-/* Copyright (C) 2006-2008 Joris Mooij [j dot mooij at science dot ru dot nl]
- Radboud University Nijmegen, The Netherlands
-
- This file is part of libDAI.
+/* This file is part of libDAI - http://www.libdai.org/
+ *
+ * libDAI is licensed under the terms of the GNU General Public License version
+ * 2, or (at your option) any later version. libDAI is distributed without any
+ * warranty. See the file COPYING for more details.
+ *
+ * Copyright (C) 2006-2009 Joris Mooij [joris dot mooij at libdai dot org]
+ * Copyright (C) 2006-2007 Radboud University Nijmegen, The Netherlands
+ */
- libDAI is free software; you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 2 of the License, or
- (at your option) any later version.
- libDAI is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with libDAI; if not, write to the Free Software
- Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
-*/
+/// \file
+/// \brief Defines class HAK.
+/// \todo Improve documentation
#ifndef __defined_libdai_hak_h
namespace dai {
-/// HAK provides an implementation of the single and double-loop algorithms by Heskes, Albers and Kappen
+/// Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen
+/** \todo Optimize HAK with precalculated indices, similarly to BP.
+ */
class HAK : public DAIAlgRG {
- protected:
+ private:
std::vector<Factor> _Qa;
std::vector<Factor> _Qb;
std::vector<std::vector<Factor> > _muab;
std::vector<std::vector<Factor> > _muba;
+ /// Maximum difference encountered so far
+ double _maxdiff;
+ /// Number of iterations needed
+ size_t _iters;
public:
+ /// Parameters of this inference algorithm
struct Properties {
+ /// Enumeration of possible cluster choices
+ DAI_ENUM(ClustersType,MIN,DELTA,LOOP)
+
+ /// Verbosity
size_t verbose;
+
+ /// Maximum number of iterations
size_t maxiter;
+
+ /// Tolerance
double tol;
- DAI_ENUM(ClustersType,MIN,DELTA,LOOP)
+
+ /// Damping constant
+ double damping;
+
+ /// How to choose the clusters
ClustersType clusters;
+
+ /// Use single-loop (GBP) or double-loop (HAK)
bool doubleloop;
+
+ /// Depth of loops (only relevant for clusters == ClustersType::LOOP)
size_t loopdepth;
} props;
- double maxdiff;
-
+
+ /// Name of this inference algorithm
+ static const char *Name;
+
public:
/// Default constructor
- HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), props(), maxdiff() {}
-
- /// Copy constructor
- HAK(const HAK & x) : DAIAlgRG(x), _Qa(x._Qa), _Qb(x._Qb), _muab(x._muab), _muba(x._muba), props(x.props), maxdiff(x.maxdiff) {}
-
- /// Clone function
- HAK* clone() const { return new HAK(*this); }
-
- /// Construct from RegionGraph
- HAK(const RegionGraph & rg, const PropertySet &opts);
-
- /// Construct from RactorGraph using "clusters" option
- HAK(const FactorGraph & fg, const PropertySet &opts);
-
- /// Assignment operator
- HAK & operator=(const HAK & x) {
- if( this != &x ) {
- DAIAlgRG::operator=(x);
- _Qa = x._Qa;
- _Qb = x._Qb;
- _muab = x._muab;
- _muba = x._muba;
- props = x.props;
- maxdiff = x.maxdiff;
- }
- return *this;
- }
-
- static const char *Name;
+ HAK() : DAIAlgRG(), _Qa(), _Qb(), _muab(), _muba(), _maxdiff(0.0), _iters(0U), props() {}
+
+ /// Construct from FactorGraph fg and PropertySet opts
+ HAK( const FactorGraph &fg, const PropertySet &opts );
+
+ /// Construct from RegionGraph rg and PropertySet opts
+ HAK( const RegionGraph &rg, const PropertySet &opts );
+
+ /// @name General InfAlg interface
+ //@{
+ virtual HAK* clone() const { return new HAK(*this); }
+ virtual std::string identify() const;
+ virtual Factor belief( const Var &n ) const;
+ virtual Factor belief( const VarSet &ns ) const;
+ virtual std::vector<Factor> beliefs() const;
+ virtual Real logZ() const;
+ virtual void init();
+ virtual void init( const VarSet &ns );
+ virtual double run();
+ virtual double maxDiff() const { return _maxdiff; }
+ virtual size_t Iterations() const { return _iters; }
+ //@}
+
+
+ /// @name Additional interface specific for HAK
+ //@{
Factor & muab( size_t alpha, size_t _beta ) { return _muab[alpha][_beta]; }
Factor & muba( size_t alpha, size_t _beta ) { return _muba[alpha][_beta]; }
const Factor& Qa( size_t alpha ) const { return _Qa[alpha]; };
double doGBP();
double doDoubleLoop();
- double run();
- void init();
- std::string identify() const;
- Factor belief( const Var &n ) const;
- Factor belief( const VarSet &ns ) const;
- std::vector<Factor> beliefs() const;
- Real logZ () const;
-
- void init( const VarSet &ns );
- void undoProbs( const VarSet &ns ) { RegionGraph::undoProbs( ns ); init( ns ); }
- void setProperties( const PropertySet &opts );
- PropertySet getProperties() const;
- std::string printProperties() const;
- double maxDiff() const { return maxdiff; }
+ //@}
private:
void constructMessages();
void findLoopClusters( const FactorGraph &fg, std::set<VarSet> &allcl, VarSet newcl, const Var & root, size_t length, VarSet vars );
+
+ void setProperties( const PropertySet &opts );
+ PropertySet getProperties() const;
+ std::string printProperties() const;
};
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