Uses of Class
de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

Packages that use AbstractMaterializeKNNPreprocessor

Package	Description
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections.

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.database.query.knn

Fields in de.lmu.ifi.dbs.elki.database.query.knn declared as AbstractMaterializeKNNPreprocessor

Modifier and Type	Field and Description
private AbstractMaterializeKNNPreprocessor<O,D,T>	PreprocessorKNNQuery.preprocessor The last preprocessor result

Methods in de.lmu.ifi.dbs.elki.database.query.knn that return AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
AbstractMaterializeKNNPreprocessor<O,D,T>	PreprocessorKNNQuery.getPreprocessor() Get the preprocessor instance.

Constructors in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type AbstractMaterializeKNNPreprocessor

Constructor and Description
PreprocessorKNNQuery(Relation<O> database, AbstractMaterializeKNNPreprocessor<O,D,T> preprocessor) Constructor.

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Subclasses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Modifier and Type	Class and Description
class	CachedDoubleDistanceKNNPreprocessor<O> Preprocessor that loads an existing cached kNN result.
class	KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<?>,D extends Distance<D>> Class to materialize the kNN using a spatial join on an R-tree.
class	MaterializeKNNAndRKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors and the reverse k nearest neighbors (and their distances) to each database object.
class	MaterializeKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	PartitionApproximationMaterializeKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	RandomSampleKNNPreprocessor<O,D extends Distance<D>> Class that computed the kNN only on a random sample.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
abstract AbstractMaterializeKNNPreprocessor<O,D,T>	AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection declared as AbstractMaterializeKNNPreprocessor

Modifier and Type	Field and Description
private AbstractMaterializeKNNPreprocessor<? extends NumberVector<?>,D,?>	DistanceFunctionVisualization.Instance.result The selection result we work on

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection with parameters of type AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
static double	DistanceFunctionVisualization.getLPNormP(AbstractMaterializeKNNPreprocessor<?,?,?> kNN) Get the "p" value of an Lp norm.
static boolean	DistanceFunctionVisualization.isAngularDistance(AbstractMaterializeKNNPreprocessor<?,?,?> kNN) Test whether the given preprocessor used an angular distance function