GibbsKL computes the KL divergence betweens 2 BNs using an approximation pattern: GIBBS sampling. More...

#include <GibbsBNdistance.h>

Inheritance diagram for gum::GibbsBNdistance< GUM_SCALAR >:

Collaboration diagram for gum::GibbsBNdistance< GUM_SCALAR >:

Public Attributes
Signaler3< Size, double, double >	onProgress
	Progression, error and time. More...

Signaler1< std::string >	onStop
	Criteria messageApproximationScheme. More...

Public Member Functions
	GibbsBNdistance (const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
	constructor must give 2 BNs More...

	GibbsBNdistance (const BNdistance< GUM_SCALAR > &kl)
	copy constructor More...

	~GibbsBNdistance ()
	destructor More...

void	setBurnIn (Size b)
	Number of burn in for one iteration. More...

Size	burnIn () const
	Returns the number of burn in. More...

Complexity	difficulty () const
	return KL::Complexity::Heavy,KL::Complexity::Difficult,KL::Complexity::Correct depending on the BNs __p and __q More...

Size	nbrDrawnVar () const
	Getters and setters. More...

void	setNbrDrawnVar (Size nbr)

bool	isDrawnAtRandom () const

void	setDrawnAtRandom (bool atRandom)

Instantiation	monteCarloSample ()
	draws a Monte Carlo sample More...

Instantiation	nextSample (Instantiation prev)
	draws next sample of Gibbs sampling More...

Accessors to results. The first call do the computations. The
others do not.
double	klPQ ()

Size	errorPQ ()

double	klQP ()

Size	errorQP ()

double	hellinger ()

double	bhattacharya ()

double	jsd ()

const IBayesNet< GUM_SCALAR > &	p () const

const IBayesNet< GUM_SCALAR > &	q () const

Getters and setters
void	setEpsilon (double eps)
	Given that we approximate f(t), stopping criterion on \|f(t+1)-f(t)\|. More...

double	epsilon () const
	Returns the value of epsilon. More...

void	disableEpsilon ()
	Disable stopping criterion on epsilon. More...

void	enableEpsilon ()
	Enable stopping criterion on epsilon. More...

bool	isEnabledEpsilon () const
	Returns true if stopping criterion on epsilon is enabled, false otherwise. More...

void	setMinEpsilonRate (double rate)
	Given that we approximate f(t), stopping criterion on d/dt(\|f(t+1)-f(t)\|). More...

double	minEpsilonRate () const
	Returns the value of the minimal epsilon rate. More...

void	disableMinEpsilonRate ()
	Disable stopping criterion on epsilon rate. More...

void	enableMinEpsilonRate ()
	Enable stopping criterion on epsilon rate. More...

bool	isEnabledMinEpsilonRate () const
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise. More...

void	setMaxIter (Size max)
	Stopping criterion on number of iterations. More...

Size	maxIter () const
	Returns the criterion on number of iterations. More...

void	disableMaxIter ()
	Disable stopping criterion on max iterations. More...

void	enableMaxIter ()
	Enable stopping criterion on max iterations. More...

bool	isEnabledMaxIter () const
	Returns true if stopping criterion on max iterations is enabled, false otherwise. More...

void	setMaxTime (double timeout)
	Stopping criterion on timeout. More...

double	maxTime () const
	Returns the timeout (in seconds). More...

double	currentTime () const
	Returns the current running time in second. More...

void	disableMaxTime ()
	Disable stopping criterion on timeout. More...

void	enableMaxTime ()
	Enable stopping criterion on timeout. More...

bool	isEnabledMaxTime () const
	Returns true if stopping criterion on timeout is enabled, false otherwise. More...

void	setPeriodSize (Size p)
	How many samples between two stopping is enable. More...

Size	periodSize () const
	Returns the period size. More...

void	setVerbosity (bool v)
	Set the verbosity on (true) or off (false). More...

bool	verbosity () const
	Returns true if verbosity is enabled. More...

ApproximationSchemeSTATE	stateApproximationScheme () const
	Returns the approximation scheme state. More...

Size	nbrIterations () const
	Returns the number of iterations. More...

const std::vector< double > &	history () const
	Returns the scheme history. More...

void	initApproximationScheme ()
	Initialise the scheme. More...

bool	startOfPeriod ()
	Returns true if we are at the beginning of a period (compute error is mandatory). More...

void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps. More...

Size	remainingBurnIn ()
	Returns the remaining burn in. More...

void	stopApproximationScheme ()
	Stop the approximation scheme. More...

bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error. More...

Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message. More...

Public Types
enum	ApproximationSchemeSTATE : char { ApproximationSchemeSTATE::Undefined, ApproximationSchemeSTATE::Continue, ApproximationSchemeSTATE::Epsilon, ApproximationSchemeSTATE::Rate, ApproximationSchemeSTATE::Limit, ApproximationSchemeSTATE::TimeLimit, ApproximationSchemeSTATE::Stopped }
	The different state of an approximation scheme. More...

Protected Attributes
const IBayesNet< GUM_SCALAR > &	_p

const IBayesNet< GUM_SCALAR > &	_q

GUM_SCALAR	_klPQ

GUM_SCALAR	_klQP

GUM_SCALAR	_hellinger

GUM_SCALAR	_bhattacharya

GUM_SCALAR	_jsd

Size	_errorPQ

Size	_errorQP

double	_current_epsilon
	Current epsilon. More...

double	_last_epsilon
	Last epsilon value. More...

double	_current_rate
	Current rate. More...

Size	_current_step
	The current step. More...

Timer	_timer
	The timer. More...

ApproximationSchemeSTATE	_current_state
	The current state. More...

std::vector< double >	_history
	The scheme history, used only if verbosity == true. More...

double	_eps
	Threshold for convergence. More...

bool	_enabled_eps
	If true, the threshold convergence is enabled. More...

double	_min_rate_eps
	Threshold for the epsilon rate. More...

bool	_enabled_min_rate_eps
	If true, the minimal threshold for epsilon rate is enabled. More...

double	_max_time
	The timeout. More...

bool	_enabled_max_time
	If true, the timeout is enabled. More...

Size	_max_iter
	The maximum iterations. More...

bool	_enabled_max_iter
	If true, the maximum iterations stopping criterion is enabled. More...

Size	_burn_in
	Number of iterations before checking stopping criteria. More...

Size	_period_size
	Checking criteria frequency. More...

bool	_verbosity
	If true, verbosity is enabled. More...

