Uses of Class
jsat.classifiers.DataPoint

Packages that use DataPoint

Package	Description
jsat
jsat.classifiers
jsat.classifiers.bayesian
jsat.classifiers.bayesian.graphicalmodel
jsat.classifiers.boosting
jsat.classifiers.calibration
jsat.classifiers.knn
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.classifiers.svm.extended
jsat.classifiers.trees
jsat.clustering
jsat.clustering.dissimilarity
jsat.clustering.evaluation
jsat.clustering.evaluation.intra
jsat.clustering.hierarchical
jsat.clustering.kmeans
jsat.datatransform
jsat.datatransform.featureselection
jsat.datatransform.kernel
jsat.distributions.multivariate
jsat.linear
jsat.parameters
jsat.regression

Uses of DataPoint in jsat

Fields in jsat with type parameters of type DataPoint

Modifier and Type	Field and Description
protected List<DataPoint>	SimpleDataSet.dataPoints

Methods in jsat that return DataPoint

Modifier and Type	Method and Description
DataPoint	SimpleDataSet.getDataPoint(int i)
abstract DataPoint	DataSet.getDataPoint(int i) Returns the i'th data point in this set.

Methods in jsat that return types with arguments of type DataPoint

Modifier and Type	Method and Description
List<DataPoint>	SimpleDataSet.getBackingList()
Iterator<DataPoint>	DataSet.getDataPointIterator() Returns an iterator that will iterate over all data points in the set.
List<DataPoint>	DataSet.getDataPoints() Creates a list containing the same DataPoints in this set.

Methods in jsat with parameters of type DataPoint

Modifier and Type	Method and Description
void	SimpleDataSet.add(DataPoint dp) Adds a new datapoint to this set.
void	SimpleDataSet.setDataPoint(int i, DataPoint dp)
abstract void	DataSet.setDataPoint(int i, DataPoint dp) Replaces an already existing data point with the one given.

Constructor parameters in jsat with type arguments of type DataPoint

Constructor and Description
SimpleDataSet(List<DataPoint> dataPoints)

Uses of DataPoint in jsat.classifiers

Fields in jsat.classifiers with type parameters of type DataPoint

Modifier and Type	Field and Description
protected List<DataPoint>	ClassificationDataSet.datapoints

Methods in jsat.classifiers that return DataPoint

Modifier and Type	Method and Description
DataPoint	DataPoint.clone() Creates a deep clone of this data point, such that altering either data point does not effect the other one.
DataPoint	DataPointPair.getDataPoint()
DataPoint	ClassificationDataSet.getDataPoint(int i) Returns the i'th data point from the data set

Methods in jsat.classifiers that return types with arguments of type DataPoint

Modifier and Type	Method and Description
List<DataPoint>	ClassificationDataSet.getSamples(int category) Returns the list of all examples that belong to the given category.

Methods in jsat.classifiers with parameters of type DataPoint

Modifier and Type	Method and Description
void	ClassificationDataSet.addDataPoint(DataPoint dp, int classification) Creates a new data point and add it
CategoricalResults	Rocchio.classify(DataPoint data)
CategoricalResults	RegressorToClassifier.classify(DataPoint data)
CategoricalResults	PriorClassifier.classify(DataPoint data)
CategoricalResults	OneVSOne.classify(DataPoint data)
CategoricalResults	OneVSAll.classify(DataPoint data)
CategoricalResults	MultinomialLogisticRegression.classify(DataPoint data)
CategoricalResults	MajorityVote.classify(DataPoint data)
CategoricalResults	DDAG.classify(DataPoint data)
CategoricalResults	Classifier.classify(DataPoint data) Performs classification on the given data point.
double	RegressorToClassifier.getScore(DataPoint dp)
void	DataPointPair.setDataPoint(DataPoint dataPoint)
void	ClassificationDataSet.setDataPoint(int i, DataPoint dp)
void	UpdateableClassifier.update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Constructors in jsat.classifiers with parameters of type DataPoint

Constructor and Description
DataPointPair(DataPoint dataPoint, P pair)

Constructor parameters in jsat.classifiers with type arguments of type DataPoint

Constructor and Description
ClassificationDataSet(List<DataPoint> data, int predicting) Creates a new data set for classification problems from the given list of data points.

Uses of DataPoint in jsat.classifiers.bayesian

Methods in jsat.classifiers.bayesian with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	ODE.classify(DataPoint data)
CategoricalResults	NaiveBayesUpdateable.classify(DataPoint data)
CategoricalResults	NaiveBayes.classify(DataPoint data)
CategoricalResults	MultinomialNaiveBayes.classify(DataPoint data)
CategoricalResults	ConditionalProbabilityTable.classify(DataPoint data)
CategoricalResults	BestClassDistribution.classify(DataPoint data)
CategoricalResults	AODE.classify(DataPoint data)
void	ODE.update(DataPoint dataPoint, int targetClass)
void	NaiveBayesUpdateable.update(DataPoint dataPoint, int targetClass)
void	MultinomialNaiveBayes.update(DataPoint dataPoint, int targetClass)
void	AODE.update(DataPoint dataPoint, int targetClass)

Uses of DataPoint in jsat.classifiers.bayesian.graphicalmodel

Methods in jsat.classifiers.bayesian.graphicalmodel with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	DiscreteBayesNetwork.classify(DataPoint data)

Uses of DataPoint in jsat.classifiers.boosting

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

Modifier and Type	Method and Description
CategoricalResults	Wagging.classify(DataPoint data)
CategoricalResults	UpdatableStacking.classify(DataPoint data)
CategoricalResults	Stacking.classify(DataPoint data)
CategoricalResults	SAMME.classify(DataPoint data)
CategoricalResults	ModestAdaBoost.classify(DataPoint data)
CategoricalResults	LogitBoost.classify(DataPoint data)
CategoricalResults	EmphasisBoost.classify(DataPoint data)
CategoricalResults	Bagging.classify(DataPoint data)
CategoricalResults	ArcX4.classify(DataPoint data)
CategoricalResults	AdaBoostM1.classify(DataPoint data)
double	ModestAdaBoost.getScore(DataPoint dp)
double	EmphasisBoost.getScore(DataPoint dp)
protected double	LogitBoost.P(DataPoint x) Returns the probability that a given data point belongs to class 1
double	Wagging.regress(DataPoint data)
double	UpdatableStacking.regress(DataPoint data)
double	Stacking.regress(DataPoint data)
double	Bagging.regress(DataPoint data)
void	UpdatableStacking.update(DataPoint dataPoint, double targetValue)
void	UpdatableStacking.update(DataPoint dataPoint, int targetClass)

Uses of DataPoint in jsat.classifiers.calibration

Methods in jsat.classifiers.calibration with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	PlattCalibration.classify(DataPoint data)
CategoricalResults	IsotonicCalibration.classify(DataPoint data)
double	BinaryScoreClassifier.getScore(DataPoint dp) Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.

Uses of DataPoint in jsat.classifiers.knn

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

Modifier and Type	Method and Description
CategoricalResults	NearestNeighbour.classify(DataPoint data)
CategoricalResults	LWL.classify(DataPoint data)
CategoricalResults	DANN.classify(DataPoint data)
double	NearestNeighbour.regress(DataPoint data)
double	LWL.regress(DataPoint data)

