'Predict posterior probability of data under each Gaussian
in the model.
Parameters
X : array-like, shape = [n_samples, n_features]
Returns
responsibilities : array-like, shape = (n_samples, n_components)
Returns the probability of the sample for each Gaussian
(state) in the model.'

def predict_proba(self, X):

    (logprob, responsibilities) = self.score_samples(X)
    return responsibilities
