wolffd@0: function bnet = mk_fhmm(N, Q, Y, discrete_obs)
wolffd@0: % MK_FHMM Make a factorial Hidden Markov Model
wolffd@0: %
wolffd@0: % There are N independent parallel hidden chains, each connected to the output
wolffd@0: %
wolffd@0: % e.g., N = 2 (vertical/diagonal edges point down)
wolffd@0: %
wolffd@0: % A1--->A2
wolffd@0: % | B1--|->B2
wolffd@0: % | /   |/
wolffd@0: % Y1    Y2
wolffd@0: %
wolffd@0: % [bnet, onode] = mk_chmm(n, q, y, discrete_obs)
wolffd@0: %
wolffd@0: % Each hidden node is discrete and has Q values.
wolffd@0: % If discrete_obs = 1, each observed node is discrete and has values 1..Y.
wolffd@0: % If discrete_obs = 0, each observed node is a Gaussian vector of length Y.
wolffd@0: 
wolffd@0: if nargin < 2, Q = 2; end
wolffd@0: if nargin < 3, Y = 2; end
wolffd@0: if nargin < 4, discrete_obs = 1; end
wolffd@0: 
wolffd@0: ss = N+1;
wolffd@0: hnodes = 1:N;
wolffd@0: onode = N+1;
wolffd@0: 
wolffd@0: intra = zeros(ss);
wolffd@0: intra(hnodes, onode) = 1;
wolffd@0: 
wolffd@0: inter = eye(ss);
wolffd@0: inter(onode,onode) = 0;
wolffd@0: 
wolffd@0: ns = [Q*ones(1,N) Y];
wolffd@0: 
wolffd@0: eclass1 = [hnodes onode];
wolffd@0: eclass2 = [hnodes+ss onode];
wolffd@0: if discrete_obs
wolffd@0:   dnodes = 1:ss;
wolffd@0: else
wolffd@0:   dnodes = hnodes;
wolffd@0: end
wolffd@0: bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
wolffd@0: 	      'observed', onode);
wolffd@0: 
wolffd@0: for i=hnodes(:)'
wolffd@0:   bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0: end
wolffd@0: i = onode;
wolffd@0: if discrete_obs
wolffd@0:   bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0: else
wolffd@0:   bnet.CPD{i} = gaussian_CPD(bnet, i);
wolffd@0: end
wolffd@0: for i=hnodes(:)'+ss
wolffd@0:   bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0: end
wolffd@0: 
wolffd@0: