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