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, 41 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/maximize_params.m b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/maximize_params.m
new file mode 100644
index 00000000..15c94dd5
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/maximize_params.m
@@ -0,0 +1,41 @@
+function CPD = maximize_params(CPD, temp)
+% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (dsoftmax) using IRLS
+% CPD = maximize_params(CPD, temperature)
+% temperature parameter is ignored
+
+% Written by Pierpaolo Brutti
+
+if ~adjustable_CPD(CPD), return; end
+options = foptions;
+
+if CPD.verbose
+  options(1) = 1;
+else
+  options(1) = -1;
+end
+%options(1) = CPD.verbose;
+
+options(2) = CPD.wthresh;
+options(3) = CPD.llthresh;
+options(5) = CPD.approx_hess;
+options(14) = CPD.max_iter;
+
+dpsize = size(CPD.self_vals,3);
+for i=1:dpsize,
+  mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example
+  if  ~isempty(mask),
+    if ~isempty(CPD.dps_as_cps.ndx),
+        puredp_map = find_equiv_posns(CPD.dpndx, union(CPD.dpndx, CPD.dps_as_cps.ndx)); % find the glm  structure
+        subs       = ind2subv(CPD.sizes(union(CPD.dpndx, CPD.dps_as_cps.ndx)),i);       % that corrisponds to the
+        active_glm = max([1,subv2ind(CPD.sizes(CPD.dpndx), subs(puredp_map))]);         % i-th 'fictitious' example
+        
+        CPD.glim{active_glm} = netopt_weighted(CPD.glim{active_glm}, options, CPD.parent_vals(mask',:,i),...
+            CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg');
+    else
+        alfa = 0.4; if CPD.solo, alfa = 1; end % learning step = 1 <=> self is all alone in the net
+        CPD.glim{i} = glmtrain_weighted(CPD.glim{i}, options, CPD.parent_vals(mask',:),...
+            CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), alfa);
+    end               
+  end
+  mask=[];
+end