weka.classifiers
Class LWR

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.LWR

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, UpdateableClassifier, WeightedInstancesHandler

public class LWR

extends Classifier

implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler

Locally-weighted regression. Uses an instance-based algorithm to assign instance weights which are then used by a linear regression model. For more information, see

Atkeson, C., A. Moore, and S. Schaal (1996) Locally weighted learning download postscript.

Valid options are:

-D
Produce debugging output.

-K num
Set the number of neighbours used for setting kernel bandwidth. (default all)

-W num
Set the weighting kernel shape to use. 1 = Inverse, 2 = Gaussian. (default 0 = Linear)

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected static int	GAUSS
protected static int	INVERSE
protected static int	LINEAR
protected boolean	m_Debug True if debugging output should be printed
protected int	m_kNN The number of neighbours used to select the kernel bandwidth
protected double[]	m_Max The maximum values for numeric attributes.
protected double[]	m_Min The minimum values for numeric attributes.
protected Instances	m_Train The training instances used for classification.
protected boolean	m_UseAllK True if m_kNN should be set to all instances
protected int	m_WeightKernel The weighting kernel method currently selected

Constructor Summary

LWR()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Predicts the class value for the given test instance.
protected double	getAttributeMax(int index) Gets an attributes maximum observed value
protected double	getAttributeMin(int index) Gets an attributes minimum observed value
boolean	getDebug() SGts whether debugging output should be produced
int	getKNN() Gets the number of neighbours used for kernel bandwidth setting.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
int	getWeightingKernel() Gets the kernel weighting method to use.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setDebug(boolean debug) Sets whether debugging output should be produced
void	setKNN(int knn) Sets the number of neighbours used for kernel bandwidth setting.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setWeightingKernel(int kernel) Sets the kernel weighting method to use.
java.lang.String	toString() Returns a description of this classifier.
void	updateClassifier(Instance instance) Adds the supplied instance to the training set

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_Train

protected Instances m_Train

The training instances used for classification.

m_Min

protected double[] m_Min

The minimum values for numeric attributes.

m_Max

protected double[] m_Max

The maximum values for numeric attributes.

m_Debug

protected boolean m_Debug

True if debugging output should be printed

m_kNN

protected int m_kNN

The number of neighbours used to select the kernel bandwidth

m_WeightKernel

protected int m_WeightKernel

The weighting kernel method currently selected

m_UseAllK

protected boolean m_UseAllK

True if m_kNN should be set to all instances

LINEAR

protected static final int LINEAR

INVERSE

protected static final int INVERSE

GAUSS

protected static final int GAUSS

Constructor Detail

LWR

public LWR()

Method Detail

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-D
Produce debugging output.

-K num
Set the number of neighbours used for setting kernel bandwidth. (default all)

-W num
Set the weighting kernel shape to use. 1 = Inverse, 2 = Gaussian. (default 0 = Linear)

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setDebug

public void setDebug(boolean debug)

Sets whether debugging output should be produced

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

SGts whether debugging output should be produced

Returns:

true if debugging output should be printed

setKNN

public void setKNN(int knn)

Sets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.

Parameters:

knn - the number of neighbours included inside the kernel bandwidth, or 0 to specify using all neighbors.

getKNN

public int getKNN()

Gets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.

Returns:

the number of neighbours included inside the kernel bandwidth, or 0 for all neighbours

setWeightingKernel

public void setWeightingKernel(int kernel)

Sets the kernel weighting method to use. Must be one of LINEAR, INVERSE, or GAUSS, other values are ignored.

Parameters:

kernel - the new kernel method to use. Must be one of LINEAR, INVERSE, or GAUSS

getWeightingKernel

public int getWeightingKernel()

Gets the kernel weighting method to use.

Returns:

the new kernel method to use. Will be one of LINEAR, INVERSE, or GAUSS

getAttributeMin

protected double getAttributeMin(int index)

Gets an attributes minimum observed value

Parameters:

index - the index of the attribute

Returns:

the minimum observed value

getAttributeMax

protected double getAttributeMax(int index)

Gets an attributes maximum observed value

Parameters:

index - the index of the attribute

Returns:

the maximum observed value

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Overrides:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

updateClassifier

public void updateClassifier(Instance instance)
                      throws java.lang.Exception

Adds the supplied instance to the training set

Specified by:

updateClassifier in interface UpdateableClassifier

Parameters:

instance - the instance to add

Throws:

java.lang.Exception - if instance could not be incorporated successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Predicts the class value for the given test instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted class value

Throws:

java.lang.Exception - if an error occurred during the prediction

toString

public java.lang.String toString()

Returns a description of this classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of this classifier as a string.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options