A class that, given a structure and a parameter estimator returns a full Bayes net. More...

#include <agrum/BN/learning/paramUtils/DAG2BNLearner.h>

Inheritance diagram for gum::learning::DAG2BNLearner:

Collaboration diagram for gum::learning::DAG2BNLearner:

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

Public Member Functions
template<typename GUM_SCALAR>
INLINE BayesNet< GUM_SCALAR >	createBN (ParamEstimator &estimator, const DAG &dag)
	create a BN
template<typename GUM_SCALAR>
INLINE BayesNet< GUM_SCALAR >	createBNwithEM (ParamEstimator &bootstrap_estimator, ParamEstimator &EM_estimator, const DAG &dag)
	create a BN with EM: initialized by an estimator
template<typename GUM_SCALAR>
INLINE BayesNet< GUM_SCALAR >	createBNwithEM (ParamEstimator &bootstrap_estimator, ParamEstimator &EM_estimator, const BayesNet< GUM_SCALAR > &bn)
	create a BN with EM: initialized by the parameters of a BN
void	setEpsilon (double eps) override
	sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelihoods
void	setMinDiffEpsilon (double eps)
	sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelihoods
void	setMinEpsilonRate (double rate) override
	sets the stopping criterion of EM as being the minimal log-likelihood's evolution rate
Constructors / Destructors
	DAG2BNLearner ()
	default constructor
	DAG2BNLearner (const DAG2BNLearner &from)
	copy constructor
	DAG2BNLearner (DAG2BNLearner &&from)
	move constructor
virtual DAG2BNLearner *	clone () const
	virtual copy constructor
virtual	~DAG2BNLearner ()
	destructor
Operators
DAG2BNLearner &	operator= (const DAG2BNLearner &from)
	copy operator
DAG2BNLearner &	operator= (DAG2BNLearner &&from)
	move operator
Getters and setters
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.
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
Signaler3< Size, double, double >	onProgress
	Progression, error and time.
Signaler1< const std::string & >	onStop
	Criteria messageApproximationScheme.

Protected Attributes
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.

Private Member Functions
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.

Accessors / Modifiers
DAG2BNLearner &	setNoise (const double noise)
	sets the noise amount used to perturb the initial CPTs used by EM
template<typename GUM_SCALAR = double>
BayesNet< GUM_SCALAR >	createBNwithEM (ParamEstimator &bootstrap_estimator, ParamEstimator &EM_estimator, const DAG &dag)
	creates a BN with a given structure (dag) using the EM algorithm
template<typename GUM_SCALAR>
BayesNet< GUM_SCALAR >	createBNwithEM (ParamEstimator &bootstrap_estimator, ParamEstimator &EM_estimator, const BayesNet< GUM_SCALAR > &bn)
	creates a BN using the EM algorithm with the structure specified by bn, initialized by the parameters of bn
template<typename GUM_SCALAR>
BayesNet< GUM_SCALAR >	createBNwithEM (ParamEstimator &bootstrap_estimator, ParamEstimator &EM_estimator, BayesNet< GUM_SCALAR > &&bn)
	creates a BN using the EM algorithm with the structure specified by bn, initialized by the parameters of bn
EMApproximationScheme &	approximationScheme ()
	returns the approximation policy of the EM learning algorithm
template<typename GUM_SCALAR = double>
static BayesNet< GUM_SCALAR >	createBN (ParamEstimator &estimator, const DAG &dag)
	create a BN from a DAG using a one pass generator (typically ML)

Detailed Description

A class that, given a structure and a parameter estimator returns a full Bayes net.

Definition at line 70 of file DAG2BNLearner.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 86 of file IApproximationSchemeConfiguration.h.

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

Constructor & Destructor Documentation

◆ DAG2BNLearner() [1/3]

gum::learning::DAG2BNLearner::DAG2BNLearner ( )

default constructor

Referenced by DAG2BNLearner(), DAG2BNLearner(), clone(), createBN(), operator=(), operator=(), and setNoise().

Here is the caller graph for this function:

◆ DAG2BNLearner() [2/3]

gum::learning::DAG2BNLearner::DAG2BNLearner ( const DAG2BNLearner & from )

copy constructor

References DAG2BNLearner().

Here is the call graph for this function:

◆ DAG2BNLearner() [3/3]

gum::learning::DAG2BNLearner::DAG2BNLearner ( DAG2BNLearner && from )

move constructor

References DAG2BNLearner().

Here is the call graph for this function:

◆ ~DAG2BNLearner()

virtual gum::learning::DAG2BNLearner::~DAG2BNLearner ( )

virtual

destructor

Member Function Documentation

◆ approximationScheme()

EMApproximationScheme & gum::learning::DAG2BNLearner::approximationScheme ( )

returns the approximation policy of the EM learning algorithm

References gum::learning::EMApproximationScheme::EMApproximationScheme().

Here is the call graph for this function:

◆ clone()

virtual DAG2BNLearner * gum::learning::DAG2BNLearner::clone ( ) const

virtual

virtual copy constructor

References DAG2BNLearner().

Here is the call graph for this function:

◆ 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 229 of file approximationScheme_inl.h.

                                                                           {
    // 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 enabled_max_time_, and timer_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::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_().

Here is the caller graph for this function:

◆ createBN() [1/2]

template<typename GUM_SCALAR = double>

BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBN	(	ParamEstimator &	estimator,
		const DAG &	dag )

static

create a BN from a DAG using a one pass generator (typically ML)

Referenced by createBNwithEM(), gum::learning::GreedyHillClimbing::learnBN(), gum::learning::LocalSearchWithTabuList::learnBN(), gum::learning::Miic::learnBN(), and gum::learning::SimpleMiic::learnBN().

Here is the caller graph for this function:

◆ createBN() [2/2]

template<typename GUM_SCALAR>

INLINE BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBN	(	ParamEstimator &	estimator,
		const DAG &	dag )

create a BN

Definition at line 73 of file DAG2BNLearner_tpl.h.

                                                                               {
      return DAG2BNLearner()._createBN_(estimator, dag, false);
    }

References DAG2BNLearner().

Here is the call graph for this function:

◆ createBNwithEM() [1/5]

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBNwithEM	(	ParamEstimator &	bootstrap_estimator,
		ParamEstimator &	EM_estimator,
		BayesNet< GUM_SCALAR > &&	bn )

creates a BN using the EM algorithm with the structure specified by bn, initialized by the parameters of bn

create a BN with EM: initialized by the parameters of a BN

Parameters

bn	a Bayes Net that is used both for specifying the structure of the returned BN and for initializing the parameters for the EM algorithm. CPTs filled exclusively with only zeroes are initialized directly the BNLearner using the bootstrap_estimator.

Warning: : the bn is modified by EM. If you do not want its value to be altered, prefer using the createBNwithEM with the const ref parameter

Parameters

bootstrap_estimator	the ParamEstimator used to initialize the CPTs of bn when those are filled exclusively with zeroes
EM_estimator	the ParamEstimator used in all the EM steps (except the initialization). It is used in a loop until the stopping condition is met (max of relative error on every parameter<epsilon)

Returns: a BN whose structure is the same as bn and whose parameters are learnt by EM.

Definition at line 158 of file DAG2BNLearner_tpl.h.

                                                                                      {
      // estimate the parameters of the fully zeroed CPTs using the bootstrap estimator
      const VariableNodeMap& varmap = bn.variableNodeMap();
      for (const auto id: bn.dag()) {
        // get the CPT of node id and its variables in the correct order
        auto& pot = const_cast< Tensor< GUM_SCALAR >& >(bn.cpt(id));
 
        // check if the CPT contains only zeroes
        bool all_zeroed = true;
        for (gum::Instantiation inst(pot); !inst.end(); inst.inc()) {
          if (pot[inst] != 0.0) {
            all_zeroed = false;
            break;
          }
        }
 
        // estimate the initial parameters of pot if all_zeroed
        if (all_zeroed) {
          // get the conditioning variables: they are all the variables except
          // the first one in pot
          const auto&           vars = pot.variablesSequence();
          std::vector< NodeId > conditioning_ids(vars.size() - 1);
          for (auto i = std::size_t(1); i < vars.size(); ++i) {
            conditioning_ids[i - 1] = varmap.get(*(vars[i]));
          }
 
          // estimate the initial parameters of pot
          bootstrap_estimator.setParameters(id, conditioning_ids, pot, false);
        }
      }
 
      return _performEM_(bootstrap_estimator, EM_estimator, std::move(bn));
    }

References gum::Instantiation::end(), gum::VariableNodeMap::get(), gum::learning::ParamEstimator::setParameters(), and gum::MultiDimDecorator< GUM_SCALAR >::variablesSequence().

Here is the call graph for this function:

◆ createBNwithEM() [2/5]

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBNwithEM	(	ParamEstimator &	bootstrap_estimator,
		ParamEstimator &	EM_estimator,
		const BayesNet< GUM_SCALAR > &	bn )

creates a BN using the EM algorithm with the structure specified by bn, initialized by the parameters of bn

Parameters

bn	a Bayes Net that is used both for specifying the structure of the returned BN and for initializing the parameters for the EM algorithm. CPTs filled exclusively with only zeroes are initialized directly the BNLearner using the bootstrap_estimator.

Warning: : the bn is copied, so that it is unmodified by EM. If you do not care about its current value, prefer using the createBNwithEM with the ref ref parameter

Parameters

bootstrap_estimator	the ParamEstimator used to initialize the CPTs of bn when those are filled exclusively with zeroes
EM_estimator	the ParamEstimator used in all the EM steps (except the initialization). It is used in a loop until the stopping condition is met (max of relative error on every parameter<epsilon)

Returns: a BN whose structure is the same as bn and whose parameters are learnt by EM.

◆ createBNwithEM() [3/5]

template<typename GUM_SCALAR>

INLINE BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBNwithEM	(	ParamEstimator &	bootstrap_estimator,
		ParamEstimator &	EM_estimator,
		const BayesNet< GUM_SCALAR > &	bn )

create a BN with EM: initialized by the parameters of a BN

Definition at line 145 of file DAG2BNLearner_tpl.h.

                                                                                                  {
      // for EM estimations, we need to disable caches
      bootstrap_estimator.clear();
      EM_estimator.clear();
 
      auto bn_copy(bn);
      return createBNwithEM(bootstrap_estimator, EM_estimator, std::move(bn_copy));
    }

References gum::learning::ParamEstimator::clear(), and createBNwithEM().

Here is the call graph for this function:

◆ createBNwithEM() [4/5]

template<typename GUM_SCALAR = double>

BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBNwithEM	(	ParamEstimator &	bootstrap_estimator,
		ParamEstimator &	EM_estimator,
		const DAG &	dag )

creates a BN with a given structure (dag) using the EM algorithm

Parameters

bootstrap_estimator	the ParamEstimator used to initialize EM
EM_estimator	the ParamEstimator used in all the EM steps (except the initialization). It is used in a loop until the stopping condition is met (max of relative error on every parameter<epsilon)
dag	the graphical structure of the returned BN

Returns: a BN learnt by EM whose graphical structure is dag

Referenced by createBNwithEM().

Here is the caller graph for this function:

◆ createBNwithEM() [5/5]

template<typename GUM_SCALAR>

INLINE BayesNet< GUM_SCALAR > gum::learning::DAG2BNLearner::createBNwithEM	(	ParamEstimator &	bootstrap_estimator,
		ParamEstimator &	EM_estimator,
		const DAG &	dag )

create a BN with EM: initialized by an estimator

Definition at line 130 of file DAG2BNLearner_tpl.h.

                                                                                     {
      // for EM estimations, we need to disable caches
      bootstrap_estimator.clear();
      EM_estimator.clear();
 
      // bootstrap EM by learning an initial model
      BayesNet< GUM_SCALAR > bn = createBN< GUM_SCALAR >(bootstrap_estimator, dag);
 
      return _performEM_(bootstrap_estimator, EM_estimator, std::move(bn));
    }

References gum::learning::ParamEstimator::clear(), and createBN().

Here is the call graph for this function:

◆ 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 136 of file approximationScheme_inl.h.

136{ return timer_.step(); }

References timer_.

◆ disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

overridevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 74 of file approximationScheme_inl.h.

74{ enabled_eps_ = false; }

References enabled_eps_.

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

Here is the caller graph for this function:

◆ disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

overridevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 115 of file approximationScheme_inl.h.

115{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

Here is the caller graph for this function:

◆ disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

overridevirtualinherited

Disable stopping criterion on timeout.

Returns: Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 139 of file approximationScheme_inl.h.

139{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

Here is the caller graph for this function:

◆ disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 95 of file approximationScheme_inl.h.

95{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

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

Here is the caller graph for this function:

◆ enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

77{ 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 118 of file approximationScheme_inl.h.

118{ 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 142 of file approximationScheme_inl.h.

142{ 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 98 of file approximationScheme_inl.h.

98{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

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

Here is the caller graph for this function:

◆ 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 71 of file approximationScheme_inl.h.

71{ return eps_; }

References eps_.

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

Here is the caller graph for this function:

◆ 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 178 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.

Here is the call graph for this function:

◆ initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 189 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_epsilon_, current_rate_, current_state_, current_step_, and initApproximationScheme().

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

Here is the call graph for this function:

Here is the caller graph for this function:

◆ 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 81 of file approximationScheme_inl.h.

81{ 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 122 of file approximationScheme_inl.h.

122{ 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 146 of file approximationScheme_inl.h.

