weka.classifiers.functions

## Class SMOreg

- java.lang.Object
- - weka.classifiers.AbstractClassifier
  - - weka.classifiers.functions.SMOreg

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

      public class SMOreg
      extends AbstractClassifier
      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: 10141 $
  Author:
    Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int` | `FILTER_NONE` The filter to apply to the training data: None
    `static int` | `FILTER_NORMALIZE` The filter to apply to the training data: Normalzie
    `static int` | `FILTER_STANDARDIZE` The filter to apply to the training data: Standardize
    `static Tag[]` | `TAGS_FILTER` The filter to apply to the training data

    - ### Fields inherited from class weka.classifiers.AbstractClassifier

      `NUM_DECIMAL_PLACES_DEFAULT`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `SMOreg()`

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `void` | `buildClassifier(Instances instances)` Method for building the classifier.
    `double` | `classifyInstance(Instance instance)` Classifies the given instance using the linear regression function.
    `java.lang.String` | `cTipText()` Returns the tip text for this property
    `java.util.Enumeration<java.lang.String>` | `enumerateMeasures()` Returns an enumeration of the measure names.
    `java.lang.String` | `filterTypeTipText()` Returns the tip text for this property
    `double` | `getC()` Get the value of C.
    `Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
    `SelectedTag` | `getFilterType()` Gets how the training data will be transformed.
    `Kernel` | `getKernel()` Returns the kernel to use
    `double` | `getMeasure(java.lang.String measureName)` Returns the value of the named measure
    `java.lang.String[]` | `getOptions()` Gets the current settings of the classifier.
    `RegOptimizer` | `getRegOptimizer()` returns the learning algorithm
    `java.lang.String` | `getRevision()` Returns the revision string.
    `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.
    `java.lang.String` | `globalInfo()` Returns a string describing classifier
    `java.lang.String` | `kernelTipText()` Returns the tip text for this property
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `static void` | `main(java.lang.String[] args)` Main method for running this classifier.
    `java.lang.String` | `regOptimizerTipText()` Returns the tip text for this property
    `void` | `setC(double v)` Set the value of C.
    `void` | `setFilterType(SelectedTag newType)` Sets how the training data will be transformed.
    `void` | `setKernel(Kernel value)` sets the kernel to use
    `void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
    `void` | `setRegOptimizer(RegOptimizer regOptimizer)` sets the learning algorithm
    `java.lang.String` | `toString()` Prints out the classifier.

    - ### Methods inherited from class weka.classifiers.AbstractClassifier

      `batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### 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<Option> listOptions()

      Returns an enumeration describing the available options.
      Specified by:
        `listOptions` in interface `OptionHandler`
      Overrides:
        `listOptions` in class `AbstractClassifier`
      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 `AbstractClassifier`
      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 `AbstractClassifier`
      Returns:
        an array of strings suitable for passing to setOptions

    - #### getCapabilities

          public Capabilities getCapabilities()

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

    - #### buildClassifier

          public void buildClassifier(Instances instances)
                               throws java.lang.Exception

      Method for building the classifier.
      Specified by:
        `buildClassifier` in interface `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.
      Specified by:
        `classifyInstance` in interface `Classifier`
      Overrides:
        `classifyInstance` in class `AbstractClassifier`
      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<java.lang.String> 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

    - #### getRevision

          public java.lang.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(java.lang.String[] args)

      Main method for running this classifier.
      Parameters:
        `args` - the commandline options