www.pudn.com > HMM1.zip > dhmm_em_demo.m

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