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

Public Member Functions
	GibbsBNdistance (const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
	constructor must give 2 BNs
	GibbsBNdistance (const BNdistance< GUM_SCALAR > &kl)
	copy constructor
	~GibbsBNdistance () override
	destructor
void	setBurnIn (Size b)
	Number of burn in for one iteration.
Size	burnIn () const
	Returns the number of burn in.
Complexity	difficulty () const
	return KL::Complexity::Heavy,KL::Complexity::Difficult,KL::Complexity::Correct depending on the BNs p and q
Size	nbrDrawnVar () const
	Getters and setters.
void	setNbrDrawnVar (Size nbr)
bool	isDrawnAtRandom () const
void	setDrawnAtRandom (bool atRandom)
Instantiation	monteCarloSample ()
	draws a Monte Carlo sample
Instantiation	nextSample (Instantiation prev)
	draws next sample of Gibbs sampling
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) override
	Given that we approximate f(t), stopping criterion on \|f(t+1)-f(t)\|.
double	epsilon () const override
	Returns the value of epsilon.
void	disableEpsilon () override
	Disable stopping criterion on epsilon.
void	enableEpsilon () override
	Enable stopping criterion on epsilon.
bool	isEnabledEpsilon () const override
	Returns true if stopping criterion on epsilon is enabled, false otherwise.
void	setMinEpsilonRate (double rate) override
	Given that we approximate f(t), stopping criterion on d/dt(\|f(t+1)-f(t)\|).
double	minEpsilonRate () const override
	Returns the value of the minimal epsilon rate.
void	disableMinEpsilonRate () override
	Disable stopping criterion on epsilon rate.
void	enableMinEpsilonRate () override
	Enable stopping criterion on epsilon rate.
bool	isEnabledMinEpsilonRate () const override
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise.
void	setMaxIter (Size max) override
	Stopping criterion on number of iterations.
Size	maxIter () const override
	Returns the criterion on number of iterations.
void	disableMaxIter () override
	Disable stopping criterion on max iterations.
void	enableMaxIter () override
	Enable stopping criterion on max iterations.
bool	isEnabledMaxIter () const override
	Returns true if stopping criterion on max iterations is enabled, false otherwise.
void	setMaxTime (double timeout) override
	Stopping criterion on timeout.
double	maxTime () const override
	Returns the timeout (in seconds).
double	currentTime () const override
	Returns the current running time in second.
void	disableMaxTime () override
	Disable stopping criterion on timeout.
void	enableMaxTime () override
	Enable stopping criterion on timeout.
bool	isEnabledMaxTime () const override
	Returns true if stopping criterion on timeout is enabled, false otherwise.
void	setPeriodSize (Size p) override
	How many samples between two stopping is enable.
Size	periodSize () const override
	Returns the period size.
void	setVerbosity (bool v) override
	Set the verbosity on (true) or off (false).
bool	verbosity () const override
	Returns true if verbosity is enabled.
ApproximationSchemeSTATE	stateApproximationScheme () const override
	Returns the approximation scheme state.
Size	nbrIterations () const override
	Returns the number of iterations.
const std::vector< double > &	history () const override
	Returns the scheme history.
void	initApproximationScheme ()
	Initialise the scheme.
bool	startOfPeriod () const
	Returns true if we are at the beginning of a period (compute error is mandatory).
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps.
Size	remainingBurnIn () const
	Returns the remaining burn in.
void	stopApproximationScheme ()
	Stop the approximation scheme.
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error.
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message.

