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

first hg version after svn

+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