


"""simple docstring"""


import numpy as np

import datasets


A     :   List[Any]			    =			"\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     :   Dict			    =			"\\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     :   Optional[int]			    =			"\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 			snake_case				(     self	):
										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 			snake_case				(     self    ,   __a    ,   __a	):
										# convert to numpy arrays
										__lowerCAmelCase				    =			np.array(__a	)
										__lowerCAmelCase				    =			np.array(__a	)

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

										return {"mahalanobis": mahal_dist}








"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ):
						raise ValueError("iterations must be defined as integers"      )
			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ) or not number >= 1:
						raise ValueError(
						    "starting number must be\n                         and integer and be more than 0"      )
			if not iterations >= 1:
						raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"      )

			__lowerCAmelCase				    =			""
			while number <= iterations:
						if number % 3 == 0:
									out += "Fizz"
						if number % 5 == 0:
									out += "Buzz"
						if 0 not in (number % 3, number % 5):
									out += str(_UpperCamelCase      )

						# print(out)
						number += 1
						out += " "
			return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()




