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ActiveLearningMonteCarloSampler Class Reference

A class used to perform Monte Carlo Sampling with active learning. More...

#include <ActiveLearningMonteCarloSampler.h>

Inheritance diagram for ActiveLearningMonteCarloSampler:
[legend]

Public Types

enum  SampleMode { SampleMode::GLOBAL, SampleMode::LOCAL }
 
typedef DataFileName DataFileParameterType
 

Public Member Functions

 ActiveLearningMonteCarloSampler (const InputParameters &parameters)
 
virtual bool isAdaptiveSamplingCompleted () const override
 Returns true if the adaptive sampling is completed. More...
 
std::vector< Real > getNextLocalRow ()
 
dof_id_type getNumberOfRows () const
 
dof_id_type getNumberOfCols () const
 
dof_id_type getNumberOfLocalRows () const
 
const LocalRankConfig & getRankConfig (bool batch_mode) const
 
libMesh::Parallel::Communicator & getLocalComm ()
 
virtual bool enabled () const
 
std::shared_ptr< MooseObject > getSharedPtr ()
 
std::shared_ptr< const MooseObject > getSharedPtr () const
 
MooseApp & getMooseApp () const
 
const std::string & type () const
 
const std::string & name () const
 
std::string typeAndName () const
 
MooseObjectParameterName uniqueParameterName (const std::string &parameter_name) const
 
MooseObjectName uniqueName () const
 
const InputParameters & parameters () const
 
const hit::Node * getHitNode () const
 
bool hasBase () const
 
const std::string & getBase () const
 
const T & getParam (const std::string &name) const
 
std::vector< std::pair< T1, T2 > > getParam (const std::string &param1, const std::string &param2) const
 
const T * queryParam (const std::string &name) const
 
const T & getRenamedParam (const std::string &old_name, const std::string &new_name) const
 
T getCheckedPointerParam (const std::string &name, const std::string &error_string="") const
 
bool isParamValid (const std::string &name) const
 
bool isParamSetByUser (const std::string &name) const
 
void connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const
 
void paramError (const std::string &param, Args... args) const
 
void paramWarning (const std::string &param, Args... args) const
 
void paramInfo (const std::string &param, Args... args) const
 
std::string messagePrefix (const bool hit_prefix=true) const
 
std::string errorPrefix (const std::string &) const
 
void mooseError (Args &&... args) const
 
void mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const
 
void mooseErrorNonPrefixed (Args &&... args) const
 
void mooseWarning (Args &&... args) const
 
void mooseWarningNonPrefixed (Args &&... args) const
 
void mooseDeprecated (Args &&... args) const
 
void mooseInfo (Args &&... args) const
 
void callMooseError (std::string msg, const bool with_prefix, const hit::Node *node=nullptr) const
 
std::string getDataFileName (const std::string &param) const
 
std::string getDataFileNameByName (const std::string &relative_path) const
 
std::string getDataFilePath (const std::string &relative_path) const
 
virtual void initialSetup ()
 
virtual void timestepSetup ()
 
virtual void jacobianSetup ()
 
virtual void residualSetup ()
 
virtual void subdomainSetup ()
 
virtual void customSetup (const ExecFlagType &)
 
const ExecFlagEnum & getExecuteOnEnum () const
 
PerfGraph & perfGraph ()
 
T & getSampler (const std::string &name)
 
Sampler & getSampler (const std::string &name)
 
T & getSamplerByName (const SamplerName &name)
 
Sampler & getSamplerByName (const SamplerName &name)
 
const VectorPostprocessorValue & getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const
 
const VectorPostprocessorValue & getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const
 
const VectorPostprocessorValue & getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const
 
const ScatterVectorPostprocessorValue & getScatterVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const
 
const ScatterVectorPostprocessorValue & getScatterVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const
 
const ScatterVectorPostprocessorValue & getScatterVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const
 
const ScatterVectorPostprocessorValue & getScatterVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const
 
bool hasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const
 
bool hasVectorPostprocessor (const std::string &param_name) const
 
bool hasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const
 
bool hasVectorPostprocessorByName (const VectorPostprocessorName &name) const
 
const VectorPostprocessorName & getVectorPostprocessorName (const std::string &param_name) const
 
DenseMatrix< Real > getGlobalSamples ()
 
DenseMatrix< Real > getGlobalSamples ()
 
DenseMatrix< Real > getLocalSamples ()
 
DenseMatrix< Real > getLocalSamples ()
 
dof_id_type getLocalRowBegin () const
 
dof_id_type getLocalRowBegin () const
 
dof_id_type getLocalRowEnd () const
 
dof_id_type getLocalRowEnd () const
 
const Distribution & getDistribution (const std::string &name) const
 
const T & getDistribution (const std::string &name) const
 
const Distribution & getDistribution (const std::string &name) const
 
const T & getDistribution (const std::string &name) const
 
const Distribution & getDistributionByName (const DistributionName &name) const
 
const T & getDistributionByName (const std::string &name) const
 
const Distribution & getDistributionByName (const DistributionName &name) const
 
const T & getDistributionByName (const std::string &name) const
 
bool isVectorPostprocessorDistributed (const std::string &param_name) const
 
bool isVectorPostprocessorDistributed (const std::string &param_name) const
 
bool isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const
 
bool isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const
 
const Parallel::Communicator & comm () const
 
processor_id_type n_processors () const
 
processor_id_type processor_id () const
 

Static Public Member Functions

static InputParameters validParams ()
 
static void callMooseError (MooseApp *const app, const InputParameters &params, std::string msg, const bool with_prefix, const hit::Node *node)
 

Public Attributes

const ConsoleStream _console
 

Static Public Attributes

static const std::string type_param
 
static const std::string name_param
 
static const std::string unique_name_param
 
static const std::string app_param
 
static const std::string moose_base_param
 

Protected Types

enum  CommMethod
 

Protected Member Functions

virtual void sampleSetUp (const Sampler::SampleMode mode) override
 Gather all the samples. More...
 
virtual Real computeSample (dof_id_type row_index, dof_id_type col_index) override
 Return the sample for the given row and column. More...
 
void setNumberOfRandomSeeds (std::size_t number)
 
Real getRand (unsigned int index=0)
 
uint32_t getRandl (unsigned int index, uint32_t lower, uint32_t upper)
 
virtual LocalRankConfig constructRankConfig (bool batch_mode) const
 
PerfID registerTimedSection (const std::string &section_name, const unsigned int level) const
 
PerfID registerTimedSection (const std::string &section_name, const unsigned int level, const std::string &live_message, const bool print_dots=true) const
 
std::string timedSectionName (const std::string &section_name) const
 
virtual void addVectorPostprocessorDependencyHelper (const VectorPostprocessorName &) const
 
const ReporterContextBase & getReporterContextBaseByName (const ReporterName &reporter_name) const
 
const ReporterName & getReporterName (const std::string &param_name) const
 
virtual void addReporterDependencyHelper (const ReporterName &)
 
void setNumberOfRows (dof_id_type n_rows)
 
void setNumberOfRows (dof_id_type n_rows)
 
void setNumberOfCols (dof_id_type n_cols)
 
void setNumberOfCols (dof_id_type n_cols)
 
virtual void sampleTearDown (const SampleMode)
 
virtual void sampleTearDown (const SampleMode)
 
virtual void computeSampleMatrix (DenseMatrix< Real > &matrix)
 
virtual void computeSampleMatrix (DenseMatrix< Real > &matrix)
 
virtual void computeLocalSampleMatrix (DenseMatrix< Real > &matrix)
 
virtual void computeLocalSampleMatrix (DenseMatrix< Real > &matrix)
 
virtual void computeSampleRow (dof_id_type i, std::vector< Real > &data)
 
virtual void computeSampleRow (dof_id_type i, std::vector< Real > &data)
 
virtual void advanceGenerators (const dof_id_type count)
 
virtual void advanceGenerators (const dof_id_type count)
 
virtual void advanceGenerator (const unsigned int seed_index, const dof_id_type count)
 
virtual void advanceGenerator (const unsigned int seed_index, const dof_id_type count)
 
void setAutoAdvanceGenerators (const bool state)
 
void setAutoAdvanceGenerators (const bool state)
 
void shuffle (std::vector< T > &data, const std::size_t seed_index=0, const CommMethod method=CommMethod::LOCAL)
 
void shuffle (std::vector< T > &data, const std::size_t seed_index=0, const CommMethod method=CommMethod::LOCAL)
 
virtual void executeSetUp ()
 
virtual void executeSetUp ()
 
virtual void executeTearDown ()
 
virtual void executeTearDown ()
 
void saveGeneratorState ()
 
void saveGeneratorState ()
 
void restoreGeneratorState ()
 
void restoreGeneratorState ()
 
const T & getReporterValue (const std::string &param_name, const std::size_t time_index=0)
 
const T & getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)
 
const T & getReporterValue (const std::string &param_name, const std::size_t time_index=0)
 
const T & getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)
 
const T & getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)
 
const T & getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)
 
const T & getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)
 
const T & getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)
 
bool hasReporterValue (const std::string &param_name) const
 
bool hasReporterValue (const std::string &param_name) const
 
bool hasReporterValue (const std::string &param_name) const
 
bool hasReporterValue (const std::string &param_name) const
 
bool hasReporterValueByName (const ReporterName &reporter_name) const
 
bool hasReporterValueByName (const ReporterName &reporter_name) const
 
bool hasReporterValueByName (const ReporterName &reporter_name) const
 
bool hasReporterValueByName (const ReporterName &reporter_name) const
 

Protected Attributes

std::vector< Distribution const * > _distributions
 Storage for distribution objects to be utilized. More...
 
const std::vector< bool > & _flag_sample
 Flag samples if the surrogate prediction was inadequate. More...
 
bool _is_sampling_completed = false
 True if the sampling is completed. More...
 
 NONE
 
 LOCAL
 
 SEMI_LOCAL
 
const dof_id_type _min_procs_per_row
 
const dof_id_type _max_procs_per_row
 
libMesh::Parallel::Communicator _local_comm
 
const bool & _enabled
 
MooseApp & _app
 
Factory & _factory
 
ActionFactory & _action_factory
 
const std::string & _type
 
const std::string & _name
 
const InputParameters & _pars
 
const ExecFlagEnum & _execute_enum
 
const ExecFlagType & _current_execute_flag
 
MooseApp & _pg_moose_app
 
const std::string _prefix
 
const Parallel::Communicator & _communicator
 

Private Attributes

const int & _step
 Track the current step of the main App. More...
 
const unsigned int _num_batch
 The maximum number of GP fails. More...
 
int _check_step
 Ensure that the sampler proceeds in a sequential fashion. More...
 
const int & _num_samples
 Number of samples requested. More...
 
int _retraining_steps = 0
 Number of retraining performed. More...
 
std::vector< std::vector< Real > > _inputs_sto
 Storage for previously accepted samples by the decision reporter system. More...
 
std::vector< std::vector< Real > > _inputs_gp_fails
 Store the input params for which the GP fails. More...
 

Detailed Description

A class used to perform Monte Carlo Sampling with active learning.

Definition at line 17 of file ActiveLearningMonteCarloSampler.h.

Constructor & Destructor Documentation

◆ ActiveLearningMonteCarloSampler()

ActiveLearningMonteCarloSampler::ActiveLearningMonteCarloSampler ( const InputParameters &  parameters)

Definition at line 40 of file ActiveLearningMonteCarloSampler.C.

