% Test jtree_compiled on a toy QMR network.rand('state', 0);randn('state', 0);pMax = 0.01;Nfindings = 10;Ndiseases = 5;N=Nfindings+Ndiseases;findings = Ndiseases+1:N;diseases = 1:Ndiseases;G = zeros(Ndiseases, Nfindings);for i=1:Nfindings  v= rand(1,Ndiseases);  rents = find(v<0.8);  if (length(rents)==0)    rents=ceil(rand(1)*Ndiseases);  end  G(rents,i)=1;end       prior = pMax*rand(1,Ndiseases);leak = 0.5*rand(1,Nfindings); % in real QMR, leak approx exp(-0.02) = 0.98     %leak = ones(1,Nfindings); % turns off leaks, which makes inference much harderinhibit = rand(Ndiseases, Nfindings);inhibit(not(G)) = 1;% first half of findings are +ve, second half -ve% The very first and last findings are hiddenpos = 2:floor(Nfindings/2);neg = (pos(end)+1):(Nfindings-1);big = 1;if big  % Make the bnet in the straightforward way  tabular_leaves = 1;  obs_nodes = myunion(pos, neg) + Ndiseases;  bnet = mk_qmr_bnet(G, inhibit, leak, prior, tabular_leaves, obs_nodes);  evidence = cell(1, N);  evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos)));  evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg)));else  % Marginalize out hidden leaves apriori  positive_leaves_only = 1;  [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, positive_leaves_only);  obs_nodes = bnet.observed;  evidence = cell(1, Ndiseases + length(obs_nodes));  evidence(obs_nodes) = num2cell(vals);endengine = {};engine{end+1} = jtree_inf_engine(bnet);E = length(engine);exact = 1:E;ll = zeros(1,E);for e=1:E  tic; [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); tocendassert(all(approxeq(ll(exact), ll(exact(1)))))post = zeros(E, Ndiseases);for e=1:E  for i=diseases(:)'    m = marginal_nodes(engine{e}, i);    post(e, i) = m.T(2);  endendfor e=exact(:)'  for i=diseases(:)'    assert(approxeq(post(1, i), post(e, i)));  endend