Public Attributes
Signaler< Size, double, double >	onProgress
	Progression, error and time.
Signaler< std::string_view >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
void	computeKL_ () final
void	process_ ()
void	updateSamplingNodes_ ()

Protected Attributes
const IBayesNet< GUM_SCALAR > &	p_
const IBayesNet< GUM_SCALAR > &	q_
GUM_SCALAR	klPQ_
GUM_SCALAR	klQP_
Size	errorPQ_
Size	errorQP_
GUM_SCALAR	hellinger_
GUM_SCALAR	bhattacharya_
GUM_SCALAR	jsd_
double	current_epsilon_
	Current epsilon.
double	last_epsilon_
	Last epsilon value.
double	current_rate_
	Current rate.
Size	current_step_
	The current step.
Timer	timer_
	The timer.
ApproximationSchemeSTATE	current_state_
	The current state.
std::vector< double >	history_
	The scheme history, used only if verbosity == true.
double	eps_
	Threshold for convergence.
bool	enabled_eps_
	If true, the threshold convergence is enabled.
double	min_rate_eps_
	Threshold for the epsilon rate.
bool	enabled_min_rate_eps_
	If true, the minimal threshold for epsilon rate is enabled.
double	max_time_
	The timeout.
bool	enabled_max_time_
	If true, the timeout is enabled.
Size	max_iter_
	The maximum iterations.
bool	enabled_max_iter_
	If true, the maximum iterations stopping criterion is enabled.
Size	burn_in_
	Number of iterations before checking stopping criteria.
Size	period_size_
	Checking criteria frequency.
bool	verbosity_
	If true, verbosity is enabled.
Size	counting_
const IBayesNet< GUM_SCALAR > &	samplingBn_
const NodeProperty< Idx > *	hardEv_
Sequence< NodeId >	samplingNodes_
Size	nbr_
bool	atRandom_

Private Member Functions
bool	_checkCompatibility_ () const
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.
void	_drawVarMonteCarlo_ (NodeId nod, Instantiation *I)
void	_GibbsSample_ (NodeId id, Instantiation *I)
	change in Instantiation I a new drawn value for id

Private Attributes
Complexity	_difficulty_
bool	_done_

Detailed Description

template<GUM_Numeric 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 96 of file GibbsBNdistance.h.

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

enum class gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 87 of file IApproximationSchemeConfiguration.h.

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

Constructor & Destructor Documentation

◆ GibbsBNdistance() [1/2]

template<GUM_Numeric 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 74 of file GibbsBNdistance_tpl.h.

                                                                                   :
      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);
  }

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

Referenced by GibbsBNdistance(), GibbsBNdistance(), and ~GibbsBNdistance().

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◆ GibbsBNdistance() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

explicit

copy constructor

Definition at line 93 of file GibbsBNdistance_tpl.h.

                                                                                   :
      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);
  }

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

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◆ ~GibbsBNdistance()

template<GUM_Numeric GUM_SCALAR>

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

override

destructor

Definition at line 113 of file GibbsBNdistance_tpl.h.

                                                  {
    GUM_DESTRUCTOR(GibbsBNdistance);
  }

References GibbsBNdistance().

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Member Function Documentation

◆ _checkCompatibility_()

template<GUM_Numeric GUM_SCALAR>

bool gum::BNdistance< GUM_SCALAR >::_checkCompatibility_ ( ) const

privateinherited

Definition at line 145 of file BNdistance_tpl.h.

                                                            {
    // should not be used
    if (p_.size() != q_.size())
      GUM_ERROR(OperationNotAllowed,
                "BNdistance : the 2 BNs are not compatible (not the same size)")
 
    for (auto node: p_.nodes()) {
      const DiscreteVariable& vp = p_.variable(node);
      if (!q_.exists(vp.name())) {
        GUM_ERROR(OperationNotAllowed,
                  "BNdistance : the 2 BNs are not compatible (variable : " + vp.name() + ")");
      }
      const DiscreteVariable& vq = q_.variableFromName(vp.name());
      if (vp != vq)
        GUM_ERROR(OperationNotAllowed,
                  "BNdistance : the 2 BNs are not compatible "
                  "(not the same variable for the same name : "
                      + vp.toString() + "and " + vq.toString() + ")");
    }
 
    if (std::fabs(p_.log10DomainSize() - q_.log10DomainSize()) > 1e-14) {
      GUM_ERROR(OperationNotAllowed,
                "BNdistance : the 2 BNs are not compatible (not the same domainSize) : p="
                    << p_.log10DomainSize() << " q=" << q_.log10DomainSize() << " => "
                    << p_.log10DomainSize() - q_.log10DomainSize());
    }
 
    return true;
  }

References GUM_ERROR, gum::Variable::name(), p_, q_, and gum::DiscreteVariable::toString().

