comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/maximize_params.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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