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