sgpp::datadriven::AlgorithmAdaBoostIdentity Class Reference

#include <AlgorithmAdaBoostIdentity.hpp>

Inheritance diagram for sgpp::datadriven::AlgorithmAdaBoostIdentity:

Public Member Functions
	AlgorithmAdaBoostIdentity (base::Grid &SparseGrid, base::GridType 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.
virtual	~AlgorithmAdaBoostIdentity ()
	Std-Deconstructor.
Public Member Functions inherited from sgpp::datadriven::AlgorithmAdaBoostBase
	AlgorithmAdaBoostBase (base::Grid &SparseGrid, base::GridType 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.
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.
void	doAdaBoostR2 (base::DataMatrix &weights, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest, std::string lossFucType)
	Performs the Adaboost.R2(a regression algorithm)
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)
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.
void	doRealAdaBoost (base::DataMatrix &weights, base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs the Real Adaboost.
void	doRefinement (base::DataVector &alpha_ada, base::DataVector &weight_ada, size_t curBaseLearner)
	Performs refinement of grid to get an adaptive grid.
void	eval (base::DataMatrix &testData, base::DataMatrix &algorithmValueTrain, base::DataMatrix &algorithmValueTest)
	Performs a real value calculate for the testing dataset.
void	getAccuracy (base::DataMatrix &testData, base::DataVector &testDataClass, double *accuracy_train, double *accuracy_test)
	Performs an accuracy evaluation for the testing dataset.
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.
size_t	getActualBL ()
	Get the actual base learners after doing adaboosting.
size_t	getMaxGridPoint (size_t baseLearner)
	Get the max GridPoint ever used in adaboosting.
size_t	getMeanGridPoint (size_t baseLearner)
	Get the mean GridPoint ever used in adaboosting.
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.
size_t	getSumGridPoint (size_t baseLearner)
	Get the sum GridPoint ever used in adaboosting.
double	hValue (double realValue)
	Performs a hypothesis classifier.
virtual	~AlgorithmAdaBoostBase ()
	Std-Deconstructor.

Protected Member Functions
virtual void	alphaSolver (double &lambda, base::DataVector &weight, base::DataVector &alpha, bool final)
	Performs a solver to get alpha use DMWeightMatrix as the System Matrix.

Additional Inherited Members
Protected Attributes inherited from sgpp::datadriven::AlgorithmAdaBoostBase
size_t	actualBaseLearners
	Actual base learners number for Adaboosting.
size_t	boostMode
	Set the boost mode (1: Discrete Adaboost, 2: Real Adaboost)
base::DataVector *	classes
	Pointer to the class(real value in regression) of the data vector.
base::DataMatrix *	data
	Pointer to the data matrix.
size_t	dim
	DataSet Dimension.
double	epsilon
	Parameter for CG solver(during the refinement)
double	epsilon_final
	Parameter for CG solver(for the last refinement)
base::Grid *	grid
	the grid
size_t	gridPoint
	Number of grid points.
size_t	imax
	Parameter for CG solver(during the refinement)
size_t	imax_final
	Parameter for CG solver(for the last refinement)
double	labelOne
	One label of the DataSet.
double	labelTwo
	Another label of the DataSet.
double	lamb
	the lambda, the regularisation parameter
double	lambLogMax
	Log of the Max lambda in searching for optimal lambda.
size_t	lambSteps
	Number of iteration in searching for optimal lambda.
double	lambStepsize
	Interval size with logrange used in searching optimal lambda.
base::level_t	level
	grid level
base::DataVector *	maxGridPoint
	Number of the maximum grid points used in the algorithm.
size_t	numBaseLearners
	Number of base learner for Adaboosting.
size_t	numData
	the size of the grid
size_t	numOfAda
	Number of Grid points to refine.
double	perOfAda
	Percentage of Grid points to refine(between 0 and 1)
bool	refinement
	Judgement of grid refine.
size_t	refineMode
	Select the refine mode(1:use grid number, 2: use grid number percentage)
size_t	refineTimes
	Number of refinement with a certain percentage of Grid points.
base::DataVector *	sumGridPoint
	Number of the sum grid points used in the algorithm.
double	threshold
	Threshold to predict class.
base::GridType	type
	type of grid; implemented cases: Linear Grid, LinearL0Boundary Grid, ModLinear Grid;

Constructor & Destructor Documentation

AlgorithmAdaBoostIdentity()

sgpp::datadriven::AlgorithmAdaBoostIdentity::AlgorithmAdaBoostIdentity	(	base::Grid &	SparseGrid,
		base::GridType	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 type of grid
gridLevel	reference to the level of grid
trainData	reference to the training dataset
trainDataClass	reference to the class 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

~AlgorithmAdaBoostIdentity()

sgpp::datadriven::AlgorithmAdaBoostIdentity::~AlgorithmAdaBoostIdentity ( )

virtual

Std-Deconstructor.

Member Function Documentation

alphaSolver()

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

protectedvirtual

Performs a solver to get alpha use DMWeightMatrix as the System Matrix.

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

Implements sgpp::datadriven::AlgorithmAdaBoostBase.

References alpha, sgpp::datadriven::AlgorithmAdaBoostBase::classes, sgpp::op_factory::createOperationIdentity(), sgpp::datadriven::AlgorithmAdaBoostBase::data, sgpp::datadriven::AlgorithmAdaBoostBase::epsilon, sgpp::datadriven::AlgorithmAdaBoostBase::epsilon_final, sgpp::datadriven::DMWeightMatrix::generateb(), sgpp::base::DataVector::getSize(), sgpp::datadriven::AlgorithmAdaBoostBase::grid, sgpp::datadriven::AlgorithmAdaBoostBase::imax, sgpp::datadriven::AlgorithmAdaBoostBase::imax_final, lambda, and sgpp::solver::ConjugateGradients::solve().

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

• datadriven/src/sgpp/datadriven/algorithm/AlgorithmAdaBoostIdentity.hpp
• datadriven/src/sgpp/datadriven/algorithm/AlgorithmAdaBoostIdentity.cpp