



"""simple docstring"""

import numpy as np
import requests



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



def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     str       ,     lowercase__  :     Optional[int]       ,     lowercase__  :     int       ,     lowercase__  :     Optional[Any]   ):
		snake_case		:						str								=				len_data

		snake_case		:						Dict								=				np.dot(lowercase__       ,     data_x.transpose()   )
		prod -= data_y.transpose()
		snake_case		:						Optional[Any]								=				np.dot(lowercase__       ,     lowercase__   )
		snake_case		:						Tuple								=				theta - (alpha / n) * sum_grad
		return theta



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]       ,     lowercase__  :     List[Any]       ,     lowercase__  :     Union[str, Any]   ):
		snake_case		:						Any								=				np.dot(lowercase__       ,     data_x.transpose()   )
		prod -= data_y.transpose()
		snake_case		:						Optional[int]								=				np.sum(np.square(lowercase__   )   )
		snake_case		:						Union[str, Any]								=				sum_elem / (2 * len_data)
		return error



def   UpperCamelCase__     (		lowercase__  :     Dict       ,     lowercase__  :     Optional[int]   ):
		snake_case		:						Union[str, Any]								=				10_0000
		snake_case		:						int								=				0.0_00_15_50

		snake_case		:						List[str]								=				data_x.shape[1]
		snake_case		:						int								=				data_x.shape[0] - 1

		snake_case		:						List[Any]								=				np.zeros((1, no_features)   )

		for i in range(0       ,     lowercase__   ):
				snake_case		:						Tuple								=				run_steep_gradient_descent(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__   )
				snake_case		:						Optional[int]								=				sum_of_square_error(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__   )
				print(F'''At Iteration {i + 1} - Error is {error:.5f}'''   )

		return theta



def   UpperCamelCase__     (		lowercase__  :     Dict       ,     lowercase__  :     List[Any]   ):
		snake_case		:						int								=				sum(abs(y - predicted_y[i]   ) for i, y in enumerate(lowercase__   )   )
		return total / len(lowercase__   )



def   UpperCamelCase__     (		):
		snake_case		:						int								=				collect_dataset()

		snake_case		:						Dict								=				data.shape[0]
		snake_case		:						str								=				np.c_[np.ones(lowercase__   ), data[:, :-1]].astype(lowercase__   )
		snake_case		:						Optional[int]								=				data[:, -1].astype(lowercase__   )

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


if __name__ == "__main__":
							main()







"""simple docstring"""

import random



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):

		def is_sorted(lowercase__  :     int   ):
				for i in range(len(lowercase__   ) - 1   ):
						if collection[i] > collection[i + 1]:
								return False
				return True

		while not is_sorted(lowercase__   ):
				random.shuffle(lowercase__   )
		return collection


if __name__ == "__main__":
							__A 		=			input("Enter numbers separated by a comma:\n").strip()
							__A 		=			[int(item) for item in user_input.split(",")]
							print(bogo_sort(unsorted))

