




"""simple docstring"""

import numpy as np

import datasets


__magic_name__				     =     """
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/]
"""

__magic_name__				     =     """\
@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}
}
"""

__magic_name__				     =     """
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        SCREAMING_SNAKE_CASE__   (						datasets.Metric      ):
   def    __UpperCAmelCase       (       self			:				List[str]							):
          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    __UpperCAmelCase       (       self			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				Any							):
          lowerCamelCase__          =						np.array(_snake_case							)
          lowerCamelCase__          =						np.array(_snake_case							)

          # 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
          lowerCamelCase__          =						X - np.mean(_snake_case							)
          lowerCamelCase__          =						np.cov(reference_distribution.T							)
          try:
                 lowerCamelCase__          =						np.linalg.inv(_snake_case							)
          except np.linalg.LinAlgError:
                 lowerCamelCase__          =						np.linalg.pinv(_snake_case							)
          lowerCamelCase__          =						np.dot(_snake_case   ,	_snake_case							)
          lowerCamelCase__          =						np.dot(_snake_case   ,	X_minus_mu.T							).diagonal()

          return {"mahalanobis": mahal_dist}











SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




