Uses of Interface
org.openimaj.knn.NearestNeighboursFactory

Packages that use NearestNeighboursFactory

Package	Description
org.openimaj.knn
org.openimaj.knn.approximate
org.openimaj.ml.clustering.dbscan
org.openimaj.ml.clustering.kmeans	K-Means in OpenIMAJ is designed to be both extremely fast and flexible.

Uses of NearestNeighboursFactory in org.openimaj.knn

Classes in org.openimaj.knn that implement NearestNeighboursFactory

Modifier and Type	Class and Description
static class	ByteNearestNeighboursExact.Factory NearestNeighboursFactory for producing ByteNearestNeighboursExacts.
static class	DoubleNearestNeighboursExact.Factory NearestNeighboursFactory for producing DoubleNearestNeighboursExacts.
static class	FloatNearestNeighboursExact.Factory NearestNeighboursFactory for producing FloatNearestNeighboursExacts.
static class	IntNearestNeighboursExact.Factory NearestNeighboursFactory for producing IntNearestNeighboursExacts.
static class	LongNearestNeighboursExact.Factory NearestNeighboursFactory for producing LongNearestNeighboursExacts.
static class	ObjectNearestNeighboursExact.Factory<T> NearestNeighboursFactory for producing ObjectNearestNeighboursExacts.
static class	ShortNearestNeighboursExact.Factory NearestNeighboursFactory for producing ShortNearestNeighboursExacts.

Uses of NearestNeighboursFactory in org.openimaj.knn.approximate

Classes in org.openimaj.knn.approximate that implement NearestNeighboursFactory

Modifier and Type	Class and Description
static class	ByteNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing ByteNearestNeighboursKDTrees.
static class	DoubleNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing DoubleNearestNeighboursKDTrees.
static class	FloatNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing FloatNearestNeighboursKDTrees.
static class	IntNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing IntNearestNeighboursKDTrees.
static class	LongNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing LongNearestNeighboursKDTrees.
static class	ShortNearestNeighboursKDTree.Factory NearestNeighboursFactory for producing ShortNearestNeighboursKDTrees.

Uses of NearestNeighboursFactory in org.openimaj.ml.clustering.dbscan

Constructors in org.openimaj.ml.clustering.dbscan with parameters of type NearestNeighboursFactory

Constructor and Description
DoubleNNDBSCAN(double eps, int minPts, NearestNeighboursFactory<? extends DoubleNearestNeighbours,double[]> nnf) Perform a DBScane with this configuration

Uses of NearestNeighboursFactory in org.openimaj.ml.clustering.kmeans

Fields in org.openimaj.ml.clustering.kmeans declared as NearestNeighboursFactory

Modifier and Type	Field and Description
protected NearestNeighboursFactory<? extends NN,DATA>	KMeansConfiguration.factory The factory for producing the NearestNeighbours objects used in assignment.

Methods in org.openimaj.ml.clustering.kmeans that return NearestNeighboursFactory

Modifier and Type	Method and Description
NearestNeighboursFactory<? extends NN,DATA>	KMeansConfiguration.getNearestNeighbourFactory() Get the factory that produces the NearestNeighbours during clustering.

Methods in org.openimaj.ml.clustering.kmeans with parameters of type NearestNeighboursFactory

Modifier and Type	Method and Description
void	KMeansConfiguration.setNearestNeighbourFactory(NearestNeighboursFactory<? extends NN,DATA> factory) Set the factory that produces the NearestNeighbours during clustering.

Constructors in org.openimaj.ml.clustering.kmeans with parameters of type NearestNeighboursFactory

Constructor and Description
KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory) Create configuration for data that will create K clusters.
KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters) Create configuration for data that will create K clusters.
KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters, ExecutorService threadpool) Create configuration for data that will create K clusters.
KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters, int blockSize, ExecutorService threadpool) Create configuration for data with M dimensions that will create K clusters.