Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03]. More...

#include <dai/hak.h>

Inheritance diagram for dai::HAK:

Classes
struct	Properties
	Parameters for HAK. More...

Public Member Functions
Constructors/destructors
	HAK ()
	Default constructor. More...

	HAK (const FactorGraph &fg, const PropertySet &opts)
	Construct from FactorGraph fg and PropertySet opts. More...

	HAK (const RegionGraph &rg, const PropertySet &opts)
	Construct from RegionGraph rg and PropertySet opts. More...

General InfAlg interface
virtual HAK *	clone () const
	Returns a pointer to a new, cloned copy of `*this` (i.e., virtual copy constructor) More...

virtual HAK *	construct (const FactorGraph &fg, const PropertySet &opts) const
	Returns a pointer to a newly constructed inference algorithm. More...

virtual std::string	name () const
	Returns the name of the algorithm. More...

virtual Factor	belief (const VarSet &vs) const
	Returns the (approximate) marginal probability distribution of a set of variables. More...

virtual std::vector< Factor >	beliefs () const
	Returns all beliefs (approximate marginal probability distributions) calculated by the algorithm. More...

virtual Real	logZ () const
	Returns the logarithm of the (approximated) partition sum (normalizing constant of the factor graph). More...

virtual void	init ()
	Initializes all data structures of the approximate inference algorithm. More...

virtual void	init (const VarSet &vs)
	Initializes all data structures corresponding to some set of variables. More...

virtual Real	run ()
	Runs the approximate inference algorithm. More...

virtual Real	maxDiff () const
	Returns maximum difference between single variable beliefs in the last iteration. More...

virtual size_t	Iterations () const
	Returns number of iterations done (one iteration passes over the complete factorgraph). More...

virtual void	setMaxIter (size_t maxiter)
	Sets maximum number of iterations (one iteration passes over the complete factorgraph). More...

virtual void	setProperties (const PropertySet &opts)
	Set parameters of this inference algorithm. More...

virtual PropertySet	getProperties () const
	Returns parameters of this inference algorithm converted into a PropertySet. More...

virtual std::string	printProperties () const
	Returns parameters of this inference algorithm formatted as a string in the format "[key1=val1,key2=val2,...,keyn=valn]". More...

Additional interface specific for HAK
Factor &	muab (size_t alpha, size_t _beta)
	Returns reference to message from outer region alpha to its _beta 'th neighboring inner region. More...

Factor &	muba (size_t alpha, size_t _beta)
	Returns reference to message the _beta 'th neighboring inner region of outer region alpha to that outer region. More...

const Factor &	Qa (size_t alpha) const
	Returns belief of outer region alpha. More...

const Factor &	Qb (size_t beta) const
	Returns belief of inner region beta. More...

Real	doGBP ()
	Runs single-loop algorithm (algorithm 1 in [HAK03]) More...

Real	doDoubleLoop ()
	Runs double-loop algorithm (as described in section 4.2 of [HAK03]), which always convergences. More...

Public Member Functions inherited from dai::DAIAlg< GRM >
	DAIAlg ()
	Default constructor. More...

	DAIAlg (const GRM &grm)
	Construct from GRM. More...

FactorGraph &	fg ()
	Returns reference to underlying FactorGraph. More...

const FactorGraph &	fg () const
	Returns constant reference to underlying FactorGraph. More...

void	clamp (size_t i, size_t x, bool backup=false)
	Clamp variable with index i to value x (i.e. multiply with a Kronecker delta $\delta_{x_i, x}$ ) More...

void	makeCavity (size_t i, bool backup=false)
	Sets all factors interacting with variable with index i to one. More...

void	makeRegionCavity (std::vector< size_t > facInds, bool backup)
	Sets all factors indicated by facInds to one. More...

void	backupFactor (size_t I)
	Make a backup copy of factor I. More...

void	backupFactors (const VarSet &vs)
	Make backup copies of all factors involving the variables in vs. More...

void	restoreFactor (size_t I)
	Restore factor I from its backup copy. More...

void	restoreFactors (const VarSet &vs)
	Restore the factors involving the variables in vs from their backup copies. More...

void	restoreFactors ()
	Restore all factors from their backup copies. More...

Public Member Functions inherited from dai::InfAlg
virtual	~InfAlg ()
	Virtual destructor (needed because this class contains virtual functions) More...

virtual std::string	identify () const
	Identifies itself for logging purposes. More...

virtual Factor	belief (const Var &v) const
	Returns the (approximate) marginal probability distribution of a variable. More...

virtual Factor	beliefV (size_t i) const
	Returns the (approximate) marginal probability distribution of the variable with index i. More...

virtual Factor	beliefF (size_t I) const
	Returns the (approximate) marginal probability distribution of the variables on which factor I depends. More...

virtual std::vector< size_t >	findMaximum () const
	Calculates the joint state of all variables that has maximum probability. More...

Public Attributes
struct dai::HAK::Properties	props

Private Member Functions
void	construct ()
	Helper function for constructors. More...

void	findLoopClusters (const FactorGraph &fg, std::set< VarSet > &allcl, VarSet newcl, const Var &root, size_t length, VarSet vars)
	Recursive procedure for finding clusters of variables containing loops of length at most length. More...

Private Attributes
std::vector< Factor >	_Qa
	Outer region beliefs. More...

std::vector< Factor >	_Qb
	Inner region beliefs. More...

std::vector< std::vector< Factor > >	_muab
	Messages from outer to inner regions. More...

std::vector< std::vector< Factor > >	_muba
	Messages from inner to outer regions. More...

Real	_maxdiff
	Maximum difference encountered so far. More...

size_t	_iters
	Number of iterations needed. More...

Detailed Description

Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03].

Constructor & Destructor Documentation

dai::HAK::HAK ( )

inline

Default constructor.

dai::HAK::HAK	(	const FactorGraph &	fg,
		const PropertySet &	opts
	)

Construct from FactorGraph fg and PropertySet opts.

Parameters

fg	Factor graph.
opts	Parameters

See also: Properties

dai::HAK::HAK	(	const RegionGraph &	rg,
		const PropertySet &	opts
	)

Construct from RegionGraph rg and PropertySet opts.

Member Function Documentation

virtual HAK* dai::HAK::clone ( ) const

inlinevirtual

Returns a pointer to a new, cloned copy of *this (i.e., virtual copy constructor)

Implements dai::InfAlg.

virtual HAK* dai::HAK::construct	(	const FactorGraph &	fg,
		const PropertySet &	opts
	)		const

inlinevirtual

Returns a pointer to a newly constructed inference algorithm.

Parameters

fg	Factor graph on which to perform the inference algorithm;
opts	Parameters passed to constructor of inference algorithm;

Implements dai::InfAlg.

virtual std::string dai::HAK::name ( ) const

inlinevirtual

Returns the name of the algorithm.

Implements dai::InfAlg.

Factor dai::HAK::belief ( const VarSet & vs ) const

virtual

Returns the (approximate) marginal probability distribution of a set of variables.

Note: Before this method is called, run() should have been called.

Exceptions

NOT_IMPLEMENTED	if not implemented/supported.
BELIEF_NOT_AVAILABLE	if the requested belief cannot be calculated with this algorithm.

Implements dai::InfAlg.

vector< Factor > dai::HAK::beliefs ( ) const

virtual

Returns all beliefs (approximate marginal probability distributions) calculated by the algorithm.

Note: Before this method is called, run() should have been called.

Implements dai::InfAlg.

Real dai::HAK::logZ ( ) const

virtual

Returns the logarithm of the (approximated) partition sum (normalizing constant of the factor graph).

Note: Before this method is called, run() should have been called.

Exceptions

NOT_IMPLEMENTED if not implemented/supported

Implements dai::InfAlg.

void dai::HAK::init ( )

virtual

Initializes all data structures of the approximate inference algorithm.

Note: This method should be called at least once before run() is called.

Implements dai::InfAlg.

void dai::HAK::init ( const VarSet & vs )

virtual

Initializes all data structures corresponding to some set of variables.

