comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/ghmm1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Make an HMM with Gaussian 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; % size of observed vector
+ns = [Q O];
+bnet = mk_dbn(intra, inter, ns, 'discrete', 1, 'observed', 2);
+
+prior0 = normalise(rand(Q,1));
+transmat0 = mk_stochastic(rand(Q,Q));
+mu0 = rand(O,Q);
+Sigma0 = repmat(eye(O), [1 1 Q]);
+bnet.CPD{1} = tabular_CPD(bnet, 1, prior0);
+%% we set the cov prior to 0 to give same results as HMM toolbox
+%bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0, 'cov_prior_weight', 0);
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0);
+bnet.CPD{3} = tabular_CPD(bnet, 3, transmat0);
+
+
+T = 5; % fixed length sequences
+
+engine = {};
+engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
+engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet));
+engine{end+1} = hmm_inf_engine(bnet);
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
+%engine{end+1} = frontier_inf_engine(bnet);
+engine{end+1} = bk_inf_engine(bnet, 'clusters', {[1]});
+engine{end+1} = jtree_dbn_inf_engine(bnet);
+
+
+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(O, T, ncases);
+for i=1:ncases
+  data(:,:,i) = cell2num(cases{i}(bnet.observed, :));  
+end
+
+tic
+[LL2, prior2, transmat2, mu2, Sigma2] = mhmm_em(data, prior0, transmat0, mu0, Sigma0, [],  'max_iter', max_iter);
+t=toc;
+disp(['HMM toolbox took ' num2str(t) ' seconds '])
+
+e = 1;
+assert(approxeq(prior2, CPD{e,1}.CPT))
+assert(approxeq(mu2, CPD{e,2}.mean))
+assert(approxeq(Sigma2, CPD{e,2}.cov))
+assert(approxeq(transmat2, CPD{e,3}.CPT))
+assert(approxeq(LL2, LL{e}))