Referenced by BNdistance().

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◆ _drawVarMonteCarlo_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::_drawVarMonteCarlo_	(	NodeId	nod,
		Instantiation *	I )

privateinherited

Definition at line 103 of file gibbsOperator_tpl.h.

                                                                                    {
    gum::Instantiation Itop(*I);
    Itop.erase(samplingBn_.variable(nod));
    I->chgVal(samplingBn_.variable(nod), samplingBn_.cpt(nod).extract(Itop).draw());
  }

References gum::Instantiation::chgVal(), gum::Instantiation::erase(), and samplingBn_.

Referenced by monteCarloSample().

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◆ _GibbsSample_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::_GibbsSample_	(	NodeId	id,
		Instantiation *	I )

privateinherited

change in Instantiation I a new drawn value for id

Definition at line 123 of file gibbsOperator_tpl.h.

                                                                             {
    gum::Instantiation Itop(*I);
    Itop.erase(samplingBn_.variable(id));
    gum::Tensor< GUM_SCALAR > p = samplingBn_.cpt(id).extract(Itop);
    for (const auto nod: samplingBn_.children(id))
      p *= samplingBn_.cpt(nod).extract(Itop);
    GUM_ASSERT(p.nbrDim() == 1);
    if (p.sum() != 0) {
      p.normalize();
      I->chgVal(samplingBn_.variable(id), p.draw());
    }
  }

References gum::Instantiation::chgVal(), gum::Tensor< GUM_SCALAR >::draw(), gum::Instantiation::erase(), gum::MultiDimDecorator< GUM_ELEMENT >::nbrDim(), gum::Tensor< GUM_SCALAR >::normalize(), samplingBn_, and gum::Tensor< GUM_SCALAR >::sum().

Referenced by nextSample().

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◆ bhattacharya()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: Bhattacharya distance (

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

Definition at line 110 of file BNdistance_tpl.h.

                                                {
    process_();
    return bhattacharya_;
  }

References bhattacharya_, and process_().

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◆ burnIn()

template<GUM_Numeric 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 206 of file GibbsBNdistance_tpl.h.

                                                   {
    return this->burn_in_;
  }

References gum::ApproximationScheme::burn_in_.

Referenced by computeKL_().

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◆ computeKL_()

template<GUM_Numeric GUM_SCALAR>

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

finalprotectedvirtual

Reimplemented from gum::BNdistance< GUM_SCALAR >.

Definition at line 118 of file GibbsBNdistance_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
    this->updateSamplingNodes_();
    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)std::log2(ratio);
          klPQ_ += delta;
 
          // pmid!=0
          jsd_ -= std::log2(pp / pmid) + ratio * std::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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◆ continueApproximationScheme()

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 69 of file approximationScheme.cpp.

                                                                    {
    // 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;
    }
  }

References gum::IApproximationSchemeConfiguration::Continue, current_epsilon_, current_rate_, current_state_, current_step_, enabled_eps_, enabled_max_iter_, enabled_max_time_, enabled_min_rate_eps_, eps_, gum::IApproximationSchemeConfiguration::Epsilon, GUM_EMIT3, GUM_ERROR, history_, last_epsilon_, gum::IApproximationSchemeConfiguration::Limit, max_iter_, max_time_, gum::IApproximationSchemeConfiguration::messageApproximationScheme(), min_rate_eps_, gum::IApproximationSchemeConfiguration::onProgress, gum::IApproximationSchemeConfiguration::Rate, startOfPeriod(), stateApproximationScheme(), stopScheme_(), gum::IApproximationSchemeConfiguration::TimeLimit, timer_, and verbosity().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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◆ currentTime()

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

overridevirtualinherited

Returns the current running time in second.

