type": "slide"} # # Let's practice solving hypothesis test problems! # + [markdown] slideshow={"slide_type": "slide"} # ## Example 1 # Let's revisit our Gabonese elephant weight example. # # Suppose that African elephants have weights distributed normally around a mean of 9000 lbs with a standard deviation of 900 lbs. _Pachyderm Adventures_ has recently measured the weights of **35** Gabonese elephants and has calculated their average weight at 8637 lbs. # # Is the average weight of Gabonese elephants _less_ than the average weight of African elephants? Use significance level $\alpha = 0.05$. # # **What are the null and alternative hypothesis in this case?** # # **What kind of test do we need to run?** # # **What's the critical test statistic value we should use?** # # **Perform the test and make a decision regarding the null hypothesis.** # + [markdown] slideshow={"slide_type": "notes"} # * Null hypothesis # * The average weight of Gabonese elephants is the same as the average weight of African elephants. # # * Alternative hypothesis # * The average weight of Gabonese elephants is less than the average weight of African elephants. # - - - # # We need to run a lower-tailed one-sample z-test. # + slideshow={"slide_type": "notes"} # + [markdown] slideshow={"slide_type": "slide"} # ## Example 2 # Next, let's finish working through our coffee shop example... # # A coffee shop relocates from Manhattan to Brooklyn and wants to make sure that all lattes are consistent before and after their move. They buy a new machine and hire a new barista. In Manhattan, lattes are made with 4 oz of espresso. A random sample of 25 lattes made in their new store in Brooklyn shows a mean of 4.6 oz and standard deviation of 0.22 oz. Are their lattes different now that they've relocated to Brooklyn? Use a significance level of $\alpha = 0.01$. # + [markdown] slideshow={"slide_type": "notes"} # State null and alternative hypothesis # 1. Null: the amount of espresso in the lattes is the same as before the move. # 2. Alternative: the amount of espresso in the lattes is different before and after the move. # # What kind of test? # * two-tailed one-sample t-test # * small sample size # * unknown population standard deviation # * two-tailed because we want to know if amounts are same or different # + slideshow={"slide_type": "notes"} x_bar = 4.6 mu = 4 s = 0.22 n = 25 df = n-1 t = (x_bar - mu)/(s/n**0.5) print("The t-statistic for our sample is {}.".format(round(t, 2))) # + slideshow={"slide_type": "notes"} # critical t-statistic values stats.t.ppf(0.005, df), stats.t.ppf(1-0.005, df) # + [markdown] slideshow={"slide_type": "notes"} # Can we reject the null hypothesis? # # > Yes. t > |t_critical|. we can reject the null hypothesis in favor of the alternative at $\alpha = 0.01$. # + [markdown] slideshow={"slide_type": "slide"} # ## Example 3 # # I'm buying jeans from store A and store B. I know nothing about their inventory other than prices. # # ``` python # store1 = [20,30,30,50,75,25,30,30,40,80] # store2 = [60,30,70,90,60,40,70,40] # ``` # # Should I go just to one store for a less expensive pair of jeans? I'm pretty apprehensive about my decision, so $\alpha = 0.1$. It's okay to assume the samples have equal variances. # + [markdown] slideshow={"slide_type": "slide"} # **State the null and alternative hypotheses** # + [markdown] slideshow={"slide_type": "notes"} # > Null: Store A and B have the same jean prices. # # > Alternative: Store A and B do not have the same jean prices. # + [markdown] slideshow={"slide_type": "slide"} # **What kind of test should we run? Why?** # + [markdown] slideshow={"slide_type": "notes"} # > Run a two-tailed two independent sample t-test. Sample sizes are small. # + [markdown] slideshow={"slide_type": "slide"} # **Perform the test.** # + slideshow={"slide_type": "notes"} store1 = [20,30,30,50,75,25,30,30,40,80] store2 = [60,30,70,90,60,40,70,40] stats.ttest_ind(store1, store2) # + [markdown] slideshow={"slide_type": "slide"} # **Make decision.** # + [markdown] slideshow={"slide_type": "notes"} # > We fail to reject the null hypothesis at a significance level of $\alpha = 0.1$. We do not have evidence to support that jean prices are different in store A and store B. # + [markdown] slideshow={"slide_type": "slide"} # ## Example 4 # # Next, let's finish working through the restaurant delivery times problem. # # You measure the delivery times of ten different restaurants in two different neighborhoods. You want to know if restaurants in the different neighborhoods have the same delivery times. It's okay to assume both samples have equal variances. Set your significance threshold to 0.05. # # ``` python # delivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8] # delivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3] # ``` # + [markdown] slideshow={"slide_type": "slide"} # State null and alternative hypothesis. What type of test should we perform? # + [markdown] slideshow={"slide_type": "notes"} # > Null hypothesis: The delivery times for restaurants in neighborhood A are equal to delivery times for restaurants in neighborhood B. # # > Alternative hypothesis: Delivery times for restaurants in neighborhood A are not equal to delivery times for restaurants in neighborhood B. # # > Two-sided unpaired two-sample t-test # + slideshow={"slide_type": "notes"} delivery_times_A = [28.4, 23.3, 30.4, 28.1, 29.4, 30.6, 27.8, 30.9, 27.0, 32.8] delivery_times_B = [26.4, 26.3, 27.4, 30.4, 25.1, 28.4, 23.3, 24.7, 31.8, 24.3] # + slideshow={"slide_type": "notes"} stats.ttest_ind(delivery_times_A, delivery_times_B) # + [markdown] slideshow={"slide_type": "notes"} # > We cannot reject the null hypothesis that restaurant A and B have equal delivery times. p-value > $\alpha$. # + [markdown] slideshow={"slide_type": "slide"} # # Level Up: More practice problems! # + [markdown] slideshow={"slide_type": "slide"} # A rental car company claims the mean time to rent a car on their website is 60 seconds with a standard deviation of 30 seconds. A random sample of 36 customers attempted to rent a car on the website. The mean time to rent was 75 seconds. Is this enough evidence to contradict the company's claim at a significance level of $\alpha = 0.05$? # + [markdown] slideshow={"slide_type": "notes"} # Null hypothesis: # # Alternative hypothesis: # # + slideshow={"slide_type": "notes"} # one-sample z-test # + [markdown] slideshow={"slide_type": "notes"} # Reject?: # + [markdown] slideshow={"slide_type": "slide"} # Consider the gain in weight (in grams) of 19 female rats between 28 and 84 days after birth. # # Twelve rats were fed on a high protein diet and seven rats were fed on a low protein diet. # # ``` python # high_protein = [134, 146, 104, 119, 124, 161, 107, 83, 113, 129, 97, 123] # low_protein = [70, 118, 101, 85, 107, 132, 94] # ``` # # Is there any difference in the weight gain of rats fed on high protein diet vs low protein diet? It's OK to assume equal sample variances. # + [markdown] slideshow={"slide_type": "notes"} # Null and alternative hypotheses? # + [markdown] slideshow={"slide_type": "notes"} # > null: # # > alternative: # + [markdown] slideshow={"slide_type": "notes"} # What kind of test should we perform and why? # + [markdown] slideshow={"slide_type": "notes"} # > Test: # + slideshow={"slide_type": "notes"} # + [markdown] slideshow={"slide_type": "notes"} # We fail to reject the null hypothesis at a significance level of $\alpha = 0.05$. # + [markdown] slideshow={"slide_type": "slide"} # **What if we wanted to test if the rats who ate a high protein diet gained more weight than those who ate a low-protein diet?** # + [markdown] slideshow={"slide_type": "notes"} # Null: # # alternative: # + [markdown] slideshow={"slide_type": "notes"} # Kind of test? # + [markdown] slideshow={"slide_type": "notes"} # Critical test statistic value? # + slideshow={"slide_type": "notes"} # + [markdown] slideshow={"slide_type": "notes"} # Can we reject? # + [markdown] slideshow={"slide_type": "slide"} # # Summary # # Key Takeaways: # # * A statistical hypothesis test is a method for testing a hypothesis about a parameter in a population using data measured in a sample. # * Hypothesis tests consist of a null hypothesis and an alternative hypothesis. # * We test a hypothesis by determining the chance of obtaining a sample statistic if the null hypothesis regarding the population parameter is true. # * One-sample z-tests and one-sample t-tests are hypothesis tests for the population mean $\mu$. # * We use a one-sample z-test for the population mean when the population standard deviation is known and the sample size is sufficiently large. We use a one-sample t-test for the population mean when the population standard deviation is unknown or when the sample size is small. # * Two-sample t-tests are hypothesis tests for differences in two population means.
28,259
/Basic Python/Variable.ipynb
5612c86d5474ad4b0cec8bfc77654d6b2747fa12
[]
no_license
kausikporey/Python
https://github.com/kausikporey/Python
0
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Jupyter Notebook
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- num = 5 id(num) a = 5 b = a id(a) id(b) id(10) PI = 22/7 PI PI = 2 PI type(PI) num = 3.2 num = int(num) type(num) num list(range(2,10)) list(range(5)) list(range(2,10,2)) d = {'Kausik':'Samsung','Rahul':'Iphone','Goutam':'Moto'} d.keys() d.values() d['Rahul'] y = 5 + 6j y ing) in a retriever. This builds on top of ideas in the [ContextualCompressionRetriever](/docs/modules/data_connection/retrievers/contextual_compression/). # + # #!pip install cohere # + # #!pip install faiss # OR (depending on Python version) # #!pip install faiss-cpu # + # get a new token: https://dashboard.cohere.ai/ import os import getpass os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:") # - os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") # + # Helper function for printing docs def pretty_print_docs(docs): print( f"\n{'-' * 100}\n".join( [f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)] ) ) # + [markdown] jp-MarkdownHeadingCollapsed=true # ## Set up the base vector store retriever # Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs. # + from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.document_loaders import TextLoader from langchain.vectorstores import FAISS documents = TextLoader("../../../state_of_the_union.txt").load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) texts = text_splitter.split_documents(documents) retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever( search_kwargs={"k": 20} ) query = "What did the president say about Ketanji Brown Jackson" docs = retriever.get_relevant_documents(query) pretty_print_docs(docs) # - # ## Doing reranking with CohereRerank # Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `CohereRerank`, uses the Cohere rerank endpoint to rerank the returned results. # + from langchain.llms import OpenAI from langchain.retrievers import ContextualCompressionRetriever from langchain.retrievers.document_compressors import CohereRerank llm = OpenAI(temperature=0) compressor = CohereRerank() compression_retriever = ContextualCompressionRetriever( base_compressor=compressor, base_retriever=retriever ) compressed_docs = compression_retriever.get_relevant_documents( "What did the president say about Ketanji Jackson Brown" ) pretty_print_docs(compressed_docs) # - # You can of course use this retriever within a QA pipeline from langchain.chains import RetrievalQA chain = RetrievalQA.from_chain_type( llm=OpenAI(temperature=0), retriever=compression_retriever ) chain({"query": query})
3,158
/_5_task.ipynb
2e5cf8f1ef1bab2403ff7daec0f76e3295df9a93
[]
no_license
qu4n7/geek_prob
https://github.com/qu4n7/geek_prob
0
1
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10,372
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # <h1>Can a large scale dewatering project replace a pumping test?</h1> # <h2>A show case of the Tim groundwater familiy by an assessment of the Vlaketunnel dewatering in 2011.</h2> # # The primary goal of this notebook is to illustrate the use of the Tim groundwater family, especially the use of QGIS-Tim, TimML and data available on the internet. # # This notebook is presented during a workshop at the NHV Spring meeting on April 13, 2023. # # Prepared by Mark Bakker (TU Delft) and Hendrik Meuwese (Waterboard Scheldestromen). # <img style="float: right;" src="attachment:b772e179-45a7-471d-a136-7563cdb35352.png"> # ## Some Python imports # + # import general packages from io import StringIO import numpy as np import matplotlib.pyplot as plt import pandas as pd # install timml if it is not installed already try: import timml as tml except: # !pip install timml import timml as tml # import specific functions for this notebook. not used now to make notebook run on colab # import vlaketunnel_functions as vlake_func # some plotting parameters plt.rcParams["figure.figsize"] = (12, 4) # set default figure size plt.rcParams["contour.negative_linestyle"] = 'solid' # set default line style plt.rcParams["figure.autolayout"] = True # same at tight_layout after every plot # - # # This is TimML in Python # # A short example of TimML in the Python interface. # # The code cell below is copied from the example notebook (https://github.com/mbakker7/timml/blob/master/notebooks/timml_notebook0_sol.ipynb). It creates a groundwater model with uniform flow and an extraction of 400 m$^3$/day by a well. # # Do you want to know more about Tim? For Dutch people, TKI TIM is active now: https://publicwiki.deltares.nl/display/TKIP/DEL156+TKI+TIM # + ml = tml.ModelMaq(kaq=10, z=[10, 0]) rf = tml.Constant(ml, xr=-1000, yr=0, hr=41) uf = tml.Uflow(ml, slope=0.001, angle=0) w = tml.Well(ml, xw=-400, yw=0, Qw=50., rw=0.2) ml.solve() ml.contour(win=[-1000, 100, -500, 500], ngr=50, levels=np.arange(39, 42, 0.1), ) #figsize=(6, 6)) ml.tracelines(-800 * np.ones(1), -200 * np.ones(1), np.zeros(1), hstepmax=20, color='C1') # - # The example is a simple synthetic model. # The construction of more complicated TimML models is facilitated by a QGIS plus-in. We will use QGIS to create a TimML model later in this Notebook. # # QGIS-TIM models may be exported to Python scripts and imported in a notebook. Some pre-processing of model input and observations is done in a separate notebook. # # First we give an overview of the modeling case. # # Short overview of the case and model input # # ## Why was dewatering started? # The eastern part of the Vlaketunnel (A58 highway in province Zeeland) lifted up by 10-15 cm on November 12, 2010. The tunnel was closed immediately. Dumper trucks delivered sand to try to stabilize the tunnel. # A large-scale dewatering was started on November 26, 2010. # # ![image.png](attachment:8e8328db-5651-45ab-ae31-f7ded25d32f5.png) # ## Boundary conditions # # ### GeoTop # Lithology according to BRO GeoTop v1.5 # # ![image.png](attachment:f3552384-e244-4cb8-a202-8ef5a598984e.png) # # ### Layer composition # # | top (m NAP) | bottom (m NAP) | hydrogeology | dewatering | channel | kh | # |--- |--- |--- |--- |--- | --- | # | -1 | -7 | semi-confining toplayer | | yes | c=1000 | # | -7 | -15 | upper part aquifer | yes | yes | kh=5 | # | -15 | -30 | middle part aquifer | | | kh=15 | # | -30 | -40 | lower part aquifer | | | kh=5 | # ### Discharge # # Actual discharges are: 325 m$^3$/hour in the eastern part and 75 m$^3$/hour in the western part. # + q_east_total = 325 * 24 # m^3/d q_east_nr_wells = 4 print(f'EAST discharge per well = {q_east_total/q_east_nr_wells} m3/day') q_west_total = 75 * 24 # m^3/d q_west_nr_wells = 2 print(f'WEST discharge per well = {q_west_total/q_west_nr_wells} m3/day') # - # ## Observed drawdowns, relative to center of eastern dewatering site # # Major dewatering is on eastern shore, see observed drawdown of 8 m at x=0. # # # Red color for observations west of Kanaal door Zuid-Beveland, maroon color for eastern shore. Mind the difference of drawdown near $x=1700$ on both shores. # + # import on your laptop # import pickle # with open(r'data/df_dh.pkl', 'rb') as f: # df_dh = pickle.load(f) # binder does not support pickle, we use text import data_as_string = StringIO("""name;x;y;screen_top;tube_nr;dh_obs;ha;va;color;r eastern_tunnel;59313.0;387345.0;-10.00;1.0;-8.000000;left;top;maroon;0.000000 pb6;58774.0;386980.0;-10.00;1.0;-2.000000;left;top;r;650.957756 B48F0233-001;58803.0;388144.0;-6.45;1.0;-0.549122;left;top;r;947.892926 B48F0233-002;58803.0;388144.0;-8.81;2.0;-0.307027;left;bottom;r;947.892926 B48F0233-003;58803.0;388144.0;-14.89;3.0;-0.338581;right;top;r;947.892926 B48F0203-002;59400.0;389050.0;-8.60;2.0;-0.821750;left;bottom;maroon;1707.218205 B48F0203-003;59400.0;389050.0;-14.40;3.0;-0.767000;right;top;maroon;1707.218205 B48F0232-002;58615.0;389021.0;-9.57;2.0;-0.029118;left;bottom;r;1815.538488 B48F0232-003;58615.0;389021.0;-14.61;3.0;-0.026286;right;top;r;1815.538488 B48F0231-002;57381.0;389003.0;-10.58;2.0;0.000676;left;bottom;r;2545.896306 B48F0231-003;57381.0;389003.0;-14.27;3.0;-0.016892;right;top;r;2545.896306""") df_dh = pd.read_table(data_as_string, header=0, sep=";", index_col=0) # - df_dh.plot.scatter(x='r', y='dh_obs', c='color', xlabel='distance to eastern dewatering site (m)', ylabel='drawdown (m)', figsize=(10,4), grid=True); # # Create TimML model using QGIS-Tim # # QGIS-Tim is a graphical user interface in QGIS for TimML (steady-state) and TTim (transient) models (https://deltares.gitlab.io/imod/qgis-tim/index.html). QGIS-Tim can export a Python file with the model input. # # ## Model set-up # Because of the limited time during this workshop, the QGIS-Tim is prepared. The result is posted in the code cell below. # + #Video.from_file("data/screen_capture_qgis_tim_compressed.mp4", width=320, height=320) #Video.from_file("data/screen_capture_qgis_tim_full.mp4", width=320, height=320) # - # ## Functions to create the model and display the model results # These are commonly stored in a separate Python file. They are included here to make it easier to run the notebook on google colab. # + def create_model(kaq=[0.1, 5.0, 15.0, 5.0], c=[1000.0, 2.0, 2.0, 2.0],hstar=0, c_channel_bot=30, do_plot=True, df_dh=None): """ Create a TimML model for Vlaketunnel case Parameters ---------- kaq : list, optional Kh of aquifers. The default is [0.1, 5.0, 15.0, 5.0]. c : list, optional c of aquitards. The default is [1000.0, 2.0, 2.0, 2.0]. hstar : float, optional Top boundary condition of semi-confining toplayer. The default is 0. c_channel_bot : float, optional resistance of Kanaal door Zuid-Beveland. The default is 30. do_plot : boolean, optional Plot results? The default is True. df_dh: pd.DataFrame, optional Information about observed drawdowns, required for plotting. The default is None. Returns ------- ml : timml model The model """ # create model ml = tml.ModelMaq( kaq=kaq, z=[1.0, -3.0, -7.0, -7.0, -14.0, -14.0, -30.0, -30.0, -40.0], c=c, topboundary="semi", npor=[None, None, None, None, None, None, None, None], hstar=hstar, ) # add dewatering dewatering_east_xys = [[59224, 387382], [59359, 387375], [59360, 387311], [59234, 387298], ] q_east_total = 325*24 q_west_total = 75*24 dewatering_west_xys = [[58781, 387375], [58785, 387307],] for dewatering_xys, q_total in zip([dewatering_east_xys, dewatering_west_xys], [q_east_total, q_west_total]): # loop over both dewatering locations for dewatering_xy in dewatering_xys: # loop over the modelled wells, in pratice a lot of more wells are used. Current model has focus on regional effect, therefore limited number of wells are considered sufficient dewatering_east = tml.Well( xw=dewatering_xy[0], yw=dewatering_xy[1], Qw=q_total/len(dewatering_xys), rw=0.5, res=1.0, layers=1, label=None, model=ml, ) c_channel = ml.aq.c.copy() c_channel[0] = c_channel_bot channel_0 = tml.PolygonInhomMaq( kaq=ml.aq.kaq, z=ml.aq.z, c=c_channel, topboundary="semi", npor=[None, None, None, None, None, None, None, None], hstar=0.0, # compared to QGIS-Tim export the channel is extended to the north in order to cover the northern observation wells better xy= [ [58921, 390500], [59065, 390500], [59110, 387996], [59146, 387447], [59263, 386809], [59317, 386260], [59110, 386251], [58966, 386863], [58921, 388617], ], order=4, ndeg=6, model=ml, ) ml.solve() if do_plot and (df_dh is not None): plot_model_results(ml, df_dh) return ml def plot_model_input(ml): """ Plot model input in schematic section Parameters ---------- ml : timml Model The model Returns ------- None. """ # some plotting constants xmin=-1 xchannel=-0.25 xhinter=-0.2 xmax=1 zaqmid = np.mean([ml.aq.zaqtop,ml.aq.zaqbot],axis=0) # plot layers plt.hlines(y=ml.aq.zlltop,xmin=xmin,xmax=xmax,color='darkgray') plt.hlines(y=ml.aq.zaqbot,xmin=xmin,xmax=xmax,color='darkgray') # plot kh for kh, z in zip(ml.aq.kaq, zaqmid): plt.annotate(f'kh={kh:0.1f}m/d',(0,z),ha='center') # plot c for c, z in zip(ml.aq.c, ml.aq.zaqtop): plt.annotate(f'c={c:0.1f}d',(0.5,z),ha='center',va='center') # plot channel plt.plot([xmin,xchannel],[ml.aq.inhomlist[0].hstar]*2,color='blue') plt.annotate(f'h_ch={ml.aq.inhomlist[0].hstar:0.1f}',(xchannel,ml.aq.inhomlist[0].hstar),ha='right',va='bottom') plt.annotate(f'c_ch={ml.aq.inhomlist[0].c[0]:0.1f}',(xchannel,ml.aq.zaqtop[0]),ha='right',va='bottom') # plot hinterland plt.plot([xhinter,xmax],[ml.aq.hstar]*2,color='darkblue') plt.annotate(f'h_polder={ml.aq.hstar:0.1f}',(xhinter,ml.aq.hstar),ha='left',va='bottom') plt.xlim([xmin, xmax]) def plot_model_results(ml, df_dh): """ Plot results of TimML model of Vlaketunnel case Parameters ---------- ml : timml Model, The model. df_dh : pd.DataFrame Observed drawdowns Returns ------- None. """ # contour plot plt.subplot(221) ml.contour(win=[57000, 60000, 386900, 389100], ngr=50, layers=1, levels=[-5,-2,-1,-0.5,-0.1], labels=True, decimals=2, legend=False, newfig=False); plt.scatter(df_dh.x, df_dh.y, 20, c=df_dh.color) for index, row in df_dh.iterrows(): plt.annotate(f'{row.dh_obs:0.2f}', (row.x, row.y),ha=row.ha,va=row.va) plt.title('contours in layer 1'); # plot model input plt.subplot(222) plot_model_input(ml) for plotid in (223, 224): plt.subplot(plotid) if plotid == 223: # first plot, get model results df_dh['ml_layer'] = None df_dh['dh_calc'] = None for index, row in df_dh.iterrows(): df_dh.loc[index,'ml_layer'] = np.where(ml.aq.zaqtop > row.screen_top)[0][-1] df_dh.loc[index,'dh_calc'] = ml.headalongline(row.x, row.y, row.ml_layer)[0][0] # plot all model results plot_df = df_dh else: # second plot, only plot outside dewatering area plot_df = df_dh.loc[df_dh.r > 100] plt.scatter(plot_df.r, plot_df.dh_obs, 50, c=plot_df.color, alpha=0.3, label='observed') plt.scatter(plot_df.r, plot_df.dh_calc, 40, marker='+', label='modelled') plt.legend() plt.title('heads from screened modellayer'); plt.grid() # - # ## The model is now a function # The Python script exported by QGIS-Tim is relatively long, because each well is stored separately. The code is included in the function `create_model`. The function builds the same model as created in QGIS-Tim. Some of the model input can be changed through the input arguments of the function. When no arguments are given, the model uses the default parameters from QGIS-Tim. The only change is that for the dewatering, all heads are computed with respect to the situation before the start of dewatering. Hence, waterlevel in the canal and polders is specified as 0. # # The mode is built and solved as follows: ml = create_model(do_plot=False) # create and solve model, but don't plot results # The aquifer parameters of the model may be visualized with the `plot_model_input` function. plot_model_input(ml) # A contour plot of the computed head changed caused by the dewatering are shown below. The figure also included the measured head changes (note that the head change is the opposite of the drawdown). ml.contour(win=[57000, 60000, 386900, 389100], ngr=50, layers=1, levels=[-5,-2,-1,-0.5,-0.1], labels=True, decimals=2, legend=False, newfig=False); plt.scatter(df_dh.x, df_dh.y, 20, c=df_dh.color) for index, row in df_dh.iterrows(): plt.annotate(f'{row.dh_obs:0.2f}', (row.x, row.y),ha=row.ha,va=row.va) plt.title('contours in layer 1'); # Both plots, and sections over the observation locations are combined in one plotting function: `plot_model_results`. plot_model_results(ml, df_dh) # ## Next: vary the aquifer parameters to better match the observed head changes # # The calculated head change is (far) larger than the observed head change. Different aquifer parameters are specified as input to the `create_model` function. The function automtically calls the plot function by default. # ### First attempt: larger bottom resistance of Kanaal door Zuid-Beveland ml = create_model(kaq=[0.1, 2.0, 10.0, 5.0], c_channel_bot=250, df_dh=df_dh) # ### Second attempt: higher resistance of semi-confining top layer below polders ml = create_model(kaq=[0.1, 2.0, 10.0, 5.0], c_channel_bot=250, c=[5000.0, 2.0, 2.0, 2.0], df_dh=df_dh) # # Up to you! # # Which model input gives the best representation of the observation? # # Is there one best solution? Is this relatively simple schematization a reasonable representation?
