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, 41 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/chmm1.m b/sourcecodes/bnt-master/BNT/examples/dynamic/chmm1.m
new file mode 100644
index 00000000..10df79d8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/chmm1.m
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+% Compare the speeds of various inference engines on a coupled HMM
+
+N = 3;
+Q = 2;
+rand('state', 0);
+randn('state', 0);
+discrete = 0;
+if discrete
+  Y = 2; % size of output alphabet
+else
+  Y = 3; % size of observed vectors
+end
+coupled = 1;
+bnet = mk_chmm(N, Q, Y, discrete, coupled); 
+%bnet = mk_fhmm(N, Q, Y, discrete);  % factorial HMM
+ss = length(bnet.node_sizes_slice);
+
+T = 3;
+
+USEC = exist('@jtree_C_inf_engine/collect_evidence','file');
+
+engine = {};
+engine{end+1} = jtree_dbn_inf_engine(bnet);
+%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'SD');
+%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'D');
+%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'B');
+if USEC, engine{end+1} = jtree_C_dbn_inf_engine(bnet); end
+engine{end+1} = hmm_inf_engine(bnet);
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); 
+
+% times in matlab N=4 Q=4 T=5 (* = winner)
+%     jtree    SD       B          hmm       dhmm      unrolled
+%    0.6266    1.1563    8.3815    0.3069    0.1948*    0.8654  inf
+%    0.9057*   2.1522   12.6314    2.6847    2.3107    3.1905  learn
+
+%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes);
+%engine{end+1} = pearl_unrolled_dbn_inf_engine(bnet, T);
+
+inf_time = cmp_inference_dbn(bnet, engine, T)
+learning_time = cmp_learning_dbn(bnet, engine, T)
+