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, 118 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Models/mk_alarm_bnet.m b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_alarm_bnet.m
new file mode 100644
index 00000000..5705909b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_alarm_bnet.m
@@ -0,0 +1,118 @@
+function bnet = mk_alarm_bnet()
+
+% Written by Qian Diao <qian.diao@intel.com> on 11 Dec 01
+
+N = 37;
+dag = zeros(N,N);
+dag(21,23) = 1 ;
+dag(21,24) = 1 ;
+dag(1,24) = 1 ;
+dag(1,23) = 1 ;
+dag(2,26) = 1 ;
+dag(2,25) = 1 ;
+dag(2,24) = 1 ;
+dag(2,13) = 1 ;
+dag(2,23) = 1 ;
+dag(13,30) = 1 ;
+dag(30,31) = 1 ;
+dag(3,14) = 1 ;
+dag(3,19) = 1 ;
+dag(4,36) = 1 ;
+dag(14,35) = 1 ;
+dag(32,33) = 1 ;
+dag(32,35) = 1 ;
+dag(32,34) = 1 ;
+dag(32,36) = 1 ;
+dag(15,21) = 1 ;
+dag(5,31) = 1 ;
+dag(27,30) = 1 ;
+dag(28,31) = 1 ;
+dag(28,29) = 1 ;
+dag(26,28) = 1 ;
+dag(26,27) = 1 ;
+dag(16,31) = 1 ;
+dag(16,37) = 1 ;
+dag(23,26) = 1 ;
+dag(23,29) = 1 ;
+dag(23,25) = 1 ;
+dag(6,15) = 1 ;
+dag(7,27) = 1 ;
+dag(8,21) = 1 ;
+dag(19,20) = 1 ;
+dag(19,22) = 1 ;
+dag(31,32) = 1 ;
+dag(9,14) = 1 ;
+dag(9,17) = 1 ;
+dag(9,19) = 1 ;
+dag(10,33) = 1 ;
+dag(10,34) = 1 ;
+dag(11,16) = 1 ;
+dag(12,13) = 1 ;
+dag(12,18) = 1 ;
+dag(35,37) = 1 ;
+
+node_sizes = 2*ones(1,N);
+node_sizes(2) = 3;
+node_sizes(6) = 3;
+node_sizes(14) = 3;
+node_sizes(15) = 4;
+node_sizes(16) = 3;
+node_sizes(18) = 3;
+node_sizes(19) = 3;
+node_sizes(20) = 3;
+node_sizes(21) = 4;
+node_sizes(22) = 3;
+node_sizes(23) = 4;
+node_sizes(24) = 4;
+node_sizes(25) = 4;
+node_sizes(26) = 4;
+node_sizes(27) = 3;
+node_sizes(28) = 3;
+node_sizes(29) = 4;
+node_sizes(30) = 3;
+node_sizes(32) = 3;
+node_sizes(33) = 3;
+node_sizes(34) = 3;
+node_sizes(35) = 3;
+node_sizes(36) = 3;
+node_sizes(37) = 3;
+
+bnet = mk_bnet(dag, node_sizes);
+
+bnet.CPD{1} = tabular_CPD(bnet, 1,[0.96 0.04 ]);
+bnet.CPD{2} = tabular_CPD(bnet, 2,[0.92 0.03 0.05 ]);
+bnet.CPD{3} = tabular_CPD(bnet, 3,[0.8 0.2 ]);
+bnet.CPD{4} = tabular_CPD(bnet, 4,[0.95 0.05 ]);
+bnet.CPD{5} = tabular_CPD(bnet, 5,[0.8 0.2 ]);
+bnet.CPD{6} = tabular_CPD(bnet, 6,[0.01 0.98 0.01 ]);
+bnet.CPD{7} = tabular_CPD(bnet, 7,[0.01 0.99 ]);
+bnet.CPD{8} = tabular_CPD(bnet, 8,[0.95 0.05 ]);
+bnet.CPD{9} = tabular_CPD(bnet, 9,[0.95 0.05 ]);
+bnet.CPD{10} = tabular_CPD(bnet, 10,[0.9 0.1 ]);
+bnet.CPD{11} = tabular_CPD(bnet, 11,[0.99 0.01 ]);
+bnet.CPD{12} = tabular_CPD(bnet, 12,[0.99 0.01 ]);
+bnet.CPD{13} = tabular_CPD(bnet, 13,[0.95 0.95 0.05 0.1 0.1 0.01 0.05 0.05 0.95 0.9 0.9 0.99 ]);
+bnet.CPD{14} = tabular_CPD(bnet, 14,[0.05 0.95 0.5 0.98 0.9 0.04 0.49 0.01 0.05 0.01 0.01 0.01 ]);
+bnet.CPD{15} = tabular_CPD(bnet, 15,[0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 ]);
+bnet.CPD{16} = tabular_CPD(bnet, 16,[0.3 0.98 0.4 0.01 0.3 0.01 ]);
+bnet.CPD{17} = tabular_CPD(bnet, 17,[0.99 0.1 0.01 0.9 ]);
+bnet.CPD{18} = tabular_CPD(bnet, 18,[0.05 0.01 0.9 0.19 0.05 0.8 ]);
+bnet.CPD{19} = tabular_CPD(bnet, 19,[0.05 0.98 0.01 0.95 0.9 0.01 0.09 0.04 0.05 0.01 0.9 0.01 ]);
+bnet.CPD{20} = tabular_CPD(bnet, 20,[0.95 0.04 0.01 0.04 0.95 0.29 0.01 0.01 0.7 ]);
+bnet.CPD{21} = tabular_CPD(bnet, 21,[0.97 0.97 0.01 0.97 0.01 0.97 0.01 0.97 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 ]);