Uses of DataPoint in jsat.classifiers.linear

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

Modifier and Type	Method and Description
CategoricalResults	StochasticMultinomialLogisticRegression.classify(DataPoint data)
CategoricalResults	STGD.classify(DataPoint data)
CategoricalResults	SPA.classify(DataPoint data)
CategoricalResults	SMIDAS.classify(DataPoint data)
CategoricalResults	SCW.classify(DataPoint data)
CategoricalResults	SCD.classify(DataPoint data)
CategoricalResults	ROMMA.classify(DataPoint data)
CategoricalResults	PassiveAggressive.classify(DataPoint data)
CategoricalResults	NHERD.classify(DataPoint data)
CategoricalResults	NewGLMNET.classify(DataPoint data)
CategoricalResults	LogisticRegressionDCD.classify(DataPoint data)
CategoricalResults	LinearSGD.classify(DataPoint data)
CategoricalResults	LinearL1SCD.classify(DataPoint data)
CategoricalResults	LinearBatch.classify(DataPoint data)
CategoricalResults	BBR.classify(DataPoint data)
CategoricalResults	AROW.classify(DataPoint data)
CategoricalResults	ALMA2.classify(DataPoint data)
double	STGD.getScore(DataPoint dp)
double	SCW.getScore(DataPoint dp)
double	ROMMA.getScore(DataPoint dp)
double	PassiveAggressive.getScore(DataPoint dp)
double	NHERD.getScore(DataPoint dp)
double	AROW.getScore(DataPoint dp)
double	ALMA2.getScore(DataPoint dp)
double	STGD.regress(DataPoint data)
double	SMIDAS.regress(DataPoint data)
double	SCD.regress(DataPoint data)
double	PassiveAggressive.regress(DataPoint data)
double	LinearSGD.regress(DataPoint data)
double	LinearL1SCD.regress(DataPoint data)
double	LinearBatch.regress(DataPoint data)
void	STGD.update(DataPoint dataPoint, double y)
void	PassiveAggressive.update(DataPoint dataPoint, double targetValue)
void	LinearSGD.update(DataPoint dataPoint, double targetValue)
void	STGD.update(DataPoint dataPoint, int targetClass)
void	SPA.update(DataPoint dataPoint, int targetClass)
void	SCW.update(DataPoint dataPoint, int targetClass)
void	ROMMA.update(DataPoint dataPoint, int targetClass)
void	PassiveAggressive.update(DataPoint dataPoint, int targetClass)
void	NHERD.update(DataPoint dataPoint, int targetClass)
void	LinearSGD.update(DataPoint dataPoint, int targetClass)
void	AROW.update(DataPoint dataPoint, int targetClass)
void	ALMA2.update(DataPoint dataPoint, int targetClass)

Uses of DataPoint in jsat.classifiers.linear.kernelized

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

Modifier and Type	Method and Description
CategoricalResults	Projectron.classify(DataPoint data)
CategoricalResults	OSKL.classify(DataPoint data)
CategoricalResults	KernelSGD.classify(DataPoint data)
CategoricalResults	Forgetron.classify(DataPoint data)
CategoricalResults	DUOL.classify(DataPoint data)
CategoricalResults	CSKLRBatch.classify(DataPoint data)
CategoricalResults	CSKLR.classify(DataPoint data)
CategoricalResults	BOGD.classify(DataPoint data)
CategoricalResults	ALMA2K.classify(DataPoint data)
double	Projectron.getScore(DataPoint dp)
double	OSKL.getScore(DataPoint dp)
double	Forgetron.getScore(DataPoint dp)
double	DUOL.getScore(DataPoint dp)
double	BOGD.getScore(DataPoint dp)
double	ALMA2K.getScore(DataPoint dp)
double	KernelSGD.regress(DataPoint data)
void	KernelSGD.update(DataPoint dataPoint, double targetValue)
void	Projectron.update(DataPoint dataPoint, int targetClass)
void	OSKL.update(DataPoint dataPoint, int targetClass)
void	KernelSGD.update(DataPoint dataPoint, int targetClass)
void	Forgetron.update(DataPoint dataPoint, int targetClass)
void	DUOL.update(DataPoint dataPoint, int targetClass)
void	CSKLR.update(DataPoint dataPoint, int targetClass)
void	BOGD.update(DataPoint dataPoint, int targetClass)
void	ALMA2K.update(DataPoint dataPoint, int targetClass)

Uses of DataPoint in jsat.classifiers.neuralnetwork

Methods in jsat.classifiers.neuralnetwork that return DataPoint

Modifier and Type	Method and Description
DataPoint	RBFNet.transform(DataPoint dp)

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

Modifier and Type	Method and Description
CategoricalResults	SOM.classify(DataPoint data)
CategoricalResults	RBFNet.classify(DataPoint data)
CategoricalResults	Perceptron.classify(DataPoint data)
CategoricalResults	LVQLLC.classify(DataPoint data)
CategoricalResults	LVQ.classify(DataPoint data)
CategoricalResults	DReDNetSimple.classify(DataPoint data)
CategoricalResults	BackPropagationNet.classify(DataPoint data)
double	Perceptron.getScore(DataPoint dp)
double	RBFNet.regress(DataPoint data)
double	BackPropagationNet.regress(DataPoint data)
DataPoint	RBFNet.transform(DataPoint dp)

Uses of DataPoint in jsat.classifiers.svm

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

Modifier and Type	Method and Description
CategoricalResults	SVMnoBias.classify(DataPoint data)
CategoricalResults	SBP.classify(DataPoint data)
CategoricalResults	PlattSMO.classify(DataPoint data)
CategoricalResults	PegasosK.classify(DataPoint data)
CategoricalResults	Pegasos.classify(DataPoint data)
CategoricalResults	LSSVM.classify(DataPoint data)
CategoricalResults	DCSVM.classify(DataPoint data)
CategoricalResults	DCDs.classify(DataPoint data)
CategoricalResults	DCD.classify(DataPoint data)
double	SVMnoBias.getScore(DataPoint dp)
double	SBP.getScore(DataPoint dp)
double	PlattSMO.getScore(DataPoint dp)
double	PegasosK.getScore(DataPoint dp)
double	Pegasos.getScore(DataPoint dp)
double	LSSVM.getScore(DataPoint dp)
double	DCSVM.getScore(DataPoint dp)
double	DCDs.getScore(DataPoint dp)
double	DCD.getScore(DataPoint dp)
double	PlattSMO.regress(DataPoint data)
double	LSSVM.regress(DataPoint data)
double	DCDs.regress(DataPoint data)
double	DCD.regress(DataPoint data)

Uses of DataPoint in jsat.classifiers.svm.extended

Methods in jsat.classifiers.svm.extended with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	OnlineAMM.classify(DataPoint data)
CategoricalResults	CPM.classify(DataPoint data)
double	CPM.getScore(DataPoint dp)
void	OnlineAMM.update(DataPoint dataPoint, int y_t)
protected int	OnlineAMM.update(DataPoint dataPoint, int y_t, int z_t) Performs the work for an update.

Uses of DataPoint in jsat.classifiers.trees

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

Modifier and Type	Method and Description
void	ImpurityScore.addPoint(DataPoint dp, int targetClass) Adds one more point to the impurity score
CategoricalResults	TreeNodeVisitor.classify(DataPoint dp)
CategoricalResults	RandomForest.classify(DataPoint data)
CategoricalResults	ID3.classify(DataPoint data)
CategoricalResults	ExtraTree.classify(DataPoint data)
CategoricalResults	ERTrees.classify(DataPoint data)
CategoricalResults	DecisionTree.classify(DataPoint data)
CategoricalResults	DecisionStump.classify(DataPoint data)
abstract int	TreeNodeVisitor.getPath(DataPoint dp) Returns the path down the tree the given data point would have taken, or -1 if this node was a leaf node OR if a missing value prevent traversal down the path
int	DecisionTree.Node.getPath(DataPoint dp)
CategoricalResults	TreeNodeVisitor.localClassify(DataPoint dp) Returns the classification result that would have been obtained if the current node was a leaf node.
CategoricalResults	DecisionTree.Node.localClassify(DataPoint dp)
double	TreeNodeVisitor.localRegress(DataPoint dp) Returns the regression result that would have been obtained if the current node was a leaf node.
double	DecisionTree.Node.localRegress(DataPoint dp)
double	TreeNodeVisitor.regress(DataPoint dp) Performs regression on the given data point by following it down the tree until it finds the correct terminal node.
double	RandomForest.regress(DataPoint data)
double	ExtraTree.regress(DataPoint data)
double	ERTrees.regress(DataPoint data)
double	DecisionTree.regress(DataPoint data)
double	DecisionStump.regress(DataPoint data)
void	ImpurityScore.removePoint(DataPoint dp, int targetClass) Removes one point from the impurity score
int	DecisionStump.whichPath(DataPoint data) Determines which split path this data point would follow from this decision stump.

