LibSVM

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.functions.LibSVM

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

public class LibSVM
extends RandomizableClassifier
implements TechnicalInformationHandler

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.).

Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/.

Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

BibTeX:

 @misc{EL-Manzalawy2005,
    author = {Yasser EL-Manzalawy},
    note = {You don't need to include the WLSVM package in the CLASSPATH},
    title = {WLSVM},
    year = {2005},
    URL = {http://www.cs.iastate.edu/\~yasser/wlsvm/}
 }
 
 @misc{Chang2001,
    author = {Chih-Chung Chang and Chih-Jen Lin},
    note = {The Weka classifier works with version 2.82 of LIBSVM},
    title = {LIBSVM - A Library for Support Vector Machines},
    year = {2001},
    URL = {http://www.csie.ntu.edu.tw/\~cjlin/libsvm/}
 }

Valid options are:

 -S <int>
  Set type of SVM (default: 0)
    0 = C-SVC
    1 = nu-SVC
    2 = one-class SVM
    3 = epsilon-SVR
    4 = nu-SVR

 -K <int>
  Set type of kernel function (default: 2)
    0 = linear: u'*v
    1 = polynomial: (gamma*u'*v + coef0)^degree
    2 = radial basis function: exp(-gamma*|u-v|^2)
    3 = sigmoid: tanh(gamma*u'*v + coef0)

 -D <int>
  Set degree in kernel function (default: 3)

 -G <double>
  Set gamma in kernel function (default: 1/k)

 -R <double>
  Set coef0 in kernel function (default: 0)

 -C <double>
  Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   (default: 1)

 -N <double>
  Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
   (default: 0.5)

 -Z
  Turns on normalization of input data (default: off)

 -J
  Turn off nominal to binary conversion.
  WARNING: use only if your data is all numeric!

 -V
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.

 -P <double>
  Set the epsilon in loss function of epsilon-SVR (default: 0.1)

 -M <double>
  Set cache memory size in MB (default: 40)

 -E <double>
  Set tolerance of termination criterion (default: 0.001)

 -H
  Turns the shrinking heuristics off (default: on)

 -W <double>
  Set the parameters C of class i to weight[i]*C, for C-SVC
  E.g., for a 3-class problem, you could use "1 1 1" for equally
  weighted classes.
  (default: 1 for all classes)

 -B
  Generate probability estimates for classification

 -seed <num>
  Random seed
  (default = 1)

Version:: $Revision: 10660 $
Author:: Yasser EL-Manzalawy, FracPete (fracpete at waikato dot ac dot nz)
See Also:: LibSVMLoader, LibSVMSaver, Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`KERNELTYPE_LINEAR` kernel type linear: u'*v
`static int`	`KERNELTYPE_POLYNOMIAL` kernel type polynomial: (gammau'v + coef0)^degree
`static int`	`KERNELTYPE_RBF` kernel type radial basis function: exp(-gamma*\|u-v\|^2)
`static int`	`KERNELTYPE_SIGMOID` kernel type sigmoid: tanh(gammau'v + coef0)
`static int`	`SVMTYPE_C_SVC` SVM type C-SVC (classification)
`static int`	`SVMTYPE_EPSILON_SVR` SVM type epsilon-SVR (regression)
`static int`	`SVMTYPE_NU_SVC` SVM type nu-SVC (classification)
`static int`	`SVMTYPE_NU_SVR` SVM type nu-SVR (regression)
`static int`	`SVMTYPE_ONE_CLASS_SVM` SVM type one-class SVM (classification)
`static Tag[]`	`TAGS_KERNELTYPE` the different kernel types
`static Tag[]`	`TAGS_SVMTYPE` SVM types

Constructor Summary

Constructors
Constructor and Description

LibSVM()

