aGrUM  0.13.2
gum::learning::GreedyHillClimbing Class Reference

The greedy hill climbing learning algorithm (for directed graphs) More...

#include <greedyHillClimbing.h>

+ Inheritance diagram for gum::learning::GreedyHillClimbing:
+ Collaboration diagram for gum::learning::GreedyHillClimbing:

Public Attributes

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

Public Member Functions

Constructors / Destructors
 GreedyHillClimbing ()
 default constructor More...
 
 GreedyHillClimbing (const GreedyHillClimbing &from)
 copy constructor More...
 
 GreedyHillClimbing (GreedyHillClimbing &&from)
 move constructor More...
 
 ~GreedyHillClimbing ()
 destructor More...
 
Operators
GreedyHillClimbingoperator= (const GreedyHillClimbing &from)
 copy operator More...
 
GreedyHillClimbingoperator= (GreedyHillClimbing &&from)
 move operator More...
 
Accessors / Modifiers
ApproximationSchemeapproximationScheme ()
 returns the approximation policy of the learning algorithm More...
 
template<typename GRAPH_CHANGES_SELECTOR >
DAG learnStructure (GRAPH_CHANGES_SELECTOR &selector, const std::vector< Size > &modal, DAG initial_dag=DAG())
 learns the structure of a Bayes net More...
 
template<typename GUM_SCALAR = double, typename GRAPH_CHANGES_SELECTOR , typename PARAM_ESTIMATOR , typename CELL_TRANSLATORS >
BayesNet< GUM_SCALAR > learnBN (GRAPH_CHANGES_SELECTOR &selector, PARAM_ESTIMATOR &estimator, const std::vector< std::string > &names, const std::vector< Size > &modal, const CELL_TRANSLATORS &translator, DAG initial_dag=DAG())
 learns the structure and the parameters of a BN 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

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...
 

Detailed Description

The greedy hill climbing learning algorithm (for directed graphs)

The GreedyHillClimbing class implements a greedy search in which the only the graph changes that increase the global score are applied. Those that increase it the more are applied first. The algorithm stops when no single change can increase the score anymore.

Definition at line 56 of file greedyHillClimbing.h.

Member Enumeration Documentation

The different state of an approximation scheme.

Enumerator
Undefined 
Continue 
Epsilon 
Rate 
Limit 
TimeLimit 
Stopped 

Definition at line 64 of file IApproximationSchemeConfiguration.h.

64  : char {
65  Undefined,
66  Continue,
67  Epsilon,
68  Rate,
69  Limit,
70  TimeLimit,
71  Stopped
72  };

Constructor & Destructor Documentation

gum::learning::GreedyHillClimbing::GreedyHillClimbing ( )

default constructor

Definition at line 33 of file greedyHillClimbing.cpp.

References gum::ApproximationScheme::disableMaxIter(), gum::ApproximationScheme::disableMaxTime(), gum::ApproximationScheme::disableMinEpsilonRate(), and gum::ApproximationScheme::setEpsilon().

33  {
34  setEpsilon(0);
38  GUM_CONSTRUCTOR(GreedyHillClimbing);
39  }
void disableMinEpsilonRate()
Disable stopping criterion on epsilon rate.
void disableMaxTime()
Disable stopping criterion on timeout.
void setEpsilon(double eps)
Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.
void disableMaxIter()
Disable stopping criterion on max iterations.
GreedyHillClimbing()
default constructor

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gum::learning::GreedyHillClimbing::GreedyHillClimbing ( const GreedyHillClimbing from)

copy constructor

Definition at line 42 of file greedyHillClimbing.cpp.

42  :
43  ApproximationScheme(from) {
44  GUM_CONS_CPY(GreedyHillClimbing);
45  }
GreedyHillClimbing()
default constructor
gum::learning::GreedyHillClimbing::GreedyHillClimbing ( GreedyHillClimbing &&  from)

move constructor

Definition at line 48 of file greedyHillClimbing.cpp.

