






import numpy as np
import requests





def 						A_					(						)				->List[str]:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		len_data

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





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Union[str, Any]:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		1_0_0_0_0_0
 SCREAMING_SNAKE_CASE				    =		0.0_0_0_1_5_5_0

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

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

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

 return theta





def 						A_					(						lowercase_	,  lowercase_			)				->Any:







 """simple docstring"""

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





def 						A_					(						)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		collect_dataset()

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

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


if __name__ == "__main__":
      main()






from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
