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1 function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
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2 % SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data
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3 % score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
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4 %
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5 % data(i,m) is the value of node i in case m (can be a cell array)
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6 % args is a cell array containing optional arguments passed to the constructor,
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7 % or is [] if none
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8 %
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9 % We create a whole Bayes net which only connects parents to node,
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10 % where node has a CPD of the specified type (with default parameters).
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11 % We then evaluate its score ('bic' or 'bayesian')
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12
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13 % We should use a cache to avoid unnecessary computation.
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14 % In particular, log_marginal_prob_node for tabular CPDs calls gammaln
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15 % and compute_counts, both of which are slow.
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16
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17 [n ncases] = size(data);
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18 dag = zeros(n,n);
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19 % SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make
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20 % CPTs
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21 % Kevin had: if ~isempty(ps), dag(ps, j) = 1; end
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22 if ~isempty(ps), dag(ps, j) = 1;, ps = sort(ps);, end
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23
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24 bnet = mk_bnet(dag, ns, 'discrete', discrete);
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25 %bnet.CPD{j} = xxx_CPD(bnet, j);
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26 %eval(sprintf('bnet.CPD{j} = %s_CPD(bnet, j);', node_type));
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27 fname = sprintf('%s_CPD', node_type);
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28 %fprintf('score CPD %d\n', j);
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29 if isempty(args)
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30 bnet.CPD{j} = feval(fname, bnet, j);
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31 else
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32 bnet.CPD{j} = feval(fname, bnet, j, args{:});
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33 end
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34 switch scoring_fn
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35 case 'bic',
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36 fam = [ps j];
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37 %score = BIC_score_CPD(bnet.CPD{j}, fam, data, ns, bnet.cnodes);
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38 %bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes);
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39
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40 % SML 03/16/04 had to special case gaussian b/c generic_CPD/learn_params
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41 % no longer supported because of simple interface to learn_params
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42 % introduced by KPM for tabular nodes below:
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43 % KPM 9 June 04 - tabular nodes have changed back!
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44 if 1 % (isempty(find(j==discrete)))
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45 bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes);
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46 else
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47 bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam, :));
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48 end
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49 L = log_prob_node(bnet.CPD{j}, data(j,:), data(ps,:));
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50 S = struct(bnet.CPD{j}); % violate object privacy
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51 score = L - 0.5*S.nparams*log(ncases);
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52 case 'bayesian',
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53 %score = bayesian_score_CPD(bnet.CPD{j}, data(fam, :));
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54 score = log_marg_prob_node(bnet.CPD{j}, data(j,:), data(ps,:));
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55 otherwise,
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56 error(['unrecognized scoring fn ' scoring_fn]);
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57 end
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