Exact inference algorithm using junction tree. More...

#include <dai/jtree.h>

Inheritance diagram for dai::JTree:

Classes
struct	Properties
	Parameters for JTree. More...

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

	JTree (const FactorGraph &fg, const PropertySet &opts, bool automatic=true)
	Construct from FactorGraph fg and PropertySet opts. More...

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

virtual JTree *	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...

std::vector< size_t >	findMaximum () const

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

virtual void	init (const VarSet &)
	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	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 JTree
void	construct (const FactorGraph &fg, const std::vector< VarSet > &cl, bool verify=false)
	Constructs a junction tree based on the cliques cl (corresponding to some elimination sequence). More...

void	GenerateJT (const FactorGraph &fg, const std::vector< VarSet > &cl)
	Constructs a junction tree based on the cliques cl (corresponding to some elimination sequence). More...

const Factor &	message (size_t alpha, size_t _beta) const
	Returns constant reference to the message from outer region alpha to its _beta 'th neighboring inner region. More...

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

void	runHUGIN ()
	Runs junction tree algorithm using HUGIN (message-free) updates. More...

void	runShaferShenoy ()
	Runs junction tree algorithm using Shafer-Shenoy updates. More...

size_t	findEfficientTree (const VarSet &vs, RootedTree &Tree, size_t PreviousRoot=(size_t)-1) const
	Finds an efficient subtree for calculating the marginal of the variables in vs. More...

Factor	calcMarginal (const VarSet &vs)
	Calculates the marginal of a set of variables (using cutset conditioning, if necessary) 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 void	setMaxIter (size_t)
	Sets maximum number of iterations (one iteration passes over the complete factorgraph). More...

Public Attributes
RootedTree	RTree
	The junction tree (stored as a rooted tree) More...

std::vector< Factor >	Qa
	Outer region beliefs. More...

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

struct dai::JTree::Properties	props

Private Attributes
std::vector< std::vector< Factor > >	_mes
	Stores the messages. More...

Real	_logZ
	Stores the logarithm of the partition sum. More...

Related Functions
(Note that these are not member functions.)
std::pair< size_t, BigInt >	boundTreewidth (const FactorGraph &fg, greedyVariableElimination::eliminationCostFunction fn, size_t maxStates=0)
	Calculates upper bound to the treewidth of a FactorGraph, using the specified heuristic. More...

Detailed Description

Exact inference algorithm using junction tree.

The junction tree algorithm uses message passing on a junction tree to calculate exact marginal probability distributions ("beliefs") for specified cliques (outer regions) and separators (intersections of pairs of cliques).

There are two variants, the sum-product algorithm (corresponding to finite temperature) and the max-product algorithm (corresponding to zero temperature).

Examples:: example.cpp.

Constructor & Destructor Documentation

dai::JTree::JTree ( )

inline

Default constructor.

dai::JTree::JTree	(	const FactorGraph &	fg,
		const PropertySet &	opts,
		bool	automatic = `true`
	)

Construct from FactorGraph fg and PropertySet opts.

Parameters

fg	factor graph
opts	Parameters

See also: Properties

Parameters

automatic if true, construct the junction tree automatically, using the heuristic in opts['heuristic'].

Member Function Documentation

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

inlinevirtual

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

Implements dai::InfAlg.

Reimplemented in dai::TreeEP.

virtual JTree* dai::JTree::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.

Reimplemented in dai::TreeEP.

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

inlinevirtual

Returns the name of the algorithm.

Implements dai::InfAlg.

Reimplemented in dai::TreeEP.

Factor dai::JTree::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.

Examples:: example.cpp.

vector< Factor > dai::JTree::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::JTree::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.

Reimplemented in dai::TreeEP.

Examples:: example.cpp.

std::vector< size_t > dai::JTree::findMaximum ( ) const

virtual

Precondition: Assumes that run() has been called and that props.inference == MAXPROD

Reimplemented from dai::InfAlg.

Examples:: example.cpp.

virtual void dai::JTree::init ( )

inlinevirtual

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.

Reimplemented in dai::TreeEP.

Examples:: example.cpp.

virtual void dai::JTree::init ( const VarSet & vs )

inlinevirtual

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.

Reimplemented in dai::TreeEP.

