wolffd@0: % Check that adding soft evidence to a hidden node is equivalent to evaluating its leaf CPD.
wolffd@0: 
wolffd@0: % Make an HMM
wolffd@0: T = 3; Q = 2; O = 2; cts_obs = 0; param_tying = 0;
wolffd@0: bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying);
wolffd@0: N = 2*T;
wolffd@0: onodes = bnet.observed;
wolffd@0: hnodes = mysetdiff(1:N, onodes);
wolffd@0: for i=1:N
wolffd@0:   bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0: end
wolffd@0: 
wolffd@0: ev = sample_bnet(bnet);
wolffd@0: evidence = cell(1,N);
wolffd@0: evidence(onodes) = ev(onodes);
wolffd@0: 
wolffd@0: engine = jtree_inf_engine(bnet);
wolffd@0: 
wolffd@0: [engine, ll] = enter_evidence(engine, evidence);
wolffd@0: query = 1;
wolffd@0: m = marginal_nodes(engine, query);
wolffd@0: 
wolffd@0: 
wolffd@0: % Make a Markov chain with the same backbone
wolffd@0: bnet2 = mk_markov_chain_bnet(T, Q);
wolffd@0: for i=1:T
wolffd@0:   S = struct(bnet.CPD{hnodes(i)}); % violate object privacy
wolffd@0:   bnet2.CPD{i} = tabular_CPD(bnet2, i, S.CPT);
wolffd@0: end
wolffd@0: 
wolffd@0: % Evaluate the observed leaves of the HMM
wolffd@0: soft_ev = cell(1,T);
wolffd@0: for i=1:T
wolffd@0:   S = struct(bnet.CPD{onodes(i)}); % violate object privacy
wolffd@0:   dist = S.CPT(:, evidence{onodes(i)});
wolffd@0:   soft_ev{i} = dist;
wolffd@0: end
wolffd@0: 
wolffd@0: % Use the leaf potentials as soft evidence
wolffd@0: engine2 = jtree_inf_engine(bnet2);
wolffd@0: [engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev);
wolffd@0: m2 = marginal_nodes(engine2, query);
wolffd@0: 
wolffd@0: assert(approxeq(m2.T, m.T))
wolffd@0: assert(approxeq(ll2, ll))
wolffd@0: 
wolffd@0: 
wolffd@0: 
wolffd@0: % marginal on node 1 without evidence
wolffd@0: [engine2, ll2] = enter_evidence(engine2, cell(1,T));
wolffd@0: m2 = marginal_nodes(engine2, 1);
wolffd@0: 
wolffd@0: % add soft evidence
wolffd@0: soft_ev=cell(1,T);
wolffd@0: soft_ev{1}=[0.7 0.3]; 
wolffd@0: [engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev);
wolffd@0: m3 = marginal_nodes(engine2, 1);
wolffd@0: 
wolffd@0: assert(approxeq(normalise(m2.T .* [0.7 0.3]'), m3.T))
wolffd@0: