The statement/case tells to build and train a hidden Markov's model having following components specially using murphyk's
toolbox for HMM as per the choice:
- O = Observation's vector
- Q = States vector
- T = vectors sequence
- nex = number of sequences
- M = number of mixtures
Demo Code (from murphyk's
toolbox):
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
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, 'full');
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);