Real dai::JTree::run ( )

virtual

Runs the approximate inference algorithm.

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

Implements dai::InfAlg.

Reimplemented in dai::TreeEP.

Examples:: example.cpp.

virtual Real dai::JTree::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.

Reimplemented in dai::TreeEP.

virtual size_t dai::JTree::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.

Reimplemented in dai::TreeEP.

void dai::JTree::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.

Reimplemented in dai::TreeEP.

PropertySet dai::JTree::getProperties ( ) const

virtual

Returns parameters of this inference algorithm converted into a PropertySet.

Implements dai::InfAlg.

Reimplemented in dai::TreeEP.

string dai::JTree::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.

Reimplemented in dai::TreeEP.

void dai::JTree::construct	(	const FactorGraph &	fg,
		const std::vector< VarSet > &	cl,
		bool	verify = `false`
	)

Constructs a junction tree based on the cliques cl (corresponding to some elimination sequence).

First, constructs a weighted graph, where the nodes are the elements of cl, and each edge is weighted with the cardinality of the intersection of the state spaces of the nodes. Then, a maximal spanning tree for this weighted graph is calculated. Subsequently, a corresponding region graph is built:

the outer regions correspond with the cliques and have counting number 1;
the inner regions correspond with the seperators, i.e., the intersections of two cliques that are neighbors in the spanning tree, and have counting number -1 (except empty ones, which have counting number 0);
inner and outer regions are connected by an edge if the inner region is a seperator for the outer region. Finally, Beliefs are constructed. If verify == true, checks whether each factor is subsumed by a clique.

void dai::JTree::GenerateJT	(	const FactorGraph &	fg,
		const std::vector< VarSet > &	cl
	)

Constructs a junction tree based on the cliques cl (corresponding to some elimination sequence).

Invokes construct() and then constructs messages.

See also: construct()

const Factor& dai::JTree::message	(	size_t	alpha,
		size_t	_beta
	)		const

inline

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

Factor& dai::JTree::message	(	size_t	alpha,
		size_t	_beta
	)

inline

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

void dai::JTree::runHUGIN ( )

Runs junction tree algorithm using HUGIN (message-free) updates.

Note: The initial messages may be arbitrary; actually they are not used at all.

void dai::JTree::runShaferShenoy ( )

Runs junction tree algorithm using Shafer-Shenoy updates.

Note: The initial messages may be arbitrary.

size_t dai::JTree::findEfficientTree	(	const VarSet &	vs,
		RootedTree &	Tree,
		size_t	PreviousRoot = `(size_t)-1`
	)		const

Finds an efficient subtree for calculating the marginal of the variables in vs.

First, the current junction tree is reordered such that it gets as root the clique that has maximal state space overlap with vs. Then, the minimal subtree (starting from the root) is identified that contains all the variables in vs and also the outer region with index PreviousRoot (if specified). Finally, the current junction tree is reordered such that this minimal subtree comes before the other edges, and the size of the minimal subtree is returned.

Factor dai::JTree::calcMarginal ( const VarSet & vs )

Calculates the marginal of a set of variables (using cutset conditioning, if necessary)

Precondition: assumes that run() has been called already

Friends And Related Function Documentation

std::pair< size_t, BigInt > boundTreewidth	(	const FactorGraph &	fg,
		greedyVariableElimination::eliminationCostFunction	fn,
		size_t	maxStates = `0`
	)

Parameters

fg	the factor graph for which the treewidth should be bounded
fn	the heuristic cost function used for greedy variable elimination
maxStates	maximum total number of states in outer regions of junction tree (0 means no limit)

Exceptions

OUT_OF_MEMORY if the total number of states becomes larger than maxStates

Returns: a pair (number of variables in largest clique, number of states in largest clique)

Member Data Documentation

std::vector<std::vector<Factor> > dai::JTree::_mes

private

Stores the messages.

Real dai::JTree::_logZ

private

Stores the logarithm of the partition sum.

RootedTree dai::JTree::RTree

The junction tree (stored as a rooted tree)

std::vector<Factor> dai::JTree::Qa

Outer region beliefs.

std::vector<Factor> dai::JTree::Qb

Inner region beliefs.

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

include/dai/jtree.h
src/jtree.cpp

Classes

Public Member Functions

Public Attributes

Private Attributes

Related Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation