

'''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 (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					seed(1							)
					return [gauss(__magic_name__			,   __magic_name__							) for _ in range(__magic_name__							)]





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return [k for k in range(__magic_name__							) for _ in range(instance_count[k]							)]





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return sum(__magic_name__							) / instance_count





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return instance_count / total_count





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







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





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							int        =       []
					# for loop iterates over number of elements in list
					for i in range(len(__magic_name__							)							):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i]							)							):
															UpperCAmelCase		:							Optional[Any]        =       []  # 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(__magic_name__							)							):
																				# 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(__magic_name__							)

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





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]        =       sum(1 for i, j in zip(__magic_name__			,   __magic_name__							) 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(__magic_name__							)) * 100


a       :      List[str]  	=			TypeVar("num")





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__ = lambda __magic_name__							: True			,   __magic_name__ = None			,   ):



					'''simple docstring'''







					while True:
										try:
															UpperCAmelCase		:							str        =       input_type(input(__magic_name__							).strip() or default							)
															if condition(__magic_name__							):
																				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 (					):



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

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

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

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

										# Generating Ys to detecting corresponding classes
										UpperCAmelCase		:							Union[str, Any]        =       y_generator(__magic_name__			,   __magic_name__							)
										print("Generated Corresponding Ys: \n"			,   __magic_name__							)
										print("-" * 100							)

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

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

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

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

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

										# Calculating Accuracy of the model
										print(F"Accuracy: {accuracy(__magic_name__			,   __magic_name__							)}"							)
										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 ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