Uses of DataPoint in jsat.clustering

Methods in jsat.clustering that return types with arguments of type DataPoint

Modifier and Type	Method and Description
List<List<DataPoint>>	ClustererBase.cluster(DataSet dataSet)
List<List<DataPoint>>	Clusterer.cluster(DataSet dataSet) Performs clustering on the given data set.
List<List<DataPoint>>	DBSCAN.cluster(DataSet dataSet, double eps, int minPts)
List<List<DataPoint>>	DBSCAN.cluster(DataSet dataSet, double eps, int minPts, ExecutorService threadpool)
List<List<DataPoint>>	ClustererBase.cluster(DataSet dataSet, ExecutorService threadpool)
List<List<DataPoint>>	Clusterer.cluster(DataSet dataSet, ExecutorService threadpool) Performs clustering on the given data set.
List<List<DataPoint>>	KClustererBase.cluster(DataSet dataSet, int clusters)
List<List<DataPoint>>	KClusterer.cluster(DataSet dataSet, int clusters) Performs clustering on the given data set.
List<List<DataPoint>>	DBSCAN.cluster(DataSet dataSet, int minPts)
List<List<DataPoint>>	KClustererBase.cluster(DataSet dataSet, int clusters, ExecutorService threadpool)
List<List<DataPoint>>	KClusterer.cluster(DataSet dataSet, int clusters, ExecutorService threadpool) Performs clustering on the given data set.
List<List<DataPoint>>	DBSCAN.cluster(DataSet dataSet, int minPts, ExecutorService threadpool)
List<List<DataPoint>>	KClustererBase.cluster(DataSet dataSet, int lowK, int highK)
List<List<DataPoint>>	KClusterer.cluster(DataSet dataSet, int lowK, int highK) Performs clustering on the given data set.
List<List<DataPoint>>	KClustererBase.cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool)
List<List<DataPoint>>	KClusterer.cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool) Performs clustering on the given data set.
static List<List<DataPoint>>	ClustererBase.createClusterListFromAssignmentArray(int[] assignments, DataSet dataSet) Convenient helper method.
static List<DataPoint>	ClustererBase.getDatapointsFromCluster(int c, int[] assignments, DataSet dataSet, int[] indexFrom) Gets a list of the datapoints in a data set that belong to the indicated cluster

Method parameters in jsat.clustering with type arguments of type DataPoint

Modifier and Type	Method and Description
boolean	EMGaussianMixture.setUsingDataList(List<DataPoint> dataPoint)
boolean	EMGaussianMixture.setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool)

Uses of DataPoint in jsat.clustering.dissimilarity

Methods in jsat.clustering.dissimilarity with parameters of type DataPoint

Modifier and Type	Method and Description
double	DistanceMetricDissimilarity.distance(DataPoint a, DataPoint b)
double	ClusterDissimilarity.distance(DataPoint a, DataPoint b) Provides the notion of distance, or dissimilarity, between two data points

Method parameters in jsat.clustering.dissimilarity with type arguments of type DataPoint

