class_docstr: Maximum likelihood covariance estimator.
  
  Read more in the :ref:`User Guide <covariance>`.
  
  Parameters
  ----------
  store_precision : bool
      Specifies if the estimated precision is stored.
  
  assume_centered : bool
      If True, data are not centered before computation.
      Useful when working with data whose mean is almost, but not exactly
      zero.
      If False (default), data are centered before computation.
  
  Attributes
  ----------
  covariance_ : 2D ndarray, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : 2D ndarray, shape (n_features, n_features)
      Estimated pseudo-inverse matrix.
      (stored only if store_precision is True)
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docstr: Compute the log-likelihood of a Gaussian dataset.
  
  Uses ``self.covariance_`` as an estimator of its covariance matrix.
  
  Parameters
  ----------
  X_test : array-like, shape = [n_samples, n_features]
      Test data of which we compute the likelihood, where n_samples is
      the number of samples and n_features is the number of features.
      X_test is assumed to be drawn from the same distribution than
      the data used in fit (including centering).
  y : ndarray | None
      Not used, present for API consistency.
  
  Returns
  -------
  res : float
      The likelihood of the data set with `self.covariance_` as an
      estimator of its covariance matrix.
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code: def score(self, X_test, y=None):
      """Compute the log-likelihood of a Gaussian dataset.
  
      Uses ``self.covariance_`` as an estimator of its covariance matrix.
  
      Parameters
      ----------
      X_test : array-like, shape = [n_samples, n_features]
          Test data of which we compute the likelihood, where n_samples is
          the number of samples and n_features is the number of features.
          X_test is assumed to be drawn from the same distribution than
          the data used in fit (including centering).
      y : ndarray | None
          Not used, present for API consistency.
  
      Returns
      -------
      res : float
          The likelihood of the data set with `self.covariance_` as an
          estimator of its covariance matrix.
      """
      # compute empirical covariance of the test set
      test_cov = empirical_covariance(
          X_test - self.location_, assume_centered=True
      )
      # compute log likelihood
      res = log_likelihood(test_cov, self.get_precision())
  
      return res
  
imports: import inspect
  import operator
  import os
  import warnings
  from contextlib import contextmanager
  from io import StringIO
  from math import log
  from pprint import pprint
  import numpy
label_desc: Use image node_id 5 for calling the EmpiricalCovariance obj's underlying member method code with example usage: obj.score(X_test, y) and returns: res