14,775
/classifiers/decision-tree/team_v_team_cross_validated_over_and_under_sampled_decision_tree.ipynb
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psalire/cmpe255-term-project
https://github.com/psalire/cmpe255-term-project
0
0
null
null
null
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Jupyter Notebook
false
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6,808,693
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Economics Problem Set 1 # # # # ## Question 1 # # # ### 1. State variables: # # Stock of oil $S_t$ # Price $p_t$ # # ### 2. Control variables: # # Amount of oil to sell $x_t$ # # ### 3. Transition equation: # # $$S_{t+1} = S_t - x_t$$ # # ### 4. Sequence problem # # Max $E_{t}\{\Sigma_{s=1}^{\infty}p_{t+s}x_{t+s}(\frac{1}{1+r})^s) \}$ # subject to $S_{t+s+1} = S_{t+s} - x_{t+s}$ $\forall s$ and $S_t = B$ and $S_{t+s} \geqslant 0$ # # The Bellman equation is of the form: # # $$V(S) = Max_x\{px + \frac{1}{1+r} V(S - x)\}$$ # # ### 5. The Euler equation # # $$p_{t+s} = p_{t+1+s}(\frac{1}{1+r})$$ # # ### 6. The solution # # Since the payoff function is linear, the solution is piecewise. # # If $p_{t+1+s} = p_{t+s}$ for all s, then $x_t = B$, i.e. we sell everything today. This is because we get the same absolute payoff from selling a marginal unit in any period, but we discount the future, so we sell everything today. # # If $p_{t+1+s} > (1+r)p_{t+s}$ then $x_{t+s} = 0$ for all s. This is actually a violation of the transversality condition, and so the model is not stationary in this case. # # A necessary condition for an interior solution is $p_{t+s}(1+r) = p_{t+s+1}$ # # ## Question two # # ### 1. State variables: # # Capital today: $k_t$ # Shock today: $z_t$ # # ### 2. Control variables: # # Consumption today: $c_t$ # # ### 3. Bellman Equation: # # $$V(z_t, k_t) = Max_c\{U(C_{t}) + \beta E_tV(z_{t+1}, k_{t+1})\}$$ # # subject to the resource contraint: # # $$k_{t+1} + c_{t} = z_{t}k_{t}^\alpha + (1-\delta)k_{t}$$ # # # ### Import some packages # + #Imports import numpy as np import matplotlib.pyplot as plt # to print plots inline # %matplotlib inline # - # ### Set Parameters # # Parameters: # * $\gamma$ : Coefficient of Relative Risk Aversion # * $\beta$ : Discount factor # * $\delta$ : Rate of depreciation # * $\alpha$ : Curvature of production function # * $\sigma_z$ : Standard dev of productivity shocks # * $\mu$ : Centre of log normal distribution # * $\rho$ : Persistence parameter # gamma = 0.5 beta = 0.96 delta = 0.05 alpha = 0.4 sigmaz = 0.2 mu = 0 rho = 0 # ### Create Grid Space # + ''' ------------------------------------------------------------------------ Create Grid for State Space - Capital and Shock ------------------------------------------------------------------------ lb_k = scalar, lower bound of capital grid ub_k = scalar, upper bound of capital grid size_k = integer, number of grid points in capital state space k_grid = vector, size_k x 1 vector of capital grid points ------------------------------------------------------------------------ ''' lb_k = 10 ub_k = 13 size_k = 60 # Number of grid points of k size_z = 60 # Number of grid points of z k_grid = np.linspace(lb_k, ub_k, size_k) import ar1_approx as ar1 ln_z_grid, pi = ar1.addacooper(size_z, mu, rho, sigmaz) z_grid = np.exp(ln_z_grid) pi_z = np.transpose(pi) # + ''' ------------------------------------------------------------------------ Create grid of current utility values ------------------------------------------------------------------------ C = matrix, current consumption (c=z_tk_t^a - k_t+1 + (1-delta)k_t) U = matrix, current period utility value for all possible choices of w and w' (rows are w, columns w') ------------------------------------------------------------------------ ''' C = np.zeros((size_k, size_k, size_z)) for i in range(size_k): # loop over k_t for j in range(size_k): # loop over k_t+1 for q in range(size_z): #loop over z_t C[i, j, q] = z_grid[q]* k_grid[i]**alpha + (1 - delta)*k_grid[i] - k_grid[j] # replace 0 and negative consumption with a tiny value # This is a way to impose non-negativity on cons C[C<=0] = 1e-15 if gamma == 1: U = np.log(C) else: U = (C ** (1 - gamma)) / (1 - gamma) U[C<0] = -9999999 # - # ### Value function iteration # + ''' ------------------------------------------------------------------------ Value Function Iteration ------------------------------------------------------------------------ VFtol = scalar, tolerance required for value function to converge VFdist = scalar, distance between last two value functions VFmaxiter = integer, maximum number of iterations for value function V = vector, the value functions at each iteration Vmat = matrix, the value for each possible combination of w and w' Vstore = matrix, stores V at each iteration VFiter = integer, current iteration number TV = vector, the value function after applying the Bellman operator PF = vector, indicies of choices of w' for all w VF = vector, the "true" value function ------------------------------------------------------------------------ ''' VFtol = 1e-6 VFdist = 7.0 VFmaxiter = 500 V = np.zeros((size_k, size_z)) # initial guess at value function Vmat = np.zeros((size_k, size_k, size_z)) # initialize Vmat matrix Vstore = np.zeros((size_k, size_z, VFmaxiter)) #initialize Vstore array VFiter = 1 while VFdist > VFtol and VFiter < VFmaxiter: print('This is the distance', VFdist, VFiter) for i in range(size_k): # loop over k_t for j in range(size_k): # loop over k_t+1 for q in range(size_z): #loop over z_t EV = 0 for qq in range(size_z): EV += pi_z[q, qq]*V[j, qq] Vmat[i, j, q] = U[i, j, q] + beta * EV Vstore[:,:, VFiter] = V.reshape(size_k, size_z,) # store value function at each iteration for graphing later TV = Vmat.max(1) # apply max operator over k_t+1 PF = np.argmax(Vmat, axis=1) VFdist = (np.absolute(V - TV)).max() # check distance V = TV VFiter += 1 if VFiter < VFmaxiter: print('Value function converged after this many iterations:', VFiter) else: print('Value function did not converge') VF = V # solution to the functional equation # - # Plot value function plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[1:], VF[1:, 0], label='$z$ = ' + str(z_grid[0])) ax.plot(k_grid[1:], VF[1:, 5], label='$z$ = ' + str(z_grid[5])) ax.plot(k_grid[1:], VF[1:, 15], label='$z$ = ' + str(z_grid[15])) ax.plot(k_grid[1:], VF[1:, 19], label='$z$ = ' + str(z_grid[19])) # Now add the legend with some customizations. legend = ax.legend(loc='lower right', shadow=False) # Set the fontsize for label in legend.get_texts(): label.set_fontsize('large') for label in legend.get_lines(): label.set_linewidth(1.5) # the legend line width plt.xlabel('Size of Capital') plt.ylabel('Value Function') plt.title('Value Function') plt.show() #Plot optimal consumption rule as a function of capital optK = k_grid[PF] optC = z_grid * k_grid ** (alpha) + (1 - delta) * k_grid - optK plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[:], optC[:][18], label='Consumption') # Now add the legend with some customizations. #legend = ax.legend(loc='upper left', shadow=False) # Set the fontsize for label in legend.get_texts(): label.set_fontsize('large') for label in legend.get_lines(): label.set_linewidth(1.5) # the legend line width plt.xlabel('Size of Capital') plt.ylabel('Optimal Consumption') plt.title('Policy Function, consumption - growth model') plt.show() # + #Plot optimal capital in period t + 1 rule as a function of cake size optK = k_grid[PF] plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[:], optK[:][18], label='Capital in period t+1') # Now add the legend with some customizations. #legend = ax.legend(loc='upper left', shadow=False) # Set the fontsize for label in legend.get_texts(): label.set_fontsize('large') for label in legend.get_lines(): label.set_linewidth(1.5) # the legend line width plt.xlabel('Size of Capital in period t') plt.ylabel('Optimal Capital in period t+1') plt.title('Policy Function, capital next period - growth model') plt.show() # - # ## Question 3 # ### The Bellman equation # # $$V(z_t, k_t) = Max_c\{U(C_{t}) + \beta E_{z_{t+1} | z_t} V(z_{t+1}, k_{t+1})\}$$ # # subject to the resource contraint: # # $$k_{t+1} + c_{t} = z_{t}k_{t}^\alpha + (1-\delta)k_{t}$$ # ### Set Parameters # # Parameters: # * $\gamma$ : Coefficient of Relative Risk Aversion # * $\beta$ : Discount factor # * $\delta$ : Rate of depreciation # * $\alpha$ : Curvature of production function # * $\sigma_v$ : Standard deviation of iid shock to log z # * $\rho$ : Persistence parameter # * $\sigma_v$: stdev of iid shock gamma = 0.5 beta = 0.96 delta = 0.05 alpha = 0.4 sigma_v = 0.1 mu = 0 rho = 0.8 # ### Create Grid Space # + ''' ------------------------------------------------------------------------ Create Grid for State Space - Capital and Shock ------------------------------------------------------------------------ lb_k = scalar, lower bound of capital grid ub_k = scalar, upper bound of capital grid size_k = integer, number of grid points in capital state space k_grid = vector, size_k x 1 vector of capital grid points ------------------------------------------------------------------------ ''' lb_k = 10 ub_k = 13 size_k = 30 # Number of grid points of k size_z = 30 # Number of grid points of z k_grid = np.linspace(lb_k, ub_k, size_k) import ar1_approx as ar1 ln_z_grid, pi = ar1.addacooper(size_z, mu, rho, sigma_v) z_grid = np.exp(ln_z_grid) pi_z = np.transpose(pi) # + ''' ------------------------------------------------------------------------ Create grid of current utility values ------------------------------------------------------------------------ C = matrix, current consumption (c=z_tk_t^a - k_t+1 + (1-delta)k_t) U = matrix, current period utility value for all possible choices of k and k' ------------------------------------------------------------------------ ''' C = np.zeros((size_k, size_k, size_z)) for i in range(size_k): # loop over k_t for j in range(size_k): # loop over k_t+1 for q in range(size_z): #loop over z_t C[i, j, q] = z_grid[q]* k_grid[i]**alpha + (1 - delta)*k_grid[i] - k_grid[j] # replace 0 and negative consumption with a tiny value # This is a way to impose non-negativity on cons C[C<=0] = 1e-15 if gamma == 1: U = np.log(C) else: U = (C ** (1 - gamma)) / (1 - gamma) U[C<0] = -9999999 # - # ### Value function iteration # + ''' ------------------------------------------------------------------------ Value Function Iteration ------------------------------------------------------------------------ VFtol = scalar, tolerance required for value function to converge VFdist = scalar, distance between last two value functions VFmaxiter = integer, maximum number of iterations for value function V = vector, the value functions at each iteration Vmat = matrix, the value for each possible combination of w and w' Vstore = matrix, stores V at each iteration VFiter = integer, current iteration number TV = vector, the value function after applying the Bellman operator PF = vector, indicies of choices of w' for all w VF = vector, the "true" value function ------------------------------------------------------------------------ ''' VFtol = 1e-6 VFdist = 7.0 VFmaxiter = 500 V = np.zeros((size_k, size_z)) # initial guess at value function Vmat = np.zeros((size_k, size_k, size_z)) # initialize Vmat matrix Vstore = np.zeros((size_k, size_z, VFmaxiter)) #initialize Vstore array VFiter = 1 while VFdist > VFtol and VFiter < VFmaxiter: print('This is the distance', VFdist, VFiter) for i in range(size_k): # loop over k_t for j in range(size_k): # loop over k_t+1 for q in range(size_z): #loop over z_t EV = 0 for qq in range(size_z): EV += pi_z[q, qq]*V[j, qq] Vmat[i, j, q] = U[i, j, q] + beta * EV Vstore[:,:, VFiter] = V.reshape(size_k, size_z,) # store value function at each iteration for graphing later TV = Vmat.max(1) # apply max operator over k_t+1 PF = np.argmax(Vmat, axis=1) VFdist = (np.absolute(V - TV)).max() # check distance V = TV VFiter += 1 if VFiter < VFmaxiter: print('Value function converged after this many iterations:', VFiter) else: print('Value function did not converge') VF = V # solution to the functional equation # - # Plot value function plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[1:], VF[1:, 0], label='$z$ = ' + str(z_grid[0])) ax.plot(k_grid[1:], VF[1:, 5], label='$z$ = ' + str(z_grid[5])) ax.plot(k_grid[1:], VF[1:, 15], label='$z$ = ' + str(z_grid[15])) ax.plot(k_grid[1:], VF[1:, 19], label='$z$ = ' + str(z_grid[19])) # Now add the legend with some customizations. legend = ax.legend(loc='lower right', shadow=False) # Set the fontsize for label in legend.get_texts(): label.set_fontsize('large') for label in legend.get_lines(): label.set_linewidth(1.5) # the legend line width plt.xlabel('Size of Capital') plt.ylabel('Value Function') plt.title('Value Function') plt.show() #Plot optimal consumption rule as a function of capital optK = k_grid[PF] optC = z_grid * k_grid ** (alpha) + (1 - delta) * k_grid - optK plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[:], optC[:][12], label='Consumption') ax.plot(k_grid[:], optC[:][18], label='Consumption') ax.plot(k_grid[:], optC[:][19], label='Consumption') # Now add the legend with some customizations. #legend = ax.legend(loc='upper left', shadow=False) plt.xlabel('Size of Capital') plt.ylabel('Optimal Consumption') plt.title('Policy Function, consumption - growth model') plt.show() #Plot optimal capital in period t + 1 rule as a function of cake size optK = k_grid[PF] plt.figure() fig, ax = plt.subplots() ax.plot(k_grid[:], optK[:][4], label='Capital in period t+1') ax.plot(k_grid[:], optK[:][12], label='Capital in period t+1') ax.plot(k_grid[:], optK[:][14], label='Capital in period t+1') # Now add the legend with some customizations. #legend = ax.legend(loc='upper left', shadow=False) # Set the fontsize for label in legend.get_texts(): label.set_fontsize('large') for label in legend.get_lines(): label.set_linewidth(1.5) # the legend line width plt.xlabel('Size of Capital in period t') plt.ylabel('Optimal Capital in period t+1') plt.title('Policy Function, capital next period - growth model') plt.show() # ## Question 4 # ## 1. Bellman Equation: # # $$V(w) = Max\{V^U(w), V^J(w)\}$$ # where: # $$V^U(w)= b + \beta E V(w)$$ # and # $$V^J(w) = E_0 \sum_{t=0}^{\infty} \beta^t w = \frac{w}{1 - \beta} $$ # Declare parameters # Preference parameters beta = 0.96 b = 0.05 # Taste shock parameters: AR(1) process: mu = 0 sigma = .15 size_w = 100 rho = 0 # + ''' ------------------------------------------------------------------------ Create Grid for State Space ------------------------------------------------------------------------ ub_w = scalar, upper bound grid size_w = integer, number of grid points in state space w_grid = vector, size_w x 1 vector of grid points ------------------------------------------------------------------------ ''' import ar1_approx as ar1 ln_w_grid, pi_t = ar1.addacooper(size_w, mu, rho, sigma) w_grid = np.exp(ln_w_grid) pi = np.transpose(pi_t) # - ''' ------------------------------------------------------------------------ Create grid of current utility values ------------------------------------------------------------------------ U = matrix, current period utility value for all possible choices of w and w' (rows are w, columns w') ------------------------------------------------------------------------ ''' U = np.zeros(size_w) for i in range(size_w): # loop over w U[i] = (w_grid[i])/(1-beta) # + ''' ------------------------------------------------------------------------ Value Function Iteration ------------------------------------------------------------------------ VFtol = scalar, tolerance required for value function to converge VFdist = scalar, distance between last two value functions VFmaxiter = integer, maximum number of iterations for value function V = matrix, the value functions at each iteration TV = matrix, the value function after applying the Bellman operator PF_discrete = matrix, matrix of policy function: eat=1, not eat=0 Vstore = array, stores V at each iteration VFiter = integer, current iteration number EV = scalar, expected value function for a given state U_eat = matrix, utility from eating cake now Vwait = matrix, value of waiting to eat the cake VF = vector, the "true" value function ------------------------------------------------------------------------ ''' VFtol = 1e-8 VFdist = 7.0 VFmaxiter = 500 V = np.zeros(size_w) # initial guess at value function TV = np.zeros(size_w) PF_discrete = np.zeros(size_w) Vstore = np.zeros((size_w, VFmaxiter)) #initialize Vstore array VFiter = 1 while VFdist > VFtol and VFiter < VFmaxiter: print('This is the distance', VFdist, VFiter) for i in range(size_w): # loop over w EV = 0 for ii in range(size_w): # loop over w EV += pi[i, ii] * V[ii] # note can move one space because of how we constructed grid U_emp = U[i] Vun = b + beta * EV TV[i] = max(U_emp, Vun) PF_discrete[i] = U_emp >= Vun # = 1 if take job Vstore[:, VFiter] = TV # store value function at each iteration for graphing later VFdist = (np.absolute(V - TV)).max() # check distance V = TV VFiter += 1 if VFiter < VFmaxiter: print('Value function converged after this many iterations:', VFiter) else: print('Value function did not converge') VF = V # solution to the functional equation # - # ### Threshold ''' ------------------------------------------------------------------------ Find threshold policy functions ------------------------------------------------------------------------ ''' threshold_w = w_grid[np.argmax(PF_discrete)] print(threshold_w) # Plot value function plt.figure() fig, ax = plt.subplots() ax.plot(w_grid[:], VF[:]) # Set the fontsize plt.xlabel('Wage offer') plt.ylabel('Value Function') plt.title('Value Function - search model') plt.show() # + #Set grid of b grid_b = np.linspace(0.05, 1, 20) threshold_vec = np.zeros(20) #Begin for loop for q in range(20): VFtol = 1e-8 VFdist = 7.0 VFmaxiter = 500 V = np.zeros(size_w) # initial guess at value function TV = np.zeros(size_w) PF_discrete = np.zeros(size_w) Vstore = np.zeros((size_w, VFmaxiter)) #initialize Vstore array VFiter = 1 while VFdist > VFtol and VFiter < VFmaxiter: print('This is the distance', VFdist, VFiter) for i in range(size_w): # loop over w EV = 0 for ii in range(size_w): # loop over w EV += pi[i, ii] * V[ii] # note can move one space because of how we constructed grid U_emp = U[i] Vun = grid_b[q] + beta * EV TV[i] = max(U_emp, Vun) PF_discrete[i] = U_emp >= Vun # = 1 if take job Vstore[:, VFiter] = TV # store value function at each iteration for graphing later VFdist = (np.absolute(V - TV)).max() # check distance V = TV VFiter += 1 if VFiter < VFmaxiter: print('Value function converged after this many iterations:', VFiter) else: print('Value function did not converge') VF = V # solution to the functional equation threshold_vec[q]=w_grid[np.argmax(PF_discrete)] # - print(threshold_vec) # Plot resevation wage as function of benefits plt.figure() fig, ax = plt.subplots() ax.plot(grid_b[:], threshold_vec[:]) # Set the fontsize plt.xlabel('Benefits') plt.ylabel('Threshold wage offer') plt.title('Threshold wage - search model') plt.show()
20,187
/muhammad-njyb-python-1.ipynb
51784872dbe5df53083398d71820d686980cc0c4
[]
no_license
Kurakuratempur/gettoknow
https://github.com/Kurakuratempur/gettoknow
0
0
null
2020-09-27T10:32:40
2020-09-27T09:54:10
