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Public Attributes | List of all members
sgpp::base::AdaptivityConfiguration Struct Reference

structure that can be used by application to define adaptivity strategies More...

#include <Grid.hpp>

Public Attributes

CoarseningFunctorType coarseningFunctorType_ = CoarseningFunctorType::Surplus
 coarsening indicator
 
double coarseningThreshold_ = 0.0
 coarsening threshold for surpluses
 
bool coarsenInitialPoints_ = false
 prevent coarsening of initial grid points, needed for some decompositions
 
bool errorBasedRefinement_ = false
 other refinement strategy, that is more expensive, but yields better results
 
size_t errorBufferSize_ = 3
 amount of error values to consider when checking for convergence in case of error based refinement
 
double errorConvergenceThreshold_ = 0.001
 threshold for convergence in case error based refinement is applied
 
size_t errorMinInterval_ = 0
 minimum amount of iterations before the next refinement is allowed to happen in case of error based refinement
 
bool levelPenalize_ = false
 determines if finer grid levels should be penalized when finding points to refine
 
bool maxLevelType_ = false
 refinement type: false: classic, true: maxLevel
 
size_t numCoarseningPoints_ = 5
 max. number of points to be coarsened
 
size_t numRefinementPoints_ = 5
 max. number of points to be refined
 
size_t numRefinements_ = 1
 number of refinements
 
double percent_ = 1.0
 max. percent of points to be refined/coarsened
 
bool precomputeEvaluations_ = true
 in case of zero corssing based refinement: determines if evaluations should be precomupted
 
RefinementFunctorType refinementFunctorType_ = RefinementFunctorType::Surplus
 refinement indicator
 
size_t refinementPeriod_ = 1
 refinement will be triggered each refinementPeriod instances (approximately) in case of non error based refinement
 
double refinementThreshold_ = 0.0
 refinement threshold for surpluses
 
std::vector< double > scalingCoefficients_ = std::vector<double>()
 in case of data based refinements: determines the scaling coefficients for each class
 
AdaptivityThresholdType thresholdType_ = AdaptivityThresholdType::Absolute
 threshold type
 

Detailed Description

structure that can be used by application to define adaptivity strategies

Member Data Documentation

◆ coarseningFunctorType_

CoarseningFunctorType sgpp::base::AdaptivityConfiguration::coarseningFunctorType_ = CoarseningFunctorType::Surplus

coarsening indicator

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::ModelFittingDensityEstimation::getCoarseningFunctor(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ coarseningThreshold_

double sgpp::base::AdaptivityConfiguration::coarseningThreshold_ = 0.0

coarsening threshold for surpluses

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ coarsenInitialPoints_

bool sgpp::base::AdaptivityConfiguration::coarsenInitialPoints_ = false

prevent coarsening of initial grid points, needed for some decompositions

Referenced by sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ errorBasedRefinement_

bool sgpp::base::AdaptivityConfiguration::errorBasedRefinement_ = false

other refinement strategy, that is more expensive, but yields better results

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RefinementMonitorFactory::createRefinementMonitor(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ errorBufferSize_

size_t sgpp::base::AdaptivityConfiguration::errorBufferSize_ = 3

amount of error values to consider when checking for convergence in case of error based refinement

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RefinementMonitorFactory::createRefinementMonitor(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ errorConvergenceThreshold_

double sgpp::base::AdaptivityConfiguration::errorConvergenceThreshold_ = 0.001

threshold for convergence in case error based refinement is applied

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RefinementMonitorFactory::createRefinementMonitor(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ errorMinInterval_

size_t sgpp::base::AdaptivityConfiguration::errorMinInterval_ = 0

minimum amount of iterations before the next refinement is allowed to happen in case of error based refinement

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RefinementMonitorFactory::createRefinementMonitor(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ levelPenalize_

bool sgpp::base::AdaptivityConfiguration::levelPenalize_ = false

determines if finer grid levels should be penalized when finding points to refine

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ maxLevelType_

bool sgpp::base::AdaptivityConfiguration::maxLevelType_ = false

refinement type: false: classic, true: maxLevel

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::LearnerScenario::getAdaptivityConfiguration(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), hpx_main(), main(), main(), sgpp::datadriven::LearnerScenario::setAdaptivityConfiguration(), sgpp::datadriven::FitterConfiguration::setupDefaults(), sgpp::datadriven::LearnerBase::train(), and sgpp::datadriven::LearnerBaseSP::train().