Size	_counting
	number of samples drawn More...

const IBayesNet< GUM_SCALAR > &	_sampling_bn

const NodeProperty< Idx > *	_hardEv

Sequence< NodeId >	_samplingNodes

Size	_nbr

bool	_atRandom

Protected Member Functions
void	_computeKL () final

void	_process ()

Detailed Description

template<typename GUM_SCALAR>
class gum::GibbsBNdistance< GUM_SCALAR >

GibbsKL computes the KL divergence betweens 2 BNs using an approximation pattern: GIBBS sampling.

KL.process() computes KL(P||Q) using klPQ() and KL(Q||P) using klQP(). The computations are made once. The second is for free :) GibbsKL allows as well to compute in the same time the Hellinger distance ( \( *\sqrt{\sum_i (\sqrt{p_i}-\sqrt{q_i})^2}\)) (Kokolakis and Nanopoulos, 2001) and Bhattacharya distance (Kaylath,T. 1967)

It may happen that P*ln(P/Q) is not computable (Q=0 and P!=0). In such a case, KL keeps working but trace this error (errorPQ() and errorQP()). In those cases, Hellinger distance approximation is under-evaluated.

Warning: : convergence and stop criteria are designed w.r.t the main computation : KL(P||Q). The 3 others have no guarantee.

snippets :

gum::KL base_kl(net1,net2);
if (base_kl.difficulty()!=KL::HEAVY) {
 gum::ExactBNdistance kl(base_kl);
 std::cout<<"KL net1||net2 :"<<kl.klPQ()<<std::endl;
} else {
 gum::GibbsBNdistance kl(base_kl);
 std::cout<<"KL net1||net2 :"<<kl.klPQ()<<std::endl;
}

Definition at line 76 of file GibbsBNdistance.h.

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

enum gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 63 of file IApproximationSchemeConfiguration.h.

                                         : char {
       Undefined,
       Continue,
       Epsilon,
       Rate,
       Limit,
       TimeLimit,
       Stopped
     };

Constructor & Destructor Documentation

◆ GibbsBNdistance() [1/2]

template<typename GUM_SCALAR >

gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance	(	const IBayesNet< GUM_SCALAR > &	P,
		const IBayesNet< GUM_SCALAR > &	Q
	)

constructor must give 2 BNs

Exceptions

gum::OperationNotAllowed if the 2 BNs have not the same domainSize or compatible node sets.

Definition at line 49 of file GibbsKL_tpl.h.

References GIBBSKL_DEFAULT_BURNIN, GIBBSKL_DEFAULT_EPSILON, GIBBSKL_DEFAULT_MAXITER, GIBBSKL_DEFAULT_MIN_EPSILON_RATE, GIBBSKL_DEFAULT_PERIOD_SIZE, GIBBSKL_DEFAULT_TIMEOUT, GIBBSKL_DEFAULT_VERBOSITY, gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), gum::ApproximationScheme::setEpsilon(), gum::ApproximationScheme::setMaxIter(), gum::ApproximationScheme::setMaxTime(), gum::ApproximationScheme::setMinEpsilonRate(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::setVerbosity().

                                                                          :
       BNdistance< GUM_SCALAR >(P, Q),
       ApproximationScheme(),
       GibbsOperator< GUM_SCALAR >(
          P,
          nullptr,
          1 + (P.size() * GIBBSKL_POURCENT_DRAWN_SAMPLE / 100),
          GIBBSKL_DRAWN_AT_RANDOM) {
     GUM_CONSTRUCTOR(GibbsBNdistance);
 
     setEpsilon(GIBBSKL_DEFAULT_EPSILON);
     setMinEpsilonRate(GIBBSKL_DEFAULT_MIN_EPSILON_RATE);
     setMaxIter(GIBBSKL_DEFAULT_MAXITER);
     setVerbosity(GIBBSKL_DEFAULT_VERBOSITY);
     setBurnIn(GIBBSKL_DEFAULT_BURNIN);
     setPeriodSize(GIBBSKL_DEFAULT_PERIOD_SIZE);
     setMaxTime(GIBBSKL_DEFAULT_TIMEOUT);
   }

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◆ GibbsBNdistance() [2/2]

template<typename GUM_SCALAR >

gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance ( const BNdistance< GUM_SCALAR > & kl )

explicit

copy constructor

Definition at line 70 of file GibbsKL_tpl.h.

References GIBBSKL_DEFAULT_BURNIN, GIBBSKL_DEFAULT_EPSILON, GIBBSKL_DEFAULT_MAXITER, GIBBSKL_DEFAULT_MIN_EPSILON_RATE, GIBBSKL_DEFAULT_PERIOD_SIZE, GIBBSKL_DEFAULT_TIMEOUT, GIBBSKL_DEFAULT_VERBOSITY, gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), gum::ApproximationScheme::setEpsilon(), gum::ApproximationScheme::setMaxIter(), gum::ApproximationScheme::setMaxTime(), gum::ApproximationScheme::setMinEpsilonRate(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::setVerbosity().

                                          :
       BNdistance< GUM_SCALAR >(kl),
       ApproximationScheme()
       // Gibbs operator with 10% of nodes changes at random between each samples
       ,
       GibbsOperator< GUM_SCALAR >(
          kl.p(),
          nullptr,
          1 + (kl.p().size() * GIBBSKL_POURCENT_DRAWN_SAMPLE / 100),
          true) {
     GUM_CONSTRUCTOR(GibbsBNdistance);
 
     setEpsilon(GIBBSKL_DEFAULT_EPSILON);
     setMinEpsilonRate(GIBBSKL_DEFAULT_MIN_EPSILON_RATE);
     setMaxIter(GIBBSKL_DEFAULT_MAXITER);
     setVerbosity(GIBBSKL_DEFAULT_VERBOSITY);
     setBurnIn(GIBBSKL_DEFAULT_BURNIN);
     setPeriodSize(GIBBSKL_DEFAULT_PERIOD_SIZE);
     setMaxTime(GIBBSKL_DEFAULT_TIMEOUT);
   }

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◆ ~GibbsBNdistance()

template<typename GUM_SCALAR >

gum::GibbsBNdistance< GUM_SCALAR >::~GibbsBNdistance ( )

destructor

Definition at line 93 of file GibbsKL_tpl.h.

