Uses of Class
tutorial.clustering.SameSizeKMeans.Meta

Packages that use SameSizeKMeans.Meta

Package	Description
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of SameSizeKMeans.Meta in tutorial.clustering

Fields in tutorial.clustering declared as SameSizeKMeans.Meta

Modifier and Type	Field	Description
(package private) SameSizeKMeans.Meta	SameSizeKMeans.PreferenceComparator.c	Meta to use for comparison.

Methods in tutorial.clustering that return types with arguments of type SameSizeKMeans.Meta

Modifier and Type	Method	Description
protected WritableDataStore<SameSizeKMeans.Meta>	SameSizeKMeans.initializeMeta(Relation<V> relation, double[][] means)	Initialize the metadata storage.

Methods in tutorial.clustering with parameters of type SameSizeKMeans.Meta

Modifier and Type	Method	Description
it.unimi.dsi.fastutil.ints.IntComparator	SameSizeKMeans.PreferenceComparator.select(SameSizeKMeans.Meta c)	Set the meta to sort by
protected void	SameSizeKMeans.transfer(WritableDataStore<SameSizeKMeans.Meta> metas, SameSizeKMeans.Meta meta, ModifiableDBIDs src, ModifiableDBIDs dst, DBIDRef id, int dstnum)	Transfer a single element from one cluster to another.

Method parameters in tutorial.clustering with type arguments of type SameSizeKMeans.Meta

Modifier and Type	Method	Description
protected ArrayModifiableDBIDs	SameSizeKMeans.initialAssignment(java.util.List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeans.Meta> metas, DBIDs ids)
protected double[][]	SameSizeKMeans.refineResult(Relation<V> relation, double[][] means, java.util.List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeans.Meta> metas, ArrayModifiableDBIDs tids)	Perform k-means style iterations to improve the clustering result.
protected void	SameSizeKMeans.transfer(WritableDataStore<SameSizeKMeans.Meta> metas, SameSizeKMeans.Meta meta, ModifiableDBIDs src, ModifiableDBIDs dst, DBIDRef id, int dstnum)	Transfer a single element from one cluster to another.
protected void	SameSizeKMeans.updateDistances(Relation<V> relation, double[][] means, WritableDataStore<SameSizeKMeans.Meta> metas, NumberVectorDistance<? super V> df)	Compute the distances of each object to all means.