"""simple docstring"""

import numpy as np

import datasets


UpperCAmelCase       =  '\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'

UpperCAmelCase       =  '\\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'

UpperCAmelCase       =  '\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      UpperCAmelCase_       (						datasets.Metric):






							def       _UpperCamelCase			(		self :      Any  )       ->       Any:
												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 :      Dict     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      int  )       ->       Optional[int]:
												# convert to numpy arrays
												_UpperCamelCase      						=	np.array(__lowerCamelCase  )
												_UpperCamelCase      						=	np.array(__lowerCamelCase  )

												# 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(__lowerCamelCase  )
												_UpperCamelCase      						=	np.cov(reference_distribution.T  )
												try:
																	_UpperCamelCase      						=	np.linalg.inv(__lowerCamelCase  )
												except np.linalg.LinAlgError:
																	_UpperCamelCase      						=	np.linalg.pinv(__lowerCamelCase  )
												_UpperCamelCase      						=	np.dot(__lowerCamelCase     ,						__lowerCamelCase  )
												_UpperCamelCase      						=	np.dot(__lowerCamelCase     ,						X_minus_mu.T  ).diagonal()

												return {"mahalanobis": mahal_dist}







import numpy as np


def 	UpperCamelCase__(       UpperCamelCase__			:       np.array  )->np.array:
       return (2 / (1 + np.exp(-2 * vector  ))) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()





