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, 48 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m
new file mode 100644
index 00000000..f4e18147
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m
@@ -0,0 +1,48 @@
+function KLdiv = KL_divergence(bnetP, bnetQ)
+% KL_DIVERGENCE computes the Kullback-Leibler divergence between two BNET distributions
+% KLdiv = KL_divergence(bnetP, bnetQ)
+%
+% Output :
+%   div = sum_x  P(x).log(P(x)/Q(x))
+%
+% Rem : 
+%   This version is optimized for speed, but can use too many memory
+%     ==> if you have a memory problem, use kl_divergence2 instead
+%
+
+%   ONLY FOR TABULAR NODES
+%   Make sure that you have done the params learning.
+%  
+%   V1.1 : 8 oct 2004 (Ph. Leray - philippe.leray@univ-nantes.fr)
+
+N = size(bnetP.dag,1);
+N2 = size(bnetQ.dag,1);
+ns= bnetP.node_sizes;
+ns2= bnetQ.node_sizes;
+if N~=N2, error('size of dags must be the same'), end
+if ns~=ns2, error('node sizes of dags must be the same'), end
+tiny = exp(-700);
+KLdiv=0;
+
+inst = ind2subv(ns, 1:prod(ns)); 
+  %Px=1; Qx=1;
+  for i=1:N,
+    ps = parents(bnetP.dag, i);
+    %e = bnetP.equiv_class(i);
+    %[tmp Px(:,i)] = prob_node(bnetP.CPD{e}, inst(:,i)', inst(:,ps)');
+    [tmp Px(:,i)] = prob_node(bnetP.CPD{i}, inst(:,i)', inst(:,ps)');
+
+    ps = parents(bnetQ.dag, i);
+    %e = bnetQ.equiv_class(i);
+    %[tmp Qx(:,i)] = prob_node(bnetQ.CPD{e}, inst(:,i)', inst(:,ps)');
+    [tmp Qx(:,i)] = prob_node(bnetQ.CPD{i}, inst(:,i)', inst(:,ps)');
+  end
+ Px=prod(Px,2);
+ Px = Px + (Px==0)*tiny; % replace 0s by tiny
+ Qx=prod(Qx,2);
+ Qx = Qx + (Qx==0)*tiny; % replace 0s by tiny
+
+ %%%%% Faut-il diviser par le nb de configurations possibles ? (sum => mean)
+ KLdiv = sum(Px.*log(Px./Qx));
+  %end
+