




import numpy as np
import requests




def  a_		(							)	->			Optional[Any]:

			'''simple docstring'''


			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(__snake_case					)
			data.pop(0					)  # This is for removing the labels from the list
			UpperCamelCase_     				= np.matrix(__snake_case					)
			return dataset




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= len_data

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




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.dot(__snake_case     ,		data_x.transpose()					)
			prod -= data_y.transpose()
			UpperCamelCase_     				= np.sum(np.square(__snake_case					)					)
			UpperCamelCase_     				= sum_elem / (2 * len_data)
			return error




def  a_		(							__snake_case     ,		__snake_case					)	->			Dict:

			'''simple docstring'''


			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     ,		__snake_case					):
						UpperCamelCase_     				= run_steep_gradient_descent(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
						UpperCamelCase_     				= sum_of_square_error(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
						print(F'''At Iteration {i + 1} - Error is {error:.5f}'''					)

			return theta




def  a_		(							__snake_case     ,		__snake_case					)	->			Union[str, Any]:

			'''simple docstring'''


			UpperCamelCase_     				= sum(abs(y - predicted_y[i]					) for i, y in enumerate(__snake_case					)					)
			return total / len(__snake_case					)




def  a_		(							)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= collect_dataset()

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

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


if __name__ == "__main__":
				main()






def  a_		(							__snake_case					)	->			str:

			'''simple docstring'''


			return " ".join([word[::-1] for word in input_str.split()]					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