48  :
49  ApproximationScheme(std::move(from)) {
50  GUM_CONS_MOV(GreedyHillClimbing);
51  }
GreedyHillClimbing()
default constructor
gum::learning::GreedyHillClimbing::~GreedyHillClimbing ( )

destructor

Definition at line 54 of file greedyHillClimbing.cpp.

References operator=().

54  {
55  GUM_DESTRUCTOR(GreedyHillClimbing);
56  }
GreedyHillClimbing()
default constructor

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Member Function Documentation

ApproximationScheme & gum::learning::GreedyHillClimbing::approximationScheme ( )

returns the approximation policy of the learning algorithm

Definition at line 72 of file greedyHillClimbing.cpp.

72  {
73  return *this;
74  }
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
errorThe new error value.
Returns
false if state become != ApproximationSchemeSTATE::Continue
Exceptions
OperationNotAllowedRaised 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::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

225  {
226  // For coherence, we fix the time used in the method
227 
228  double timer_step = _timer.step();
229 
230  if (_enabled_max_time) {
231  if (timer_step > _max_time) {
233  return false;
234  }
235  }
236 
237  if (!startOfPeriod()) { return true; }
238 
240  GUM_ERROR(OperationNotAllowed,
241  "state of the approximation scheme is not correct : "
243  }
244 
245  if (verbosity()) { _history.push_back(error); }
246 
247  if (_enabled_max_iter) {
248  if (_current_step > _max_iter) {
250  return false;
251  }
252  }
253 
255  _current_epsilon = error; // eps rate isEnabled needs it so affectation was
256  // moved from eps isEnabled below
257 
258  if (_enabled_eps) {
259  if (_current_epsilon <= _eps) {
261  return false;
262  }
263  }
264 
265  if (_last_epsilon >= 0.) {
266  if (_current_epsilon > .0) {
267  // ! _current_epsilon can be 0. AND epsilon
268  // isEnabled can be disabled !
269  _current_rate =
271  }
272  // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
273  // infinity the else means a return false if we isEnabled the rate below,
274  // as we would have returned false if epsilon isEnabled was enabled
275  else {
277  }
278 
279  if (_enabled_min_rate_eps) {
280  if (_current_rate <= _min_rate_eps) {
282  return false;
283  }
284  }
285  }
286 
288  if (onProgress.hasListener()) {
290  }
291 
292  return true;
293  } else {
294  return false;
295  }
296  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
bool verbosity() const
Returns true if verbosity is enabled.
Signaler3< Size, double, double > onProgress
Progression, error and time.
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.
std::string messageApproximationScheme() const
Returns the approximation scheme message.
bool _enabled_eps
If true, the threshold convergence is enabled.
void _stopScheme(ApproximationSchemeSTATE new_state)
Stop the scheme given a new state.
double _current_epsilon
Current epsilon.
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.
bool startOfPeriod()
Returns true if we are at the beginning of a period (compute error is mandatory). ...
double _eps
Threshold for convergence.
double step() const
Returns the delta time between now and the last reset() call (or the constructor).
Definition: timer_inl.h:39
double _current_rate
Current rate.
bool _enabled_max_time
If true, the timeout is enabled.
Size _current_step
The current step.
std::vector< double > _history
The scheme history, used only if verbosity == true.
double _min_rate_eps
Threshold for the epsilon rate.
double _last_epsilon
Last epsilon value.
Size _max_iter
The maximum iterations.
#define GUM_EMIT3(signal, arg1, arg2, arg3)
Definition: signaler3.h:40
ApproximationSchemeSTATE _current_state
The current state.
double _max_time
The timeout.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

Returns the current running time in second.

Returns
Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 126 of file approximationScheme_inl.h.

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

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

126 { return _timer.step(); }
double step() const
Returns the delta time between now and the last reset() call (or the constructor).
Definition: timer_inl.h:39

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INLINE void gum::ApproximationScheme::disableEpsilon ( )
virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 52 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

52 { _enabled_eps = false; }
bool _enabled_eps
If true, the threshold convergence is enabled.

