include/dai/hak.h

   1 /*  This file is part of libDAI - http://www.libdai.org/
   2  *
   3  *  Copyright (c) 2006-2011, The libDAI authors. All rights reserved.
   4  *
   5  *  Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.
   6  */
   7
   8
   9 /// \file
  10 /// \brief Defines class HAK, which implements a variant of Generalized Belief Propagation.
  11 /// \idea Implement more general region graphs and corresponding Generalized Belief Propagation updates as described in [\ref YFW05].
  12 /// \todo Use ClusterGraph instead of a vector<VarSet> for speed.
  13 /// \todo Optimize this code for large factor graphs.
  14 /// \todo Implement GBP parent-child  algorithm.
  15
  16
  17 #include <dai/dai_config.h>
  18 #ifdef DAI_WITH_HAK
  19
  20
  21 #ifndef __defined_libdai_hak_h
  22 #define __defined_libdai_hak_h
  23
  24
  25 #include <string>
  26 #include <dai/daialg.h>
  27 #include <dai/regiongraph.h>
  28 #include <dai/enum.h>
  29 #include <dai/properties.h>
  30
  31
  32 namespace dai {
  33
  34
  35 /// Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [\ref HAK03]
  36 class HAK : public DAIAlgRG {
  37     private:
  38         /// Outer region beliefs
  39         std::vector<Factor>                _Qa;
  40         /// Inner region beliefs
  41         std::vector<Factor>                _Qb;
  42         /// Messages from outer to inner regions
  43         std::vector<std::vector<Factor> >  _muab;
  44         /// Messages from inner to outer regions
  45         std::vector<std::vector<Factor> >  _muba;
  46         /// Maximum difference encountered so far
  47         Real _maxdiff;
  48         /// Number of iterations needed
  49         size_t _iters;
  50
  51     public:
  52         /// Parameters for HAK
  53         struct Properties {
  54             /// Enumeration of possible cluster choices
  55             /** The following cluster choices are defined:
  56              *   - MIN minimal clusters, i.e., one outer region for each maximal factor
  57              *   - DELTA one outer region for each variable and its Markov blanket
  58              *   - LOOP one cluster for each loop of length at most \a Properties::loopdepth, and in addition one cluster for each maximal factor
  59              *   - BETHE Bethe approximation (one outer region for each maximal factor, inner regions are single variables)
  60              */
  61             DAI_ENUM(ClustersType,MIN,BETHE,DELTA,LOOP);
  62
  63             /// Enumeration of possible message initializations
  64             DAI_ENUM(InitType,UNIFORM,RANDOM);
  65
  66             /// Verbosity (amount of output sent to stderr)
  67             size_t verbose;
  68
  69             /// Maximum number of iterations
  70             size_t maxiter;
  71
  72             /// Maximum time (in seconds)
  73             double maxtime;
  74
  75             /// Tolerance for convergence test
  76             Real tol;
  77
  78             /// Damping constant (0.0 means no damping, 1.0 is maximum damping)
  79             Real damping;
  80
  81             /// How to choose the outer regions
  82             ClustersType clusters;
  83
  84             /// How to initialize the messages
  85             InitType init;
  86
  87             /// Use single-loop (GBP) or double-loop (HAK)
  88             bool doubleloop;
  89
  90             /// Depth of loops (only relevant for \a clusters == \c ClustersType::LOOP)
  91             size_t loopdepth;
  92         } props;
  93
  94     public:
  95     /// \name Constructors/destructors
  96     //@{
  97         /// Default constructor
  98         HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), _maxdiff(0.0), _iters(0U), props() {}
  99
 100         /// Construct from FactorGraph \a fg and PropertySet \a opts
 101         /** \param fg Factor graph.
 102          *  \param opts Parameters @see Properties
 103          */
 104         HAK( const FactorGraph &fg, const PropertySet &opts );
 105
 106         /// Construct from RegionGraph \a rg and PropertySet \a opts
 107         HAK( const RegionGraph &rg, const PropertySet &opts );
 108     //@}
 109
 110
 111     /// \name General InfAlg interface
 112     //@{
 113         virtual HAK* clone() const { return new HAK(*this); }
 114         virtual HAK* construct( const FactorGraph &fg, const PropertySet &opts ) const { return new HAK( fg, opts ); }
 115         virtual std::string name() const { return "HAK"; }
 116         virtual Factor belief( const VarSet &vs ) const;
 117         virtual std::vector<Factor> beliefs() const;
 118         virtual Real logZ() const;
 119         virtual void init();
 120         virtual void init( const VarSet &vs );
 121         virtual Real run();
 122         virtual Real maxDiff() const { return _maxdiff; }
 123         virtual size_t Iterations() const { return _iters; }
 124         virtual void setMaxIter( size_t maxiter ) { props.maxiter = maxiter; }
 125         virtual void setProperties( const PropertySet &opts );
 126         virtual PropertySet getProperties() const;
 127         virtual std::string printProperties() const;
 128     //@}
 129
 130
 131     /// \name Additional interface specific for HAK
 132     //@{
 133         /// Returns reference to message from outer region \a alpha to its \a _beta 'th neighboring inner region
 134         Factor & muab( size_t alpha, size_t _beta ) { return _muab[alpha][_beta]; }
 135         /// Returns reference to message the \a _beta 'th neighboring inner region of outer region \a alpha to that outer region
 136         Factor & muba( size_t alpha, size_t _beta ) { return _muba[alpha][_beta]; }
 137         /// Returns belief of outer region \a alpha
 138         const Factor& Qa( size_t alpha ) const { return _Qa[alpha]; };
 139         /// Returns belief of inner region \a beta
 140         const Factor& Qb( size_t beta ) const { return _Qb[beta]; };
 141
 142         /// Runs single-loop algorithm (algorithm 1 in [\ref HAK03])
 143         Real doGBP();
 144         /// Runs double-loop algorithm (as described in section 4.2 of [\ref HAK03]), which always convergences
 145         Real doDoubleLoop();
 146     //@}
 147
 148     private:
 149         /// Helper function for constructors
 150         void construct();
 151         /// Recursive procedure for finding clusters of variables containing loops of length at most \a length
 152         /** \param fg the factor graph
 153          *  \param allcl the clusters found so far
 154          *  \param newcl partial candidate cluster
 155          *  \param root start (and end) point of the loop
 156          *  \param length number of variables that may be added to \a newcl
 157          *  \param vars neighboring variables of \a newcl
 158          *  \return allcl all clusters of variables with loops of length at most \a length passing through root
 159          */
 160         void findLoopClusters( const FactorGraph &fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars );
 161 };
 162
 163
 164 } // end of namespace dai
 165
 166
 167 #endif
 168
 169
 170 #endif