


"""simple docstring"""


import numpy

# List of input, output pairs
__lowerCamelCase      :int	    =		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__lowerCamelCase      :int	    =		(((515, 22, 13), 555), ((61, 35, 49), 150))
__lowerCamelCase      :List[Any]	    =		[2, 4, 1, 5]
__lowerCamelCase      :Optional[Any]	    =		len(train_data)
__lowerCamelCase      :Tuple	    =		0.009




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						int="train"    )   ->      Any:
      return calculate_hypothesis_value(__snake_case			,				__snake_case    ) - output(
          __snake_case			,				__snake_case    )




def   snake_case		(     UpperCamelCase__       :						int    )   ->      Tuple:
      lowerCamelCase					:					Union[str, Any]        =	0
      for i in range(len(__snake_case    ) - 1    ):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val




def   snake_case		(     UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      str:


      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						List[str]    )   ->      Union[str, 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   snake_case		(     UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						Union[str, Any]=m    )   ->      Tuple:
      lowerCamelCase					:					Union[str, Any]        =	0
      for i in range(__snake_case    ):
            if index == -1:
                  summation_value += _error(__snake_case    )
            else:
                  summation_value += _error(__snake_case    ) * train_data[i][0][index]
      return summation_value




def   snake_case		(     UpperCamelCase__       :						List[str]    )   ->      Any:
      lowerCamelCase					:					str        =	summation_of_cost_derivative(__snake_case			,				__snake_case    ) / m
      return cost_derivative_value




def   snake_case		(     )   ->      Union[str, Any]:
      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      lowerCamelCase					:					Dict        =	0.0_0_0_0_0_2
      lowerCamelCase					:					List[str]        =	0
      lowerCamelCase					:					Tuple        =	0
      while True:
            j += 1
            lowerCamelCase					:					str        =	[0, 0, 0, 0]
            for i in range(0			,				len(__snake_case    )    ):
                  lowerCamelCase					:					Any        =	get_cost_derivative(i - 1    )
                  lowerCamelCase					:					str        =	(
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                __snake_case			,				__snake_case			,				atol=__snake_case			,				rtol=__snake_case			,				):
                  break
            lowerCamelCase					:					List[str]        =	temp_parameter_vector
      print(("""Number of iterations:""", j)    )




def   snake_case		(     )   ->      Union[str, Any]:


      for i in range(len(__snake_case    )    ):
            print(("""Actual output value:""", output(__snake_case			,				"""test"""    ))    )
            print(("""Hypothesis output:""", calculate_hypothesis_value(__snake_case			,				"""test"""    ))    )


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










"""simple docstring"""


from typing import TYPE_CHECKING

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


__lowerCamelCase      :Optional[Any]	    =		{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Union[str, Any]	    =		[
        'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EncodecModel',
        'EncodecPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_encodec import (
        ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
        EncodecConfig,
    )
    from .feature_extraction_encodec import EncodecFeatureExtractor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encodec import (
            ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
            EncodecModel,
            EncodecPreTrainedModel,
        )

else:
    import sys

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




