diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CPD_to_scgpot.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CPD_to_scgpot.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,58 @@
+function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence)
+% CPD_TO_CGPOT Convert a Gaussian CPD to a CG potential, incorporating any evidence   
+% pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence)
+
+self = CPD.self;
+dnodes = mysetdiff(1:length(ns), cnodes);
+odom = domain(~isemptycell(evidence(domain)));
+cdom = myintersect(cnodes, domain);
+cheaddom = myintersect(self, domain);
+ctaildom = mysetdiff(cdom,cheaddom);
+ddom = myintersect(dnodes, domain);
+cobs = myintersect(cdom, odom);
+dobs = myintersect(ddom, odom);
+ens = ns; % effective node size
+ens(cobs) = 0;
+ens(dobs) = 1;
+
+% Extract the params compatible with the observations (if any) on the discrete parents (if any)
+% parents are all but the last domain element
+ps = domain(1:end-1);
+dps = myintersect(ps, ddom);
+dops = myintersect(dps, odom);
+
+map = find_equiv_posns(dops, dps);
+dpvals = cat(1, evidence{dops});
+index = mk_multi_index(length(dps), map, dpvals);
+
+dpsize = prod(ens(dps));
+cpsize = size(CPD.weights(:,:,1), 2); % cts parents size
+ss = size(CPD.mean, 1); % self size
+% the reshape acts like a squeeze
+m = reshape(CPD.mean(:, index{:}), [ss dpsize]);
+C = reshape(CPD.cov(:, :, index{:}), [ss ss dpsize]);
+W = reshape(CPD.weights(:, :, index{:}), [ss cpsize dpsize]);
+
+
+% Convert each conditional Gaussian to a canonical potential
+pot = cell(1, dpsize);
+for i=1:dpsize
+  %pot{i} = linear_gaussian_to_scgcpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence);
+  pot{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i));
+end
+
+pot = scgpot(ddom, cheaddom, ctaildom, ens, pot);
+
+
+function pot = linear_gaussian_to_scgcpot(mu, Sigma, W, domain, ns, cnodes, evidence)
+% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD  to a stable conditional potential element.
+% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
+
+p = 1;
+A = mu;
+B = W;
+C = Sigma;
+ns(odom) = 0;
+%pot = scgcpot(, ns(domain), p, A, B, C);
+
+