comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/log_marg_prob_node.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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
+
+