weka.classifiers.lazy
Class LWL

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.lazy.LWL
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, 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: 1.21 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]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.
 java.util.Enumeration 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(java.lang.String additionalMeasureName)
          Returns the value of the named measure from the neighbour search algorithm.
 NearestNeighbourSearch getNearestNeighbourSearchAlgorithm()
          Returns the current nearestNeighbourSearch algorithm in use.
 java.lang.String[] getOptions()
          Gets the current settings of the classifier.
 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.
 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.
 java.lang.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(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, makeCopy, 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

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 java.util.Enumeration 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(java.lang.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:
java.lang.IllegalArgumentException - if the named measure is not supported

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:

 -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:
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, 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 java.lang.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 CapabilitiesHandler
Overrides:
getCapabilities in class SingleClassifierEnhancer
Returns:
the capabilities of this classifier
See Also:
Capabilities

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