Daniel@0: % Check that online inference gives same results as filtering for various algorithms
Daniel@0: 
Daniel@0: N = 3;
Daniel@0: Q = 2;
Daniel@0: ss = N*2;
Daniel@0: 
Daniel@0: rand('state', 0);
Daniel@0: randn('state', 0);
Daniel@0: 
Daniel@0: 
Daniel@0: obs_size = 1;
Daniel@0: discrete_obs = 0;
Daniel@0: bnet = mk_chmm(N, Q, obs_size, discrete_obs);
Daniel@0: ns = bnet.node_sizes_slice;
Daniel@0: 
Daniel@0: engine = {};
Daniel@0: engine{end+1} = hmm_inf_engine(bnet);
Daniel@0: E = length(engine);
Daniel@0: 
Daniel@0: onodes = (1:N)+N;
Daniel@0: 
Daniel@0: T = 4;
Daniel@0: ev = cell(ss,T);
Daniel@0: ev(onodes,:) = num2cell(randn(N, T));
Daniel@0: 
Daniel@0: 
Daniel@0: filter = 1;
Daniel@0: loglik2 = zeros(1,E);
Daniel@0: for e=1:E
Daniel@0:   [engine2{e}, loglik2(e)] = enter_evidence(engine{e}, ev, 'filter', filter);
Daniel@0: end
Daniel@0: 
Daniel@0: loglik = zeros(1,E);
Daniel@0: marg1 = cell(E,N,T);
Daniel@0: for e=1:E
Daniel@0:   ll = zeros(1,T);
Daniel@0:   engine{e} = dbn_init_bel(engine{e});
Daniel@0:   for t=1:T
Daniel@0:     [engine{e}, ll(t)] = dbn_update_bel(engine{e}, ev(:,t), t);
Daniel@0:     for i=1:N
Daniel@0:       marg1{e,i,t} = dbn_marginal_from_bel(engine{e}, i);
Daniel@0:     end
Daniel@0:   end
Daniel@0:   loglik1(e) = sum(ll);
Daniel@0: end
Daniel@0: 
Daniel@0: assert(approxeq(loglik1, loglik2))
Daniel@0: 
Daniel@0: a = zeros(E,N,T);
Daniel@0: for e=1:E
Daniel@0:   for t=1:T
Daniel@0:     for i=1:N
Daniel@0:       marg2{e,i,t} = marginal_nodes(engine2{e}, i, t);
Daniel@0:       a(e,i,t) = (approxeq(marg2{e,i,t}.T(:), marg1{e,i,t}.T(:)));
Daniel@0:     end
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: assert(all(a(:)==1))