wolffd@0: % Compute Viterbi path discrete HMM by different methods
wolffd@0: 
wolffd@0: intra = zeros(2);
wolffd@0: intra(1,2) = 1;
wolffd@0: inter = zeros(2);
wolffd@0: inter(1,1) = 1;
wolffd@0: n = 2;
wolffd@0: 
wolffd@0: Q = 2; % num hidden states
wolffd@0: O = 2; % num observable symbols
wolffd@0: 
wolffd@0: ns = [Q O];
wolffd@0: dnodes = 1:2;
wolffd@0: onodes = [2];
wolffd@0: eclass1 = [1 2];
wolffd@0: eclass2 = [3 2];
wolffd@0: bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
wolffd@0: 	      'observed', onodes);
wolffd@0: 
wolffd@0: for seed=1:10
wolffd@0: rand('state', seed);
wolffd@0: prior = normalise(rand(Q,1));
wolffd@0: transmat = mk_stochastic(rand(Q,Q));
wolffd@0: obsmat = mk_stochastic(rand(Q,O));
wolffd@0: bnet.CPD{1} = tabular_CPD(bnet, 1, prior);
wolffd@0: bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat);
wolffd@0: bnet.CPD{3} = tabular_CPD(bnet, 3, transmat);
wolffd@0: 
wolffd@0: 
wolffd@0: % Create a sequence
wolffd@0: T = 5; 
wolffd@0: ev = sample_dbn(bnet, T);
wolffd@0: evidence = cell(2,T);
wolffd@0: evidence(2,:) = ev(2,:); % extract observed component
wolffd@0: data = cell2num(ev(2,:));
wolffd@0: 
wolffd@0: %obslik = mk_dhmm_obs_lik(data, obsmat);
wolffd@0: obslik = multinomial_prob(data, obsmat);
wolffd@0: path = viterbi_path(prior, transmat, obslik);
wolffd@0: 
wolffd@0: engine = {};
wolffd@0: engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
wolffd@0: 
wolffd@0: mpe = find_mpe(engine{1}, evidence);
wolffd@0: 
wolffd@0: assert(isequal(cell2num(mpe(1,:)), path)) % extract values of hidden nodes
wolffd@0: end