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [default] # language: python # name: python3 # --- # # Backpropagation from IPython.display import Image Image("mlp.png", height=200, width=600) # # Variables & Terminology # * ## $W_{i}$ - weights of the $i$th layer # * ## $B_{i}$ - biases of the $i$th layer # * ## $L_{a}^{i}$ - _activation_ (Inner product of weights and inputs of previous layer) of the $i$th layer. # * ## $L_{o}^{i}$ - _output_ of the $i$th layer. (This is $f(L_{a}^{i})$, where $f$ is the activation function) # # # MLP with one input, one hidden, one output layer # * ## $X, y$ are the training samples # * ## $\mathbf{W_{1}}$ and $\mathbf{W_{2}}$ are the weights for first (hidden) and the second (output) layer. # * ## $\mathbf{B_{1}}$ and $\mathbf{B_{2}}$ are the biases for first (hidden) and the second (output) layer. # * ## $L_{a}^{0} = L_{o}^{0}$, since the first (zeroth) layers is just the input. # # # Activations and outputs # * ## $L_{a}^{1} = X\mathbf{W_{1}} + \mathbf{B_{1}}$ # * ## $L_{o}^{1} = \frac{1}{1 + e^{-L_{a}^{1}}}$ # * ## $L_{a}^{2} = L_{o}^{1}\mathbf{W_{2}} + \mathbf{B_{2}}$ # * ## $L_{o}^{2} = \frac{1}{1 + e^{-L_{a}^{2}}}$ # * ## Loss $E = \frac{1}{2} \sum_{S}(y - L_{o}^{2})^{2}$ # # ---- # Derivation of backpropagation learning rule: from IPython.display import YouTubeVideo YouTubeVideo("LOc_y67AzCA") import numpy as np from utils import backprop_decision_boundary, backprop_make_classification, backprop_make_moons from sklearn.metrics import accuracy_score from theano import tensor as T from theano import function, shared import matplotlib.pyplot as plt plt.style.use('ggplot') plt.rc('figure', figsize=(8, 6)) # %matplotlib inline # + x, y = T.dmatrices('xy') # weights and biases w1 = shared(np.random.rand(2, 3), name="w1") b1 = shared(np.random.rand(1, 3), name="b1") w2 = shared(np.random.rand(3, 2), name="w2") b2 = shared(np.random.rand(1, 2), name="b2") # layer activations l1_activation = T.dot(x, w1) + b1.repeat(x.shape[0], axis=0) l1_output = 1.0 / (1 + T.exp(-l1_activation)) l2_activation = T.dot(l1_output, w2) + b2.repeat(l1_output.shape[0], axis=0) l2_output = 1.0 / (1 + T.exp(-l2_activation)) # losses and gradients loss = 0.5 * T.sum((y - l2_output) ** 2) gw1, gb1, gw2, gb2 = T.grad(loss, [w1, b1, w2, b2]) # functions alpha = 0.2 predict = function([x], l2_output) train = function([x, y], loss, updates=[(w1, w1 - alpha * gw1), (b1, b1 - alpha * gb1), (w2, w2 - alpha * gw2), (b2, b2 - alpha * gb2)]) # - # make dummy data X, Y = backprop_make_classification() backprop_decision_boundary(predict, X, Y) y_hat = predict(X) print("Accuracy: ", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1))) for i in range(500): l = train(X, Y) if i % 100 == 0: print(l) backprop_decision_boundary(predict, X, Y) y_hat = predict(X) print("Accuracy: ", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1))) # # Exercise: Implement an MLP with two hidden layers, for the following dataset X, Y = backprop_make_moons() plt.scatter(X[:, 0], X[:, 1], c=np.argmax(Y, axis=1)) # ### Hints: # 1. Use two hidden layers, one containing 3 and the other containing 4 neurons # 2. Use learning rate $\alpha$ = 0.2 # 3. Try to make the network converge in 1000 iterations # + # enter code here # - # ### Tips & Tricks for backprogation: # [Efficient BackProp, LeCun et al](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) wah ini dan jalankan perhitungannya. # # | barang | harga | # |-------------|--------| # | ayam | 20000 | # | ikan kembung| 15000 | # | sayur kangkung | 10000 | # | susu | 22000 | # + colab={} colab_type="code" id="px1m7D9NTd9C" Ayam = 20000 Kembung = 15000 Kangkung = 10000 Susu = 22000 Diskon = 0.25 Biaya = (1*Ayam + 4*Kembung + 3*Kangkung + 2*Susu) Biayafinal = Biaya - Biaya*Diskon jawaban_12 = Biayafinal # + [markdown] colab_type="text" id="apXGBudnTd9E" # 13. Surti, remaja anak bapak kades dan si Tejo, jejaka yang baru saja mudik berencana untuk menikah 2 tahun dari sekarang. Jika biaya pernikahan dan resepsi di kampung mereka adalah 48.000.000, berapa uang yang harus ditabung mereka berdua per bulannya agar 2 tahun lagi mereka bisa menikah? # # asumsi: tidak ada inflasi, dan semua harga selalu konstan. # + colab={} colab_type="code" id="SJZeQlRGTd9E" biayanikah = 48000000 estimasiwaktu = 24 #bulan tabungan = biayanikah / estimasiwaktu #total tabungan berdua, per orang berapa tidak cukup informasinya jawaban_13 = tabungan # + [markdown] colab_type="text" id="t6zq8ndETd9G" # 14. Sebuah bioskop ingin memutar film dan menampilkan judul film tersebut di website mereka. Namun judul film tersebut semuanya memakai huruf kecil. Bantulah bioskop tersebut # # Hint: Pakai method di dalam string # + colab={} colab_type="code" id="QppBED31Td9H" judul = 'the lord of the rings: the return of the king' jawaban_14 = print(judul.title()) # + [markdown] colab_type="text" id="SGZoVCxvTd9J" # 15. Carilah ada berapa kata 'gandalf' di dalam teks berikut. (tidak case sensitive) # + colab={} colab_type="code" id="Phdw2URbTd9J" teks = "Centuries later, during the War of the Ring, Gandalf leads Aragorn, Legolas, Gimli, and King Théoden to Isengard, \ where they reunite with Merry and Pippin. Gandalf retrieves the defeated Saruman's palantír. Pippin later looks \ into the seeing-stone and is seen by Sauron. From Pippin's description of his visions, Gandalf surmises that Sauron \ will attack Gondor's capital Minas Tirith. He rides there to warn Gondor's steward Denethor, taking Pippin with him." a = teks.count("Gandalf") b = teks.count("gandalf") total = a + b jawaban_15 = total # -
5,913
/Iris.ipynb
f757a0cec578c5f74b237efac68701679d683c4b
[]
no_license
thomasdubois18/Data_Science
https://github.com/thomasdubois18/Data_Science
0
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785,257
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="Ro5oRBZgdhbU" colab_type="text" # # return Anahtar Sözcüğü # # Kabaca fonksiyonun o noktada durduran ve önündeki değeri dışarıya döndüren anahtar sözcüktür. # + id="PKNTQ6laevFp" colab_type="code" outputId="d91db9a1-b61e-434b-c366-2c45012a770f" colab={"base_uri": "https://localhost:8080/", "height": 136} #@title Fonksiyon dışında kullanılamaz. return # + id="kn7xutzmfC0i" colab_type="code" outputId="6eb95766-0373-4e2a-ae2a-e672df4c9115" colab={"base_uri": "https://localhost:8080/", "height": 36} #@title Basit kullanımı def deneme(): return 1 print(deneme()) # + id="s2rXkGiCfVwZ" colab_type="code" outputId="4b321431-0503-4f7c-c87c-27962e1e1922" colab={"base_uri": "https://localhost:8080/", "height": 36} #@title Önündeki değeri fonksiyonun dışına döndürür def deneme(): return 'cagatay' deger = deneme() print(deger) # + id="v97XmU_ffw_c" colab_type="code" outputId="12b72c3a-9fd7-4c12-b59e-bc08728290ae" colab={"base_uri": "https://localhost:8080/", "height": 36} #@title Kullanıldığı satırda fonksiyon biter, sonra yazılanlar anlamsızdır def deneme(): a = 5 return a a = 456 print(deneme()) # + id="c1gDX_qDgJnZ" colab_type="code" outputId="71ad3139-e1e1-4e0e-c781-eb0d44c71200" colab={"base_uri": "https://localhost:8080/", "height": 36} #@title Önünde değer yerine işlem varsa, bu işlemin gerçekleşmesini bekler ve sonucunu döndürür def deneme(): return (1 + 4 + 9)*0 > 1 and True print(deneme()) # + id="ykKWPtPNiLLZ" colab_type="code" outputId="801387f1-b2ef-447e-9901-147622d17056" colab={"base_uri": "https://localhost:8080/", "height": 36} #@title Önünde hiçbir şey yoksa None döndürür def deneme(): return print(deneme()) # + [markdown] id="GgrMiEnEgfKG" colab_type="text" # **Python oldukça esnektir, yalnızca değer döndürmek zorunda değilsiniz.** # + id="iGnJpusOgtgu" colab_type="code" outputId="3cdc5eef-b596-41e0-ec2d-e8d1b974fbc4" colab={"base_uri": "https://localhost:8080/", "height": 36} def deneme(): return 1, 2, 'cagatay' print(deneme()) # + id="WpSQdCKi2Bsb" colab_type="code" outputId="3c0a7ba2-c197-4faa-b156-dbe5322f9e58" colab={"base_uri": "https://localhost:8080/", "height": 36} def deneme(a): if a: return 'cagatay' else: return 1.4 print(deneme(False)) # + id="jododA_3g0N_" colab_type="code" outputId="fc605d9b-c3a1-431e-9640-ad71ad74ce76" colab={"base_uri": "https://localhost:8080/", "height": 36} def deneme(): a = {'a': 1, 'b': None, 'c': 3.2} return a print(deneme()) # + id="tmiTcuZ1hFtD" colab_type="code" outputId="bf134d7d-e0b8-43a1-ba1c-db4e1a2f2f4d" colab={"base_uri": "https://localhost:8080/", "height": 36} class A: def __init__(self): self.ad = 'cagatay' def deneme(): obje = A() return obje disaridaki_obje = deneme() print(disaridaki_obje.ad) # + id="lauoTmtdiEBO" colab_type="code" cellView="both" outputId="683957fa-7186-454b-e552-0bcbc4532432" colab={"base_uri": "https://localhost:8080/", "height": 54} def baska(): def deneme(): print('deneme') return deneme cikti = baska() print(cikti()) map(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True)) # Fortes correlations ! mise à part peut etre pour l'épaisseur des sepales. Il peut être interessant de faire une ACP pour voir les nouvelles variables ainsi construites ! Ce n'est pas forcement interessant de part le nombre de variables faible mais peu etre interessant étant donné la correlation. # # ACP df.head() liste_df = list(df) df[liste_df[0:-2]].head() n = df[liste_df[0:-2]].shape[0] p = df[liste_df[0:-2]].shape[1] #car target te label print(n,p) # On standardise les données # + from sklearn.preprocessing import StandardScaler sc = StandardScaler() df_standardise = sc.fit_transform(df[liste_df[0:-2]]) df_standardise print(np.mean(df_standardise,axis=0)) #presque 0 car erreur de trancature print(np.std(df_standardise,axis=0)) #1 # - from sklearn.decomposition import PCA acp = PCA(svd_solver='full') coord = acp.fit_transform(df_standardise) print(acp.n_components_) #car on a tout gardé ici eigval = (n-1)/n*acp.explained_variance_ print(eigval) #valeur corrigée plt.plot(np.arange(1,p+1),eigval) plt.title("Scree plot") plt.ylabel("Eigen values") plt.xlabel("Factor number") plt.show() # Compliqué à choisir car peu de variables, mais on peut choisir entre 2 et 3. print(acp.singular_values_**2/n) #ou valeurs singulieres prop_var = acp.explained_variance_ratio_ #proportion de variance expliquée prop_var = np.cumsum(prop_var) print(prop_var) #cumul de variance expliquée plt.plot(np.arange(1,p+1),prop_var) plt.title("Explained variance vs. # of factors") plt.ylabel("Cumsum explained variance ratio") plt.xlabel("Factor number") plt.show() # Cette fois on semble plus s'orrienter vers 2-3 valeurs. #seuils pour test des bâtons brisés bs = 1/np.arange(p,0,-1) bs = np.cumsum(bs) bs = bs[::-1] #test des bâtons brisés print(pd.DataFrame({'Val.Propre':eigval,'Seuils':bs})) # De part la règle de Kaiser, pour une ACP normée, la somme des valeurs propres étant égale au nombre de variables, leur moyenne vaut 1. On considère ainsi qu’un axe est intéressant si sa valeur propre est supérieure 1. # # Ici on est tenté entre 1 variable et 2 car la deuxieme est à 0,9... color_list = ['blue', 'red', 'green'] colors = [color_list[c] for c in df['target']] #positionnement des individus dans le premier plan fig, axes = plt.subplots(figsize=(12,12)) axes.set_xlim(-5,5) #même limites en abscisse axes.set_ylim(-5,5) #et en ordonnée #placement des étiquettes des observations for i in range(n): plt.annotate(df.index[i],(coord[i,0],coord[i,1]),color=colors[i]) #ajouter les axes plt.plot([-5,5],[0,0],color='silver',linestyle='-',linewidth=1) plt.plot([0,0],[-5,5],color='silver',linestyle='-',linewidth=1) #affichage plt.show() # Pas sur qu'on soit mieux qu'avec les variables de départ, surement du au faite que l'on avait peu de variables (4). # La classe bleue est très bien séparée, les deux autres beaucoup moins. # # Résolution par Apprentissage # # ## Naive Bayes # "modèle à caractéristiques statistiquement indépendantes " : créé donc des classes en partant du principe que chaque paramètre est indépendant from sklearn.naive_bayes import GaussianNB df2 = df[liste_df[0:-2]] clf = GaussianNB() clf.fit(df2, df['target']) #on entraine notre modele clf.get_params() result = clf.predict(df2) result # On a testé les résultats sur le même jeu de données, testons la qualité des prédictions : nb_error = 0 for i in (result - target): if i !