146{ 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 102 of file approximationScheme_inl.h.

102{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

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

Here is the caller graph for this function:

◆ 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 112 of file approximationScheme_inl.h.

112{ 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 133 of file approximationScheme_inl.h.

133{ return max_time_; }

References max_time_.

◆ messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns: Returns the approximation scheme message.

Definition at line 59 of file IApproximationSchemeConfiguration_inl.h.

                                                                                {
    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();
  }

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

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

Here is the call graph for this function:

Here is the caller graph for this function:

◆ 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 92 of file approximationScheme_inl.h.

92{ return min_rate_eps_; }

References min_rate_eps_.

◆ 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 169 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_().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ operator=() [1/2]

DAG2BNLearner & gum::learning::DAG2BNLearner::operator= ( const DAG2BNLearner & from )

copy operator

References DAG2BNLearner().

Here is the call graph for this function:

◆ operator=() [2/2]

DAG2BNLearner & gum::learning::DAG2BNLearner::operator= ( DAG2BNLearner && from )

move operator

References DAG2BNLearner().

Here is the call graph for this function:

◆ periodSize()

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

overridevirtualinherited

Returns the period size.

Returns: Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 155 of file approximationScheme_inl.h.

155{ return period_size_; }

gum::ApproximationScheme::period_size_

Size period_size_

Checking criteria frequency.

Definition approximationScheme.h:426

References period_size_.

◆ remainingBurnIn()

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

inherited

Returns the remaining burn in.

Returns: Returns the remaining burn in.

Definition at line 212 of file approximationScheme_inl.h.

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

References burn_in_, and current_step_.

◆ setEpsilon()

void gum::learning::EMApproximationScheme::setEpsilon ( double eps )

inlineoverridevirtualinherited

sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelihoods

Parameters

eps	the log-likelihood difference below which EM stops its iterations

Warning: setting this stopping criterion disables the min rate criterion (if it was enabled)

Exceptions

OutOfBounds Raised if eps <= 0

Reimplemented from gum::ApproximationScheme.

Definition at line 89 of file EMApproximationScheme.h.

                                           {
        if (eps <= 0)
          GUM_ERROR(OutOfBounds, "EM's min diff epsilon value must be strictly positive")
        ApproximationScheme::setEpsilon(eps);
        ApproximationScheme::disableMinEpsilonRate();
      }

References gum::ApproximationScheme::disableMinEpsilonRate(), GUM_ERROR, and gum::ApproximationScheme::setEpsilon().

Referenced by setMinDiffEpsilon().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ 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 105 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(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

◆ 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 126 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(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

◆ setMinDiffEpsilon()

void gum::learning::EMApproximationScheme::setMinDiffEpsilon ( double eps )

inlineinherited

sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelihoods

Parameters

eps	the log-likelihood difference below which EM stops its iterations

Warning: setting this stopping criterion disables the min rate criterion (if it was enabled)

Exceptions

OutOfBounds Raised if eps <= 0

Definition at line 104 of file EMApproximationScheme.h.

104{ setEpsilon(eps); }

References setEpsilon().

Here is the call graph for this function:

◆ setMinEpsilonRate()

void gum::learning::EMApproximationScheme::setMinEpsilonRate ( double rate )

inlineoverridevirtualinherited

sets the stopping criterion of EM as being the minimal log-likelihood's evolution rate

Parameters

rate	the log-likelihood evolution rate below which EM stops its iterations

Warning: setting this stopping criterion disables the min diff criterion (if it was enabled)

Exceptions

OutOfBounds if rate<0

Reimplemented from gum::ApproximationScheme.

Definition at line 114 of file EMApproximationScheme.h.

                                                   {
        if (rate <= 0.0)
          GUM_ERROR(OutOfBounds, "EM's min log-likelihood evolution rate must be strictly positive")
        ApproximationScheme::setMinEpsilonRate(rate);
        ApproximationScheme::disableEpsilon();
      }

References gum::ApproximationScheme::disableEpsilon(), GUM_ERROR, and gum::ApproximationScheme::setMinEpsilonRate().

Here is the call graph for this function:

◆ setNoise()

DAG2BNLearner & gum::learning::DAG2BNLearner::setNoise ( const double noise )

sets the noise amount used to perturb the initial CPTs used by EM

Parameters

noise When EM starts, it initializes all the CPTs of the Bayes net. EM adds a noise to these CPTs by mixing their values with some random noise. The formula used is, up to some normalizing constant: new_cpt = (1-noise) * cpt + noise * random_cpt(). Of course, noise must belong to interval [0,1].

References DAG2BNLearner().