◆ numCoarseningPoints_

size_t sgpp::base::AdaptivityConfiguration::numCoarseningPoints_ = 5

max. number of points to be coarsened

Referenced by sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ numRefinementPoints_

size_t sgpp::base::AdaptivityConfiguration::numRefinementPoints_ = 5

max. number of points to be refined

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), doAllRefinements(), doAllRefinements(), doAllRefinements(), doAllRefinements(), sgpp::datadriven::LearnerSGDEOnOffParallel::doRefinementForAll(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::LearnerScenario::getAdaptivityConfiguration(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), sgpp::datadriven::RefinementHandler::handleSurplusBasedRefinement(), hpx_main(), sgpp::datadriven::LearnerSGDEConfiguration::initConfig(), sgpp::datadriven::SparseGridDensityEstimatorConfiguration::initConfig(), sgpp::datadriven::LearnerSGDEConfiguration::LearnerSGDEConfiguration(), main(), main(), sgpp::datadriven::LearnerScenario::setAdaptivityConfiguration(), sgpp::datadriven::FitterConfiguration::setupDefaults(), sgpp::datadriven::SparseGridDensityEstimatorConfiguration::SparseGridDensityEstimatorConfiguration(), sgpp::datadriven::SparseGridDensityEstimator::train(), sgpp::datadriven::LearnerSGDE::train(), sgpp::datadriven::LearnerBase::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerSVM::train(), sgpp::datadriven::LearnerSGD::train(), and sgpp::datadriven::LearnerSGDE::trainOnline().

◆ numRefinements_

size_t sgpp::base::AdaptivityConfiguration::numRefinements_ = 1

number of refinements

Referenced by sgpp::datadriven::ModelFittingClassification::adapt(), sgpp::datadriven::RefinementHandler::checkRefinementNecessary(), sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RosenblattTransformation::createSGDELearner(), doAllRefinements(), doAllRefinements(), doAllRefinements(), doAllRefinements(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::LearnerScenario::getAdaptivityConfiguration(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), hpx_main(), sgpp::datadriven::LearnerSGDEConfiguration::initConfig(), sgpp::datadriven::SparseGridDensityEstimatorConfiguration::initConfig(), sgpp::datadriven::LearnerSGDEConfiguration::LearnerSGDEConfiguration(), main(), main(), sgpp::datadriven::LearnerScenario::setAdaptivityConfiguration(), sgpp::datadriven::FitterConfiguration::setupDefaults(), sgpp::datadriven::SparseGridDensityEstimatorConfiguration::SparseGridDensityEstimatorConfiguration(), sgpp::datadriven::SparseGridDensityEstimator::train(), sgpp::datadriven::LearnerSGDE::train(), sgpp::datadriven::RegressionLearner::train(), sgpp::datadriven::LearnerBase::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerSGD::train(), and sgpp::datadriven::LearnerSGDE::trainOnline().

◆ percent_

double sgpp::base::AdaptivityConfiguration::percent_ = 1.0

max. percent of points to be refined/coarsened

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::LearnerScenario::getAdaptivityConfiguration(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), hpx_main(), main(), main(), sgpp::datadriven::LearnerScenario::setAdaptivityConfiguration(), sgpp::datadriven::FitterConfiguration::setupDefaults(), sgpp::datadriven::LearnerBase::train(), and sgpp::datadriven::LearnerBaseSP::train().

◆ precomputeEvaluations_

bool sgpp::base::AdaptivityConfiguration::precomputeEvaluations_ = true

in case of zero corssing based refinement: determines if evaluations should be precomupted

Referenced by sgpp::datadriven::ModelFittingClassification::adapt(), sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ refinementFunctorType_

RefinementFunctorType sgpp::base::AdaptivityConfiguration::refinementFunctorType_ = RefinementFunctorType::Surplus

refinement indicator

Referenced by sgpp::datadriven::ModelFittingClassification::adapt(), sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), sgpp::datadriven::ModelFittingDensityEstimation::getRefinementFunctor(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ refinementPeriod_

size_t sgpp::base::AdaptivityConfiguration::refinementPeriod_ = 1

refinement will be triggered each refinementPeriod instances (approximately) in case of non error based refinement

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::RefinementMonitorFactory::createRefinementMonitor(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ refinementThreshold_

double sgpp::base::AdaptivityConfiguration::refinementThreshold_ = 0.0

refinement threshold for surpluses

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), doAllRefinements(), doAllRefinements(), doAllRefinements(), doAllRefinements(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::LearnerScenario::getAdaptivityConfiguration(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), hpx_main(), main(), main(), sgpp::datadriven::LearnerScenario::setAdaptivityConfiguration(), sgpp::datadriven::FitterConfiguration::setupDefaults(), sgpp::datadriven::SparseGridDensityEstimator::train(), sgpp::datadriven::LearnerSGDE::train(), sgpp::datadriven::LearnerBase::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerBaseSP::train(), sgpp::datadriven::LearnerSVM::train(), sgpp::datadriven::LearnerSGD::train(), and sgpp::datadriven::LearnerSGDE::trainOnline().

◆ scalingCoefficients_

std::vector<double> sgpp::base::AdaptivityConfiguration::scalingCoefficients_ = std::vector<double>()

in case of data based refinements: determines the scaling coefficients for each class

Referenced by sgpp::datadriven::DBMatObjectStore::ObjectContainer::configMatches(), sgpp::datadriven::DMConfigTools::dumpToStream(), sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig(), and sgpp::datadriven::FitterConfiguration::setupDefaults().

◆ thresholdType_

AdaptivityThresholdType sgpp::base::AdaptivityConfiguration::thresholdType_ = AdaptivityThresholdType::Absolute

threshold type

Referenced by sgpp::datadriven::DMConfigTools::dumpToStream(), and sgpp::datadriven::DataMiningConfigParser::getFitterAdaptivityConfig().

The documentation for this struct was generated from the following file: