code: 
  '''simple docstring'''
  
  import numpy as np
  import requests
  
  
  def 	_UpperCAmelCase				(  )    ->						str:
    _lowerCAmelCase       :  List[str]   				=					requests.get(
        """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
        """master/Week1/ADRvsRating.csv"""		)
    _lowerCAmelCase       :  Tuple   				=					response.text.splitlines()
    _lowerCAmelCase       :  Dict   				=					[]
    for item in lines:
      _lowerCAmelCase       :  str   				=					item.split(""","""		)
      data.append(_lowerCamelCase		)
    data.pop(0		)  # This is for removing the labels from the list
    _lowerCAmelCase       :  Union[str, Any]   				=					np.matrix(_lowerCamelCase		)
    return dataset
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Optional[Any]  ,      _lowerCamelCase						:       Dict		)    ->						Tuple:
    _lowerCAmelCase       :  Optional[int]   				=					len_data
  
    _lowerCAmelCase       :  Dict   				=					np.dot(_lowerCamelCase  ,      data_x.transpose()		)
    prod -= data_y.transpose()
    _lowerCAmelCase       :  Union[str, Any]   				=					np.dot(_lowerCamelCase  ,      _lowerCamelCase		)
    _lowerCAmelCase       :  Any   				=					theta - (alpha / n) * sum_grad
    return theta
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Optional[int]  ,      _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       List[str]		)    ->						str:
    _lowerCAmelCase       :  Tuple   				=					np.dot(_lowerCamelCase  ,      data_x.transpose()		)
    prod -= data_y.transpose()
    _lowerCAmelCase       :  Optional[int]   				=					np.sum(np.square(_lowerCamelCase		)		)
    _lowerCAmelCase       :  Optional[Any]   				=					sum_elem / (2 * len_data)
    return error
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       List[str]		)    ->						List[Any]:
    _lowerCAmelCase       :  Tuple   				=					10_00_00
    _lowerCAmelCase       :  Optional[Any]   				=					0.000_1550
  
    _lowerCAmelCase       :  Dict   				=					data_x.shape[1]
    _lowerCAmelCase       :  List[str]   				=					data_x.shape[0] - 1
  
    _lowerCAmelCase       :  List[Any]   				=					np.zeros((1, no_features)		)
  
    for i in range(0  ,      _lowerCamelCase		):
      _lowerCAmelCase       :  Dict   				=					run_steep_gradient_descent(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      _lowerCAmelCase       :  List[Any]   				=					sum_of_square_error(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      print(f'At Iteration {i + 1} - Error is {error:.5f}'		)
  
    return theta
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[Any]  ,      _lowerCamelCase						:       Optional[int]		)    ->						Tuple:
    _lowerCAmelCase       :  List[Any]   				=					sum(abs(y - predicted_y[i]		) for i, y in enumerate(_lowerCamelCase		)		)
    return total / len(_lowerCamelCase		)
  
  
  def 	_UpperCAmelCase				(  )    ->						Tuple:
    _lowerCAmelCase       :  int   				=					collect_dataset()
  
    _lowerCAmelCase       :  Any   				=					data.shape[0]
    _lowerCAmelCase       :  Tuple   				=					np.c_[np.ones(_lowerCamelCase		), data[:, :-1]].astype(_lowerCamelCase		)
    _lowerCAmelCase       :  Any   				=					data[:, -1].astype(_lowerCamelCase		)
  
    _lowerCAmelCase       :  str   				=					run_linear_regression(_lowerCamelCase  ,      _lowerCamelCase		)
    _lowerCAmelCase       :  Tuple   				=					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: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  