Returns: Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 137 of file approximationScheme_inl.h.

137{ return timer_.step(); }

References timer_.

◆ difficulty()

template<GUM_Numeric 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 87 of file BNdistance_tpl.h.

                                                        {
    return _difficulty_;
  }

References _difficulty_.

◆ disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

overridevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 75 of file approximationScheme_inl.h.

75{ enabled_eps_ = false; }

References enabled_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

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◆ disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

overridevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

116{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

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◆ disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

overridevirtualinherited

Disable stopping criterion on timeout.

Returns: Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 140 of file approximationScheme_inl.h.

140{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

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◆ disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 96 of file approximationScheme_inl.h.

96{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), and gum::learning::EMApproximationScheme::setEpsilon().

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◆ enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 78 of file approximationScheme_inl.h.

78{ enabled_eps_ = true; }

References enabled_eps_.

◆ enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

overridevirtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 119 of file approximationScheme_inl.h.

119{ enabled_max_iter_ = true; }

References enabled_max_iter_.

◆ enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

overridevirtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 143 of file approximationScheme_inl.h.

143{ enabled_max_time_ = true; }

References enabled_max_time_.

◆ enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

overridevirtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 99 of file approximationScheme_inl.h.

99{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::MCBNDistance< GUM_SCALAR >::computeKL_().

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◆ epsilon()

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

overridevirtualinherited

Returns the value of epsilon.

Returns: Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

72{ return eps_; }

References eps_.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::onContextualize_(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

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◆ errorPQ()

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

Definition at line 122 of file BNdistance_tpl.h.

                                         {
    process_();
    return errorPQ_;
  }

References errorPQ_, and process_().

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◆ errorQP()

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

Definition at line 128 of file BNdistance_tpl.h.

                                         {
    process_();
    return errorQP_;
  }

References errorQP_, and process_().

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◆ hellinger()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: hellinger distance (

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

Definition at line 104 of file BNdistance_tpl.h.

                                             {
    process_();
    return hellinger_;
  }

References hellinger_, and process_().

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◆ history()

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

overridevirtualinherited

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 179 of file approximationScheme_inl.h.

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

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

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◆ initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 190 of file approximationScheme_inl.h.

                                                           {
    current_state_   = ApproximationSchemeSTATE::Continue;
    current_step_    = 0;
    current_epsilon_ = current_rate_ = -1.0;
    history_.clear();
    timer_.reset();
  }

References ApproximationScheme(), gum::IApproximationSchemeConfiguration::Continue, current_state_, current_step_, and initApproximationScheme().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), initApproximationScheme(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInference(), and gum::SamplingInference< GUM_SCALAR >::onStateChanged_().

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◆ isDrawnAtRandom()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Definition at line 147 of file gibbsOperator_tpl.h.

                                                          {
    return atRandom_;
  }

References atRandom_.

◆ isEnabledEpsilon()

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

overridevirtualinherited

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 82 of file approximationScheme_inl.h.

82{ return enabled_eps_; }

References enabled_eps_.

◆ isEnabledMaxIter()

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

overridevirtualinherited

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 123 of file approximationScheme_inl.h.

123{ return enabled_max_iter_; }

References enabled_max_iter_.

◆ isEnabledMaxTime()

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

overridevirtualinherited

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 147 of file approximationScheme_inl.h.

147{ return enabled_max_time_; }

References enabled_max_time_.

◆ isEnabledMinEpsilonRate()

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

overridevirtualinherited

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 103 of file approximationScheme_inl.h.

103{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::MCBNDistance< GUM_SCALAR >::computeKL_().

