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, 53 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/mpe2.m b/sourcecodes/bnt-master/BNT/examples/static/mpe2.m
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index 00000000..032cc0b1
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+% Computing most probable explanation.
+
+% If you don't break ties consistently, loopy can give wrong mpe
+% even though the graph has no cycles, and even though the max-marginals are the same.
+% This example was contributed by Wentau Yih <wtyih@yahoo.com> 29 Jan 02.
+
+% define loop-free graph structure (all edges point down)
+%
+% Xe1   Xe2
+%  |    |
+%  E1   E2
+%    \ /
+%     R1
+%     |
+%    Xr1
+
+N = 6;
+dag = zeros(N,N);
+Xe1 = 1; Xe2 = 2; E1 = 3; E2 = 4; R1 = 5; Xr1 = 6;
+dag(Xe1, E1) = 1;
+dag(Xe2, E2) = 1;
+dag([E1 E2], R1) = 1;
+dag(R1, Xr1) = 1;
+
+node_sizes = [ 1 1 2 2 2 1 ];
+
+% create BN
+
+bnet = mk_bnet(dag, node_sizes, 'observed', [Xe1 Xe2 Xr1]);
+
+% fill in CPT
+
+bnet.CPD{Xe1} = tabular_CPD(bnet, Xe1, [1]);
+bnet.CPD{Xe2} = tabular_CPD(bnet, Xe2, [1]);
+bnet.CPD{E1} = tabular_CPD(bnet, E1, [0.2 0.8]);
+bnet.CPD{E2} = tabular_CPD(bnet, E2, [0.3 0.7]);
+bnet.CPD{R1} = tabular_CPD(bnet, R1, [1 1 1 0.8 0 0 0 0.2]);
+bnet.CPD{Xr1} = tabular_CPD(bnet, Xr1, [0.15 0.85]);
+
+clear engine;
+engine{1} = belprop_inf_engine(bnet);
+engine{2} = jtree_inf_engine(bnet);
+engine{3} = global_joint_inf_engine(bnet);
+engine{4} = var_elim_inf_engine(bnet);
+
+evidence = cell(1,N);
+evidence{Xe1} = 1;  evidence{Xe2} = 1;  evidence{Xr1} = 1;
+
+mpe = find_mpe(engine{1}, evidence, 'break_ties', 0) % gives wrong results
+mpe = find_mpe(engine{1}, evidence)
+for i=2:4
+  mpe = find_mpe(engine{i}, evidence)
+end