Package elki.index.tree.spatial.kd

Class MemoryKDTree.KDTreeKNNSearcher

java.lang.Object

elki.index.tree.spatial.kd.MemoryKDTree.KDTreeKNNSearcher

All Implemented Interfaces:

KNNSearcher<O>

Enclosing class:

MemoryKDTree<O extends NumberVector>

@Reference(authors="S. Arya and D. M. Mount",
           title="Algorithms for fast vector quantization",
           booktitle="Proc. DCC \'93: Data Compression Conference",
           url="https://doi.org/10.1109/DCC.1993.253111",
           bibkey="doi:10.1109/DCC.1993.253111")
public class MemoryKDTree.KDTreeKNNSearcher
extends java.lang.Object
implements KNNSearcher<O>

kNN query for the k-d-tree.

Reference:

S. Arya and D. M. Mount
Algorithms for fast vector quantization
Proc. DCC '93: Data Compression Conference

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
private PartialDistance<? super O>	distance	Distance to use.

Constructor Summary

Constructors

Constructor	Description
KDTreeKNNSearcher(PartialDistance<? super O> distance)	Constructor.

Method Summary

Modifier and Type	Method	Description
KNNList	getKNN(O obj, int k)	Get the k nearest neighbors for a particular object.
private double	kdKNNSearch(java.lang.Object cur, O query, KNNHeap knns, DBIDArrayIter iter, double[] bounds, double rawdist, double maxdist)	Perform a kNN search on the k-d-tree.

Methods inherited from class java.lang.Object

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

Field Detail

distance

private PartialDistance<? super O extends NumberVector> distance

Distance to use.

Constructor Detail

KDTreeKNNSearcher

public KDTreeKNNSearcher(PartialDistance<? super O> distance)

Constructor.

Parameters:

distance - Distance to use

Method Detail

getKNN

public KNNList getKNN(O obj,
                      int k)

Description copied from interface: KNNSearcher

Get the k nearest neighbors for a particular object.

Specified by:

getKNN in interface KNNSearcher<O extends NumberVector>

Parameters:

obj - query object

k - Number of neighbors requested

Returns:

neighbors

kdKNNSearch

private double kdKNNSearch(java.lang.Object cur,
                           O query,
                           KNNHeap knns,
                           DBIDArrayIter iter,
                           double[] bounds,
                           double rawdist,
                           double maxdist)

Perform a kNN search on the k-d-tree.

Parameters:

cur - Current node

query - Query object

knns - kNN heap

iter - Iterator variable (reduces memory footprint!)

bounds - current bounds

rawdist - Raw distance to current rectangle (usually squared)

maxdist - Current upper bound of kNN distance.

Returns:

New upper bound of kNN distance.