BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 37 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/docs/dbn_hmm_demo.m b/sourcecodes/bnt-master/docs/dbn_hmm_demo.m
new file mode 100644
index 00000000..30933b8f
--- /dev/null
+++ b/sourcecodes/bnt-master/docs/dbn_hmm_demo.m
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+ Example due to Wang Hee Lin" <engp1622@nus.edu.sg
+
+
+intra = zeros(2);
+intra(1,2) = 1; 
+inter = zeros(2);
+inter(1,1) = 1; 
+
+Q = 2; % num hidden states
+O = 2; % num observable symbols
+ns = [Q O];%number of states
+dnodes = 1:2;
+%onodes = [1:2]; % only possible with jtree, not hmm
+onodes = [2]; 
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes);
+for i=1:4
+  bnet.CPD{i} = tabular_CPD(bnet, i);
+end
+
+prior0 = normalise(rand(Q,1));
+transmat0 = mk_stochastic(rand(Q,Q));
+obsmat0 = mk_stochastic(rand(Q,O));
+
+%engine = smoother_engine(hmm_2TBN_inf_engine(bnet));
+engine = smoother_engine(jtree_2TBN_inf_engine(bnet));
+
+ss = 2;%slice size(ss)
+ncases = 10;%number of examples
+T=10;
+max_iter=2;%iterations for EM
+cases = cell(1, ncases);
+for i=1:ncases
+  ev = sample_dbn(bnet, T);
+  cases{i} = cell(ss,T);
+  cases{i}(onodes,:) = ev(onodes, :);
+end
+[bnet2, LLtrace] = learn_params_dbn_em(engine, cases, 'max_iter', 4);