Package elki.index.preprocessed.knn

Class SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>

java.lang.Object

elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor<O>

elki.index.preprocessed.knn.SpacefillingMaterializeKNNPreprocessor<O>

Type Parameters:

O - Vector type indexed

All Implemented Interfaces:

Index, KNNIndex<O>

@Reference(authors="Erich Schubert, Arthur Zimek, Hans-Peter Kriegel",
           title="Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles",
           booktitle="Proc. 20th Int. Conf. Database Systems for Advanced Applications (DASFAA 2015)",
           url="https://doi.org/10.1007/978-3-319-18123-3_2",
           bibkey="DBLP:conf/dasfaa/SchubertZK15")
public class SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
extends AbstractMaterializeKNNPreprocessor<O>

Compute the nearest neighbors approximatively using space filling curves.

This version does the bulk kNN-join operation, i.e. precomputes the k nearest neighbors for every object, then discards the curves. This is usually more memory intensive but faster than SpacefillingKNNPreprocessor.

Reference:

Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
Proc. 20th Int. Conf. Database Systems for Advanced Applications (DASFAA 2015)

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	SpacefillingMaterializeKNNPreprocessor.Factory<V extends NumberVector>	Index factory class

Field Summary

Fields

Modifier and Type	Field	Description
(package private) java.util.List<? extends SpatialSorter>	curvegen	Spatial curve generators
private static Logging	LOG	Class logger
(package private) Mean	mean	Mean number of distance computations
(package private) java.util.Random	random	Random number generator.
(package private) int	variants	Number of variants to generate for each curve
(package private) double	window	Curve window size

Fields inherited from class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

distance, distanceQuery, k, relation, storage

Constructor Summary

Constructors

Constructor	Description
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, java.util.List<? extends SpatialSorter> curvegen, double window, int variants, java.util.Random random)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected Logging	getLogger()	Get the classes static logger.
KNNSearcher<O>	kNNByObject(DistanceQuery<O> distQ, int maxk, int flags)	Get a KNN query object for the given distance query and k.
void	logStatistics()	Send statistics to the logger, if enabled.
protected void	preprocess()	Perform the preprocessing step.

Methods inherited from class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

createStorage, get, getDistanceQuery, getK, initialize, kNNByDBID

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

Class logger

curvegen

final java.util.List<? extends SpatialSorter> curvegen

Spatial curve generators

window

final double window

Curve window size

variants

final int variants

Number of variants to generate for each curve

mean

Mean mean

Mean number of distance computations

random

java.util.Random random

Random number generator.

Constructor Detail

SpacefillingMaterializeKNNPreprocessor

public SpacefillingMaterializeKNNPreprocessor(Relation<O> relation,
                                              Distance<? super O> distance,
                                              int k,
                                              java.util.List<? extends SpatialSorter> curvegen,
                                              double window,
                                              int variants,
                                              java.util.Random random)

Constructor.

Parameters:

relation - Relation to index.

distance - Distance function

k - k

curvegen - Curve generators

window - Window multiplicator

variants - Number of curve variants to generate

random - Random number generator

Method Detail

preprocess

protected void preprocess()

Description copied from class: AbstractMaterializeKNNPreprocessor

Perform the preprocessing step.

Specified by:

preprocess in class AbstractMaterializeKNNPreprocessor<O extends NumberVector>

logStatistics

public void logStatistics()

Description copied from interface: Index

Send statistics to the logger, if enabled.

Note: you must have set the logging level appropriately before initializing the index! Otherwise, the index might not have collected the desired statistics.

getLogger

protected Logging getLogger()

Description copied from class: AbstractMaterializeKNNPreprocessor

Get the classes static logger.

Specified by:

getLogger in class AbstractMaterializeKNNPreprocessor<O extends NumberVector>

Returns:

Logger

kNNByObject

public KNNSearcher<O> kNNByObject(DistanceQuery<O> distQ,
                                  int maxk,
                                  int flags)

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:

kNNByObject in interface KNNIndex<O extends NumberVector>

Overrides:

kNNByObject in class AbstractMaterializeKNNPreprocessor<O extends NumberVector>

Parameters:

distQ - Distance query

maxk - Maximum value of k

flags - Hints for the optimizer

Returns:

KNN Query object or null