libDAI
Classes | Public Attributes | Private Member Functions | Private Attributes
dai::MF Class Reference

Approximate inference algorithm "Mean Field". More...

#include <dai/mf.h>

Inheritance diagram for dai::MF:
dai::DAIAlg< GRM > dai::InfAlg

List of all members.

Classes

struct  Properties
 Parameters for MF. More...

Public Member Functions

Constructors/destructors
 MF ()
 Default constructor.
 MF (const FactorGraph &fg, const PropertySet &opts)
 Construct from FactorGraph fg and PropertySet opts.
General InfAlg interface
virtual MFclone () const
 Returns a pointer to a new, cloned copy of *this (i.e., virtual copy constructor)
virtual MFconstruct (const FactorGraph &fg, const PropertySet &opts) const
 Returns a pointer to a newly constructed inference algorithm.
virtual std::string name () const
 Returns the name of the algorithm.
virtual Factor belief (const Var &v) const
 Returns the (approximate) marginal probability distribution of a variable.
virtual Factor belief (const VarSet &vs) const
 Returns the (approximate) marginal probability distribution of a set of variables.
virtual Factor beliefV (size_t i) const
 Returns the (approximate) marginal probability distribution of the variable with index i.
virtual std::vector< Factorbeliefs () const
 Returns all beliefs (approximate marginal probability distributions) calculated by the algorithm.
virtual Real logZ () const
 Returns the logarithm of the (approximated) partition sum (normalizing constant of the factor graph).
virtual void init ()
 Initializes all data structures of the approximate inference algorithm.
virtual void init (const VarSet &ns)
 Initializes all data structures corresponding to some set of variables.
virtual Real run ()
 Runs the approximate inference algorithm.
virtual Real maxDiff () const
 Returns maximum difference between single variable beliefs in the last iteration.
virtual size_t Iterations () const
 Returns number of iterations done (one iteration passes over the complete factorgraph).
virtual void setMaxIter (size_t maxiter)
 Sets maximum number of iterations (one iteration passes over the complete factorgraph).
virtual void setProperties (const PropertySet &opts)
 Set parameters of this inference algorithm.
virtual PropertySet getProperties () const
 Returns parameters of this inference algorithm converted into a PropertySet.
virtual std::string printProperties () const
 Returns parameters of this inference algorithm formatted as a string in the format "[key1=val1,key2=val2,...,keyn=valn]".

Public Attributes

struct dai::MF::Properties props

Private Member Functions

void construct ()
 Helper function for constructors.
Factor calcNewBelief (size_t i)
 Calculates an updated belief of variable i.

Private Attributes

std::vector< Factor_beliefs
 Current approximations of single variable marginals.
Real _maxdiff
 Maximum difference encountered so far.
size_t _iters
 Number of iterations needed.

Detailed Description

Approximate inference algorithm "Mean Field".

The Mean Field algorithm iteratively calculates approximations of single variable marginals (beliefs). The update equation for a single belief $b_i$ is given by:

\[ b_i^{\mathrm{new}}(x_i) \propto \prod_{I\in N_i} \exp \left( \sum_{x_{N_I \setminus \{i\}}} \log f_I(x_I) \prod_{j \in N_I \setminus \{i\}} b_j(x_j) \right) \]

for naive mean field and by

\[ b_i^{\mathrm{new}}(x_i) \propto \prod_{I\in N_i} \left( \sum_{x_{N_I \setminus \{i\}}} f_I(x_I) \prod_{j \in N_I \setminus \{i\}} b_j(x_j) \right) \]

for hard-spin mean field. These update equations are performed for all variables until convergence.


Constructor & Destructor Documentation

dai::MF::MF ( ) [inline]

Default constructor.

dai::MF::MF ( const FactorGraph fg,
const PropertySet opts 
) [inline]

Construct from FactorGraph fg and PropertySet opts.

Parameters:
fgFactor graph.
optsParameters
See also:
Properties

Member Function Documentation

virtual MF* dai::MF::clone ( ) const [inline, virtual]

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

Implements dai::InfAlg.

virtual MF* dai::MF::construct ( const FactorGraph fg,
const PropertySet opts 
) const [inline, virtual]

Returns a pointer to a newly constructed inference algorithm.

Parameters:
fgFactor graph on which to perform the inference algorithm;
optsParameters passed to constructor of inference algorithm;

Implements dai::InfAlg.

virtual std::string dai::MF::name ( ) const [inline, virtual]

Returns the name of the algorithm.

Implements dai::InfAlg.

virtual Factor dai::MF::belief ( const Var v) const [inline, virtual]

Returns the (approximate) marginal probability distribution of a variable.

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

Reimplemented from dai::InfAlg.

Factor dai::MF::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_IMPLEMENTEDif not implemented/supported.
BELIEF_NOT_AVAILABLEif the requested belief cannot be calculated with this algorithm.

Implements dai::InfAlg.

Factor dai::MF::beliefV ( size_t  i) const [virtual]

Returns the (approximate) marginal probability distribution of the variable with index i.

For some approximate inference algorithms, using beliefV() is preferred to belief() for performance reasons.

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

Reimplemented from dai::InfAlg.

vector< Factor > dai::MF::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::MF::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_IMPLEMENTEDif not implemented/supported

Implements dai::InfAlg.

void dai::MF::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::MF::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_IMPLEMENTEDif not implemented/supported

Implements dai::InfAlg.

Real dai::MF::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::MF::maxDiff ( ) const [inline, virtual]

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

Exceptions:
NOT_IMPLEMENTEDif not implemented/supported

Reimplemented from dai::InfAlg.

virtual size_t dai::MF::Iterations ( ) const [inline, virtual]

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

Exceptions:
NOT_IMPLEMENTEDif not implemented/supported

Reimplemented from dai::InfAlg.

virtual void dai::MF::setMaxIter ( size_t  ) [inline, virtual]

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

Exceptions:
NOT_IMPLEMENTEDif not implemented/supported

Reimplemented from dai::InfAlg.

void dai::MF::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::MF::getProperties ( ) const [virtual]

Returns parameters of this inference algorithm converted into a PropertySet.

Implements dai::InfAlg.

string dai::MF::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.

void dai::MF::construct ( ) [private]

Helper function for constructors.

Factor dai::MF::calcNewBelief ( size_t  i) [private]

Calculates an updated belief of variable i.


Member Data Documentation

std::vector<Factor> dai::MF::_beliefs [private]

Current approximations of single variable marginals.

Maximum difference encountered so far.

size_t dai::MF::_iters [private]

Number of iterations needed.


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