


'''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_					:    int          =    "\\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_					:    List[str]          =    "\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  a     (     datasets.Metric       ):
 def 							__UpperCAmelCase		( self						)					->							Dict:
      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      ,				__magic_name__      ,				__magic_name__						)					->							str:
      # convert to numpy arrays
      _a			      =							np.array(a_						)
      _a			      =							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
      _a			      =							X - np.mean(a_						)
      _a			      =							np.cov(reference_distribution.T						)
      try:
           _a			      =							np.linalg.inv(a_						)
      except np.linalg.LinAlgError:
           _a			      =							np.linalg.pinv(a_						)
      _a			      =							np.dot(a_      ,				a_						)
      _a			      =							np.dot(a_      ,				X_minus_mu.T						).diagonal()

      return {"mahalanobis": mahal_dist}





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

