#include <dai/gibbs.h>
Public Member Functions | |
Gibbs () | |
Default constructor. | |
Gibbs (const FactorGraph &fg, const PropertySet &opts) | |
Construct from FactorGraph fg and PropertySet opts. | |
General InfAlg interface | |
virtual Gibbs * | clone () const |
Returns a pointer to a new, cloned copy of *this (i.e., virtual copy constructor). | |
virtual std::string | identify () const |
Identifies itself for logging purposes. | |
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 Factor | beliefF (size_t I) const |
Returns the (approximate) marginal probability distribution of the variables on which factor I depends. | |
virtual std::vector< Factor > | beliefs () 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). | |
std::vector< std::size_t > | findMaximum () const |
Calculates the joint state of all variables that has maximum probability. | |
virtual void | init () |
Initializes all data structures of the approximate inference algorithm. | |
virtual void | init (const VarSet &) |
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]". | |
Additional interface specific for Gibbs | |
void | randomizeState () |
Draw the current joint state of all variables from a uniform random distribution. | |
std::vector< size_t > & | state () |
Return reference to current state of all variables. | |
const std::vector< size_t > & | state () const |
Return constant reference to current state of all variables. | |
Public Attributes | |
struct dai::Gibbs::Properties | props |
Static Public Attributes | |
static const char * | Name = "GIBBS" |
Name of this inference algorithm. | |
Private Types | |
typedef std::vector< size_t > | _count_t |
Type used to store the counts of various states. | |
typedef std::vector< size_t > | _state_t |
Type used to store the joint state of all variables. | |
Private Member Functions | |
void | construct () |
Helper function for constructors. | |
void | updateCounts () |
Updates all counts (_sample_count, _var_counts, _factor_counts) based on current state. | |
Prob | getVarDist (size_t i) |
Calculate conditional distribution of variable i, given the current state. | |
void | resampleVar (size_t i) |
Draw state of variable i randomly from its conditional distribution and update the current state. | |
size_t | getFactorEntry (size_t I) |
Calculates linear index into factor I corresponding to the current state. | |
size_t | getFactorEntryDiff (size_t I, size_t i) |
Calculates the differences between linear indices into factor I corresponding with a state change of variable i. | |
Private Attributes | |
size_t | _sample_count |
Number of samples counted so far (excluding burn-in periods). | |
std::vector< _count_t > | _var_counts |
State counts for each variable. | |
std::vector< _count_t > | _factor_counts |
State counts for each factor. | |
size_t | _iters |
Number of iterations done (including burn-in periods). | |
_state_t | _state |
Current joint state of all variables. | |
_state_t | _max_state |
Joint state with maximum probability seen so far. | |
Real | _max_score |
Highest score so far. | |
Related Functions | |
(Note that these are not member functions.) | |
std::vector< size_t > | getGibbsState (const FactorGraph &fg, size_t iters) |
Runs Gibbs sampling for iters iterations (of which burnin for burn-in) on FactorGraph fg, and returns the resulting state. | |
Classes | |
struct | Properties |
Parameters for Gibbs. More... |
typedef std::vector<size_t> dai::Gibbs::_count_t [private] |
Type used to store the counts of various states.
typedef std::vector<size_t> dai::Gibbs::_state_t [private] |
Type used to store the joint state of all variables.
dai::Gibbs::Gibbs | ( | ) | [inline] |
Default constructor.
dai::Gibbs::Gibbs | ( | const FactorGraph & | fg, | |
const PropertySet & | opts | |||
) | [inline] |
Construct from FactorGraph fg and PropertySet opts.
fg | Factor graph. | |
opts | Parameters |
virtual Gibbs* dai::Gibbs::clone | ( | ) | const [inline, virtual] |
Returns a pointer to a new, cloned copy of *this
(i.e., virtual copy constructor).
Implements dai::InfAlg.
virtual std::string dai::Gibbs::identify | ( | ) | const [inline, virtual] |
Returns the (approximate) marginal probability distribution of a variable.
Reimplemented from dai::InfAlg.
Returns the (approximate) marginal probability distribution of a set of variables.
NOT_IMPLEMENTED | if not implemented/supported. | |
BELIEF_NOT_AVAILABLE | if the requested belief cannot be calculated with this algorithm. |
Implements dai::InfAlg.
Factor dai::Gibbs::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.
Reimplemented from dai::InfAlg.
Factor dai::Gibbs::beliefF | ( | size_t | I | ) | const [virtual] |
Returns the (approximate) marginal probability distribution of the variables on which factor I depends.
