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