This class implements standard sparse grid regression with an Identity matrix as regularization operator. More...

#include <LearnerLeastSquaresIdentity.hpp>

Inheritance diagram for sgpp::datadriven::LearnerLeastSquaresIdentity:

Public Member Functions
std::vector< std::pair< size_t, double > >	getRefinementExecTimes ()

	LearnerLeastSquaresIdentity (const bool isRegression, const bool isVerbose=true)
	Constructor.

void	multTranspose (sgpp::base::DataMatrix &dataset, sgpp::base::DataVector &multiplier, sgpp::base::DataVector &result) override

virtual void	predict (sgpp::base::DataMatrix &testDataset, sgpp::base::DataVector &classesComputed)
	executes a Regression test for a given dataset and returns the result

void	predict (sgpp::base::DataMatrix &testDataset, sgpp::base::DataVector &classesComputed) override
	executes a Regression test for a given dataset and returns the result

void	setImplementation (sgpp::datadriven::OperationMultipleEvalConfiguration operationConfiguration)

double	testRegular (const sgpp::base::RegularGridConfiguration &gridConfig, sgpp::base::DataMatrix &testDataset)

	~LearnerLeastSquaresIdentity () override
	Destructor.

Public Member Functions inherited from sgpp::datadriven::LearnerBase
void	dumpFunction (std::string tFilename, size_t resolution)
	simple dump of sparse grid function into file, e.g.

void	dumpGrid (std::string tFilename)
	simple dump of grid points into file, e.g.

virtual double	getAccuracy (const sgpp::base::DataVector &classesComputed, const sgpp::base::DataVector &classesReference, const double threshold=0.0)
	compute the accuracy for given testDataset.

virtual double	getAccuracy (sgpp::base::DataMatrix &testDataset, const sgpp::base::DataVector &classesReference, const double threshold=0.0)
	compute the accuracy for given testDataset.

sgpp::base::DataVector &	getAlpha ()

virtual ClassificatorQuality	getCassificatorQuality (const sgpp::base::DataVector &classesComputed, const sgpp::base::DataVector &classesReference, const double threshold=0.0)
	compute the quality for given testDataset, classification ONLY!

virtual ClassificatorQuality	getCassificatorQuality (sgpp::base::DataMatrix &testDataset, const sgpp::base::DataVector &classesReference, const double threshold=0.0)
	compute the quality for given testDataset, classification ONLY! test is automatically called in order to determine the regression values of the current learner

sgpp::base::Grid &	getGrid ()

bool	getIsRegression () const
	determines the current mode

bool	getIsVerbose () const
	determines the current verbose mode of learner

std::vector< std::pair< size_t, double > >	getRefinementExecTimes ()

	LearnerBase (const bool isRegression, const bool isVerbose=true)
	Constructor.

	LearnerBase (const LearnerBase &copyMe)
	Copy-Constructor.

void	setIsVerbose (const bool isVerbose)
	sets the current verbose mode of learner

void	setReuseCoefficients (bool reuseCoefficients)

void	setSolverVerbose (bool solverVerbose)

void	store (std::string tGridFilename, std::string tAlphaFilename)
	store the grid and its current coefficients into files for further usage.

LearnerTiming	train (sgpp::base::DataMatrix &trainDataset, sgpp::base::DataVector &classes, const sgpp::base::RegularGridConfiguration &gridConfig, const sgpp::solver::SLESolverConfiguration &SolverConfig, const double lambdaRegularization)
	Learning a dataset with regular sparse grids.

virtual LearnerTiming	train (sgpp::base::DataMatrix &trainDataset, sgpp::base::DataVector &classes, const sgpp::base::RegularGridConfiguration &gridConfig, const sgpp::solver::SLESolverConfiguration &SolverConfigRefine, const sgpp::solver::SLESolverConfiguration &SolverConfigFinal, const sgpp::base::AdaptivityConfiguration &adaptivityConfig, bool testAccDuringAdapt, const double lambdaRegularization, sgpp::base::DataMatrix testDataset=nullptr, sgpp::base::DataVector testClasses=nullptr)
	Learning a dataset with spatially adaptive sparse grids.

virtual	~LearnerBase ()
	Destructor.

