code: 
  
  
  
  
  
  
  import numpy as np
  import requests
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= requests.get(
           """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
           """master/Week1/ADRvsRating.csv"""					)
       UpperCAmelCase__			:     Any										= response.text.splitlines()
       UpperCAmelCase__			:     str										= []
       for item in lines:
            UpperCAmelCase__			:     Union[str, Any]										= item.split(""","""					)
            data.append(__lowerCamelCase					)
       data.pop(0					)  # This is for removing the labels from the list
       UpperCAmelCase__			:     Union[str, Any]										= np.matrix(__lowerCamelCase					)
       return dataset
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= len_data
  
       UpperCAmelCase__			:     Dict										= np.dot(__lowerCamelCase   ,       data_x.transpose()					)
       prod -= data_y.transpose()
       UpperCAmelCase__			:     Optional[Any]										= np.dot(__lowerCamelCase   ,       __lowerCamelCase					)
       UpperCAmelCase__			:     Optional[int]										= theta - (alpha / n) * sum_grad
       return theta
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[str]										= np.dot(__lowerCamelCase   ,       data_x.transpose()					)
       prod -= data_y.transpose()
       UpperCAmelCase__			:     Any										= np.sum(np.square(__lowerCamelCase					)					)
       UpperCAmelCase__			:     Union[str, Any]										= sum_elem / (2 * len_data)
       return error
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= 10_0000
       UpperCAmelCase__			:     Union[str, Any]										= 0.0_001_550
  
       UpperCAmelCase__			:     Optional[Any]										= data_x.shape[1]
       UpperCAmelCase__			:     Tuple										= data_x.shape[0] - 1
  
       UpperCAmelCase__			:     Optional[int]										= np.zeros((1, no_features)					)
  
       for i in range(0   ,       __lowerCamelCase					):
            UpperCAmelCase__			:     Any										= run_steep_gradient_descent(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)
            UpperCAmelCase__			:     Optional[int]										= sum_of_square_error(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)
            print(F"At Iteration {i + 1} - Error is {error:.5f}"					)
  
       return theta
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Any										= sum(abs(y - predicted_y[i]					) for i, y in enumerate(__lowerCamelCase					)					)
       return total / len(__lowerCamelCase					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= collect_dataset()
  
       UpperCAmelCase__			:     str										= data.shape[0]
       UpperCAmelCase__			:     List[str]										= np.c_[np.ones(__lowerCamelCase					), data[:, :-1]].astype(__lowerCamelCase					)
       UpperCAmelCase__			:     int										= data[:, -1].astype(__lowerCamelCase					)
  
       UpperCAmelCase__			:     List[str]										= run_linear_regression(__lowerCamelCase   ,       __lowerCamelCase					)
       UpperCAmelCase__			:     Any										= theta.shape[1]
       print("""Resultant Feature vector : """					)
       for i in range(0   ,       __lowerCamelCase					):
            print(F"{theta[0, i]:.5f}"					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  