

'''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  UpperCAmelCase (							datasets.Metric    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							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   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      np.array(_UpperCamelCase		)
							lowerCamelCase_  				=      np.array(_UpperCamelCase		)

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

							return {"mahalanobis": mahal_dist}










from ..utils import DummyObject, requires_backends




class 			__UpperCamelCase    (							metaclass=A__    ):
     __A	:  str							  =   ["""torch""", """scipy"""]





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(self				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)