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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 GreedyHillClimbing().

103 { _enabled_max_iter = false; }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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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 GreedyHillClimbing().

129 { _enabled_max_time = false; }
bool _enabled_max_time
If true, the timeout is enabled.

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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::GibbsKL< GUM_SCALAR >::_computeKL(), gum::learning::genericBNLearner::disableMinEpsilonRate(), and GreedyHillClimbing().

77  {
78  _enabled_min_rate_eps = false;
79  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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INLINE void gum::ApproximationScheme::enableEpsilon ( )
virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 55 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

55 { _enabled_eps = true; }
bool _enabled_eps
If true, the threshold convergence is enabled.

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INLINE void gum::ApproximationScheme::enableMaxIter ( )
virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

106 { _enabled_max_iter = true; }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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INLINE void gum::ApproximationScheme::enableMaxTime ( )
virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 132 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

132 { _enabled_max_time = true; }
bool _enabled_max_time
If true, the timeout is enabled.

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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::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::enableMinEpsilonRate().

82  {
83  _enabled_min_rate_eps = true;
84  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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

Returns the value of epsilon.

Returns
Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 49 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

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

49 { return _eps; }
double _eps
Threshold for convergence.

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

Returns the scheme history.

Returns
Returns the scheme history.
Exceptions
OperationNotAllowedRaised 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().

171  {
173  GUM_ERROR(OperationNotAllowed,
174  "state of the approximation scheme is udefined");
175  }
176 
177  if (verbosity() == false) {
178  GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
179  }
180 
181  return _history;
182  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
bool verbosity() const
Returns true if verbosity is enabled.
std::vector< double > _history
The scheme history, used only if verbosity == true.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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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::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

185  {
187  _current_step = 0;
189  _history.clear();
190  _timer.reset();
191  }
double _current_epsilon
Current epsilon.
void reset()
Reset the timer.
Definition: timer_inl.h:29
double _current_rate
Current rate.
Size _current_step
The current step.
std::vector< double > _history
The scheme history, used only if verbosity == true.
ApproximationSchemeSTATE _current_state
The current state.

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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().

59  {
60  return _enabled_eps;
61  }
bool _enabled_eps
If true, the threshold convergence is enabled.

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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().

110  {
111  return _enabled_max_iter;
112  }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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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().

136  {
137  return _enabled_max_time;
138  }
bool _enabled_max_time
If true, the timeout is enabled.

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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::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::isEnabledMinEpsilonRate().

88  {
89  return _enabled_min_rate_eps;
90  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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template<typename GUM_SCALAR , typename GRAPH_CHANGES_SELECTOR , typename PARAM_ESTIMATOR , typename CELL_TRANSLATORS >
BayesNet< GUM_SCALAR > gum::learning::GreedyHillClimbing::learnBN ( GRAPH_CHANGES_SELECTOR &  selector,
PARAM_ESTIMATOR &  estimator,
const std::vector< std::string > &  names,
const std::vector< Size > &  modal,
const CELL_TRANSLATORS &  translator,
DAG  initial_dag = DAG() 
)

learns the structure and the parameters of a BN

Parameters
selectorA selector class that computes the best changes that can be applied and that enables the user to get them very easily. Typically, the selector is a GraphChangesSelector4DiGraph<SCORE, STRUCT_CONSTRAINT, GRAPH_CHANGES_GENERATOR>.
estimatorA estimator.
namesThe variables names.
modalthe domain sizes of the random variables observed in the database
translatorThe cell translator to use.
initial_dagthe DAG we start from for our learning

Definition at line 136 of file greedyHillClimbing_tpl.h.

References learnStructure().

