gum::learning::DAG2BNLearner< ALLOC > Class Template Reference

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

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

Inheritance diagram for gum::learning::DAG2BNLearner< ALLOC >:

Collaboration diagram for gum::learning::DAG2BNLearner< ALLOC >:

Public Attributes
Signaler3< Size, double, double >	onProgress
	Progression, error and time. More...
Signaler1< std::string >	onStop
	Criteria messageApproximationScheme. More...

Public Member Functions
Constructors / Destructors
	DAG2BNLearner (const allocator_type &alloc=allocator_type())
	default constructor More...
	DAG2BNLearner (const DAG2BNLearner< ALLOC > &from)
	copy constructor More...
	DAG2BNLearner (const DAG2BNLearner< ALLOC > &from, const allocator_type &alloc)
	copy constructor with a given allocator More...
	DAG2BNLearner (DAG2BNLearner< ALLOC > &&from)
	move constructor More...
	DAG2BNLearner (DAG2BNLearner< ALLOC > &&from, const allocator_type &alloc)
	move constructor with a given allocator More...
virtual DAG2BNLearner< ALLOC > *	clone () const
	virtual copy constructor More...
virtual DAG2BNLearner< ALLOC > *	clone (const allocator_type &alloc) const
	virtual copy constructor with a given allocator More...
virtual	~DAG2BNLearner ()
	destructor More...
Operators
DAG2BNLearner< ALLOC > &	operator= (const DAG2BNLearner< ALLOC > &from)
	copy operator More...
DAG2BNLearner< ALLOC > &	operator= (DAG2BNLearner< ALLOC > &&from)
	move operator More...
Getters and setters
void	setEpsilon (double eps)
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|. More...
double	epsilon () const
	Returns the value of epsilon. More...
void	disableEpsilon ()
	Disable stopping criterion on epsilon. More...
void	enableEpsilon ()
	Enable stopping criterion on epsilon. More...
bool	isEnabledEpsilon () const
	Returns true if stopping criterion on epsilon is enabled, false otherwise. More...
void	setMinEpsilonRate (double rate)
	Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|). More...
double	minEpsilonRate () const
	Returns the value of the minimal epsilon rate. More...
void	disableMinEpsilonRate ()
	Disable stopping criterion on epsilon rate. More...
void	enableMinEpsilonRate ()
	Enable stopping criterion on epsilon rate. More...
bool	isEnabledMinEpsilonRate () const
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise. More...
void	setMaxIter (Size max)
	Stopping criterion on number of iterations. More...
Size	maxIter () const
	Returns the criterion on number of iterations. More...
void	disableMaxIter ()
	Disable stopping criterion on max iterations. More...
void	enableMaxIter ()
	Enable stopping criterion on max iterations. More...
bool	isEnabledMaxIter () const
	Returns true if stopping criterion on max iterations is enabled, false otherwise. More...
void	setMaxTime (double timeout)
	Stopping criterion on timeout. More...
double	maxTime () const
	Returns the timeout (in seconds). More...
double	currentTime () const
	Returns the current running time in second. More...
void	disableMaxTime ()
	Disable stopping criterion on timeout. More...
void	enableMaxTime ()
	Enable stopping criterion on timeout. More...
bool	isEnabledMaxTime () const
	Returns true if stopping criterion on timeout is enabled, false otherwise. More...
void	setPeriodSize (Size p)
	How many samples between two stopping is enable. More...
Size	periodSize () const
	Returns the period size. More...
void	setVerbosity (bool v)
	Set the verbosity on (true) or off (false). More...
bool	verbosity () const
	Returns true if verbosity is enabled. More...
ApproximationSchemeSTATE	stateApproximationScheme () const
	Returns the approximation scheme state. More...
Size	nbrIterations () const
	Returns the number of iterations. More...
const std::vector< double > &	history () const
	Returns the scheme history. More...
void	initApproximationScheme ()
	Initialise the scheme. More...
bool	startOfPeriod ()
	Returns true if we are at the beginning of a period (compute error is mandatory). More...
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps. More...
Size	remainingBurnIn ()
	Returns the remaining burn in. More...
void	stopApproximationScheme ()
	Stop the approximation scheme. More...
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error. More...
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message. More...

Public Types
using	allocator_type = ALLOC< NodeId >
	type for the allocators passed in arguments of methods More...
enum	ApproximationSchemeSTATE : char {   ApproximationSchemeSTATE::Undefined, ApproximationSchemeSTATE::Continue, ApproximationSchemeSTATE::Epsilon, ApproximationSchemeSTATE::Rate,   ApproximationSchemeSTATE::Limit, ApproximationSchemeSTATE::TimeLimit, ApproximationSchemeSTATE::Stopped }
	The different state of an approximation scheme. More...

Protected Attributes
double	_current_epsilon
	Current epsilon. More...
double	_last_epsilon
	Last epsilon value. More...
double	_current_rate
	Current rate. More...
Size	_current_step
	The current step. More...
Timer	_timer
	The timer. More...
ApproximationSchemeSTATE	_current_state
	The current state. More...
std::vector< double >	_history
	The scheme history, used only if verbosity == true. More...
double	_eps
	Threshold for convergence. More...
bool	_enabled_eps
	If true, the threshold convergence is enabled. More...
double	_min_rate_eps
	Threshold for the epsilon rate. More...
bool	_enabled_min_rate_eps
	If true, the minimal threshold for epsilon rate is enabled. More...
double	_max_time
	The timeout. More...
bool	_enabled_max_time
	If true, the timeout is enabled. More...
Size	_max_iter
	The maximum iterations. More...
bool	_enabled_max_iter
	If true, the maximum iterations stopping criterion is enabled. More...
Size	_burn_in
	Number of iterations before checking stopping criteria. More...
Size	_period_size
	Checking criteria frequency. More...
bool	_verbosity
	If true, verbosity is enabled. More...

Accessors / Modifiers
template<typename GUM_SCALAR = double>
BayesNet< GUM_SCALAR >	createBN (ParamEstimator< ALLOC > &bootstrap_estimator, ParamEstimator< ALLOC > &general_estimator, const DAG &dag)
	create a BN from a DAG using a two pass generator (typically EM) More...
ApproximationScheme &	approximationScheme ()
	returns the approximation policy of the learning algorithm More...
allocator_type	getAllocator () const
	returns the allocator used by the score More...
template<typename GUM_SCALAR = double>
static BayesNet< GUM_SCALAR >	createBN (ParamEstimator< ALLOC > &estimator, const DAG &dag)
	create a BN from a DAG using a one pass generator (typically ML) More...

Detailed Description

template<template< typename > class ALLOC = std::allocator>
class gum::learning::DAG2BNLearner< ALLOC >

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

Definition at line 49 of file DAG2BNLearner.h.

Member Typedef Documentation

allocator_type

template<template< typename > class ALLOC = std::allocator>

using gum::learning::DAG2BNLearner< ALLOC >::allocator_type = ALLOC< NodeId >

type for the allocators passed in arguments of methods

Definition at line 54 of file DAG2BNLearner.h.

Member Enumeration Documentation

ApproximationSchemeSTATE

enum gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 63 of file IApproximationSchemeConfiguration.h.

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

Constructor & Destructor Documentation

DAG2BNLearner() [1/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner

(

const allocator_type &

alloc = allocator_type()

)

default constructor

Referenced by gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner(), and gum::learning::DAG2BNLearner< ALLOC >::getAllocator().

Here is the caller graph for this function:

DAG2BNLearner() [2/5]

template<template< typename > class ALLOC>

gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner

(

const DAG2BNLearner< ALLOC > &

from

)

copy constructor

Definition at line 66 of file DAG2BNLearner_tpl.h.

References gum::ApproximationScheme::ApproximationScheme(), and gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner().

                                                                            :
        DAG2BNLearner(from, from.getAllocator()) {}

Here is the call graph for this function:

DAG2BNLearner() [3/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner	(	const DAG2BNLearner< ALLOC > &	from,
		const allocator_type &	alloc
	)

copy constructor with a given allocator

DAG2BNLearner() [4/5]

template<template< typename > class ALLOC>

gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner

(

DAG2BNLearner< ALLOC > &&

from

)

move constructor

Definition at line 83 of file DAG2BNLearner_tpl.h.

