de.lmu.ifi.dbs.elki.algorithm.benchmark

Class KNNBenchmarkAlgorithm<O,D extends Distance<D>>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,Result>

de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm<O,D>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, InspectionUtilFrequentlyScanned, Parameterizable

public class KNNBenchmarkAlgorithm<O,D extends Distance<D>>
extends AbstractDistanceBasedAlgorithm<O,D,Result>

Benchmarking algorithm that computes the k nearest neighbors for each query point. The query points can either come from a separate data source, or from the original database.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KNNBenchmarkAlgorithm.Parameterizer<O,D extends Distance<D>> Parameterization class

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k Number of neighbors to retrieve.
private static Logging	LOG The logger for this class.
protected DatabaseConnection	queries The alternate query point source.
protected RandomFactory	random Random generator factory
protected double	sampling Sampling size.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KNNBenchmarkAlgorithm(DistanceFunction<? super O,D> distanceFunction, int k, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Result	run(Database database, Relation<O> relation) Run the algorithm.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction, run

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

The logger for this class.

k

protected int k

Number of neighbors to retrieve.

queries

protected DatabaseConnection queries

The alternate query point source. Optional.

sampling

protected double sampling

Sampling size.

random

protected RandomFactory random

Random generator factory

Constructor Detail

KNNBenchmarkAlgorithm

public KNNBenchmarkAlgorithm(DistanceFunction<? super O,D> distanceFunction,
                     int k,
                     DatabaseConnection queries,
                     double sampling,
                     RandomFactory random)

Constructor.

Parameters:

distanceFunction - Distance function to use

k - K parameter

queries - Query data set (may be null!)

sampling - Sampling rate

random - Random factory

Method Detail

run

public Result run(Database database,
         Relation<O> relation)

Run the algorithm.

Parameters:

database - Database

relation - Relation

Returns:

Null result

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Result>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Result>

Returns:

the static logger