This method can be used to do a partial initialization after a part of the factor graph has changed. Instead of initializing all data structures, it only initializes those involving the variables in vs.

Exceptions

NOT_IMPLEMENTED if not implemented/supported

Implements dai::InfAlg.

Real dai::HAK::run ( )

virtual

Runs the approximate inference algorithm.

Note: Before run() is called the first time, init() should have been called.

Implements dai::InfAlg.

virtual Real dai::HAK::maxDiff ( ) const

inlinevirtual

Returns maximum difference between single variable beliefs in the last iteration.

Exceptions

NOT_IMPLEMENTED if not implemented/supported

Reimplemented from dai::InfAlg.

virtual size_t dai::HAK::Iterations ( ) const

inlinevirtual

Returns number of iterations done (one iteration passes over the complete factorgraph).

Exceptions

NOT_IMPLEMENTED if not implemented/supported

Reimplemented from dai::InfAlg.

virtual void dai::HAK::setMaxIter ( size_t )

inlinevirtual

Sets maximum number of iterations (one iteration passes over the complete factorgraph).

Exceptions

NOT_IMPLEMENTED if not implemented/supported

Reimplemented from dai::InfAlg.

void dai::HAK::setProperties ( const PropertySet & opts )

virtual

Set parameters of this inference algorithm.

The parameters are set according to the PropertySet opts. The values can be stored either as std::string or as the type of the corresponding MF::props member.

Implements dai::InfAlg.

PropertySet dai::HAK::getProperties ( ) const

virtual

Returns parameters of this inference algorithm converted into a PropertySet.

Implements dai::InfAlg.

string dai::HAK::printProperties ( ) const

virtual

Returns parameters of this inference algorithm formatted as a string in the format "[key1=val1,key2=val2,...,keyn=valn]".

Implements dai::InfAlg.

Factor& dai::HAK::muab	(	size_t	alpha,
		size_t	_beta
	)

inline

Returns reference to message from outer region alpha to its _beta 'th neighboring inner region.

Factor& dai::HAK::muba	(	size_t	alpha,
		size_t	_beta
	)

inline

Returns reference to message the _beta 'th neighboring inner region of outer region alpha to that outer region.

const Factor& dai::HAK::Qa ( size_t alpha ) const

inline

Returns belief of outer region alpha.

const Factor& dai::HAK::Qb ( size_t beta ) const

inline

Returns belief of inner region beta.

Real dai::HAK::doGBP ( )

Runs single-loop algorithm (algorithm 1 in [HAK03])

Real dai::HAK::doDoubleLoop ( )

Runs double-loop algorithm (as described in section 4.2 of [HAK03]), which always convergences.

void dai::HAK::construct ( )

private

Helper function for constructors.

void dai::HAK::findLoopClusters	(	const FactorGraph &	fg,
		std::set< VarSet > &	allcl,
		VarSet	newcl,
		const Var &	root,
		size_t	length,
		VarSet	vars
	)

private

Recursive procedure for finding clusters of variables containing loops of length at most length.

Parameters

fg	the factor graph
allcl	the clusters found so far
newcl	partial candidate cluster
root	start (and end) point of the loop
length	number of variables that may be added to newcl
vars	neighboring variables of newcl

Returns: allcl all clusters of variables with loops of length at most length passing through root

Member Data Documentation

std::vector<Factor> dai::HAK::_Qa

private

Outer region beliefs.

std::vector<Factor> dai::HAK::_Qb

private

Inner region beliefs.

std::vector<std::vector<Factor> > dai::HAK::_muab

private

Messages from outer to inner regions.

std::vector<std::vector<Factor> > dai::HAK::_muba

private

Messages from inner to outer regions.

Real dai::HAK::_maxdiff

private

Maximum difference encountered so far.

size_t dai::HAK::_iters

private

Number of iterations needed.

The documentation for this class was generated from the following files:

include/dai/hak.h
src/hak.cpp

Classes

Public Member Functions

Public Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation