comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/dhmm1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Make an HMM with discrete observations
+%   X1 -> X2
+%   |     | 
+%   v     v
+%   Y1    Y2 
+
+intra = zeros(2);
+intra(1,2) = 1;
+inter = zeros(2);
+inter(1,1) = 1;
+n = 2;
+
+Q = 2; % num hidden states
+O = 2; % num observable symbols
+
+ns = [Q O];
+dnodes = 1:2;
+onodes = [2];
+eclass1 = [1 2];
+eclass2 = [3 2];
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
+	      'observed', onodes);
+
+rand('state', 0);
+prior1 = normalise(rand(Q,1));
+transmat1 = mk_stochastic(rand(Q,Q));
+obsmat1 = mk_stochastic(rand(Q,O));
+bnet.CPD{1} = tabular_CPD(bnet, 1, prior1);
+bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat1);
+bnet.CPD{3} = tabular_CPD(bnet, 3, transmat1);
+
+
+T = 5; % fixed length sequences
+
+engine = {};
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
+engine{end+1} = hmm_inf_engine(bnet);
+engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet));
+engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
+if 1
+%engine{end+1} = frontier_inf_engine(bnet); % broken
+engine{end+1} = bk_inf_engine(bnet, 'clusters', {[1]});
+engine{end+1} = jtree_dbn_inf_engine(bnet);
+end
+
+inf_time = cmp_inference_dbn(bnet, engine, T);
+
+ncases = 2;
+max_iter = 2;
+[learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter);
+
+% Compare to HMM toolbox
+
+data = zeros(ncases, T);
+for i=1:ncases
+  %data(i,:) = cat(2, cases{i}{onodes,:});
+  data(i,:) = cell2num(cases{i}(onodes,:));
+end
+[LL2, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', max_iter);
+
+e = 1;
+assert(approxeq(prior2, CPD{e,1}.CPT))
+assert(approxeq(obsmat2, CPD{e,2}.CPT))
+assert(approxeq(transmat2, CPD{e,3}.CPT))
+assert(approxeq(LL2, LL{e}))        
+