de.lmu.ifi.dbs.elki.index.preprocessed.knn

Class AbstractMaterializeKNNPreprocessor<O>

java.lang.Object

de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex<O,KNNList>

de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Index, KNNIndex<O>, Result

Direct Known Subclasses:

CachedDoubleDistanceKNNPreprocessor, KNNJoinMaterializeKNNPreprocessor, MaterializeKNNPreprocessor, MetricalIndexApproximationMaterializeKNNPreprocessor, NNDescent, PartitionApproximationMaterializeKNNPreprocessor, RandomSampleKNNPreprocessor, SpacefillingMaterializeKNNPreprocessor, SpatialApproximationMaterializeKNNPreprocessor

public abstract class AbstractMaterializeKNNPreprocessor<O>
extends AbstractPreprocessorIndex<O,KNNList>
implements KNNIndex<O>

Abstract base class for KNN Preprocessors.

Since:

0.4.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AbstractMaterializeKNNPreprocessor.Factory<O> The parameterizable factory.

Field Summary

Fields

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	distanceFunction The distance function to be used.
protected DistanceQuery<O>	distanceQuery The distance query we used.
protected int	k The query k value.

Fields inherited from class de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex

relation, storage

Constructor Summary

Constructors

Constructor and Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.

Method Summary

Modifier and Type	Method and Description
(package private) void	createStorage() Create the default storage.
KNNList	get(DBIDRef id) Get the k nearest neighbors.
DistanceQuery<O>	getDistanceQuery() The distance query we used.
int	getK() Get the value of 'k' supported by this preprocessor.
KNNQuery<O>	getKNNQuery(DistanceQuery<O> distQ, java.lang.Object... hints) Get a KNN query object for the given distance query and k.
void	initialize() Initialize the index.
protected abstract void	preprocess() Perform the preprocessing step.

Methods inherited from class de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex

getLogger

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.index.Index

logStatistics

Methods inherited from interface de.lmu.ifi.dbs.elki.result.Result

getLongName, getShortName

Field Detail

k

protected final int k

The query k value.

distanceFunction

protected final DistanceFunction<? super O> distanceFunction

The distance function to be used.

distanceQuery

protected final DistanceQuery<O> distanceQuery

The distance query we used.

Constructor Detail

AbstractMaterializeKNNPreprocessor

public AbstractMaterializeKNNPreprocessor(Relation<O> relation,
                                          DistanceFunction<? super O> distanceFunction,
                                          int k)

Constructor.

Parameters:

relation - Relation

distanceFunction - Distance function

k - k

Method Detail

getDistanceQuery

public DistanceQuery<O> getDistanceQuery()

The distance query we used.

Returns:

Distance query

getK

public int getK()

Get the value of 'k' supported by this preprocessor.

Returns:

k

preprocess

protected abstract void preprocess()

Perform the preprocessing step.

get

public KNNList get(DBIDRef id)

Get the k nearest neighbors.

Parameters:

id - Object ID

Returns:

Neighbors

createStorage

void createStorage()

Create the default storage.

initialize

public void initialize()

Description copied from interface: Index

Initialize the index. For static indexes, this is the moment the index is bulk loaded.

Specified by:

initialize in interface Index

getKNNQuery

public KNNQuery<O> getKNNQuery(DistanceQuery<O> distQ,
                               java.lang.Object... hints)

Description copied from interface: KNNIndex

Get a KNN query object for the given distance query and k. This function MAY return null, when the given distance is not supported!

Specified by:

getKNNQuery in interface KNNIndex<O>

Parameters:

distQ - Distance query

hints - Hints for the optimizer

Returns:

KNN Query object or null