Uses of Class
jsat.clustering.SeedSelectionMethods.SeedSelection

Packages that use SeedSelectionMethods.SeedSelection

Package	Description
jsat.classifiers.neuralnetwork
jsat.clustering
jsat.clustering.kmeans

Uses of SeedSelectionMethods.SeedSelection in jsat.classifiers.neuralnetwork

Fields in jsat.classifiers.neuralnetwork declared as SeedSelectionMethods.SeedSelection

Modifier and Type	Field and Description
static SeedSelectionMethods.SeedSelection	LVQ.DEFAULT_SEED_SELECTION The default seed selection method is SeedSelection.KPP

Methods in jsat.classifiers.neuralnetwork that return SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
SeedSelectionMethods.SeedSelection	LVQ.getSeedSelection() Returns the method of seed selection used

Methods in jsat.classifiers.neuralnetwork with parameters of type SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
void	LVQ.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the seed selection method used to select the initial learning vectors

Uses of SeedSelectionMethods.SeedSelection in jsat.clustering

Fields in jsat.clustering declared as SeedSelectionMethods.SeedSelection

Modifier and Type	Field and Description
protected SeedSelectionMethods.SeedSelection	PAM.seedSelection

Methods in jsat.clustering that return SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
SeedSelectionMethods.SeedSelection	PAM.getSeedSelection()
SeedSelectionMethods.SeedSelection	EMGaussianMixture.getSeedSelection()
static SeedSelectionMethods.SeedSelection	SeedSelectionMethods.SeedSelection.valueOf(String name) Returns the enum constant of this type with the specified name.
static SeedSelectionMethods.SeedSelection[]	SeedSelectionMethods.SeedSelection.values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods in jsat.clustering with parameters of type SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
static void	SeedSelectionMethods.selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod) Selects seeds from a data set to use for a clustering algorithm.
static void	SeedSelectionMethods.selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool) Selects seeds from a data set to use for a clustering algorithm.
static void	SeedSelectionMethods.selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod) Selects seeds from a data set to use for a clustering algorithm.
static void	SeedSelectionMethods.selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool) Selects seeds from a data set to use for a clustering algorithm.
static List<Vec>	SeedSelectionMethods.selectIntialPoints(DataSet d, int k, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod)
static List<Vec>	SeedSelectionMethods.selectIntialPoints(DataSet d, int k, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool) Selects seeds from a data set to use for a clustering algorithm.
static List<Vec>	SeedSelectionMethods.selectIntialPoints(DataSet d, int k, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod) Selects seeds from a data set to use for a clustering algorithm.
static List<Vec>	SeedSelectionMethods.selectIntialPoints(DataSet d, int k, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool) Selects seeds from a data set to use for a clustering algorithm.
void	PAM.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection used by this algorithm
void	EMGaussianMixture.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection to use for this algorithm.

Constructors in jsat.clustering with parameters of type SeedSelectionMethods.SeedSelection

Constructor and Description
CLARA(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)
CLARA(int sampleCount, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)
CLARA(int sampleSize, int sampleCount, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)
EMGaussianMixture(SeedSelectionMethods.SeedSelection seedSelection)
PAM(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)

Uses of SeedSelectionMethods.SeedSelection in jsat.clustering.kmeans

Fields in jsat.clustering.kmeans declared as SeedSelectionMethods.SeedSelection

Modifier and Type	Field and Description
static SeedSelectionMethods.SeedSelection	KMeans.DEFAULT_SEED_SELECTION This is the default seed selection method used in ElkanKMeans.
protected SeedSelectionMethods.SeedSelection	KMeans.seedSelection

Methods in jsat.clustering.kmeans that return SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
SeedSelectionMethods.SeedSelection	XMeans.getSeedSelection()
SeedSelectionMethods.SeedSelection	MiniBatchKMeans.getSeedSelection() Returns the method of seed selection to use
SeedSelectionMethods.SeedSelection	KMeans.getSeedSelection()
SeedSelectionMethods.SeedSelection	GMeans.getSeedSelection()

Methods in jsat.clustering.kmeans with parameters of type SeedSelectionMethods.SeedSelection

Modifier and Type	Method and Description
void	XMeans.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection)
void	MiniBatchKMeans.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of selecting the initial data points to seed the clustering algorithm.
void	KMeans.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection to use for this algorithm.
void	GMeans.setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection)

Constructors in jsat.clustering.kmeans with parameters of type SeedSelectionMethods.SeedSelection

Constructor and Description
ElkanKMeans(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection) Creates a new KMeans instance.
HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection) Creates a new k-Means object
HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand) Creates a new k-Means object
KMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand)
MiniBatchKMeans(DistanceMetric dm, int batchSize, int iterations, SeedSelectionMethods.SeedSelection seedSelection) Creates a new Mini-Batch k-Means object
NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection) Creates a new naive k-Means cluster
NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand) Creates a new naive k-Means cluster