Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/update_tied_ess.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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