141  {
142  return DAG2BNLearner::
143  createBN< GUM_SCALAR, PARAM_ESTIMATOR, CELL_TRANSLATORS >(
144  estimator,
145  learnStructure(selector, modal, initial_dag),
146  names,
147  modal,
148  translator);
149  }
DAG learnStructure(GRAPH_CHANGES_SELECTOR &selector, const std::vector< Size > &modal, DAG initial_dag=DAG())
learns the structure of a Bayes net

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template<typename GRAPH_CHANGES_SELECTOR >
DAG gum::learning::GreedyHillClimbing::learnStructure ( GRAPH_CHANGES_SELECTOR &  selector,
const std::vector< Size > &  modal,
DAG  initial_dag = DAG() 
)

learns the structure of a Bayes net

Parameters
selectorA selector class that computes the best changes that can be applied and that enables the user to get them very easily. Typically, the selector is a GraphChangesSelector4DiGraph<SCORE, STRUCT_CONSTRAINT, GRAPH_CHANGES_GENERATOR>.
modalthe domain sizes of the random variables observed in the database
initial_dagthe DAG we start from for our learning

Definition at line 35 of file greedyHillClimbing_tpl.h.

References gum::DAG::addArc(), gum::learning::ARC_ADDITION, gum::learning::ARC_DELETION, gum::learning::ARC_REVERSAL, gum::ApproximationScheme::continueApproximationScheme(), gum::ArcGraphPart::eraseArc(), GUM_ERROR, gum::ApproximationScheme::initApproximationScheme(), gum::learning::GraphChange::node1(), gum::learning::GraphChange::node2(), gum::NodeGraphPart::size(), gum::ApproximationScheme::stopApproximationScheme(), gum::learning::GraphChange::type(), and gum::ApproximationScheme::updateApproximationScheme().

Referenced by gum::learning::genericBNLearner::__learnDAG(), learnBN(), and gum::learning::K2::learnStructure().

37  {
38  selector.setGraph(dag, modal);
39 
40  Size nb_changes_applied = 1;
41  double delta_score;
42 
44 
45  // a vector that indicates which queues have valid scores, i.e., scores
46  // that were not invalidated by previously applied changes
47  std::vector< bool > impacted_queues(dag.size(), false);
48 
49  do {
50  nb_changes_applied = 0;
51  delta_score = 0;
52 
53  std::vector< std::pair< NodeId, double > > ordered_queues =
54  selector.nodesSortedByBestScore();
55 
56  for (Idx j = 0; j < dag.size(); ++j) {
57  Idx i = ordered_queues[j].first;
58 
59  if (!(selector.empty(i)) && (selector.bestScore(i) > 0)) {
60  // pick up the best change
61  const GraphChange& change = selector.bestChange(i);
62 
63  // perform the change
64  switch (change.type()) {
66  if (!impacted_queues[change.node2()]
67  && selector.isChangeValid(change)) {
68  delta_score += selector.bestScore(i);
69  dag.addArc(change.node1(), change.node2());
70  impacted_queues[change.node2()] = true;
71  selector.applyChangeWithoutScoreUpdate(change);
72  ++nb_changes_applied;
73  }
74 
75  break;
76 
78  if (!impacted_queues[change.node2()]
79  && selector.isChangeValid(change)) {
80  delta_score += selector.bestScore(i);
81  dag.eraseArc(Arc(change.node1(), change.node2()));
82  impacted_queues[change.node2()] = true;
83  selector.applyChangeWithoutScoreUpdate(change);
84  ++nb_changes_applied;
85  }
86 
87  break;
88 
90  if ((!impacted_queues[change.node1()])
91  && (!impacted_queues[change.node2()])
92  && selector.isChangeValid(change)) {
93  delta_score += selector.bestScore(i);
94  dag.eraseArc(Arc(change.node1(), change.node2()));
95  dag.addArc(change.node2(), change.node1());
96  impacted_queues[change.node1()] = true;
97  impacted_queues[change.node2()] = true;
98  selector.applyChangeWithoutScoreUpdate(change);
99  ++nb_changes_applied;
100  }
101 
102  break;
103 
104  default:
105  GUM_ERROR(OperationNotAllowed,
106  "edge modifications are not supported by local search");
107  }
108  }
109  }
110 
111  selector.updateScoresAfterAppliedChanges();
112 
113  // reset the impacted queue and applied changes structures
114  for (auto iter = impacted_queues.begin(); iter != impacted_queues.end();
115  ++iter) {
116  *iter = false;
117  }
118 
119  updateApproximationScheme(nb_changes_applied);
120 
121  } while (nb_changes_applied && continueApproximationScheme(delta_score));
122 
123  stopApproximationScheme(); // just to be sure of the approximationScheme
124  // has
125  // been notified of the end of looop
126 
127  return dag;
128  }
unsigned long Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:50
void initApproximationScheme()
Initialise the scheme.
bool continueApproximationScheme(double error)
Update the scheme w.r.t the new error.
void stopApproximationScheme()
Stop the approximation scheme.
unsigned long Idx
Type for indexes.
Definition: types.h:43
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66
void updateApproximationScheme(unsigned int incr=1)
Update the scheme w.r.t the new error and increment steps.

