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, 104 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m
new file mode 100644
index 00000000..df122b43
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m
@@ -0,0 +1,104 @@
+function dag = learn_struct_tan(data, class_node, root, node_sizes, scoring_fn)
+% LEARN_STRUCT_TAN Learn the structure of the tree augmented naive bayesian network 
+% (with discrete nodes)
+% dag = learn_struct_tan(app, class, root, node_sizes)
+%
+% Input :
+% 	data(i,m) is the value of node i in case m
+% 	class_node is the class node
+% 	root is the root node of the tree part of the dag (must be different from the class node)
+%   	node_sizes = 1 if gaussian node,
+%   	scoring_fn = 'bic' (default value) or 'mutual_info'
+%
+% Output :
+%	dag = adjacency matrix of the dag
+%
+% V1.1 : 21 may 2003, (O. Francois - francois.olivier.c.h@gmail.com, Ph. Leray - philippe.leray@univ-nantes.fr)
+% V1.2 : may 2005 bug correction about node types (Navid Serrano <Navid.Serrano@jpl.nasa.gov>)
+
+
+if nargin <4
+    error('Requires at least 4 arguments.')
+end
+
+if nargin == 4
+    scoring_fn='bic';
+end;
+
+if class_node==root
+    error(' The root node can''t be the class node.');
+end
+
+%  if root>class_node
+%      root=root-1;
+%  end
+
+N=size(data,1);
+node_types=cell(N-1,1);
+notclass=setdiff(1:N,class_node);
+for i=1:N
+    if node_sizes(i)==1
+        node_types{i}='gaussian';
+    else
+        node_types{i}='tabular';
+    end
+end
+
+dag=zeros(N);
+T = learn_struct_mwst4tan(data, ones(1,N), node_sizes, node_types, scoring_fn, root, class_node);
+dag=T;
+dag(class_node,notclass)=1;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function [T, score_mat] = learn_struct_mwst4tan(data, discrete, node_sizes, node_type, scoring_fn, root, class)
+
+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);
+score_mat(class,:)=Inf;
+score_mat(:,class)=Inf;
+
+switch scoring_fn
+case 'bic',
+    for i=mysetdiff(1:(N-1), class)
+            score2 = score_family(i, [class], node_type{i}, scoring_fn, node_sizes, discrete, data,[]);
+        for j=mysetdiff((i+1):N, class)
+            score1 = score_family(i, [j,class], 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',   % tabular nodes only
+    for i=mysetdiff(1:(N-1), class)
+        for j=mysetdiff((i+1):N, class)
+            score_mat(i,j)= -cond_mutual_info_score(i,node_sizes(i),j,node_sizes(j),class,node_sizes(class),data);
+            score_mat(j,i)=score_mat(i,j);
+        end
+    end
+otherwise,
+    error(['unrecognized scoring fn ' scoring_fn]);
+end
+
+variab = mysetdiff(1:N,class);
+%score_mat
+G = minimum_spanning_tree(score_mat(variab,variab));
+if root>class, root=root-1;end
+T = mk_rooted_tree(G, root);
+T1 = full(T);
+T=zeros(N);
+T(variab,variab)=T1;
+
+
+