                                                   {
     GUM_DESTRUCTOR(GibbsBNdistance);
   }

Member Function Documentation

◆ _computeKL()

template<typename GUM_SCALAR >

void gum::GibbsBNdistance< GUM_SCALAR >::_computeKL ( )

finalprotectedvirtual

Reimplemented from gum::BNdistance< GUM_SCALAR >.

Definition at line 98 of file GibbsKL_tpl.h.

                                                  {
     auto Iq = _q.completeInstantiation();
 
     gum::Instantiation I = this->monteCarloSample();
     initApproximationScheme();
 
     // map between particle() variables and _q variables (using name of vars)
     HashTable< const DiscreteVariable*, const DiscreteVariable* > map;
 
     for (Idx ite = 0; ite < I.nbrDim(); ++ite) {
       map.insert(&I.variable(ite), &_q.variableFromName(I.variable(ite).name()));
     }
 
     // BURN IN
     for (Idx i = 0; i < burnIn(); i++)
       I = this->nextSample(I);
 
     // SAMPLING
     _klPQ = _klQP = _hellinger = _jsd = (GUM_SCALAR)0.0;
     _errorPQ = _errorQP = 0;
     GUM_SCALAR delta, ratio, error;
     delta = ratio = error = (GUM_SCALAR)-1;
     GUM_SCALAR oldPQ = 0.0;
     GUM_SCALAR pp, pq, pmid;
 
     do {
       this->disableMinEpsilonRate();
       I = this->nextSample(I);
       updateApproximationScheme();
 
       //_p.synchroInstantiations( Ip,I);
       Iq.setValsFrom(map, I);
 
       pp = _p.jointProbability(I);
       pq = _q.jointProbability(Iq);
       pmid = (pp + pq) / 2.0;
 
       if (pp != (GUM_SCALAR)0.0) {
         _hellinger += std::pow(std::sqrt(pp) - std::sqrt(pq), 2) / pp;
 
         if (pq != (GUM_SCALAR)0.0) {
           _bhattacharya += std::sqrt(pq / pp);   // std::sqrt(pp*pq)/pp
           this->enableMinEpsilonRate();   // replace check_rate=true;
           ratio = pq / pp;
           delta = (GUM_SCALAR)log2(ratio);
           _klPQ += delta;
 
           // pmid!=0
           _jsd -= log2(pp / pmid) + ratio * log2(pq / pmid);
         } else {
           _errorPQ++;
         }
       }
 
       if (pq != (GUM_SCALAR)0.0) {
         if (pp != (GUM_SCALAR)0.0) {
           // if we are here, it is certain that delta and ratio have been
           // computed
           // further lines above. (for now #112-113)
           _klQP += (GUM_SCALAR)(-delta * ratio);
         } else {
           _errorQP++;
         }
       }
 
       if (this->isEnabledMinEpsilonRate()) {   // replace check_rate
         // delta is used as a temporary variable
         delta = _klPQ / nbrIterations();
         error = (GUM_SCALAR)std::abs(delta - oldPQ);
         oldPQ = delta;
       }
     } while (continueApproximationScheme(error));   //
 
     _klPQ = -_klPQ / (nbrIterations());
     _klQP = -_klQP / (nbrIterations());
     _jsd = -0.5 * _jsd / (nbrIterations());
     _hellinger = std::sqrt(_hellinger / nbrIterations());
     _bhattacharya = -std::log(_bhattacharya / (nbrIterations()));
   }

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◆ _process()

template<typename GUM_SCALAR >

void gum::BNdistance< GUM_SCALAR >::_process ( )

protectedinherited

Definition at line 175 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::__done, and gum::BNdistance< GUM_SCALAR >::_computeKL().

Referenced by gum::BNdistance< GUM_SCALAR >::bhattacharya(), gum::BNdistance< GUM_SCALAR >::errorPQ(), gum::BNdistance< GUM_SCALAR >::errorQP(), gum::BNdistance< GUM_SCALAR >::hellinger(), gum::BNdistance< GUM_SCALAR >::jsd(), gum::BNdistance< GUM_SCALAR >::klPQ(), and gum::BNdistance< GUM_SCALAR >::klQP().

                                           {
     if (!__done) {
       _computeKL();
       __done = true;
     }
   }

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◆ bhattacharya()

template<typename GUM_SCALAR >

INLINE double gum::BNdistance< GUM_SCALAR >::bhattacharya ( )

inherited

Returns: Bhattacharya distance (

See also: http://en.wikipedia.org/wiki/Bhattacharya_distance)

Definition at line 90 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_bhattacharya, and gum::BNdistance< GUM_SCALAR >::_process().

                                                        {
     _process();
     return _bhattacharya;
   }

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◆ burnIn()

template<typename GUM_SCALAR >

Size gum::GibbsBNdistance< GUM_SCALAR >::burnIn ( ) const

Returns the number of burn in.

Returns: Returns the number of burn in.

Definition at line 186 of file GibbsKL_tpl.h.

References gum::ApproximationScheme::_burn_in.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL().

                                                    {
     return this->_burn_in;
   }

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◆ continueApproximationScheme()

INLINE bool gum::ApproximationScheme::continueApproximationScheme ( double error )

inherited

Update the scheme w.r.t the new error.

Test the stopping criterion that are enabled.

Parameters

error The new error value.

Returns: false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 225 of file approximationScheme_inl.h.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                            {
     // For coherence, we fix the time used in the method
 
     double timer_step = _timer.step();
 
     if (_enabled_max_time) {
       if (timer_step > _max_time) {
         _stopScheme(ApproximationSchemeSTATE::TimeLimit);
         return false;
       }
     }
 
     if (!startOfPeriod()) { return true; }
 
     if (_current_state != ApproximationSchemeSTATE::Continue) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is not correct : "
                    + messageApproximationScheme());
     }
 
     if (verbosity()) { _history.push_back(error); }
 
     if (_enabled_max_iter) {
       if (_current_step > _max_iter) {
         _stopScheme(ApproximationSchemeSTATE::Limit);
         return false;
       }
     }
 
