

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



def  __lowerCAmelCase (  A    ,       A      ):
	return [k for k in range(A      ) for _ in range(instance_count[k]      )]



def  __lowerCAmelCase (  A    ,       A      ):
	return sum(A      ) / instance_count



def  __lowerCAmelCase (  A    ,       A      ):
	return instance_count / total_count



def  __lowerCAmelCase (  A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     []  # 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
	UpperCAmelCase_    			=     len(A      )  # Number of classes in dataset
	return 1 / (total_count - n_classes) * sum(A      )



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     []
	# 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]      )      ):
			UpperCAmelCase_    			=     []  # 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      ):
	UpperCAmelCase_    			=     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


_a:      str								=     TypeVar("""num""")



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A = lambda A      : True    ,       A = None    ,       ):
	while True:
		try:
			UpperCAmelCase_    			=     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 (  ):
	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_    			=     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
		UpperCAmelCase_    			=     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
		UpperCAmelCase_    			=     []  # An empty list to store instance counts of classes in dataset
		for i in range(A      ):
			UpperCAmelCase_    			=     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
		UpperCAmelCase_    			=     []
		for a in range(A      ):
			UpperCAmelCase_    			=     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
		UpperCAmelCase_    			=     [
		    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
		UpperCAmelCase_    			=     y_generator(A    ,       A      )
		print("Generated Corresponding Ys: \n"    ,       A      )
		print("-" * 100      )

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

		# Predicting Y values
		# storing predicted Y values in 'pre_indexes' variable
		UpperCAmelCase_    			=     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 string import ascii_uppercase



def  __lowerCAmelCase (  A    ,       A    ,       A = False    ,       A = ascii_uppercase      ):
	UpperCAmelCase_    			=     keyword.upper()
	UpperCAmelCase_    			=     plaintext.upper()
	UpperCAmelCase_    			=     set(A      )

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	UpperCAmelCase_    			=     []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(A      )
    # the number of those unique characters will determine the number of rows
	UpperCAmelCase_    			=     len(A      )

	# create a shifted version of the alphabet
	UpperCAmelCase_    			=     unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	UpperCAmelCase_    			=     [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0    ,       26    ,       A      )
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	UpperCAmelCase_    			=     {}
	UpperCAmelCase_    			=     0
	for column in range(A      ):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(A      ) <= column:
				break

			# map current letter to letter in modified alphabet
			UpperCAmelCase_    			=     row[column]
			letter_index += 1

	if verbose:
		print(A      )
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext      )


if __name__ == "__main__":
				# example use
				print(mixed_keyword("""college""", """UNIVERSITY"""))