References gum::learning::DAG2BNLearner< ALLOC >::clone().

                                                                       :
        DAG2BNLearner(std::move(from), from.getAllocator()) {}

Here is the call graph for this function:

DAG2BNLearner() [5/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner	(	DAG2BNLearner< ALLOC > &&	from,
		const allocator_type &	alloc
	)

move constructor with a given allocator

~DAG2BNLearner()

template<template< typename > class ALLOC>

gum::learning::DAG2BNLearner< ALLOC >::~DAG2BNLearner ( )

virtual

destructor

Definition at line 113 of file DAG2BNLearner_tpl.h.

References gum::learning::DAG2BNLearner< ALLOC >::operator=().

                                           {
      GUM_DESTRUCTOR(DAG2BNLearner);
    }

Here is the call graph for this function:

Member Function Documentation

approximationScheme()

template<template< typename > class ALLOC>

INLINE ApproximationScheme & gum::learning::DAG2BNLearner< ALLOC >::approximationScheme

(

)

returns the approximation policy of the learning algorithm

Definition at line 272 of file DAG2BNLearner_tpl.h.

                                                                            {
      return *this;
    }

clone() [1/2]

template<template< typename > class ALLOC>

DAG2BNLearner< ALLOC > * gum::learning::DAG2BNLearner< ALLOC >::clone ( ) const

virtual

virtual copy constructor

Definition at line 106 of file DAG2BNLearner_tpl.h.

References gum::learning::DAG2BNLearner< ALLOC >::getAllocator().

Referenced by gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner().

                                                                {
      return clone(this->getAllocator());
    }

Here is the call graph for this function:

Here is the caller graph for this function:

clone() [2/2]

template<template< typename > class ALLOC = std::allocator>

virtual DAG2BNLearner< ALLOC >* gum::learning::DAG2BNLearner< ALLOC >::clone ( const allocator_type &  alloc ) const

virtual

virtual copy constructor with a given allocator

continueApproximationScheme()

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

inherited

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

Test the stopping criterion that are enabled.

Parameters

error

The new error value.

Returns

false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed

Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 225 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_epsilon, gum::ApproximationScheme::_current_rate, gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_current_step, gum::ApproximationScheme::_enabled_eps, gum::ApproximationScheme::_enabled_max_iter, gum::ApproximationScheme::_enabled_max_time, gum::ApproximationScheme::_enabled_min_rate_eps, gum::ApproximationScheme::_eps, gum::ApproximationScheme::_history, gum::ApproximationScheme::_last_epsilon, gum::ApproximationScheme::_max_iter, gum::ApproximationScheme::_max_time, gum::ApproximationScheme::_min_rate_eps, gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::_timer, gum::IApproximationSchemeConfiguration::Continue, gum::IApproximationSchemeConfiguration::Epsilon, GUM_EMIT3, GUM_ERROR, gum::IApproximationSchemeConfiguration::Limit, gum::IApproximationSchemeConfiguration::messageApproximationScheme(), gum::IApproximationSchemeConfiguration::onProgress, gum::IApproximationSchemeConfiguration::Rate, gum::ApproximationScheme::startOfPeriod(), gum::ApproximationScheme::stateApproximationScheme(), gum::Timer::step(), gum::IApproximationSchemeConfiguration::TimeLimit, and gum::ApproximationScheme::verbosity().

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

                                                                           {
    // For coherence, we fix the time used in the method

    double timer_step = _timer.step();

    if (_enabled_max_time) {
      if (timer_step > _max_time) {
        _stopScheme(ApproximationSchemeSTATE::TimeLimit);
        return false;
      }
    }

    if (!startOfPeriod()) { return true; }

    if (_current_state != ApproximationSchemeSTATE::Continue) {
      GUM_ERROR(OperationNotAllowed,
                "state of the approximation scheme is not correct : "
                   + messageApproximationScheme());
    }

    if (verbosity()) { _history.push_back(error); }

    if (_enabled_max_iter) {
      if (_current_step > _max_iter) {
        _stopScheme(ApproximationSchemeSTATE::Limit);
        return false;
      }
    }

    _last_epsilon = _current_epsilon;
    _current_epsilon = error;   // eps rate isEnabled needs it so affectation was
    // moved from eps isEnabled below

    if (_enabled_eps) {
      if (_current_epsilon <= _eps) {
        _stopScheme(ApproximationSchemeSTATE::Epsilon);
        return false;
      }
    }

    if (_last_epsilon >= 0.) {
      if (_current_epsilon > .0) {
        // ! _current_epsilon can be 0. AND epsilon
        // isEnabled can be disabled !
        _current_rate =
           std::fabs((_current_epsilon - _last_epsilon) / _current_epsilon);
      }
      // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
      // infinity the else means a return false if we isEnabled the rate below,
      // as we would have returned false if epsilon isEnabled was enabled
      else {
        _current_rate = _min_rate_eps;
      }

      if (_enabled_min_rate_eps) {
        if (_current_rate <= _min_rate_eps) {
          _stopScheme(ApproximationSchemeSTATE::Rate);
          return false;
        }
      }
    }

    if (stateApproximationScheme() == ApproximationSchemeSTATE::Continue) {
      if (onProgress.hasListener()) {
        GUM_EMIT3(onProgress, _current_step, _current_epsilon, timer_step);
      }

      return true;
    } else {
      return false;
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

createBN() [1/2]

template<template< typename > class ALLOC>

template<typename GUM_SCALAR >

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

static

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

create a BN

Definition at line 162 of file DAG2BNLearner_tpl.h.

References gum::BayesNet< GUM_SCALAR >::add(), gum::BayesNet< GUM_SCALAR >::addArc(), gum::ArcGraphPart::arcs(), gum::BayesNet< GUM_SCALAR >::beginTopologyTransformation(), gum::BayesNet< GUM_SCALAR >::cpt(), gum::learning::ParamEstimator< ALLOC >::database(), gum::BayesNet< GUM_SCALAR >::endTopologyTransformation(), gum::BayesNet< GUM_SCALAR >::generateCPT(), gum::VariableNodeMap::get(), gum::learning::ParamEstimator< ALLOC >::nodeId2Columns(), gum::learning::ParamEstimator< ALLOC >::setParameters(), gum::BayesNet< GUM_SCALAR >::variableNodeMap(), and gum::MultiDimDecorator< GUM_SCALAR >::variablesSequence().

                                                                      {
      BayesNet< GUM_SCALAR > bn;

      // create a bn with dummy parameters corresponding to the dag
      const auto& node2cols = estimator.nodeId2Columns();
      const auto& database = estimator.database();
      if (node2cols.empty()) {
        for (const auto id : dag) {
          bn.add(dynamic_cast< const DiscreteVariable& >(database.variable(id)),
                 id);
        }
      } else {
        for (const auto id : dag) {
          const std::size_t col = node2cols.second(id);
          bn.add(dynamic_cast< const DiscreteVariable& >(database.variable(col)),
                 id);
        }
      }

      // add the arcs
      bn.beginTopologyTransformation();
      for (const auto& arc : dag.arcs()) {
        bn.addArc(arc.tail(), arc.head());
      }
      bn.endTopologyTransformation();

      // estimate the parameters
      const VariableNodeMap& varmap = bn.variableNodeMap();
      for (const auto id : dag) {
        try {
          // get the sequence of variables and make the targets be the last
          Potential< GUM_SCALAR >& pot =
             const_cast< Potential< GUM_SCALAR >& >(bn.cpt(id));

          // get the variables of the CPT of id in the correct order
          const Sequence< const DiscreteVariable* >& vars =
             pot.variablesSequence();

          // setup the estimation
          std::vector< NodeId > conditioning_ids(vars.size() - 1);
          for (std::size_t i = std::size_t(1); i < vars.size(); ++i) {
            conditioning_ids[i - 1] = varmap.get(*(vars[i]));
          }
          estimator.setParameters(id, conditioning_ids, pot);
        } catch (DatabaseError&) { bn.generateCPT(id); }
      }

      return bn;
    }

Here is the call graph for this function:

createBN() [2/2]

template<template< typename > class ALLOC>

template<typename GUM_SCALAR >

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

create a BN from a DAG using a two pass generator (typically EM)

create a BN

The bootstrap estimator is used once to provide an inital BN. This one is used by the second estimator. The later is exploited in a loop until the stopping condition is met (max of relative error on every parameter<epsilon)

Definition at line 216 of file DAG2BNLearner_tpl.h.

References gum::ApproximationScheme::continueApproximationScheme(), gum::BayesNet< GUM_SCALAR >::cpt(), gum::DAGmodel::dag(), gum::Instantiation::end(), gum::ApproximationScheme::initApproximationScheme(), gum::learning::ParamEstimator< ALLOC >::setBayesNet(), gum::ApproximationScheme::stopApproximationScheme(), and gum::ApproximationScheme::updateApproximationScheme().

                                     {
      // bootstrap EM by learning an initial model
      BayesNet< GUM_SCALAR > bn = createBN< GUM_SCALAR >(bootstrap_estimator, dag);
      general_estimator.setBayesNet(bn);

      // perform EM
      initApproximationScheme();

      GUM_SCALAR delta;
      do {
        // bugfix for parallel execution of VariableElimination
        const auto& xdag = bn.dag();
        for (const auto node : xdag) {
          xdag.parents(node);
          xdag.children(node);
        }

        BayesNet< GUM_SCALAR > new_bn =
           createBN< GUM_SCALAR >(general_estimator, dag);
        updateApproximationScheme();

        delta = GUM_SCALAR(0.0);
        for (const auto node : dag) {
          const auto& old_cpt = bn.cpt(node);
          const auto& new_cpt = new_bn.cpt(node);

          Instantiation old_inst(old_cpt);
          Instantiation new_inst(new_cpt);

          for (; !old_inst.end(); ++old_inst, ++new_inst) {
            const GUM_SCALAR old_val = old_cpt.get(old_inst);
            if (old_val > 0.0) {
              const GUM_SCALAR new_val = new_cpt.get(new_inst);
              const GUM_SCALAR diff = new_val - old_val;
              const auto       diffrel =
                 (diff < 0.0) ? (-diff / old_val) : (diff / old_val);
              if (delta < diffrel) delta = diffrel;
            }
          }
        }

        bn = std::move(new_bn);
      } while (continueApproximationScheme(double(delta)));

      stopApproximationScheme();   // just to be sure of the approximationScheme
                                   // has been notified of the end of loop

      return bn;
    }   // namespace learning

Here is the call graph for this function:

currentTime()

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

virtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 126 of file approximationScheme_inl.h.

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

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

{ return _timer.step(); }

Here is the call graph for this function:

Here is the caller graph for this function:

disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 52 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

{ _enabled_eps = false; }

Here is the caller graph for this function:

disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 103 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

{ _enabled_max_iter = false; }

Here is the caller graph for this function:

disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 129 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

{ _enabled_max_time = false; }

Here is the caller graph for this function:

disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                         {
    _enabled_min_rate_eps = false;
  }

Here is the caller graph for this function:

enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 55 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

{ _enabled_eps = true; }

Here is the caller graph for this function:

enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

{ _enabled_max_iter = true; }

Here is the caller graph for this function:

enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 132 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

{ _enabled_max_time = true; }

Here is the caller graph for this function:

enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 82 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                        {
    _enabled_min_rate_eps = true;
  }

Here is the caller graph for this function:

epsilon()

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

virtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 49 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

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

{ return _eps; }

Here is the caller graph for this function:

getAllocator()

template<template< typename > class ALLOC>

INLINE DAG2BNLearner< ALLOC >::allocator_type gum::learning::DAG2BNLearner< ALLOC >::getAllocator

(

)

const

returns the allocator used by the score

Definition at line 39 of file DAG2BNLearner_tpl.h.

References gum::learning::DAG2BNLearner< ALLOC >::DAG2BNLearner().

Referenced by gum::learning::DAG2BNLearner< ALLOC >::clone().

                                                  {
      return *this;
    }

Here is the call graph for this function:

Here is the caller graph for this function:

history()

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

virtualinherited

Returns the scheme history.

Returns

Returns the scheme history.

