934f26003a1b794a3a03ca824817fb1252dff270,hmmlearn/hmm.py,GMMHMM,_generate_sample_from_state,#GMMHMM#,577

Before Change

        return np.array([g.score(X) for g in self.gmms_]).T

    def _generate_sample_from_state(self, state, random_state=None):
        return self.gmms_[state].sample(, random_state=random_state).flatten()

    def _initialize_sufficient_statistics(self):
        stats = super(GMMHMM, self)._initialize_sufficient_statistics()

After Change

            random_state = self.random_state
        random_state = check_random_state(random_state)

        cur_means = self.means_[state]
        cur_covs = self.covars_[state]
        cur_weights = self.weights_[state]

        i_gauss = random_state.choice(self.n_mix, p=cur_weights)

Instances