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, 56 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/examples/test_sem1.m b/sourcecodes/bnt-master/SLP/examples/test_sem1.m
new file mode 100644
index 00000000..942ec7dd
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/examples/test_sem1.m
@@ -0,0 +1,56 @@
+% Lawn sprinker example from Russell and Norvig p454
+% See www.cs.berkeley.edu/~murphyk/Bayes/usage.html for details.
+
+rand('state', 0);
+randn('state', 0);
+
+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;
+
+false = 1; true = 2;
+ns = 2*ones(1,N); % binary nodes
+
+bnet = mk_bnet(dag, ns);
+bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]);
+bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]);
+bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]);
+bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
+
+nsamples = 500;
+samplesM = cell(N, nsamples);
+for i=1:nsamples
+  samplesM(:,i) = sample_bnet(bnet);
+end
+
+hide = rand(N, nsamples) > 0.9;
+[I,J]=find(hide);
+for k=1:length(I)
+  samplesM{I(k), J(k)} = [];
+end
+
+% Make a initial chain like dag
+G0 = zeros(N,N);
+for i=1:N-1
+   G0(i, i+1) = 1;
+end
+
+figure;
+draw_graph(G0);
+
+B0 = mk_bnet(G0, ns);
+% use random params
+for i=1:N
+  B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);
+end
+
+max_loop = 30;
+%profile on -detail mmex
+[B0, order, best_score] = learn_struct_EM(B0, samplesM, max_loop);
+%profile report
+
+dag1 = B0.dag;
+dag1 = dag1(order,order);