



import numpy as np

import datasets


UpperCAmelCase   :							str							 ="""
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/]
"""

UpperCAmelCase   :							int							 ="""\
@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}
}
"""

UpperCAmelCase   :							Any							 ="""
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       _lowercase   (datasets.Metric      ):
				'''simple docstring'''







				def       _lowerCamelCase					( self    ):

									'''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       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


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

									return {"mahalanobis": mahal_dist}





import re



def    _lowerCAmelCase    (_lowerCAmelCase):
					UpperCamelCase_		  =   re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$")
					if match := re.search(_lowerCAmelCase   ,	_lowerCAmelCase):
										return match.string == phone
					return False


if __name__ == "__main__":
	print(indian_phone_validator("""+918827897895"""))
