



import numpy as np

import datasets


SCREAMING_SNAKE_CASE__   			=   '''
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/]
'''

SCREAMING_SNAKE_CASE__   			=   '''\
@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}
}
'''

SCREAMING_SNAKE_CASE__   			=   '''
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   __lowerCAmelCase  (		datasets.Metric   ):
					"""simple docstring"""






					def      _a				(  self       :							List[str]       ):






								"""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      _a				(  self       :							Tuple					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Any       ):






								"""simple docstring"""


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

								return {"mahalanobis": mahal_dist}









from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     stack.pop(), stack.pop()
									if token == "+":
												stack.append(a + b       )
									elif token == "-":
												stack.append(a - b       )
									elif token == "*":
												stack.append(a * b       )
									else:
												if a * b < 0 and a % b != 0:
															stack.append(a // b + 1       )
												else:
															stack.append(a // b       )
						else:
									stack.append(int(__UpperCamelCase       )       )

			return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




