





import numpy as np
import requests




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




def  A_(      A					,   A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  len_data

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




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  np.dot(UpperCamelCase_					,   data_x.transpose() )
	prod -= data_y.transpose()
	UpperCAmelCase_		    =  np.sum(np.square(UpperCamelCase_ ) )
	UpperCAmelCase_		    =  sum_elem / (2 * len_data)
	return error




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  100000
	UpperCAmelCase_		    =  0.0_001_550

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

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

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

	return theta




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  sum(abs(y - predicted_y[i] ) for i, y in enumerate(UpperCamelCase_ ) )
	return total / len(UpperCamelCase_ )




def  A_(      ):
	UpperCAmelCase_		    =  collect_dataset()

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

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


if __name__ == "__main__":
				main()










"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





