hmm_class.py 文件源码

def _train_transition(self,observations):
        # Initialize transition matrix
        new_trans_prob = np.asmatrix(np.zeros(self.trans_prob.shape))

        # Find alpha and beta
        alpha,c = self._alpha_cal(observations)
        beta = self._beta_cal(observations,c)

        # calculate transition matrix values
        for t in range(len(observations)-1):
            temp1 = np.multiply(alpha[:,t],beta[:,t+1].transpose())
            temp1 = np.multiply(self.trans_prob,temp1)
            new_trans_prob = new_trans_prob + np.multiply(temp1,self.em_prob[:,self.obs_map[observations[t+1]]].transpose())

        # Normalize values so that sum of probabilities is 1
        for i in range(self.trans_prob.shape[0]):
            new_trans_prob[i,:] = new_trans_prob[i,:]/np.sum(new_trans_prob[i,:])

        return new_trans_prob