'''simple docstring'''




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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     seed(1				)
     return [gauss(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				) for _ in range(SCREAMING_SNAKE_CASE_				)]

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return [k for k in range(SCREAMING_SNAKE_CASE_				) for _ in range(instance_count[k]				)]

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return sum(SCREAMING_SNAKE_CASE_				) / instance_count

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return instance_count / total_count

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Any  					=       []  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(SCREAMING_SNAKE_CASE_				)				):
          # 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
     lowercase_       :							Optional[Any]  					=       len(SCREAMING_SNAKE_CASE_				)  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE_				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       []
     # for loop iterates over number of elements in list
     for i in range(len(SCREAMING_SNAKE_CASE_				)				):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i]				)				):
               lowercase_       :							Union[str, Any]  					=       []  # 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(SCREAMING_SNAKE_CASE_				)				):
                    # 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(SCREAMING_SNAKE_CASE_				)

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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Union[str, Any]  					=       sum(1 for i, j in zip(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				) 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(SCREAMING_SNAKE_CASE_				)) * 100


_A										= TypeVar('num')

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_ = lambda SCREAMING_SNAKE_CASE_				: True	,							SCREAMING_SNAKE_CASE_ = None	,							):
     while True:
          try:
               lowercase_       :							Dict  					=       input_type(input(SCREAMING_SNAKE_CASE_				).strip() or default				)
               if condition(SCREAMING_SNAKE_CASE_				):
                    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 _UpperCamelCase (							):
     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
          lowercase_       :							List[Any]  					=       valid_input(
              input_type=SCREAMING_SNAKE_CASE_	,							condition=lambda SCREAMING_SNAKE_CASE_				: x > 0	,							input_msg='Enter the number of classes (Data Groupings): '	,							err_msg='Number of classes should be positive!'	,							)

          print('-' * 100				)

          # Trying to get the value of standard deviation
          lowercase_       :							Dict  					=       valid_input(
              input_type=SCREAMING_SNAKE_CASE_	,							condition=lambda SCREAMING_SNAKE_CASE_				: 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('-' * 100				)

          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          lowercase_       :							Union[str, Any]  					=       []  # An empty list to store instance counts of classes in dataset
          for i in range(SCREAMING_SNAKE_CASE_				):
               lowercase_       :							Optional[int]  					=       valid_input(
                   input_type=SCREAMING_SNAKE_CASE_	,							condition=lambda SCREAMING_SNAKE_CASE_				: x > 0	,							input_msg=(f'''Enter The number of instances for class_{i+1}: ''')	,							err_msg='Number of instances should be positive!'	,							)
               counts.append(SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          # An empty list to store values of user-entered means of classes
          lowercase_       :							Any  					=       []
          for a in range(SCREAMING_SNAKE_CASE_				):
               lowercase_       :							str  					=       valid_input(
                   input_type=SCREAMING_SNAKE_CASE_	,							input_msg=(f'''Enter the value of mean for class_{a+1}: ''')	,							err_msg='This is an invalid value.'	,							)
               user_means.append(SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          print('Standard deviation: '	,							SCREAMING_SNAKE_CASE_				)
          # print out the number of instances in classes in separated line
          for i, count in enumerate(SCREAMING_SNAKE_CASE_	,							1				):
               print(f'''Number of instances in class_{i} is: {count}'''				)
          print('-' * 100				)

          # print out mean values of classes separated line
          for i, user_mean in enumerate(SCREAMING_SNAKE_CASE_	,							1				):
               print(f'''Mean of class_{i} is: {user_mean}'''				)
          print('-' * 100				)

          # Generating training dataset drawn from gaussian distribution
          lowercase_       :							Optional[Any]  					=       [
              gaussian_distribution(user_means[j]	,							SCREAMING_SNAKE_CASE_	,							counts[j]				)
              for j in range(SCREAMING_SNAKE_CASE_				)
          ]
          print('Generated Normal Distribution: \n'	,							SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          # Generating Ys to detecting corresponding classes
          lowercase_       :							List[Any]  					=       y_generator(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
          print('Generated Corresponding Ys: \n'	,							SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          # Calculating the value of actual mean for each class
          lowercase_       :							Tuple  					=       [calculate_mean(counts[k]	,							x[k]				) for k in range(SCREAMING_SNAKE_CASE_				)]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE_	,							1				):
               print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''				)
          print('-' * 100				)

          # Calculating the value of probabilities for each class
          lowercase_       :							str  					=       [
              calculate_probabilities(counts[i]	,							sum(SCREAMING_SNAKE_CASE_				)				) for i in range(SCREAMING_SNAKE_CASE_				)
          ]

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(SCREAMING_SNAKE_CASE_	,							1				):
               print(f'''Probability of class_{i} is: {probability}'''				)
          print('-' * 100				)

          # Calculating the values of variance for each class
          lowercase_       :							Optional[int]  					=       calculate_variance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							sum(SCREAMING_SNAKE_CASE_				)				)
          print('Variance: '	,							SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          lowercase_       :							int  					=       predict_y_values(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
          print('-' * 100				)

          # Calculating Accuracy of the model
          print(f'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)}'''				)
          print('-' * 100				)
          print(' DONE '.center(100	,							'+'				)				)

          if input('Press any key to restart or \'q\' for quit: '				).strip().lower() == "q":
               print('\n' + 'GoodBye!'.center(100	,							'-'				) + '\n'				)
               break
          system('cls' if name == 'nt' else 'clear'				)  # noqa: S605


if __name__ == "__main__":
 main()





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




