Package weka.classifiers.functions

Class LeastMedSq

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.functions.LeastMedSq
All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
public class LeastMedSq extends Classifier implements OptionHandler, TechnicalInformationHandler
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model.

The basis of the algorithm is

Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection. .

BibTeX: 

 @book{Rousseeuw1987,
    author = {Peter J. Rousseeuw and Annick M. Leroy},
    title = {Robust regression and outlier detection},
    year = {1987}
 }

Valid options are: 

 -S <sample size>
  Set sample size
  (default: 4)
 
 -G <seed>
  Set the seed used to generate samples
  (default: 0)
 
 -D
  Produce debugging output
  (default no debugging output)
Version:
$Revision: 5523 $
Author:
Tony Voyle (tv6@waikato.ac.nz)
See Also:

  • Constructor Details

    • LeastMedSq

      public LeastMedSq()

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing this classifier
      Returns:
      a description of the classifier 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

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Overrides:
      getCapabilities in class Classifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Build lms regression
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      data - training data
      Throws:
      Exception - if an error occurs

    • classifyInstance

      public double classifyInstance(Instance instance) throws Exception
      Classify a given instance using the best generated LinearRegression Classifier.
      Overrides:
      classifyInstance in class Classifier
      Parameters:
      instance - instance to be classified
      Returns:
      class value
      Throws:
      Exception - if an error occurs

    • toString

      public String toString()
      Returns a string representing the best LinearRegression classifier found.
      Overrides:
      toString in class Object
      Returns:
      String representing the regression

    • sampleSizeTipText

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

    • setSampleSize

      public void setSampleSize(int samplesize)
      sets number of samples
      Parameters:
      samplesize - value

    • getSampleSize

      public int getSampleSize()
      gets number of samples
      Returns:
      value

    • randomSeedTipText

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

    • setRandomSeed

      public void setRandomSeed(long randomseed)
      Set the seed for the random number generator
      Parameters:
      randomseed - the seed

    • getRandomSeed

      public long getRandomSeed()
      get the seed for the random number generator
      Returns:
      the seed value

    • setDebug

      public void setDebug(boolean debug)
      sets whether or not debugging output shouild be printed
      Overrides:
      setDebug in class Classifier
      Parameters:
      debug - true if debugging output selected

    • getDebug

      public boolean getDebug()
      Returns whether or not debugging output shouild be printed
      Overrides:
      getDebug in class Classifier
      Returns:
      true if debuging output selected

    • listOptions

      public Enumeration listOptions()
      Returns an enumeration of all the available options..
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class Classifier
      Returns:
      an enumeration of all available options.

    • setOptions

      public void setOptions(String[] options) throws Exception
      Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible). Valid options are: 

       -S <sample size>
  Set sample size
  (default: 4)
 
       -G <seed>
  Set the seed used to generate samples
  (default: 0)
 
       -D
  Produce debugging output
  (default no debugging output)
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class Classifier
      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 option settings for the OptionHandler.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class Classifier
      Returns:
      the list of current option settings as an array of strings

    • combinations

      public static int combinations(int n, int r) throws Exception
      Produces the combination nCr
      Parameters:
      n -
      r -
      Returns:
      the combination
      Throws:
      Exception - if r is greater than n

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision

    • main

      public static void main(String[] argv)
      generate a Linear regression predictor for testing
      Parameters:
      argv - options