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, CommandlineRunnable, 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: 15519 $
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
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

SMOreg()

Constructors
Constructor and Description
`SMOreg()`

Method Summary

All Methods Static Methods Instance Methods Concrete 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 java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- 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

Class SMOreg

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

Constructor Detail

SMOreg

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

getCapabilities

buildClassifier

classifyInstance

regOptimizerTipText

setRegOptimizer

getRegOptimizer

kernelTipText

setKernel

getKernel

cTipText

getC

setC

filterTypeTipText

getFilterType

setFilterType

toString

enumerateMeasures

getMeasure

getRevision

main