weka.classifiers.lazy

Class LWL

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.lazy.LWL

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class LWL
extends SingleClassifierEnhancer
implements UpdateableClassifier, WeightedInstancesHandler, TechnicalInformationHandler

Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).

For more info, see

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.

C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..

BibTeX:

 @inproceedings{Frank2003,
    author = {Eibe Frank and Mark Hall and Bernhard Pfahringer},
    booktitle = {19th Conference in Uncertainty in Artificial Intelligence},
    pages = {249-256},
    publisher = {Morgan Kaufmann},
    title = {Locally Weighted Naive Bayes},
    year = {2003}
 }
 
 @article{Atkeson1996,
    author = {C. Atkeson and A. Moore and S. Schaal},
    journal = {AI Review},
    title = {Locally weighted learning},
    year = {1996}
 }
 

Valid options are:

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 

 -K <number of neighbours>
  Set the number of neighbours used to set the kernel bandwidth.
  (default all)

 -U <number of weighting method>
  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
  2=Tricube, 3=Inverse, 4=Gaussian.
  (default 0 = Linear)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 10141 $

Author:

Len Trigg ([email protected]), Eibe Frank ([email protected]), Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	CONSTANT
static int	EPANECHNIKOV
static int	GAUSS
static int	INVERSE
static int	LINEAR The available kernel weighting methods.
static int	TRICUBE

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
LWL() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
Enumeration<String>	enumerateMeasures() Returns an enumeration of the additional measure names produced by the neighbour search algorithm.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getKNN() Gets the number of neighbours used for kernel bandwidth setting.
double	getMeasure(String additionalMeasureName) Returns the value of the named measure from the neighbour search algorithm.
NearestNeighbourSearch	getNearestNeighbourSearchAlgorithm() Returns the current nearestNeighbourSearch algorithm in use.
String[]	getOptions() Gets the current settings of the classifier.
String	getRevision() Returns the revision string.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
int	getWeightingKernel() Gets the kernel weighting method to use.
String	globalInfo() Returns a string describing classifier.
String	KNNTipText() Returns the tip text for this property.
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	nearestNeighbourSearchAlgorithmTipText() Returns the tip text for this property.
void	setKNN(int knn) Sets the number of neighbours used for kernel bandwidth setting.
void	setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm) Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
void	setOptions(String[] options) Parses a given list of options.
void	setWeightingKernel(int kernel) Sets the kernel weighting method to use.
String	toString() Returns a description of this classifier.
void	updateClassifier(Instance instance) Adds the supplied instance to the training set.
String	weightingKernelTipText() Returns the tip text for this property.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Field Detail

LINEAR

public static final int LINEAR

The available kernel weighting methods.

See Also:

Constant Field Values

EPANECHNIKOV

public static final int EPANECHNIKOV

See Also:

Constant Field Values

TRICUBE

public static final int TRICUBE

See Also:

Constant Field Values

INVERSE

public static final int INVERSE

See Also:

Constant Field Values

GAUSS

public static final int GAUSS

See Also:

Constant Field Values

CONSTANT

public static final int CONSTANT

See Also:

Constant Field Values

Constructor Detail

LWL

public LWL()

Constructor.

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier.

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

enumerateMeasures

public Enumeration<String> enumerateMeasures()

Returns an enumeration of the additional measure names produced by the neighbour search algorithm.

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(String additionalMeasureName)

Returns the value of the named measure from the neighbour search algorithm.

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

IllegalArgumentException - if the named measure is not supported

listOptions

public Enumeration<Option> 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(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 

 -K <number of neighbours>
  Set the number of neighbours used to set the kernel bandwidth.
  (default all)

 -U <number of weighting method>
  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
  2=Tricube, 3=Inverse, 4=Gaussian.
  (default 0 = Linear)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class SingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public 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 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 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, EPANECHNIKOV, TRICUBE, INVERSE, GAUSS or CONSTANT, other values are ignored.

Parameters:

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

getWeightingKernel

public int getWeightingKernel()

Gets the kernel weighting method to use.

Returns:

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

nearestNeighbourSearchAlgorithmTipText

public String nearestNeighbourSearchAlgorithmTipText()

Returns the tip text for this property.

Returns:

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

getNearestNeighbourSearchAlgorithm

public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm()

Returns the current nearestNeighbourSearch algorithm in use.

Returns:

the NearestNeighbourSearch algorithm currently in use.

setNearestNeighbourSearchAlgorithm

public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)

Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).

Parameters:

nearestNeighbourSearchAlgorithm - - The NearestNeighbourSearch class.

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

updateClassifier

public void updateClassifier(Instance instance)
                      throws Exception

Adds the supplied instance to the training set.

Specified by:

updateClassifier in interface UpdateableClassifier

Parameters:

instance - the instance to add

Throws:

Exception - if instance could not be incorporated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

Exception - if distribution can't be computed successfully

toString

public String toString()

Returns a description of this classifier.

Overrides:

toString in class Object

Returns:

a description of this classifier as a string.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options