




import numpy

# List of input, output pairs
_lowerCamelCase      			=						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_lowerCamelCase      			=						(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_lowerCamelCase      			=						[2, 4, 1, 5]
_lowerCamelCase      			=						len(train_data)
_lowerCamelCase      			=						0.009




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						Dict="train"				)  ->							int:
    return calculate_hypothesis_value(__UpperCamelCase	,					__UpperCamelCase				) - output(
        __UpperCamelCase	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							Any:
    UpperCAmelCase_										=					0
    for i in range(len(__UpperCamelCase				) - 1				):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]				)  ->							Dict:
    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Any				)  ->							Any:
    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]				)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]				)
    return None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Any=m				)  ->							Optional[Any]:
    UpperCAmelCase_										=					0
    for i in range(__UpperCamelCase				):
        if index == -1:
            summation_value += _error(__UpperCamelCase				)
        else:
            summation_value += _error(__UpperCamelCase				) * train_data[i][0][index]
    return summation_value




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]				)  ->							str:
    UpperCAmelCase_										=					summation_of_cost_derivative(__UpperCamelCase	,					__UpperCamelCase				) / m
    return cost_derivative_value




def  SCREAMING_SNAKE_CASE     (	)  ->							Any:
    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    UpperCAmelCase_										=					0.000_002
    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					0
    while True:
        j += 1
        UpperCAmelCase_										=					[0, 0, 0, 0]
        for i in range(0	,					len(__UpperCamelCase				)				):
            UpperCAmelCase_										=					get_cost_derivative(i - 1				)
            UpperCAmelCase_										=					(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            __UpperCamelCase	,					__UpperCamelCase	,					atol=__UpperCamelCase	,					rtol=__UpperCamelCase	,					):
            break
        UpperCAmelCase_										=					temp_parameter_vector
    print(('''Number of iterations:''', j)				)




def  SCREAMING_SNAKE_CASE     (	)  ->							Optional[Any]:
    for i in range(len(__UpperCamelCase				)				):
        print(('''Actual output value:''', output(__UpperCamelCase	,					'''test'''				))				)
        print(('''Hypothesis output:''', calculate_hypothesis_value(__UpperCamelCase	,					'''test'''				))				)


if __name__ == "__main__":
 run_gradient_descent()
 print('\nTesting gradient descent for a linear hypothesis function.\n')
 test_gradient_descent()











from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  ['pixel_values']
     def __init__(     self       :      Any ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      bool = True ,   __snake_case       :      Union[int, float] = 1 / 2_55 ,   __snake_case       :      bool = True ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,   **__snake_case       :      Optional[Any] ,   ):
         super().__init__(**__snake_case    )
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					resample
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size
         UpperCAmelCase_										=					do_rescale
         UpperCAmelCase_										=					rescale_factor
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_										=					image_std if image_std is not None else IMAGENET_DEFAULT_STD
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Tuple ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
             UpperCAmelCase_										=					int((2_56 / 2_24) * size['''shortest_edge''']    )
             UpperCAmelCase_										=					get_resize_output_image_size(__snake_case ,   size=__snake_case ,   default_to_square=__snake_case    )
             UpperCAmelCase_										=					{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'    )
         return resize(
             __snake_case ,   size=(size_dict['''height'''], size_dict['''width''']) ,   resample=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         if "height" not in size or "width" not in size:
             raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'    )
         return center_crop(__snake_case ,   size=(size['''height'''], size['''width''']) ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[int, float] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      List[Any] ,   ):
         return rescale(__snake_case ,   scale=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Union[str, Any] ,   ):
         return normalize(__snake_case ,   mean=__snake_case ,   std=__snake_case ,   data_format=__snake_case ,   **__snake_case    )




     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      ImageInput ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      PILImageResampling = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[float] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[TensorType] = None ,   __snake_case       :      ChannelDimension = ChannelDimension.FIRST ,   **__snake_case       :      List[str] ,   ):
         UpperCAmelCase_										=					do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_										=					resample if resample is not None else self.resample
         UpperCAmelCase_										=					do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_										=					do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_										=					rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_										=					do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_										=					image_std if image_std is not None else self.image_std

         UpperCAmelCase_										=					size if size is not None else self.size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					make_list_of_images(__snake_case    )

         if not valid_images(__snake_case    ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

         if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''    )

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

         # All transformations expect numpy arrays.
         UpperCAmelCase_										=					[to_numpy_array(__snake_case    ) for image in images]

         if do_resize:
             UpperCAmelCase_										=					[self.resize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         if do_center_crop:
             UpperCAmelCase_										=					[self.center_crop(__snake_case ,   __snake_case    ) for image in images]

         if do_rescale:
             UpperCAmelCase_										=					[self.rescale(__snake_case ,   __snake_case    ) for image in images]

         if do_normalize:
             UpperCAmelCase_										=					[self.normalize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					[to_channel_dimension_format(__snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					{'''pixel_values''': images}
         return BatchFeature(data=__snake_case ,   tensor_type=__snake_case    )





