Package weka.classifiers.lazy

Class LWLSynchro

  • All Implemented Interfaces:
    Serializable, Cloneable, weka.classifiers.Classifier, ThreadSafeClassifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
    Direct Known Subclasses:
    AbstainingLWL, LWLIntervalEstimator
    public class LWLSynchro
extends weka.classifiers.lazy.LWL
implements ThreadSafeClassifier
    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: 

     -no-update
  Suppresses the update of the nearest neighbor search (nns)
  algorithm with the data that is to be classified.
 (default: nns gets updated).
 
     -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.functions.GaussianProcesses)
     Options specific to classifier weka.classifiers.functions.GaussianProcesses:
 
     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
     -L <double>
  Level of Gaussian Noise wrt transformed target. (default 1)
     -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
     -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
     Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 
     -D
  Enables debugging output (if available) to be printed.
  (default: off)
     -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
     -C <num>
  The size of the cache (a prime number), 0 for full cache and
  -1 to turn it off.
  (default: 250007)
     -E <num>
  The Exponent to use.
  (default: 1.0)
     -L
  Use lower-order terms.
  (default: no)
    Note: the build(Instance) needs manual syncing with the original WEKA classifier (distributionForInstance(Instance) method).
    Version:
    $Revision$
    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:
    LWL.distributionForInstance(Instance), Serialized Form

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected boolean m_NoUpdate
      whether to suppress the update of the nearest-neighbor search algorithm when making predictions.

      • Fields inherited from class weka.classifiers.lazy.LWL

        CONSTANT, EPANECHNIKOV, GAUSS, INVERSE, LINEAR, m_kNN, m_NNSearch, m_Train, m_UseAllK, m_WeightKernel, m_ZeroR, TRICUBE

      • Fields inherited from class weka.classifiers.SingleClassifierEnhancer

        m_Classifier

      • Fields inherited from class weka.classifiers.AbstractClassifier

        BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

    • Constructor Summary

      Constructors 
      Constructor Description
      LWLSynchro()
      Initializes the classifier.

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      protected void build​(weka.core.Instance instance)
      Builds the classifier.
      protected String defaultClassifierString()
      Default classifier classname.
      double[] distributionForInstance​(weka.core.Instance instance)
      Calculates the class membership probabilities for the given test instance.
      boolean getNoUpdate()
      Returns whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
      String[] getOptions()
      Gets the current settings of the classifier.
      String getRevision()
      Returns the revision string.
      Enumeration listOptions()
      Returns an enumeration describing the available options.
      static void main​(String[] argv)
      Main method for testing this class.
      String noUpdateTipText()
      Returns the tip text for this property.
      void setNoUpdate​(boolean value)
      Sets whether to suppress updating the nearest-neighbor search algorithm when making predictions.
      void setOptions​(String[] options)
      Parses a given list of options.
      String toString()
      Returns a description of this classifier.

      • Methods inherited from class weka.classifiers.lazy.LWL

        buildClassifier, enumerateMeasures, getCapabilities, getKNN, getMeasure, getNearestNeighbourSearchAlgorithm, getTechnicalInformation, getWeightingKernel, globalInfo, KNNTipText, nearestNeighbourSearchAlgorithmTipText, setKNN, setNearestNeighbourSearchAlgorithm, setWeightingKernel, updateClassifier, weightingKernelTipText

      • Methods inherited from class weka.classifiers.SingleClassifierEnhancer

        classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, 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 interface weka.classifiers.Classifier

        buildClassifier, classifyInstance, getCapabilities

    • Field Detail

      • m_NoUpdate

        protected boolean m_NoUpdate
        whether to suppress the update of the nearest-neighbor search algorithm when making predictions.

    • Constructor Detail

      • LWLSynchro

        public LWLSynchro()
        Initializes the classifier.

    • Method Detail

      • defaultClassifierString

        protected String defaultClassifierString()
        Default classifier classname.
        Overrides:
        defaultClassifierString in class weka.classifiers.lazy.LWL
        Returns:
        the classname

      • listOptions

        public Enumeration listOptions()
        Returns an enumeration describing the available options.
        Specified by:
        listOptions in interface weka.core.OptionHandler
        Overrides:
        listOptions in class weka.classifiers.lazy.LWL
        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: 

         -no-update
  Suppresses the update of the nearest neighbor search (nns)
  algorithm with the data that is to be classified.
 (default: nns gets updated).
 
         -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.functions.GaussianProcesses)
         Options specific to classifier weka.classifiers.functions.GaussianProcesses:
 
         -D
  If set, classifier is run in debug mode and
  may output additional info to the console
         -L <double>
  Level of Gaussian Noise wrt transformed target. (default 1)
         -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
         -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
         Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 
         -D
  Enables debugging output (if available) to be printed.
  (default: off)
         -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
         -C <num>
  The size of the cache (a prime number), 0 for full cache and
  -1 to turn it off.
  (default: 250007)
         -E <num>
  The Exponent to use.
  (default: 1.0)
         -L
  Use lower-order terms.
  (default: no)
        Specified by:
        setOptions in interface weka.core.OptionHandler
        Overrides:
        setOptions in class weka.classifiers.lazy.LWL
        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 weka.core.OptionHandler
        Overrides:
        getOptions in class weka.classifiers.lazy.LWL
        Returns:
        an array of strings suitable for passing to setOptions

      • noUpdateTipText

        public String noUpdateTipText()
        Returns the tip text for this property.
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setNoUpdate

        public void setNoUpdate​(boolean value)
        Sets whether to suppress updating the nearest-neighbor search algorithm when making predictions.
        Parameters:
        value - if true then no update happens.

      • getNoUpdate

        public boolean getNoUpdate()
        Returns whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
        Returns:
        true if the update is suppressed

      • build

        protected void build​(weka.core.Instance instance)
              throws Exception
        Builds the classifier.

        Note: needs manual syncing with the distributionForInstance method of the original WEKA classifier.
        Parameters:
        instance - the instance to make prediction for
        Throws:
        Exception - if build fails
        See Also:
        LWL.distributionForInstance(Instance)

      • distributionForInstance

        public double[] distributionForInstance​(weka.core.Instance instance)
                                 throws Exception
        Calculates the class membership probabilities for the given test instance.
        Specified by:
        distributionForInstance in interface weka.classifiers.Classifier
        Overrides:
        distributionForInstance in class weka.classifiers.lazy.LWL
        Parameters:
        instance - the instance to be classified
        Returns:
        predicted 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 weka.classifiers.lazy.LWL
        Returns:
        a description of this classifier as a string.

      • getRevision

        public String getRevision()
        Returns the revision string.
        Specified by:
        getRevision in interface weka.core.RevisionHandler
        Overrides:
        getRevision in class weka.classifiers.lazy.LWL
        Returns:
        the revision

      • main

        public static void main​(String[] argv)
        Main method for testing this class.
        Parameters:
        argv - the options