Uses of Interface
it.uniroma2.sag.kelp.data.dataset.Dataset

Packages that use Dataset

Package	Description
it.uniroma2.sag.kelp.data.dataset
it.uniroma2.sag.kelp.data.dataset.selector
it.uniroma2.sag.kelp.kernel.cache
it.uniroma2.sag.kelp.learningalgorithm
it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
it.uniroma2.sag.kelp.learningalgorithm.classification.dcd
it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear
it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm
it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification
it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos
it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
it.uniroma2.sag.kelp.learningalgorithm.classification.scw
it.uniroma2.sag.kelp.learningalgorithm.clustering
it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans
it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear
it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm
it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
it.uniroma2.sag.kelp.linearization
it.uniroma2.sag.kelp.linearization.nystrom
it.uniroma2.sag.kelp.utils

Uses of Dataset in it.uniroma2.sag.kelp.data.dataset

Classes in it.uniroma2.sag.kelp.data.dataset that implement Dataset

Modifier and Type	Class and Description
class	SequenceDataset A dataset made of SequenceExamples
class	SimpleDataset A SimpleDataset that represent a whole dataset in memory.

Methods in it.uniroma2.sag.kelp.data.dataset that return Dataset

Modifier and Type	Method and Description
static Dataset	SimpleDataset.extractExamplesOfClasses(Dataset dataset, List<Label> labels) This method extracts examples of given labels from dataset
Dataset	Dataset.getShuffledDataset()

Methods in it.uniroma2.sag.kelp.data.dataset with parameters of type Dataset

Modifier and Type	Method and Description
void	SimpleDataset.addExamples(Dataset datasetToBeAdded) Add all the examples contained in datasetToBeAdded
static Dataset	SimpleDataset.extractExamplesOfClasses(Dataset dataset, List<Label> labels) This method extracts examples of given labels from dataset

Uses of Dataset in it.uniroma2.sag.kelp.data.dataset.selector

Methods in it.uniroma2.sag.kelp.data.dataset.selector with parameters of type Dataset

Modifier and Type	Method and Description
List<Example>	RandomExampleSelector.select(Dataset dataset)
List<Example>	FirstExamplesSelector.select(Dataset dataset)
List<Example>	ExampleSelector.select(Dataset dataset) This function allows to select a subset of Examples from the input Dataset

Uses of Dataset in it.uniroma2.sag.kelp.kernel.cache

Constructors in it.uniroma2.sag.kelp.kernel.cache with parameters of type Dataset

Constructor and Description
StripeKernelCache(Dataset dataset)
StripeKernelCache(Dataset dataset, int maxNumberOfRows)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm

Methods in it.uniroma2.sag.kelp.learningalgorithm with parameters of type Dataset

Modifier and Type	Method and Description
void	PassiveAggressive.learn(Dataset dataset)
void	MultiEpochLearning.learn(Dataset dataset)
void	LearningAlgorithm.learn(Dataset dataset) It starts the training process exploiting the provided dataset

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm

Methods in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm with parameters of type Dataset

Modifier and Type	Method and Description
void	BudgetedLearningAlgorithm.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.dcd

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.dcd with parameters of type Dataset

Modifier and Type	Method and Description
void	DCDLearningAlgorithm.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.hmm

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.hmm with parameters of type Dataset

Modifier and Type	Method and Description
void	SequenceClassificationLearningAlgorithm.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear with parameters of type Dataset

Modifier and Type	Method and Description
void	LibLinearLearningAlgorithm.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver

Constructors in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver with parameters of type Dataset

Constructor and Description
Problem(Dataset dataset, String reprentationName, Label label, Problem.LibLinearSolverType solverType)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm with parameters of type Dataset

Modifier and Type	Method and Description
void	OneClassSvmClassification.learn(Dataset trainingSet)
void	BinaryNuSvmClassification.learn(Dataset trainingSet)
void	BinaryCSvmClassification.learn(Dataset trainingSet)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver with parameters of type Dataset

