'Compute the total log probability under the model.
Parameters
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points.  Each row
corresponds to a single data point.
Returns
logprob : float
Total log-likelihood of the data in X.'

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

    return np.sum(self.score_samples(X))
