


"""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 __lowercase  (   lowerCamelCase_	:   float    ,       lowerCamelCase_	:   float    ,       lowerCamelCase_	:   int	):
				seed(1	)
				return [gauss(lowerCamelCase_    ,       lowerCamelCase_	) for _ in range(lowerCamelCase_	)]


def __lowercase  (   lowerCamelCase_	:   int    ,       lowerCamelCase_	:   list	):
				return [k for k in range(lowerCamelCase_	) for _ in range(instance_count[k]	)]


def __lowercase  (   lowerCamelCase_	:   int    ,       lowerCamelCase_	:   list	):
				return sum(lowerCamelCase_	) / instance_count


def __lowercase  (   lowerCamelCase_	:   int    ,       lowerCamelCase_	:   int	):
				return instance_count / total_count


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


def __lowercase  (   lowerCamelCase_	:   list    ,       lowerCamelCase_	:   list    ,       lowerCamelCase_	:   float    ,       lowerCamelCase_	:   list	):
				SCREAMING_SNAKE_CASE__  				=					[]
				# 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]	)	):
												SCREAMING_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(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 __lowercase  (   lowerCamelCase_	:   list    ,       lowerCamelCase_	:   list	):
				SCREAMING_SNAKE_CASE__  				=					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


_lowerCamelCase  =	TypeVar('num')


def __lowercase  (   lowerCamelCase_	:   Callable[[object], num]    ,       lowerCamelCase_	:   str    ,       lowerCamelCase_	:   str    ,       lowerCamelCase_	:   Callable[[num], bool] = lambda lowerCamelCase_	: True    ,       lowerCamelCase_	:   str | None = None    ,       ):
				while True:
								try:
												SCREAMING_SNAKE_CASE__  				=					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 __lowercase  (   ):
				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
								SCREAMING_SNAKE_CASE__  				=					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
								SCREAMING_SNAKE_CASE__  				=					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
								SCREAMING_SNAKE_CASE__  				=					[]  # An empty list to store instance counts of classes in dataset
								for i in range(lowerCamelCase_	):
												SCREAMING_SNAKE_CASE__  				=					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
								SCREAMING_SNAKE_CASE__  				=					[]
								for a in range(lowerCamelCase_	):
												SCREAMING_SNAKE_CASE__  				=					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
								SCREAMING_SNAKE_CASE__  				=					[
								    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
								SCREAMING_SNAKE_CASE__  				=					y_generator(lowerCamelCase_    ,       lowerCamelCase_	)
								print("Generated Corresponding Ys: \n"    ,       lowerCamelCase_	)
								print("-" * 100	)

								# Calculating the value of actual mean for each class
								SCREAMING_SNAKE_CASE__  				=					[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
								SCREAMING_SNAKE_CASE__  				=					[
								    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
								SCREAMING_SNAKE_CASE__  				=					calculate_variance(lowerCamelCase_    ,       lowerCamelCase_    ,       sum(lowerCamelCase_	)	)
								print("Variance: "    ,       lowerCamelCase_	)
								print("-" * 100	)

								# Predicting Y values
								# storing predicted Y values in 'pre_indexes' variable
								SCREAMING_SNAKE_CASE__  				=					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()




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					str(lowerCamelCase_	).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					oct_string[0] == "-"
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_	) <= 7 for char in oct_string	):
								raise ValueError("Non-octal value was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					0
				for char in oct_string:
								SCREAMING_SNAKE_CASE__  				=					8 * decimal_number + int(lowerCamelCase_	)
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					-decimal_number
				return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
