"""simple docstring"""







import numpy as np
import requests


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


def   a__      (		UpperCAmelCase__       :			Any		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			Optional[Any]			)->	int:
						UpperCAmelCase__						=		len_data

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


def   a__      (		UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			int		,			UpperCAmelCase__       :			Optional[int]		,			UpperCAmelCase__       :			Dict			)->	Optional[Any]:
						UpperCAmelCase__						=		np.dot(__lowerCamelCase		,			data_x.transpose()			)
						prod -= data_y.transpose()
						UpperCAmelCase__						=		np.sum(np.square(__lowerCamelCase			)			)
						UpperCAmelCase__						=		sum_elem / (2 * len_data)
						return error


def   a__      (		UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			Union[str, Any]			)->	str:
						UpperCAmelCase__						=		1_0_0_0_0_0
						UpperCAmelCase__						=		0.0_00_15_50

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

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

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

						return theta


def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Optional[int]			)->	Optional[int]:
						UpperCAmelCase__						=		sum(abs(y - predicted_y[i]			) for i, y in enumerate(__lowerCamelCase			)			)
						return total / len(__lowerCamelCase			)


def   a__      (		)->	List[str]:
						UpperCAmelCase__						=		collect_dataset()

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

						UpperCAmelCase__						=		run_linear_regression(__lowerCamelCase		,			__lowerCamelCase			)
						UpperCAmelCase__						=		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"""







import math
from timeit import timeit


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						UpperCAmelCase__						=		0
						UpperCAmelCase__						=		abs(UpperCAmelCase__			)
						while True:
												UpperCAmelCase__						=		n // 1_0
												digits += 1
												if n == 0:
																		break
						return digits


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						return 1 if n == 0 else math.floor(math.log(abs(UpperCAmelCase__			)		,			1_0			) + 1			)


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						return len(str(abs(UpperCAmelCase__			)			)			)


def   a__      (		)->	None:
						from collections.abc import Callable

						def benchmark_a_function(UpperCAmelCase__       :			Callable		,			UpperCAmelCase__       :			int			) -> None:
												UpperCAmelCase__						=		f'{func.__name__}({value})'
												UpperCAmelCase__						=		timeit(f'__main__.{call}'		,			setup="""import __main__"""			)
												print(f'{call}: {func(UpperCAmelCase__			)} -- {timing} seconds'			)

						for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(UpperCAmelCase__		,			UpperCAmelCase__			)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

