


"""simple docstring"""





from sklearn.metrics import matthews_corrcoef

import datasets


snake_case__		:     List[Any]    				=				'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

snake_case__		:     Union[str, Any]    				=				'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

snake_case__		:     Tuple    				=				'''\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,					_KWARGS_DESCRIPTION			)
class 		snake_case_(       datasets.Metric			):

     '''simple docstring'''





     def      lowerCamelCase__     (	self  :					List[str]				):
           return datasets.MetricInfo(
               description=_DESCRIPTION					,				citation=_CITATION					,				inputs_description=_KWARGS_DESCRIPTION					,				features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''int32'''				),
                       '''references''': datasets.Value('''int32'''				),
                   }				)					,				reference_urls=[
                   '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
               ]					,				)






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Any=None				):
           return {
               "matthews_correlation": float(matthews_corrcoef(UpperCamelCase_					,				UpperCamelCase_					,				sample_weight=UpperCamelCase_				)				),
           }






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			int			):
      return base * power(_snake_case ,   (exponent - 1)			) if exponent else 1


if __name__ == "__main__":
 print('''Raise base to the power of exponent using recursion...''')
 snake_case__		:     Union[str, Any]    				=				int(input('''Enter the base: ''').strip())
 snake_case__		:     Optional[Any]    				=				int(input('''Enter the exponent: ''').strip())
 snake_case__		:     Any    				=				power(base, abs(exponent))
 if exponent < 0:  # power() does not properly deal w/ negative exponents
  snake_case__		:     Dict    				=				1 / result
 print(f"""{base} to the power of {exponent} is {result}""")


