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, 79 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/StructLearn/bic1.m b/sourcecodes/bnt-master/BNT/examples/static/StructLearn/bic1.m
new file mode 100644
index 00000000..22473564
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/StructLearn/bic1.m
@@ -0,0 +1,79 @@
+% compare BIC and Bayesian score 
+
+N = 4;
+dag = zeros(N,N);
+%C = 1; S = 2; R = 3; W = 4; % topological order
+C = 4; S = 2; R = 3; W = 1; % arbitrary order
+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, 'CPT', [0.5 0.5]);
+bnet.CPD{R} = tabular_CPD(bnet, R, 'CPT', [0.8 0.2 0.2 0.8]);
+bnet.CPD{S} = tabular_CPD(bnet, S, 'CPT', [0.5 0.9 0.5 0.1]);
+bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
+
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+ncases = 1000;
+data = cell(N, ncases);
+for m=1:ncases
+  data(:,m) = sample_bnet(bnet);
+end
+
+priors = [0.1 1 10];
+P = length(priors);
+params = cell(1,P);
+for p=1:P
+  params{p} = cell(1,N);
+  for i=1:N
+    %params{p}{i} = {'prior', priors(p)};
+    params{p}{i} = {'prior_type', 'dirichlet', 'dirichlet_weight', priors(p)};
+  end
+end
+
+%sz = 1000:1000:10000;
+sz = 10:10:100;
+S = length(sz);
+bic_score = zeros(S, 1);
+bayes_score = zeros(S, P);
+for i=1:S
+  bic_score(i) = score_dags(data(:,1:sz(i)), ns, {dag}, 'scoring_fn', 'bic', 'params', []);
+end
+diff = zeros(S,P);
+for p=1:P
+  for i=1:S
+    bayes_score(i,p) = score_dags(data(:,1:sz(i)), ns, {dag}, 'params', params{p});
+  end
+end
+
+for p=1:P
+  for i=1:S
+    diff(i,p) = bayes_score(i,p)/ bic_score(i);
+    %diff(i,p) = abs(bayes_score(i,p) - bic_score(i));
+  end
+end
+
+if 0
+plot(sz, diff(:,1), 'g--*', sz, diff(:,2), 'b-.+', sz, diff(:,3), 'k:s');
+title('Relative BIC error vs. size of data set')
+legend('BDeu 0.1', 'BDeu 1', 'Bdeu 10', 2)
+end
+
+if 0
+plot(sz, bic_score, 'r-o',  sz, bayes_score(:,1), 'g--*', sz, bayes_score(:,2), 'b-.+', sz, bayes_score(:,3), 'k:s');
+legend('bic', 'BDeu 0.01', 'BDeu 1', 'Bdeu 100')
+ylabel('score')
+title('score vs. size of data set')
+end
+
+%xlabel('num. data cases')
+
+%previewfig(gcf, 'format', 'png', 'height', 2, 'color', 'rgb')
+%exportfig(gcf, '/home/cs/murphyk/public_html/Bayes/Figures/bic.png', 'format', 'png', 'height', 2, 'color', 'rgb')