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function pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD to a canonical potential.
% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
%
% We include any cts evidence, but ignore any discrete evidence.
% (Use gaussian_CPD_params_given_dps to use discrete evidence to select mu, Sigma, W.)
odom = domain(~isemptycell(evidence(domain)));
hdom = domain(isemptycell(evidence(domain)));
cobs = myintersect(cnodes, odom);
chid = myintersect(cnodes, hdom);
cvals = cat(1, evidence{cobs});
%[g,h,K] = gaussian_to_canonical(mu, Sigma, W);
Sinv = inv(Sigma);
g = -0.5*mu'*Sinv*mu + log(normal_coef(Sigma));
if isempty(W) || (size(W,2)==0) % no cts parents
h = Sinv*mu;
K = Sinv;
else
h = [-W'*Sinv*mu; Sinv*mu];
K = [W'*Sinv*W -W'*Sinv';
-Sinv*W Sinv];
end
if ~isempty(cvals)
%[g, h, K] = enter_evidence_canonical(g, h, K, chid, cobs, cvals(:), ns);
[hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(h, K, chid, cobs, ns);
y = cvals(:);
g = g + hy'*y - 0.5*y'*KYY*y;
if length(hx)==0 % isempty(X) % i.e., we have instantiated everything away
h = [];
K = [];
else
h = hx - KXY*y;
K = KXX;
end
end
ns(odom) = 0;
pot = cpot(domain, ns(domain), g, h, K);
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