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
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
function bnet = mk_dbn(intra, inter, node_sizes, varargin)
% MK_DBN Make a Dynamic Bayesian Network.
%
% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs
% from i in slice t to j in slice t iff intra(i,j) = 1, and
% from i in slice t to j in slice t+1 iff inter(i,j) = 1,
% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1.
% node_sizes(i) is the number of values node i can take on.
% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
% See also mk_bnet.
%
% Optional arguments [default in brackets]
% 'discrete' - list of discrete nodes [1:n]
% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ]
% 'eclass1' - equiv class for slice 1 [1:n]
% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1]
% equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j},
% i.e., nodes i and j have tied parameters.
% 'intra1' - topology of first slice, if different from others
% 'names' - a cell array of strings to be associated with nodes 1:n [{}]
% This creates an associative array, so you write e.g.
% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42'
% assuming names = { 'foo', 'bar', ...}.
%
% For backwards compatibility with BNT2, arguments can also be specified as follows
% bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1)
%
% After calling this function, you must specify the parameters (conditional probability
% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc.
n = length(intra);
ss = n;
bnet.nnodes_per_slice = ss;
bnet.intra = intra;
bnet.inter = inter;
bnet.intra1 = intra;
dag = zeros(2*n);
dag(1:n,1:n) = bnet.intra1;
dag(1:n,(1:n)+n) = bnet.inter;
dag((1:n)+n,(1:n)+n) = bnet.intra;
bnet.dag = dag;
bnet.names = {};
directed = 1;
if ~acyclic(dag,directed)
error('graph must be acyclic')
end
bnet.eclass1 = 1:n;
%bnet.eclass2 = (1:n)+n;
bnet.eclass2 = bnet.eclass1;
for i=1:ss
if isequal(parents(dag, i+ss), parents(dag, i)+ss)
%fprintf('%d has isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
else
bnet.eclass2(i) = max(bnet.eclass2) + 1;
%fprintf('%d has non isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
end
end
dnodes = 1:n;
bnet.observed = [];
if nargin >= 4
args = varargin;
nargs = length(args);
if ~isstr(args{1})
if nargs >= 1, dnodes = args{1}; end
if nargs >= 2, bnet.eclass1 = args{2}; end
if nargs >= 3, bnet.eclass2 = args{3}; end
if nargs >= 4, bnet.intra1 = args{4}; end
else
for i=1:2:nargs
switch args{i},
case 'discrete', dnodes = args{i+1};
case 'observed', bnet.observed = args{i+1};
case 'eclass1', bnet.eclass1 = args{i+1};
case 'eclass2', bnet.eclass2 = args{i+1};
case 'intra1', bnet.intra1 = args{i+1};
%case 'ar_hmm', bnet.ar_hmm = args{i+1}; % should check topology
case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n));
otherwise,
error(['invalid argument name ' args{i}]);
end
end
end
end
bnet.observed = sort(bnet.observed); % for comparing sets
ns = node_sizes;
bnet.node_sizes_slice = ns(:)';
bnet.node_sizes = [ns(:) ns(:)];
cnodes = mysetdiff(1:n, dnodes);
bnet.dnodes_slice = dnodes;
bnet.cnodes_slice = cnodes;
bnet.dnodes = [dnodes dnodes+n];
bnet.cnodes = [cnodes cnodes+n];
bnet.equiv_class = [bnet.eclass1(:) bnet.eclass2(:)];
bnet.CPD = cell(1,max(bnet.equiv_class(:)));
eclass = bnet.equiv_class(:);
E = max(eclass);
bnet.rep_of_eclass = zeros(1,E);
for e=1:E
mems = find(eclass==e);
bnet.rep_of_eclass(e) = mems(1);
end
ss = n;
onodes = bnet.observed;
hnodes = mysetdiff(1:ss, onodes);
bnet.hidden_bitv = zeros(1,2*ss);
bnet.hidden_bitv(hnodes) = 1;
bnet.hidden_bitv(hnodes+ss) = 1;
bnet.parents = cell(1, 2*ss);
for i=1:ss
bnet.parents{i} = parents(bnet.dag, i);
bnet.parents{i+ss} = parents(bnet.dag, i+ss);
end
bnet.auto_regressive = zeros(1,ss);
% ar(i)=1 means (observed) node i depends on i in the previous slice
for o=bnet.observed(:)'
if any(bnet.parents{o+ss} <= ss)
bnet.auto_regressive(o) = 1;
end
end
|
