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

first hg version after svn

+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+
+%if nargin < 6
+%  hidden_bitv = zeros(1, max(fmarginal.domain));
+%  hidden_bitv(find(isempty(evidence)))=1;
+%end
+
+dom = fmarginal.domain;
+self = dom(end);
+ps = dom(1:end-1);
+hidden_self = hidden_bitv(self);
+cps = myintersect(ps, cnodes);
+dps = mysetdiff(ps, cps);
+hidden_cps = all(hidden_bitv(cps));
+hidden_dps = all(hidden_bitv(dps));
+
+CPD.nsamples = CPD.nsamples + 1;            
+[ss cpsz dpsz] = size(CPD.weights); % ss = self size
+
+% Let X be the cts parent (if any), Y be the cts child (self).
+
+if ~hidden_self & (isempty(cps) | ~hidden_cps) & hidden_dps % all cts nodes are observed, all discrete nodes are hidden
+  % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0
+  % Since discrete parents are hidden, we do not need to add evidence to w.
+  w = fmarginal.T(:);
+  CPD.Wsum = CPD.Wsum + w;
+  y = evidence{self};
+  Cyy = y*y';
+  if ~CPD.useC
+     W = repmat(w(:)',ss,1); % W(y,i) = w(i)
+     W2 = repmat(reshape(W, [ss 1 dpsz]), [1 ss 1]); % W2(x,y,i) = w(i)
+     CPD.WYsum = CPD.WYsum +  W .* repmat(y(:), 1, dpsz);
+     CPD.WYYsum = CPD.WYYsum + W2  .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]);
+  else
+     W = w(:)';
+     W2 = reshape(W, [1 1 dpsz]);
+     CPD.WYsum = CPD.WYsum +  rep_mult(W, y(:), size(CPD.WYsum)); 
+     CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum));
+  end
+  if cpsz > 0 % X exists
+    x = cat(1, evidence{cps}); x = x(:);
+    Cxx = x*x';
+    Cxy = x*y';
+    if ~CPD.useC
+       CPD.WXsum = CPD.WXsum + W .* repmat(x(:), 1, dpsz);
+       CPD.WXXsum = CPD.WXXsum + W2 .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]);
+       CPD.WXYsum = CPD.WXYsum + W2 .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]);
+    else
+       CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum));
+       CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum));
+       CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum));
+    end
+  end
+  return;
+end
+
+% general (non-vectorized) case
+fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow!
+
+if dpsz == 1 % no discrete parents
+  w = 1;
+else
+  w = fullm.T(:);
+end
+
+CPD.Wsum = CPD.Wsum + w;
+xi = 1:cpsz;
+yi = (cpsz+1):(cpsz+ss);
+for i=1:dpsz
+  muY = fullm.mu(yi, i);
+  SYY = fullm.Sigma(yi, yi, i);
+  CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY;
+  CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y]
+  if cpsz > 0
+    muX = fullm.mu(xi, i);
+    SXX = fullm.Sigma(xi, xi, i);
+    SXY = fullm.Sigma(xi, yi, i);
+    CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX;
+    CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');
+    CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');
+  end
+end                
+