     _last_epsilon = _current_epsilon;
     _current_epsilon = error;   // eps rate isEnabled needs it so affectation was
     // moved from eps isEnabled below
 
     if (_enabled_eps) {
       if (_current_epsilon <= _eps) {
         _stopScheme(ApproximationSchemeSTATE::Epsilon);
         return false;
       }
     }
 
     if (_last_epsilon >= 0.) {
       if (_current_epsilon > .0) {
         // ! _current_epsilon can be 0. AND epsilon
         // isEnabled can be disabled !
         _current_rate =
            std::fabs((_current_epsilon - _last_epsilon) / _current_epsilon);
       }
       // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
       // infinity the else means a return false if we isEnabled the rate below,
       // as we would have returned false if epsilon isEnabled was enabled
       else {
         _current_rate = _min_rate_eps;
       }
 
       if (_enabled_min_rate_eps) {
         if (_current_rate <= _min_rate_eps) {
           _stopScheme(ApproximationSchemeSTATE::Rate);
           return false;
         }
       }
     }
 
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Continue) {
       if (onProgress.hasListener()) {
         GUM_EMIT3(onProgress, _current_step, _current_epsilon, timer_step);
       }
 
       return true;
     } else {
       return false;
     }
   }

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◆ currentTime()

INLINE double gum::ApproximationScheme::currentTime ( ) const

virtualinherited

Returns the current running time in second.

Returns: Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 126 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_timer, and gum::Timer::step().

Referenced by gum::learning::genericBNLearner::currentTime().

126 { return _timer.step(); }

gum::Timer::step

double step() const

Returns the delta time between now and the last reset() call (or the constructor).

Definition: timer_inl.h:39

gum::ApproximationScheme::_timer

Timer _timer

The timer.

Definition: approximationScheme.h:379

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◆ difficulty()

template<typename GUM_SCALAR >

Complexity gum::BNdistance< GUM_SCALAR >::difficulty ( ) const

inherited

return KL::Complexity::Heavy,KL::Complexity::Difficult,KL::Complexity::Correct depending on the BNs __p and __q

Definition at line 67 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::__difficulty.

                                                         {
     return __difficulty;
   }

◆ disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 52 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::disableEpsilon().

52 { _enabled_eps = false; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:391

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◆ disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 103 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::learning::genericBNLearner::disableMaxIter(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

103 { _enabled_max_iter = false; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:409

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◆ disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns: Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 129 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::disableMaxTime(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

129 { _enabled_max_time = false; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:403

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◆ disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::learning::genericBNLearner::disableMinEpsilonRate(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

                                                          {
     _enabled_min_rate_eps = false;
   }

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◆ enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 55 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), and gum::learning::genericBNLearner::enableEpsilon().

55 { _enabled_eps = true; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:391

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◆ enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::enableMaxIter().

106 { _enabled_max_iter = true; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:409

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◆ enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 132 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), and gum::learning::genericBNLearner::enableMaxTime().

132 { _enabled_max_time = true; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:403

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◆ enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 82 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::enableMinEpsilonRate().

                                                         {
     _enabled_min_rate_eps = true;
   }

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◆ epsilon()

INLINE double gum::ApproximationScheme::epsilon ( ) const

virtualinherited

Returns the value of epsilon.

Returns: Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 49 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), and gum::learning::genericBNLearner::epsilon().

49 { return _eps; }

gum::ApproximationScheme::_eps

double _eps

Threshold for convergence.

Definition: approximationScheme.h:388

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◆ errorPQ()

template<typename GUM_SCALAR >

INLINE Size gum::BNdistance< GUM_SCALAR >::errorPQ ( )

inherited

Returns: the number of errors while processing divergence KL(P||Q)

Definition at line 102 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_errorPQ, and gum::BNdistance< GUM_SCALAR >::_process().

                                                 {
     _process();
     return _errorPQ;
   }

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◆ errorQP()

template<typename GUM_SCALAR >

INLINE Size gum::BNdistance< GUM_SCALAR >::errorQP ( )

inherited

Returns: the number of errors while processing divergence KL(Q||P)

Definition at line 108 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_errorQP, and gum::BNdistance< GUM_SCALAR >::_process().

                                                 {
     _process();
     return _errorQP;
   }

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◆ hellinger()

template<typename GUM_SCALAR >

INLINE double gum::BNdistance< GUM_SCALAR >::hellinger ( )

inherited

Returns: hellinger distance (

See also: http://en.wikipedia.org/wiki/Hellinger_distance)

Definition at line 84 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_hellinger, and gum::BNdistance< GUM_SCALAR >::_process().

                                                     {
     _process();
     return _hellinger;
   }

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◆ history()

INLINE const std::vector< double > & gum::ApproximationScheme::history ( ) const

virtualinherited

Returns the scheme history.

Returns: Returns the scheme history.

Exceptions

OperationNotAllowed Raised if the scheme did not performed or if verbosity is set to false.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 171 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_history, GUM_ERROR, gum::ApproximationScheme::stateApproximationScheme(), gum::IApproximationSchemeConfiguration::Undefined, and gum::ApproximationScheme::verbosity().

Referenced by gum::learning::genericBNLearner::history().

                                                                        {
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is udefined");
     }
 
     if (verbosity() == false) {
       GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
     }
 
     return _history;
   }

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◆ initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 185 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_epsilon, gum::ApproximationScheme::_current_rate, gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_current_step, gum::ApproximationScheme::_history, gum::ApproximationScheme::_timer, gum::IApproximationSchemeConfiguration::Continue, and gum::Timer::reset().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

                                                            {
     _current_state = ApproximationSchemeSTATE::Continue;
     _current_step = 0;
     _current_epsilon = _current_rate = -1.0;
     _history.clear();
     _timer.reset();
   }

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◆ isDrawnAtRandom()

template<typename GUM_SCALAR >

bool gum::GibbsOperator< GUM_SCALAR >::isDrawnAtRandom ( ) const

inlineinherited

Definition at line 67 of file gibbsOperator.h.

References gum::GibbsOperator< GUM_SCALAR >::_atRandom.

67 { return _atRandom; }

gum::GibbsOperator::_atRandom

bool _atRandom

Definition: gibbsOperator.h:87

◆ isEnabledEpsilon()

INLINE bool gum::ApproximationScheme::isEnabledEpsilon ( ) const

virtualinherited

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Returns: Returns true if stopping criterion on epsilon is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 59 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::isEnabledEpsilon().

                                                           {
     return _enabled_eps;
   }

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◆ isEnabledMaxIter()

INLINE bool gum::ApproximationScheme::isEnabledMaxIter ( ) const

virtualinherited

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Returns: Returns true if stopping criterion on max iterations is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 110 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::isEnabledMaxIter().

