Package ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories

Class GmeansSamplingFactory<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>>

java.lang.Object

ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory<D,GmeansSampling<I,D>>

ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory<I,D>

All Implemented Interfaces:

IRerunnableSamplingAlgorithmFactory<D,GmeansSampling<I,D>>, ISamplingAlgorithmFactory<D,GmeansSampling<I,D>>, org.api4.java.common.reconstruction.IReconstructible

public class GmeansSamplingFactory<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>>
extends ASampleAlgorithmFactory<D,GmeansSampling<I,D>>
implements IRerunnableSamplingAlgorithmFactory<D,GmeansSampling<I,D>>

Constructor Summary

Constructors

Constructor	Description
GmeansSamplingFactory()

Method Summary

Modifier and Type	Method	Description
GmeansSampling<I,D>	getAlgorithm(int sampleSize, D inputDataset, java.util.Random random)	After the necessary config is done, this method returns a fully configured instance of a sampling algorithm.
int	getMaxIterationsInnerLoop()
void	setClusterSeed(long clusterSeed)	Set the seed the clustering will use for initialization.
void	setDistanceMeassure(org.apache.commons.math3.ml.distance.DistanceMeasure distanceMeassure)	Set the distance measure for the clustering.
void	setMaxIterationsInnerLoop(int maxIterationsInnerLoop)
void	setPreviousRun(GmeansSampling<I,D> previousRun)	Set the previous run of the sampling algorithm, if one occurred, can be set here to get data from it.

Methods inherited from class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory

addInstruction, create, getAlgorithm, getConstructionInstruction, getConstructionPlan, getRandom, getSampleSize, getSeed, setRandom, setSampleSize, setSeed

Methods inherited from class java.lang.Object

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

Methods inherited from interface ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory

getAlgorithm

Constructor Detail

GmeansSamplingFactory

public GmeansSamplingFactory()

Method Detail

setPreviousRun

public void setPreviousRun(GmeansSampling<I,D> previousRun)

Description copied from interface: IRerunnableSamplingAlgorithmFactory

Set the previous run of the sampling algorithm, if one occurred, can be set here to get data from it.

Specified by:

setPreviousRun in interface IRerunnableSamplingAlgorithmFactory<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>>

Parameters:

previousRun - Algorithm object of the previous of the sampling algorithm.

setClusterSeed

public void setClusterSeed(long clusterSeed)

Set the seed the clustering will use for initialization. Default is without a fix seed and the system time instead.

Parameters:

clusterSeed -

setDistanceMeassure

public void setDistanceMeassure(org.apache.commons.math3.ml.distance.DistanceMeasure distanceMeassure)

Set the distance measure for the clustering. Default is the Manhattan distance.

Parameters:

distanceMeassure -

getMaxIterationsInnerLoop

public int getMaxIterationsInnerLoop()

setMaxIterationsInnerLoop

public void setMaxIterationsInnerLoop(int maxIterationsInnerLoop)

getAlgorithm

public GmeansSampling<I,D> getAlgorithm(int sampleSize,
                                              D inputDataset,
                                              java.util.Random random)

Description copied from interface: ISamplingAlgorithmFactory

After the necessary config is done, this method returns a fully configured instance of a sampling algorithm.

Specified by:

getAlgorithm in interface ISamplingAlgorithmFactory<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>>

Parameters:

sampleSize - Desired size of the sample that will be created.

inputDataset - Dataset where the sample will be drawn from.

random - Random object to make samples reproducible.

Returns:

Configured sampling algorithm object.