org.apache.ignite.ml.knn.classification

Class KNNClassificationModel<K,V>

java.lang.Object

org.apache.ignite.ml.knn.classification.KNNClassificationModel<K,V>

All Implemented Interfaces:

Serializable, Function<Vector,Double>, Exportable<KNNModelFormat>, IgniteFunction<Vector,Double>, Model<Vector,Double>

Direct Known Subclasses:

KNNRegressionModel

public class KNNClassificationModel<K,V>
extends Object
implements Model<Vector,Double>, Exportable<KNNModelFormat>

kNN algorithm model to solve multi-class classification task.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected DistanceMeasure	distanceMeasure Distance measure.
protected int	k Amount of nearest neighbors.
protected KNNStrategy	stgy kNN strategy.

Constructor Summary

Constructors

Constructor and Description
KNNClassificationModel(Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> dataset) Builds the model via prepared dataset.

Method Summary

Modifier and Type	Method and Description
Double	apply(Vector v)
boolean	equals(Object obj)
protected List<LabeledVector>	findKNearestNeighbors(Vector v) The main idea is calculation all distance pairs between given vector and all vectors in training set, sorting them and finding k vectors with min distance with the given vector.
int	hashCode()
<P> void	saveModel(Exporter<KNNModelFormat,P> exporter, P path) Save model by the given path.
KNNClassificationModel<K,V>	withDistanceMeasure(DistanceMeasure distanceMeasure) Set up parameter of the kNN model.
KNNClassificationModel<K,V>	withK(int k) Set up parameter of the kNN model.
KNNClassificationModel<K,V>	withStrategy(KNNStrategy stgy) Set up parameter of the kNN model.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.ignite.ml.Model

combine

Methods inherited from interface java.util.function.Function

andThen, compose, identity

Field Detail

k

protected int k

Amount of nearest neighbors.

distanceMeasure

protected DistanceMeasure distanceMeasure

Distance measure.

stgy

protected KNNStrategy stgy

kNN strategy.

Constructor Detail

KNNClassificationModel

public KNNClassificationModel(Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> dataset)

Builds the model via prepared dataset.

Parameters:

dataset - Specially prepared object to run algorithm over it.

Method Detail

apply

public Double apply(Vector v)

Specified by:

apply in interface Function<Vector,Double>

saveModel

public <P> void saveModel(Exporter<KNNModelFormat,P> exporter,
                          P path)

Description copied from interface: Exportable

Save model by the given path.

Specified by:

saveModel in interface Exportable<KNNModelFormat>

Parameters:

exporter - Exporter.

path - Path to saved model.

withK

public KNNClassificationModel<K,V> withK(int k)

Set up parameter of the kNN model.

Parameters:

k - Amount of nearest neighbors.

Returns:

Model.

withStrategy

public KNNClassificationModel<K,V> withStrategy(KNNStrategy stgy)

Set up parameter of the kNN model.

Parameters:

stgy - Strategy of calculations.

Returns:

Model.

withDistanceMeasure

public KNNClassificationModel<K,V> withDistanceMeasure(DistanceMeasure distanceMeasure)

Set up parameter of the kNN model.

Parameters:

distanceMeasure - Distance measure.

Returns:

Model.

findKNearestNeighbors

protected List<LabeledVector> findKNearestNeighbors(Vector v)

The main idea is calculation all distance pairs between given vector and all vectors in training set, sorting them and finding k vectors with min distance with the given vector.

Parameters:

v - The given vector.

Returns:

K-nearest neighbors.

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object