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, 62 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m
new file mode 100644
index 00000000..8437385e
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m
@@ -0,0 +1,62 @@
+function [T, score_mat] = learn_struct_mwst(data, discrete, node_sizes, node_type, scoring_fn, root)
+% LEARN_STRUCT_MWST Learn an oriented tree using the MSWT algorithm
+% T = learn_struct_mwst(data, discrete, node_sizes, node_type, scoring_fn, root)
+%
+% Input : 
+%   data(i,m) is the node i in the case m,
+%   discrete = [ 1 if discret-node 0 if not ],
+%   node_sizes = 1 if gaussian node,
+%   node_type = {'tabular','gaussian',...},
+%   score = 'bic' (for complete data and any node types) or 'mutual_info' (tabular nodes),
+%   root is the futur root-node of the tree T.
+%
+% Output :
+%	T = adjacency matrix of the tree
+%
+% V1.2 : 17 feb 2003 (O. Francois - francois.olivier.c.h@gmail.com, Ph. Leray - philippe.leray@univ-nantes.fr)
+%
+%
+% See Chow&Liu 1968 for the original algorithm using Mutual Information scoring.
+% Or Heckerman 1994.
+
+if nargin <4
+    error('Requires at least 4 arguments.')
+end
+
+if nargin == 4
+    scoring_fn='bic'; root=1;
+end;
+
+if nargin == 5
+    root=1;
+end;
+
+
+N=size(data,1);
+score_mat=zeros(N,N);
+
+switch scoring_fn
+case 'bic',
+    for i=1:(N-1)
+            score2 = score_family(i, [], node_type{i}, scoring_fn, node_sizes, discrete, data,[]);
+        for j=(i+1):N
+            score1 = score_family(i, [j], node_type{i}, scoring_fn, node_sizes, discrete, data,[]);
+            score = score2-score1;
+            score_mat(i,j)=score;
+            score_mat(j,i)=score;
+        end
+    end
+case 'mutual_info',
+    for i=1:(N-1)
+        for j=(i+1):N
+            score_mat(i,j)= -mutual_info_score(i,node_sizes(i),j,node_sizes(j),data);
+            score_mat(j,i)=score_mat(i,j);
+        end
+    end
+otherwise,
+    error(['unrecognized scoring fn ' scoring_fn]);
+end
+
+G = minimum_spanning_tree(score_mat);
+T = mk_rooted_tree(G, root);
+T=full(T);
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