

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       (				lowercase__  :float      ,						lowercase__  :float      ,						lowercase__  :int				)       ->  list:
						seed(1				)
						return [gauss(lowercase__      ,						lowercase__				) for _ in range(lowercase__				)]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :list				)       ->  list:
						return [k for k in range(lowercase__				) for _ in range(instance_count[k]				)]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :list				)       ->  float:
						return sum(lowercase__				) / instance_count
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :int				)       ->  float:
						return instance_count / total_count
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :list      ,						lowercase__  :list      ,						lowercase__  :int				)       ->  float:
						__magic_name__				:							str				=      []  # An empty list to store all squared differences
						# iterate over number of elements in items
						for i in range(len(lowercase__				)				):
												# 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__				:							Tuple				=      len(lowercase__				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :list      ,						lowercase__  :list      ,						lowercase__  :float      ,						lowercase__  :list				)       ->  list:
						__magic_name__				:							Optional[Any]				=      []
						# for loop iterates over number of elements in list
						for i in range(len(lowercase__				)				):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i]				)				):
																		__magic_name__				:							Optional[int]				=      []  # 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(lowercase__				)				):
																								# 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(lowercase__				)

						return [result.index(max(lowercase__				)				) for result in results]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :list      ,						lowercase__  :list				)       ->  float:
						__magic_name__				:							Any				=      sum(1 for i, j in zip(lowercase__      ,						lowercase__				) 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(lowercase__				)) * 1_0_0


snake_case__     							= TypeVar('num')
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[object], num]      ,						lowercase__  :str      ,						lowercase__  :str      ,						lowercase__  :Callable[[num], bool] = lambda lowercase__				: True      ,						lowercase__  :str | None = None      ,						)       ->  num:
						while True:
												try:
																		__magic_name__				:							Any				=      input_type(input(lowercase__				).strip() or default				)
																		if condition(lowercase__				):
																								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       (				)       ->  List[Any]:
						while True:
												print(' Linear Discriminant Analysis '.center(5_0      ,						'*'				)				)
												print('*' * 5_0      ,						'\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__				:							int				=      valid_input(
												    input_type=lowercase__      ,						condition=lambda lowercase__				: x > 0      ,						input_msg='Enter the number of classes (Data Groupings): '      ,						err_msg='Number of classes should be positive!'      ,						)

												print('-' * 1_0_0				)

												# Trying to get the value of standard deviation
												__magic_name__				:							Optional[int]				=      valid_input(
												    input_type=lowercase__      ,						condition=lambda lowercase__				: 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('-' * 1_0_0				)

												# Trying to get number of instances in classes and theirs means to generate
												# dataset
												__magic_name__				:							Union[str, Any]				=      []  # An empty list to store instance counts of classes in dataset
												for i in range(lowercase__				):
																		__magic_name__				:							Union[str, Any]				=      valid_input(
																		    input_type=lowercase__      ,						condition=lambda lowercase__				: x > 0      ,						input_msg=(f"Enter The number of instances for class_{i+1}: ")      ,						err_msg='Number of instances should be positive!'      ,						)
																		counts.append(lowercase__				)
												print('-' * 1_0_0				)

												# An empty list to store values of user-entered means of classes
												__magic_name__				:							str				=      []
												for a in range(lowercase__				):
																		__magic_name__				:							Union[str, Any]				=      valid_input(
																		    input_type=lowercase__      ,						input_msg=(f"Enter the value of mean for class_{a+1}: ")      ,						err_msg='This is an invalid value.'      ,						)
																		user_means.append(lowercase__				)
												print('-' * 1_0_0				)

												print('Standard deviation: '      ,						lowercase__				)
												# print out the number of instances in classes in separated line
												for i, count in enumerate(lowercase__      ,						1				):
																		print(f"Number of instances in class_{i} is: {count}"				)
												print('-' * 1_0_0				)

												# print out mean values of classes separated line
												for i, user_mean in enumerate(lowercase__      ,						1				):
																		print(f"Mean of class_{i} is: {user_mean}"				)
												print('-' * 1_0_0				)

												# Generating training dataset drawn from gaussian distribution
												__magic_name__				:							List[Any]				=      [
												    gaussian_distribution(user_means[j]      ,						lowercase__      ,						counts[j]				)
												    for j in range(lowercase__				)
												]
												print('Generated Normal Distribution: \n'      ,						lowercase__				)
												print('-' * 1_0_0				)

												# Generating Ys to detecting corresponding classes
												__magic_name__				:							str				=      y_generator(lowercase__      ,						lowercase__				)
												print('Generated Corresponding Ys: \n'      ,						lowercase__				)
												print('-' * 1_0_0				)

												# Calculating the value of actual mean for each class
												__magic_name__				:							Optional[int]				=      [calculate_mean(counts[k]      ,						x[k]				) for k in range(lowercase__				)]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(lowercase__      ,						1				):
																		print(f"Actual(Real) mean of class_{i} is: {actual_mean}"				)
												print('-' * 1_0_0				)

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

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(lowercase__      ,						1				):
																		print(f"Probability of class_{i} is: {probability}"				)
												print('-' * 1_0_0				)

												# Calculating the values of variance for each class
												__magic_name__				:							Any				=      calculate_variance(lowercase__      ,						lowercase__      ,						sum(lowercase__				)				)
												print('Variance: '      ,						lowercase__				)
												print('-' * 1_0_0				)

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												__magic_name__				:							Optional[Any]				=      predict_y_values(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
												print('-' * 1_0_0				)

												# Calculating Accuracy of the model
												print(f"Accuracy: {accuracy(lowercase__      ,						lowercase__				)}"				)
												print('-' * 1_0_0				)
												print(' DONE '.center(1_0_0      ,						'+'				)				)

												if input('Press any key to restart or \'q\' for quit: '				).strip().lower() == "q":
																		print('\n' + 'GoodBye!'.center(1_0_0      ,						'-'				) + '\n'				)
																		break
												system('cls' if name == 'nt' else 'clear'				)  # noqa: S605


if __name__ == "__main__":
			main()






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


