Fixed example_imagesegmentation by adding InfAlg::setMaxIter(size_t)
[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) 2006-2010 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 /// \idea Implement more general region graphs and corresponding Generalized Belief Propagation updates as described in [\ref YFW05].
15 /// \todo Use ClusterGraph instead of a vector<VarSet> for speed.
16 /// \todo Optimize this code for large factor graphs.
17 /// \todo Implement GBP parent-child algorithm.
18
19
20 #ifndef __defined_libdai_hak_h
21 #define __defined_libdai_hak_h
22
23
24 #include <string>
25 #include <dai/daialg.h>
26 #include <dai/regiongraph.h>
27 #include <dai/enum.h>
28 #include <dai/properties.h>
29
30
31 namespace dai {
32
33
34 /// Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [\ref HAK03]
35 class HAK : public DAIAlgRG {
36 private:
37 /// Outer region beliefs
38 std::vector<Factor> _Qa;
39 /// Inner region beliefs
40 std::vector<Factor> _Qb;
41 /// Messages from outer to inner regions
42 std::vector<std::vector<Factor> > _muab;
43 /// Messages from inner to outer regions
44 std::vector<std::vector<Factor> > _muba;
45 /// Maximum difference encountered so far
46 Real _maxdiff;
47 /// Number of iterations needed
48 size_t _iters;
49
50 public:
51 /// Parameters for HAK
52 struct Properties {
53 /// Enumeration of possible cluster choices
54 /** The following cluster choices are defined:
55 * - MIN minimal clusters, i.e., one outer region for each maximal factor
56 * - DELTA one outer region for each variable and its Markov blanket
57 * - LOOP one cluster for each loop of length at most \a Properties::loopdepth, and in addition one cluster for each maximal factor
58 * - BETHE Bethe approximation (one outer region for each maximal factor, inner regions are single variables)
59 */
60 DAI_ENUM(ClustersType,MIN,BETHE,DELTA,LOOP);
61
62 /// Enumeration of possible message initializations
63 DAI_ENUM(InitType,UNIFORM,RANDOM);
64
65 /// Verbosity (amount of output sent to stderr)
66 size_t verbose;
67
68 /// Maximum number of iterations
69 size_t maxiter;
70
71 /// Maximum time (in seconds)
72 double maxtime;
73
74 /// Tolerance for convergence test
75 Real tol;
76
77 /// Damping constant (0.0 means no damping, 1.0 is maximum damping)
78 Real damping;
79
80 /// How to choose the outer regions
81 ClustersType clusters;
82
83 /// How to initialize the messages
84 InitType init;
85
86 /// Use single-loop (GBP) or double-loop (HAK)
87 bool doubleloop;
88
89 /// Depth of loops (only relevant for \a clusters == \c ClustersType::LOOP)
90 size_t loopdepth;
91 } props;
92
93 /// Name of this inference algorithm
94 static const char *Name;
95
96 public:
97 /// \name Constructors/destructors
98 //@{
99 /// Default constructor
100 HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), _maxdiff(0.0), _iters(0U), props() {}
101
102 /// Construct from FactorGraph \a fg and PropertySet \a opts
103 /** \param fg Factor graph.
104 * \param opts Parameters @see Properties
105 */
106 HAK( const FactorGraph &fg, const PropertySet &opts );
107
108 /// Construct from RegionGraph \a rg and PropertySet \a opts
109 HAK( const RegionGraph &rg, const PropertySet &opts );
110 //@}
111
112
113 /// \name General InfAlg interface
114 //@{
115 virtual HAK* clone() const { return new HAK(*this); }
116 virtual std::string identify() const;
117 virtual Factor belief( const VarSet &vs ) const;
118 virtual std::vector<Factor> beliefs() const;
119 virtual Real logZ() const;
120 virtual void init();
121 virtual void init( const VarSet &vs );
122 virtual Real run();
123 virtual Real maxDiff() const { return _maxdiff; }
124 virtual size_t Iterations() const { return _iters; }
125 virtual void setMaxIter( size_t maxiter ) { props.maxiter = maxiter; }
126 virtual void setProperties( const PropertySet &opts );
127 virtual PropertySet getProperties() const;
128 virtual std::string printProperties() const;
129 //@}
130
131
132 /// \name Additional interface specific for HAK
133 //@{
134 /// Returns reference to message from outer region \a alpha to its \a _beta 'th neighboring inner region
135 Factor & muab( size_t alpha, size_t _beta ) { return _muab[alpha][_beta]; }
136 /// Returns reference to message the \a _beta 'th neighboring inner region of outer region \a alpha to that outer region
137 Factor & muba( size_t alpha, size_t _beta ) { return _muba[alpha][_beta]; }
138 /// Returns belief of outer region \a alpha
139 const Factor& Qa( size_t alpha ) const { return _Qa[alpha]; };
140 /// Returns belief of inner region \a beta
141 const Factor& Qb( size_t beta ) const { return _Qb[beta]; };
142
143 /// Runs single-loop algorithm (algorithm 1 in [\ref HAK03])
144 Real doGBP();
145 /// Runs double-loop algorithm (as described in section 4.2 of [\ref HAK03]), which always convergences
146 Real doDoubleLoop();
147 //@}
148
149 private:
150 /// Helper function for constructors
151 void construct();
152 /// Recursive procedure for finding clusters of variables containing loops of length at most \a length
153 /** \param fg the factor graph
154 * \param allcl the clusters found so far
155 * \param newcl partial candidate cluster
156 * \param root start (and end) point of the loop
157 * \param length number of variables that may be added to \a newcl
158 * \param vars neighboring variables of \a newcl
159 * \return allcl all clusters of variables with loops of length at most \a length passing through root
160 */
161 void findLoopClusters( const FactorGraph &fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars );
162 };
163
164
165 } // end of namespace dai
166
167
168 #endif