Modifier and Type	Method and Description
double	SingleLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	SingleLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	LanceWilliamsDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	LanceWilliamsDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	CompleteLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	CompleteLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	ClusterDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.
double	ClusterDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.
double	CentroidDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	CentroidDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	AverageLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)
double	AverageLinkDissimilarity.dissimilarity(List<DataPoint> a, List<DataPoint> b)

Uses of DataPoint in jsat.clustering.evaluation

Method parameters in jsat.clustering.evaluation with type arguments of type DataPoint

Modifier and Type	Method and Description
double	NormalizedMutualInformation.evaluate(List<List<DataPoint>> dataSets)
double	IntraClusterSumEvaluation.evaluate(List<List<DataPoint>> dataSets)
double	DunnIndex.evaluate(List<List<DataPoint>> dataSets)
double	DaviesBouldinIndex.evaluate(List<List<DataPoint>> dataSets)
double	ClusterEvaluation.evaluate(List<List<DataPoint>> dataSets) Evaluates the clustering of the given set of clusters.
double	AdjustedRandIndex.evaluate(List<List<DataPoint>> dataSets)

Uses of DataPoint in jsat.clustering.evaluation.intra

Method parameters in jsat.clustering.evaluation.intra with type arguments of type DataPoint

Modifier and Type	Method and Description
double	SumOfSqrdPairwiseDistances.evaluate(List<DataPoint> dataPoints)
double	SoSCentroidDistance.evaluate(List<DataPoint> dataPoints)
double	MeanDistance.evaluate(List<DataPoint> dataPoints)
double	MeanCentroidDistance.evaluate(List<DataPoint> dataPoints)
double	MaxDistance.evaluate(List<DataPoint> dataPoints)
double	IntraClusterEvaluation.evaluate(List<DataPoint> dataPoints) Evaluates the cluster represented by the given list of data points.

Uses of DataPoint in jsat.clustering.hierarchical

Methods in jsat.clustering.hierarchical that return types with arguments of type DataPoint

Modifier and Type	Method and Description
List<List<DataPoint>>	PriorityHAC.getClusterDesignations(int clusters) Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.
List<List<DataPoint>>	NNChainHAC.getClusterDesignations(int clusters, DataSet data) Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.

Uses of DataPoint in jsat.clustering.kmeans

Methods in jsat.clustering.kmeans that return types with arguments of type DataPoint

Modifier and Type	Method and Description
protected static List<List<DataPoint>>	KMeans.getListOfLists(int k)

Uses of DataPoint in jsat.datatransform

Methods in jsat.datatransform that return DataPoint

Modifier and Type	Method and Description
DataPoint	ZeroMeanTransform.inverse(DataPoint dp)
DataPoint	LinearTransform.inverse(DataPoint dp)
DataPoint	InvertibleTransform.inverse(DataPoint dp) Applies the inverse or "reverse" transform to approximately undo the effect of DataTransform.transform(jsat.classifiers.DataPoint) to recover an approximation of the original data point.
DataPoint	FastICA.inverse(DataPoint dp)
DataPoint	ZeroMeanTransform.transform(DataPoint dp)
DataPoint	WhitenedPCA.transform(DataPoint dp)
DataPoint	UnitVarianceTransform.transform(DataPoint dp)
DataPoint	StandardizeTransform.transform(DataPoint dp)
DataPoint	RemoveAttributeTransform.transform(DataPoint dp)
DataPoint	PolynomialTransform.transform(DataPoint dp)
DataPoint	PNormNormalization.transform(DataPoint dp)
DataPoint	PCA.transform(DataPoint dp)
DataPoint	NumericalToHistogram.transform(DataPoint dp)
DataPoint	NominalToNumeric.transform(DataPoint dp)
DataPoint	LinearTransform.transform(DataPoint dp)
DataPoint	JLTransform.transform(DataPoint dp)
DataPoint	InverseOfTransform.transform(DataPoint dp)
DataPoint	InsertMissingValuesTransform.transform(DataPoint dp)
DataPoint	Imputer.transform(DataPoint dp)
DataPoint	FastICA.transform(DataPoint dp)
DataPoint	DenseSparceTransform.transform(DataPoint dp)
DataPoint	DataTransformProcess.transform(DataPoint dp)
DataPoint	DataTransform.transform(DataPoint dp) Returns a new data point that is a transformation of the original data point.
DataPoint	AutoDeskewTransform.transform(DataPoint dp)

Methods in jsat.datatransform with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	DataModelPipeline.classify(DataPoint data)
DataPoint	ZeroMeanTransform.inverse(DataPoint dp)
DataPoint	LinearTransform.inverse(DataPoint dp)
DataPoint	InvertibleTransform.inverse(DataPoint dp) Applies the inverse or "reverse" transform to approximately undo the effect of DataTransform.transform(jsat.classifiers.DataPoint) to recover an approximation of the original data point.
DataPoint	FastICA.inverse(DataPoint dp)
void	ZeroMeanTransform.mutableInverse(DataPoint dp)
void	LinearTransform.mutableInverse(DataPoint dp)
void	InPlaceInvertibleTransform.mutableInverse(DataPoint dp) Mutates the given data point.
void	ZeroMeanTransform.mutableTransform(DataPoint dp)
void	WhitenedZCA.mutableTransform(DataPoint dp)
void	UnitVarianceTransform.mutableTransform(DataPoint dp)
void	StandardizeTransform.mutableTransform(DataPoint dp)
void	PNormNormalization.mutableTransform(DataPoint dp)
void	LinearTransform.mutableTransform(DataPoint dp)
void	InsertMissingValuesTransform.mutableTransform(DataPoint dp)
void	InPlaceTransform.mutableTransform(DataPoint dp) Mutates the given data point.
void	Imputer.mutableTransform(DataPoint dp)
void	AutoDeskewTransform.mutableTransform(DataPoint dp)
double	DataModelPipeline.regress(DataPoint data)
DataPoint	ZeroMeanTransform.transform(DataPoint dp)
DataPoint	WhitenedPCA.transform(DataPoint dp)
DataPoint	UnitVarianceTransform.transform(DataPoint dp)
DataPoint	StandardizeTransform.transform(DataPoint dp)
DataPoint	RemoveAttributeTransform.transform(DataPoint dp)
DataPoint	PolynomialTransform.transform(DataPoint dp)
DataPoint	PNormNormalization.transform(DataPoint dp)
DataPoint	PCA.transform(DataPoint dp)
DataPoint	NumericalToHistogram.transform(DataPoint dp)
DataPoint	NominalToNumeric.transform(DataPoint dp)
DataPoint	LinearTransform.transform(DataPoint dp)
DataPoint	JLTransform.transform(DataPoint dp)
DataPoint	InverseOfTransform.transform(DataPoint dp)
DataPoint	InsertMissingValuesTransform.transform(DataPoint dp)
DataPoint	Imputer.transform(DataPoint dp)
DataPoint	FastICA.transform(DataPoint dp)
DataPoint	DenseSparceTransform.transform(DataPoint dp)
DataPoint	DataTransformProcess.transform(DataPoint dp)
DataPoint	DataTransform.transform(DataPoint dp) Returns a new data point that is a transformation of the original data point.
DataPoint	AutoDeskewTransform.transform(DataPoint dp)

Uses of DataPoint in jsat.datatransform.featureselection

Methods in jsat.datatransform.featureselection that return DataPoint

Modifier and Type	Method and Description
DataPoint	SFS.transform(DataPoint dp)
DataPoint	LRS.transform(DataPoint dp)
DataPoint	BDS.transform(DataPoint dp)

Methods in jsat.datatransform.featureselection with parameters of type DataPoint

Modifier and Type	Method and Description
DataPoint	SFS.transform(DataPoint dp)
DataPoint	LRS.transform(DataPoint dp)
DataPoint	BDS.transform(DataPoint dp)

