ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Class KmeansSampling<I extends IInstance>

java.lang.Object

ai.libs.jaicore.basic.algorithm.AAlgorithm<IDataset<I>,IDataset<I>>

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm<I>

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling<I>

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling<I>

All Implemented Interfaces:

ai.libs.jaicore.basic.algorithm.IAlgorithm<IDataset<I>,IDataset<I>>, ai.libs.jaicore.basic.Cancelable, ai.libs.jaicore.basic.ILoggingCustomizable, java.lang.Iterable<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>, java.util.concurrent.Callable<IDataset<I>>, java.util.Iterator<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>

public class KmeansSampling<I extends IInstance>
extends ClusterSampling<I>

Implementation of a sampling method using kmeans-clustering. This algorithm produces clusters of the given points and checks weather all points in a cluster have the same target Attribute. If yes only the point nearest to the center is added, otherwise the whole cluster is added to the sample.

Caution: This does ignore the given sample size!

Field Summary

Fields inherited from class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

clusterResults, currentCluster, distanceMeassure, seed

Fields inherited from class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

sample, sampleSize

Constructor Summary

Constructors

Constructor and Description
KmeansSampling(long seed, org.apache.commons.math3.ml.distance.DistanceMeasure dist, IDataset<I> input) Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long seed, int k, org.apache.commons.math3.ml.distance.DistanceMeasure dist, IDataset<I> input) Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long seed, int k, IDataset<I> input) Implementation of a sampling method using kmeans-clustering.

Method Summary

Modifier and Type	Method and Description
ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent	nextWithException()

Methods inherited from class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

doAlgorithmStep, getClusterResults, setClusterResults, setDistanceMeassure

Methods inherited from class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

call, doInactiveStep, setSampleSize

Methods inherited from class ai.libs.jaicore.basic.algorithm.AAlgorithm

activate, announceTimeoutDetected, avoidReinterruptionOnShutdownOnCurrentThread, cancel, checkAndConductTermination, checkTermination, computeTimeoutAware, getActivationTime, getConfig, getId, getInput, getLoggerName, getNumCPUs, getRemainingTimeToDeadline, getState, getTimeout, getTimeoutPrecautionOffset, hasNext, hasThreadBeenInterruptedDuringShutdown, interruptThreadAsPartOfShutdown, isCanceled, isShutdownInitialized, isStopCriterionSatisfied, isTimeouted, iterator, next, post, registerActiveThread, registerListener, resolveShutdownInterruptOnCurrentThread, setConfig, setLoggerName, setMaxNumThreads, setNumCPUs, setState, setTimeout, setTimeout, setTimeoutPrecautionOffset, shutdown, terminate, unregisterActiveThread, unregisterThreadAndShutdown

Methods inherited from class java.lang.Object

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

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Methods inherited from interface java.util.Iterator

forEachRemaining, remove

Constructor Detail

KmeansSampling

public KmeansSampling(long seed,
                      int k,
                      IDataset<I> input)

Implementation of a sampling method using kmeans-clustering.

Parameters:

seed - RAndom Seed

k - number of clusters

KmeansSampling

public KmeansSampling(long seed,
                      org.apache.commons.math3.ml.distance.DistanceMeasure dist,
                      IDataset<I> input)

Implementation of a sampling method using kmeans-clustering. The sample size will be used as the number of clusters.

Parameters:

seed - Random Seed

dist - DistanceMeasure to be used

KmeansSampling

public KmeansSampling(long seed,
                      int k,
                      org.apache.commons.math3.ml.distance.DistanceMeasure dist,
                      IDataset<I> input)

Implementation of a sampling method using kmeans-clustering.

Parameters:

seed - Random Seed

k - number of clusters

dist - DistanceMeasure to be used

Method Detail

nextWithException

public ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent nextWithException()
                                                                        throws ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException

Throws:

ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException