


"""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        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     str:
   seed(1				)
   return [gauss(UpperCAmelCase__       ,   UpperCAmelCase__				) for _ in range(UpperCAmelCase__				)]




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   return [k for k in range(UpperCAmelCase__				) for _ in range(instance_count[k]				)]




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Dict:
   return sum(UpperCAmelCase__				) / instance_count




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   return instance_count / total_count




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Union[str, Any]:
   a   :	Tuple						=    []  # 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   :	Optional[Any]						=    len(UpperCAmelCase__				)  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(UpperCAmelCase__				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Any:
   a   :	Optional[Any]						=    []
   # 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   :	int						=    []  # 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        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[str]:
   a   :	Any						=    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__				)) * 100


SCREAMING_SNAKE_CASE__				:  List[Any]						= TypeVar("num")




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__ = lambda UpperCAmelCase__				: True       ,   UpperCAmelCase__ = None       ,   )     ->     List[str]:
   while True:
      try:
         a   :	List[Any]						=    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        A_					(					)     ->     int:
   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   :	Optional[Any]						=    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('-' * 100				)

      # Trying to get the value of standard deviation
      a   :	Optional[int]						=    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('-' * 100				)

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      a   :	Tuple						=    []  # An empty list to store instance counts of classes in dataset
      for i in range(UpperCAmelCase__				):
         a   :	Union[str, Any]						=    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('-' * 100				)

      # An empty list to store values of user-entered means of classes
      a   :	str						=    []
      for a in range(UpperCAmelCase__				):
         a   :	Dict						=    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('-' * 100				)

      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('-' * 100				)

      # 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('-' * 100				)

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

      # Generating Ys to detecting corresponding classes
      a   :	str						=    y_generator(UpperCAmelCase__       ,   UpperCAmelCase__				)
      print('Generated Corresponding Ys: \n'       ,   UpperCAmelCase__				)
      print('-' * 100				)

      # Calculating the value of actual mean for each class
      a   :	Dict						=    [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('-' * 100				)

      # Calculating the value of probabilities for each class
      a   :	Optional[Any]						=    [
          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('-' * 100				)

      # Calculating the values of variance for each class
      a   :	List[str]						=    calculate_variance(UpperCAmelCase__       ,   UpperCAmelCase__       ,   sum(UpperCAmelCase__				)				)
      print('Variance: '       ,   UpperCAmelCase__				)
      print('-' * 100				)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      a   :	Any						=    predict_y_values(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)
      print('-' * 100				)

      # Calculating Accuracy of the model
      print(F'Accuracy: {accuracy(UpperCAmelCase__       ,   UpperCAmelCase__				)}'				)
      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		for i, _ in enumerate(SCREAMING_SNAKE_CASE     ):
				if (i % 2 == 1) == (nums[i - 1] > nums[i]):
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Dict										=      nums[i], nums[i - 1]

		return nums


if __name__ == "__main__":
							print("""Enter the array elements:""")
							SCREAMING_SNAKE_CASE__     : Tuple				=   list(map(int, input().split()))
							print("""The unsorted array is:""")
							print(array)
							print("""Array after Wiggle sort:""")
							print(wiggle_sort(array))
