






"""simple docstring"""





import numpy as np
import requests






def 							A(    ):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     len_data

       lowercase__:							Tuple         =     np.dot(__UpperCamelCase		,  data_x.transpose()						)
       prod -= data_y.transpose()
       lowercase__:							Tuple         =     np.dot(__UpperCamelCase		,  __UpperCamelCase						)
       lowercase__:							Union[str, Any]         =     theta - (alpha / n) * sum_grad
       return theta






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     np.dot(__UpperCamelCase		,  data_x.transpose()						)
       prod -= data_y.transpose()
       lowercase__:							str         =     np.sum(np.square(__UpperCamelCase						)						)
       lowercase__:							List[Any]         =     sum_elem / (2 * len_data)
       return error






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Any         =     100000
       lowercase__:							Dict         =     0.0_00_15_50

       lowercase__:							Optional[Any]         =     data_x.shape[1]
       lowercase__:							int         =     data_x.shape[0] - 1

       lowercase__:							str         =     np.zeros((1, no_features)						)

       for i in range(0		,  __UpperCamelCase						):
              lowercase__:							int         =     run_steep_gradient_descent(__UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase						)
              lowercase__:							Union[str, Any]         =     sum_of_square_error(__UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase						)
              print(F"""At Iteration {i + 1} - Error is {error:.5f}"""						)

       return theta






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     sum(abs(y - predicted_y[i]						) for i, y in enumerate(__UpperCamelCase						)						)
       return total / len(__UpperCamelCase						)






def 							A(    ):
       """simple docstring"""





       lowercase__:							Optional[int]         =     collect_dataset()

       lowercase__:							Tuple         =     data.shape[0]
       lowercase__:							str         =     np.c_[np.ones(__UpperCamelCase						), data[:, :-1]].astype(__UpperCamelCase						)
       lowercase__:							Optional[Any]         =     data[:, -1].astype(__UpperCamelCase						)

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


if __name__ == "__main__":
  main()








def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list[int]							)			->							int:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()