For some approximate inference algorithms, using beliefF() is preferred to belief() for performance reasons.
Reimplemented from dai::InfAlg.
vector< Factor > dai::Gibbs::beliefs | ( | ) | const [virtual] |
Returns all beliefs (approximate marginal probability distributions) calculated by the algorithm.
Implements dai::InfAlg.
virtual Real dai::Gibbs::logZ | ( | ) | const [inline, virtual] |
Returns the logarithm of the (approximated) partition sum (normalizing constant of the factor graph).
NOT_IMPLEMENTED | if not implemented/supported |
Implements dai::InfAlg.
std::vector<std::size_t> dai::Gibbs::findMaximum | ( | ) | const [inline, virtual] |
Calculates the joint state of all variables that has maximum probability.
NOT_IMPLEMENTED | if not implemented/supported |
Reimplemented from dai::InfAlg.
void dai::Gibbs::init | ( | ) | [virtual] |
Initializes all data structures of the approximate inference algorithm.
Implements dai::InfAlg.
virtual void dai::Gibbs::init | ( | const VarSet & | vs | ) | [inline, 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.
NOT_IMPLEMENTED | if not implemented/supported |
Implements dai::InfAlg.
Real dai::Gibbs::run | ( | ) | [virtual] |
Runs the approximate inference algorithm.
Implements dai::InfAlg.
virtual Real dai::Gibbs::maxDiff | ( | ) | const [inline, virtual] |
Returns maximum difference between single variable beliefs in the last iteration.
NOT_IMPLEMENTED | if not implemented/supported |
Reimplemented from dai::InfAlg.
virtual size_t dai::Gibbs::Iterations | ( | ) | const [inline, virtual] |
Returns number of iterations done (one iteration passes over the complete factorgraph).
NOT_IMPLEMENTED | if not implemented/supported |
Reimplemented from dai::InfAlg.
virtual void dai::Gibbs::setMaxIter | ( | size_t | ) | [inline, virtual] |
Sets maximum number of iterations (one iteration passes over the complete factorgraph).
NOT_IMPLEMENTED | if not implemented/supported |
Reimplemented from dai::InfAlg.
void dai::Gibbs::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::Gibbs::getProperties | ( | ) | const [virtual] |
Returns parameters of this inference algorithm converted into a PropertySet.
Implements dai::InfAlg.
string dai::Gibbs::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::Gibbs::randomizeState | ( | ) |
Draw the current joint state of all variables from a uniform random distribution.
std::vector<size_t>& dai::Gibbs::state | ( | ) | [inline] |
const std::vector<size_t>& dai::Gibbs::state | ( | ) | const [inline] |
Return constant reference to current state of all variables.
void dai::Gibbs::construct | ( | ) | [private] |
Helper function for constructors.
void dai::Gibbs::updateCounts | ( | ) | [private] |
Updates all counts (_sample_count, _var_counts, _factor_counts) based on current state.
Prob dai::Gibbs::getVarDist | ( | size_t | i | ) | [private] |
Calculate conditional distribution of variable i, given the current state.
void dai::Gibbs::resampleVar | ( | size_t | i | ) | [private] |
Draw state of variable i randomly from its conditional distribution and update the current state.
size_t dai::Gibbs::getFactorEntry | ( | size_t | I | ) | [private] |
Calculates linear index into factor I corresponding to the current state.
size_t dai::Gibbs::getFactorEntryDiff | ( | size_t | I, | |
size_t | i | |||
) | [private] |
Calculates the differences between linear indices into factor I corresponding with a state change of variable i.
std::vector< size_t > getGibbsState | ( | const FactorGraph & | fg, | |
size_t | iters | |||
) | [related] |
Runs Gibbs sampling for iters iterations (of which burnin for burn-in) on FactorGraph fg, and returns the resulting state.
size_t dai::Gibbs::_sample_count [private] |
Number of samples counted so far (excluding burn-in periods).
std::vector<_count_t> dai::Gibbs::_var_counts [private] |
State counts for each variable.
std::vector<_count_t> dai::Gibbs::_factor_counts [private] |
State counts for each factor.
size_t dai::Gibbs::_iters [private] |
Number of iterations done (including burn-in periods).
_state_t dai::Gibbs::_state [private] |
Current joint state of all variables.
_state_t dai::Gibbs::_max_state [private] |
Joint state with maximum probability seen so far.
Real dai::Gibbs::_max_score [private] |
Highest score so far.
const char * dai::Gibbs::Name = "GIBBS" [static] |
Name of this inference algorithm.