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

first hg version after svn

+function CPD = update_tied_ess(CPD, domain, engine, evidence, ns, cnodes)
+
+if ~adjustable_CPD(CPD), return; end
+nCPDs = size(domain, 2);
+fmarginal = cell(1, nCPDs);
+for l=1:nCPDs
+  fmarginal{l} = marginal_family(engine, nodes(l));
+end
+
+[ss cpsz dpsz] = size(CPD.weights);
+if const_evidence_pattern(engine)
+  dom = domain(:,1);
+  dnodes = mysetdiff(1:length(ns), cnodes);
+  ddom = myintersect(dom, dnodes);
+  cdom = myintersect(dom, cnodes);
+  odom = dom(~isemptycell(evidence(dom)));
+  hdom = dom(isemptycell(evidence(dom)));
+  % If all hidden nodes are discrete and all cts nodes are observed 
+  % (e.g., HMM with Gaussian output)
+  % we can add the observed evidence in parallel
+  if mysubset(ddom, hdom) & mysubset(cdom, odom)
+    [mu, Sigma, T] = add_cts_ev_to_marginals(fmarginal, evidence, ns, cnodes);
+  else
+    mu = zeros(ss, dpsz, nCPDs);
+    Sigma = zeros(ss, ss, dpsz, nCPDs);
+    T = zeros(dpsz, nCPDs);
+    for l=1:nCPDs
+      [mu(:,:,l), Sigma(:,:,:,l), T(:,l)] = add_ev_to_marginals(fmarginal{l}, evidence, ns, cnodes);
+    end
+  end
+end
+CPD.nsamples = CPD.nsamples + nCPDs;            
+
+
+if dpsz == 1 % no discrete parents
+  w = 1;
+else
+  w = fullm.T(:);
+end
+CPD.Wsum = CPD.Wsum + w;
+% Let X be the cts parent (if any), Y be the cts child (self).
+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.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');
+    CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');
+  end
+end                
+
+
+%%%%%%%%%%%%%
+
+function fullm = add_evidence_to_marginal(fmarginal, evidence, ns, cnodes)
+
+
+dom = fmarginal.domain;
+
+% Find out which values of the discrete parents (if any) are compatible with 
+% the discrete evidence (if any).
+dnodes = mysetdiff(1:length(ns), cnodes);
+ddom = myintersect(dom, dnodes);
+cdom = myintersect(dom, cnodes);
+odom = dom(~isemptycell(evidence(dom)));
+hdom = dom(isemptycell(evidence(dom)));
+
+dobs = myintersect(ddom, odom);
+dvals = cat(1, evidence{dobs});
+ens = ns; % effective node sizes
+ens(dobs) = 1;
+S = prod(ens(ddom));
+subs = ind2subv(ens(ddom), 1:S);
+mask = find_equiv_posns(dobs, ddom);
+subs(mask) = dvals;
+supportedQs = subv2ind(ns(ddom), subs);
+
+if isempty(ddom)
+  Qarity = 1;
+else
+  Qarity = prod(ns(ddom));
+end
+fullm.T = zeros(Qarity, 1);
+fullm.T(supportedQs) = fmarginal.T(:);
+
+% Now put the hidden cts parts into their right blocks,
+% leaving the observed cts parts as 0.
+cobs = myintersect(cdom, odom);
+chid = myintersect(cdom, hdom);
+cvals = cat(1, evidence{cobs});
+n = sum(ns(cdom));
+fullm.mu = zeros(n,Qarity);
+fullm.Sigma = zeros(n,n,Qarity);
+
+if ~isempty(chid)
+  chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom));
+end
+if ~isempty(cobs)
+  cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom));
+end
+
+for i=1:length(supportedQs)
+  Q = supportedQs(i);
+  if ~isempty(chid)
+    fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i);
+    fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i);
+  end
+  if ~isempty(cobs)
+    fullm.mu(cobs_blocks, Q) = cvals(:);
+  end
+end