Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/general/Old/mk_gdl_graph.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function gdl = mk_gdl_graph(G, domains, node_sizes, kernels, varargin) | |
2 % MK_GDL_GRAPH Make a GDL (generalized distributed law) graph | |
3 % gdl = mk_gdl_graph(G, domains, node_sizes, kernels, ...) | |
4 % | |
5 % A GDL graph is like a moralized, but untriangulated, Bayes net: | |
6 % each "node" represents a domain with a corresponding kernel function. | |
7 % For details, see "The Generalized Distributive Law", Aji and McEliece, | |
8 % IEEE Trans. Info. Theory, 46(2): 325--343, 2000 | |
9 % | |
10 % G(i,j) = 1 if there is an (undirected) edge between domains i,j | |
11 % | |
12 % domains{i} is the domain of node i | |
13 % | |
14 % node_sizes(i) is the number of values node i can take on, | |
15 % or the length of node i if i is a continuous-valued vector. | |
16 % node_sizes(i) = 1 if i is a utility node. | |
17 % | |
18 % kernels is the list of kernel functions | |
19 % | |
20 % The list below gives optional arguments [default value in brackets]. | |
21 % | |
22 % equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] | |
23 % discrete - the list of nodes which are discrete random variables [1:N] | |
24 % chance - the list of nodes which are random variables [1:N] | |
25 % decision - the list of nodes which are decision nodes [ [] ] | |
26 % utility - the list of nodes which are utility nodes [ [] ] | |
27 | |
28 | |
29 ns = node_sizes; | |
30 N = length(domains); | |
31 vars = []; | |
32 for i=1:N | |
33 vars = myunion(vars, domains{i}); | |
34 end | |
35 Nvars = length(vars); | |
36 | |
37 gdl.equiv_class = 1:length(kernels); | |
38 gdl.chance_nodes = 1:Nvars; | |
39 gdl.utility_nodes = []; | |
40 gdl.decision_nodes = []; | |
41 gdl.dnodes = 1:Nvars; | |
42 | |
43 if nargin >= 5 | |
44 args = varargin; | |
45 nargs = length(args); | |
46 for i=1:2:nargs | |
47 switch args{i}, | |
48 case 'equiv_class', bnet.equiv_class = args{i+1}; | |
49 case 'chance', bnet.chance_nodes = args{i+1}; | |
50 case 'utility', bnet.utility_nodes = args{i+1}; | |
51 case 'decision', bnet.decision_nodes = args{i+1}; | |
52 case 'discrete', bnet.dnodes = args{i+1}; | |
53 otherwise, | |
54 error(['invalid argument name ' args{i}]); | |
55 end | |
56 end | |
57 end | |
58 | |
59 | |
60 gdl.G = G; | |
61 gdl.vars = vars; | |
62 gdl.doms = domains; | |
63 gdl.node_sizes = node_sizes; | |
64 gdl.cnodes = mysetdiff(vars, gdl.dnodes); | |
65 gdl.kernels = kernels; | |
66 gdl.type = 'gdl'; | |
67 | |
68 % Compute a bit vector representation of the set of domains | |
69 % dom_bitv(i,j) = 1 iff variable j occurs in domain i | |
70 gdl.dom_bitv = zeros(N, length(vars)); | |
71 for i=1:N | |
72 gdl.dom_bitv(i, domains{i}) = 1; | |
73 end | |
74 | |
75 % compute the interesection of the domains on either side of each edge (separating set) | |
76 gdl.sepset = cell(N, N); | |
77 gdl.nbrs = cell(1,N); | |
78 for i=1:N | |
79 nbrs = neighbors(G, i); | |
80 gdl.nbrs{i} = nbrs; | |
81 for j = nbrs(:)' | |
82 gdl.sepset{i,j} = myintersect(domains{i}, domains{j}); | |
83 end | |
84 end | |
85 | |
86 |