Exceptions

OperationNotAllowed

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 171 of file approximationScheme_inl.h.

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

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

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

    if (verbosity() == false) {
      GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
    }

    return _history;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 185 of file approximationScheme_inl.h.

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

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

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

Here is the call graph for this function:

Here is the caller graph for this function:

isEnabledEpsilon()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 59 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

                                                          {
    return _enabled_eps;
  }

Here is the caller graph for this function:

isEnabledMaxIter()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 110 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

                                                          {
    return _enabled_max_iter;
  }

Here is the caller graph for this function:

isEnabledMaxTime()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 136 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

                                                          {
    return _enabled_max_time;
  }

Here is the caller graph for this function:

isEnabledMinEpsilonRate()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 88 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                                 {
    return _enabled_min_rate_eps;
  }

Here is the caller graph for this function:

maxIter()

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

virtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 100 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

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

{ return _max_iter; }

Here is the caller graph for this function:

maxTime()

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

virtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

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

{ return _max_time; }

Here is the caller graph for this function:

messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

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

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

                                                                         {
    std::stringstream s;

    switch (stateApproximationScheme()) {
      case ApproximationSchemeSTATE::Continue: s << "in progress"; break;

      case ApproximationSchemeSTATE::Epsilon:
        s << "stopped with epsilon=" << epsilon();
        break;

      case ApproximationSchemeSTATE::Rate:
        s << "stopped with rate=" << minEpsilonRate();
        break;

      case ApproximationSchemeSTATE::Limit:
        s << "stopped with max iteration=" << maxIter();
        break;

      case ApproximationSchemeSTATE::TimeLimit:
        s << "stopped with timeout=" << maxTime();
        break;

      case ApproximationSchemeSTATE::Stopped: s << "stopped on request"; break;

      case ApproximationSchemeSTATE::Undefined: s << "undefined state"; break;
    };

    return s.str();
  }

Here is the call graph for this function:

Here is the caller graph for this function:

minEpsilonRate()

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

virtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_min_rate_eps.

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

                                                          {
    return _min_rate_eps;
  }

Here is the caller graph for this function:

nbrIterations()

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

virtualinherited

Returns the number of iterations.

Returns

Returns the number of iterations.

Exceptions

OperationNotAllowed

Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

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

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

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

    return _current_step;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

operator=() [1/2]

template<template< typename > class ALLOC>

DAG2BNLearner< ALLOC > & gum::learning::DAG2BNLearner< ALLOC >::operator=

(

const DAG2BNLearner< ALLOC > &

from

)

copy operator

Definition at line 121 of file DAG2BNLearner_tpl.h.

Referenced by gum::learning::DAG2BNLearner< ALLOC >::~DAG2BNLearner().

                                                                          {
      ApproximationScheme::operator=(from);
      return *this;
    }

Here is the caller graph for this function:

operator=() [2/2]

template<template< typename > class ALLOC>

DAG2BNLearner< ALLOC > & gum::learning::DAG2BNLearner< ALLOC >::operator=

(

DAG2BNLearner< ALLOC > &&

from

)

move operator

Definition at line 130 of file DAG2BNLearner_tpl.h.

References gum::Instantiation::end(), GUM_ERROR, gum::MultiDimDecorator< GUM_SCALAR >::set(), gum::Instantiation::setFirst(), gum::Instantiation::setVals(), and gum::MultiDimDecorator< GUM_SCALAR >::variablesSequence().

                                                                     {
      ApproximationScheme::operator=(std::move(from));
      return *this;
    }

Here is the call graph for this function:

periodSize()

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

virtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

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

{ return _period_size; }

Here is the caller graph for this function:

remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 208 of file approximationScheme_inl.h.

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

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

setEpsilon()

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

virtualinherited

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

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfLowerBound

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 41 of file approximationScheme_inl.h.

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

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

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

    _eps = eps;
    _enabled_eps = true;
  }

Here is the caller graph for this function:

setMaxIter()

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

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

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

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

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

Here is the caller graph for this function:

setMaxTime()

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

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfLowerBound

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

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

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

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

Here is the caller graph for this function:

setMinEpsilonRate()

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

virtualinherited

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

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfLowerBound

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 64 of file approximationScheme_inl.h.

