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/SCG/scg1.m b/sourcecodes/bnt-master/BNT/examples/static/SCG/scg1.m
new file mode 100644
index 00000000..f504c1fe
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/SCG/scg1.m
@@ -0,0 +1,77 @@
+% Same as cg1, except we call stab_cond_gauss_inf_engine
+
+bnet  = mk_incinerator_bnet;
+
+engines = {};
+engines{end+1} = stab_cond_gauss_inf_engine(bnet);
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = cond_gauss_inf_engine(bnet);
+nengines = length(engines);
+
+F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9;
+n = 9;
+dnodes = [B F W];
+cnodes = mysetdiff(1:n, dnodes);
+
+evidence = cell(1,n); % no evidence
+ll = zeros(1, nengines);
+for e=1:nengines
+  [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence);
+end
+%assert(approxeq(ll(1), ll)))
+ll
+
+% Compare to the results in table on p1107.
+% These results are printed to 3dp in Cowell p150
+
+mu = zeros(1,n);
+sigma = zeros(1,n);
+dprob = zeros(1,n);
+addev = 1;
+tol = 1e-2;
+for e=1:nengines
+  for i=cnodes(:)'
+    m = marginal_nodes(engines{e}, i, addev);
+    mu(i) = m.mu;
+    sigma(i) = sqrt(m.Sigma);
+  end
+  for i=dnodes(:)'
+    m = marginal_nodes(engines{e}, i, addev);
+    dprob(i) = m.T(1);
+  end
+  assert(approxeq(mu([E D C L Min Mout]), [-3.25 3.04 -1.85 1.48 -0.214 2.83], tol))
+  assert(approxeq(sigma([E D C L Min Mout]), [0.709 0.770 0.507 0.631 0.459 0.860], tol))
+  assert(approxeq(dprob([B F W]), [0.85 0.95 0.29], tol))
+  %m = marginal_nodes(engines{e}, bnet.names('E'), addev);
+  %assert(approxeq(m.mu, -3.25, tol))
+  %assert(approxeq(sqrt(m.Sigma), 0.709, tol))
+end
+
+% Add evidence (p 1105, top right)
+evidence = cell(1,n);
+evidence{W} = 1; % industrial
+evidence{L} = 1.1;
+evidence{C} = -0.9;
+
+ll = zeros(1, nengines);
+for e=1:nengines
+  [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence);
+end
+%assert(all(approxeq(ll(1), ll)))
+ll
+
+for e=1:nengines
+  for i=cnodes(:)'
+    m = marginal_nodes(engines{e}, i, addev);
+    mu(i) = m.mu;
+    sigma(i) = sqrt(m.Sigma);
+  end
+  for i=dnodes(:)'
+    m = marginal_nodes(engines{e}, i, addev);
+    dprob(i) = m.T(1);
+  end
+  assert(approxeq(mu([E D C L Min Mout]), [-3.90 3.61 -0.9 1.1 0.5 4.11], tol))
+  assert(approxeq(sigma([E D C L Min Mout]), [0.076 0.326 0 0 0.1 0.344], tol))
+  assert(approxeq(dprob([B F W]), [0.0122 0.9995 1], tol))
+end
+