comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_fhmm.m @ 0:e9a9cd732c1e tip

first hg version after svn

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