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◆ jsd()

template<GUM_Numeric GUM_SCALAR>

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 116 of file BNdistance_tpl.h.

                                       {
    process_();
    return jsd_;
  }

References jsd_, and process_().

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◆ klPQ()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: divergence KL(P||Q)

Definition at line 92 of file BNdistance_tpl.h.

                                        {
    process_();
    return klPQ_;
  }

References klPQ_, and process_().

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◆ klQP()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: divergence KL(Q||P)

Definition at line 98 of file BNdistance_tpl.h.

                                        {
    process_();
    return klQP_;
  }

References klQP_, and process_().

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◆ maxIter()

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

overridevirtualinherited

Returns the criterion on number of iterations.

Returns: Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 113 of file approximationScheme_inl.h.

113{ return max_iter_; }

References max_iter_.

◆ maxTime()

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

overridevirtualinherited

Returns the timeout (in seconds).

Returns: Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 134 of file approximationScheme_inl.h.

134{ return max_time_; }

References max_time_.

◆ messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns: Returns the approximation scheme message.

Definition at line 64 of file IApproximationSchemeConfiguration.cpp.

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

References Continue, Epsilon, epsilon(), Limit, maxIter(), maxTime(), minEpsilonRate(), Rate, stateApproximationScheme(), Stopped, TimeLimit, and Undefined.

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

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◆ minEpsilonRate()

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

overridevirtualinherited

Returns the value of the minimal epsilon rate.

Returns: Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

93{ return min_rate_eps_; }

References min_rate_eps_.

◆ monteCarloSample()

template<GUM_Numeric 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 88 of file gibbsOperator_tpl.h.

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

References _drawVarMonteCarlo_(), gum::Instantiation::add(), gum::Instantiation::chgVal(), hardEv_, and samplingBn_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::GibbsSampling< GUM_SCALAR >::monteCarloSample_().

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◆ nbrDrawnVar()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Getters and setters.

Definition at line 137 of file gibbsOperator_tpl.h.

                                                      {
    return nbr_;
  }

References nbr_.

◆ nbrIterations()

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

overridevirtualinherited

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 170 of file approximationScheme_inl.h.

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

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

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::MCBNDistance< GUM_SCALAR >::computeKL_().

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◆ nextSample()

template<GUM_Numeric GUM_SCALAR>

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

inherited

draws next sample of Gibbs sampling

Definition at line 110 of file gibbsOperator_tpl.h.

                                                                          {
    for (Idx i = 0; i < nbr_; i++) {
      auto pos
          = atRandom_ ? randomValue(samplingNodes_.size()) : (counting_ % samplingNodes_.size());
      this->_GibbsSample_(samplingNodes_[pos], &prev);
      counting_++;
    }
    return prev;
  }

References _GibbsSample_(), atRandom_, counting_, nbr_, gum::randomValue(), and samplingNodes_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::GibbsSampling< GUM_SCALAR >::draw_().

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◆ p()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: p

Definition at line 134 of file BNdistance_tpl.h.

                                                                   {
    return p_;
  }

References p_.

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

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◆ periodSize()

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

overridevirtualinherited

Returns the period size.

Returns: Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

156{ return period_size_; }

gum::ApproximationScheme::period_size_

Size period_size_

Checking criteria frequency.

Definition approximationScheme.h:426

References period_size_.

◆ process_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 177 of file BNdistance_tpl.h.

                                          {
    if (!_done_) {
      computeKL_();
      _done_ = true;
    }
  }

References _done_, and computeKL_().

Referenced by bhattacharya(), errorPQ(), errorQP(), hellinger(), jsd(), klPQ(), and klQP().

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◆ q()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns: q

Definition at line 139 of file BNdistance_tpl.h.

                                                                   {
    return q_;
  }

References q_.

◆ remainingBurnIn()

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

inherited

Returns the remaining burn in.

Returns: Returns the remaining burn in.