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

Returns the criterion on number of iterations.

Returns
Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 100 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

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

100 { return _max_iter; }
Size _max_iter
The maximum iterations.

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

Returns the timeout (in seconds).

Returns
Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

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

123 { return _max_time; }
double _max_time
The timeout.

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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().

38  {
39  std::stringstream s;
40 
41  switch (stateApproximationScheme()) {
42  case ApproximationSchemeSTATE::Continue: s << "in progress"; break;
43 
45  s << "stopped with epsilon=" << epsilon();
46  break;
47 
49  s << "stopped with rate=" << minEpsilonRate();
50  break;
51 
53  s << "stopped with max iteration=" << maxIter();
54  break;
55 
57  s << "stopped with timeout=" << maxTime();
58  break;
59 
60  case ApproximationSchemeSTATE::Stopped: s << "stopped on request"; break;
61 
62  case ApproximationSchemeSTATE::Undefined: s << "undefined state"; break;
63  };
64 
65  return s.str();
66  }
virtual double epsilon() const =0
Returns the value of epsilon.
virtual ApproximationSchemeSTATE stateApproximationScheme() const =0
Returns the approximation scheme state.
virtual double maxTime() const =0
Returns the timeout (in seconds).
virtual Size maxIter() const =0
Returns the criterion on number of iterations.
virtual double minEpsilonRate() const =0
Returns the value of the minimal epsilon rate.

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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().

72  {
73  return _min_rate_eps;
74  }
double _min_rate_eps
Threshold for the epsilon rate.

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

Returns the number of iterations.

Returns
Returns the number of iterations.
Exceptions
OperationNotAllowedRaised 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::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::nbrIterations().

161  {
163  GUM_ERROR(OperationNotAllowed,
164  "state of the approximation scheme is undefined");
165  }
166 
167  return _current_step;
168  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
Size _current_step
The current step.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

copy operator

Definition at line 60 of file greedyHillClimbing.cpp.

Referenced by ~GreedyHillClimbing().

60  {
61  ApproximationScheme::operator=(from);
62  return *this;
63  }

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

move operator

Definition at line 66 of file greedyHillClimbing.cpp.

66  {
67  ApproximationScheme::operator=(std::move(from));
68  return *this;
69  }
INLINE Size gum::ApproximationScheme::periodSize ( ) const
virtualinherited

Returns the period size.

Returns
Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

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

147 { return _period_size; }
Size _period_size
Checking criteria frequency.

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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.

208  {
209  if (_burn_in > _current_step) {
210  return _burn_in - _current_step;
211  } else {
212  return 0;
213  }
214  }
Size _burn_in
Number of iterations before checking stopping criteria.
Size _current_step
The current step.
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
epsThe new epsilon value.
Exceptions
OutOfLowerBoundRaised 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), GreedyHillClimbing(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setEpsilon().

