



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      (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 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return sum(SCREAMING_SNAKE_CASE__ ) / instance_count


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return instance_count / total_count


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			[]  # 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
						snake_case_     =			len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			[]
						# 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] ) ):
																		snake_case_     =			[]  # 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 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			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


lowerCAmelCase_ 			=			TypeVar('''num''')


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = lambda SCREAMING_SNAKE_CASE__ : True						,	SCREAMING_SNAKE_CASE__ = None						,	):
						while True:
												try:
																		snake_case_     =			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 						__SCREAMING_SNAKE_CASE      ():
						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
												snake_case_     =			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
												snake_case_     =			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
												snake_case_     =			[]  # An empty list to store instance counts of classes in dataset
												for i in range(SCREAMING_SNAKE_CASE__ ):
																		snake_case_     =			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
												snake_case_     =			[]
												for a in range(SCREAMING_SNAKE_CASE__ ):
																		snake_case_     =			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
												snake_case_     =			[
												    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
												snake_case_     =			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
												snake_case_     =			[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
												snake_case_     =			[
												    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
												snake_case_     =			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
												snake_case_     =			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()


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return (input_a, input_a).count(0 ) % 2


def 						__SCREAMING_SNAKE_CASE      ():
						assert xor_gate(0						,	0 ) == 0
						assert xor_gate(0						,	1 ) == 1
						assert xor_gate(1						,	0 ) == 1
						assert xor_gate(1						,	1 ) == 0


if __name__ == "__main__":
					print(xor_gate(0, 0))
					print(xor_gate(0, 1))