code: 
  
  
  
  import numpy as np
  import requests
  def 	UpperCamelCase			(     )			->  Dict:
  			UpperCamelCase					:						Union[str, Any]					=			requests.get(
  			    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
  			    'master/Week1/ADRvsRating.csv'			)
  			UpperCamelCase					:						Optional[Any]					=			response.text.splitlines()
  			UpperCamelCase					:						Optional[Any]					=			[]
  			for item in lines:
  						UpperCamelCase					:						Any					=			item.split(','			)
  						data.append(snake_case__			)
  			data.pop(0			)  # This is for removing the labels from the list
  			UpperCamelCase					:						Tuple					=			np.matrix(snake_case__			)
  			return dataset
  def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					List[Any] ,  snake_case__ :					Any ,  snake_case__ :					Optional[Any] ,  snake_case__ :					Union[str, Any]			)			->  List[Any]:
  			UpperCamelCase					:						List[Any]					=			len_data
  
  			UpperCamelCase					:						Union[str, Any]					=			np.dot(snake_case__ ,  data_x.transpose()			)
  			prod -= data_y.transpose()
  			UpperCamelCase					:						List[str]					=			np.dot(snake_case__ ,  snake_case__			)
  			UpperCamelCase					:						str					=			theta - (alpha / n) * sum_grad
  			return theta
  def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					List[Any] ,  snake_case__ :					Any ,  snake_case__ :					List[Any]			)			->  Optional[int]:
  			UpperCamelCase					:						Union[str, Any]					=			np.dot(snake_case__ ,  data_x.transpose()			)
  			prod -= data_y.transpose()
  			UpperCamelCase					:						Any					=			np.sum(np.square(snake_case__			)			)
  			UpperCamelCase					:						Tuple					=			sum_elem / (2 * len_data)
  			return error
  def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[str]			)			->  Union[str, Any]:
  			UpperCamelCase					:						Tuple					=			100000
  			UpperCamelCase					:						Dict					=			0.0001550
  
  			UpperCamelCase					:						str					=			data_x.shape[1]
  			UpperCamelCase					:						List[Any]					=			data_x.shape[0] - 1
  
  			UpperCamelCase					:						Optional[int]					=			np.zeros((1, no_features)			)
  
  			for i in range(0 ,  snake_case__			):
  						UpperCamelCase					:						Optional[int]					=			run_steep_gradient_descent(snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__			)
  						UpperCamelCase					:						str					=			sum_of_square_error(snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__			)
  						print(F"""At Iteration {i + 1} - Error is {error:.5f}"""			)
  
  			return theta
  def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[Any]			)			->  Union[str, Any]:
  			UpperCamelCase					:						Any					=			sum(abs(y - predicted_y[i]			) for i, y in enumerate(snake_case__			)			)
  			return total / len(snake_case__			)
  def 	UpperCamelCase			(     )			->  Tuple:
  			UpperCamelCase					:						List[Any]					=			collect_dataset()
  
  			UpperCamelCase					:						Dict					=			data.shape[0]
  			UpperCamelCase					:						Optional[int]					=			np.c_[np.ones(snake_case__			), data[:, :-1]].astype(snake_case__			)
  			UpperCamelCase					:						Dict					=			data[:, -1].astype(snake_case__			)
  
  			UpperCamelCase					:						int					=			run_linear_regression(snake_case__ ,  snake_case__			)
  			UpperCamelCase					:						List[Any]					=			theta.shape[1]
  			print('Resultant Feature vector : '			)
  			for i in range(0 ,  snake_case__			):
  						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
  
  
  __UpperCAmelCase      = {
      '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __UpperCAmelCase      = ['''DetaImageProcessor''']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __UpperCAmelCase      = [
          '''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
  
      __UpperCAmelCase      = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  