Removed deprecated interfaces
[libdai.git] / src / clustergraph.cpp
1 /* This file is part of libDAI - http://www.libdai.org/
2 *
3 * libDAI is licensed under the terms of the GNU General Public License version
4 * 2, or (at your option) any later version. libDAI is distributed without any
5 * warranty. See the file COPYING for more details.
6 *
7 * Copyright (C) 2006-2010 Joris Mooij [joris dot mooij at libdai dot org]
8 * Copyright (C) 2006-2007 Radboud University Nijmegen, The Netherlands
9 */
10
11
12 #include <set>
13 #include <vector>
14 #include <iostream>
15 #include <dai/varset.h>
16 #include <dai/clustergraph.h>
17
18
19 namespace dai {
20
21
22 using namespace std;
23
24
25 ClusterGraph::ClusterGraph( const std::vector<VarSet> & cls ) : G(), vars(), clusters() {
26 // construct vars, clusters and edge list
27 vector<Edge> edges;
28 foreach( const VarSet &cl, cls ) {
29 if( find( clusters.begin(), clusters.end(), cl ) == clusters.end() ) {
30 // add cluster
31 size_t n2 = clusters.size();
32 clusters.push_back( cl );
33 for( VarSet::const_iterator n = cl.begin(); n != cl.end(); n++ ) {
34 size_t n1 = find( vars.begin(), vars.end(), *n ) - vars.begin();
35 if( n1 == vars.size() )
36 // add variable
37 vars.push_back( *n );
38 edges.push_back( Edge( n1, n2 ) );
39 }
40 } // disregard duplicate clusters
41 }
42
43 // Create bipartite graph
44 G.construct( vars.size(), clusters.size(), edges.begin(), edges.end() );
45 }
46
47
48 size_t sequentialVariableElimination::operator()( const ClusterGraph &cl, const std::set<size_t> &/*remainingVars*/ ) {
49 return cl.findVar( seq.at(i++) );
50 }
51
52
53 size_t greedyVariableElimination::operator()( const ClusterGraph &cl, const std::set<size_t> &remainingVars ) {
54 set<size_t>::const_iterator lowest = remainingVars.end();
55 size_t lowest_cost = -1UL;
56 for( set<size_t>::const_iterator i = remainingVars.begin(); i != remainingVars.end(); i++ ) {
57 size_t cost = heuristic( cl, *i );
58 if( lowest == remainingVars.end() || lowest_cost > cost ) {
59 lowest = i;
60 lowest_cost = cost;
61 }
62 }
63 return *lowest;
64 }
65
66
67 size_t eliminationCost_MinNeighbors( const ClusterGraph &cl, size_t i ) {
68 std::vector<size_t> id_n = cl.G.delta1( i );
69 return id_n.size();
70 }
71
72
73 size_t eliminationCost_MinWeight( const ClusterGraph &cl, size_t i ) {
74 std::vector<size_t> id_n = cl.G.delta1( i );
75
76 size_t cost = 1;
77 for( size_t _i = 0; _i < id_n.size(); _i++ )
78 cost *= cl.vars[id_n[_i]].states();
79
80 return cost;
81 }
82
83
84 size_t eliminationCost_MinFill( const ClusterGraph &cl, size_t i ) {
85 std::vector<size_t> id_n = cl.G.delta1( i );
86
87 size_t cost = 0;
88 // for each unordered pair {i1,i2} adjacent to n
89 for( size_t _i1 = 0; _i1 < id_n.size(); _i1++ )
90 for( size_t _i2 = _i1 + 1; _i2 < id_n.size(); _i2++ ) {
91 // if i1 and i2 are not adjacent, eliminating n would make them adjacent
92 if( !cl.adj(id_n[_i1], id_n[_i2]) )
93 cost++;
94 }
95
96 return cost;
97 }
98
99
100 size_t eliminationCost_WeightedMinFill( const ClusterGraph &cl, size_t i ) {
101 std::vector<size_t> id_n = cl.G.delta1( i );
102
103 size_t cost = 0;
104 // for each unordered pair {i1,i2} adjacent to n
105 for( size_t _i1 = 0; _i1 < id_n.size(); _i1++ )
106 for( size_t _i2 = _i1 + 1; _i2 < id_n.size(); _i2++ ) {
107 // if i1 and i2 are not adjacent, eliminating n would make them adjacent
108 if( !cl.adj(id_n[_i1], id_n[_i2]) )
109 cost += cl.vars[id_n[_i1]].states() * cl.vars[id_n[_i2]].states();
110 }
111
112 return cost;
113 }
114
115
116 } // end of namespace dai