Protected Member Functions
std::unique_ptr< sgpp::datadriven::DMSystemMatrixBase >	createDMSystem (sgpp::base::DataMatrix &trainDataset, double lambda) override
	abstract method that constructs the corresponding system of linear equations Derived classes MUST overwrite this functions!

void	postProcessing (const sgpp::base::DataMatrix &trainDataset, const sgpp::solver::SLESolverType &solver, const size_t numNeededIterations) override
	Hook-Method for post-processing after each refinement learning.

Protected Member Functions inherited from sgpp::datadriven::LearnerBase
virtual void	InitializeGrid (const sgpp::base::RegularGridConfiguration &gridConfig)
	Initialize the grid and its coefficients.

virtual void	preProcessing ()
	Hook-Method for pre-processing before starting learning.

Additional Inherited Members
Protected Attributes inherited from sgpp::datadriven::LearnerBase
std::unique_ptr< sgpp::base::DataVector >	alpha
	the grid's coefficients

size_t	currentRefinementStep
	the current refinment step during training

double	execTime
	execution time

std::vector< std::pair< size_t, double > >	ExecTimeOnStep

double	GByte
	number of transferred Gbytes

double	GFlop
	number of executed Floating Point operations

std::unique_ptr< sgpp::base::Grid >	grid
	sparse grid object

bool	isRegression
	is regression selected

bool	isTrained
	is the grid trained

bool	isVerbose
	is verbose output enabled

bool	reuseCoefficients
	shall the coefficients be reused between refinement steps

bool	solverVerbose
	sets the verbose option for the solver

double	stepExecTime
	execution time for current refinement to calculate the GFlops at the current timestep only otherwise accumulated GFlops (all refinement steps) are calculated

double	stepGByte
	number of transferred Gbytes in the current refinement step

double	stepGFlop
	number of executed Floating Point operations in the current refinement step

Detailed Description

This class implements standard sparse grid regression with an Identity matrix as regularization operator.

Furthermore this Learner provides support for several vectorization approaches covering GPUs, CPUs and coprocessors.

Constructor & Destructor Documentation

◆ LearnerLeastSquaresIdentity()

sgpp::datadriven::LearnerLeastSquaresIdentity::LearnerLeastSquaresIdentity	(	const bool	isRegression,
		const bool	isVerbose = `true`
	)

explicit

Constructor.

Parameters

isRegression	set to true if a regression task should be executed
isVerbose	set to true in order to allow console output

◆ ~LearnerLeastSquaresIdentity()

sgpp::datadriven::LearnerLeastSquaresIdentity::~LearnerLeastSquaresIdentity ( )

override

Destructor.

Member Function Documentation

◆ createDMSystem()

std::unique_ptr< sgpp::datadriven::DMSystemMatrixBase > sgpp::datadriven::LearnerLeastSquaresIdentity::createDMSystem	(	sgpp::base::DataMatrix &	trainDataset,
		double	lambda
	)

overrideprotectedvirtual

abstract method that constructs the corresponding system of linear equations Derived classes MUST overwrite this functions!

Parameters

trainDataset	training dataset
lambda	lambda regularization parameter

Implements sgpp::datadriven::LearnerBase.

References sgpp::datadriven::LearnerBase::grid, and lambda.

◆ getRefinementExecTimes()

std::vector< std::pair< size_t, double > > sgpp::datadriven::LearnerLeastSquaresIdentity::getRefinementExecTimes ( )

◆ multTranspose()

void sgpp::datadriven::LearnerLeastSquaresIdentity::multTranspose	(	sgpp::base::DataMatrix &	dataset,
		sgpp::base::DataVector &	multiplier,
		sgpp::base::DataVector &	result
	)

overridevirtual

Reimplemented from sgpp::datadriven::LearnerBase.

