Uses of Class
jsat.regression.RegressionDataSet

Packages that use RegressionDataSet

Package	Description
jsat
jsat.classifiers.boosting
jsat.classifiers.knn
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.classifiers.trees
jsat.datatransform
jsat.datatransform.featureselection
jsat.io
jsat.linear.distancemetrics
jsat.parameters
jsat.regression

Uses of RegressionDataSet in jsat

Methods in jsat that return RegressionDataSet

Modifier and Type	Method and Description
RegressionDataSet	SimpleDataSet.asRegressionDataSet(int index) Converts this dataset into one meant for regression problems.

Uses of RegressionDataSet in jsat.classifiers.boosting

Methods in jsat.classifiers.boosting that return RegressionDataSet

Modifier and Type	Method and Description
static RegressionDataSet	Bagging.getSampledDataSet(RegressionDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
static RegressionDataSet	Bagging.getWeightSampledDataSet(RegressionDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.

Methods in jsat.classifiers.boosting with parameters of type RegressionDataSet

Modifier and Type	Method and Description
static RegressionDataSet	Bagging.getSampledDataSet(RegressionDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
static RegressionDataSet	Bagging.getWeightSampledDataSet(RegressionDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
void	Wagging.train(RegressionDataSet dataSet)
void	UpdatableStacking.train(RegressionDataSet dataSet)
void	Stacking.train(RegressionDataSet dataSet)
void	Bagging.train(RegressionDataSet dataSet)
void	Wagging.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	UpdatableStacking.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	Stacking.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	Bagging.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.knn

Methods in jsat.classifiers.knn with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	NearestNeighbour.train(RegressionDataSet dataSet)
void	LWL.train(RegressionDataSet dataSet)
void	NearestNeighbour.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LWL.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.linear

Methods in jsat.classifiers.linear with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	STGD.train(RegressionDataSet dataSet)
void	SMIDAS.train(RegressionDataSet dataSet)
void	SCD.train(RegressionDataSet dataSet)
void	PassiveAggressive.train(RegressionDataSet dataSet)
void	LinearSGD.train(RegressionDataSet dataSet)
void	LinearL1SCD.train(RegressionDataSet dataSet)
void	LinearBatch.train(RegressionDataSet dataSet)
void	STGD.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	SMIDAS.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	SCD.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	PassiveAggressive.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LinearSGD.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LinearL1SCD.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LinearBatch.train(RegressionDataSet D, ExecutorService threadPool)
void	LinearBatch.train(RegressionDataSet dataSet, Regressor warmSolution)
void	LinearBatch.train(RegressionDataSet D, Regressor warmSolution, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.linear.kernelized

Methods in jsat.classifiers.linear.kernelized with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	KernelSGD.train(RegressionDataSet dataSet)
void	KernelSGD.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.neuralnetwork

Methods in jsat.classifiers.neuralnetwork with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	RBFNet.train(RegressionDataSet dataSet)
void	BackPropagationNet.train(RegressionDataSet dataSet)
void	RBFNet.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	BackPropagationNet.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.svm

Methods in jsat.classifiers.svm with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	PlattSMO.train(RegressionDataSet dataSet)
void	LSSVM.train(RegressionDataSet dataSet)
void	DCDs.train(RegressionDataSet dataSet)
void	DCD.train(RegressionDataSet dataSet)
void	PlattSMO.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LSSVM.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	DCDs.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	DCD.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	PlattSMO.train(RegressionDataSet dataSet, Regressor warmSolution)
void	LSSVM.train(RegressionDataSet dataSet, Regressor warmSolution)
void	DCDs.train(RegressionDataSet dataSet, Regressor warmSolution)
void	PlattSMO.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)
void	LSSVM.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)
void	DCDs.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.classifiers.trees

Methods in jsat.classifiers.trees with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	RandomForest.train(RegressionDataSet dataSet)
void	ExtraTree.train(RegressionDataSet dataSet)
void	ERTrees.train(RegressionDataSet dataSet)
void	DecisionTree.train(RegressionDataSet dataSet)
void	DecisionStump.train(RegressionDataSet dataSet)
void	RandomForest.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	ExtraTree.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	ERTrees.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	DecisionTree.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	DecisionStump.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	DecisionTree.train(RegressionDataSet dataSet, Set<Integer> options)
void	DecisionTree.train(RegressionDataSet dataSet, Set<Integer> options, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.datatransform

