from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   list:
							seed(1							)
							return [gauss(snake_case_,    snake_case_							) for _ in range(snake_case_							)]




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   list:
							return [k for k in range(snake_case_							) for _ in range(instance_count[k]							)]




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   float:
							return sum(snake_case_							) / instance_count




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   float:
							return instance_count / total_count




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   float:
							__UpperCAmelCase						:			List[Any]         =   []  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(snake_case_							)							):
														# 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						:			Tuple         =   len(snake_case_							)  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(snake_case_							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   list:
							__UpperCAmelCase						:			List[Any]         =   []
							# for loop iterates over number of elements in list
							for i in range(len(snake_case_							)							):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i]							)							):
																					__UpperCAmelCase						:			List[str]         =   []  # 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(snake_case_							)							):
																												# 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(snake_case_							)

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




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   float:
							__UpperCAmelCase						:			Dict         =   sum(1 for i, j in zip(snake_case_,    snake_case_							) 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(snake_case_							)) * 100


_snake_case			=						TypeVar('''num''')




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__ = lambda snake_case__							: True,    snake_case__ = None,    )						->   num:
							while True:
														try:
																					__UpperCAmelCase						:			Union[str, Any]         =   input_type(input(snake_case_							).strip() or default							)
																					if condition(snake_case_							):
																												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   _UpperCamelCase   (			)						->   Optional[int]:
							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						:			Union[str, Any]         =   valid_input(
														    input_type=snake_case_,    condition=lambda snake_case__							: 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						:			Any         =   valid_input(
														    input_type=snake_case_,    condition=lambda snake_case__							: 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						:			Optional[int]         =   []  # An empty list to store instance counts of classes in dataset
														for i in range(snake_case_							):
																					__UpperCAmelCase						:			int         =   valid_input(
																					    input_type=snake_case_,    condition=lambda snake_case__							: x > 0,    input_msg=(f'''Enter The number of instances for class_{i+1}: '''),    err_msg="Number of instances should be positive!",    )
																					counts.append(snake_case_							)
														print("-" * 100							)

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

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

														# Generating training dataset drawn from gaussian distribution
														__UpperCAmelCase						:			Optional[int]         =   [
														    gaussian_distribution(user_means[j],    snake_case_,    counts[j]							)
														    for j in range(snake_case_							)
														]
														print("Generated Normal Distribution: \n",    snake_case_							)
														print("-" * 100							)

														# Generating Ys to detecting corresponding classes
														__UpperCAmelCase						:			Any         =   y_generator(snake_case_,    snake_case_							)
														print("Generated Corresponding Ys: \n",    snake_case_							)
														print("-" * 100							)

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

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

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

														# Calculating the values of variance for each class
														__UpperCAmelCase						:			Any         =   calculate_variance(snake_case_,    snake_case_,    sum(snake_case_							)							)
														print("Variance: ",    snake_case_							)
														print("-" * 100							)

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														__UpperCAmelCase						:			Tuple         =   predict_y_values(snake_case_,    snake_case_,    snake_case_,    snake_case_							)
														print("-" * 100							)

														# Calculating Accuracy of the model
														print(f'''Accuracy: {accuracy(snake_case_,    snake_case_							)}'''							)
														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()





import numpy as np
import requests




def   _UpperCamelCase   (			)						->   Tuple:
							__UpperCAmelCase						:			Tuple         =   requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"							)
							__UpperCAmelCase						:			List[Any]         =   response.text.splitlines()
							__UpperCAmelCase						:			Any         =   []
							for item in lines:
														__UpperCAmelCase						:			Any         =   item.split(","							)
														data.append(snake_case__							)
							data.pop(0							)  # This is for removing the labels from the list
							__UpperCAmelCase						:			List[str]         =   np.matrix(snake_case__							)
							return dataset




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[Any]:
							__UpperCAmelCase						:			Tuple         =   len_data

							__UpperCAmelCase						:			int         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.dot(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[Any]         =   theta - (alpha / n) * sum_grad
							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			Any         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.sum(np.square(snake_case__							)							)
							__UpperCAmelCase						:			List[Any]         =   sum_elem / (2 * len_data)
							return error




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			Any         =   10_0000
							__UpperCAmelCase						:			Dict         =   0.000_1550

							__UpperCAmelCase						:			str         =   data_x.shape[1]
							__UpperCAmelCase						:			Any         =   data_x.shape[0] - 1

							__UpperCAmelCase						:			List[str]         =   np.zeros((1, no_features)							)

							for i in range(0,    snake_case__							):
														__UpperCAmelCase						:			Union[str, Any]         =   run_steep_gradient_descent(snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														__UpperCAmelCase						:			Any         =   sum_of_square_error(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														print(f'''At Iteration {i + 1} - Error is {error:.5f}'''							)

							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			str         =   sum(abs(y - predicted_y[i]							) for i, y in enumerate(snake_case__							)							)
							return total / len(snake_case__							)




def   _UpperCamelCase   (			)						->   List[str]:
							__UpperCAmelCase						:			str         =   collect_dataset()

							__UpperCAmelCase						:			List[Any]         =   data.shape[0]
							__UpperCAmelCase						:			Union[str, Any]         =   np.c_[np.ones(snake_case__							), data[:, :-1]].astype(snake_case__							)
							__UpperCAmelCase						:			int         =   data[:, -1].astype(snake_case__							)

							__UpperCAmelCase						:			List[Any]         =   run_linear_regression(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[str]         =   theta.shape[1]
							print("Resultant Feature vector : "							)
							for i in range(0,    snake_case__							):
														print(f'''{theta[0, i]:.5f}'''							)


if __name__ == "__main__":
	main()




