wolffd@0: % to test whether scg inference engine can handl dynameic BN
wolffd@0: % Make a linear dynamical system
wolffd@0: %   X1 -> X2
wolffd@0: %   |     | 
wolffd@0: %   v     v
wolffd@0: %   Y1    Y2 
wolffd@0: 
wolffd@0: intra = zeros(2);
wolffd@0: intra(1,2) = 1;
wolffd@0: inter = zeros(2);
wolffd@0: inter(1,1) = 1;
wolffd@0: n = 2;
wolffd@0: 
wolffd@0: X = 2; % size of hidden state
wolffd@0: Y = 2; % size of observable state
wolffd@0: 
wolffd@0: ns = [X Y];
wolffd@0: dnodes = [];
wolffd@0: onodes = [2];
wolffd@0: eclass1 = [1 2];
wolffd@0: eclass2 = [3 2];
wolffd@0: bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2);
wolffd@0: 
wolffd@0: x0 = rand(X,1);
wolffd@0: V0 = eye(X);
wolffd@0: C0 = rand(Y,X);
wolffd@0: R0 = eye(Y);
wolffd@0: A0 = rand(X,X);
wolffd@0: Q0 = eye(X);
wolffd@0: 
wolffd@0: bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0);
wolffd@0: %bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, 'full', 'untied', 'clamped_mean');
wolffd@0: %bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, 'full', 'untied', 'clamped_mean');
wolffd@0: bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0);
wolffd@0: bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0);
wolffd@0: 
wolffd@0: 
wolffd@0: T = 5; % fixed length sequences
wolffd@0: 
wolffd@0: clear engine;
wolffd@0: %engine{1} = kalman_inf_engine(bnet, onodes);
wolffd@0: engine{1} = scg_unrolled_dbn_inf_engine(bnet, T, onodes);
wolffd@0: engine{2} = jtree_unrolled_dbn_inf_engine(bnet, T);
wolffd@0: 
wolffd@0: N = length(engine);
wolffd@0: 
wolffd@0: % inference
wolffd@0: 
wolffd@0: ev = sample_dbn(bnet, T);
wolffd@0: evidence = cell(n,T);
wolffd@0: evidence(onodes,:) = ev(onodes, :);
wolffd@0: 
wolffd@0: t = 2;
wolffd@0: query = [1 3];
wolffd@0: m = cell(1, N);
wolffd@0: ll = zeros(1, N);
wolffd@0: 
wolffd@0: engine{1} = enter_evidence(engine{1}, evidence);
wolffd@0: [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence);
wolffd@0: m{1} = marginal_nodes(engine{1}, query);
wolffd@0: m{2} = marginal_nodes(engine{2}, query, t);
wolffd@0: 
wolffd@0: 
wolffd@0: % compare all engines to engine{1}
wolffd@0: for i=2:N
wolffd@0:   assert(approxeq(m{1}.mu, m{i}.mu));
wolffd@0:   assert(approxeq(m{1}.Sigma, m{i}.Sigma));
wolffd@0: %  assert(approxeq(ll(1), ll(i)));
wolffd@0: end
wolffd@0: