comparison toolboxes/FullBNT-1.0.7/HMM/dhmm_em_demo.m @ 0:e9a9cd732c1e tip

first hg version after svn

+O = 3;
+Q = 2;
+
+% "true" parameters
+prior0 = normalise(rand(Q,1));
+transmat0 = mk_stochastic(rand(Q,Q));
+obsmat0 = mk_stochastic(rand(Q,O));
+
+% training data
+T = 5;
+nex = 10;
+data = dhmm_sample(prior0, transmat0, obsmat0, T, nex);
+
+% initial guess of parameters
+prior1 = normalise(rand(Q,1));
+transmat1 = mk_stochastic(rand(Q,Q));
+obsmat1 = mk_stochastic(rand(Q,O));
+
+% improve guess of parameters using EM
+[LL, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', 5);
+LL
+
+% use model to compute log likelihood
+loglik = dhmm_logprob(data, prior2, transmat2, obsmat2)
+% log lik is slightly different than LL(end), since it is computed after the final M step