


'''simple docstring'''


import numpy as np
import requests







def       snake_case_       (  ):







				"""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			:	str							=				response.text.splitlines()
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				[]
				for item in lines:
								_SCREAMING_SNAKE_CASE			:	Dict							=				item.split(""","""					)
								data.append(SCREAMING_SNAKE_CASE__					)
				data.pop(0					)  # This is for removing the labels from the list
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.matrix(SCREAMING_SNAKE_CASE__					)
				return dataset







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				len_data

				_SCREAMING_SNAKE_CASE			:	List[str]							=				np.dot(SCREAMING_SNAKE_CASE__							,				data_x.transpose()					)
				prod -= data_y.transpose()
				_SCREAMING_SNAKE_CASE			:	Dict							=				np.dot(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Any							=				theta - (alpha / n) * sum_grad
				return theta







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Dict							=				10_0000
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				0.0_0_0_1_5_5_0

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

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				np.zeros((1, no_features)					)

				for i in range(0							,				SCREAMING_SNAKE_CASE__					):
								_SCREAMING_SNAKE_CASE			:	List[str]							=				run_steep_gradient_descent(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	Tuple							=				sum_of_square_error(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
								print(f"""At Iteration {i + 1} - Error is {error:.5f}"""					)

				return theta







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	int							=				sum(abs(y - predicted_y[i]					) for i, y in enumerate(SCREAMING_SNAKE_CASE__					)					)
				return total / len(SCREAMING_SNAKE_CASE__					)







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				collect_dataset()

				_SCREAMING_SNAKE_CASE			:	str							=				data.shape[0]
				_SCREAMING_SNAKE_CASE			:	Tuple							=				np.c_[np.ones(SCREAMING_SNAKE_CASE__					), data[:, :-1]].astype(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	str							=				data[:, -1].astype(SCREAMING_SNAKE_CASE__					)

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


if __name__ == "__main__":
				main()








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if len(str(SCREAMING_SNAKE_CASE__					)					) == 1 or len(str(SCREAMING_SNAKE_CASE__					)					) == 1:
								return a * b

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max(len(str(SCREAMING_SNAKE_CASE__					)					)							,				len(str(SCREAMING_SNAKE_CASE__					)					)					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				ma // 2

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba((aa + aa)							,				(ba + ba)					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def       snake_case_       (  ):







				"""simple docstring"""





				print(karatsuba(1_5463							,				2_3489					)					)


if __name__ == "__main__":
				main()




