hmm_utils.py 文件源码

def _compute_log_likelihood(self, X):
        seq_len = X.shape[0]
        n_states = self.n_components
        n_dim = X.shape[1]
        p = np.zeros((seq_len,n_states))
        for i in range(seq_len):
            miss = np.isnan(X[i])
            p[i] = np.sum(miss * np.log(self.miss_probs_) + (1-miss) * np.log(1-self.miss_probs_), axis=1)
            if not np.all(miss):
                for state in range(n_states):
                    mean = self.means_[state][miss==0]
                    cov = self.covars_[state][np.ix_(miss==0,miss==0)]
                    p[i][state] = p[i][state] + np.log(multivariate_normal.pdf(X[i][miss==0],mean=mean,cov=cov))
        return p