



import numpy as np
import requests

def      A_ (		):

				"""simple docstring"""
				a__			 =						requests.get(
				    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
				    """master/Week1/ADRvsRating.csv"""						)
				a__			 =						response.text.splitlines()
				a__			 =						[]
				for item in lines:
								a__			 =						item.split(""","""						)
								data.append(__a						)
				data.pop(0						)  # This is for removing the labels from the list
				a__			 =						np.matrix(__a						)
				return dataset

def      A_ (		__a       :			Any	,						__a       :			Optional[Any]	,						__a       :			List[Any]	,						__a       :			Any	,						__a       :			List[Any]						):

				"""simple docstring"""
				a__			 =						len_data

				a__			 =						np.dot(__a	,						data_x.transpose()						)
				prod -= data_y.transpose()
				a__			 =						np.dot(__a	,						__a						)
				a__			 =						theta - (alpha / n) * sum_grad
				return theta

def      A_ (		__a       :			Tuple	,						__a       :			Dict	,						__a       :			Optional[int]	,						__a       :			List[str]						):

				"""simple docstring"""
				a__			 =						np.dot(__a	,						data_x.transpose()						)
				prod -= data_y.transpose()
				a__			 =						np.sum(np.square(__a						)						)
				a__			 =						sum_elem / (2 * len_data)
				return error

def      A_ (		__a       :			Union[str, Any]	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__			 =						100_000
				a__			 =						0.0_0_0_1_5_5_0

				a__			 =						data_x.shape[1]
				a__			 =						data_x.shape[0] - 1

				a__			 =						np.zeros((1, no_features)						)

				for i in range(0	,						__a						):
								a__			 =						run_steep_gradient_descent(__a	,						__a	,						__a	,						__a	,						__a						)
								a__			 =						sum_of_square_error(__a	,						__a	,						__a	,						__a						)
								print(F'''At Iteration {i + 1} - Error is {error:.5f}'''						)

				return theta

def      A_ (		__a       :			List[str]	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						sum(abs(y - predicted_y[i]						) for i, y in enumerate(__a						)						)
				return total / len(__a						)

def      A_ (		):

				"""simple docstring"""
				a__			 =						collect_dataset()

				a__			 =						data.shape[0]
				a__			 =						np.c_[np.ones(__a						), data[:, :-1]].astype(__a						)
				a__			 =						data[:, -1].astype(__a						)

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


if __name__ == "__main__":
	main()






def      A_ (		__a       :			int						):

				"""simple docstring"""
				if number != int(__a						):
								raise ValueError("""factorial() only accepts integral values"""						)
				if number < 0:
								raise ValueError("""factorial() not defined for negative values"""						)
				a__			 =						1
				for i in range(1	,						number + 1						):
								value *= i
				return value

def      A_ (		__a       :			int						):

				"""simple docstring"""
				if not isinstance(__a	,						__a						):
								raise ValueError("""factorial() only accepts integral values"""						)
				if n < 0:
								raise ValueError("""factorial() not defined for negative values"""						)
				return 1 if n == 0 or n == 1 else n * factorial(n - 1						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase								=   int(input("""Enter a positive integer: """).strip() or 0)
	print(f"""factorial{n} is {factorial(n)}""")

