




from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     seed(1)
     return [gauss(__UpperCamelCase	,      __UpperCamelCase) for _ in range(__UpperCamelCase)]

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     return [k for k in range(__UpperCamelCase) for _ in range(instance_count[k])]

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     return sum(__UpperCamelCase) / instance_count

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     return instance_count / total_count

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     a											=   []  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(__UpperCamelCase)):
          # for loop iterates over number of elements in inner layer of items
          for j in range(len(items[i])):
               # appending squared differences to 'squared_diff' list
               squared_diff.append((items[i][j] - means[i]) ** 2)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
     a											=   len(__UpperCamelCase)  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(__UpperCamelCase)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     a											=   []
     # for loop iterates over number of elements in list
     for i in range(len(__UpperCamelCase)):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i])):
               a											=   []  # to store all discriminant values of each item as a list
               # for loop iterates over number of classes we have in our dataset
               for k in range(len(__UpperCamelCase)):
                    # appending values of discriminants for each class to 'temp' list
                    temp.append(
                        x_items[i][j] * (means[k] / variance)
                        - (means[k] ** 2 / (2 * variance))
                        + log(probabilities[k]))
               # appending discriminant values of each item to 'results' list
               results.append(__UpperCamelCase)

     return [result.index(max(__UpperCamelCase)) for result in results]

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     a											=   sum(1 for i, j in zip(__UpperCamelCase	,      __UpperCamelCase) if i == j)
     # percentage of accuracy equals to number of correct predictions divided by number
     # of all data and multiplied by 100
     return (correct / len(__UpperCamelCase)) * 1_00


lowercase__   :    List[str] 					=      TypeVar("num")

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase = lambda __UpperCamelCase: True	,      __UpperCamelCase = None	,      )    ->  num:
     while True:
          try:
               a											=   input_type(input(__UpperCamelCase).strip() or default)
               if condition(__UpperCamelCase):
                    return user_input
               else:
                    print(f'''{user_input}: {err_msg}''')
                    continue
          except ValueError:
               print(
                   f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}''')

def       SCREAMING_SNAKE_CASE							(     )    ->  List[Any]:
     while True:
          print(" Linear Discriminant Analysis ".center(50	,      "*"))
          print("*" * 50	,      "\n")
          print("First of all we should specify the number of classes that")
          print("we want to generate as training dataset")
          # Trying to get number of classes
          a											=   valid_input(
              input_type=__UpperCamelCase	,      condition=lambda __UpperCamelCase: x > 0	,      input_msg="Enter the number of classes (Data Groupings): "	,      err_msg="Number of classes should be positive!"	,      )

          print("-" * 1_00)

          # Trying to get the value of standard deviation
          a											=   valid_input(
              input_type=__UpperCamelCase	,      condition=lambda __UpperCamelCase: x >= 0	,      input_msg=(
                  "Enter the value of standard deviation"
                  "(Default value is 1.0 for all classes): "
              )	,      err_msg="Standard deviation should not be negative!"	,      default="1.0"	,      )

          print("-" * 1_00)

          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          a											=   []  # An empty list to store instance counts of classes in dataset
          for i in range(__UpperCamelCase):
               a											=   valid_input(
                   input_type=__UpperCamelCase	,      condition=lambda __UpperCamelCase: x > 0	,      input_msg=(f'''Enter The number of instances for class_{i+1}: ''')	,      err_msg="Number of instances should be positive!"	,      )
               counts.append(__UpperCamelCase)
          print("-" * 1_00)

          # An empty list to store values of user-entered means of classes
          a											=   []
          for a in range(__UpperCamelCase):
               a											=   valid_input(
                   input_type=__UpperCamelCase	,      input_msg=(f'''Enter the value of mean for class_{a+1}: ''')	,      err_msg="This is an invalid value."	,      )
               user_means.append(__UpperCamelCase)
          print("-" * 1_00)

          print("Standard deviation: "	,      __UpperCamelCase)
          # print out the number of instances in classes in separated line
          for i, count in enumerate(__UpperCamelCase	,      1):
               print(f'''Number of instances in class_{i} is: {count}''')
          print("-" * 1_00)

          # print out mean values of classes separated line
          for i, user_mean in enumerate(__UpperCamelCase	,      1):
               print(f'''Mean of class_{i} is: {user_mean}''')
          print("-" * 1_00)

          # Generating training dataset drawn from gaussian distribution
          a											=   [
              gaussian_distribution(user_means[j]	,      __UpperCamelCase	,      counts[j])
              for j in range(__UpperCamelCase)
          ]
          print("Generated Normal Distribution: \n"	,      __UpperCamelCase)
          print("-" * 1_00)

          # Generating Ys to detecting corresponding classes
          a											=   y_generator(__UpperCamelCase	,      __UpperCamelCase)
          print("Generated Corresponding Ys: \n"	,      __UpperCamelCase)
          print("-" * 1_00)

          # Calculating the value of actual mean for each class
          a											=   [calculate_mean(counts[k]	,      x[k]) for k in range(__UpperCamelCase)]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(__UpperCamelCase	,      1):
               print(f'''Actual(Real) mean of class_{i} is: {actual_mean}''')
          print("-" * 1_00)

          # Calculating the value of probabilities for each class
          a											=   [
              calculate_probabilities(counts[i]	,      sum(__UpperCamelCase)) for i in range(__UpperCamelCase)
          ]

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(__UpperCamelCase	,      1):
               print(f'''Probability of class_{i} is: {probability}''')
          print("-" * 1_00)

          # Calculating the values of variance for each class
          a											=   calculate_variance(__UpperCamelCase	,      __UpperCamelCase	,      sum(__UpperCamelCase))
          print("Variance: "	,      __UpperCamelCase)
          print("-" * 1_00)

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          a											=   predict_y_values(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
          print("-" * 1_00)

          # Calculating Accuracy of the model
          print(f'''Accuracy: {accuracy(__UpperCamelCase	,      __UpperCamelCase)}''')
          print("-" * 1_00)
          print(" DONE ".center(1_00	,      "+"))

          if input("Press any key to restart or 'q' for quit: ").strip().lower() == "q":
               print("\n" + "GoodBye!".center(1_00	,      "-") + "\n")
               break
          system("cls" if name == "nt" else "clear")  # noqa: S605


if __name__ == "__main__":
   main()







from sklearn.metrics import mean_absolute_error

import datasets


lowercase__   :    int 					=      "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

lowercase__   :    str 					=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__   :    Tuple 					=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,      _KWARGS_DESCRIPTION   )
class     a__ (      datasets.Metric   ):







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          return datasets.MetricInfo(
              description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(self._get_feature_types()				)						,	reference_urls=[
                  "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
              ]						,	)







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"				)				),
                   "references": datasets.Sequence(datasets.Value("float"				)				),
               }
          else:
               return {
                   "predictions": datasets.Value("float"				),
                   "references": datasets.Value("float"				),
               }







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=None						,	A="uniform_average"				)						->					List[Any]:



          '''simple docstring'''



          a											=   mean_absolute_error(A						,	A						,	sample_weight=A						,	multioutput=A				)

          return {"mae": mae_score}

