wolffd@0: function bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
wolffd@0: % MK_HMM_BNET Make a (static) bnet to represent a hidden Markov model
wolffd@0: % bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
wolffd@0: %
wolffd@0: % T = num time slices
wolffd@0: % Q = num hidden states
wolffd@0: % O = size of the observed node (num discrete values or length of vector)
wolffd@0: % cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete
wolffd@0: % param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node
wolffd@0: 
wolffd@0: N = 2*T;
wolffd@0: dag = zeros(N);
wolffd@0: %hnodes = 1:2:2*T;
wolffd@0: hnodes = 1:T;
wolffd@0: for i=1:T-1
wolffd@0:   dag(hnodes(i), hnodes(i+1))=1;
wolffd@0: end
wolffd@0: %onodes = 2:2:2*T;
wolffd@0: onodes = T+1:2*T;
wolffd@0: for i=1:T
wolffd@0:   dag(hnodes(i), onodes(i)) = 1;
wolffd@0: end
wolffd@0: 
wolffd@0: if cts_obs
wolffd@0:   dnodes = hnodes;
wolffd@0: else
wolffd@0:   dnodes = 1:N;
wolffd@0: end
wolffd@0: ns = ones(1,N);
wolffd@0: ns(hnodes) = Q;
wolffd@0: ns(onodes) = O;
wolffd@0: 
wolffd@0: if param_tying
wolffd@0:   H1class = 1; Hclass = 2; Oclass = 3;
wolffd@0:   eclass = ones(1,N);
wolffd@0:   eclass(hnodes(2:end)) = Hclass;
wolffd@0:   eclass(hnodes(1)) = H1class;
wolffd@0:   eclass(onodes) = Oclass;
wolffd@0: else
wolffd@0:   eclass = 1:N;
wolffd@0: end
wolffd@0: 
wolffd@0: bnet = mk_bnet(dag, ns, 'observed', onodes, 'discrete', dnodes, 'equiv_class', eclass);
wolffd@0: 
wolffd@0: hnodes = mysetdiff(1:N, onodes);
wolffd@0: if ~param_tying
wolffd@0:   for i=hnodes(:)'
wolffd@0:     bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0:   end
wolffd@0:   if cts_obs
wolffd@0:     for i=onodes(:)'
wolffd@0:       bnet.CPD{i} = gaussian_CPD(bnet, i);
wolffd@0:     end
wolffd@0:   else
wolffd@0:     for i=onodes(:)'
wolffd@0:       bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0:     end
wolffd@0:   end
wolffd@0: else
wolffd@0:   bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior
wolffd@0:   bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix
wolffd@0:   if cts_obs
wolffd@0:     bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1));
wolffd@0:   else
wolffd@0:     bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1));
wolffd@0:   end
wolffd@0: end