Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/log_marg_prob_node.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/log_marg_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function L = log_marg_prob_node(CPD, self_ev, pev, usecell) +% LOG_MARG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m)) for node i (tabular) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% 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 may also be cell arrays.) + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(ncases == size(pev, 2)); + +if nargin < 4 + %usecell = 0; + if iscell(self_ev) + usecell = 1; + else + usecell = 0; + end +end + + +if ncases==0 + L = 0; + return; +elseif ncases==1 % speedup the sequential learning case + CPT = CPD.CPT; + % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params) + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + %x = pev(:)'; + x = pev; + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPT(ind); + end + L = log(p); +else + % We ignore the CPTs here and assume the prior has not been changed + + % We arrange the data as in the following example. + % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m, + % and y(m) be the child in case m. Then we create the data matrix + % + % p(1,1) p(1,2) p(1,3) + % p(2,1) p(2,2) p(2,3) + % y(1) y(2) y(3) + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + %S = struct(CPD); fprintf('log marg prob node %d, ps\n', S.self); disp(S.parents) + counts = compute_counts(data, sz); + L = dirichlet_score_family(counts, CPD.dirichlet); +end + +