


'''simple docstring'''
def 				lowerCamelCase_   (   __UpperCamelCase				:					list		)				->  bool:







     """simple docstring"""
     if not isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          raise ValueError('Input series is not valid, valid series - [2, 4, 8]'		)
     if len(__UpperCamelCase		) == 0:
          raise ValueError('Input list must be a non empty list'		)
     if len(__UpperCamelCase		) == 1:
          return True
     try:
          _A				=       series[1] / series[0]
          for index in range(len(__UpperCamelCase		) - 1		):
               if series[index + 1] / series[index] != common_ratio:
                    return False
     except ZeroDivisionError:
          return False
     return True

def 				lowerCamelCase_   (   __UpperCamelCase				:					list		)				->  float:







     """simple docstring"""
     if not isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          raise ValueError('Input series is not valid, valid series - [2, 4, 8]'		)
     if len(__UpperCamelCase		) == 0:
          raise ValueError('Input list must be a non empty list'		)
     _A				=       1
     for value in series:
          answer *= value
     return pow(__UpperCamelCase       ,						1 / len(__UpperCamelCase		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







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

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

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

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

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

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

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

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

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

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


if __name__ == "__main__":
    main()





