class_docstr: An object for detecting outliers in a Gaussian distributed dataset.
  
  Read more in the :ref:`User Guide <outlier_detection>`.
  
  Parameters
  ----------
  store_precision : bool, default=True
      Specify if the estimated precision is stored.
  
  assume_centered : bool, default=False
      If True, the support of robust location and covariance estimates
      is computed, and a covariance estimate is recomputed from it,
      without centering the data.
      Useful to work with data whose mean is significantly equal to
      zero but is not exactly zero.
      If False, the robust location and covariance are directly computed
      with the FastMCD algorithm without additional treatment.
  
  support_fraction : float, default=None
      The proportion of points to be included in the support of the raw
      MCD estimate. If None, the minimum value of support_fraction will
      be used within the algorithm: `[n_sample + n_features + 1] / 2`.
      Range is (0, 1).
  
  contamination : float, default=0.1
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set. Range is (0, 0.5].
  
  random_state : int, RandomState instance or None, default=None
      Determines the pseudo random number generator for shuffling
      the data. Pass an int for reproducible results across multiple function
      calls. See :term:`Glossary <random_state>`.
  
  Attributes
  ----------
  location_ : ndarray of shape (n_features,)
      Estimated robust location.
  
  covariance_ : ndarray of shape (n_features, n_features)
      Estimated robust covariance matrix.
  
  precision_ : ndarray of shape (n_features, n_features)
      Estimated pseudo inverse matrix.
      (stored only if store_precision is True)
  
  support_ : ndarray of shape (n_samples,)
      A mask of the observations that have been used to compute the
      robust estimates of location and shape.
  
  offset_ : float
      Offset used to define the decision function from the raw scores.
      We have the relation: ``decision_function = score_samples - offset_``.
      The offset depends on the contamination parameter and is defined in
      such a way we obtain the expected number of outliers (samples with
      decision function < 0) in training.
  
      .. versionadded:: 0.20
  
  raw_location_ : ndarray of shape (n_features,)
      The raw robust estimated location before correction and re-weighting.
  
  raw_covariance_ : ndarray of shape (n_features, n_features)
      The raw robust estimated covariance before correction and re-weighting.
  
  raw_support_ : ndarray of shape (n_samples,)
      A mask of the observations that have been used to compute
      the raw robust estimates of location and shape, before correction
      and re-weighting.
  
  dist_ : ndarray of shape (n_samples,)
      Mahalanobis distances of the training set (on which :meth:`fit` is
      called) observations.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  EmpiricalCovariance : Maximum likelihood covariance estimator.
  GraphicalLasso : Sparse inverse covariance estimation
      with an l1-penalized estimator.
  LedoitWolf : LedoitWolf Estimator.
  MinCovDet : Minimum Covariance Determinant
      (robust estimator of covariance).
  OAS : Oracle Approximating Shrinkage Estimator.
  ShrunkCovariance : Covariance estimator with shrinkage.
  
  Notes
  -----
  Outlier detection from covariance estimation may break or not
  perform well in high-dimensional settings. In particular, one will
  always take care to work with ``n_samples > n_features ** 2``.
  
  References
  ----------
  .. [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the
     minimum covariance determinant estimator" Technometrics 41(3), 212
     (1999)
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn.covariance import EllipticEnvelope
  >>> true_cov = np.array([[.8, .3],
  ...                      [.3, .4]])
  >>> X = np.random.RandomState(0).multivariate_normal(mean=[0, 0],
  ...                                                  cov=true_cov,
  ...                                                  size=500)
  >>> cov = EllipticEnvelope(random_state=0).fit(X)
  >>> # predict returns 1 for an inlier and -1 for an outlier
  >>> cov.predict([[0, 0],
  ...              [3, 3]])
  array([ 1, -1])
  >>> cov.covariance_
  array([[0.7411..., 0.2535...],
         [0.2535..., 0.3053...]])
  >>> cov.location_
  array([0.0813... , 0.0427...])
docstr: Compute the decision function of the given observations.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The data matrix.
  
  Returns
  -------
  decision : ndarray of shape (n_samples,)
      Decision function of the samples.
      It is equal to the shifted Mahalanobis distances.
      The threshold for being an outlier is 0, which ensures a
      compatibility with other outlier detection algorithms.
docstr_tok: ["Compute","the","decision","function","of","the","given","observations",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","The","data","matrix",".","Returns","--","--","--","-","decision",":","ndarray","of","shape","(","n_samples",",",")","Decision","function","of","the","samples",".","It","is","equal","to","the","shifted","Mahalanobis","distances",".","The","threshold","for","being","an","outlier","is","0",",","which","ensures","a","compatibility","with","other","outlier","detection","algorithms","."]
code: def decision_function(self, X):
      """Compute the decision function of the given observations.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          The data matrix.
  
      Returns
      -------
      decision : ndarray of shape (n_samples,)
          Decision function of the samples.
          It is equal to the shifted Mahalanobis distances.
          The threshold for being an outlier is 0, which ensures a
          compatibility with other outlier detection algorithms.
      """
      check_is_fitted(self)
      negative_mahal_dist = self.score_samples(X)
      return negative_mahal_dist - self.offset_
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/covariance/_elliptic_envelope.py
imports: from numbers import Real
  import numpy
  from ..base import OutlierMixin, _fit_context
  from ..metrics import accuracy_score
  from ..utils._param_validation import Interval
  from ..utils.validation import check_is_fitted
  from ._robust_covariance import MinCovDet
label_desc: Use image node_id 3 for calling the EllipticEnvelope obj's underlying member method code with example usage: obj.decision_function(X) and returns: unknown