Package weka.classifiers.functions

Class SMOreg

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class SMOreg
extends Classifier
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
    SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting the RegOptimizer. The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer.

    For more information see:

    S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999.

    A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.

    BibTeX: 

     @inproceedings{Shevade1999,
    author = {S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy},
    booktitle = {IEEE Transactions on Neural Networks},
    title = {Improvements to the SMO Algorithm for SVM Regression},
    year = {1999},
    PS = {http://guppy.mpe.nus.edu.sg/\~mpessk/svm/ieee_smo_reg.ps.gz}
 }
 
 @techreport{Smola1998,
    author = {A.J. Smola and B. Schoelkopf},
    note = {NeuroCOLT2 Technical Report NC2-TR-1998-030},
    title = {A tutorial on support vector regression},
    year = {1998}
 }

    Valid options are: 

     -C <double>
  The complexity constant C.
  (default 1)
     -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)
     -I <classname and parameters>
  Optimizer class used for solving quadratic optimization problem
  (default weka.classifiers.functions.supportVector.RegSMOImproved)
     -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
     
 Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
 
     -T <double>
  The tolerance parameter for checking the stopping criterion.
  (default 0.001)
     -V
  Use variant 1 of the algorithm when true, otherwise use variant 2.
  (default true)
     -P <double>
  The epsilon for round-off error.
  (default 1.0e-12)
     -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)
     -W <double>
  The random number seed.
  (default 1)
     
 Options specific to kernel ('-K') 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)
    Version:
    $Revision: 8126 $
    Author:
    Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
    See Also:
    Serialized Form

    • Field Detail

      • FILTER_NORMALIZE

        public static final int FILTER_NORMALIZE
        The filter to apply to the training data: Normalzie
        See Also:
        Constant Field Values

      • FILTER_STANDARDIZE

        public static final int FILTER_STANDARDIZE
        The filter to apply to the training data: Standardize
        See Also:
        Constant Field Values

      • FILTER_NONE

        public static final int FILTER_NONE
        The filter to apply to the training data: None
        See Also:
        Constant Field Values

      • TAGS_FILTER

        public static final Tag[] TAGS_FILTER
        The filter to apply to the training data

    • Constructor Detail

      • SMOreg

        public SMOreg()

    • 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

      • listOptions

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

         -C <double>
  The complexity constant C.
  (default 1)
         -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)
         -I <classname and parameters>
  Optimizer class used for solving quadratic optimization problem
  (default weka.classifiers.functions.supportVector.RegSMOImproved)
         -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
         
 Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
 
         -T <double>
  The tolerance parameter for checking the stopping criterion.
  (default 0.001)
         -V
  Use variant 1 of the algorithm when true, otherwise use variant 2.
  (default true)
         -P <double>
  The epsilon for round-off error.
  (default 1.0e-12)
         -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)
         -W <double>
  The random number seed.
  (default 1)
         
 Options specific to kernel ('-K') 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 OptionHandler
        Overrides:
        setOptions in class Classifier
        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 Classifier
        Returns:
        an array of strings suitable for passing to setOptions

      • buildClassifier

        public void buildClassifier​(Instances instances)
                     throws java.lang.Exception
        Method for building the classifier.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        instances - the set of training instances
        Throws:
        java.lang.Exception - if the classifier can't be built successfully

      • classifyInstance

        public double classifyInstance​(Instance instance)
                        throws java.lang.Exception
        Classifies the given instance using the linear regression function.
        Overrides:
        classifyInstance in class Classifier
        Parameters:
        instance - the test instance
        Returns:
        the classification
        Throws:
        java.lang.Exception - if classification can't be done successfully

      • regOptimizerTipText

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

      • setRegOptimizer

        public void setRegOptimizer​(RegOptimizer regOptimizer)
        sets the learning algorithm
        Parameters:
        regOptimizer - the learning algorithm

      • getRegOptimizer

        public RegOptimizer getRegOptimizer()
        returns the learning algorithm
        Returns:
        the learning algorithm

      • kernelTipText

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

      • setKernel

        public void setKernel​(Kernel value)
        sets the kernel to use
        Parameters:
        value - the kernel to use

      • getKernel

        public Kernel getKernel()
        Returns the kernel to use
        Returns:
        the current kernel

      • cTipText

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

      • getC

        public double getC()
        Get the value of C.
        Returns:
        Value of C.

      • setC

        public void setC​(double v)
        Set the value of C.
        Parameters:
        v - Value to assign to C.

      • filterTypeTipText

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

      • getFilterType

        public SelectedTag getFilterType()
        Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Returns:
        the filtering mode

      • setFilterType

        public void setFilterType​(SelectedTag newType)
        Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Parameters:
        newType - the new filtering mode

      • toString

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

      • enumerateMeasures

        public java.util.Enumeration enumerateMeasures()
        Returns an enumeration of the measure names. Additional measures must follow the naming convention of starting with "measure", eg. double measureBlah()
        Specified by:
        enumerateMeasures in interface AdditionalMeasureProducer
        Returns:
        an enumeration of the measure names

      • getMeasure

        public double getMeasure​(java.lang.String measureName)
        Returns the value of the named measure
        Specified by:
        getMeasure in interface AdditionalMeasureProducer
        Parameters:
        measureName - 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

      • main

        public static void main​(java.lang.String[] args)
        Main method for running this classifier.
        Parameters:
        args - the commandline options