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function marginal = marginal_nodes(engine, nodes)
% MARGINAL_NODES Compute the marginal on the specified query nodes (likelihood_weighting)
% marginal = marginal_nodes(engine, nodes)
bnet = bnet_from_engine(engine);
ddom = myintersect(nodes, bnet.dnodes);
cdom = myintersect(nodes, bnet.cnodes);
nsamples = size(engine.samples, 1);
ns = bnet.node_sizes;
%w = normalise(engine.weights);
w = engine.weights;
if mysubset(nodes, ddom)
T = 0*myones(ns(nodes));
P = prod(ns(nodes));
indices = ind2subv(ns(nodes), 1:P);
samples = reshape(cat(1, engine.samples{:,nodes}), nsamples, length(nodes));
for j = 1:P
rows = find_rows(samples, indices(j,:));
T(j) = sum(w(rows));
end
T = normalise(T);
marginal.T = T;
elseif subset(nodes, cdom)
samples = reshape(cat(1, engine.samples{:,nodes}), nsamples*sum(ns(nodes)), length(nodes));
[marginal.mu, marginal.Sigma] = wstats(samples', normalise(w));
else
error('can''t handle mixed marginals yet');
end
marginal.domain = nodes;
%%%%%%%%%
function rows = find_rows(M, v)
% FINDROWS Find rows which are equal to a specified vector
% rows = findrows(M, v)
% Each row of M is a sample
temp = abs(M - repmat(v, size(M, 1), 1));
rows = find(sum(temp,2) == 0);
%%%%%%%%
function [mu, Sigma] = wstats(X, w)
% Computes the weighted mean and weighted covariance matrix for a given
% set of observations X(:,i), and a set of normalised weights w(i).
% Each column of X is a sample.
d = X - repmat(X * w', 1, size(X, 2));
mu = sum(X .* repmat(w, size(X, 1), 1), 2);
Sigma = d * diag(w) * d';
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