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

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function CPD = update_tied_ess(CPD, domain, engine, evidence, ns, cnodes)
wolffd@0 2
wolffd@0 3 if ~adjustable_CPD(CPD), return; end
wolffd@0 4 nCPDs = size(domain, 2);
wolffd@0 5 fmarginal = cell(1, nCPDs);
wolffd@0 6 for l=1:nCPDs
wolffd@0 7 fmarginal{l} = marginal_family(engine, nodes(l));
wolffd@0 8 end
wolffd@0 9
wolffd@0 10 [ss cpsz dpsz] = size(CPD.weights);
wolffd@0 11 if const_evidence_pattern(engine)
wolffd@0 12 dom = domain(:,1);
wolffd@0 13 dnodes = mysetdiff(1:length(ns), cnodes);
wolffd@0 14 ddom = myintersect(dom, dnodes);
wolffd@0 15 cdom = myintersect(dom, cnodes);
wolffd@0 16 odom = dom(~isemptycell(evidence(dom)));
wolffd@0 17 hdom = dom(isemptycell(evidence(dom)));
wolffd@0 18 % If all hidden nodes are discrete and all cts nodes are observed
wolffd@0 19 % (e.g., HMM with Gaussian output)
wolffd@0 20 % we can add the observed evidence in parallel
wolffd@0 21 if mysubset(ddom, hdom) & mysubset(cdom, odom)
wolffd@0 22 [mu, Sigma, T] = add_cts_ev_to_marginals(fmarginal, evidence, ns, cnodes);
wolffd@0 23 else
wolffd@0 24 mu = zeros(ss, dpsz, nCPDs);
wolffd@0 25 Sigma = zeros(ss, ss, dpsz, nCPDs);
wolffd@0 26 T = zeros(dpsz, nCPDs);
wolffd@0 27 for l=1:nCPDs
wolffd@0 28 [mu(:,:,l), Sigma(:,:,:,l), T(:,l)] = add_ev_to_marginals(fmarginal{l}, evidence, ns, cnodes);
wolffd@0 29 end
wolffd@0 30 end
wolffd@0 31 end
wolffd@0 32 CPD.nsamples = CPD.nsamples + nCPDs;
wolffd@0 33
wolffd@0 34
wolffd@0 35 if dpsz == 1 % no discrete parents
wolffd@0 36 w = 1;
wolffd@0 37 else
wolffd@0 38 w = fullm.T(:);
wolffd@0 39 end
wolffd@0 40 CPD.Wsum = CPD.Wsum + w;
wolffd@0 41 % Let X be the cts parent (if any), Y be the cts child (self).
wolffd@0 42 xi = 1:cpsz;
wolffd@0 43 yi = (cpsz+1):(cpsz+ss);
wolffd@0 44 for i=1:dpsz
wolffd@0 45 muY = fullm.mu(yi, i);
wolffd@0 46 SYY = fullm.Sigma(yi, yi, i);
wolffd@0 47 CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY;
wolffd@0 48 CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y]
wolffd@0 49 if cpsz > 0
wolffd@0 50 muX = fullm.mu(xi, i);
wolffd@0 51 SXX = fullm.Sigma(xi, xi, i);
wolffd@0 52 SXY = fullm.Sigma(xi, yi, i);
wolffd@0 53 CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX;
wolffd@0 54 CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');
wolffd@0 55 CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');
wolffd@0 56 end
wolffd@0 57 end
wolffd@0 58
wolffd@0 59
wolffd@0 60 %%%%%%%%%%%%%
wolffd@0 61
wolffd@0 62 function fullm = add_evidence_to_marginal(fmarginal, evidence, ns, cnodes)
wolffd@0 63
wolffd@0 64
wolffd@0 65 dom = fmarginal.domain;
wolffd@0 66
wolffd@0 67 % Find out which values of the discrete parents (if any) are compatible with
wolffd@0 68 % the discrete evidence (if any).
wolffd@0 69 dnodes = mysetdiff(1:length(ns), cnodes);
wolffd@0 70 ddom = myintersect(dom, dnodes);
wolffd@0 71 cdom = myintersect(dom, cnodes);
wolffd@0 72 odom = dom(~isemptycell(evidence(dom)));
wolffd@0 73 hdom = dom(isemptycell(evidence(dom)));
wolffd@0 74
wolffd@0 75 dobs = myintersect(ddom, odom);
wolffd@0 76 dvals = cat(1, evidence{dobs});
wolffd@0 77 ens = ns; % effective node sizes
wolffd@0 78 ens(dobs) = 1;
wolffd@0 79 S = prod(ens(ddom));
wolffd@0 80 subs = ind2subv(ens(ddom), 1:S);
wolffd@0 81 mask = find_equiv_posns(dobs, ddom);
wolffd@0 82 subs(mask) = dvals;
wolffd@0 83 supportedQs = subv2ind(ns(ddom), subs);
wolffd@0 84
wolffd@0 85 if isempty(ddom)
wolffd@0 86 Qarity = 1;
wolffd@0 87 else
wolffd@0 88 Qarity = prod(ns(ddom));
wolffd@0 89 end
wolffd@0 90 fullm.T = zeros(Qarity, 1);
wolffd@0 91 fullm.T(supportedQs) = fmarginal.T(:);
wolffd@0 92
wolffd@0 93 % Now put the hidden cts parts into their right blocks,
wolffd@0 94 % leaving the observed cts parts as 0.
wolffd@0 95 cobs = myintersect(cdom, odom);
wolffd@0 96 chid = myintersect(cdom, hdom);
wolffd@0 97 cvals = cat(1, evidence{cobs});
wolffd@0 98 n = sum(ns(cdom));
wolffd@0 99 fullm.mu = zeros(n,Qarity);
wolffd@0 100 fullm.Sigma = zeros(n,n,Qarity);
wolffd@0 101
wolffd@0 102 if ~isempty(chid)
wolffd@0 103 chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom));
wolffd@0 104 end
wolffd@0 105 if ~isempty(cobs)
wolffd@0 106 cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom));
wolffd@0 107 end
wolffd@0 108
wolffd@0 109 for i=1:length(supportedQs)
wolffd@0 110 Q = supportedQs(i);
wolffd@0 111 if ~isempty(chid)
wolffd@0 112 fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i);
wolffd@0 113 fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i);
wolffd@0 114 end
wolffd@0 115 if ~isempty(cobs)
wolffd@0 116 fullm.mu(cobs_blocks, Q) = cvals(:);
wolffd@0 117 end
wolffd@0 118 end