



import numpy as np
import requests






def 						_lowerCAmelCase    (					):



 """simple docstring"""





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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						len_data

 __SCREAMING_SNAKE_CASE							:     Dict							=						np.dot(__lowerCamelCase		,						data_x.transpose()  )
 prod -= data_y.transpose()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						np.dot(__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						theta - (alpha / n) * sum_grad
 return theta






def 						_lowerCAmelCase    (					__lowerCamelCase :  Tuple		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.dot(__lowerCamelCase		,						data_x.transpose()  )
 prod -= data_y.transpose()
 __SCREAMING_SNAKE_CASE							:     Dict							=						np.sum(np.square(__lowerCamelCase  )  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						sum_elem / (2 * len_data)
 return error






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  List[str]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						100000
 __SCREAMING_SNAKE_CASE							:     int							=						0.0001550

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

 __SCREAMING_SNAKE_CASE							:     Any							=						np.zeros((1, no_features)  )

 for i in range(0		,						__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						run_steep_gradient_descent(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						sum_of_square_error(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
  print(F"""At Iteration {i + 1} - Error is {error:.5f}"""  )

 return theta






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Optional[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						sum(abs(y - predicted_y[i]  ) for i, y in enumerate(__lowerCamelCase  )  )
 return total / len(__lowerCamelCase  )






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						collect_dataset()

 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						data.shape[0]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						np.c_[np.ones(__lowerCamelCase  ), data[:, :-1]].astype(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						data[:, -1].astype(__lowerCamelCase  )

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


if __name__ == "__main__":
      main()








import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