Definition at line 213 of file approximationScheme_inl.h.

                                                         {
    if (burn_in_ > current_step_) {
      return burn_in_ - current_step_;
    } else {
      return 0;
    }
  }

References burn_in_, and current_step_.

◆ setBurnIn()

template<GUM_Numeric 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

OutOfBounds Raised if b < 1.

Definition at line 201 of file GibbsBNdistance_tpl.h.

                                                      {
    this->burn_in_ = b;
  }

References gum::ApproximationScheme::burn_in_.

Referenced by GibbsBNdistance(), and GibbsBNdistance().

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◆ setDrawnAtRandom()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Definition at line 152 of file gibbsOperator_tpl.h.

                                                                  {
    atRandom_ = atRandom;
  }

References atRandom_.

◆ setEpsilon()

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

overridevirtualinherited

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

OutOfBounds Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 64 of file approximationScheme_inl.h.

                                                        {
    if (eps < 0.) { GUM_ERROR(OutOfBounds, "eps should be >=0") }
 
    eps_         = eps;
    enabled_eps_ = true;
  }

References enabled_eps_, eps_, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setEpsilon().

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◆ setMaxIter()

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

overridevirtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfBounds Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

                                                      {
    if (max < 1) { GUM_ERROR(OutOfBounds, "max should be >=1") }
    max_iter_         = max;
    enabled_max_iter_ = true;
  }

References enabled_max_iter_, GUM_ERROR, and max_iter_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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◆ setMaxTime()

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

overridevirtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout The timeout value in seconds.

Exceptions

OutOfBounds Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 127 of file approximationScheme_inl.h.

                                                            {
    if (timeout <= 0.) { GUM_ERROR(OutOfBounds, "timeout should be >0.") }
    max_time_         = timeout;
    enabled_max_time_ = true;
  }

References enabled_max_time_, GUM_ERROR, and max_time_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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◆ setMinEpsilonRate()

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

overridevirtualinherited

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

OutOfBounds if rate<0

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 85 of file approximationScheme_inl.h.

                                                                {
    if (rate < 0) { GUM_ERROR(OutOfBounds, "rate should be >=0") }
 
    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

References enabled_min_rate_eps_, GUM_ERROR, and min_rate_eps_.

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

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◆ setNbrDrawnVar()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Definition at line 142 of file gibbsOperator_tpl.h.

                                                           {
    nbr_ = nbr;
  }

References nbr_.

◆ setPeriodSize()

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

overridevirtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfBounds Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

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

References GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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◆ setVerbosity()

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

overridevirtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 159 of file approximationScheme_inl.h.

159{ verbosity_ = v; }

gum::ApproximationScheme::verbosity_

bool verbosity_

If true, verbosity is enabled.

Definition approximationScheme.h:429

References verbosity_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), gum::MCBNDistance< GUM_SCALAR >::MCBNDistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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◆ startOfPeriod()

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

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 200 of file approximationScheme_inl.h.

                                                       {
    if (current_step_ < burn_in_) { return false; }
 
    if (period_size_ == 1) { return true; }
 
    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

Referenced by continueApproximationScheme().

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◆ stateApproximationScheme()

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

overridevirtualinherited

Returns the approximation scheme state.

Returns: Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 165 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

Referenced by continueApproximationScheme(), history(), and nbrIterations().

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◆ stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 222 of file approximationScheme_inl.h.

                                                           {
    if (current_state_ == ApproximationSchemeSTATE::Continue) {
      stopScheme_(ApproximationSchemeSTATE::Stopped);
    }
  }

Referenced by gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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◆ stopScheme_()

INLINE void gum::ApproximationScheme::stopScheme_ ( ApproximationSchemeSTATE new_state )

privateinherited

Stop the scheme given a new state.

Parameters

new_state The scheme new state.

Definition at line 231 of file approximationScheme_inl.h.

