Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_hmm_bnet.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) % MK_HMM_BNET Make a (static) bnet to represent a hidden Markov model % bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) % % T = num time slices % Q = num hidden states % O = size of the observed node (num discrete values or length of vector) % cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete % param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node N = 2*T; dag = zeros(N); %hnodes = 1:2:2*T; hnodes = 1:T; for i=1:T-1 dag(hnodes(i), hnodes(i+1))=1; end %onodes = 2:2:2*T; onodes = T+1:2*T; for i=1:T dag(hnodes(i), onodes(i)) = 1; end if cts_obs dnodes = hnodes; else dnodes = 1:N; end ns = ones(1,N); ns(hnodes) = Q; ns(onodes) = O; if param_tying H1class = 1; Hclass = 2; Oclass = 3; eclass = ones(1,N); eclass(hnodes(2:end)) = Hclass; eclass(hnodes(1)) = H1class; eclass(onodes) = Oclass; else eclass = 1:N; end bnet = mk_bnet(dag, ns, 'observed', onodes, 'discrete', dnodes, 'equiv_class', eclass); hnodes = mysetdiff(1:N, onodes); if ~param_tying for i=hnodes(:)' bnet.CPD{i} = tabular_CPD(bnet, i); end if cts_obs for i=onodes(:)' bnet.CPD{i} = gaussian_CPD(bnet, i); end else for i=onodes(:)' bnet.CPD{i} = tabular_CPD(bnet, i); end end else bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix if cts_obs bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); else bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); end end