Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/update_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_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