'Computes the log-likelihood of a Gaussian data set with
`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 : not used, present for API consistence purpose.
Returns
res : float
The likelihood of the data set with `self.covariance_` as an
estimator of its covariance matrix.'

def score(self, X_test, y=None):

    test_cov = empirical_covariance((X_test - self.location_), assume_centered=True)
    res = log_likelihood(test_cov, self.get_precision())
    return res
