






"""simple docstring"""





import numpy as np
import requests



def      __lowercase       (     )       ->       Any:
      __snake_case   :     Dict	      =requests.get(
          '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
          '''master/Week1/ADRvsRating.csv'''			)
      __snake_case   :     Union[str, Any]	      =response.text.splitlines()
      __snake_case   :     Optional[int]	      =[]
      for item in lines:
            __snake_case   :     List[Any]	      =item.split(''','''			)
            data.append(a			)
      data.pop(0			)  # This is for removing the labels from the list
      __snake_case   :     int	      =np.matrix(a			)
      return dataset



def      __lowercase       (     a			:					Tuple							,		a			:					Any							,		a			:					Tuple							,		a			:					int							,		a			:					Union[str, Any]			)       ->       Union[str, Any]:
      __snake_case   :     int	      =len_data

      __snake_case   :     Any	      =np.dot(a							,		data_x.transpose()			)
      prod -= data_y.transpose()
      __snake_case   :     List[Any]	      =np.dot(a							,		a			)
      __snake_case   :     List[str]	      =theta - (alpha / n) * sum_grad
      return theta



def      __lowercase       (     a			:					str							,		a			:					Union[str, Any]							,		a			:					str							,		a			:					Any			)       ->       Optional[Any]:
      __snake_case   :     int	      =np.dot(a							,		data_x.transpose()			)
      prod -= data_y.transpose()
      __snake_case   :     Union[str, Any]	      =np.sum(np.square(a			)			)
      __snake_case   :     Optional[int]	      =sum_elem / (2 * len_data)
      return error



def      __lowercase       (     a			:					Dict							,		a			:					str			)       ->       Optional[int]:
      __snake_case   :     str	      =100_000
      __snake_case   :     int	      =0.0_0_0_1_5_5_0

      __snake_case   :     Optional[int]	      =data_x.shape[1]
      __snake_case   :     Union[str, Any]	      =data_x.shape[0] - 1

      __snake_case   :     int	      =np.zeros((1, no_features)			)

      for i in range(0							,		a			):
            __snake_case   :     Any	      =run_steep_gradient_descent(a							,		a							,		a							,		a							,		a			)
            __snake_case   :     Tuple	      =sum_of_square_error(a							,		a							,		a							,		a			)
            print(f'''At Iteration {i + 1} - Error is {error:.5f}'''			)

      return theta



def      __lowercase       (     a			:					Optional[Any]							,		a			:					Optional[Any]			)       ->       Optional[Any]:
      __snake_case   :     str	      =sum(abs(y - predicted_y[i]			) for i, y in enumerate(a			)			)
      return total / len(a			)



def      __lowercase       (     )       ->       Union[str, Any]:
      __snake_case   :     str	      =collect_dataset()

      __snake_case   :     str	      =data.shape[0]
      __snake_case   :     str	      =np.c_[np.ones(a			), data[:, :-1]].astype(a			)
      __snake_case   :     Union[str, Any]	      =data[:, -1].astype(a			)

      __snake_case   :     Any	      =run_linear_regression(a							,		a			)
      __snake_case   :     Optional[int]	      =theta.shape[1]
      print('''Resultant Feature vector : '''			)
      for i in range(0							,		a			):
            print(f'''{theta[0, i]:.5f}'''			)


if __name__ == "__main__":
    main()










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


