#include <AlgorithmAdaBoostBase.hpp>

Inheritance diagram for sgpp::datadriven::AlgorithmAdaBoostBase:

Public Member Functions
	AlgorithmAdaBoostBase (base::Grid &SparseGrid, size_t gridType, base::level_t gridLevel, base::DataMatrix &trainData, base::DataVector &trainDataClass, size_t NUM, double lambda, size_t IMAX, double eps, size_t IMAX_final, double eps_final, double firstLabel, double secondLabel, double threshold, double maxLambda, double minLambda, size_t searchNum, bool refine, size_t refineMode, size_t refineNum, size_t numberOfAda, double percentOfAda, size_t mode)
	Std-Constructor. More...

void	classif (base::DataMatrix &testData, base::DataVector &algorithmClassTrain, base::DataVector &algorithmClassTest, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs a classify for the testing dataset according to the baselearners get from the algorithm. More...

void	doAdaBoostR2 (base::DataMatrix &weights, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest, std::string lossFucType)
	Performs the Adaboost.R2(a regression algorithm) More...

void	doAdaBoostRT (base::DataMatrix &weights, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest, double Tvalue, std::string powerType)
	Performs the Adaboost.RT(a regression algorithm) More...

void	doDiscreteAdaBoost (base::DataVector &hypoWeight, base::DataVector &weightError, base::DataMatrix &weights, base::DataMatrix &decision, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs the Discrete Adaboost. More...

void	doRealAdaBoost (base::DataMatrix &weights, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs the Real Adaboost. More...

void	doRefinement (base::DataVector &alpha_ada, base::DataVector &weight_ada, size_t curBaseLearner)
	Performs refinement of grid to get an adaptive grid. More...

void	eval (base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs a real value calculate for the testing dataset. More...

void	getAccuracy (base::DataMatrix &testData, base::DataVector &testDataClass, double accuracy_train, double accuracy_test)
	Performs an accuracy evaluation for the testing dataset. More...

void	getAccuracyBL (base::DataMatrix &testData, base::DataVector &testDataClass, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest, double accuracy_train, double accuracy_test, size_t yourBaseLearner)
	Performs an accuracy evaluation for the testing dataset with a specified number of base learner. More...

size_t	getActualBL ()
	Get the actual base learners after doing adaboosting. More...

size_t	getMaxGridPoint (size_t baseLearner)
	Get the max GridPoint ever used in adaboosting. More...

size_t	getMeanGridPoint (size_t baseLearner)
	Get the mean GridPoint ever used in adaboosting. More...

void	getROC (base::DataMatrix &validationData, base::DataVector &validationDataClass, double acc, double sensitivity, double specificity, double precision, double recall, double fOneScore)
	Performs an evaluation to get ROC related parameter. More...

size_t	getSumGridPoint (size_t baseLearner)
	Get the sum GridPoint ever used in adaboosting. More...

double	hValue (double realValue)
	Performs a hypothesis classifier. More...

virtual	~AlgorithmAdaBoostBase ()
	Std-Deconstructor. More...

Protected Member Functions
virtual void	alphaSolver (double &lambda, base::DataVector &weight, base::DataVector &alpha, bool final)=0
	Performs a solver to get alpha. More...

Protected Attributes
size_t	actualBaseLearners
	Actual base learners number for Adaboosting. More...

size_t	boostMode
	Set the boost mode (1: Discrete Adaboost, 2: Real Adaboost) More...

base::DataVector *	classes
	Pointer to the class(real value in regression) of the data vector. More...

base::DataMatrix *	data
	Pointer to the data matrix. More...

size_t	dim
	DataSet Dimension. More...

double	epsilon
	Parameter for CG solver(during the refinement) More...

double	epsilon_final
	Parameter for CG solver(for the last refinement) More...

base::Grid *	grid
	the grid More...

size_t	gridPoint
	Number of grid points. More...

size_t	imax
	Parameter for CG solver(during the refinement) More...

size_t	imax_final
	Parameter for CG solver(for the last refinement) More...

double	labelOne
	One label of the DataSet. More...

double	labelTwo
	Another label of the DataSet. More...

double	lamb
	the lambda, the regularisation parameter More...

double	lambLogMax
	Log of the Max lambda in searching for optimal lambda. More...

size_t	lambSteps
	Number of iteration in searching for optimal lambda. More...

double	lambStepsize
	Interval size with logrange used in searching optimal lambda. More...

