





'''simple docstring'''




import numpy as np
import requests



def 						__A						(       ):


		"""simple docstring"""



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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Dict						=	len_data

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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							str						=	np.dot(lowerCamelCase_	,	data_x.transpose()					)
		prod -= data_y.transpose()
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.sum(np.square(lowerCamelCase_					)					)
		SCREAMING_SNAKE_CASE				:							Dict						=	sum_elem / (2 * len_data)
		return error



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	10_00_00
		SCREAMING_SNAKE_CASE				:							str						=	0.0_001_550

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

		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.zeros((1, no_features)					)

		for i in range(0	,	lowerCamelCase_					):
				SCREAMING_SNAKE_CASE				:							Any						=	run_steep_gradient_descent(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)
				SCREAMING_SNAKE_CASE				:							Tuple						=	sum_of_square_error(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)
				print(f'''At Iteration {i + 1} - Error is {error:.5f}'''					)

		return theta



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							str						=	sum(abs(y - predicted_y[i]					) for i, y in enumerate(lowerCamelCase_					)					)
		return total / len(lowerCamelCase_					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	collect_dataset()

		SCREAMING_SNAKE_CASE				:							str						=	data.shape[0]
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.c_[np.ones(lowerCamelCase_					), data[:, :-1]].astype(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	data[:, -1].astype(lowerCamelCase_					)

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


if __name__ == "__main__":
							main()









'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		if len(lowerCamelCase_					) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	min(lowerCamelCase_	,	lowerCamelCase_					)
		return min_num


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)



