weka.classifiers.lazy
Class LWL

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.SingleClassifierEnhancer
              |
              +--weka.classifiers.lazy.LWL

All Implemented Interfaces:

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

public class LWL

extends SingleClassifierEnhancer

implements UpdateableClassifier, WeightedInstancesHandler

Locally-weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler. A good choice for classification is NaiveBayes. LinearRegression is suitable for regression problems. For more information, see

Eibe Frank, Mark Hall, and Bernhard Pfahringer (2003). Locally Weighted Naive Bayes. Working Paper 04/03, Department of Computer Science, University of Waikato. 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)

-B classname
Specify the full class name of a base classifier (which needs to be a WeightedInstancesHandler).

Version:

$Revision: 1.9 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

LWL()
Constructor.

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
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.lang.String	globalInfo() Returns a string describing classifier
java.lang.String	KNNTipText() Returns the tip text for this property
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	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
java.lang.String	weightingKernelTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

LWL

public LWL()

Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class SingleClassifierEnhancer

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)

-B classname
Specify the full class name of a base classifier (which needs to be a WeightedInstancesHandler).

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class SingleClassifierEnhancer

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

Overrides:

getOptions in class SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

KNNTipText

public java.lang.String KNNTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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

weightingKernelTipText

public java.lang.String weightingKernelTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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

buildClassifier

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

Generates the classifier.

Specified by:

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

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed successfully

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