1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
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;
|
