Package 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 (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:

  • Field Details

  • Constructor Details

    • LWL

      public LWL()
      Constructor.

  • Method Details

    • 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 CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class SingleClassifierEnhancer
      Returns:
      the capabilities of this classifier
      See Also:

    • 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