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


if 1
O = 4;
T = 10;
nex = 50;
M = 2;
Q = 3;
else
O = 8; >Number of coefficients in a vector
T = 420; >Number of vectors in a sequence
nex = 1; >Number of sequences
M = 1; >Number of mixtures
Q = 6; >Number of states
end
cov_type = 'full';

data = randn(O,T,nex);

> initial guess of parameters
prior0 = normalise(rand(Q,1));
transmat0 = mk_stochastic(rand(Q,Q));

if 0
Sigma0 = repmat(eye(O), [1 1 Q M]);
> Initialize each mean to a random data point
indices = randperm(T*nex);
mu0 = reshape(data(:,indices(1:(Q*M))), [O Q M]);
mixmat0 = mk_stochastic(rand(Q,M));
else
[mu0, Sigma0] = mixgauss_init(Q*M, data, cov_type);
mu0 = reshape(mu0, [O Q M]);
Sigma0 = reshape(Sigma0, [O O Q M]);
mixmat0 = mk_stochastic(rand(Q,M));
end

[LL, prior1, transmat1, mu1, Sigma1, mixmat1] = ...
mhmm_em(data, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', 5);


loglik = mhmm_logprob(data, prior1, transmat1, mu1, Sigma1, mixmat1);