References sgpp::op_factory::createOperationMultipleEval(), sgpp::datadriven::LearnerBase::grid, and sgpp::base::OperationMultipleEval::mult().

◆ postProcessing()

void sgpp::datadriven::LearnerLeastSquaresIdentity::postProcessing	(	const sgpp::base::DataMatrix &	trainDataset,
		const sgpp::solver::SLESolverType &	solver,
		const size_t	numNeededIterations
	)

overrideprotectedvirtual

Hook-Method for post-processing after each refinement learning.

can be overwritten by derived classes

Parameters

trainDataset	matrix with training data
solver	solver
numNeededIterations	number of required iterations

Reimplemented from sgpp::datadriven::LearnerBase.

References sgpp::datadriven::LearnerBase::execTime, sgpp::datadriven::LearnerBase::GByte, sgpp::datadriven::LearnerVectorizedPerformance::GByte_, sgpp::datadriven::LearnerVectorizedPerformanceCalculator::getGFlopAndGByte(), sgpp::base::DataMatrix::getNrows(), sgpp::datadriven::LearnerBase::GFlop, sgpp::datadriven::LearnerVectorizedPerformance::GFlop_, sgpp::datadriven::LearnerBase::grid, sgpp::datadriven::LearnerBase::isVerbose, and sgpp::datadriven::LearnerBase::reuseCoefficients.

◆ predict() [1/2]

void sgpp::datadriven::LearnerBase::predict	(	sgpp::base::DataMatrix &	testDataset,
		sgpp::base::DataVector &	classesComputed
	)

virtual

executes a Regression test for a given dataset and returns the result

Parameters

testDataset	dataset that is evaluated with the current learner
classesComputed	result of the evaluation of the data set

Reimplemented from sgpp::datadriven::LearnerBase.

◆ predict() [2/2]

void sgpp::datadriven::LearnerLeastSquaresIdentity::predict	(	sgpp::base::DataMatrix &	testDataset,
		sgpp::base::DataVector &	classesComputed
	)

overridevirtual

executes a Regression test for a given dataset and returns the result

Parameters

testDataset	dataset that is evaluated with the current learner
classesComputed	result of the evaluation of the data set

Reimplemented from sgpp::datadriven::LearnerBase.

References sgpp::datadriven::LearnerBase::alpha, sgpp::op_factory::createOperationMultipleEval(), sgpp::base::DataMatrix::getNrows(), sgpp::datadriven::LearnerBase::grid, and sgpp::base::OperationMultipleEval::mult().

◆ setImplementation()

void sgpp::datadriven::LearnerLeastSquaresIdentity::setImplementation ( sgpp::datadriven::OperationMultipleEvalConfiguration operationConfiguration )

inline

◆ testRegular()

double sgpp::datadriven::LearnerLeastSquaresIdentity::testRegular	(	const sgpp::base::RegularGridConfiguration &	gridConfig,
		sgpp::base::DataMatrix &	testDataset
	)

References sgpp::datadriven::LearnerBase::alpha, sgpp::op_factory::createOperationMultipleEval(), sgpp::datadriven::LearnerBase::execTime, sgpp::base::DataMatrix::getNrows(), sgpp::datadriven::LearnerBase::grid, sgpp::datadriven::LearnerBase::InitializeGrid(), sgpp::base::SGppStopwatch::start(), and sgpp::base::SGppStopwatch::stop().

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

datadriven/src/sgpp/datadriven/application/LearnerLeastSquaresIdentity.hpp
datadriven/src/sgpp/datadriven/application/LearnerLeastSquaresIdentity.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ LearnerLeastSquaresIdentity()

◆ ~LearnerLeastSquaresIdentity()

Member Function Documentation

◆ createDMSystem()

◆ getRefinementExecTimes()

◆ multTranspose()

◆ postProcessing()

◆ predict() [1/2]

◆ predict() [2/2]

◆ setImplementation()

◆ testRegular()