



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()








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__)



