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annotate toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/scg_dbn.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Test whether stable conditional Gaussian inference works
wolffd@0 2 % Make a linear dynamical system
wolffd@0 3 % X1 -> X2
wolffd@0 4 % | |
wolffd@0 5 % v v
wolffd@0 6 % Y1 Y2
wolffd@0 7
wolffd@0 8 intra = zeros(2);
wolffd@0 9 intra(1,2) = 1;
wolffd@0 10 inter = zeros(2);
wolffd@0 11 inter(1,1) = 1;
wolffd@0 12 n = 2;
wolffd@0 13
wolffd@0 14 X = 2; % size of hidden state
wolffd@0 15 Y = 2; % size of observable state
wolffd@0 16
wolffd@0 17 ns = [X Y];
wolffd@0 18 bnet = mk_dbn(intra, inter, ns, 'discrete', [], 'observed', 2);
wolffd@0 19
wolffd@0 20 x0 = rand(X,1);
wolffd@0 21 V0 = eye(X);
wolffd@0 22 C0 = rand(Y,X);
wolffd@0 23 R0 = eye(Y);
wolffd@0 24 A0 = rand(X,X);
wolffd@0 25 Q0 = eye(X);
wolffd@0 26
wolffd@0 27 bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0);
wolffd@0 28 bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0);
wolffd@0 29 bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0);
wolffd@0 30
wolffd@0 31
wolffd@0 32 T = 5; % fixed length sequences
wolffd@0 33
wolffd@0 34 engine = {};
wolffd@0 35 engine{end+1} = kalman_inf_engine(bnet);
wolffd@0 36 engine{end+1} = scg_unrolled_dbn_inf_engine(bnet, T);
wolffd@0 37 engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
wolffd@0 38
wolffd@0 39 inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll', 0);