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, 86 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/cg1.m b/sourcecodes/bnt-master/BNT/examples/static/cg1.m
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+% Conditional Gaussian network
+% The waste incinerator emissions example from Lauritzen (1992),
+% "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models", 
+% JASA 87(420): 1098--1108
+%
+% This example is reprinted on p145 of "Probabilistic Networks and Expert Systems",
+% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer.
+%
+% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#cg_model
+
+ns = 2*ones(1,9);
+%bnet  = mk_incinerator_bnet(ns);
+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)))
+
+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
+