41  {
42  if (eps < 0.) { GUM_ERROR(OutOfLowerBound, "eps should be >=0"); }
43 
44  _eps = eps;
45  _enabled_eps = true;
46  }
bool _enabled_eps
If true, the threshold convergence is enabled.
double _eps
Threshold for convergence.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters
maxThe maximum number of iterations.
Exceptions
OutOfLowerBoundRaised 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxIter().

93  {
94  if (max < 1) { GUM_ERROR(OutOfLowerBound, "max should be >=1"); }
95  _max_iter = max;
96  _enabled_max_iter = true;
97  }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.
Size _max_iter
The maximum iterations.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters
timeoutThe timeout value in seconds.
Exceptions
OutOfLowerBoundRaised 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxTime().

116  {
117  if (timeout <= 0.) { GUM_ERROR(OutOfLowerBound, "timeout should be >0."); }
118  _max_time = timeout;
119  _enabled_max_time = true;
120  }
bool _enabled_max_time
If true, the timeout is enabled.
double _max_time
The timeout.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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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
rateThe minimal epsilon rate.
Exceptions
OutOfLowerBoundif 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMinEpsilonRate().

64  {
65  if (rate < 0) { GUM_ERROR(OutOfLowerBound, "rate should be >=0"); }
66 
67  _min_rate_eps = rate;
68  _enabled_min_rate_eps = true;
69  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.
double _min_rate_eps
Threshold for the epsilon rate.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

How many samples between two stopping is enable.

Parameters
pThe new period value.
Exceptions
OutOfLowerBoundRaised 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setPeriodSize().

141  {
142  if (p < 1) { GUM_ERROR(OutOfLowerBound, "p should be >=1"); }
143 
144  _period_size = p;
145  }
Size _period_size
Checking criteria frequency.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

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

Parameters
vIf 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::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setVerbosity().

150 { _verbosity = v; }
bool _verbosity
If true, verbosity is enabled.

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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().

195  {
196  if (_current_step < _burn_in) { return false; }
197 
198  if (_period_size == 1) { return true; }
199 
200  return ((_current_step - _burn_in) % _period_size == 0);
201  }
Size _burn_in
Number of iterations before checking stopping criteria.
Size _current_step
The current step.
Size _period_size
Checking criteria frequency.

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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().

156  {
157  return _current_state;
158  }
ApproximationSchemeSTATE _current_state
The current state.

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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 learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

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

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

Parameters
incrThe new increment steps.

Definition at line 204 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

204  {
205  _current_step += incr;
206  }
Size _current_step
The current step.

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

Returns true if verbosity is enabled.

Returns
Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

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

152 { return _verbosity; }
bool _verbosity
If true, verbosity is enabled.

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Member Data Documentation

double gum::ApproximationScheme::_current_epsilon
protectedinherited
double gum::ApproximationScheme::_current_rate
protectedinherited
bool gum::ApproximationScheme::_enabled_max_iter
protectedinherited
bool gum::ApproximationScheme::_enabled_min_rate_eps
protectedinherited
double gum::ApproximationScheme::_eps
protectedinherited
std::vector< double > gum::ApproximationScheme::_history
protectedinherited
double gum::ApproximationScheme::_last_epsilon
protectedinherited

Last epsilon value.

Definition at line 371 of file approximationScheme.h.

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

Size gum::ApproximationScheme::_max_iter
protectedinherited
double gum::ApproximationScheme::_max_time
protectedinherited
double gum::ApproximationScheme::_min_rate_eps
protectedinherited
Size gum::ApproximationScheme::_period_size
protectedinherited
bool gum::ApproximationScheme::_verbosity
protectedinherited

If true, verbosity is enabled.

Definition at line 419 of file approximationScheme.h.

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

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

Criteria messageApproximationScheme.

Definition at line 61 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: