Catalano.MachineLearning.Classification

Class KNearestNeighbors

java.lang.Object

Catalano.MachineLearning.Classification.KNearestNeighbors

All Implemented Interfaces:

IClassifier, java.io.Serializable, java.lang.Cloneable

public class KNearestNeighbors
extends java.lang.Object
implements IClassifier, java.io.Serializable

K Nearest Neighbour classifier.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KNearestNeighbors() Initializes a new instance of the KNearestNeighbors class.
KNearestNeighbors(int k) Initializes a new instance of the KNearestNeighbors class.
KNearestNeighbors(int k, IDivergence divergence) Initializes a new instance of the KNearestNeighbors class.
KNearestNeighbors(int k, IMercerKernel kernel) Initializes a new instance of the KNearestNeighbors class.

Method Summary

Modifier and Type	Method and Description
IClassifier	clone() Clone of the object.
IDivergence	getDistance() Get the divergence function.
int	getK() Get number of neighbours.
IMercerKernel	getKernel() Get the kernel metric.
void	Learn(DatasetClassification dataset) Learn from a dataset.
void	Learn(double[][] input, int[] output) Learn.
int	Predict(double[] feature) Compute.
void	setDistance(IDivergence divergence) Set the divergence function.
void	setK(int k) Set number of neighbours.
void	setMercerKernel(IMercerKernel kernel) Set the kernel metric.

Methods inherited from class java.lang.Object

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

Constructor Detail

KNearestNeighbors

public KNearestNeighbors()

Initializes a new instance of the KNearestNeighbors class.

KNearestNeighbors

public KNearestNeighbors(int k)

Initializes a new instance of the KNearestNeighbors class.

Parameters:

k - Number of neighbors.

KNearestNeighbors

public KNearestNeighbors(int k,
                         IDivergence divergence)

Initializes a new instance of the KNearestNeighbors class.

Parameters:

k - Number of neighbors.

divergence - Distance.

KNearestNeighbors

public KNearestNeighbors(int k,
                         IMercerKernel kernel)

Initializes a new instance of the KNearestNeighbors class.

Parameters:

k - Number of neighbors.

kernel - Kernel.

Method Detail

getK

public int getK()

Get number of neighbours.

Returns:

Number of neighbours.

setK

public void setK(int k)

Set number of neighbours.

Parameters:

k - Number of neighbours.

getKernel

public IMercerKernel getKernel()

Get the kernel metric.

Returns:

Kernel.

setMercerKernel

public void setMercerKernel(IMercerKernel kernel)

Set the kernel metric.

Parameters:

kernel - Kernel.

getDistance

public IDivergence getDistance()

Get the divergence function.

Returns:

Divergence.

setDistance

public void setDistance(IDivergence divergence)

Set the divergence function.

Parameters:

divergence - Divergence.

Learn

public void Learn(DatasetClassification dataset)

Description copied from interface: IClassifier

Learn from a dataset.

Specified by:

Learn in interface IClassifier

Parameters:

dataset - Dataset.

Learn

public void Learn(double[][] input,
                  int[] output)

Description copied from interface: IClassifier

Learn.

Specified by:

Learn in interface IClassifier

Parameters:

input - Matrix of features.

output - Labels.

Predict

public int Predict(double[] feature)

Compute.

Specified by:

Predict in interface IClassifier

Parameters:

feature - Feature to compute.

Returns:

Object.

clone

public IClassifier clone()

Description copied from interface: IClassifier

Clone of the object.

Specified by:

clone in interface IClassifier

Overrides:

clone in class java.lang.Object

Returns:

A new copy of the object.