org.apache.ignite.ml.clustering.kmeans

Class KMeansTrainer

java.lang.Object

org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

All Implemented Interfaces:

DatasetTrainer<KMeansModel,Double>, SingleLabelDatasetTrainer<KMeansModel>

public class KMeansTrainer
extends Object
implements SingleLabelDatasetTrainer<KMeansModel>

The trainer for KMeans algorithm.

Constructor Summary

Constructors

Constructor and Description
KMeansTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> KMeansModel	fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,double[]> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
DistanceMeasure	getDistance() Gets the distance.
double	getEpsilon() Gets the epsilon.
int	getK() Gets the amount of clusters.
int	getMaxIterations() Gets the max number of iterations before convergence.
long	getSeed() Gets the seed number.
KMeansTrainer	withDistance(DistanceMeasure distance) Set up the distance.
KMeansTrainer	withEpsilon(double epsilon) Set up the epsilon.
KMeansTrainer	withK(int k) Set up the amount of clusters.
KMeansTrainer	withMaxIterations(int maxIterations) Set up the max number of iterations before convergence.
KMeansTrainer	withSeed(long seed) Set up the seed.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.ignite.ml.trainers.DatasetTrainer

fit, fit

Constructor Detail

KMeansTrainer

public KMeansTrainer()

Method Detail

fit

public <K,V> KMeansModel fit(DatasetBuilder<K,V> datasetBuilder,
                             IgniteBiFunction<K,V,double[]> featureExtractor,
                             IgniteBiFunction<K,V,Double> lbExtractor)

Trains model based on the specified data.

Specified by:

fit in interface DatasetTrainer<KMeansModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

datasetBuilder - Dataset builder.

featureExtractor - Feature extractor.

lbExtractor - Label extractor.

Returns:

Model.

getK

public int getK()

Gets the amount of clusters.

Returns:

The parameter value.

withK

public KMeansTrainer withK(int k)

Set up the amount of clusters.

Parameters:

k - The parameter value.

Returns:

Model with new amount of clusters parameter value.

getMaxIterations

public int getMaxIterations()

Gets the max number of iterations before convergence.

Returns:

The parameter value.

withMaxIterations

public KMeansTrainer withMaxIterations(int maxIterations)

Set up the max number of iterations before convergence.

Parameters:

maxIterations - The parameter value.

Returns:

Model with new max number of iterations before convergence parameter value.

getEpsilon

public double getEpsilon()

Gets the epsilon.

Returns:

The parameter value.

withEpsilon

public KMeansTrainer withEpsilon(double epsilon)

Set up the epsilon.

Parameters:

epsilon - The parameter value.

Returns:

Model with new epsilon parameter value.

getDistance

public DistanceMeasure getDistance()

Gets the distance.

Returns:

The parameter value.

withDistance

public KMeansTrainer withDistance(DistanceMeasure distance)

Set up the distance.

Parameters:

distance - The parameter value.

Returns:

Model with new distance parameter value.

getSeed

public long getSeed()

Gets the seed number.

Returns:

The parameter value.

withSeed

public KMeansTrainer withSeed(long seed)

Set up the seed.

Parameters:

seed - The parameter value.

Returns:

Model with new seed parameter value.