BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 77 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/qmr2.m b/sourcecodes/bnt-master/BNT/examples/static/qmr2.m
new file mode 100644
index 00000000..921cf57e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/qmr2.m
@@ -0,0 +1,77 @@
+% 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 harder
+inhibit = rand(Ndiseases, Nfindings);
+inhibit(not(G)) = 1;
+
+% first half of findings are +ve, second half -ve
+% The very first and last findings are hidden
+pos = 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);
+end
+
+engine = {};
+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); toc
+end
+
+assert(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);
+  end
+end
+for e=exact(:)'
+  for i=diseases(:)'
+    assert(approxeq(post(1, i), post(e, i)));
+  end
+end
+