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function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Set the params of a tabular node to their ML/MAP values.
% CPD = maximize_params(CPD, temp)
if ~adjustable_CPD(CPD), return; end
%assert(approxeq(sum(CPD.counts(:)), CPD.nsamples)); % false!
switch CPD.prior_type
case 'none',
counts = reshape(CPD.counts, size(CPD.CPT));
CPD.CPT = mk_stochastic(counts);
case 'dirichlet',
counts = reshape(CPD.counts, size(CPD.CPT));
CPD.CPT = mk_stochastic(counts + CPD.dirichlet);
% case 'entropic',
% % For an HMM,
% % CPT(i,j) = pr(X(t)=j | X(t-1)=i) = transprob(i,j)
% % counts(i,j) = E #(X(t-1)=i, X(t)=j) = exp_num_trans(i,j)
% Z = 1-temp;
% fam_sz = CPD.sizes;
% psz = prod(fam_sz(1:end-1));
% ssz = fam_sz(end);
% counts = reshape(CPD.counts, psz, ssz);
% CPT = zeros(psz, ssz);
% for i=CPD.entropic_pcases(:)'
% [CPT(i,:), logpost] = entropic_map_estimate(counts(i,:), Z);
% end
% non_entropic_pcases = mysetdiff(1:psz, CPD.entropic_pcases);
% for i=non_entropic_pcases(:)'
% CPT(i,:) = mk_stochastic(counts(i,:));
% end
% %for i=1:psz
% % [CPT(i,:), logpost] = entropic_map(counts(i,:), Z);
% %end
% if CPD.trim & (temp < 2) % at high temps, we would trim everything!
% % grad(j) = d log lik / d theta(i ->j)
% % CPT(i,j) = 0 => counts(i,j) = 0
% % so we can safely replace 0s by 1s in the denominator
% denom = CPT(i,:) + (CPT(i,:)==0);
% grad = counts(i,:) ./ denom;
% trim = find(CPT(i,:) <= exp(-(1/Z)*grad)); % eqn 32
% if ~isempty(trim)
% CPT(i,trim) = 0;
% if all(CPD.trimmed_trans(i,trim)==0) % trimming for 1st time
% disp(['trimming CPT(' num2str(i) ',' num2str(trim) ')'])
% end
% CPD.trimmed_trans(i,trim) = 1;
% end
% end
% CPD.CPT = myreshape(CPT, CPD.sizes);
end
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