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, 31 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/potentials/@cpot/marginalize_pot.m b/sourcecodes/bnt-master/BNT/potentials/@cpot/marginalize_pot.m
new file mode 100644
index 00000000..ae9ee839
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/potentials/@cpot/marginalize_pot.m
@@ -0,0 +1,31 @@
+function smallpot = marginalize_pot(bigpot, keep, maximize, useC)
+% MARGINALIZE_POT Marginalize a cpot onto a smaller domain.
+% smallpot = marginalize_pot(bigpot, keep, maximize, useC)
+%
+% The maximize argument is ignored - maxing out a Gaussian is the same as summing it out,
+% since the mode and mean are equal.
+% The useC argument is ignored.
+
+node_sizes = sparse(1, max(bigpot.domain));
+node_sizes(bigpot.domain) = bigpot.sizes;
+sum_over = mysetdiff(bigpot.domain, keep);
+
+if sum(node_sizes(sum_over))==0 % isempty(sum_over)
+  %smallpot = bigpot;
+  smallpot = cpot(keep, node_sizes(keep), bigpot.g, bigpot.h, bigpot.K);
+else
+  [h1, h2, K11, K12, K21, K22] = partition_matrix_vec(bigpot.h, bigpot.K, sum_over, keep, node_sizes);
+  n = length(h1);
+  K11inv = inv(K11);
+  g = bigpot.g + 0.5*(n*log(2*pi) - log(det(K11)) + h1'*K11inv*h1);
+  if length(h2) > 0 % ~isempty(keep) % we are are actually keeping something
+    A = K21*K11inv;
+    h = h2 - A*h1;
+    K = K22 - A*K12;
+  else
+    h = [];
+    K = [];
+  end
+  smallpot = cpot(keep, node_sizes(keep), g, h, K);
+end
+