






from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    list:
							seed(1			)
							return [gauss(a_				,		a_			) for _ in range(a_			)]
def   lowerCamelCase      (		a_				,		a_			)			->    list:
							return [k for k in range(a_			) for _ in range(instance_count[k]			)]
def   lowerCamelCase      (		a_				,		a_			)			->    float:
							return sum(a_			) / instance_count
def   lowerCamelCase      (		a_				,		a_			)			->    float:
							return instance_count / total_count
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    float:
							lowerCAmelCase_       		=  []  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(a_			)			):
														# 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
							lowerCAmelCase_       		=  len(a_			)  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(a_			)
def   lowerCamelCase      (		a_				,		a_				,		a_				,		a_			)			->    list:
							lowerCAmelCase_       		=  []
							# for loop iterates over number of elements in list
							for i in range(len(a_			)			):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i]			)			):
																					lowerCAmelCase_       		=  []  # 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(a_			)			):
																												# 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(a_			)

							return [result.index(max(a_			)			) for result in results]
def   lowerCamelCase      (		a_				,		a_			)			->    float:
							lowerCAmelCase_       		=  sum(1 for i, j in zip(a_				,		a_			) 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(a_			)) * 100


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

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

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

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

														# Generating Ys to detecting corresponding classes
														lowerCAmelCase_       		=  y_generator(a_				,		a_			)
														print('Generated Corresponding Ys: \n'				,		a_			)
														print('-' * 100			)

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

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

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

														# Calculating the values of variance for each class
														lowerCAmelCase_       		=  calculate_variance(a_				,		a_				,		sum(a_			)			)
														print('Variance: '				,		a_			)
														print('-' * 100			)

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														lowerCAmelCase_       		=  predict_y_values(a_				,		a_				,		a_				,		a_			)
														print('-' * 100			)

														# Calculating Accuracy of the model
														print(F'''Accuracy: {accuracy(a_				,		a_			)}'''			)
														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()











from __future__ import annotations

from string import ascii_letters
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# The final result string
							lowerCAmelCase_       		=  ''

							for character in input_string:
														if character not in alpha:
																					# Append without encryption if character is not in the alphabet
																					result += character
														else:
																					# Get the index of the new key and make sure it isn't too large
																					lowerCAmelCase_       		=  (alpha.index(a_			) + key) % len(a_			)

																					# Append the encoded character to the alphabet
																					result += alpha[new_key]

							return result
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							key *= -1

							return encrypt(a_				,		a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_ = None			)			->    dict[int, str]:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# To store data on all the combinations
							lowerCAmelCase_       		=  {}

							# Cycle through each combination
							for key in range(1				,		len(a_			) + 1			):
														# Decrypt the message and store the result in the data
														lowerCAmelCase_       		=  decrypt(a_				,		a_				,		a_			)

							return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 1_0}\n Menu\n{"-" * 1_0}''')
				print(*["""1.Encrypt""", """2.Decrypt""", """3.BruteForce""", """4.Quit"""], sep="""\n""")

				# get user input
				lowerCamelCase_		  =						input("""\nWhat would you like to do?: """).strip() or """4"""

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print("""Invalid choice, please enter a valid choice""")
				elif choice == "1":
						lowerCamelCase_		  =						input("""Please enter the string to be encrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print("""Goodbye.""")
						break



