Package weka.classifiers.functions.supportVector

Class RegOptimizer

java.lang.Object
weka.classifiers.functions.supportVector.RegOptimizer
All Implemented Interfaces:
Serializable, OptionHandler, RevisionHandler
Direct Known Subclasses:
RegSMO
public class RegOptimizer extends Object implements OptionHandler, Serializable, RevisionHandler
Base class implementation for learning algorithm of SMOreg Valid options are: 

 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)
 
 -W <double>
  The random number seed.
  (default 1)
Version:
$Revision: 15561 $
Author:
Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
See Also:

  • Field Details

    • m_alpha

      public double[] m_alpha
      alpha and alpha* arrays containing weights for solving dual problem

    • m_alphaStar

      public double[] m_alphaStar

  • Constructor Details

    • RegOptimizer

      public RegOptimizer()
      the default constructor

  • Method Details

    • listOptions

      public Enumeration<Option> listOptions()
      Gets an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      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: 

       -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)
 
       -W <double>
  The random number seed.
  (default 1)
      Specified by:
      setOptions in interface OptionHandler
      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
      Returns:
      an array of strings suitable for passing to setOptions

    • modelBuilt

      public boolean modelBuilt()
      flag to indicate whether the model was built yet
      Returns:
      true if the model was built

    • setSMOReg

      public void setSMOReg(SMOreg value)
      sets the parent SVM
      Parameters:
      value - the parent SVM

    • getKernelEvaluations

      public long getKernelEvaluations()
      returns the number of kernel evaluations
      Returns:
      the number of kernel evaluations

    • getCacheHits

      public int getCacheHits()
      return the number of kernel cache hits
      Returns:
      the number of hits

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      learn SVM parameters from data. Subclasses should implement something more interesting.
      Parameters:
      data - the data to work with
      Throws:
      Exception - always an Exceoption since subclasses must override it

    • SVMOutput

      public double SVMOutput(Instance inst) throws Exception
      Parameters:
      inst -
      Returns:
      Throws:
      Exception

    • seedTipText

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

    • getSeed

      public int getSeed()
      Gets the current seed value for the random number generator
      Returns:
      the seed value

    • setSeed

      public void setSeed(int value)
      Sets the seed value for the random number generator
      Parameters:
      value - the seed value

    • epsilonParameterTipText

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

    • getEpsilonParameter

      public double getEpsilonParameter()
      Get the value of epsilon parameter of the epsilon insensitive loss function.
      Returns:
      Value of epsilon parameter.

    • setEpsilonParameter

      public void setEpsilonParameter(double v)
      Set the value of epsilon parameter of the epsilon insensitive loss function.
      Parameters:
      v - Value to assign to epsilon parameter.

    • toString

      public String toString()
      Prints out the classifier.
      Overrides:
      toString in class Object
      Returns:
      a description of the classifier as a string

    • getRevision

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