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

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

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

    _min_rate_eps = rate;
    _enabled_min_rate_eps = true;
  }

Here is the caller graph for this function:

setPeriodSize()

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

virtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfLowerBound

Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 141 of file approximationScheme_inl.h.

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

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

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

    _period_size = p;
  }

Here is the caller graph for this function:

setVerbosity()

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

virtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

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

{ _verbosity = v; }

Here is the caller graph for this function:

startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

inherited

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

Returns

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

Definition at line 195 of file approximationScheme_inl.h.

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

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

                                                 {
    if (_current_step < _burn_in) { return false; }

    if (_period_size == 1) { return true; }

    return ((_current_step - _burn_in) % _period_size == 0);
  }

Here is the caller graph for this function:

stateApproximationScheme()

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

virtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state.

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

                                                             {
    return _current_state;
  }

Here is the caller graph for this function:

stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 217 of file approximationScheme_inl.h.

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

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

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

Here is the call graph for this function:

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 204 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

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

                                                                              {
    _current_step += incr;
  }

Here is the caller graph for this function:

verbosity()

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

virtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

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

{ return _verbosity; }

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 412 of file approximationScheme.h.

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

_current_epsilon

double gum::ApproximationScheme::_current_epsilon

protectedinherited

Current epsilon.

Definition at line 367 of file approximationScheme.h.

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

_current_rate

double gum::ApproximationScheme::_current_rate

protectedinherited

Current rate.

Definition at line 373 of file approximationScheme.h.

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

_current_state

ApproximationSchemeSTATE gum::ApproximationScheme::_current_state

protectedinherited

The current state.

Definition at line 382 of file approximationScheme.h.

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

_current_step

Size gum::ApproximationScheme::_current_step

protectedinherited

The current step.

Definition at line 376 of file approximationScheme.h.

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

_enabled_eps

bool gum::ApproximationScheme::_enabled_eps

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 391 of file approximationScheme.h.

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

_enabled_max_iter

bool gum::ApproximationScheme::_enabled_max_iter

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 409 of file approximationScheme.h.

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

_enabled_max_time

bool gum::ApproximationScheme::_enabled_max_time

protectedinherited

If true, the timeout is enabled.

Definition at line 403 of file approximationScheme.h.

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

_enabled_min_rate_eps

bool gum::ApproximationScheme::_enabled_min_rate_eps

protectedinherited

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

Definition at line 397 of file approximationScheme.h.

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

_eps

double gum::ApproximationScheme::_eps

protectedinherited

Threshold for convergence.

Definition at line 388 of file approximationScheme.h.

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

_history

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

protectedinherited

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

Definition at line 385 of file approximationScheme.h.

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

_last_epsilon

double gum::ApproximationScheme::_last_epsilon

protectedinherited

Last epsilon value.

Definition at line 370 of file approximationScheme.h.

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

_max_iter

Size gum::ApproximationScheme::_max_iter

protectedinherited

The maximum iterations.

Definition at line 406 of file approximationScheme.h.

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

_max_time

double gum::ApproximationScheme::_max_time

protectedinherited

The timeout.

Definition at line 400 of file approximationScheme.h.

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

_min_rate_eps

double gum::ApproximationScheme::_min_rate_eps

protectedinherited

Threshold for the epsilon rate.

Definition at line 394 of file approximationScheme.h.

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

_period_size

Size gum::ApproximationScheme::_period_size

protectedinherited

Checking criteria frequency.

Definition at line 415 of file approximationScheme.h.

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

_timer

Timer gum::ApproximationScheme::_timer

protectedinherited

The timer.

Definition at line 379 of file approximationScheme.h.

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

_verbosity

bool gum::ApproximationScheme::_verbosity

protectedinherited

If true, verbosity is enabled.

Definition at line 418 of file approximationScheme.h.

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

onProgress

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

inherited

Progression, error and time.

Definition at line 57 of file IApproximationSchemeConfiguration.h.

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

onStop

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

inherited

Criteria messageApproximationScheme.

Definition at line 60 of file IApproximationSchemeConfiguration.h.

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

---

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

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