comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/log_prob_node.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function L = log_prob_node(CPD, self_ev, pev)
+% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian)
+% L = log_prob_node(CPD, self_ev, pev)
+%
+% 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.)
+
+if iscell(self_ev), usecell = 1; else usecell = 0; end
+
+use_log = 1;
+ncases = length(self_ev);
+nparents = length(CPD.sizes)-1;
+assert(ncases == size(pev, 2));
+
+if ncases == 0
+  L = 0;
+  return;
+end
+
+if length(CPD.dps)==0 % no discrete parents, so we can vectorize
+  i = 1;
+  if usecell
+    Y = cell2num(self_ev);
+  else
+    Y = self_ev;
+  end
+  if length(CPD.cps) == 0 
+    L = gaussian_prob(Y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);
+  else
+    if usecell
+      X = cell2num(pev);
+    else
+      X = pev;
+    end
+    L = gaussian_prob(Y, CPD.mean(:,i) + CPD.weights(:,:,i)*X, CPD.cov(:,:,i), use_log);
+  end
+else % each case uses a (potentially) different set of parameters
+  L = 0;
+  for m=1:ncases
+    if usecell
+      dpvals = cat(1, pev{CPD.dps, m});
+    else
+      dpvals = pev(CPD.dps, m);
+    end
+    i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)');
+    y = self_ev{m};
+    if length(CPD.cps) == 0 
+      L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);
+    else
+      if usecell
+	x = cat(1, pev{CPD.cps, m});
+      else
+	x = pev(CPD.cps, m);
+      end
+      L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log);
+    end
+  end
+end