base::level_t	level
	grid level More...

base::DataVector *	maxGridPoint
	Number of the maximum grid points used in the algorithm. More...

size_t	numBaseLearners
	Number of base learner for Adaboosting. More...

size_t	numData
	the size of the grid More...

size_t	numOfAda
	Number of Grid points to refine. More...

double	perOfAda
	Percentage of Grid points to refine(between 0 and 1) More...

bool	refinement
	Judgement of grid refine. More...

size_t	refineMode
	Select the refine mode(1:use grid number, 2: use grid number percentage) More...

size_t	refineTimes
	Number of refinement with a certain percentage of Grid points. More...

base::DataVector *	sumGridPoint
	Number of the sum grid points used in the algorithm. More...

double	threshold
	Threshold to predict class. More...

size_t	type
	type of grid possible value are 1, 2 or 3(1 = Linear Grid, 2 = LinearL0Boundary Grid, 3 = ModLinear Grid); More...

Constructor & Destructor Documentation

◆ AlgorithmAdaBoostBase()

sgpp::datadriven::AlgorithmAdaBoostBase::AlgorithmAdaBoostBase	(	base::Grid &	SparseGrid,
		size_t	gridType,
		base::level_t	gridLevel,
		base::DataMatrix &	trainData,
		base::DataVector &	trainDataClass,
		size_t	NUM,
		double	lambda,
		size_t	IMAX,
		double	eps,
		size_t	IMAX_final,
		double	eps_final,
		double	firstLabel,
		double	secondLabel,
		double	threshold,
		double	maxLambda,
		double	minLambda,
		size_t	searchNum,
		bool	refine,
		size_t	refineMode,
		size_t	refineNum,
		size_t	numberOfAda,
		double	percentOfAda,
		size_t	mode
	)

Std-Constructor.

Parameters

SparseGrid	reference to the sparse grid
gridType	reference to the of grid type(1 = Linear Grid, 2 = LinearL0Boundary Grid, 3 = ModLinear Grid)
gridLevel	reference to the level of grid
trainData	reference to the training dataset
trainDataClass	reference to the class(real value in regression) of training dataset
NUM	the number of baselearner for Adaboosting
lambda	the regularisation parameter
IMAX	the parameter for ConjugateGradients
eps	the parameter for ConjugateGradients
IMAX_final	the parameter for ConjugateGradients used for last refinement step
eps_final	the parameter for ConjugateGradients used for last refinement step
firstLabel	one label from training dataset
secondLabel	another label from training dataset
threshold	the parameter for predicting a class
maxLambda	the max lambda used in searching optimal lambda
minLambda	the min lambda used in searching optimal lambda
searchNum	the searching times used in searching for optimal lambda
refine	the judgement of refine
refineMode	Select the refine mode
refineNum	the Number of refinement with a certain percentage of Grid points
numberOfAda	the number of Grid points to refine
percentOfAda	the percentage of Grid points to refine
mode	the adaboost type to choose

References actualBaseLearners, boostMode, classes, data, dim, friedman::eps, epsilon, epsilon_final, sgpp::base::HashGridStorage::getDimension(), sgpp::base::DataMatrix::getNrows(), sgpp::base::HashGridStorage::getSize(), sgpp::base::Grid::getStorage(), grid, gridPoint, python.utils.sg_projections::gridStorage, imax, imax_final, labelOne, labelTwo, lamb, lambda, lambLogMax, lambSteps, lambStepsize, level, maxGridPoint, numBaseLearners, numData, numOfAda, perOfAda, python.uq.operations.discretizeProduct::refine(), refinement, refineMode, refineTimes, sumGridPoint, threshold, and type.

◆ ~AlgorithmAdaBoostBase()

sgpp::datadriven::AlgorithmAdaBoostBase::~AlgorithmAdaBoostBase ( )

virtual

Std-Deconstructor.

Member Function Documentation

◆ alphaSolver()

virtual void sgpp::datadriven::AlgorithmAdaBoostBase::alphaSolver	(	double &	lambda,
		base::DataVector &	weight,
		base::DataVector &	alpha,
		bool	final
	)

protectedpure virtual

Performs a solver to get alpha.

Parameters

lambda	the regularisation parameter
weight	the weights of examples
alpha	output the coefficients of the sparse grid's basis functions
final	judgement the final step of this base learner

Implemented in sgpp::datadriven::AlgorithmAdaBoostIdentity.

Referenced by doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), and doRefinement().

