comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/arhmm1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Make an HMM with autoregressive Gaussian observations (switching AR model)
+%   X1 -> X2
+%   |     | 
+%   v     v
+%   Y1 -> Y2 
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+intra = zeros(2);
+intra(1,2) = 1;
+inter = zeros(2);
+inter(1,1) = 1;
+inter(2,2) = 1;
+n = 2;
+
+Q = 2; % num hidden states
+O = 2; % size of observed vector
+
+ns = [Q O];
+dnodes = 1;
+onodes = [2];
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes);
+
+bnet.CPD{1} = tabular_CPD(bnet, 1);
+bnet.CPD{2} = gaussian_CPD(bnet, 2);
+bnet.CPD{3} = tabular_CPD(bnet, 3);
+bnet.CPD{4} = gaussian_CPD(bnet, 4);
+
+
+T = 10; % fixed length sequences
+
+engine = {};
+%engine{end+1} = hmm_inf_engine(bnet);
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
+%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet));
+%engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
+
+inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll',1);
+learning_time = cmp_learning_dbn(bnet, engine, T, 'check_ll', 1);
+