Mercurial > hg > camir-aes2014
view 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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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