◆ classif()

void sgpp::datadriven::AlgorithmAdaBoostBase::classif	(	base::DataMatrix &	testData,
		base::DataVector &	algorithmClassTrain,
		base::DataVector &	algorithmClassTest,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest
	)

Performs a classify for the testing dataset according to the baselearners get from the algorithm.

Parameters

testData	reference to the testing dataset
algorithmClassTrain	reference to the class of training dataset got from the algorithm
algorithmClassTest	reference to the class of testing dataset got from the algorithm
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners

References eval(), sgpp::base::DataMatrix::get(), sgpp::base::DataMatrix::getNrows(), hValue(), python.statsfileInfo::i, numData, and sgpp::base::DataVector::set().

Referenced by getAccuracy(), and getROC().

◆ doAdaBoostR2()

void sgpp::datadriven::AlgorithmAdaBoostBase::doAdaBoostR2	(	base::DataMatrix &	weights,
		base::DataMatrix &	testData,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest,
		std::string	lossFucType
	)

Performs the Adaboost.R2(a regression algorithm)

Parameters

weights	the matrix to store weights of every training date for every weak learner
testData	reference to the testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners
lossFucType	the loss function type(linear, square or exponential)

◆ doAdaBoostRT()

void sgpp::datadriven::AlgorithmAdaBoostBase::doAdaBoostRT	(	base::DataMatrix &	weights,
		base::DataMatrix &	testData,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest,
		double	Tvalue,
		std::string	powerType
	)

Performs the Adaboost.RT(a regression algorithm)

Parameters

weights	the matrix to store weights of every training date for every weak learner
testData	reference to the testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners
Tvalue	the threshold to demarcate the prediction correctness(only from 0 to 1)
powerType	the error rate power coefficient(linear, square or cubic)

◆ doDiscreteAdaBoost()

void sgpp::datadriven::AlgorithmAdaBoostBase::doDiscreteAdaBoost	(	base::DataVector &	hypoWeight,
		base::DataVector &	weightError,
		base::DataMatrix &	weights,
		base::DataMatrix &	decision,
		base::DataMatrix &	testData,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest
	)

Performs the Discrete Adaboost.

Parameters

hypoWeight	the vector to store hypothesis weights(Alpha-t)
weightError	the vector to store the weight error of each iteration
weights	the matrix to store weights of every training date for every weak learner
decision	the matrix to store the decision right or not according to the true class
testData	reference to the testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners

Referenced by eval().

◆ doRealAdaBoost()

void sgpp::datadriven::AlgorithmAdaBoostBase::doRealAdaBoost	(	base::DataMatrix &	weights,
		base::DataMatrix &	testData,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest
	)

Performs the Real Adaboost.

Parameters

weights	the matrix to store weights of every training date for every weak learner
testData	reference to the testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners

References actualBaseLearners, alphaSolver(), classes, sgpp::base::DataVector::copyFrom(), python.leja::count, sgpp::base::Grid::createLinearBoundaryGrid(), sgpp::base::Grid::createLinearGrid(), sgpp::base::Grid::createModLinearGrid(), sgpp::op_factory::createOperationEval(), data, dim, doRefinement(), sgpp::base::DataVector::get(), sgpp::base::DataMatrix::get(), sgpp::base::Grid::getGenerator(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataMatrix::getRow(), sgpp::base::DataVector::getSize(), grid, gridPoint, python.statsfileInfo::i, lamb, level, maxGridPoint, sgpp::base::DataVector::mult(), numBaseLearners, numData, refinement, sgpp::base::GridGenerator::regular(), sgpp::base::DataVector::resizeZero(), sgpp::base::DataVector::set(), sgpp::base::DataMatrix::set(), sgpp::base::DataVector::setAll(), sgpp::base::DataMatrix::setColumn(), sgpp::base::DataVector::sum(), sumGridPoint, type, and python.leja::weight.

Referenced by eval().

◆ doRefinement()

void sgpp::datadriven::AlgorithmAdaBoostBase::doRefinement	(	base::DataVector &	alpha_ada,
		base::DataVector &	weight_ada,
		size_t	curBaseLearner
	)

Performs refinement of grid to get an adaptive grid.