Uses of DataPoint in jsat.datatransform.kernel

Methods in jsat.datatransform.kernel that return DataPoint

Modifier and Type	Method and Description
DataPoint	RFF_RBF.transform(DataPoint dp)
DataPoint	Nystrom.transform(DataPoint dp)
DataPoint	KernelPCA.transform(DataPoint dp)

Methods in jsat.datatransform.kernel with parameters of type DataPoint

Modifier and Type	Method and Description
DataPoint	RFF_RBF.transform(DataPoint dp)
DataPoint	Nystrom.transform(DataPoint dp)
DataPoint	KernelPCA.transform(DataPoint dp)

Uses of DataPoint in jsat.distributions.multivariate

Method parameters in jsat.distributions.multivariate with type arguments of type DataPoint

Modifier and Type	Method and Description
boolean	SymmetricDirichlet.setUsingDataList(List<DataPoint> dataPoint)
boolean	ProductKDE.setUsingDataList(List<DataPoint> dataPoints)
boolean	NormalM.setUsingDataList(List<DataPoint> dataSet)
boolean	MultivariateDistribution.setUsingDataList(List<DataPoint> dataPoints) Sets the parameters of the distribution to attempt to fit the given list of data points.
boolean	MetricKDE.setUsingDataList(List<DataPoint> dataPoints)
boolean	Dirichlet.setUsingDataList(List<DataPoint> dataPoint)
boolean	MultivariateDistributionSkeleton.setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool)
boolean	MultivariateDistribution.setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of data points.
boolean	MetricKDE.setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool)

Uses of DataPoint in jsat.linear

Method parameters in jsat.linear with type arguments of type DataPoint

Modifier and Type	Method and Description
static void	MatrixStatistics.covarianceMatrix(Vec mean, List<DataPoint> dataSet, Matrix covariance) Computes the weighted result for the covariance matrix of the given data set.
static void	MatrixStatistics.covarianceMatrix(Vec mean, List<DataPoint> dataSet, Matrix covariance, double sumOfWeights, double sumOfSquaredWeights) Computes the weighted result for the covariance matrix of the given data set.

Uses of DataPoint in jsat.parameters

Methods in jsat.parameters with parameters of type DataPoint

Modifier and Type	Method and Description
CategoricalResults	ModelSearch.classify(DataPoint data)
double	ModelSearch.regress(DataPoint data)

Uses of DataPoint in jsat.regression

Methods in jsat.regression that return DataPoint

Modifier and Type	Method and Description
DataPoint	RegressionDataSet.getDataPoint(int i)

Methods in jsat.regression with parameters of type DataPoint

Modifier and Type	Method and Description
void	RegressionDataSet.addDataPoint(DataPoint dp, double val)
CategoricalResults	LogisticRegression.classify(DataPoint data)
double	StochasticRidgeRegression.regress(DataPoint data)
double	StochasticGradientBoosting.regress(DataPoint data)
double	RidgeRegression.regress(DataPoint data)
double	Regressor.regress(DataPoint data)
double	RANSAC.regress(DataPoint data)
double	OrdinaryKriging.regress(DataPoint data)
double	NadarayaWatson.regress(DataPoint data)
double	MultipleLinearRegression.regress(DataPoint data)
double	LogisticRegression.regress(DataPoint data)
double	KernelRLS.regress(DataPoint data)
double	KernelRidgeRegression.regress(DataPoint data)
double	AveragedRegressor.regress(DataPoint data)
void	RegressionDataSet.setDataPoint(int i, DataPoint dp)
void	UpdateableRegressor.update(DataPoint dataPoint, double targetValue) Updates the classifier by giving it a new data point to learn from.
void	KernelRLS.update(DataPoint dataPoint, double y_t)

Constructor parameters in jsat.regression with type arguments of type DataPoint

Constructor and Description
RegressionDataSet(List<DataPoint> data, int predicting) Creates a new data set for the given list of data points.