wolffd@0: % Make an HMM with Gaussian observations
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: Q = 2; % num hidden states
wolffd@0: O = 2; % size of observed vector
wolffd@0: ns = [Q O];
wolffd@0: bnet = mk_dbn(intra, inter, ns, 'discrete', 1, 'observed', 2);
wolffd@0: 
wolffd@0: prior0 = normalise(rand(Q,1));
wolffd@0: transmat0 = mk_stochastic(rand(Q,Q));
wolffd@0: mu0 = rand(O,Q);
wolffd@0: Sigma0 = repmat(eye(O), [1 1 Q]);
wolffd@0: bnet.CPD{1} = tabular_CPD(bnet, 1, prior0);
wolffd@0: %% we set the cov prior to 0 to give same results as HMM toolbox
wolffd@0: %bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0, 'cov_prior_weight', 0);
wolffd@0: bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0);
wolffd@0: bnet.CPD{3} = tabular_CPD(bnet, 3, transmat0);
wolffd@0: 
wolffd@0: 
wolffd@0: T = 5; % fixed length sequences
wolffd@0: 
wolffd@0: engine = {};
wolffd@0: engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
wolffd@0: engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet));
wolffd@0: engine{end+1} = hmm_inf_engine(bnet);
wolffd@0: engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
wolffd@0: %engine{end+1} = frontier_inf_engine(bnet);
wolffd@0: engine{end+1} = bk_inf_engine(bnet, 'clusters', {[1]});
wolffd@0: engine{end+1} = jtree_dbn_inf_engine(bnet);
wolffd@0: 
wolffd@0: 
wolffd@0: inf_time = cmp_inference_dbn(bnet, engine, T);
wolffd@0: 
wolffd@0: ncases = 2;
wolffd@0: max_iter = 2;
wolffd@0: [learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter);
wolffd@0: 
wolffd@0: % Compare to HMM toolbox
wolffd@0: 
wolffd@0: data = zeros(O, T, ncases);
wolffd@0: for i=1:ncases
wolffd@0:   data(:,:,i) = cell2num(cases{i}(bnet.observed, :));  
wolffd@0: end
wolffd@0: 
wolffd@0: tic
wolffd@0: [LL2, prior2, transmat2, mu2, Sigma2] = mhmm_em(data, prior0, transmat0, mu0, Sigma0, [],  'max_iter', max_iter);
wolffd@0: t=toc;
wolffd@0: disp(['HMM toolbox took ' num2str(t) ' seconds '])
wolffd@0: 
wolffd@0: e = 1;
wolffd@0: assert(approxeq(prior2, CPD{e,1}.CPT))
wolffd@0: assert(approxeq(mu2, CPD{e,2}.mean))
wolffd@0: assert(approxeq(Sigma2, CPD{e,2}.cov))
wolffd@0: assert(approxeq(transmat2, CPD{e,3}.CPT))
wolffd@0: assert(approxeq(LL2, LL{e}))