Catalano.MachineLearning.Regression

Class KNearestNeighbors

java.lang.Object

Catalano.MachineLearning.Regression.KNearestNeighbors

All Implemented Interfaces:

IRegression, java.io.Serializable, java.lang.Cloneable

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

K Nearest Neighbors for regression.

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
IRegression	clone() Clone of the object.
int	getK() Get number of neighbors.
void	Learn(DatasetRegression dataset) Learn.
void	Learn(double[][] input, double[] output) Learn.
double	Predict(double[] feature) Predict.
void	setK(int k) Set number of neighbors.

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 - Divergence.

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 neighbors.

Returns:

Number of neighbors.

setK

public void setK(int k)

Set number of neighbors.

Parameters:

k - Number of neighbors.

Learn

public void Learn(DatasetRegression dataset)

Description copied from interface: IRegression

Learn.

Specified by:

Learn in interface IRegression

Parameters:

dataset - Dataset regression.

Learn

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

Description copied from interface: IRegression

Learn.

Specified by:

Learn in interface IRegression

Parameters:

input - Input.

output - Output.

Predict

public double Predict(double[] feature)

Description copied from interface: IRegression

Predict.

Specified by:

Predict in interface IRegression

Parameters:

feature - Feature.

Returns:

Value.

clone

public IRegression clone()

Description copied from interface: IRegression

Clone of the object.

Specified by:

clone in interface IRegression

Overrides:

clone in class java.lang.Object

Returns:

A new copy of the object.