=0: nb_error += 1 print("Nombre de valeurs fausses : "+str(nb_error)) print("Pourcentage de valeurs justes : "+str((len(result)-nb_error)*100/len(result))) # on a quand meme 6 erreurs de prédiction sur 150 from sklearn.metrics import accuracy_score accuracy_score(result, target) # Nous donne un score plus "travaillé" # # # On peut décider de vouloir savoir où sont les erreurs : from sklearn.metrics import confusion_matrix mat_conf = confusion_matrix(target, result) mat_conf sns.heatmap(mat_conf, square=True, annot=True, cbar=False , xticklabels=list(iris_data.target_names) , yticklabels=list(iris_data.target_names)) plt.xlabel('predicted values') plt.ylabel('real values'); # 0 erreurs sur Setosa, si on regarde sur les Graphs ci dessus on avait bien identifié que les setosas étaient très bien séparés ! Donc, logique ! # # Apprentissage Validation # On utilise une méthode par apprentissage validation en créant des jeux de données 70% / 30% from sklearn.model_selection import train_test_split # version 0.18.1 # split la data en 70%/30% data_test = train_test_split(df2, target , random_state=0 , train_size=0.7) #data_test est une liste de 4 DF : data_train, data_test, target_train, target_test = data_test clf = GaussianNB() clf.fit(data_train, target_train) result = clf.predict(data_test) target = target_test nb_error = 0 for i in (result - target): if i !=0: nb_error += 1 print("Nombre de valeurs fausses : "+str(nb_error)) print("Pourcentage de valeurs justes : "+str((len(result)-nb_error)*100/len(result))) accuracy_score(result, target_test) mat_conf = confusion_matrix(target, result) sns.heatmap(mat_conf, square=True, annot=True, cbar=False , xticklabels=list(iris_data.target_names) , yticklabels=list(iris_data.target_names)) plt.xlabel('predicted values') plt.ylabel('real values'); # Perfect ! # # Affichage des territoires de classification # Méthode : On créé une espece de matrice avec toutes les valeurs possibles et on voit quelles valeurs leur attribu notre algo. data_sepales = df[['sepal length (cm)','sepal width (cm)']] target = df['target'] # + # On réapprend clf = GaussianNB() clf.fit(data_sepales, target) h = 0.1 #epaisseur de notre "grillage" de valeurs μ = 0.5 #valeur dont on repousse un peu les predictions par rapport aux valeurs max et min #attention ne pas trop pousser ! # On cherche les valeurs min/max de longueurs (x)/largeurs (y) des sépales x_min = df['sepal length (cm)'].min() - μ x_max = df['sepal length (cm)'].max() + μ y_min = df['sepal width (cm)'].min() - μ y_max = df['sepal width (cm)'].max() + μ x = np.arange(x_min, x_max, h) #plages de valeurs utilisées celon x y = np.arange(y_min, y_max, h) #plages de valeurs utilisées celon y # - # On créé alors une meshgrid de ces valeurs (espèce de matrice 2D de nos plages de valeurs) xx, yy = np.meshgrid(x,y ) data_vizu = list(zip(xx.ravel(), yy.ravel()) ) #ligne tres technique... explication ci apres : # Dans xx on contient len(y) vecteurs possédant chacun toutes les valeurs de x : print(xx) print(len(xx)) print(len(xx[0])) # pour yy c'est l'inverse : chaque vecteur contient une unique valeur de y repété len(x) fois print(yy) print(len(yy)) print(len(yy[0])) # ravel concatene les vetceurs d'une matrice : print(xx.ravel()) print(len(xx.ravel())) print(len(xx)*len(xx[0])) # zip quand a lui récupere pour une suite de liste la premiere valeur de chaque liste pour faire une premiere suite # # puis la deuxieme valeur de chaque liste pour en faire une nouvelle # # etc # # Ainsi : data_vizu[:10] # + z = clf.predict(data_vizu) fig = plt.figure(figsize=(8, 5)) color_list = ['blue', 'red', 'green'] colors = [color_list[c] for c in z] plt.scatter(xx.ravel(), yy.ravel(), c=colors) plt.xlim(xx.min() - .07, xx.max() + .07) plt.ylim(yy.min() - .07, yy.max() + .07) plt.xlabel('petal length (cm)') plt.ylabel('petal width (cm)') # - # Cela parait très joli, mais est-ce efficace ? On peut afficher nos valeurs connus pour voir si ce modèle est fiable ou non # # Pour cela on met z au meme format que xx et yy pour utiliser colormesh zz= z.reshape(xx.shape) zz # + fig = plt.figure(figsize=(8, 5)) plt.pcolormesh(xx, yy, zz) # Affiche les déductions en couleurs pour les couples x,y # Plot also the training points color_list = ['blue', 'red', 'green'] colors = [color_list[c] for c in target] plt.scatter(df['sepal length (cm)'],df['sepal width (cm)'], c=colors) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xlabel('petal length (cm)') plt.ylabel('petal width (cm)') # - # A noter qu'on aurait pu utiliser directement colormesh au lieu de scatter # # On remarque que notre prédiction n'est pas vraiment parfaite, c'est déjà ce que l'on avait remarqué précédemment lorsque l'on utilise uniquement les pétales et pas les sépales # # Méthode des K plus proches voisins # Cette méthode d'apprentissage utilise comme son nom l'indique la distance avec les voisins les plus proches pour déterminer les classes # # K represente le nombre de voisins que l'on veut utiliser. # # Avec k (trop) faible on risque d'avoir affaire à du sous apprentissage (underfitting) et donc pas de prédictions. # # Avec k (trop) fort on risque d'avoir affaire à du sur apprentissage (overfitting) et donc trop coller à notre échantillon. # + from sklearn import neighbors clf = neighbors.KNeighborsClassifier() # - from ipywidgets import interact @interact(k=(0,30)) def k_change(k=5): clf = neighbors.KNeighborsClassifier(n_neighbors=k) clf.fit(data_sepales, target) z = clf.predict(data_vizu) zz = z.reshape(xx.shape) fig = plt.figure(figsize=(8, 5)) plt.pcolormesh(xx, yy, zz) color_list = ['blue', 'red', 'green'] colors = [color_list[c] for c in target] plt.scatter(df['sepal length (cm)'],df['sepal width (cm)'], c=colors) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xlabel('petal length (cm)') plt.ylabel('petal width (cm)') # Etant donné que l'on a plusieurs valeurs possibles pour k (ici on a choisi de 1 à 30 mais les extremes sont forcément 'mauvais') on peu essayer de trouver un K "optimal" @interact(p=(30,70)) def p_change(p=50): # split the data in 80%/20% in each set data_test = train_test_split(data_sepales, target , random_state=0 , train_size=p/100) #data_test est une liste de 4 DF : data_train, data_test, target_train, target_test = data_test result = [] k_values = range(1,30) for k in k_values: clf = neighbors.KNeighborsClassifier(n_neighbors=k) clf.fit(data_train, target_train) z = clf.predict(data_test) score = accuracy_score(z, target_test) result.append(score) fig = plt.figure(figsize=(8, 5)) plt.plot(k_values, result) # En essayant de faire varier la proportion de l'échantillonage apprentissage/validation on voit que la valeur optimale du k reste très fou mais il semble y avoir une valeur interessante aux alentours de 20 ! # # Et si on passait par un modèle d'apprentissage non supervisé ? # Pour cela il ne faut donc pas utiliser la donnée target qui renferme les 3 espèces. # # On peut alors voir si les groupes qui font être formés vont être les mêmes ! # ## On commence par réduire le nombre de variables # En effet bien souvent pour les modèles d'apprentissage non supervisé on utilise beaucoup de paréamètres pour ne perdre aucune information, puis on essaye de créer des nouvelles variables à partir de celles de départ, on utilise ici un ACP (Analyse en Composantes Principales). # # Ici il suffit de choisir le nombre de composantes finales voulues, de donner notre jeu de données puis d'obtenir ces nouvelles composantes. df_non_sup = df[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']] # + from sklearn.decomposition import PCA model = PCA(n_components=2) model.fit(df_non_sup) df_reduc = pd.DataFrame(model.transform(df_non_sup), columns = ['C1', 'C2']) # - # Ainsi on a transformé notre data de 4 composantes original : df_non_sup.head() # En une nouvelle data contenant "autant d'informations" mais avec seulement 2 composantes (que l'on a nommé C1 et C2) : df_reduc.head() # Il pourrait être interessant de voir si les 2 nouvelles composantes différencient bien les 3 espèces de fleurs. df_reduc['label'] = df['label'] df_reduc.head() # On peut voir ca soit de manière automatique : sns.lmplot("C1", "C2", hue='label', data=df_reduc, fit_reg=False) # Soit en voulant gérer un peu plus les choses : # + color_list = ['blue', 'red', 'green'] colors = [color_list[c] for c in iris_data.target] plt.scatter(df_reduc['C1'], df_reduc['C2'], c=colors) plt.xlabel('C1') plt.ylabel('C2') for ind, s in enumerate(iris_data.target_names): # on dessine de faux points, car la légende n'affiche que les points ayant un label plt.scatter([], [], label=s, color=color_list[ind]) plt.legend(scatterpoints=1, frameon=False, labelspacing=1, bbox_to_anchor=(1.3, 0.5) , loc="center right", title='Species') # - # On remarque donc que nos 2 nouvelles composantes conservent bien les classes, et les bonnes ! # # Clustering # On va maintenant créer des regroupement par clustering, on va commencer par choisir Kmeans : # # # + from sklearn.cluster import KMeans model_kmeans = KMeans(n_clusters=3, random_state=0) model_kmeans.fit(df_reduc[['C1', 'C2']]) groups_kmeans = model_kmeans.predict(df_reduc[['C1', 'C2']]) df_reduc['group_kmeans'] = groups_kmeans # - sns.lmplot("C1", "C2", data=df_reduc, hue='label', col='group_kmeans', fit_reg=False) # Ca ne match vraiment pas bien... Il se trouve que Kmean fonctionne tres bien avec des formes... en forme de cercle ! Or nous avons ici plutot des elipses, on essaye alors GMM (Gaussian Mixture Models) qui est la méthode la plus rapide et qui s'adapte à beaucoup de "formes" de groupes, attention toutefois l'ACP est nécéssaire au préalable car il utilisera toutes composantes ! # + from sklearn.mixture import GaussianMixture model_GMM = GaussianMixture (n_components=3, covariance_type='full') model_GMM.fit(df_reduc[['C1', 'C2']]) groups_GMM = model_GMM.predict(df_reduc[['C1', 'C2']]) df_reduc['group_GMM'] = groups_GMM # - sns.lmplot("C1", "C2", data=df_reduc, hue='label', col='group_GMM', fit_reg=False) # Malgré 3 erreurs c'est bien mieux ! De toute facon les deux groupes étant très proches cela reste un très bon groupement.