                                                                                 {
    if (new_state == ApproximationSchemeSTATE::Continue) { return; }
 
    if (new_state == ApproximationSchemeSTATE::Undefined) { return; }
 
    current_state_ = new_state;
    timer_.pause();
 
    if (onStop.hasListener()) { GUM_EMIT1(onStop, messageApproximationScheme()); }
  }

References gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by continueApproximationScheme(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter().

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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 209 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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◆ updateSamplingNodes_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::updateSamplingNodes_ ( )

protectedinherited

Definition at line 73 of file gibbsOperator_tpl.h.

                                                         {
    samplingNodes_.clear();
    for (const auto node: samplingBn_.nodes())
      if (hardEv_ == nullptr || !hardEv_->exists(node)) { samplingNodes_.insert(node); }
    if (samplingNodes_.size() == 0) {
      GUM_ERROR(InvalidArgument, "No node to sample (too many nodes or too much evidence)!")
    }
    if (nbr_ > samplingNodes_.size()) nbr_ = samplingNodes_.size();
  }

References GUM_ERROR, hardEv_, nbr_, samplingBn_, and samplingNodes_.

Referenced by gum::GibbsSampling< GUM_SCALAR >::burnIn_(), and gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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◆ verbosity()

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

overridevirtualinherited

Returns true if verbosity is enabled.

Returns: Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

161{ return verbosity_; }

References verbosity_.

Referenced by ApproximationScheme(), gum::learning::EMApproximationScheme::EMApproximationScheme(), and continueApproximationScheme().

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Member Data Documentation

◆ _difficulty_

template<GUM_Numeric GUM_SCALAR>

Complexity gum::BNdistance< GUM_SCALAR >::_difficulty_

privateinherited

Definition at line 171 of file BNdistance.h.

Referenced by BNdistance(), BNdistance(), and difficulty().

◆ _done_

template<GUM_Numeric GUM_SCALAR>

bool gum::BNdistance< GUM_SCALAR >::_done_

privateinherited

Definition at line 172 of file BNdistance.h.

Referenced by BNdistance(), BNdistance(), and process_().

◆ atRandom_

template<GUM_Numeric GUM_SCALAR>

bool gum::GibbsOperator< GUM_SCALAR >::atRandom_

protectedinherited

Definition at line 104 of file gibbsOperator.h.

Referenced by GibbsOperator(), isDrawnAtRandom(), nextSample(), and setDrawnAtRandom().

◆ bhattacharya_

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::bhattacharya_

protectedinherited

Definition at line 166 of file BNdistance.h.

Referenced by bhattacharya(), gum::ExactBNdistance< GUM_SCALAR >::computeKL_(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::MCBNDistance< GUM_SCALAR >::computeKL_().

◆ burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 423 of file approximationScheme.h.

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

◆ counting_

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsOperator< GUM_SCALAR >::counting_

protectedinherited

Definition at line 98 of file gibbsOperator.h.

Referenced by GibbsOperator(), and nextSample().

◆ current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by continueApproximationScheme().

◆ current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by continueApproximationScheme().

◆ current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

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

◆ current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by continueApproximationScheme(), initApproximationScheme(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), nbrIterations(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), gum::learning::SimpleMiic::orientationMiic_(), remainingBurnIn(), and updateApproximationScheme().

◆ enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 402 of file approximationScheme.h.

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

◆ enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 420 of file approximationScheme.h.

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

◆ enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 414 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMaxTime(), enableMaxTime(), isEnabledMaxTime(), and 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 408 of file approximationScheme.h.

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

◆ eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 399 of file approximationScheme.h.

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

◆ errorPQ_

template<GUM_Numeric GUM_SCALAR>

Size gum::BNdistance< GUM_SCALAR >::errorPQ_

protectedinherited

Definition at line 162 of file BNdistance.h.

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

◆ errorQP_

template<GUM_Numeric GUM_SCALAR>

Size gum::BNdistance< GUM_SCALAR >::errorQP_

protectedinherited

Definition at line 163 of file BNdistance.h.

