weka.gui.boundaryvisualizer

Class KDDataGenerator

java.lang.Object

weka.gui.boundaryvisualizer.KDDataGenerator

All Implemented Interfaces:

Serializable, DataGenerator

public class KDDataGenerator
extends Object
implements DataGenerator, Serializable

KDDataGenerator. Class that uses kernels to generate new random instances based on a supplied set of instances.

Since:

1.0

Version:

$Revision: 8034 $

Author:

Mark Hall

See Also:

DataGenerator, Serializable, Serialized Form

Constructor Summary

Constructors

Constructor and Description
KDDataGenerator()

Method Summary

Methods

Modifier and Type	Method and Description
void	buildGenerator(Instances inputInstances) Initialize the generator using the supplied instances
double[][]	generateInstances(int[] indices) Generates a new instance using one kernel estimator.
int	getKernelBandwidth() Get the kernel bandwidth
int	getNumGeneratingModels() Return the number of kernels (there is one per training instance)
double[]	getWeights() Get weights
void	setKernelBandwidth(int kb) Set the kernel bandwidth (number of nearest neighbours to cover)
void	setSeed(int seed) Initializes a new random number generator using the supplied seed.
void	setWeightingDimensions(boolean[] dims) Set which dimensions to use when computing a weight for the next instance to generate
void	setWeightingValues(double[] vals) Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated

Methods inherited from class java.lang.Object

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

Constructor Detail

KDDataGenerator

public KDDataGenerator()

Method Detail

buildGenerator

public void buildGenerator(Instances inputInstances)
                    throws Exception

Initialize the generator using the supplied instances

Specified by:

buildGenerator in interface DataGenerator

Parameters:

inputInstances - the instances to use as the basis of the kernels

Throws:

Exception - if an error occurs

getWeights

public double[] getWeights()

Description copied from interface: DataGenerator

Get weights

Specified by:

getWeights in interface DataGenerator

generateInstances

public double[][] generateInstances(int[] indices)
                             throws Exception

Generates a new instance using one kernel estimator. Each successive call to this method incremets the index of the kernel to use.

Specified by:

generateInstances in interface DataGenerator

Returns:

the new random instance

Throws:

Exception - if an error occurs

setWeightingDimensions

public void setWeightingDimensions(boolean[] dims)

Set which dimensions to use when computing a weight for the next instance to generate

Specified by:

setWeightingDimensions in interface DataGenerator

Parameters:

dims - an array of booleans indicating which dimensions to use

setWeightingValues

public void setWeightingValues(double[] vals)

Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated

Specified by:

setWeightingValues in interface DataGenerator

Parameters:

vals - an array of doubles containing the values of the weighting dimensions (corresponding to the entries that are set to true throw setWeightingDimensions)

getNumGeneratingModels

public int getNumGeneratingModels()

Return the number of kernels (there is one per training instance)

Specified by:

getNumGeneratingModels in interface DataGenerator

Returns:

the number of kernels

setKernelBandwidth

public void setKernelBandwidth(int kb)

Set the kernel bandwidth (number of nearest neighbours to cover)

Parameters:

kb - an int value

getKernelBandwidth

public int getKernelBandwidth()

Get the kernel bandwidth

Returns:

an int value

setSeed

public void setSeed(int seed)

Initializes a new random number generator using the supplied seed.

Specified by:

setSeed in interface DataGenerator

Parameters:

seed - an int value