
"""simple docstring"""

import numpy as np
import requests
def 		UpperCAmelCase					(		):





		'''simple docstring'''


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





		'''simple docstring'''


		lowerCamelCase     :			int		=    len_data

		lowerCamelCase     :			Optional[Any]		=    np.dot(__lowerCamelCase,						data_x.transpose()			)
		prod -= data_y.transpose()
		lowerCamelCase     :			List[str]		=    np.dot(__lowerCamelCase,						__lowerCamelCase			)
		lowerCamelCase     :			List[Any]		=    theta - (alpha / n) * sum_grad
		return theta
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    np.dot(__lowerCamelCase,						data_x.transpose()			)
		prod -= data_y.transpose()
		lowerCamelCase     :			str		=    np.sum(np.square(__lowerCamelCase			)			)
		lowerCamelCase     :			Any		=    sum_elem / (2 * len_data)
		return error
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    10_0000
		lowerCamelCase     :			Any		=    0.0_0_0_1_5_5_0

		lowerCamelCase     :			Any		=    data_x.shape[1]
		lowerCamelCase     :			Any		=    data_x.shape[0] - 1

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

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

		return theta
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Any		=    sum(abs(y - predicted_y[i]			) for i, y in enumerate(__lowerCamelCase			)			)
		return total / len(__lowerCamelCase			)
def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			Any		=    collect_dataset()

		lowerCamelCase     :			Optional[int]		=    data.shape[0]
		lowerCamelCase     :			Tuple		=    np.c_[np.ones(__lowerCamelCase			), data[:, :-1]].astype(__lowerCamelCase			)
		lowerCamelCase     :			List[Any]		=    data[:, -1].astype(__lowerCamelCase			)

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


if __name__ == "__main__":
					main()













from itertools import permutations






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							list(map("".join,				permutations("0123456789"		)		)		)
     return result[999999]


if __name__ == "__main__":
       print(solution())