Here is the call graph for this function:

◆ 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 149 of file approximationScheme_inl.h.

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

References GUM_ERROR, and period_size_.

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

Here is the caller graph for this function:

◆ 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 158 of file approximationScheme_inl.h.

158{ verbosity_ = v; }

gum::ApproximationScheme::verbosity_

bool verbosity_

If true, verbosity is enabled.

Definition approximationScheme.h:429

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

Here is the caller graph for this function:

◆ 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 199 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);
  }

References burn_in_, and current_step_.

◆ 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 164 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

References current_state_.

Referenced by history(), and nbrIterations().

Here is the caller graph for this function:

◆ stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 221 of file approximationScheme_inl.h.

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

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

Here is the caller graph for this function:

◆ 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 301 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, current_state_, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxTime(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::isEnabledMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::maxTime(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::setPeriodSize().

Here is the caller graph for this function:

◆ 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 208 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::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_().

Here is the caller graph for this function:

◆ 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 160 of file approximationScheme_inl.h.

160{ return verbosity_; }

References verbosity_.

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

Here is the caller graph for this function:

Member Data Documentation

◆ 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(), gum::GibbsSampling< GUM_SCALAR >::setBurnIn(), and startOfPeriod().

◆ current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by initApproximationScheme().

◆ current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by initApproximationScheme().

◆ current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

Referenced by ApproximationScheme(), initApproximationScheme(), stateApproximationScheme(), and stopScheme_().

◆ current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by 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(), startOfPeriod(), 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(), 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(), 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(), 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(), epsilon(), and setEpsilon().

◆ history_

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

protectedinherited

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

Definition at line 396 of file approximationScheme.h.

◆ last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

◆ max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

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

◆ max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

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

◆ onProgress

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

inherited

Progression, error and time.

Definition at line 80 of file IApproximationSchemeConfiguration.h.

Referenced by 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

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

inherited

Criteria messageApproximationScheme.

Definition at line 83 of file IApproximationSchemeConfiguration.h.

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

◆ period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 426 of file approximationScheme.h.

Referenced by ApproximationScheme(), periodSize(), and setPeriodSize().

◆ 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(), and verbosity().

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

agrum/BN/learning/paramUtils/DAG2BNLearner.h
agrum/BN/learning/paramUtils/DAG2BNLearner_tpl.h

Public Types

Public Member Functions

Public Attributes

Protected Attributes

Private Member Functions

Accessors / Modifiers

Detailed Description

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

Constructor & Destructor Documentation

◆ DAG2BNLearner() [1/3]

◆ DAG2BNLearner() [2/3]

◆ DAG2BNLearner() [3/3]

◆ ~DAG2BNLearner()

Member Function Documentation

◆ approximationScheme()

◆ clone()

◆ continueApproximationScheme()

◆ createBN() [1/2]

◆ createBN() [2/2]

◆ createBNwithEM() [1/5]

◆ createBNwithEM() [2/5]

◆ createBNwithEM() [3/5]

◆ createBNwithEM() [4/5]

◆ createBNwithEM() [5/5]

◆ currentTime()

◆ disableEpsilon()

◆ disableMaxIter()

◆ disableMaxTime()

◆ disableMinEpsilonRate()

◆ enableEpsilon()

◆ enableMaxIter()

◆ enableMaxTime()

◆ enableMinEpsilonRate()

◆ epsilon()

◆ history()

◆ initApproximationScheme()

◆ isEnabledEpsilon()

◆ isEnabledMaxIter()

◆ isEnabledMaxTime()

◆ isEnabledMinEpsilonRate()

◆ maxIter()

◆ maxTime()

◆ messageApproximationScheme()

◆ minEpsilonRate()

◆ nbrIterations()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ periodSize()

◆ remainingBurnIn()

◆ setEpsilon()

◆ setMaxIter()

◆ setMaxTime()

◆ setMinDiffEpsilon()

◆ setMinEpsilonRate()

◆ setNoise()

◆ setPeriodSize()

◆ setVerbosity()

◆ startOfPeriod()

◆ stateApproximationScheme()

◆ stopApproximationScheme()

◆ stopScheme_()

◆ updateApproximationScheme()

◆ verbosity()

Member Data Documentation

◆ burn_in_

◆ current_epsilon_

◆ current_rate_

◆ current_state_

◆ current_step_

◆ enabled_eps_

◆ enabled_max_iter_

◆ enabled_max_time_

◆ enabled_min_rate_eps_

◆ eps_

◆ history_

◆ last_epsilon_

◆ max_iter_

◆ max_time_

◆ min_rate_eps_