Mauch MIREX 2010

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);