                                                           {
     return _enabled_max_iter;
   }

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◆ isEnabledMaxTime()

INLINE bool gum::ApproximationScheme::isEnabledMaxTime ( ) const

virtualinherited

Returns true if stopping criterion on timeout is enabled, false otherwise.

Returns: Returns true if stopping criterion on timeout is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 136 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::isEnabledMaxTime().

                                                           {
     return _enabled_max_time;
   }

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◆ isEnabledMinEpsilonRate()

INLINE bool gum::ApproximationScheme::isEnabledMinEpsilonRate ( ) const

virtualinherited

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Returns: Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 88 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::isEnabledMinEpsilonRate().

                                                                  {
     return _enabled_min_rate_eps;
   }

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◆ jsd()

template<typename GUM_SCALAR >

INLINE double gum::BNdistance< GUM_SCALAR >::jsd ( )

inherited

Returns: Jensen-Shannon divergence(

See also: https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence)

Definition at line 96 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_jsd, and gum::BNdistance< GUM_SCALAR >::_process().

                                               {
     _process();
     return _jsd;
   }

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◆ klPQ()

template<typename GUM_SCALAR >

INLINE double gum::BNdistance< GUM_SCALAR >::klPQ ( )

inherited

Returns: divergence KL(P||Q)

Definition at line 72 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_klPQ, and gum::BNdistance< GUM_SCALAR >::_process().

                                                {
     _process();
     return _klPQ;
   }

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◆ klQP()

template<typename GUM_SCALAR >

INLINE double gum::BNdistance< GUM_SCALAR >::klQP ( )

inherited

Returns: divergence KL(Q||P)

Definition at line 78 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_klQP, and gum::BNdistance< GUM_SCALAR >::_process().

                                                {
     _process();
     return _klQP;
   }

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◆ maxIter()

INLINE Size gum::ApproximationScheme::maxIter ( ) const

virtualinherited

Returns the criterion on number of iterations.

Returns: Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 100 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

Referenced by gum::learning::genericBNLearner::maxIter().

100 { return _max_iter; }

gum::ApproximationScheme::_max_iter

Size _max_iter

The maximum iterations.

Definition: approximationScheme.h:406

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◆ maxTime()

INLINE double gum::ApproximationScheme::maxTime ( ) const

virtualinherited

Returns the timeout (in seconds).

Returns: Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

Referenced by gum::learning::genericBNLearner::maxTime().

123 { return _max_time; }

gum::ApproximationScheme::_max_time

double _max_time

The timeout.

Definition: approximationScheme.h:400

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◆ messageApproximationScheme()

INLINE std::string gum::IApproximationSchemeConfiguration::messageApproximationScheme ( ) const

inherited

Returns the approximation scheme message.

Returns: Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

Referenced by gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), and gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg().

                                                                          {
     std::stringstream s;
 
     switch (stateApproximationScheme()) {
       case ApproximationSchemeSTATE::Continue: s << "in progress"; break;
 
       case ApproximationSchemeSTATE::Epsilon:
         s << "stopped with epsilon=" << epsilon();
         break;
 
       case ApproximationSchemeSTATE::Rate:
         s << "stopped with rate=" << minEpsilonRate();
         break;
 
       case ApproximationSchemeSTATE::Limit:
         s << "stopped with max iteration=" << maxIter();
         break;
 
       case ApproximationSchemeSTATE::TimeLimit:
         s << "stopped with timeout=" << maxTime();
         break;
 
       case ApproximationSchemeSTATE::Stopped: s << "stopped on request"; break;
 
       case ApproximationSchemeSTATE::Undefined: s << "undefined state"; break;
     };
 
     return s.str();
   }

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◆ minEpsilonRate()

INLINE double gum::ApproximationScheme::minEpsilonRate ( ) const

virtualinherited

Returns the value of the minimal epsilon rate.

Returns: Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_min_rate_eps.

Referenced by gum::learning::genericBNLearner::minEpsilonRate().

                                                           {
     return _min_rate_eps;
   }

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◆ monteCarloSample()

template<typename GUM_SCALAR >

Instantiation gum::GibbsOperator< GUM_SCALAR >::monteCarloSample ( )

inherited

draws a Monte Carlo sample

returns a MC sample This is not a really sample since we take into account evidence without care about parent of evidence, etc. This is just a not-so-bad first sample for GibbsSampler

Definition at line 67 of file gibbsOperator_tpl.h.

References gum::GibbsOperator< GUM_SCALAR >::__drawVarMonteCarlo(), gum::GibbsOperator< GUM_SCALAR >::_hardEv, gum::GibbsOperator< GUM_SCALAR >::_sampling_bn, gum::Instantiation::add(), and gum::Instantiation::chgVal().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsSampling< GUM_SCALAR >::_monteCarloSample(), and gum::GibbsOperator< GUM_SCALAR >::setDrawnAtRandom().

                                                               {
     gum::Instantiation I;
 
     for (const auto nod : _sampling_bn.topologicalOrder()) {
       I.add(_sampling_bn.variable(nod));
       if (_hardEv != nullptr && _hardEv->exists(nod)) {
         I.chgVal(_sampling_bn.variable(nod), (*_hardEv)[nod]);
       } else {
         __drawVarMonteCarlo(nod, &I);
       }
     }
     return I;
   }

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◆ nbrDrawnVar()

template<typename GUM_SCALAR >

Size gum::GibbsOperator< GUM_SCALAR >::nbrDrawnVar ( ) const

inlineinherited

Getters and setters.

Definition at line 63 of file gibbsOperator.h.

References gum::GibbsOperator< GUM_SCALAR >::_nbr.

63 { return _nbr; }

gum::GibbsOperator::_nbr

Size _nbr

Definition: gibbsOperator.h:84

◆ nbrIterations()

INLINE Size gum::ApproximationScheme::nbrIterations ( ) const

virtualinherited

Returns the number of iterations.

Returns: Returns the number of iterations.

Exceptions

OperationNotAllowed Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step, GUM_ERROR, gum::ApproximationScheme::stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::nbrIterations().

                                                        {
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is undefined");
     }
 
     return _current_step;
   }

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◆ nextSample()

template<typename GUM_SCALAR >

Instantiation gum::GibbsOperator< GUM_SCALAR >::nextSample ( Instantiation prev )

inherited

draws next sample of Gibbs sampling

Definition at line 92 of file gibbsOperator_tpl.h.

