






"""simple docstring"""
import numpy as np
import requests






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






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			int     				=						len_data

 lowerCamelCase__     :			Dict     				=						np.dot(_lowerCamelCase			,   data_x.transpose()				)
 prod -= data_y.transpose()
 lowerCamelCase__     :			Optional[int]     				=						np.dot(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			Optional[Any]     				=						theta - (alpha / n) * sum_grad
 return theta






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Dict     				=						np.dot(_lowerCamelCase			,   data_x.transpose()				)
 prod -= data_y.transpose()
 lowerCamelCase__     :			Optional[Any]     				=						np.sum(np.square(_lowerCamelCase				)				)
 lowerCamelCase__     :			str     				=						sum_elem / (2 * len_data)
 return error






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						10_0000
 lowerCamelCase__     :			Union[str, Any]     				=						0.0_001_550

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

 lowerCamelCase__     :			Union[str, Any]     				=						np.zeros((1, no_features)				)

 for i in range(0			,   _lowerCamelCase				):
  lowerCamelCase__     :			List[str]     				=						run_steep_gradient_descent(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)
  lowerCamelCase__     :			Union[str, Any]     				=						sum_of_square_error(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)
  print(f'''At Iteration {i + 1} - Error is {error:.5f}'''				)

 return theta






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						sum(abs(y - predicted_y[i]				) for i, y in enumerate(_lowerCamelCase				)				)
 return total / len(_lowerCamelCase				)






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			int     				=						collect_dataset()

 lowerCamelCase__     :			int     				=						data.shape[0]
 lowerCamelCase__     :			Optional[int]     				=						np.c_[np.ones(_lowerCamelCase				), data[:, :-1]].astype(_lowerCamelCase				)
 lowerCamelCase__     :			List[Any]     				=						data[:, -1].astype(_lowerCamelCase				)

 lowerCamelCase__     :			int     				=						run_linear_regression(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			List[str]     				=						theta.shape[1]
 print('Resultant Feature vector : '				)
 for i in range(0			,   _lowerCamelCase				):
  print(f'''{theta[0, i]:.5f}'''				)


if __name__ == "__main__":
      main()



'''simple docstring'''

from __future__ import annotations







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Optional[int]         =    {1}
     _UpperCAmelCase					:  Union[str, Any]         =    [1]
     for _ in range(2 ,       depth + 1       ):
          _UpperCAmelCase					:  Dict         =    [*previous_coefficients, 0]
          _UpperCAmelCase					:  int         =    [0, *previous_coefficients]
          _UpperCAmelCase					:  List[Any]         =    []
          for x, y in zip(lowerCAmelCase_ ,       lowerCAmelCase_       ):
               coefficients.add(x + y       )
               previous_coefficients.append(x + y       )
     return coefficients







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Dict         =    set()
     for number in unique_coefficients:
          _UpperCAmelCase					:  Tuple         =    2
          _UpperCAmelCase					:  List[str]         =    number
          while divisor**2 <= copy_number:
               _UpperCAmelCase					:  Tuple         =    0
               while copy_number % divisor == 0:
                    copy_number //= divisor
                    multiplicity += 1
               if multiplicity >= 2:
                    non_squarefrees.add(lowerCAmelCase_       )
                    break
               divisor += 1

     return unique_coefficients.difference(lowerCAmelCase_       )







def __A  (    lowerCAmelCase_ = 51       ):
     _UpperCAmelCase					:  List[str]         =    get_pascal_triangle_unique_coefficients(lowerCAmelCase_       )
     _UpperCAmelCase					:  Optional[Any]         =    get_squarefrees(lowerCAmelCase_       )
     return sum(lowerCAmelCase_       )


if __name__ == "__main__":
   print(F"{solution() = }")


