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, 18 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/check_jtree_property.m b/sourcecodes/bnt-master/graph/check_jtree_property.m
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+++ b/sourcecodes/bnt-master/graph/check_jtree_property.m
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+function check_jtree_property(cliques_containing_node, jtree)
+% CHECK_JTREE_PROPERTY Raise an error if the graph does not satisfy the join tree property.
+% check_jtree_property(cliques_containing_node, jtree_adj_mat)
+%
+% The join tree property says:
+% For each node n in the dag, compute the node-induced subgraph G by looking at all the cliques
+% that contain n, and make sure G forms a connected graph. 
+% This ensures that local propagation leads to global consistency.
+
+num_bn_nodes = length(cliques_containing_node);
+directed = 0;
+for i=1:num_bn_nodes
+  cs = cliques_containing_node{i};
+  G = jtree(cs,cs);
+  if ~connected_graph(G, directed)
+    error(['node ' num2str(i) ' violates jtree property']);
+  end
+end