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

◆ hardEv_

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 100 of file gibbsOperator.h.

Referenced by GibbsOperator(), monteCarloSample(), and updateSamplingNodes_().

◆ hellinger_

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::hellinger_

protectedinherited

Definition at line 165 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::computeKL_(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), and hellinger().

◆ history_

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

protectedinherited

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

Definition at line 396 of file approximationScheme.h.

Referenced by continueApproximationScheme().

◆ jsd_

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::jsd_

protectedinherited

Definition at line 167 of file BNdistance.h.

Referenced by gum::ExactBNdistance< GUM_SCALAR >::computeKL_(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), and jsd().

◆ klPQ_

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::klPQ_

protectedinherited

Definition at line 159 of file BNdistance.h.

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

◆ klQP_

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::BNdistance< GUM_SCALAR >::klQP_

protectedinherited

Definition at line 160 of file BNdistance.h.

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

◆ last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

Referenced by continueApproximationScheme().

◆ max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

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

◆ max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

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

◆ min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 405 of file approximationScheme.h.

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

◆ nbr_

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsOperator< GUM_SCALAR >::nbr_

protectedinherited

Definition at line 103 of file gibbsOperator.h.

Referenced by GibbsOperator(), nbrDrawnVar(), nextSample(), setNbrDrawnVar(), and updateSamplingNodes_().

◆ onProgress

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

inherited

Progression, error and time.

Definition at line 81 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::learning::IBNLearner::distributeProgress(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

◆ onStop

Signaler< std::string_view > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 84 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::IBNLearner::distributeStop().

◆ p_

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 156 of file BNdistance.h.

Referenced by BNdistance(), BNdistance(), _checkCompatibility_(), gum::ExactBNdistance< GUM_SCALAR >::computeKL_(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::drawSample_(), and p().

◆ period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 426 of file approximationScheme.h.

Referenced by ApproximationScheme(), and periodSize().

◆ q_

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 157 of file BNdistance.h.

Referenced by BNdistance(), BNdistance(), _checkCompatibility_(), gum::ExactBNdistance< GUM_SCALAR >::computeKL_(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::MCBNDistance< GUM_SCALAR >::computeKL_(), and q().

◆ samplingBn_

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 99 of file gibbsOperator.h.

Referenced by GibbsOperator(), _drawVarMonteCarlo_(), _GibbsSample_(), monteCarloSample(), and updateSamplingNodes_().

◆ samplingNodes_

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 101 of file gibbsOperator.h.

Referenced by nextSample(), and updateSamplingNodes_().

◆ timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 390 of file approximationScheme.h.

Referenced by continueApproximationScheme(), currentTime(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

◆ verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 429 of file approximationScheme.h.

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

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

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

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

Constructor & Destructor Documentation

◆ GibbsBNdistance() [1/2]

◆ GibbsBNdistance() [2/2]

◆ ~GibbsBNdistance()

Member Function Documentation

◆ _checkCompatibility_()

◆ _drawVarMonteCarlo_()

◆ _GibbsSample_()

◆ bhattacharya()

◆ burnIn()

◆ computeKL_()

◆ 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()

◆ process_()

◆ q()

◆ remainingBurnIn()

◆ setBurnIn()

◆ setDrawnAtRandom()

◆ setEpsilon()

◆ setMaxIter()

◆ setMaxTime()

◆ setMinEpsilonRate()

◆ setNbrDrawnVar()

◆ setPeriodSize()

◆ setVerbosity()

◆ startOfPeriod()

◆ stateApproximationScheme()

◆ stopApproximationScheme()

◆ stopScheme_()

◆ updateApproximationScheme()

◆ updateSamplingNodes_()

◆ verbosity()

Member Data Documentation

◆ _difficulty_

◆ _done_

◆ atRandom_

◆ bhattacharya_