

'''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       ( a		,						a		,						a )      ->	list:






     '''simple docstring'''
     seed(1 )
     return [gauss(a		,						a ) for _ in range(a )]




def 						UpperCamelCase       ( a		,						a )      ->	list:






     '''simple docstring'''
     return [k for k in range(a ) for _ in range(instance_count[k] )]




def 						UpperCamelCase       ( a		,						a )      ->	float:






     '''simple docstring'''
     return sum(a ) / instance_count




def 						UpperCamelCase       ( a		,						a )      ->	float:






     '''simple docstring'''
     return instance_count / total_count




def 						UpperCamelCase       ( a		,						a		,						a )      ->	float:






     '''simple docstring'''
     __magic_name__						   =				[]  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(a ) ):
          # 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
     __magic_name__						   =				len(a )  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(a )




def 						UpperCamelCase       ( a		,						a		,						a		,						a )      ->	list:






     '''simple docstring'''
     __magic_name__						   =				[]
     # for loop iterates over number of elements in list
     for i in range(len(a ) ):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i] ) ):
               __magic_name__						   =				[]  # 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(a ) ):
                    # 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(a )

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




def 						UpperCamelCase       ( a		,						a )      ->	float:






     '''simple docstring'''
     __magic_name__						   =				sum(1 for i, j in zip(a		,						a ) 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(a )) * 100


_lowerCAmelCase    			=						TypeVar("num")




def 						UpperCamelCase       ( a		,						a		,						a		,						a = lambda a : True		,						a = None		,						)      ->	num:






     '''simple docstring'''
     while True:
          try:
               __magic_name__						   =				input_type(input(a ).strip() or default )
               if condition(a ):
                    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       ( )      ->	List[str]:






     '''simple docstring'''
     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
          __magic_name__						   =				valid_input(
              input_type=a		,						condition=lambda a : 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
          __magic_name__						   =				valid_input(
              input_type=a		,						condition=lambda a : 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
          __magic_name__						   =				[]  # An empty list to store instance counts of classes in dataset
          for i in range(a ):
               __magic_name__						   =				valid_input(
                   input_type=a		,						condition=lambda a : x > 0		,						input_msg=(F'''Enter The number of instances for class_{i+1}: ''')		,						err_msg='''Number of instances should be positive!'''		,						)
               counts.append(a )
          print('''-''' * 100 )

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

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

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

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

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

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

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

          # Calculating the values of variance for each class
          __magic_name__						   =				calculate_variance(a		,						a		,						sum(a ) )
          print('''Variance: '''		,						a )
          print('''-''' * 100 )

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

          # Calculating Accuracy of the model
          print(F'''Accuracy: {accuracy(a		,						a )}''' )
          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'''







from collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

_a					: Optional[Any]      =    {
    """mm""": from_to(0.001, 1_0_0_0),
    """cm""": from_to(0.01, 1_0_0),
    """m""": from_to(1, 1),
    """km""": from_to(1_0_0_0, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