References gum::GibbsOperator< GUM_SCALAR >::__GibbsSample(), gum::GibbsOperator< GUM_SCALAR >::_atRandom, gum::GibbsOperator< GUM_SCALAR >::_counting, gum::GibbsOperator< GUM_SCALAR >::_nbr, gum::GibbsOperator< GUM_SCALAR >::_samplingNodes, gum::randomValue(), and gum::SequenceImplementation< Key, Alloc, Gen >::size().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsSampling< GUM_SCALAR >::_draw(), and gum::GibbsOperator< GUM_SCALAR >::setDrawnAtRandom().

                                                                           {
     for (Idx i = 0; i < _nbr; i++) {
       auto pos = _atRandom ? randomValue(_samplingNodes.size())
                            : (_counting % _samplingNodes.size());
       this->__GibbsSample(_samplingNodes[pos], &prev);
       _counting++;
     }
     return prev;
   }

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◆ p()

template<typename GUM_SCALAR >

INLINE const IBayesNet< GUM_SCALAR > & gum::BNdistance< GUM_SCALAR >::p ( ) const

inherited

Returns: p

Definition at line 114 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_p.

                                                                           {
     return _p;
   }

◆ periodSize()

INLINE Size gum::ApproximationScheme::periodSize ( ) const

virtualinherited

Returns the period size.

Returns: Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), and gum::learning::genericBNLearner::periodSize().

147 { return _period_size; }

gum::ApproximationScheme::_period_size

Size _period_size

Checking criteria frequency.

Definition: approximationScheme.h:415

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◆ q()

template<typename GUM_SCALAR >

INLINE const IBayesNet< GUM_SCALAR > & gum::BNdistance< GUM_SCALAR >::q ( ) const

inherited

Returns: q

Definition at line 119 of file BNdistance_tpl.h.

References gum::BNdistance< GUM_SCALAR >::_q.

                                                                           {
     return _q;
   }

◆ remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns: Returns the remaining burn in.

Definition at line 208 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, and gum::ApproximationScheme::_current_step.

                                                    {
     if (_burn_in > _current_step) {
       return _burn_in - _current_step;
     } else {
       return 0;
     }
   }

◆ setBurnIn()

template<typename GUM_SCALAR >

void gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn ( Size b )

Number of burn in for one iteration.

Parameters

b	The number of burn in.

Exceptions

OutOfLowerBound Raised if b < 1.

Definition at line 181 of file GibbsKL_tpl.h.

References gum::ApproximationScheme::_burn_in.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance().

                                                       {
     this->_burn_in = b;
   }

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◆ setDrawnAtRandom()

template<typename GUM_SCALAR >

void gum::GibbsOperator< GUM_SCALAR >::setDrawnAtRandom ( bool atRandom )

inlineinherited

Definition at line 69 of file gibbsOperator.h.

References gum::GibbsOperator< GUM_SCALAR >::_atRandom, gum::GibbsOperator< GUM_SCALAR >::monteCarloSample(), and gum::GibbsOperator< GUM_SCALAR >::nextSample().

69 { _atRandom = atRandom; }

gum::GibbsOperator::_atRandom

bool _atRandom

Definition: gibbsOperator.h:87

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◆ setEpsilon()

INLINE void gum::ApproximationScheme::setEpsilon ( double eps )

virtualinherited

Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfLowerBound Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 41 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps, gum::ApproximationScheme::_eps, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setEpsilon().

                                                         {
     if (eps < 0.) { GUM_ERROR(OutOfLowerBound, "eps should be >=0"); }
 
     _eps = eps;
     _enabled_eps = true;
   }

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◆ setMaxIter()

INLINE void gum::ApproximationScheme::setMaxIter ( Size max )

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter, gum::ApproximationScheme::_max_iter, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxIter().

                                                       {
     if (max < 1) { GUM_ERROR(OutOfLowerBound, "max should be >=1"); }
     _max_iter = max;
     _enabled_max_iter = true;
   }

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◆ setMaxTime()

INLINE void gum::ApproximationScheme::setMaxTime ( double timeout )

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout The timeout value in seconds.

Exceptions

OutOfLowerBound Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time, gum::ApproximationScheme::_max_time, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxTime().

                                                             {
     if (timeout <= 0.) { GUM_ERROR(OutOfLowerBound, "timeout should be >0."); }
     _max_time = timeout;
     _enabled_max_time = true;
   }

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◆ setMinEpsilonRate()

INLINE void gum::ApproximationScheme::setMinEpsilonRate ( double rate )

virtualinherited

Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfLowerBound if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 64 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps, gum::ApproximationScheme::_min_rate_eps, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMinEpsilonRate().

                                                                 {
     if (rate < 0) { GUM_ERROR(OutOfLowerBound, "rate should be >=0"); }
 
     _min_rate_eps = rate;
     _enabled_min_rate_eps = true;
   }

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◆ setNbrDrawnVar()

template<typename GUM_SCALAR >

void gum::GibbsOperator< GUM_SCALAR >::setNbrDrawnVar ( Size nbr )

inlineinherited

Definition at line 65 of file gibbsOperator.h.

References gum::GibbsOperator< GUM_SCALAR >::_nbr.

65 { _nbr = nbr; }

gum::GibbsOperator::_nbr

Size _nbr

Definition: gibbsOperator.h:84

◆ setPeriodSize()

INLINE void gum::ApproximationScheme::setPeriodSize ( Size p )

virtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfLowerBound Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 141 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setPeriodSize().

                                                        {
     if (p < 1) { GUM_ERROR(OutOfLowerBound, "p should be >=1"); }
 
     _period_size = p;
   }

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◆ setVerbosity()

INLINE void gum::ApproximationScheme::setVerbosity ( bool v )

virtualinherited

Set the verbosity on (true) or off (false).

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setVerbosity().

150 { _verbosity = v; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:418

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◆ startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

inherited

Returns true if we are at the beginning of a period (compute error is mandatory).

Returns: Returns true if we are at the beginning of a period (compute error is mandatory).

Definition at line 195 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, gum::ApproximationScheme::_current_step, and gum::ApproximationScheme::_period_size.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

                                                  {
     if (_current_step < _burn_in) { return false; }
 
     if (_period_size == 1) { return true; }
 
     return ((_current_step - _burn_in) % _period_size == 0);
   }

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◆ stateApproximationScheme()

INLINE IApproximationSchemeConfiguration::ApproximationSchemeSTATE gum::ApproximationScheme::stateApproximationScheme ( ) const

virtualinherited

Returns the approximation scheme state.