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nlzakh02/Zakharova_Metis
https://github.com/nlzakh02/Zakharova_Metis
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] colab_type="text" id="JUgOrRxl0sKq" # ## Google Colab # # Google provides a free cloud service based on Jupyter Notebooks that supports free CPU and GPU. It allows you to develop deep learning applications using popular libraries such as PyTorch, TensorFlow, Keras, and OpenCV (without installation). All these libraries are pre-installed on Google Colab along wilt Python. # - # ### 1. Notebook Creation # # Login with your account and got to [google colab](https://colab.research.google.com). You will be prompted to either create a new notebook or you can also upload your `.ipynb` notebook from your Github, Google Drive or your local machine. # # <img src="./images/colab_upload.png"> # Once you have created the notebook, you can rename it by clicking on notebook name in the upper right corner. # <img src="./images/colab_rename.png"> # # # All your notebooks will be saved in your Google Drive inside the directory `Colab Notebooks`. # ### 2. Dataset # # In upcoming assignments you would need data to train your model. The best way to use colab with your dataset is to upload your dataset to google drive and the mount your drive. You can do so with the following command # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="nyfGOBXU7cjL" outputId="f2c83b83-9633-403c-fe4e-5750acd9c33d" from google.colab import drive drive.mount("/content/gdrive", force_remount=True) # + [markdown] colab_type="text" id="hAvnynvP0sKv" # Now you should see your drive on the left-hand side of the screen!.(You may need to hit "refresh" if it doesn't occur immediately) # # <img src="./images/colab_mount.png"> # + [markdown] colab_type="text" id="YddOu3Bc0sKx" # ### 3. Installing python libraries # # In general you would not need to install anything, but incase you have then you can do so with the following command. # - # !pip3 install torch torchvision # <img src="./images/colab_install.png"> # ### 4. Download Notebooks # # Your notebooks are automatically saved in your google drive. But if you need to download them, you can do so by `File` -> `Download .ipynb` ils # logging for gensim (set to INFO) import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) # - cnts = counts.transpose() # Convert a sparse matrix into a gensim corpus corps = matutils.Sparse2Corpus(cnts) # Associating counts with words id2word = dict((v, k) for k, v in count.vocabulary_.items()) lda1 = models.LdaModel(corpus=corps, num_topics=3, minimum_probability=0.03, id2word=id2word, passes=10, alpha='auto', eta='auto') # lda1.print_topics(num_words=15) # Transform speeches from word space to topic space lda_corps = lda1[corps] #lda_corps # Store topic vectors for each document in a list of lists lda_docs = [doc for doc in lda_corps] # Check topic space vectors for first 10 documents lda_docs[0:10] # # Clustering with K-means # Create dataframe with results of transformation into topic space for clustering and plotting lda_d = [dict(doc) for doc in lda_corps] d = pd.DataFrame(lda_d).fillna(0) d.columns = ['dim1', 'dim2', 'dim3'] d1 = d.copy() d.head(2) from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score # + # Scatter Plot scale = 100 figure, tax = ternary.figure(scale=scale) figure.set_size_inches(10, 10) tax.boundary(linewidth=2.0) tax.gridlines(multiple=5, color="blue") # Set Axis labels and Title fontsize = 15 tax.set_title("Clustering of Speeches", fontsize=20) tax.left_axis_label("Government as Concept", fontsize=fontsize) tax.right_axis_label("Emotional, Aspirational", fontsize=fontsize) tax.bottom_axis_label("Act of Governing", fontsize=fontsize) # Plot a few different styles with a legend p_set = d[['dim1', 'dim2', 'dim3']] points = [tuple(x*100) for x in p_set.values] #points = random_points(30, scale=scale) tax.scatter(points, marker='s', color="red", s=50) tax.legend() tax.ticks(axis='lbr', linewidth=1, multiple=10) tax.clear_matplotlib_ticks() tax.show() # - # ### Clustering scaled data from sklearn.preprocessing import scale kmdata = scale(d) # + # List for saving silhouette score for each number of clusters sc = [] # List for saving sum of squared distances for samples to their closest cluster center for each number of clusters sse = [] # List with numbers of clusters to be tested ks = list(range(2, 58)) for k in ks: km = KMeans(n_clusters=k) km.fit(d) label = km.predict(d) sc.append(silhouette_score(d, label)) sse.append(km.inertia_) # - # Plot silhouette score plt.plot(ks, sc) #plt.xlim((0,10)) #plt.ylim((0.6, 1)) # Plot sum of squared distances for samples to their closest cluster center plt.plot(ks, sse) #plt.xlim((3, 8)) #plt.ylim((0, 2)) # ### Best number of clusters identified is 6 km = KMeans(n_clusters=6) km.fit(kmdata) km.cluster_centers_ km.labels_ d["class"] = km.labels_ colors = ["red", "blue", "green", "black", "magenta", "cyan"] d["colors"] = d["class"].map(lambda x: colors[x-1]) d.head() # + # Scatter Plot scale = 100 figure, tax = ternary.figure(scale=scale) figure.set_size_inches(10, 10) tax.boundary(linewidth=2.0) tax.gridlines(multiple=5, color="blue") # Set Axis labels and Title fontsize = 20 tax.set_title("Clustering of Speeches", fontsize=20) tax.left_axis_label("Government as Concept", fontsize=fontsize) tax.right_axis_label("Emotional, Aspirational", fontsize=fontsize) tax.bottom_axis_label("Act of Governing", fontsize=fontsize) # Plot a few different styles with a legend p_set = d[['dim1', 'dim2', 'dim3']] points = [tuple(x*100) for x in p_set.values] #points = random_points(30, scale=scale) tax.scatter(points, marker='s', color=d["colors"].values, s=150) tax.legend() tax.ticks(axis='lbr', linewidth=1, multiple=10) tax.clear_matplotlib_ticks() tax.show() # - # # Other Dimentionality Reduction Methods Trialed # ## Principal Component Analysis (PCA) # + n = list(range(59))[1:] from sklearn.decomposition import PCA var = [] for i in n: reducer = PCA(n_components=i) reduced_X = reducer.fit(counts.toarray()) var.append(sum(reduced_X.explained_variance_ratio_)) # - plt.plot(n, var); # ## Sparse PCA from sklearn.decomposition import MiniBatchSparsePCA, SparsePCA pca = MiniBatchSparsePCA(n_components=7, alpha=0.2, batch_size=5, ridge_alpha=0.2) pca_data = pca.fit(counts.toarray()) pca1 = pca.transform(counts.toarray()) #pca1 t = pd.DataFrame(pca.components_, columns=count.get_feature_names()).T g = t[(t.T != 0).any()][0].sort_values(ascending=False)[:20] print("First 20 n-grams in 1st component: ", g) plt.imshow(WordCloud().generate_from_frequencies(g.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g1 = t[(t.T != 0).any()][1].sort_values(ascending=False)[:20] print("First 20 n-grams in 2nd component: ", g1) plt.imshow(WordCloud().generate_from_frequencies(g1.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g2 = t[(t.T != 0).any()][2].sort_values(ascending=False)[:20] print("First 20 n-grams in 3rd component: ", g2) plt.imshow(WordCloud().generate_from_frequencies(g2.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g3 = t[(t.T != 0).any()][3].sort_values(ascending=False)[:20] print("First 20 n-grams in 4th component: ", g3) plt.imshow(WordCloud().generate_from_frequencies(g3.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g4 = t[(t.T != 0).any()][4].sort_values(ascending=False)[:20] print("First 20 n-grams in 5th component: ", g4) plt.imshow(WordCloud().generate_from_frequencies(g4.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g5 = t[(t.T != 0).any()][5].sort_values(ascending=False)[:15] print("First 20 n-grams in 6th component: ", g5) plt.imshow(WordCloud().generate_from_frequencies(g5.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() g6 = t[(t.T != 0).any()][6].sort_values(ascending=False)[:15] print("First 20 n-grams in 7th component: ", g6) plt.imshow(WordCloud().generate_from_frequencies(g6.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() # ## Non-negative Matrix Factorization (NMF) from sklearn.decomposition import NMF nmf = NMF(n_components=6, alpha=1.) nmf_x = nmf.fit(counts.toarray()) nmf.reconstruction_err_ n = pd.DataFrame(nmf.components_, columns=count.get_feature_names()).T f = n[(n.T != 0).any()][0].sort_values(ascending=False)[:15] print("First 15 n-grams in 1st component: ", f) plt.imshow(WordCloud().generate_from_frequencies(f.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() f1 = n[(n.T != 0).any()][1].sort_values(ascending=False)[:15] print("First 15 n-grams in 2nd component: ", f1) plt.imshow(WordCloud().generate_from_frequencies(f1.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() f2 = n[(n.T != 0).any()][2].sort_values(ascending=False)[:15] print("First 15 n-grams in 3rd component: ", f2) plt.imshow(WordCloud().generate_from_frequencies(f2.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() f3 = n[(n.T != 0).any()][3].sort_values(ascending=False)[:15] print("First 15 n-grams in 4th component: ", f3) plt.imshow(WordCloud().generate_from_frequencies(f3.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() f4 = n[(n.T != 0).any()][4].sort_values(ascending=False)[:15] print("First 15 n-grams in 5th component: ", f4) plt.imshow(WordCloud().generate_from_frequencies(f4.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() f5 = n[(n.T != 0).any()][5].sort_values(ascending=False)[:15] print("First 15 n-grams in 6th component: ", f5) plt.imshow(WordCloud().generate_from_frequencies(f5.to_dict()), interpolation='bilinear') plt.axis("off") plt.show() # # Other Clustering Methods Trialed # ## K-means Clustering with the Scaled Data from sklearn.preprocessing import scale kmdata = scale(d1) # + # List for saving silhouette score for each number of clusters sc = [] # List for saving sum of squared distances for samples to their closest cluster center for each number of clusters sse = [] # List with numbers of clusters to be tested ks = list(range(2, 58)) for k in ks: km = KMeans(n_clusters=k) km.fit(kmdata) label = km.predict(kmdata) sc.append(silhouette_score(kmdata, label)) sse.append(km.inertia_) # - # Plot silhouette score plt.plot(ks, sc) #plt.xlim((0,10)) #plt.ylim((0.6, 1)) # Plot sum of squared distances for samples to their closest cluster center plt.plot(ks, sse) #plt.xlim((4, 10)) #plt.ylim((0, 5)) # ### The best number of clusters appears to be 6. # ## DBSCAN Clustering from sklearn.cluster import DBSCAN dbs = DBSCAN(eps=0.1, min_samples=3, metric="euclidean") # Cluster unscaled data dbs.fit(d) # 4 clusters were identified, one point was identified as not belonging to any cluster set(dbs.labels_) # Cluster scaled data dbs.fit(kmdata) # 4 clusters were identified, one point was identified as not belonging to any cluster set(dbs.labels_) # ## Mean Shift Clustering from sklearn.cluster import MeanShift ms = MeanShift() # Cluster unscaled data ms.fit(d) # Identified 5 clusters ms.cluster_centers_ ms.labels_ # Cluster scaled data ms.fit(kmdata) # Identified 4 clusters ms.cluster_centers_ ms.labels_
11,524
/HW Solution/HW7.ipynb
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fayrek/Python-Lab-Fall-2020
https://github.com/fayrek/Python-Lab-Fall-2020
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null
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Jupyter Notebook
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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/Gadgeteering/LegoBrickClassification/blob/master/Lego_Sorter_Image_Classifier.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="kqd-PgheVVuG" colab_type="code" colab={} # + id="e8Zn3-MGlBL1" colab_type="code" outputId="287b270b-d86f-48f1-9140-4c4fbdf60336" colab={"base_uri": "https://localhost:8080/", "height": 1000} from __future__ import absolute_import, division, print_function, unicode_literals # !pip install -q tensorflow-gpu==2.0.0-beta1 import tensorflow as tf from tensorflow.keras import datasets, layers, models import tensorflow.keras from tensorflow import keras from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Dropout, Input from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import categorical_crossentropy from tensorflow.keras.preprocessing.image import ImageDataGenerator print(tf.