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

Class GMeansStratiAmountSelectorAndAssigner<I extends IInstance>

java.lang.Object

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

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

All Implemented Interfaces:

IStratiAmountSelector<I>, IStratiAssigner<I>

public class GMeansStratiAmountSelectorAndAssigner<I extends IInstance>
extends ClusterStratiAssigner<I>
implements IStratiAmountSelector<I>

Combined strati amount selector and strati assigner via g-means. IT can be used in 3 combinations: 1) Amount Selector and Strati Assigner: A g-means clustering is initially performed to select a strati amount via the amount of found clusters and to assign datapoints with this clusters afterwards. 2) Amount Selector: A g-means clustering is initially performed to select a suitable strati amount with the amount of found clusters. 3) Strati Assigner: Since the amount of strati is selected with another component g-means cannot be used and k-means will be used to be conform with the given strati amount. It is recommended to use combination 1, because only using this component for only one of the two tasks could yield in inconsistent results.

Field Summary

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

clusters, distanceMeasure, randomSeed

Constructor Summary

Constructors

Constructor and Description
GMeansStratiAmountSelectorAndAssigner(org.apache.commons.math3.ml.distance.DistanceMeasure distanceMeasure, int randomSeed) Constructor for GMeansStratiAmountSelectorAndAssigner with custom distanceMeasure.
GMeansStratiAmountSelectorAndAssigner(int randomSeed) Constructor for GMeansStratiAmountSelectorAndAssigner with Manhattan distanceMeasure as a default.

Method Summary

Modifier and Type	Method and Description
void	init(IDataset<I> dataset, int stratiAmount) Initialize custom assigner if necessary.
int	selectStratiAmount(IDataset<I> dataset) Select a suitable amount of strati for a Dataset.

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

assignToStrati, getNumCPUs, setNumCPUs

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.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector

getNumCPUs, setNumCPUs

Constructor Detail

GMeansStratiAmountSelectorAndAssigner

public GMeansStratiAmountSelectorAndAssigner(int randomSeed)

Constructor for GMeansStratiAmountSelectorAndAssigner with Manhattan distanceMeasure as a default.

Parameters:

randomSeed - Seed for random numbers.

GMeansStratiAmountSelectorAndAssigner

public GMeansStratiAmountSelectorAndAssigner(org.apache.commons.math3.ml.distance.DistanceMeasure distanceMeasure,
                                             int randomSeed)

Constructor for GMeansStratiAmountSelectorAndAssigner with custom distanceMeasure.

Parameters:

distanceMeasure - Distance measure for datapoints, for example Manhattan or Euclidian.

randomSeed - Seed for random numbers.

Method Detail

selectStratiAmount

public int selectStratiAmount(IDataset<I> dataset)

Description copied from interface: IStratiAmountSelector

Select a suitable amount of strati for a Dataset.

Specified by:

selectStratiAmount in interface IStratiAmountSelector<I extends IInstance>

Parameters:

dataset - The dataset that will be stratified.

Returns:

The determined amount of strati.

init

public void init(IDataset<I> dataset,
                 int stratiAmount)

Description copied from interface: IStratiAssigner

Initialize custom assigner if necessary.

Specified by:

init in interface IStratiAssigner<I extends IInstance>

Parameters:

dataset - The dataset the datapoints will be sampled from.

stratiAmount - The predetermined amount of strati the dataset will be stratified into.