org.apache.ignite.ml.clustering.kmeans

Class KMeansTrainer

java.lang.Object

org.apache.ignite.ml.trainers.DatasetTrainer<M,Double>

org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer<KMeansModel>

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

public class KMeansTrainer
extends SingleLabelDatasetTrainer<KMeansModel>

The trainer for KMeans algorithm.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansTrainer.TotalCostAndCounts Service class used for statistics.

Nested classes/interfaces inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

DatasetTrainer.EmptyDatasetException

Field Summary

Fields inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

envBuilder, environment

Constructor Summary

Constructors

Constructor and Description
KMeansTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> KMeansModel	fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
int	getAmountOfClusters() Gets the amount of clusters.
DistanceMeasure	getDistance() Gets the distance.
double	getEpsilon() Gets the epsilon.
int	getMaxIterations() Gets the max number of iterations before convergence.
boolean	isUpdateable(KMeansModel mdl)
protected <K,V> KMeansModel	updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.
KMeansTrainer	withAmountOfClusters(int k) Set up the amount of clusters.
KMeansTrainer	withDistance(DistanceMeasure distance) Set up the distance.
KMeansTrainer	withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder) Changes learning Environment.
KMeansTrainer	withEpsilon(double epsilon) Set up the epsilon.
KMeansTrainer	withMaxIterations(int maxIterations) Set up the max number of iterations before convergence.

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

fit, fit, fit, fit, fit, fit, getLastTrainedModelOrThrowEmptyDatasetException, identityTrainer, learningEnvironment, update, update, update, update, update, withConvertedLabels

Methods inherited from class java.lang.Object

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

Constructor Detail

KMeansTrainer

public KMeansTrainer()

Method Detail

fitWithInitializedDeployingContext

public <K,V> KMeansModel fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
                                                            Preprocessor<K,V> preprocessor)

Trains model based on the specified data.

Specified by:

fitWithInitializedDeployingContext in class 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.

preprocessor - Extractor of UpstreamEntry into LabeledVector.

Returns:

Model.

withEnvironmentBuilder

public KMeansTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)

Changes learning Environment.

Overrides:

withEnvironmentBuilder in class DatasetTrainer<KMeansModel,Double>

Parameters:

envBuilder - Learning environment builder.

updateModel

protected <K,V> KMeansModel updateModel(KMeansModel mdl,
                                        DatasetBuilder<K,V> datasetBuilder,
                                        Preprocessor<K,V> preprocessor)

Trains new model taken previous one as a first approximation.

Specified by:

updateModel in class DatasetTrainer<KMeansModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

mdl - Learned model.

datasetBuilder - Dataset builder.

preprocessor - Extractor of UpstreamEntry into LabeledVector.

Returns:

Updated model.

isUpdateable

public boolean isUpdateable(KMeansModel mdl)

Specified by:

isUpdateable in class DatasetTrainer<KMeansModel,Double>

Parameters:

mdl - Model.

Returns:

True if current critical for training parameters correspond to parameters from last training.

getAmountOfClusters

public int getAmountOfClusters()

Gets the amount of clusters.

Returns:

The parameter value.

withAmountOfClusters

public KMeansTrainer withAmountOfClusters(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.