


"""simple docstring"""


import numpy as np

import datasets


__snake_case    					=					'\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'

__snake_case    					=					'\\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'

__snake_case    					=					'\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 				_SCREAMING_SNAKE_CASE				(							datasets.Metric  ):
   """simple docstring"""



   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 ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[int]:
    # convert to numpy arrays
    lowercase__       :       int				=						np.array(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						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
    lowercase__       :       Optional[Any]				=						X - np.mean(lowerCamelCase__						)
    lowercase__       :       int				=						np.cov(reference_distribution.T						)
    try:
     lowercase__       :       Optional[int]				=						np.linalg.inv(lowerCamelCase__						)
    except np.linalg.LinAlgError:
     lowercase__       :       Optional[Any]				=						np.linalg.pinv(lowerCamelCase__						)
    lowercase__       :       Tuple				=						np.dot(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       int				=						np.dot(lowerCamelCase__ ,     X_minus_mu.T						).diagonal()

    return {"mahalanobis": mahal_dist}



"""simple docstring"""


import random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						partition(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
  quick_sort_random(
      lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      lowerCamelCase__       ,		pivot_index + 1       ,		lowerCamelCase__  )  # recursive quicksort to the right of the pivot point


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()