




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





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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
	return sum(__lowerCamelCase					) / instance_count





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
	return instance_count / total_count





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> float:
	a		       =							[]  # An empty list to store all squared differences
	# iterate over number of elements in items
	for i in range(len(__lowerCamelCase					)					):
		# 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(__lowerCamelCase					)  # Number of classes in dataset
	return 1 / (total_count - n_classes) * sum(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> list:
	a		       =							[]
	# for loop iterates over number of elements in list
	for i in range(len(__lowerCamelCase					)					):
		# 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(__lowerCamelCase					)					):
				# 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(__lowerCamelCase					)

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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
	a		       =							sum(1 for i, j in zip(__lowerCamelCase							,    __lowerCamelCase					) 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(__lowerCamelCase					)) * 100


__UpperCamelCase      : str					      =   TypeVar("num")





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase = lambda __lowerCamelCase					: True							,    __lowerCamelCase = None							,    )   -> num:
	while True:
		try:
			a		       =							input_type(input(__lowerCamelCase					).strip() or default					)
			if condition(__lowerCamelCase					):
				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	(			)   -> 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=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: 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		       =							valid_input(
		    input_type=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: 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		       =							[]  # An empty list to store instance counts of classes in dataset
		for i in range(__lowerCamelCase					):
			a		       =							valid_input(
			    input_type=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: x > 0							,    input_msg=(f'Enter The number of instances for class_{i+1}: ')							,    err_msg="""Number of instances should be positive!"""							,    )
			counts.append(__lowerCamelCase					)
		print("""-""" * 100					)

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

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

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

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

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

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

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

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

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

		# Calculating Accuracy of the model
		print(f'Accuracy: {accuracy(__lowerCamelCase							,    __lowerCamelCase					)}'					)
		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()







import doctest

import projectq
from projectq.ops import H, Measure





def 	__A	(			__lowerCamelCase					)   -> int:
	a		       =							quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(__lowerCamelCase					)


if __name__ == "__main__":
		doctest.testmod()

		# initialises a new quantum backend
		__UpperCamelCase      : List[Any]					      =   projectq.MainEngine()

		# Generate a list of 10 random numbers
		__UpperCamelCase      : Optional[int]					      =   [get_random_number(quantum_engine) for _ in range(10)]

		# Flushes the quantum engine from memory
		quantum_engine.flush()

		print("Random numbers", random_numbers_list)

