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diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m
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+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;
+
+
+