Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/bayes_update_params.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/bayes_update_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function CPD = bayes_update_params(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% These can be arrays or cell arrays. +% +% We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior. + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(nparents == size(pev,1)); + +if ncases == 0 | ~adjustable_CPD(CPD) + return; +elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + x = pev(:)'; + y = self_ev; + end + switch nparents + case 0, + CPD.dirichlet(y) = CPD.dirichlet(y)+1; + CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet); + case 1, + CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1; + CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :)); + case 2, + CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1; + CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :)); + case 3, + CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1; + CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :)); + otherwise, + ind = subv2ind(sz, [x y]); + CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1; + CPD.CPT = mk_stochastic(CPD.dirichlet); + end +else + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + counts = compute_counts(data, sz); + CPD.dirichlet = CPD.dirichlet + counts; + CPD.CPT = mk_stochastic(CPD.dirichlet); +end