This class implements standard sparse grid regression with an arbitrary regularization operator. More...

#include <Learner.hpp>

Inheritance diagram for sgpp::datadriven::Learner:

Public Member Functions
	Learner (sgpp::datadriven::RegularizationType &regularization, const bool isRegression, const bool isVerbose=true)
	Constructor.

virtual	~Learner ()
	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.

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

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

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
virtual std::unique_ptr< sgpp::datadriven::DMSystemMatrixBase >	createDMSystem (sgpp::base::DataMatrix &trainDataset, double lambda)
	construct system matrix

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

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

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

Protected Attributes
sgpp::datadriven::RegularizationType	CMode
	regularization mode

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 arbitrary regularization operator.

Constructor & Destructor Documentation

◆ Learner()

sgpp::datadriven::Learner::Learner	(	sgpp::datadriven::RegularizationType &	regularization,
		const bool	isRegression,
		const bool	isVerbose = `true`
	)

Constructor.

Parameters

regularization	enum that gives the regurlarization method
isRegression	flag for regression
isVerbose	flag for verbose output

◆ ~Learner()

sgpp::datadriven::Learner::~Learner ( )

virtual

Destructor.

Member Function Documentation

◆ createDMSystem()

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