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, 34 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/maximize_params.m b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/maximize_params.m
new file mode 100644
index 00000000..19d0a1be
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/maximize_params.m
@@ -0,0 +1,34 @@
+function CPD = maximize_params(CPD, temp)
+% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
+% CPD = maximize_params(CPD, temperature)
+% temperature parameter is ignored
+
+if ~adjustable_CPD(CPD), return; end
+options = foptions;
+
+% options(1) >= 0 means print an annoying message when the max. num. iter. is reached
+if CPD.verbose
+  options(1) = 1;
+else
+  options(1) = -1;
+end
+%options(1) = CPD.verbose;
+
+options(2) = CPD.wthresh;
+options(3) = CPD.llthresh;
+options(14) = CPD.max_iter;
+
+dpsz=length(CPD.mlp);
+
+for i=1:dpsz
+    mask=[];
+    mask=find(CPD.eso_weights(:,:,i)>0);    % for adapting the parameters we use only positive weighted example
+    if  ~isempty(mask),
+        CPD.mlp{i} = netopt_weighted(CPD.mlp{i}, options, CPD.parent_vals(mask',:), CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg');
+        
+        CPD.W1(:,:,i)=CPD.mlp{i}.w1;        % update the parameters matrix
+        CPD.b1(i,:)=CPD.mlp{i}.b1;          %
+        CPD.W2(:,:,i)=CPD.mlp{i}.w2;        % update the parameters matrix
+        CPD.b2(i,:)=CPD.mlp{i}.b2;          %
+    end
+end