Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/learning/score_family.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/learning/score_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data +% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% +% data(i,m) is the value of node i in case m (can be a cell array) +% args is a cell array containing optional arguments passed to the constructor, +% or is [] if none +% +% We create a whole Bayes net which only connects parents to node, +% where node has a CPD of the specified type (with default parameters). +% We then evaluate its score ('bic' or 'bayesian') + +% We should use a cache to avoid unnecessary computation. +% In particular, log_marginal_prob_node for tabular CPDs calls gammaln +% and compute_counts, both of which are slow. + +[n ncases] = size(data); +dag = zeros(n,n); +% SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make +% CPTs +% Kevin had: if ~isempty(ps), dag(ps, j) = 1; end +if ~isempty(ps), dag(ps, j) = 1;, ps = sort(ps);, end + +bnet = mk_bnet(dag, ns, 'discrete', discrete); +%bnet.CPD{j} = xxx_CPD(bnet, j); +%eval(sprintf('bnet.CPD{j} = %s_CPD(bnet, j);', node_type)); +fname = sprintf('%s_CPD', node_type); +%fprintf('score CPD %d\n', j); +if isempty(args) + bnet.CPD{j} = feval(fname, bnet, j); +else + bnet.CPD{j} = feval(fname, bnet, j, args{:}); +end +switch scoring_fn + case 'bic', + fam = [ps j]; + %score = BIC_score_CPD(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + %bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + + % SML 03/16/04 had to special case gaussian b/c generic_CPD/learn_params + % no longer supported because of simple interface to learn_params + % introduced by KPM for tabular nodes below: + % KPM 9 June 04 - tabular nodes have changed back! + if 1 % (isempty(find(j==discrete))) + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + else + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam, :)); + end + L = log_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + S = struct(bnet.CPD{j}); % violate object privacy + score = L - 0.5*S.nparams*log(ncases); + case 'bayesian', + %score = bayesian_score_CPD(bnet.CPD{j}, data(fam, :)); + score = log_marg_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + otherwise, + error(['unrecognized scoring fn ' scoring_fn]); +end