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/BN/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 51 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 64 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

DAG2BNLearner() [2/5]

template<template< typename > class ALLOC>

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

(

const DAG2BNLearner< ALLOC > &

from

)

copy constructor

Definition at line 68 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

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

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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 85 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

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

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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 115 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

                                           {
      GUM_DESTRUCTOR(DAG2BNLearner);
    }

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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 264 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 108 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

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

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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 208 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

    double timer_step = timer_.step();

    if (enabled_max_time_) {
      if (timer_step > max_time_) {
        stopScheme_(ApproximationSchemeSTATE::TimeLimit);
        return false;
      }
    }

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

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

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

    if (enabled_max_iter_) {
      if (current_step_ > max_iter_) {
        stopScheme_(ApproximationSchemeSTATE::Limit);
        return false;
      }
    }

    last_epsilon_    = current_epsilon_;
    current_epsilon_ = error;   // eps rate isEnabled needs it so affectation was
    // moved from eps isEnabled below

    if (enabled_eps_) {
      if (current_epsilon_ <= eps_) {
        stopScheme_(ApproximationSchemeSTATE::Epsilon);
        return false;
      }
    }

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

      if (enabled_min_rate_eps_) {
        if (current_rate_ <= min_rate_eps_) {
          stopScheme_(ApproximationSchemeSTATE::Rate);
          return false;
        }
      }
    }

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

      return true;
    } else {
      return false;
    }
  }

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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 159 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                          {
      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) {
        // get the sequence of variables and make the targets be the last
        auto& 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 (auto i = std::size_t(1); i < vars.size(); ++i) {
          conditioning_ids[i - 1] = varmap.get(*(vars[i]));
        }
        estimator.setParameters(id, conditioning_ids, pot);
      }

      return bn;
    }

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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 208 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                      {
      // bootstrap EM by learning an initial model
      BayesNet< GUM_SCALAR > bn = createBN< GUM_SCALAR >(bootstrap_estimator, dag);
      for (const auto& nod: bn.nodes()) {
        bn.cpt(nod).noising(0.1);
      }
      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

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currentTime()

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

virtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 115 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return timer_.step(); }

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disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 53 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_eps_ = false; }

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disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 94 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_max_iter_ = false; }

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disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 118 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_max_time_ = false; }

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disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 74 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_min_rate_eps_ = false; }

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enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 56 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_eps_ = true; }

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enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 97 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_max_iter_ = true; }

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enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 121 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_max_time_ = true; }

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enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ enabled_min_rate_eps_ = true; }

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epsilon()

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

virtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 50 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return eps_; }

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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 41 of file DAG2BNLearner_tpl.h.

                                                  {
      return *this;
    }

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 157 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

    return history_;
  }

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initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 168 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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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 60 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return enabled_eps_; }

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isEnabledMaxIter()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 101 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return enabled_max_iter_; }

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isEnabledMaxTime()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 125 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return enabled_max_time_; }

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isEnabledMinEpsilonRate()

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

virtualinherited

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

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 81 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return enabled_min_rate_eps_; }

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maxIter()

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

virtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 91 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return max_iter_; }

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maxTime()

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

virtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 112 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return max_time_; }

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messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                {
    std::stringstream s;

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

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

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

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

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

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

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

    return s.str();
  }

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minEpsilonRate()

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

virtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 71 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return min_rate_eps_; }

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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 148 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

    return current_step_;
  }

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operator=() [1/2]

template<template< typename > class ALLOC>

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

(

const DAG2BNLearner< ALLOC > &

from

)

copy operator

Definition at line 122 of file DAG2BNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

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

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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::learning::genericBNLearner::Database::Database().

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

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periodSize()

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

virtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 134 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return period_size_; }

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remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 191 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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setEpsilon()

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

virtualinherited

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

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfLowerBound

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 42 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

    eps_         = eps;
    enabled_eps_ = true;
  }

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setMaxIter()

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

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 84 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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setMaxTime()

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

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfLowerBound

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 105 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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setMinEpsilonRate()

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

virtualinherited

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

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfLowerBound

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 63 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

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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 128 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

    period_size_ = p;
  }

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setVerbosity()

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

virtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 137 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ verbosity_ = v; }

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startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

inherited

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

Returns

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

Definition at line 178 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                 {
    if (current_step_ < burn_in_) { return false; }

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

    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

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stateApproximationScheme()

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

virtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 143 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                             {
    return current_state_;
  }

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stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 200 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

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updateApproximationScheme()

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

inherited

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

Parameters

incr	The new increment steps.

Definition at line 187 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                              {
    current_step_ += incr;
  }

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verbosity()

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

virtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 139 of file approximationScheme_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return verbosity_; }

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Member Data Documentation

burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 413 of file approximationScheme.h.

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 368 of file approximationScheme.h.

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 374 of file approximationScheme.h.

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 383 of file approximationScheme.h.

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 377 of file approximationScheme.h.

enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 392 of file approximationScheme.h.

enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 410 of file approximationScheme.h.

enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 404 of file approximationScheme.h.

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 398 of file approximationScheme.h.

eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 389 of file approximationScheme.h.

history_

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

protectedinherited

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

Definition at line 386 of file approximationScheme.h.

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 371 of file approximationScheme.h.

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 407 of file approximationScheme.h.

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 401 of file approximationScheme.h.

min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 395 of file approximationScheme.h.

onProgress

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

inherited

Progression, error and time.

Definition at line 58 of file IApproximationSchemeConfiguration.h.

onStop

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

inherited

Criteria messageApproximationScheme.

Definition at line 61 of file IApproximationSchemeConfiguration.h.

period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 416 of file approximationScheme.h.

timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 380 of file approximationScheme.h.

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 419 of file approximationScheme.h.

---

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

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