
"""simple docstring"""






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def 					UpperCamelCase		( UpperCAmelCase			)		->Dict:

   """simple docstring"""



   return 1 / (1 + np.exp(-z			))
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->Any:

   """simple docstring"""



   return (-y * np.log(UpperCAmelCase			) - (1 - y) * np.log(1 - h			)).mean()
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase			)		->Any:

   """simple docstring"""



   a_							=			np.dot(UpperCAmelCase      ,					UpperCAmelCase			)
   return np.sum(y * scores - np.log(1 + np.exp(UpperCAmelCase			)			)			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase=70_000			)		->Any:

   """simple docstring"""



   a_							=			np.zeros(x.shape[1]			)

   for iterations in range(UpperCAmelCase			):
      a_							=			np.dot(UpperCAmelCase      ,					UpperCAmelCase			)
      a_							=			sigmoid_function(UpperCAmelCase			)
      a_							=			np.dot(x.T      ,					h - y			) / y.size
      a_							=			theta - alpha * gradient  # updating the weights
      a_							=			np.dot(UpperCAmelCase      ,					UpperCAmelCase			)
      a_							=			sigmoid_function(UpperCAmelCase			)
      a_							=			cost_function(UpperCAmelCase      ,					UpperCAmelCase			)
      if iterations % 100 == 0:
         print(F'''loss: {j} \t'''			)  # printing the loss after every 100 iterations
   return theta


# In[68]:

if __name__ == "__main__":
      UpperCamelCase_							=					datasets.load_iris()
      UpperCamelCase_							=					iris.data[:, :2]
      UpperCamelCase_							=					(iris.target != 0) * 1

      UpperCamelCase_							=					0.1
      UpperCamelCase_							=					logistic_reg(alpha, x, y, max_iterations=70000)
      print('theta: ', theta)  # printing the theta i.e our weights vector
      def 					UpperCamelCase		( UpperCAmelCase			)		->List[Any]:

         """simple docstring"""



         return sigmoid_function(
             np.dot(UpperCAmelCase      ,					UpperCAmelCase			)			)  # predicting the value of probability from the logistic regression algorithm

      plt.figure(figsize=(10, 6))
      plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
      plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
      (UpperCamelCase_)							=					(x[:, 0].min(), x[:, 0].max())
      (UpperCamelCase_)							=					(x[:, 1].min(), x[:, 1].max())
      (UpperCamelCase_)							=					np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
      UpperCamelCase_							=					np.c_[xxa.ravel(), xxa.ravel()]
      UpperCamelCase_							=					predict_prob(grid).reshape(xxa.shape)
      plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

      plt.legend()



      plt.show()
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CpmAntForCausalLM""",
		    """CpmAntModel""",
		    """CpmAntPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
		from .tokenization_cpmant import CpmAntTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cpmant import (
				    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntPreTrainedModel,
				)


else:
		import sys

		__a  :     Any       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)