fee03868832e3024e06ca7c40e766f1907833d1f
[libdai.git] / include / dai / hak.h
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) 2006-2009 Joris Mooij [joris dot mooij at libdai dot org]
8 * Copyright (C) 2006-2007 Radboud University Nijmegen, The Netherlands
9 */
10
11
12 /// \file
13 /// \brief Defines class HAK, which implements a variant of Generalized Belief Propagation.
14 /// \todo Implement Bethe approximation as a standard region graph choice in HAK.
15 /// \idea Implement more general region graphs and corresponding Generalized Belief Propagation updates as described in [\ref YFW05].
16
17
18 #ifndef __defined_libdai_hak_h
19 #define __defined_libdai_hak_h
20
21
22 #include <string>
23 #include <dai/daialg.h>
24 #include <dai/regiongraph.h>
25 #include <dai/enum.h>
26 #include <dai/properties.h>
27
28
29 namespace dai {
30
31
32 /// Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [\ref HAK03]
33 class HAK : public DAIAlgRG {
34 private:
35 /// Outer region beliefs
36 std::vector<Factor> _Qa;
37 /// Inner region beliefs
38 std::vector<Factor> _Qb;
39 /// Messages from outer to inner regions
40 std::vector<std::vector<Factor> > _muab;
41 /// Messages from inner to outer regions
42 std::vector<std::vector<Factor> > _muba;
43 /// Maximum difference encountered so far
44 Real _maxdiff;
45 /// Number of iterations needed
46 size_t _iters;
47
48 public:
49 /// Parameters for HAK
50 struct Properties {
51 /// Enumeration of possible cluster choices
52 /** The following cluster choices are defined:
53 * - MIN minimal clusters, i.e., one outer region for each maximal factor
54 * - DELTA one outer region for each variable and its Markov blanket
55 * - LOOP one cluster for each loop of length at most \a Properties::loopdepth, and in addition one cluster for each maximal factor
56 */
57 DAI_ENUM(ClustersType,MIN,DELTA,LOOP);
58
59 /// Enumeration of possible message initializations
60 DAI_ENUM(InitType,UNIFORM,RANDOM);
61
62 /// Verbosity (amount of output sent to stderr)
63 size_t verbose;
64
65 /// Maximum number of iterations
66 size_t maxiter;
67
68 /// Tolerance for convergence test
69 Real tol;
70
71 /// Damping constant (0.0 means no damping, 1.0 is maximum damping)
72 Real damping;
73
74 /// How to choose the outer regions
75 ClustersType clusters;
76
77 /// How to initialize the messages
78 InitType init;
79
80 /// Use single-loop (GBP) or double-loop (HAK)
81 bool doubleloop;
82
83 /// Depth of loops (only relevant for \a clusters == \c ClustersType::LOOP)
84 size_t loopdepth;
85 } props;
86
87 /// Name of this inference algorithm
88 static const char *Name;
89
90 public:
91 /// \name Constructors/destructors
92 //@{
93 /// Default constructor
94 HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), _maxdiff(0.0), _iters(0U), props() {}
95
96 /// Construct from FactorGraph \a fg and PropertySet \a opts
97 /** \param opts Parameters @see Properties
98 */
99 HAK( const FactorGraph &fg, const PropertySet &opts );
100
101 /// Construct from RegionGraph \a rg and PropertySet \a opts
102 HAK( const RegionGraph &rg, const PropertySet &opts );
103 //@}
104
105
106 /// \name General InfAlg interface
107 //@{
108 virtual HAK* clone() const { return new HAK(*this); }
109 virtual std::string identify() const;
110 virtual Factor belief( const Var &v ) const;
111 virtual Factor belief( const VarSet &vs ) const;
112 virtual std::vector<Factor> beliefs() const;
113 virtual Real logZ() const;
114 virtual void init();
115 virtual void init( const VarSet &vs );
116 virtual Real run();
117 virtual Real maxDiff() const { return _maxdiff; }
118 virtual size_t Iterations() const { return _iters; }
119 virtual void setProperties( const PropertySet &opts );
120 virtual PropertySet getProperties() const;
121 virtual std::string printProperties() const;
122 //@}
123
124
125 /// \name Additional interface specific for HAK
126 //@{
127 /// Returns reference to message from outer region \a alpha to its \a _beta 'th neighboring inner region
128 Factor & muab( size_t alpha, size_t _beta ) { return _muab[alpha][_beta]; }
129 /// Returns reference to message the \a _beta 'th neighboring inner region of outer region \a alpha to that outer region
130 Factor & muba( size_t alpha, size_t _beta ) { return _muba[alpha][_beta]; }
131 /// Returns belief of outer region \a alpha
132 const Factor& Qa( size_t alpha ) const { return _Qa[alpha]; };
133 /// Returns belief of inner region \a beta
134 const Factor& Qb( size_t beta ) const { return _Qb[beta]; };
135
136 /// Runs single-loop algorithm (algorithm 1 in [\ref HAK03])
137 Real doGBP();
138 /// Runs double-loop algorithm (as described in section 4.2 of [\ref HAK03]), which always convergences
139 Real doDoubleLoop();
140 //@}
141
142 private:
143 /// Helper function for constructors
144 void construct();
145 /// Recursive procedure for finding clusters of variables containing loops of length at most \a length
146 /** \param fg the factor graph
147 * \param allcl the clusters found so far
148 * \param newcl partial candidate cluster
149 * \param root start (and end) point of the loop
150 * \param length number of variables that may be added to \a newcl
151 * \param vars neighboring variables of \a newcl
152 * \return allcl all clusters of variables with loops of length at most \a length passing through root
153 */
154 void findLoopClusters( const FactorGraph &fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars );
155 };
156
157
158 } // end of namespace dai
159
160
161 #endif