Parameters

alpha_ada	the coefficients of the sparse grid's basis functions and to be refined
weight_ada	the weights of examples
curBaseLearner	the current base learner

References alphaSolver(), sgpp::base::DataVector::get(), sgpp::base::Grid::getGenerator(), sgpp::base::HashGridStorage::getSize(), sgpp::base::Grid::getSize(), sgpp::base::Grid::getStorage(), grid, lamb, maxGridPoint, numOfAda, perOfAda, sgpp::base::GridGenerator::refine(), refineMode, refineTimes, sgpp::base::DataVector::resizeZero(), sgpp::base::DataVector::set(), and sumGridPoint.

Referenced by doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

◆ eval()

void sgpp::datadriven::AlgorithmAdaBoostBase::eval	(	base::DataMatrix &	testData,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest
	)

Performs a real value calculate for the testing dataset.

Parameters

testData	reference to the testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners

References boostMode, doDiscreteAdaBoost(), doRealAdaBoost(), numBaseLearners, numData, sgpp::base::DataVector::setAll(), and sgpp::base::DataMatrix::setAll().

Referenced by classif().

◆ getAccuracy()

void sgpp::datadriven::AlgorithmAdaBoostBase::getAccuracy	(	base::DataMatrix &	testData,
		base::DataVector &	testDataClass,
		double *	accuracy_train,
		double *	accuracy_test
	)

Performs an accuracy evaluation for the testing dataset.

Parameters

testData	reference to the testing dataset
testDataClass	reference to the class of testing dataset
accuracy_train	reference to the accuracy for the training dataset
accuracy_test	reference to the accuracy for the testing dataset

References classes, classif(), sgpp::base::DataVector::get(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataVector::getSize(), python.statsfileInfo::i, numBaseLearners, and numData.

◆ getAccuracyBL()

void sgpp::datadriven::AlgorithmAdaBoostBase::getAccuracyBL	(	base::DataMatrix &	testData,
		base::DataVector &	testDataClass,
		base::DataMatrix &	algorithmValueTrain,
		base::DataMatrix &	algorithmValueTest,
		double *	accuracy_train,
		double *	accuracy_test,
		size_t	yourBaseLearner
	)

Performs an accuracy evaluation for the testing dataset with a specified number of base learner.

Parameters

testData	reference to the testing dataset
testDataClass	reference to the class of testing dataset
algorithmValueTrain	the matrix reference to the real value of training dataset got from the algorithm with diff base learners
algorithmValueTest	the matrix reference to the real value of testing dataset got from the algorithm with diff base learners
accuracy_train	reference to the accuracy for the training dataset
accuracy_test	reference to the accuracy for the testing dataset
yourBaseLearner	the number of base learner specified

References classes, sgpp::base::DataVector::get(), sgpp::base::DataMatrix::get(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataVector::getSize(), hValue(), python.statsfileInfo::i, and numData.

◆ getActualBL()

size_t sgpp::datadriven::AlgorithmAdaBoostBase::getActualBL ( )

Get the actual base learners after doing adaboosting.

References actualBaseLearners.

◆ getMaxGridPoint()

size_t sgpp::datadriven::AlgorithmAdaBoostBase::getMaxGridPoint ( size_t baseLearner )

Get the max GridPoint ever used in adaboosting.

Parameters

baseLearner number of baselearner

References sgpp::base::DataVector::get(), and maxGridPoint.

◆ getMeanGridPoint()

size_t sgpp::datadriven::AlgorithmAdaBoostBase::getMeanGridPoint ( size_t baseLearner )

Get the mean GridPoint ever used in adaboosting.

Parameters

baseLearner number of baselearner

References sgpp::base::DataVector::get(), python.datasetAnalysis::mean, and sumGridPoint.

◆ getROC()

void sgpp::datadriven::AlgorithmAdaBoostBase::getROC	(	base::DataMatrix &	validationData,
		base::DataVector &	validationDataClass,
		double *	acc,
		double *	sensitivity,
		double *	specificity,
		double *	precision,
		double *	recall,
		double *	fOneScore
	)

Performs an evaluation to get ROC related parameter.

Parameters

validationData	reference to the validation dataset
validationDataClass	reference to the class of validation dataset
acc	reference to the accuracy for the validation dataset
sensitivity	reference to the sensitivity for the validation dataset
specificity	reference to the specificity for the validation dataset
precision	reference to the precision for the validation dataset
recall	reference to the recall for the validation dataset
fOneScore	reference to the specificity for the validation dataset

References classif(), sgpp::base::DataVector::get(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataVector::getSize(), python.statsfileInfo::i, labelOne, labelTwo, numBaseLearners, and numData.

◆ getSumGridPoint()

size_t sgpp::datadriven::AlgorithmAdaBoostBase::getSumGridPoint ( size_t baseLearner )

Get the sum GridPoint ever used in adaboosting.

Parameters

baseLearner number of baselearner

References sgpp::base::DataVector::get(), and sumGridPoint.

◆ hValue()

double sgpp::datadriven::AlgorithmAdaBoostBase::hValue ( double realValue )

Performs a hypothesis classifier.

Parameters

realValue real value of function

References labelOne, labelTwo, and threshold.

Referenced by classif(), doDiscreteAdaBoost(), and getAccuracyBL().

Member Data Documentation

◆ actualBaseLearners

size_t sgpp::datadriven::AlgorithmAdaBoostBase::actualBaseLearners

protected

Actual base learners number for Adaboosting.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), and getActualBL().

◆ boostMode

size_t sgpp::datadriven::AlgorithmAdaBoostBase::boostMode

protected

Set the boost mode (1: Discrete Adaboost, 2: Real Adaboost)

Referenced by AlgorithmAdaBoostBase(), and eval().

◆ classes

base::DataVector* sgpp::datadriven::AlgorithmAdaBoostBase::classes

protected

Pointer to the class(real value in regression) of the data vector.

Referenced by AlgorithmAdaBoostBase(), sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), getAccuracy(), and getAccuracyBL().

◆ data

base::DataMatrix* sgpp::datadriven::AlgorithmAdaBoostBase::data

protected

Pointer to the data matrix.

Referenced by AlgorithmAdaBoostBase(), sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

◆ dim

size_t sgpp::datadriven::AlgorithmAdaBoostBase::dim

protected

DataSet Dimension.

Referenced by AlgorithmAdaBoostBase(), python.uq.dists.KDEDist.KDEDist::cov(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), python.uq.dists.NatafDist.NatafDist::getDim(), python.uq.dists.KDEDist.KDEDist::rvs(), and python.uq.dists.NatafDist.NatafDist::rvs().

◆ epsilon

double sgpp::datadriven::AlgorithmAdaBoostBase::epsilon

protected

Parameter for CG solver(during the refinement)

Referenced by AlgorithmAdaBoostBase(), and sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver().

◆ epsilon_final

double sgpp::datadriven::AlgorithmAdaBoostBase::epsilon_final

protected

Parameter for CG solver(for the last refinement)

Referenced by AlgorithmAdaBoostBase(), and sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver().

◆ grid

base::Grid* sgpp::datadriven::AlgorithmAdaBoostBase::grid

protected

the grid

◆ gridPoint

size_t sgpp::datadriven::AlgorithmAdaBoostBase::gridPoint

protected

Number of grid points.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

◆ imax

size_t sgpp::datadriven::AlgorithmAdaBoostBase::imax

protected

Parameter for CG solver(during the refinement)

Referenced by AlgorithmAdaBoostBase(), and sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver().

◆ imax_final

size_t sgpp::datadriven::AlgorithmAdaBoostBase::imax_final

protected

Parameter for CG solver(for the last refinement)

Referenced by AlgorithmAdaBoostBase(), and sgpp::datadriven::AlgorithmAdaBoostIdentity::alphaSolver().

◆ labelOne

double sgpp::datadriven::AlgorithmAdaBoostBase::labelOne

protected

One label of the DataSet.

Referenced by AlgorithmAdaBoostBase(), getROC(), and hValue().

◆ labelTwo

double sgpp::datadriven::AlgorithmAdaBoostBase::labelTwo

protected

Another label of the DataSet.

Referenced by AlgorithmAdaBoostBase(), getROC(), and hValue().

◆ lamb

double sgpp::datadriven::AlgorithmAdaBoostBase::lamb

protected

the lambda, the regularisation parameter

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), and doRefinement().

◆ lambLogMax

double sgpp::datadriven::AlgorithmAdaBoostBase::lambLogMax

protected

Log of the Max lambda in searching for optimal lambda.

Referenced by AlgorithmAdaBoostBase(), and doDiscreteAdaBoost().

◆ lambSteps

size_t sgpp::datadriven::AlgorithmAdaBoostBase::lambSteps

protected

Number of iteration in searching for optimal lambda.

Referenced by AlgorithmAdaBoostBase(), and doDiscreteAdaBoost().

