comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/maximize_params.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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