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, 26 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m
new file mode 100644
index 00000000..20faed5e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m
@@ -0,0 +1,26 @@
+% Compare different loopy belief propagation algorithms on a graph with a single loop.
+% LBP should give exact results if it converges.
+
+N = 4;
+dag = zeros(N,N);
+C = 1; S = 2; R = 3; W = 4;
+dag(C,[R S]) = 1;
+dag(R,W) = 1;
+dag(S,W)=1;
+ns = 2*ones(1,N); 
+bnet = mk_bnet(dag, ns);
+for i=1:N
+  bnet.CPD{i} = tabular_CPD(bnet, i);
+end
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+engines{end+1} = belprop_fg_inf_engine(bnet_to_fgraph(bnet));
+engines{end+1} = belprop_inf_engine(bnet, 'protocol', 'parallel');
+
+% belprop_fg does not support marginal_family
+% belprop_fg and belprop do not support loglik even on discrete
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', 2, ...
+				   'check_ll', 0, 'singletons_only', 1, 'check_converged', 2:4);
+