

"""simple docstring"""





import numpy as np

import datasets


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

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

_a       =							'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'




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







 """simple docstring"""







 def 					_lowercase (			self							:			Tuple	):
    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 					_lowercase (			self							:			int,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[Any]	):
    # convert to numpy arrays
    __lowercase								=						np.array(snake_case_	)
    __lowercase								=						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
    __lowercase								=						X - np.mean(snake_case_	)
    __lowercase								=						np.cov(reference_distribution.T	)
    try:
       __lowercase								=						np.linalg.inv(snake_case_	)
    except np.linalg.LinAlgError:
       __lowercase								=						np.linalg.pinv(snake_case_	)
    __lowercase								=						np.dot(snake_case_,					snake_case_	)
    __lowercase								=						np.dot(snake_case_,					X_minus_mu.T	).diagonal()

    return {"mahalanobis": mahal_dist}






from __future__ import annotations




def    __UpperCamelCase     (				lowerCAmelCase__							:				list[float]  ,   lowerCAmelCase__							:				list[float]  ):
 __a :  Dict						=  sorted(numsa + numsa  )
 __a				,       __a :  Optional[Any]						=  divmod(len(lowerCAmelCase__  )  ,   2  )
 if mod == 1:
  return all_numbers[div]
 else:
  return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__       =[float(x) for x in input('Enter the elements of first array: ').split()]
      lowercase__       =[float(x) for x in input('Enter the elements of second array: ').split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