◆ lambStepsize

double sgpp::datadriven::AlgorithmAdaBoostBase::lambStepsize

protected

Interval size with logrange used in searching optimal lambda.

Referenced by AlgorithmAdaBoostBase(), and doDiscreteAdaBoost().

◆ level

base::level_t sgpp::datadriven::AlgorithmAdaBoostBase::level

protected

grid level

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

◆ maxGridPoint

base::DataVector* sgpp::datadriven::AlgorithmAdaBoostBase::maxGridPoint

protected

Number of the maximum grid points used in the algorithm.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), doRefinement(), and getMaxGridPoint().

◆ numBaseLearners

size_t sgpp::datadriven::AlgorithmAdaBoostBase::numBaseLearners

protected

Number of base learner for Adaboosting.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), eval(), getAccuracy(), and getROC().

◆ numData

size_t sgpp::datadriven::AlgorithmAdaBoostBase::numData

protected

the size of the grid

Referenced by AlgorithmAdaBoostBase(), classif(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), eval(), getAccuracy(), getAccuracyBL(), and getROC().

◆ numOfAda

size_t sgpp::datadriven::AlgorithmAdaBoostBase::numOfAda

protected

Number of Grid points to refine.

Referenced by AlgorithmAdaBoostBase(), and doRefinement().

◆ perOfAda

double sgpp::datadriven::AlgorithmAdaBoostBase::perOfAda

protected

Percentage of Grid points to refine(between 0 and 1)

Referenced by AlgorithmAdaBoostBase(), and doRefinement().

◆ refinement

bool sgpp::datadriven::AlgorithmAdaBoostBase::refinement

protected

Judgement of grid refine.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

◆ refineMode

size_t sgpp::datadriven::AlgorithmAdaBoostBase::refineMode

protected

Select the refine mode(1:use grid number, 2: use grid number percentage)

Referenced by AlgorithmAdaBoostBase(), and doRefinement().

◆ refineTimes

size_t sgpp::datadriven::AlgorithmAdaBoostBase::refineTimes

protected

Number of refinement with a certain percentage of Grid points.

Referenced by AlgorithmAdaBoostBase(), and doRefinement().

◆ sumGridPoint

base::DataVector* sgpp::datadriven::AlgorithmAdaBoostBase::sumGridPoint

protected

Number of the sum grid points used in the algorithm.

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), doRealAdaBoost(), doRefinement(), getMeanGridPoint(), and getSumGridPoint().

◆ threshold

double sgpp::datadriven::AlgorithmAdaBoostBase::threshold

protected

Threshold to predict class.

Referenced by AlgorithmAdaBoostBase(), and hValue().

◆ type

size_t sgpp::datadriven::AlgorithmAdaBoostBase::type

protected

type of grid possible value are 1, 2 or 3(1 = Linear Grid, 2 = LinearL0Boundary Grid, 3 = ModLinear Grid);

Referenced by AlgorithmAdaBoostBase(), doAdaBoostR2(), doAdaBoostRT(), doDiscreteAdaBoost(), and doRealAdaBoost().

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

datadriven/src/sgpp/datadriven/algorithm/AlgorithmAdaBoostBase.hpp
datadriven/src/sgpp/datadriven/algorithm/AlgorithmAdaBoostBase.cpp

Public Member Functions

Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

◆ AlgorithmAdaBoostBase()

◆ ~AlgorithmAdaBoostBase()

Member Function Documentation

◆ alphaSolver()

◆ classif()

◆ doAdaBoostR2()

◆ doAdaBoostRT()

◆ doDiscreteAdaBoost()

◆ doRealAdaBoost()

◆ doRefinement()

◆ eval()

◆ getAccuracy()

◆ getAccuracyBL()

◆ getActualBL()

◆ getMaxGridPoint()

◆ getMeanGridPoint()

◆ getROC()

◆ getSumGridPoint()

◆ hValue()

Member Data Documentation

◆ actualBaseLearners

◆ boostMode

◆ classes

◆ data

◆ dim

◆ epsilon

◆ epsilon_final

◆ grid

◆ gridPoint

◆ imax

◆ imax_final

◆ labelOne

◆ labelTwo

◆ lamb

◆ lambLogMax

◆ lambSteps

◆ lambStepsize

◆ level

◆ maxGridPoint

◆ numBaseLearners

◆ numData

◆ numOfAda

◆ perOfAda

◆ refinement

◆ refineMode

◆ refineTimes

◆ sumGridPoint

◆ threshold

◆ type