comparison toolboxes/FullBNT-1.0.7/bnt/learning/score_family.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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