41  : Sampler(parameters),
42  _flag_sample(getReporterValue<std::vector<bool>>("flag_sample")),
43  _step(getCheckedPointerParam<FEProblemBase *>("_fe_problem_base")->timeStep()),
44  _num_batch(getParam<dof_id_type>("num_batch")),
45  _check_step(std::numeric_limits<int>::min()),
46  _num_samples(getParam<int>("num_samples"))
47 {
48  for (const DistributionName & name : getParam<std::vector<DistributionName>>("distributions"))
49  _distributions.push_back(&getDistributionByName(name));
50  setNumberOfRows(_num_batch);
51  setNumberOfCols(_distributions.size());
52  _inputs_sto.resize(_num_batch, std::vector<Real>(_distributions.size()));
53  setNumberOfRandomSeeds(getParam<unsigned int>("num_random_seeds"));
54 }
Sampler::setNumberOfRows
void setNumberOfRows(dof_id_type n_rows)
Sampler::getParam
const T & getParam(const std::string &name) const
ActiveLearningMonteCarloSampler::_flag_sample
const std::vector< bool > & _flag_sample
Flag samples if the surrogate prediction was inadequate.
Definition: ActiveLearningMonteCarloSampler.h:39
Sampler::parameters
const InputParameters & parameters() const
ActiveLearningMonteCarloSampler::_num_samples
const int & _num_samples
Number of samples requested.
Definition: ActiveLearningMonteCarloSampler.h:55
Sampler::Sampler
Sampler(const InputParameters &parameters)
Sampler::getReporterValue
const T & getReporterValue(const std::string &param_name, const std::size_t time_index=0)
Sampler::name
const std::string & name() const
ActiveLearningMonteCarloSampler::_distributions
std::vector< Distribution const * > _distributions
Storage for distribution objects to be utilized.
Definition: ActiveLearningMonteCarloSampler.h:36
ActiveLearningMonteCarloSampler::_step
const int & _step
Track the current step of the main App.
Definition: ActiveLearningMonteCarloSampler.h:46
ActiveLearningMonteCarloSampler::_inputs_sto
std::vector< std::vector< Real > > _inputs_sto
Storage for previously accepted samples by the decision reporter system.
Definition: ActiveLearningMonteCarloSampler.h:61
Sampler::getDistributionByName
const Distribution & getDistributionByName(const DistributionName &name) const
Sampler::setNumberOfCols
void setNumberOfCols(dof_id_type n_cols)
ActiveLearningMonteCarloSampler::_check_step
int _check_step
Ensure that the sampler proceeds in a sequential fashion.
Definition: ActiveLearningMonteCarloSampler.h:52
ActiveLearningMonteCarloSampler::_num_batch
const unsigned int _num_batch
The maximum number of GP fails.
Definition: ActiveLearningMonteCarloSampler.h:49
Sampler::setNumberOfRandomSeeds
void setNumberOfRandomSeeds(std::size_t number)

Member Function Documentation

◆ computeSample()

Real ActiveLearningMonteCarloSampler::computeSample ( dof_id_type  row_index,
dof_id_type  col_index 
)
overrideprotectedvirtual

Return the sample for the given row and column.

Implements Sampler.

Definition at line 102 of file ActiveLearningMonteCarloSampler.C.

103 {
104  return _inputs_sto[row_index][col_index];
105 }
ActiveLearningMonteCarloSampler::_inputs_sto
std::vector< std::vector< Real > > _inputs_sto
Storage for previously accepted samples by the decision reporter system.
Definition: ActiveLearningMonteCarloSampler.h:61

◆ isAdaptiveSamplingCompleted()

virtual bool ActiveLearningMonteCarloSampler::isAdaptiveSamplingCompleted ( ) const
inlineoverridevirtual

Returns true if the adaptive sampling is completed.

Reimplemented from Sampler.

Definition at line 27 of file ActiveLearningMonteCarloSampler.h.

27 { return _is_sampling_completed; }
ActiveLearningMonteCarloSampler::_is_sampling_completed
bool _is_sampling_completed
True if the sampling is completed.
Definition: ActiveLearningMonteCarloSampler.h:42

◆ sampleSetUp()

void ActiveLearningMonteCarloSampler::sampleSetUp ( const Sampler::SampleMode  mode)
overrideprotectedvirtual

Gather all the samples.

Reimplemented from Sampler.

Definition at line 57 of file ActiveLearningMonteCarloSampler.C.

58 {
59  // If we've already done this step, skip
60  if (_check_step == _step)
61  return;
62 
63  if (_is_sampling_completed)
64  mooseError("Internal bug: the adaptive sampling is supposed to be completed but another sample "
65  "has been requested.");
66 
67  // Keep data where the GP failed
68  if (_step > 0)
69  for (dof_id_type i = 0; i < _num_batch; ++i)
70  if (_flag_sample[i])
71  {
72  _inputs_gp_fails.push_back(_inputs_sto[i]);
73 
74  // When the GP fails, the current time step is 'wasted' and the retraining step doesn't
75  // happen until the next time step. Therefore, keep track of the number of retraining steps
76  // to increase the total number of steps taken.
77  ++_retraining_steps;
78  }
79 
80  // If we don't have enough failed inputs, generate new ones
81  if (_inputs_gp_fails.size() < _num_batch)
82  {
83  for (dof_id_type i = 0; i < _num_batch; ++i)
84  for (dof_id_type j = 0; j < _distributions.size(); ++j)
85  _inputs_sto[i][j] = _distributions[j]->quantile(getRand(_step));
86  }
87  // If we do have enough failed inputs, assign them and clear the tracked ones
88  else
89  {
90  _inputs_sto.assign(_inputs_gp_fails.begin(), _inputs_gp_fails.begin() + _num_batch);
91  _inputs_gp_fails.erase(_inputs_gp_fails.begin(), _inputs_gp_fails.begin() + _num_batch);
92  }
93 
94  _check_step = _step;
95 
96  // check if we have finished the sampling
97  if (_step >= _num_samples + _retraining_steps)
98  _is_sampling_completed = true;
99 }
ActiveLearningMonteCarloSampler::_flag_sample
const std::vector< bool > & _flag_sample
Flag samples if the surrogate prediction was inadequate.
Definition: ActiveLearningMonteCarloSampler.h:39
ActiveLearningMonteCarloSampler::_retraining_steps
int _retraining_steps
Number of retraining performed.
Definition: ActiveLearningMonteCarloSampler.h:58
Sampler::getRand
Real getRand(unsigned int index=0)
ActiveLearningMonteCarloSampler::_num_samples
const int & _num_samples
Number of samples requested.
Definition: ActiveLearningMonteCarloSampler.h:55
ActiveLearningMonteCarloSampler::_inputs_gp_fails
std::vector< std::vector< Real > > _inputs_gp_fails
Store the input params for which the GP fails.
Definition: ActiveLearningMonteCarloSampler.h:64
ActiveLearningMonteCarloSampler::_distributions
std::vector< Distribution const * > _distributions
Storage for distribution objects to be utilized.
Definition: ActiveLearningMonteCarloSampler.h:36
ActiveLearningMonteCarloSampler::_step
const int & _step
Track the current step of the main App.
Definition: ActiveLearningMonteCarloSampler.h:46
ActiveLearningMonteCarloSampler::_inputs_sto
std::vector< std::vector< Real > > _inputs_sto
Storage for previously accepted samples by the decision reporter system.
Definition: ActiveLearningMonteCarloSampler.h:61
ActiveLearningMonteCarloSampler::_is_sampling_completed
bool _is_sampling_completed
True if the sampling is completed.
Definition: ActiveLearningMonteCarloSampler.h:42
Sampler::mooseError
void mooseError(Args &&... args) const
EM::j
static const std::complex< double > j(0, 1)
Complex number "j" (also known as "i")
ActiveLearningMonteCarloSampler::_check_step
int _check_step
Ensure that the sampler proceeds in a sequential fashion.
Definition: ActiveLearningMonteCarloSampler.h:52
ActiveLearningMonteCarloSampler::_num_batch
const unsigned int _num_batch
The maximum number of GP fails.
Definition: ActiveLearningMonteCarloSampler.h:49
dof_id_type
uint8_t dof_id_type

◆ validParams()

InputParameters ActiveLearningMonteCarloSampler::validParams ( )
static

Definition at line 16 of file ActiveLearningMonteCarloSampler.C.

17 {
18  InputParameters params = Sampler::validParams();
19  params.addClassDescription("Monte Carlo Sampler for active learning with surrogate model.");
20  params.addRequiredParam<dof_id_type>("num_batch",
21  "The number of full model evaluations in the batch.");
22  params.addRequiredParam<std::vector<DistributionName>>(
23  "distributions",
24  "The distribution names to be sampled, the number of distributions provided defines the "
25  "number of columns per matrix.");
26  params.addRequiredParam<ReporterName>("flag_sample",
27  "Flag samples if the surrogate prediction was inadequate.");
28  params.addParam<unsigned int>(
29  "num_random_seeds",
30  100000,
31  "Initialize a certain number of random seeds. Change from the default only if you have to.");
32  params.addRequiredRangeCheckedParam<int>(
33  "num_samples",
34  "num_samples>0",
35  "Number of samples to use (the total number of steps taken will be equal to this number + "
36  "the number of re-training steps).");
37  return params;
38 }
InputParameters::addRequiredRangeCheckedParam
void addRequiredRangeCheckedParam(const std::string &name, const std::string &parsed_function, const std::string &doc_string)
Sampler::validParams
static InputParameters validParams()
InputParameters::addParam
void addParam(const std::string &name, const std::initializer_list< typename T::value_type > &value, const std::string &doc_string)
InputParameters::addRequiredParam
void addRequiredParam(const std::string &name, const std::string &doc_string)
InputParameters::addClassDescription
void addClassDescription(const std::string &doc_string)
dof_id_type
uint8_t dof_id_type

Member Data Documentation

◆ _check_step

int ActiveLearningMonteCarloSampler::_check_step
private

Ensure that the sampler proceeds in a sequential fashion.

Definition at line 52 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

◆ _distributions

std::vector<Distribution const *> ActiveLearningMonteCarloSampler::_distributions
protected

Storage for distribution objects to be utilized.

Definition at line 36 of file ActiveLearningMonteCarloSampler.h.

Referenced by ActiveLearningMonteCarloSampler(), and sampleSetUp().

◆ _flag_sample

const std::vector<bool>& ActiveLearningMonteCarloSampler::_flag_sample
protected

Flag samples if the surrogate prediction was inadequate.

Definition at line 39 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

◆ _inputs_gp_fails

std::vector<std::vector<Real> > ActiveLearningMonteCarloSampler::_inputs_gp_fails
private

Store the input params for which the GP fails.

Definition at line 64 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

◆ _inputs_sto

std::vector<std::vector<Real> > ActiveLearningMonteCarloSampler::_inputs_sto
private

Storage for previously accepted samples by the decision reporter system.

Definition at line 61 of file ActiveLearningMonteCarloSampler.h.

Referenced by ActiveLearningMonteCarloSampler(), computeSample(), and sampleSetUp().

◆ _is_sampling_completed

bool ActiveLearningMonteCarloSampler::_is_sampling_completed = false
protected

True if the sampling is completed.

Definition at line 42 of file ActiveLearningMonteCarloSampler.h.

Referenced by isAdaptiveSamplingCompleted(), and sampleSetUp().

◆ _num_batch

const unsigned int ActiveLearningMonteCarloSampler::_num_batch
private

The maximum number of GP fails.

Definition at line 49 of file ActiveLearningMonteCarloSampler.h.

Referenced by ActiveLearningMonteCarloSampler(), and sampleSetUp().

◆ _num_samples

const int& ActiveLearningMonteCarloSampler::_num_samples
private

Number of samples requested.

Definition at line 55 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

◆ _retraining_steps

int ActiveLearningMonteCarloSampler::_retraining_steps = 0
private

Number of retraining performed.

Definition at line 58 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

◆ _step

const int& ActiveLearningMonteCarloSampler::_step
private

Track the current step of the main App.

Definition at line 46 of file ActiveLearningMonteCarloSampler.h.

Referenced by sampleSetUp().

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