



import numpy as np
import requests


def     UpperCamelCase(						):
  lowerCAmelCase_    :  Union[str, Any]             =				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_    :  Union[str, Any]             =				item.split(''','''				)
    data.append(__UpperCamelCase				)
  data.pop(0				)  # This is for removing the labels from the list
  lowerCAmelCase_    :  Tuple             =				np.matrix(__UpperCamelCase				)
  return dataset


def     UpperCamelCase(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	str  ,__UpperCamelCase							:	Any  ,__UpperCamelCase							:	Optional[Any]				):
  lowerCAmelCase_    :  Optional[Any]             =				len_data

  lowerCAmelCase_    :  Any             =				np.dot(__UpperCamelCase  ,data_x.transpose()				)
  prod -= data_y.transpose()
  lowerCAmelCase_    :  int             =				np.dot(__UpperCamelCase  ,__UpperCamelCase				)
  lowerCAmelCase_    :  Optional[Any]             =				theta - (alpha / n) * sum_grad
  return theta


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Dict				):
  lowerCAmelCase_    :  Dict             =				np.dot(__UpperCamelCase  ,data_x.transpose()				)
  prod -= data_y.transpose()
  lowerCAmelCase_    :  Tuple             =				np.sum(np.square(__UpperCamelCase				)				)
  lowerCAmelCase_    :  Optional[int]             =				sum_elem / (2 * len_data)
  return error


def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Tuple				):
  lowerCAmelCase_    :  List[str]             =				100000
  lowerCAmelCase_    :  Tuple             =				0.0_0_0_1_5_5_0

  lowerCAmelCase_    :  str             =				data_x.shape[1]
  lowerCAmelCase_    :  int             =				data_x.shape[0] - 1

  lowerCAmelCase_    :  Optional[int]             =				np.zeros((1, no_features)				)

  for i in range(0  ,__UpperCamelCase				):
    lowerCAmelCase_    :  List[Any]             =				run_steep_gradient_descent(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)
    lowerCAmelCase_    :  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							:	Dict  ,__UpperCamelCase							:	Any				):
  lowerCAmelCase_    :  Optional[Any]             =				sum(abs(y - predicted_y[i]				) for i, y in enumerate(__UpperCamelCase				)				)
  return total / len(__UpperCamelCase				)


def     UpperCamelCase(						):
  lowerCAmelCase_    :  str             =				collect_dataset()

  lowerCAmelCase_    :  Optional[int]             =				data.shape[0]
  lowerCAmelCase_    :  List[str]             =				np.c_[np.ones(__UpperCamelCase				), data[:, :-1]].astype(__UpperCamelCase				)
  lowerCAmelCase_    :  Optional[int]             =				data[:, -1].astype(__UpperCamelCase				)

  lowerCAmelCase_    :  List[Any]             =				run_linear_regression(__UpperCamelCase  ,__UpperCamelCase				)
  lowerCAmelCase_    :  Any             =				theta.shape[1]
  print('''Resultant Feature vector : '''				)
  for i in range(0  ,__UpperCamelCase				):
    print(f"""{theta[0, i]:.5f}"""				)


if __name__ == "__main__":
      main()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





