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

Class SystematicSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>>

java.lang.Object

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

ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SystematicSamplingFactory<D>

All Implemented Interfaces:

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

public class SystematicSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>>
extends ASampleAlgorithmFactory<D,SystematicSampling<D>>
implements IRerunnableSamplingAlgorithmFactory<D,SystematicSampling<D>>

Constructor Summary

Constructors

Constructor	Description
SystematicSamplingFactory()

Method Summary

Modifier and Type	Method	Description
SystematicSampling<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.
void	setDatapointComparator(java.util.Comparator<org.api4.java.ai.ml.core.dataset.IInstance> datapointComparator)	Set a custom comparator that will be used to sort the datapoints before sampling.
void	setPreviousRun(SystematicSampling<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

SystematicSamplingFactory

public SystematicSamplingFactory()

Method Detail

setDatapointComparator

public void setDatapointComparator(java.util.Comparator<org.api4.java.ai.ml.core.dataset.IInstance> datapointComparator)

Set a custom comparator that will be used to sort the datapoints before sampling.

Parameters:

datapointComparator - Comparator for two datapoints.

setPreviousRun

public void setPreviousRun(SystematicSampling<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<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>,SystematicSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>>>

Parameters:

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

getAlgorithm

public SystematicSampling<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<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>,SystematicSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>>>

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.