Returns: Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), gum::ApproximationScheme::nbrIterations(), and gum::learning::genericBNLearner::stateApproximationScheme().

                                                              {
     return _current_state;
   }

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◆ stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 217 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_stopScheme(), gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Stopped.

Referenced by gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

                                                            {
     if (_current_state == ApproximationSchemeSTATE::Continue) {
       _stopScheme(ApproximationSchemeSTATE::Stopped);
     }
   }

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◆ updateApproximationScheme()

INLINE void gum::ApproximationScheme::updateApproximationScheme ( unsigned int incr = 1 )

inherited

Update the scheme w.r.t the new error and increment steps.

Parameters

incr	The new increment steps.

Definition at line 204 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                               {
     _current_step += incr;
   }

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◆ verbosity()

INLINE bool gum::ApproximationScheme::verbosity ( ) const

virtualinherited

Returns true if verbosity is enabled.

Returns: Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), and gum::learning::genericBNLearner::verbosity().

152 { return _verbosity; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:418

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Member Data Documentation

◆ _atRandom

template<typename GUM_SCALAR >

bool gum::GibbsOperator< GUM_SCALAR >::_atRandom

protectedinherited

Definition at line 87 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::isDrawnAtRandom(), gum::GibbsOperator< GUM_SCALAR >::nextSample(), and gum::GibbsOperator< GUM_SCALAR >::setDrawnAtRandom().

◆ _bhattacharya

template<typename GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::_bhattacharya

protectedinherited

Definition at line 139 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::bhattacharya().

◆ _burn_in

Size gum::ApproximationScheme::_burn_in

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 412 of file approximationScheme.h.

Referenced by gum::GibbsSampling< GUM_SCALAR >::burnIn(), gum::GibbsBNdistance< GUM_SCALAR >::burnIn(), gum::ApproximationScheme::remainingBurnIn(), gum::GibbsSampling< GUM_SCALAR >::setBurnIn(), gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), and gum::ApproximationScheme::startOfPeriod().

◆ _counting

template<typename GUM_SCALAR >

Size gum::GibbsOperator< GUM_SCALAR >::_counting

protectedinherited

number of samples drawn

Definition at line 79 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::nextSample().

◆ _current_epsilon

double gum::ApproximationScheme::_current_epsilon

protectedinherited

Current epsilon.

Definition at line 367 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), and gum::ApproximationScheme::initApproximationScheme().

◆ _current_rate

double gum::ApproximationScheme::_current_rate

protectedinherited

Current rate.

Definition at line 373 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), and gum::ApproximationScheme::initApproximationScheme().

◆ _current_state

ApproximationSchemeSTATE gum::ApproximationScheme::_current_state

protectedinherited

The current state.

Definition at line 382 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::initApproximationScheme(), gum::ApproximationScheme::stateApproximationScheme(), and gum::ApproximationScheme::stopApproximationScheme().

◆ _current_step

Size gum::ApproximationScheme::_current_step

protectedinherited

The current step.

Definition at line 376 of file approximationScheme.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::initApproximationScheme(), gum::learning::Miic::learnMixedStructure(), gum::ApproximationScheme::nbrIterations(), gum::ApproximationScheme::remainingBurnIn(), gum::ApproximationScheme::startOfPeriod(), and gum::ApproximationScheme::updateApproximationScheme().

◆ _enabled_eps

bool gum::ApproximationScheme::_enabled_eps

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 391 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableEpsilon(), gum::ApproximationScheme::enableEpsilon(), gum::ApproximationScheme::isEnabledEpsilon(), and gum::ApproximationScheme::setEpsilon().

◆ _enabled_max_iter

bool gum::ApproximationScheme::_enabled_max_iter

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 409 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMaxIter(), gum::ApproximationScheme::enableMaxIter(), gum::ApproximationScheme::isEnabledMaxIter(), and gum::ApproximationScheme::setMaxIter().

◆ _enabled_max_time

bool gum::ApproximationScheme::_enabled_max_time

protectedinherited

If true, the timeout is enabled.

Definition at line 403 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMaxTime(), gum::ApproximationScheme::enableMaxTime(), gum::ApproximationScheme::isEnabledMaxTime(), and gum::ApproximationScheme::setMaxTime().

◆ _enabled_min_rate_eps

bool gum::ApproximationScheme::_enabled_min_rate_eps

protectedinherited

If true, the minimal threshold for epsilon rate is enabled.

Definition at line 397 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMinEpsilonRate(), gum::ApproximationScheme::enableMinEpsilonRate(), gum::ApproximationScheme::isEnabledMinEpsilonRate(), and gum::ApproximationScheme::setMinEpsilonRate().

◆ _eps

double gum::ApproximationScheme::_eps

protectedinherited

Threshold for convergence.

Definition at line 388 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::epsilon(), and gum::ApproximationScheme::setEpsilon().

◆ _errorPQ

template<typename GUM_SCALAR>

Size gum::BNdistance< GUM_SCALAR >::_errorPQ

protectedinherited

Definition at line 142 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::errorPQ().

◆ _errorQP

template<typename GUM_SCALAR>

Size gum::BNdistance< GUM_SCALAR >::_errorQP

protectedinherited

Definition at line 143 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::errorQP().

◆ _hardEv

template<typename GUM_SCALAR >

const NodeProperty< Idx >* gum::GibbsOperator< GUM_SCALAR >::_hardEv

protectedinherited

Definition at line 81 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::__updateSamplingNodes(), and gum::GibbsOperator< GUM_SCALAR >::monteCarloSample().

◆ _hellinger

template<typename GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::_hellinger

protectedinherited

Definition at line 138 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::hellinger().

◆ _history

std::vector< double > gum::ApproximationScheme::_history

protectedinherited

The scheme history, used only if verbosity == true.

Definition at line 385 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), and gum::ApproximationScheme::initApproximationScheme().

◆ _jsd

template<typename GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::_jsd

protectedinherited

Definition at line 140 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::jsd().

◆ _klPQ

template<typename GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::_klPQ

protectedinherited

Definition at line 136 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::klPQ().

◆ _klQP

template<typename GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::_klQP

protectedinherited

Definition at line 137 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::klQP().

◆ _last_epsilon

double gum::ApproximationScheme::_last_epsilon

protectedinherited

Last epsilon value.

Definition at line 370 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

◆ _max_iter

Size gum::ApproximationScheme::_max_iter

protectedinherited

The maximum iterations.

Definition at line 406 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::maxIter(), and gum::ApproximationScheme::setMaxIter().

◆ _max_time

double gum::ApproximationScheme::_max_time

protectedinherited

The timeout.

Definition at line 400 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::maxTime(), and gum::ApproximationScheme::setMaxTime().

◆ _min_rate_eps

double gum::ApproximationScheme::_min_rate_eps

protectedinherited

Threshold for the epsilon rate.

Definition at line 394 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::minEpsilonRate(), and gum::ApproximationScheme::setMinEpsilonRate().

◆ _nbr

template<typename GUM_SCALAR >

Size gum::GibbsOperator< GUM_SCALAR >::_nbr

protectedinherited

Definition at line 84 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::__updateSamplingNodes(), gum::GibbsOperator< GUM_SCALAR >::nbrDrawnVar(), gum::GibbsOperator< GUM_SCALAR >::nextSample(), and gum::GibbsOperator< GUM_SCALAR >::setNbrDrawnVar().

◆ _p

template<typename GUM_SCALAR>

const IBayesNet< GUM_SCALAR >& gum::BNdistance< GUM_SCALAR >::_p

protectedinherited

Definition at line 133 of file BNdistance.h.

Referenced by gum::BNdistance< GUM_SCALAR >::__checkCompatibility(), gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::BNdistance< GUM_SCALAR >::BNdistance(), and gum::BNdistance< GUM_SCALAR >::p().

◆ _period_size

Size gum::ApproximationScheme::_period_size

protectedinherited

Checking criteria frequency.

Definition at line 415 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::periodSize(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::startOfPeriod().

◆ _q

template<typename GUM_SCALAR>

const IBayesNet< GUM_SCALAR >& gum::BNdistance< GUM_SCALAR >::_q

protectedinherited

Definition at line 134 of file BNdistance.h.

Referenced by gum::BNdistance< GUM_SCALAR >::__checkCompatibility(), gum::ExactBNdistance< GUM_SCALAR >::_computeKL(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::BNdistance< GUM_SCALAR >::q().

◆ _sampling_bn

template<typename GUM_SCALAR >

const IBayesNet< GUM_SCALAR >& gum::GibbsOperator< GUM_SCALAR >::_sampling_bn

protectedinherited

Definition at line 80 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::__drawVarMonteCarlo(), gum::GibbsOperator< GUM_SCALAR >::__GibbsSample(), gum::GibbsOperator< GUM_SCALAR >::__updateSamplingNodes(), and gum::GibbsOperator< GUM_SCALAR >::monteCarloSample().

◆ _samplingNodes

template<typename GUM_SCALAR >

Sequence< NodeId > gum::GibbsOperator< GUM_SCALAR >::_samplingNodes

protectedinherited

Definition at line 82 of file gibbsOperator.h.

Referenced by gum::GibbsOperator< GUM_SCALAR >::__updateSamplingNodes(), and gum::GibbsOperator< GUM_SCALAR >::nextSample().

◆ _timer

Timer gum::ApproximationScheme::_timer

protectedinherited

The timer.

Definition at line 379 of file approximationScheme.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::currentTime(), gum::ApproximationScheme::initApproximationScheme(), and gum::learning::Miic::learnMixedStructure().

◆ _verbosity

bool gum::ApproximationScheme::_verbosity

protectedinherited

If true, verbosity is enabled.

Definition at line 418 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::setVerbosity(), and gum::ApproximationScheme::verbosity().

◆ onProgress

Signaler3< Size, double, double > gum::IApproximationSchemeConfiguration::onProgress

inherited

Progression, error and time.

Definition at line 57 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::continueApproximationScheme(), and gum::learning::genericBNLearner::distributeProgress().

◆ onStop

Signaler1< std::string > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 60 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::_stopScheme(), and gum::learning::genericBNLearner::distributeStop().

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

agrum/BN/algorithms/divergence/GibbsBNdistance.h
agrum/BN/algorithms/divergence/GibbsKL_tpl.h

Public Attributes

Public Member Functions

Public Types

Protected Attributes

Protected Member Functions

Detailed Description

template<typename GUM_SCALAR> class gum::GibbsBNdistance< GUM_SCALAR >

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

Constructor & Destructor Documentation

◆ GibbsBNdistance() [1/2]

◆ GibbsBNdistance() [2/2]

◆ ~GibbsBNdistance()

Member Function Documentation

◆ _computeKL()

◆ _process()

◆ bhattacharya()

◆ burnIn()

◆ continueApproximationScheme()

◆ currentTime()

◆ difficulty()

◆ disableEpsilon()

◆ disableMaxIter()

◆ disableMaxTime()

◆ disableMinEpsilonRate()

◆ enableEpsilon()

◆ enableMaxIter()

◆ enableMaxTime()

◆ enableMinEpsilonRate()

◆ epsilon()

◆ errorPQ()

◆ errorQP()

◆ hellinger()

◆ history()

◆ initApproximationScheme()

◆ isDrawnAtRandom()

◆ isEnabledEpsilon()

◆ isEnabledMaxIter()

◆ isEnabledMaxTime()

◆ isEnabledMinEpsilonRate()

◆ jsd()

◆ klPQ()

◆ klQP()

◆ maxIter()

◆ maxTime()

◆ messageApproximationScheme()

◆ minEpsilonRate()

◆ monteCarloSample()

◆ nbrDrawnVar()

◆ nbrIterations()

◆ nextSample()

◆ p()

◆ periodSize()

◆ q()

◆ remainingBurnIn()

◆ setBurnIn()

◆ setDrawnAtRandom()

◆ setEpsilon()

◆ setMaxIter()

◆ setMaxTime()

◆ setMinEpsilonRate()

◆ setNbrDrawnVar()

◆ setPeriodSize()

◆ setVerbosity()

◆ startOfPeriod()

◆ stateApproximationScheme()

◆ stopApproximationScheme()

◆ updateApproximationScheme()

◆ verbosity()

Member Data Documentation

◆ _atRandom

◆ _bhattacharya

◆ _burn_in

◆ _counting

◆ _current_epsilon

◆ _current_rate

◆ _current_state

◆ _current_step

◆ _enabled_eps

◆ _enabled_max_iter

template<typename GUM_SCALAR>
class gum::GibbsBNdistance< GUM_SCALAR >