code: 
  
  
  import numpy as np
  import requests
  def 							_UpperCAmelCase       ():
         _A   : int       					=				requests.get(
             "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
             "master/Week1/ADRvsRating.csv"		)
         _A   : str       					=				response.text.splitlines()
         _A   : str       					=				[]
         for item in lines:
                _A   : str       					=				item.split(","		)
                data.append(UpperCamelCase__		)
         data.pop(0		)  # This is for removing the labels from the list
         _A   : List[str]       					=				np.matrix(UpperCamelCase__		)
         return dataset
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict		):
         _A   : List[Any]       					=				len_data
  
         _A   : List[Any]       					=				np.dot(UpperCamelCase__       ,							data_x.transpose()		)
         prod -= data_y.transpose()
         _A   : Union[str, Any]       					=				np.dot(UpperCamelCase__       ,							UpperCamelCase__		)
         _A   : int       					=				theta - (alpha / n) * sum_grad
         return theta
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Optional[int]		):
         _A   : List[str]       					=				np.dot(UpperCamelCase__       ,							data_x.transpose()		)
         prod -= data_y.transpose()
         _A   : Dict       					=				np.sum(np.square(UpperCamelCase__		)		)
         _A   : Tuple       					=				sum_elem / (2 * len_data)
         return error
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						int		):
         _A   : List[Any]       					=				100000
         _A   : List[str]       					=				0.0_00_15_50
  
         _A   : Union[str, Any]       					=				data_x.shape[1]
         _A   : Optional[int]       					=				data_x.shape[0] - 1
  
         _A   : Optional[int]       					=				np.zeros((1, no_features)		)
  
         for i in range(0       ,							UpperCamelCase__		):
                _A   : Optional[Any]       					=				run_steep_gradient_descent(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                _A   : Optional[int]       					=				sum_of_square_error(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                print(f"At Iteration {i + 1} - Error is {error:.5f}"		)
  
         return theta
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Union[str, Any]		):
         _A   : int       					=				sum(abs(y - predicted_y[i]		) for i, y in enumerate(UpperCamelCase__		)		)
         return total / len(UpperCamelCase__		)
  def 							_UpperCAmelCase       ():
         _A   : str       					=				collect_dataset()
  
         _A   : Optional[int]       					=				data.shape[0]
         _A   : str       					=				np.c_[np.ones(UpperCamelCase__		), data[:, :-1]].astype(UpperCamelCase__		)
         _A   : Optional[Any]       					=				data[:, -1].astype(UpperCamelCase__		)
  
         _A   : str       					=				run_linear_regression(UpperCamelCase__       ,							UpperCamelCase__		)
         _A   : Any       					=				theta.shape[1]
         print("Resultant Feature vector : "		)
         for i in range(0       ,							UpperCamelCase__		):
                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, is_vision_available
  
  
  lowerCAmelCase__   	=				{
      'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetaImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetaForObjectDetection',
          'DetaModel',
          'DetaPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .image_processing_deta import DetaImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  