__version__) import random import glob import os import pathlib import time import matplotlib.pyplot as plt from datetime import datetime from packaging import version import IPython.display as display import pandas as pd from google.colab import drive drive.mount('/content/drive') # Load the TensorBoard notebook extension # %load_ext tensorboard # Clear any logs from previous runs # !rm -rf ./logs/ path= '/content/drive/My Drive/DATA/LEGO-brick-images' data_root = pathlib.Path(path) dataset_path = pathlib.Path(path + "/dataset.csv") train_path = path+ '/train' valid_path = path+ '/valid' print (train_path) df = pd.read_csv(dataset_path, skipinitialspace=True, skip_blank_lines=True,encoding='utf-8', index_col='id') label_names = [( str(f)) for f in df.index] #label_names = ["11214 Bush 3M friction with Cross axle","18651 Cross Axle 2M with Snap friction","2357 Brick corner 1x2x2","3003 Brick 2x2","3004 Brick 1x2","3005 Brick 1x1","3022 Plate 2x2","3023 Plate 1x2","3024 Plate 1x1","3040 Roof Tile 1x2x45deg","3069 Flat Tile 1x2","32123 half Bush","3673 Peg 2M","3713 Bush for Cross Axle","3794 Plate 1X2 with 1 Knob","6632 Technic Lever 3M"] print (label_names) class_size=len(label_names) train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, vertical_flip=True) valid_datagen = ImageDataGenerator(rescale=1./255) train_batches = train_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32) valid_batches = valid_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32) test_batches = test_datagen.flow_from_directory(path, target_size=(224,224), classes=label_names, batch_size=32) image_model = tf.keras.applications.InceptionV3(include_top=False,weights='imagenet',input_tensor=Input(shape=(224,224,3))) #vgg16_model = tf.keras.applications.vgg16.VGG16(weights='imagenet', include_top=False, input_tensor=Input(shape=(224,224,3))) # Create the model model = Sequential() # Add the vgg convolutional base model model.add(image_model) # Add new layers model.add(Flatten()) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(class_size, activation='softmax')) # Show a summary of the model. Check the number of trainable parameters model.summary() model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(lr=1e-4), metrics=['acc']) # Define the Keras TensorBoard callback. logdir="logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1, write_graph=True, write_images=True, write_grads=True, batch_size=32) history = model.fit_generator( train_batches, steps_per_epoch=train_batches.samples/train_batches.batch_size , epochs=5, validation_data=valid_batches, validation_steps=valid_batches.samples/valid_batches.batch_size, verbose=1, callbacks=[tensorboard_callback]) model.evaluate(test_batches) # %tensorboard --logdir logs saved_model_path = "/content/drive/My Drive/tmp/saved_models/"+str(int(time.time())) keras.experimental.export_saved_model(model, saved_model_path) # + id="_S7pf2u1ITGI" colab_type="code" colab={} # + [markdown] id="0MxNFlxaNIii" colab_type="text" # # + [markdown] colab_type="text" id="7Z2jcRKwUHqV" # This notebook provides recipes for loading and saving data from external sources. # + [markdown] id="RGBAVArKA2U2" colab_type="text" # # + id="JLOUroipA1Jm" colab_type="code" colab={} # + id="0fd3FxU-Rv_9" colab_type="code" outputId="939dcc5f-803a-4d69-fb61-632fd2f057bd" colab={"base_uri": "https://localhost:8080/", "height": 760} from __future__ import absolute_import, division, print_function, unicode_literals import matplotlib.pylab as plt # !pip install -q tensorflow-gpu==2.0.0-beta1 import tensorflow as tf from tensorflow import keras import numpy as np import PIL.Image as Image from google.colab import drive import pathlib import csv drive.mount('/content/drive') from tensorflow.keras import layers path= "/content/drive/My Drive/DATA/LEGO brick images" with open(path+"/labels.csv", 'r') as f: reader = csv.reader(f,quoting=csv.QUOTE_ALL) label_names = list(reader) label_names=label_names[0] print (label_names) saved_model_path = "/content/drive/My Drive/tmp/saved_models/1563634289/" test_path = '/content/drive/My Drive/DATA/LEGO-brick-images_Archive/test6.JPG' IMAGE_SHAPE = (224, 224) img =Image.open(test_path).resize(IMAGE_SHAPE) print(img.format) print(img.mode) print(img.size) img=img.convert('RGB') #print(img.shape) img = np.array(img)/255.0 imgr = tf.reshape(img, [1,224, 224, 3]) print(imgr.shape) classifier = tf.keras.experimental.load_from_saved_model(saved_model_path) result = classifier.predict(imgr) print(result.shape) classifier.summary() #print(classifier.predict(img).shape) print(np.argmax(result[0])) predicted_class = np.argmax(result[0], axis=-1) print(predicted_class) img = tf.reshape(img, [224, 224, 3]) plt.imshow(img) plt.axis('off') predicted_class_name = label_names[predicted_class] _ = plt.title("Prediction: " + predicted_class_name.title()) # + id="LQz0cHX1vhFb" colab_type="code" colab={} # !pip install -q tensorflow-gpu==2.0.0-beta1 # %load_ext tensorboard # + id="JXYrSRhB-hXL" colab_type="code" outputId="0f507b03-3cfa-4cc5-894a-511ac876b009" colab={"base_uri": "https://localhost:8080/", "height": 102} # !ls # !ls 'drive/My Drive/'tmp/saved_models/ # !saved_model_cli show --dir 'drive/My Drive/tmp/saved_models/1563479506' --tag_set serve # + id="OceayWUALABE" colab_type="code" outputId="6544402d-d317-44eb-9f21-fb81d2c24de5" colab={"resources": {"http://localhost:6006/": {"data": "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 x.W/1) score = self.softmax(score) # score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16) # score = self.bn1(score) # element-wise add, size=(N, 512, x.H/16, x.W/16) # score = self.relu(self.deconv2(score)) # size=(N, 256, x.H/8, x.W/8) # score = self.bn2(score) # element-wise add, size=(N, 256, x.H/8, x.W/8) # score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4) # score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2) # score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W) # score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1) return score # size=(N, n_class, x.H/1, x.W/1) # load pretrained weights and define FCN8s if pretrainOnCAMO: vgg_model = torch.load('/content/vggmodel') fcn_model = torch.load('/content/fcnmodel') else: vgg_model = VGGNet(requires_grad=True, remove_fc=True) fcn_model = FCN8s(pretrained_net=vgg_model, n_class=num_class) ts = time.time() vgg_model = vgg_model.cuda() fcn_model = fcn_model.cuda() fcn_model = nn.DataParallel(fcn_model, device_ids=num_gpu) print("Finish cuda loading, time elapsed {}".format(time.time() - ts)) # criterion=new_loss # criterion = nn.BCEWithLogitsLoss() # criterion = FocalLoss() # criterion = DiceLoss() optimizer = optim.Adam(fcn_model.parameters(), lr=lr) scheduler = lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) print(fcn_model) # + [markdown] id="6YmWQoDPL7LD" colab_type="text" # # ## Training # + id="UADtIeFHD-hk" colab_type="code" colab={} def train(): for epoch in range(epochs): scheduler.step() ts = time.time() for iter, batch in enumerate(train_loader): optimizer.zero_grad() if use_gpu: inputs = Variable(batch['X'].cuda()) labels = Variable(batch['Y'].cuda()) else: inputs, labels = Variable(batch['X']), Variable(batch['Y']) outputs = fcn_model(inputs) # # !!!!!!! # print(outputs) # print("!!!!!!") # print(labels) # # !!!!!!! # weights=[1/((labels==1).numel()),1/((labels==0).numel())] # pos_weight=torch.tensor((labels==0).numel()/(labels==1).numel()).cuda()*1.5 criterion=nn.BCEWithLogitsLoss() # criterion = nn.L1Loss() # loss=criterion.forward(input=m(outputs),target=labels.type(torch.LongTensor).cuda()) labels/=max(labels.max(),1) loss = criterion(outputs, labels) loss.backward() optimizer.step() if iter % 10 == 0: print("epoch{}, iter{}, loss: {}".format(epoch, iter, loss.data.item())) print("Finish epoch {}, time elapsed {}".format(epoch, time.time() - ts)) val(epoch) fcn_model.train() highest_pixel_acc = max(pixel_acc_list) highest_mIOU = max(mIOU_list) highest_f_measure = max(f_measure_list) lowest_mae = min(mae_list) highest_pixel_acc_epoch = pixel_acc_list.index(highest_pixel_acc) highest_mIOU_epoch = mIOU_list.index(highest_mIOU) highest_f_measure_epoch = f_measure_list.index(highest_f_measure) lowest_mae_epoch = mae_list.index(lowest_mae) print("The highest mIOU is {} and is achieved at epoch-{}".format(highest_mIOU, highest_mIOU_epoch)) print("The lowest MAE is {} and is achieved at epoch-{}".format(lowest_mae, lowest_mae_epoch)) print("The highest f-measure is {} and is achieved at epoch-{}".format(highest_f_measure, highest_f_measure_epoch)) def save_result_comparison(input_np, output_np, gt_path): print(gt_path) means = np.array([103.939, 116.779, 123.68]) / 255. global global_index original_im_RGB = np.zeros((256,256,3)) original_im_RGB[:,:,0] = input_np[0,0,:,:] original_im_RGB[:,:,1] = input_np[0,1,:,:] original_im_RGB[:,:,2] = input_np[0,2,:,:] original_im_RGB[:,:,0] = original_im_RGB[:,:,0] + means[0] original_im_RGB[:,:,1] = original_im_RGB[:,:,1] + means[1] original_im_RGB[:,:,2] = original_im_RGB[:,:,2] + means[2] original_im_RGB[:,:,0] = original_im_RGB[:,:,0]*255.0 original_im_RGB[:,:,1] = original_im_RGB[:,:,1]*255.0 original_im_RGB[:,:,2] = original_im_RGB[:,:,2]*255.0 im_seg_RGB = np.zeros((256,256,3)) # the following version is designed for 11-class version and could still work if the number of classes is fewer. for i in range(256): for j in range(256): if output_np[i,j] == 0: im_seg_RGB[i,j,:] = [0, 0, 0] elif output_np[i,j] == 1: im_seg_RGB[i,j,:] = [255, 255, 255] elif output_np[i,j] == 2: im_seg_RGB[i,j,:] = [192, 192, 128] elif output_np[i,j] == 3: im_seg_RGB[i,j,:] = [128, 64, 128] elif output_np[i,j] == 4: im_seg_RGB[i,j,:] = [0, 0, 192] elif output_np[i,j] == 5: im_seg_RGB[i,j,:] = [128, 128, 0] elif output_np[i,j] == 6: im_seg_RGB[i,j,:] = [192, 128, 128] elif output_np[i,j] == 7: im_seg_RGB[i,j,:] = [64, 64, 128] elif output_np[i,j] == 8: im_seg_RGB[i,j,:] = [64, 0, 128] elif output_np[i,j] == 9: im_seg_RGB[i,j,:] = [64, 64, 0] elif output_np[i,j] == 10: im_seg_RGB[i,j,:] = [0, 128, 192] # horizontally stack original image and its corresponding segmentation results gt_image = Image.open(gt_path).convert('RGB') gt_image = gt_image.resize((256, 256)) slicing_vertical = np.ones((256, 2, 3)) * 255.0 hstack_image = np.hstack((original_im_RGB, slicing_vertical, im_seg_RGB, slicing_vertical, gt_image)) return hstack_image def save_image(image_stack): global global_index stack = [] slicing_horizontal = np.ones((2, 772, 3)) * 255.0 for i in image_stack: stack.append(i) stack.append(slicing_horizontal) vstack_image = np.vstack(stack) new_im = Image.fromarray(np.uint8(vstack_image)) file_name = folder_to_save_validation_result + str(global_index) + '.jpg' global_index = global_index + 1 new_im.save(file_name) # train() # + [markdown] id="P_icuXVoL9aL" colab_type="text" # ## Validation # + id="Cl0WVeoTD-be" colab_type="code" colab={} def val(epoch): fcn_model.eval() total_ious = [] pixel_accs = [] f_measures = [] maes = [] numberOfImage = 4 for iter, batch in enumerate(val_loader): ## batch is 1 in this case if use_gpu: inputs = Variable(batch['X'].cuda()) else: inputs = Variable(batch['X']) output = fcn_model(inputs) # only save the 1st image for comparison if iter <= numberOfImage: print('---------iter={}'.format(iter)) if iter == 0: vstack_image = [] # generate images images = output.data.max(1)[1].cpu().numpy()[:,:,:] image = images[0,:,:] image = save_result_comparison(batch['X'], image, batch['N'][0]) vstack_image.append(image) print(batch['N']) if iter == numberOfImage: save_image(vstack_image) output = output.data.cpu().numpy() N, _, h, w = output.shape pred = output.transpose(0, 2, 3, 1).reshape(-1, num_class).argmax(axis=1).reshape(N, h, w) target = batch['l'].cpu().numpy().reshape(N, h, w) for p, t in zip(pred, target): total_ious.append(iou(p, t)) pixel_accs.append(pixel_acc(p, t)) f_measures.append(F_Measure(p, t)) maes.append(MAE(p, t)) # Calculate average IoU total_ious = np.array(total_ious).T # n_class * val_len ious = np.nanmean(total_ious, axis=1) pixel_accs = np.array(pixel_accs).mean() f_measures = np.nanmean(np.array(f_measures)) maes = np.array(maes).mean() print("epoch{}, MAE: {}, meanIoU: {}, f_measure: {}, IoUs: {}".format(epoch, maes, np.nanmean(ious), f_measures, ious)) global pixel_acc_list global mIOU_list global f_measure_list global mae_list pixel_acc_list.append(pixel_accs) mIOU_list.append(np.nanmean(ious)) f_measure_list.append(f_measures) mae_list.append(maes) # borrow functions and modify it from https://github.com/Kaixhin/FCN-semantic-segmentation/blob/master/main.py # Calculates class intersections over unions def iou(pred, target): ious = [] target=target/max(target.max(),1) for cls in range(num_class): pred_inds = pred == cls target_inds = target == cls intersection = pred_inds[target_inds].sum() union = pred_inds.sum() + target_inds.sum() - intersection # if(cls==1): # print(pred_inds.sum()) # print(target_inds.sum()) # print(intersection) if union == 0: ious.append(float('nan')) # if there is no ground truth, do not include in evaluation else: ious.append(float(intersection) / max(union, 1)) # print("cls", cls, pred_inds.sum(), target_inds.sum(), intersection, float(intersection) / max(union, 1))\ return ious def pixel_acc(pred, target): correct = (pred == target).sum() total = (target == target).sum() return correct / total def F_Measure(pred, target): beta_sqr = 0.3 target=target/max(target.max(),1) cls = 1 pred_inds = pred == cls target_inds = target == cls TP = pred_inds[target_inds].sum() FP = pred_inds.sum() - TP FN = target_inds.sum() - TP P = TP / (TP + FP) R = TP / (TP + FN) denominator = (beta_sqr*P + R) # print(P, R) if denominator == 0: return float('nan') # if there is no ground truth, do not include in evaluation else: f_measure = (beta_sqr + 1) * P * R / denominator return f_measure def MAE(pred, target): target=target/max(target.max(),1) pred = torch.from_numpy(pred).float() target = torch.from_numpy(target).float() # print(type(target[0][0])) loss = nn.L1Loss() mae= loss(pred, target) return mae # + [markdown] id="VZz-zbB7MAtW" colab_type="text" # ## Execution # + id="ujsJzHISD-Ru" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="bfc8c141-88b7-4a6b-b145-4c322ca4448c" ## perform training and validation global_index = 0 pixel_acc_list = [] mIOU_list = [] f_measure_list = [] mae_list = [] # val(0) # show the accuracy before training train() # + id="20qijkJUChVB" colab_type="code" colab={}
23,532