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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
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