Removed stuff from InfAlg, moved it to individual inference algorithms
[libdai.git] / include / dai / hak.h
1 /* Copyright (C) 2006-2008 Joris Mooij [j dot mooij at science dot ru dot nl]
2 Radboud University Nijmegen, The Netherlands
3
4 This file is part of libDAI.
5
6 libDAI is free software; you can redistribute it and/or modify
7 it under the terms of the GNU General Public License as published by
8 the Free Software Foundation; either version 2 of the License, or
9 (at your option) any later version.
10
11 libDAI is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 GNU General Public License for more details.
15
16 You should have received a copy of the GNU General Public License
17 along with libDAI; if not, write to the Free Software
18 Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
19 */
20
21
22 #ifndef __defined_libdai_hak_h
23 #define __defined_libdai_hak_h
24
25
26 #include <string>
27 #include <dai/daialg.h>
28 #include <dai/regiongraph.h>
29 #include <dai/enum.h>
30 #include <dai/properties.h>
31
32
33 namespace dai {
34
35
36 /// HAK provides an implementation of the single and double-loop algorithms by Heskes, Albers and Kappen
37 class HAK : public DAIAlgRG {
38 protected:
39 std::vector<Factor> _Qa;
40 std::vector<Factor> _Qb;
41 std::vector<std::vector<Factor> > _muab;
42 std::vector<std::vector<Factor> > _muba;
43
44 public:
45 struct Properties {
46 size_t verbose;
47 size_t maxiter;
48 double tol;
49 ENUM3(ClustersType,MIN,DELTA,LOOP)
50 ClustersType clusters;
51 bool doubleloop;
52 size_t loopdepth;
53 } props;
54 double maxdiff;
55
56 public:
57 /// Default constructor
58 HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), props(), maxdiff() {}
59
60 /// Copy constructor
61 HAK(const HAK & x) : DAIAlgRG(x), _Qa(x._Qa), _Qb(x._Qb), _muab(x._muab), _muba(x._muba), props(x.props), maxdiff(x.maxdiff) {}
62
63 /// Clone function
64 HAK* clone() const { return new HAK(*this); }
65
66 /// Construct from RegionGraph
67 HAK(const RegionGraph & rg, const PropertySet &opts);
68
69 /// Construct from RactorGraph using "clusters" option
70 HAK(const FactorGraph & fg, const PropertySet &opts);
71
72 /// Assignment operator
73 HAK & operator=(const HAK & x) {
74 if( this != &x ) {
75 DAIAlgRG::operator=(x);
76 _Qa = x._Qa;
77 _Qb = x._Qb;
78 _muab = x._muab;
79 _muba = x._muba;
80 props = x.props;
81 maxdiff = x.maxdiff;
82 }
83 return *this;
84 }
85
86 static const char *Name;
87
88 Factor & muab( size_t alpha, size_t _beta ) { return _muab[alpha][_beta]; }
89 Factor & muba( size_t alpha, size_t _beta ) { return _muba[alpha][_beta]; }
90 const Factor& Qa( size_t alpha ) const { return _Qa[alpha]; };
91 const Factor& Qb( size_t beta ) const { return _Qb[beta]; };
92
93 double doGBP();
94 double doDoubleLoop();
95 double run();
96 void init();
97 std::string identify() const;
98 Factor belief( const Var &n ) const;
99 Factor belief( const VarSet &ns ) const;
100 std::vector<Factor> beliefs() const;
101 Complex logZ () const;
102
103 void init( const VarSet &ns );
104 void undoProbs( const VarSet &ns ) { RegionGraph::undoProbs( ns ); init( ns ); }
105 void setProperties( const PropertySet &opts );
106 PropertySet getProperties() const;
107 double maxDiff() const { return maxdiff; }
108
109 private:
110 void constructMessages();
111 void findLoopClusters( const FactorGraph &fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars );
112 };
113
114
115 } // end of namespace dai
116
117
118 #endif