


"""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 snake_case      (  datasets.Metric):







		def  a_ (					self      :       Optional[Any]		)       ->						int:





						'''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    ,   a__      :       List[Any]    ,   a__      :       List[str]		)       ->						Dict:





						'''simple docstring'''







						_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"""


import random






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			a[left_index]
				_A					  =			left_index + 1
				for j in range(left_index + 1     ,			__lowercase   ):
								if a[j] < pivot:
												_A  ,		_A					  =			a[i], a[j]
												i += 1
				_A  ,		_A					  =			a[i - 1], a[left_index]
				return i - 1






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				if left < right:
								_A					  =			random.randint(__lowercase     ,			right - 1   )
								_A  ,		_A					  =			(
								    a[left],
								    a[pivot],
								)  # switches the pivot with the left most bound
								_A					  =			partition(__lowercase     ,			__lowercase     ,			__lowercase   )
								quick_sort_random(
								    __lowercase     ,			__lowercase     ,			__lowercase   )  # recursive quicksort to the left of the pivot point
								quick_sort_random(
								    __lowercase     ,			pivot_index + 1     ,			__lowercase   )  # recursive quicksort to the right of the pivot point






def      a__ (				)	->	Dict:
				_A					  =			input("Enter numbers separated by a comma:\n"   ).strip()
				_A					  =			[int(__lowercase   ) for item in user_input.split(","   )]

				quick_sort_random(__lowercase     ,			0     ,			len(__lowercase   )   )

				print(__lowercase   )


if __name__ == "__main__":
		main()