Modifier and Type	Method and Description
protected float[]	LibCSvmSolver.getCSvmAlpha(Dataset trainingSet) Get the initial weight for the future Support Vectors
SvmSolution	LibSvmSolver.solve(int l_, Dataset dataset, float[] p_, int[] y_, float[] initial_alpha) It solves the SMO algorithm in [CC Chang & CJ Lin, 2011] min 0.5(\alpha^T Q \alpha) + p^T \alpha y^T \alpha = \delta y_i = +1 or -1 0 <= alpha_i <= Cp for y_i = 1 0 <= alpha_i <= Cn for y_i = -1 Given: Q, p, y, Cp, Cn, and an initial feasible point \alpha l is the size of vectors and matrices eps is the stopping tolerance solution will be put in \alpha, objective value will be put in obj

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification with parameters of type Dataset

Modifier and Type	Method and Description
void	OneVsOneLearning.learn(Dataset dataset) This method will cause the meta-learning algorithm to learn N*(N-1)/2 classifiers, where N is the number of classes in the dataset.
void	OneVsAllLearning.learn(Dataset dataset) This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.
void	MultiLabelClassificationLearning.learn(Dataset dataset) This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive with parameters of type Dataset

Modifier and Type	Method and Description
void	PassiveAggressiveClassification.learn(Dataset dataset)
void	BudgetedPassiveAggressiveClassification.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos with parameters of type Dataset

Modifier and Type	Method and Description
void	PegasosLearningAlgorithm.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron with parameters of type Dataset

Modifier and Type	Method and Description
void	Perceptron.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.classification.scw

Methods in it.uniroma2.sag.kelp.learningalgorithm.classification.scw with parameters of type Dataset

Modifier and Type	Method and Description
void	SoftConfidenceWeightedClassification.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.clustering

Methods in it.uniroma2.sag.kelp.learningalgorithm.clustering with parameters of type Dataset

Modifier and Type	Method and Description
List<Cluster>	ClusteringAlgorithm.cluster(Dataset dataset) It starts the clustering process exploiting the provided dataset
List<Cluster>	ClusteringAlgorithm.cluster(Dataset dataset, ExampleSelector seedSelector) It starts the clustering process exploiting the provided dataset

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans

Methods in it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans with parameters of type Dataset

Modifier and Type	Method and Description
ClusterList	KernelBasedKMeansEngine.cluster(Dataset dataset)
ClusterList	KernelBasedKMeansEngine.cluster(Dataset dataset, ExampleSelector seedSelector)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans

Methods in it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans with parameters of type Dataset

Modifier and Type	Method and Description
ClusterList	LinearKMeansEngine.cluster(Dataset dataset)
ClusterList	LinearKMeansEngine.cluster(Dataset dataset, ExampleSelector seedSelector)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear

Methods in it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear with parameters of type Dataset

Modifier and Type	Method and Description
void	LibLinearRegression.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm

Methods in it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm with parameters of type Dataset

Modifier and Type	Method and Description
void	EpsilonSvmRegression.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive

Methods in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive with parameters of type Dataset

Modifier and Type	Method and Description
void	PassiveAggressiveRegression.learn(Dataset dataset)

Uses of Dataset in it.uniroma2.sag.kelp.linearization

Methods in it.uniroma2.sag.kelp.linearization with parameters of type Dataset

Modifier and Type	Method and Description
SimpleDataset	LinearizationFunction.getLinearizedDataset(Dataset dataset, String representationName) This method linearizes all the examples in the input dataset , generating a corresponding linearized dataset.

Uses of Dataset in it.uniroma2.sag.kelp.linearization.nystrom

Methods in it.uniroma2.sag.kelp.linearization.nystrom with parameters of type Dataset

Modifier and Type	Method and Description
SimpleDataset	NystromMethodEnsemble.getLinearizedDataset(Dataset dataset, String representationName)
SimpleDataset	NystromMethod.getLinearizedDataset(Dataset dataset, String representationName)

Uses of Dataset in it.uniroma2.sag.kelp.utils

Methods in it.uniroma2.sag.kelp.utils with parameters of type Dataset

Modifier and Type	Method and Description
static List<Prediction>	ExperimentUtils.test(PredictionFunction predictionFunction, Evaluator evaluator, Dataset testset) Evaluates a prediction function over a testset