+bnet.CPD{22} = tabular_CPD(bnet, 22,[0.95 0.04 0.01 0.04 0.95 0.04 0.01 0.01 0.95 ]);
+bnet.CPD{23} = tabular_CPD(bnet, 23,[0.97 0.97 0.97 0.97 0.97 0.97 0.01 0.95 0.97 0.97 0.01 0.95 0.01 0.4 0.97 0.97 0.01 0.5 0.01 0.3 0.97 0.97 0.01 0.3 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.03 0.01 0.01 0.97 0.03 0.01 0.58 0.01 0.01 0.01 0.48 0.01 0.68 0.01 0.01 0.01 0.68 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 ]);
+bnet.CPD{24} = tabular_CPD(bnet, 24,[0.97 0.97 0.97 0.97 0.97 0.97 0.01 0.01 0.4 0.1 0.01 0.01 0.01 0.01 0.2 0.05 0.01 0.01 0.01 0.01 0.2 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.49 0.58 0.84 0.9 0.29 0.01 0.01 0.75 0.25 0.01 0.01 0.01 0.01 0.7 0.15 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.3 0.01 0.05 0.08 0.3 0.97 0.08 0.04 0.25 0.38 0.08 0.01 0.01 0.09 0.25 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.2 0.01 0.01 0.01 0.4 0.01 0.9 0.01 0.45 0.6 0.9 0.97 0.97 0.01 0.59 0.97 0.97 ]);
+bnet.CPD{25} = tabular_CPD(bnet, 25,[0.97 0.97 0.97 0.01 0.6 0.01 0.01 0.5 0.01 0.01 0.5 0.01 0.01 0.01 0.01 0.97 0.38 0.97 0.01 0.48 0.01 0.01 0.48 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.97 ]);
+bnet.CPD{26} = tabular_CPD(bnet, 26,[0.97 0.97 0.97 0.01 0.01 0.03 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.95 0.01 0.01 0.94 0.01 0.01 0.88 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.04 0.01 0.01 0.1 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.01 ]);
+bnet.CPD{27} = tabular_CPD(bnet, 27,[0.98 0.98 0.98 0.98 0.95 0.01 0.95 0.01 0.01 0.01 0.01 0.01 0.04 0.95 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.04 0.01 0.98 ]);
+bnet.CPD{28} = tabular_CPD(bnet, 28,[0.01 0.01 0.04 0.9 0.01 0.01 0.92 0.09 0.98 0.98 0.04 0.01 ]);
+bnet.CPD{29} = tabular_CPD(bnet, 29,[0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.97 0.97 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.43 ]);
+bnet.CPD{30} = tabular_CPD(bnet, 30,[0.98 0.98 0.01 0.98 0.01 0.69 0.01 0.01 0.98 0.01 0.01 0.3 0.01 0.01 0.01 0.01 0.98 0.01 ]);
+bnet.CPD{31} = tabular_CPD(bnet, 31,[0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.1 0.01 0.95 0.01 0.95 0.05 0.1 0.01 0.95 0.01 0.95 0.05 0.1 0.01 0.3 0.01 0.3 0.01 0.95 0.01 0.99 0.05 0.95 0.05 0.95 0.01 0.99 0.05 0.99 0.05 0.3 0.01 0.99 0.01 0.3 0.01 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.9 0.99 0.05 0.99 0.05 0.95 0.9 0.99 0.05 0.99 0.05 0.95 0.9 0.99 0.7 0.99 0.7 0.99 0.05 0.99 0.00999999 0.95 0.05 0.95 0.05 0.99 0.01 0.95 0.01 0.95 0.7 0.99 0.01 0.99 0.7 0.99 ]);
+bnet.CPD{32} = tabular_CPD(bnet, 32,[0.1 0.01 0.89 0.09 0.01 0.9 ]);
+bnet.CPD{33} = tabular_CPD(bnet, 33,[0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 ]);
+bnet.CPD{34} = tabular_CPD(bnet, 34,[0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 ]);
+bnet.CPD{35} = tabular_CPD(bnet, 35,[0.98 0.95 0.3 0.95 0.04 0.01 0.8 0.01 0.01 0.01 0.04 0.69 0.04 0.95 0.3 0.19 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.69 0.01 0.95 0.98 ]);
+bnet.CPD{36} = tabular_CPD(bnet, 36,[0.98 0.98 0.01 0.4 0.01 0.3 0.01 0.01 0.98 0.59 0.01 0.4 0.01 0.01 0.01 0.01 0.98 0.3 ]);
+bnet.CPD{37} = tabular_CPD(bnet, 37,[0.98 0.98 0.3 0.98 0.1 0.05 0.9 0.05 0.01 0.01 0.01 0.6 0.01 0.85 0.4 0.09 0.2 0.09 0.01 0.01 0.1 0.01 0.05 0.55 0.01 0.75 0.9 ]);