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

first hg version after svn

+% to test whether scg inference engine can handl dynameic BN
+% 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];
+dnodes = [];
+onodes = [2];
+eclass1 = [1 2];
+eclass2 = [3 2];
+bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2);
+
+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, 'full', 'untied', 'clamped_mean');
+%bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, 'full', 'untied', 'clamped_mean');
+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
+
+clear engine;
+%engine{1} = kalman_inf_engine(bnet, onodes);
+engine{1} = scg_unrolled_dbn_inf_engine(bnet, T, onodes);
+engine{2} = jtree_unrolled_dbn_inf_engine(bnet, T);
+
+N = length(engine);
+
+% inference
+
+ev = sample_dbn(bnet, T);
+evidence = cell(n,T);
+evidence(onodes,:) = ev(onodes, :);
+
+t = 2;
+query = [1 3];
+m = cell(1, N);
+ll = zeros(1, N);
+
+engine{1} = enter_evidence(engine{1}, evidence);
+[engine{2}, ll(2)] = enter_evidence(engine{2}, evidence);
+m{1} = marginal_nodes(engine{1}, query);
+m{2} = marginal_nodes(engine{2}, query, t);
+
+
+% compare all engines to engine{1}
+for i=2:N
+  assert(approxeq(m{1}.mu, m{i}.mu));
+  assert(approxeq(m{1}.Sigma, m{i}.Sigma));
+%  assert(approxeq(ll(1), ll(i)));
+end
+