Mauch MIREX 2010

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