Methods in jsat.datatransform with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	DataModelPipeline.train(RegressionDataSet dataSet)
void	DataModelPipeline.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.datatransform.featureselection

Constructors in jsat.datatransform.featureselection with parameters of type RegressionDataSet

Constructor and Description
BDS(int featureCount, RegressionDataSet dataSet, Regressor evaluator, int folds) Performs BDS feature selection for a regression problem
LRS(int L, int R, RegressionDataSet rds, Regressor evaluater, int folds) Performs LRS feature selection for a regression problem
SBS(int minFeatures, int maxFeatures, RegressionDataSet rds, Regressor evaluater, int folds, double maxDecrease) Performs SBS feature selection for a regression problem
SFS(int minFeatures, int maxFeatures, RegressionDataSet dataSet, Regressor regressor, int folds, double maxIncrease) Performs SFS feature selection for a regression problem

Uses of RegressionDataSet in jsat.io

Methods in jsat.io that return RegressionDataSet

Modifier and Type	Method and Description
static RegressionDataSet	LIBSVMLoader.loadR(File file) Loads a new regression data set from a LIBSVM file, assuming the label is a numeric target value to predict
static RegressionDataSet	LIBSVMLoader.loadR(File file, double sparseRatio) Loads a new regression data set from a LIBSVM file, assuming the label is a numeric target value to predict
static RegressionDataSet	LIBSVMLoader.loadR(File file, double sparseRatio, int vectorLength) Loads a new regression data set from a LIBSVM file, assuming the label is a numeric target value to predict
static RegressionDataSet	LIBSVMLoader.loadR(InputStreamReader isr, double sparseRatio) Loads a new regression data set from a LIBSVM file, assuming the label is a numeric target value to predict
static RegressionDataSet	LIBSVMLoader.loadR(Reader reader, double sparseRatio, int vectorLength) Loads a new regression data set from a LIBSVM file, assuming the label is a numeric target value to predict.
static RegressionDataSet	JSATData.loadRegression(InputStream inRaw) Loads in a JSAT dataset as a RegressionDataSet.
static RegressionDataSet	CSV.readR(int numeric_target_column, Path path, char delimiter, int lines_to_skip, char comment, Set<Integer> cat_cols) Reads in a CSV dataset as a regression dataset.
static RegressionDataSet	CSV.readR(int numeric_target_column, Path path, int lines_to_skip, Set<Integer> cat_cols) Reads in a CSV dataset as a regression dataset.
static RegressionDataSet	CSV.readR(int numeric_target_column, Reader reader, char delimiter, int lines_to_skip, char comment, Set<Integer> cat_cols) Reads in a CSV dataset as a regression dataset.
static RegressionDataSet	CSV.readR(int numeric_target_column, Reader reader, int lines_to_skip, Set<Integer> cat_cols) Reads in a CSV dataset as a regression dataset.

Methods in jsat.io with parameters of type RegressionDataSet

Modifier and Type	Method and Description
static void	LIBSVMLoader.write(RegressionDataSet data, OutputStream os) Writes out the given regression data set as a LIBSVM data file

Uses of RegressionDataSet in jsat.linear.distancemetrics

Methods in jsat.linear.distancemetrics with parameters of type RegressionDataSet

Modifier and Type	Method and Description
abstract void	TrainableDistanceMetric.train(RegressionDataSet dataSet) Trains this metric on the given regression problem data set
void	NormalizedEuclideanDistance.train(RegressionDataSet dataSet)
void	MahalanobisDistance.train(RegressionDataSet dataSet)
abstract void	TrainableDistanceMetric.train(RegressionDataSet dataSet, ExecutorService threadpool) Trains this metric on the given regression problem data set
void	NormalizedEuclideanDistance.train(RegressionDataSet dataSet, ExecutorService threadpool)
void	MahalanobisDistance.train(RegressionDataSet dataSet, ExecutorService threadpool)

Uses of RegressionDataSet in jsat.parameters

Methods in jsat.parameters with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	RandomSearch.train(RegressionDataSet dataSet)
void	GridSearch.train(RegressionDataSet dataSet)
void	RandomSearch.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	GridSearch.train(RegressionDataSet dataSet, ExecutorService threadPool)

