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

first hg version after svn

+% Test whether stable conditional Gaussian inference works
+% Make a linear dynamical system
+%   X1 -> X2
+%   |     | 
+%   v     v
+%   Y1    Y2 
+
+intra = zeros(2);
+intra(1,2) = 1;
+inter = zeros(2);
+inter(1,1) = 1;
+n = 2;
+
+X = 2; % size of hidden state
+Y = 2; % size of observable state
+
+ns = [X Y];
+bnet = mk_dbn(intra, inter, ns, 'discrete', [], 'observed', 2);
+
+x0 = rand(X,1);
+V0 = eye(X);
+C0 = rand(Y,X);
+R0 = eye(Y);
+A0 = rand(X,X);
+Q0 = eye(X);
+
+bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0);
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0);
+bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0);
+
+
+T = 5; % fixed length sequences
+
+engine = {};
+engine{end+1} = kalman_inf_engine(bnet);
+engine{end+1} = scg_unrolled_dbn_inf_engine(bnet, T);
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
+
+inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll', 0);