
"""simple docstring"""

import numpy as np

import datasets


SCREAMING_SNAKE_CASE_    					=						'''
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''

SCREAMING_SNAKE_CASE_    					=						'''\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
'''

SCREAMING_SNAKE_CASE_    					=						'''
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,			_KWARGS_DESCRIPTION							)
class      _UpperCAmelCase							(		datasets.Metric							):




      def     a_      (							self							)						->					int:
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,       citation=_CITATION   ,       inputs_description=_KWARGS_DESCRIPTION   ,       features=datasets.Features(
                  {
                      'X': datasets.Sequence(datasets.Value('float'   ,       id='sequence'							)   ,       id='X'							),
                  }							)   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Dict:
          # convert to numpy arrays
          UpperCAmelCase									=					np.array(lowercase_							)
          UpperCAmelCase									=					np.array(lowercase_							)

          # Assert that arrays are 2D
          if len(X.shape							) != 2:
              raise ValueError('Expected `X` to be a 2D vector'							)
          if len(reference_distribution.shape							) != 2:
              raise ValueError('Expected `reference_distribution` to be a 2D vector'							)
          if reference_distribution.shape[0] < 2:
              raise ValueError(
                  'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'							)

          # Get mahalanobis distance for each prediction
          UpperCAmelCase									=					X - np.mean(lowercase_							)
          UpperCAmelCase									=					np.cov(reference_distribution.T							)
          try:
              UpperCAmelCase									=					np.linalg.inv(lowercase_							)
          except np.linalg.LinAlgError:
              UpperCAmelCase									=					np.linalg.pinv(lowercase_							)
          UpperCAmelCase									=					np.dot(lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					np.dot(lowercase_   ,       X_minus_mu.T							).diagonal()

          return {"mahalanobis": mahal_dist}





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number | (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number & ~(1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number ^ (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      bool:






    """simple docstring"""




    return ((number >> position) & 1) == 1




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return int((number & (1 << position)) != 0 )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



