SVMreg

java.lang.Object
- keel.Algorithms.SVM.SMO.SVMreg

All Implemented Interfaces:: AdditionalMeasureProducer, TechnicalInformationHandler, WeightedInstancesHandler

public class SVMreg
extends java.lang.Object
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler

SVMreg 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).
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Version:: $Revision: 1.1 $
Author:: Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)

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
`protected double`	`m_C` capacity parameter
`protected int`	`m_filterType` Whether to normalize/standardize/neither
`protected Kernel`	`m_kernel` the configured kernel
`protected boolean`	`m_onlyNumeric` Only numeric attributes in the dataset?
`protected RegOptimizer`	`m_optimizer` contains the algorithm used for learning
`protected double`	`m_x0` coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
`protected double`	`m_x1` coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
`static Tag[]`	`TAGS_FILTER` The filter to apply to the training data

Constructor Summary

Constructors
Constructor and Description

SVMreg()

Constructors
Constructor and Description
`SVMreg()`

Method Summary

All 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`	`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.
`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
`RegOptimizer`	`getRegOptimizer()` returns the learning algorithm
`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`	`listOptions()` Returns an enumeration describing the available options.
`protected double`	`measureCacheHits()` number of kernel cache hits used during learing
`protected double`	`measureKernelEvaluations()` number of kernel evaluations used in learing
`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
clone, equals, finalize, 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
  - m_filterType
```
protected int m_filterType
```
    Whether to normalize/standardize/neither
  - m_onlyNumeric
```
protected boolean m_onlyNumeric
```
    Only numeric attributes in the dataset? If so, less need to filter
  - m_C
```
protected double m_C
```
    capacity parameter
  - m_x1
```
protected double m_x1
```
    coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
  - m_x0
```
protected double m_x0
```
    coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
  - m_optimizer
```
protected RegOptimizer m_optimizer
```
    contains the algorithm used for learning
  - m_kernel
```
protected Kernel m_kernel
```
    the configured kernel
- Constructor Detail
  - SVMreg
```
public SVMreg()
```
- 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.
    
    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).
  (default: 250007)
```
```
 -E <num>
  The Exponent to use.
  (default: 1.0)
```
```
 -L
  Use lower-order terms.
  (default: no)
```
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.Exception
```
    Method for building the 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.
    
    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
  - measureKernelEvaluations
```
protected double measureKernelEvaluations()
```
    number of kernel evaluations used in learing
    
    Returns:
    
    the number of kernel evaluations
  - measureCacheHits
```
protected double measureCacheHits()
```
    number of kernel cache hits used during learing
    
    Returns:
    
    the number of kernel cache hits

Class SVMreg

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

m_filterType

m_onlyNumeric

m_C

m_x1

m_x0

m_optimizer

m_kernel

Constructor Detail

SVMreg

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

buildClassifier

classifyInstance

regOptimizerTipText

setRegOptimizer

getRegOptimizer

kernelTipText

setKernel

getKernel

cTipText

getC

setC

filterTypeTipText

getFilterType

setFilterType

toString

enumerateMeasures

getMeasure

measureKernelEvaluations

measureCacheHits