Uses of RegressionDataSet in jsat.regression

Methods in jsat.regression that return RegressionDataSet

Modifier and Type	Method and Description
static RegressionDataSet	RegressionDataSet.comineAllBut(List<RegressionDataSet> list, int exception)
protected RegressionDataSet	RegressionDataSet.getSubset(List<Integer> indicies)
RegressionDataSet	RegressionDataSet.getTwiceShallowClone()
RegressionDataSet	RegressionDataSet.shallowClone()
static RegressionDataSet	RegressionDataSet.usingDPPList(List<DataPointPair<Double>> list) Creates a new data set that uses the given list as its backing list.

Methods in jsat.regression with parameters of type RegressionDataSet

Modifier and Type	Method and Description
void	RegressionModelEvaluation.evaluateTestSet(RegressionDataSet testSet) Performs an evaluation of the regressor using the initial data set to train, and testing on the given data set.
void	StochasticRidgeRegression.train(RegressionDataSet dataSet)
void	StochasticGradientBoosting.train(RegressionDataSet dataSet)
void	RidgeRegression.train(RegressionDataSet dataSet)
void	Regressor.train(RegressionDataSet dataSet)
void	RANSAC.train(RegressionDataSet dataSet)
void	OrdinaryKriging.train(RegressionDataSet dataSet)
void	NadarayaWatson.train(RegressionDataSet dataSet)
void	MultipleLinearRegression.train(RegressionDataSet dataSet)
void	LogisticRegression.train(RegressionDataSet dataSet)
void	KernelRLS.train(RegressionDataSet dataSet)
void	KernelRidgeRegression.train(RegressionDataSet dataSet)
void	BaseUpdateableRegressor.train(RegressionDataSet dataSet)
void	AveragedRegressor.train(RegressionDataSet dataSet)
void	OrdinaryKriging.Variogram.train(RegressionDataSet dataSet, double nugget) Sets the values of the variogram
void	OrdinaryKriging.PowVariogram.train(RegressionDataSet dataSet, double nugget)
void	StochasticRidgeRegression.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	StochasticGradientBoosting.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	RidgeRegression.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	Regressor.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	RANSAC.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	OrdinaryKriging.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	NadarayaWatson.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	MultipleLinearRegression.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	LogisticRegression.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	KernelRLS.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	KernelRidgeRegression.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	BaseUpdateableRegressor.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	AveragedRegressor.train(RegressionDataSet dataSet, ExecutorService threadPool)
void	WarmRegressor.train(RegressionDataSet dataSet, Regressor warmSolution) Trains the regressor and constructs a model for regression using the given data set.
void	WarmRegressor.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool) Trains the regressor and constructs a model for regression using the given data set.
static void	BaseUpdateableRegressor.trainEpochs(RegressionDataSet dataSet, UpdateableRegressor toTrain, int epochs) Performs training on an updateable classifier by going over the whole data set in random order one observation at a time, multiple times.

Method parameters in jsat.regression with type arguments of type RegressionDataSet

Modifier and Type	Method and Description
static RegressionDataSet	RegressionDataSet.comineAllBut(List<RegressionDataSet> list, int exception)
void	RegressionModelEvaluation.evaluateCrossValidation(List<RegressionDataSet> lcds) Performs an evaluation of the regressor using the training data set, where the folds of the training data set are provided by the user.
void	RegressionModelEvaluation.evaluateCrossValidation(List<RegressionDataSet> lcds, List<RegressionDataSet> trainCombinations) Note: Most people should never need to call this method.
void	RegressionModelEvaluation.evaluateCrossValidation(List<RegressionDataSet> lcds, List<RegressionDataSet> trainCombinations) Note: Most people should never need to call this method.

Constructors in jsat.regression with parameters of type RegressionDataSet

Constructor and Description
RegressionModelEvaluation(Regressor regressor, RegressionDataSet dataSet) Creates a new RegressionModelEvaluation that will perform serial training
RegressionModelEvaluation(Regressor regressor, RegressionDataSet dataSet, ExecutorService threadpool) Creates a new RegressionModelEvaluation that will perform parallel training.