code: 
  
  
  import numpy as np
  import requests
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     requests.get(
  			    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
  			    """master/Week1/ADRvsRating.csv"""			)
  			lowerCamelCase								=     response.text.splitlines()
  			lowerCamelCase								=     []
  			for item in lines:
  						lowerCamelCase								=     item.split(""","""			)
  						data.append(lowerCamelCase__			)
  			data.pop(0			)  # This is for removing the labels from the list
  			lowerCamelCase								=     np.matrix(lowerCamelCase__			)
  			return dataset
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Union[str, Any]     ,       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	List[str]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     len_data
  
  			lowerCamelCase								=     np.dot(lowerCamelCase__     ,       data_x.transpose()			)
  			prod -= data_y.transpose()
  			lowerCamelCase								=     np.dot(lowerCamelCase__     ,       lowerCamelCase__			)
  			lowerCamelCase								=     theta - (alpha / n) * sum_grad
  			return theta
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Dict			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     np.dot(lowerCamelCase__     ,       data_x.transpose()			)
  			prod -= data_y.transpose()
  			lowerCamelCase								=     np.sum(np.square(lowerCamelCase__			)			)
  			lowerCamelCase								=     sum_elem / (2 * len_data)
  			return error
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	int			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     100000
  			lowerCamelCase								=     0.0_0_0_1_5_5_0
  
  			lowerCamelCase								=     data_x.shape[1]
  			lowerCamelCase								=     data_x.shape[0] - 1
  
  			lowerCamelCase								=     np.zeros((1, no_features)			)
  
  			for i in range(0     ,       lowerCamelCase__			):
  						lowerCamelCase								=     run_steep_gradient_descent(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
  						lowerCamelCase								=     sum_of_square_error(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
  						print(f'At Iteration {i + 1} - Error is {error:.5f}'			)
  
  			return theta
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	str			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     sum(abs(y - predicted_y[i]			) for i, y in enumerate(lowerCamelCase__			)			)
  			return total / len(lowerCamelCase__			)
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     collect_dataset()
  
  			lowerCamelCase								=     data.shape[0]
  			lowerCamelCase								=     np.c_[np.ones(lowerCamelCase__			), data[:, :-1]].astype(lowerCamelCase__			)
  			lowerCamelCase								=     data[:, -1].astype(lowerCamelCase__			)
  
  			lowerCamelCase								=     run_linear_regression(lowerCamelCase__     ,       lowerCamelCase__			)
  			lowerCamelCase								=     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_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase      :						List[str]    =		{
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "BigBirdForCausalLM",
  		    "BigBirdForMaskedLM",
  		    "BigBirdForMultipleChoice",
  		    "BigBirdForPreTraining",
  		    "BigBirdForQuestionAnswering",
  		    "BigBirdForSequenceClassification",
  		    "BigBirdForTokenClassification",
  		    "BigBirdLayer",
  		    "BigBirdModel",
  		    "BigBirdPreTrainedModel",
  		    "load_tf_weights_in_big_bird",
  		]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "FlaxBigBirdForCausalLM",
  		    "FlaxBigBirdForMaskedLM",
  		    "FlaxBigBirdForMultipleChoice",
  		    "FlaxBigBirdForPreTraining",
  		    "FlaxBigBirdForQuestionAnswering",
  		    "FlaxBigBirdForSequenceClassification",
  		    "FlaxBigBirdForTokenClassification",
  		    "FlaxBigBirdModel",
  		    "FlaxBigBirdPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird import BigBirdTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_big_bird import (
  				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    BigBirdForCausalLM,
  				    BigBirdForMaskedLM,
  				    BigBirdForMultipleChoice,
  				    BigBirdForPreTraining,
  				    BigBirdForQuestionAnswering,
  				    BigBirdForSequenceClassification,
  				    BigBirdForTokenClassification,
  				    BigBirdLayer,
  				    BigBirdModel,
  				    BigBirdPreTrainedModel,
  				    load_tf_weights_in_big_bird,
  				)
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_big_bird import (
  				    FlaxBigBirdForCausalLM,
  				    FlaxBigBirdForMaskedLM,
  				    FlaxBigBirdForMultipleChoice,
  				    FlaxBigBirdForPreTraining,
  				    FlaxBigBirdForQuestionAnswering,
  				    FlaxBigBirdForSequenceClassification,
  				    FlaxBigBirdForTokenClassification,
  				    FlaxBigBirdModel,
  				    FlaxBigBirdPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  