Does anybody know how to train sequence using HMM using kevin murphy's toolbox? I want to train different sequences and want to test which sequence belongs to which class.
Let O = # of observations, Q = # of States, T = observation sequence length and G = # of classes. You should first preallocate PI (prior probabilities), A & B:
PI = zeros(G,Q);
A = zeros(Q,Q,G);
B = zeros(Q,O,G);
after creating observation sequences for class g, which size(Seqs) = [number_of_training_samples_in_class_g,T];
PI_g = normalise(rand(Q,1));
A_g = mk_stochastic(rand(O,O));
B_g = mk_stochastic(rand(Q,O));
if you want use another HMM topology (left-right, Bakis, linear, ...):
https://www.researchgate.net/post/How_to_determine_topology_for_HMM_in_Kevin_Morphy_Toolbox
then train HMM for class g:
[LL,PI(g,:),A(:,:,g),B(:,:,g)] = dhmm_em(Seqs, PI_g,A__g,B_g);
for test; to obtain belong log-likelihood of Seqs observation sequence to class g:
[loglik(g),errors] = dhmm_logprob(Seqs, PI(g,:), A(:,:,g), B(:,:,g));
Matlab has training software that works although the way they explain it is not the best.
Let O = # of observations, Q = # of States, T = observation sequence length and G = # of classes. You should first preallocate PI (prior probabilities), A & B:
PI = zeros(G,Q);
A = zeros(Q,Q,G);
B = zeros(Q,O,G);
after creating observation sequences for class g, which size(Seqs) = [number_of_training_samples_in_class_g,T];
PI_g = normalise(rand(Q,1));
A_g = mk_stochastic(rand(O,O));
B_g = mk_stochastic(rand(Q,O));
if you want use another HMM topology (left-right, Bakis, linear, ...):
https://www.researchgate.net/post/How_to_determine_topology_for_HMM_in_Kevin_Morphy_Toolbox
then train HMM for class g:
[LL,PI(g,:),A(:,:,g),B(:,:,g)] = dhmm_em(Seqs, PI_g,A__g,B_g);
for test; to obtain belong log-likelihood of Seqs observation sequence to class g:
[loglik(g),errors] = dhmm_logprob(Seqs, PI(g,:), A(:,:,g), B(:,:,g));
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