Constructors
Constructor and Description
`LibSVM()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances insts)` builds the classifier
`java.lang.String`	`cacheSizeTipText()` Returns the tip text for this property
`java.lang.String`	`coef0TipText()` Returns the tip text for this property
`java.lang.String`	`costTipText()` Returns the tip text for this property
`java.lang.String`	`degreeTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Computes the distribution for a given instance.
`java.lang.String`	`doNotReplaceMissingValuesTipText()` Returns the tip text for this property
`java.lang.String`	`epsTipText()` Returns the tip text for this property
`java.lang.String`	`gammaTipText()` Returns the tip text for this property
`double`	`getCacheSize()` Gets cache memory size in MB
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getCoef0()` Gets coef
`double`	`getCost()` Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
`int`	`getDegree()` Gets the degree of the kernel
`boolean`	`getDoNotReplaceMissingValues()` Gets whether automatic replacement of missing values is disabled.
`double`	`getEps()` Gets tolerance of termination criterion
`double`	`getGamma()` Gets gamma
`SelectedTag`	`getKernelType()` Gets type of kernel function
`double`	`getLoss()` Gets the epsilon in loss function of epsilon-SVR
`boolean`	`getNormalize()` whether to normalize input data
`double`	`getNu()` Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
`java.lang.String[]`	`getOptions()` Returns the current options
`boolean`	`getProbabilityEstimates()` Sets whether to generate probability estimates instead of -1/+1 for classification problems.
`java.lang.String`	`getRevision()` Returns the revision string.
`boolean`	`getShrinking()` whether to use the shrinking heuristics
`SelectedTag`	`getSVMType()` Gets type of SVM
`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`	`getWeights()` Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`static boolean`	`isPresent()` returns whether the libsvm classes are present or not, i.e.
`java.lang.String`	`kernelTypeTipText()` Returns the tip text for this property
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`lossTipText()` Returns the tip text for this property
`static void`	`main(java.lang.String[] args)` Main method for testing this class.
`java.lang.String`	`normalizeTipText()` Returns the tip text for this property
`java.lang.String`	`nuTipText()` Returns the tip text for this property
`java.lang.String`	`probabilityEstimatesTipText()` Returns the tip text for this property
`void`	`setCacheSize(double value)` Sets cache memory size in MB (default 40)
`void`	`setCoef0(double value)` Sets coef (default 0)
`void`	`setCost(double value)` Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
`void`	`setDegree(int value)` Sets the degree of the kernel
`void`	`setDoNotReplaceMissingValues(boolean b)` Whether to turn off automatic replacement of missing values.
`void`	`setEps(double value)` Sets tolerance of termination criterion (default 0.001)
`void`	`setGamma(double value)` Sets gamma (default = 1/no of attributes)
`void`	`setKernelType(SelectedTag value)` Sets type of kernel function (default KERNELTYPE_RBF)
`void`	`setLoss(double value)` Sets the epsilon in loss function of epsilon-SVR (default 0.1)
`void`	`setNormalize(boolean value)` whether to normalize input data
`void`	`setNu(double value)` Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
`void`	`setOptions(java.lang.String[] options)` Sets the classifier options Valid options are:
`void`	`setProbabilityEstimates(boolean value)` Returns whether probability estimates are generated instead of -1/+1 for classification problems.
`void`	`setShrinking(boolean value)` whether to use the shrinking heuristics
`void`	`setSVMType(SelectedTag value)` Sets type of SVM (default SVMTYPE_C_SVC)
`void`	`setWeights(java.lang.String weightsStr)` Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
`java.lang.String`	`shrinkingTipText()` Returns the tip text for this property
`java.lang.String`	`SVMTypeTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` returns a string representation
`java.lang.String`	`weightsTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableClassifier
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - SVMTYPE_C_SVC
```
public static final int SVMTYPE_C_SVC
```
    SVM type C-SVC (classification)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_NU_SVC
```
public static final int SVMTYPE_NU_SVC
```
    SVM type nu-SVC (classification)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_ONE_CLASS_SVM
```
public static final int SVMTYPE_ONE_CLASS_SVM
```
    SVM type one-class SVM (classification)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_EPSILON_SVR
```
public static final int SVMTYPE_EPSILON_SVR
```
    SVM type epsilon-SVR (regression)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_NU_SVR
```
public static final int SVMTYPE_NU_SVR
```
    SVM type nu-SVR (regression)
    
    See Also:
    
    Constant Field Values
  - TAGS_SVMTYPE
```
public static final Tag[] TAGS_SVMTYPE
```
    SVM types
  - KERNELTYPE_LINEAR
```
public static final int KERNELTYPE_LINEAR
```
    kernel type linear: u'*v
    
    See Also:
    
    Constant Field Values
  - KERNELTYPE_POLYNOMIAL
```
public static final int KERNELTYPE_POLYNOMIAL
```
    kernel type polynomial: (gamma*u'*v + coef0)^degree
    
    See Also:
    
    Constant Field Values
  - KERNELTYPE_RBF
```
public static final int KERNELTYPE_RBF
```
    kernel type radial basis function: exp(-gamma*|u-v|^2)
    
    See Also:
    
    Constant Field Values
  - KERNELTYPE_SIGMOID
```
public static final int KERNELTYPE_SIGMOID
```
    kernel type sigmoid: tanh(gamma*u'*v + coef0)
    
    See Also:
    
    Constant Field Values
  - TAGS_KERNELTYPE
```
public static final Tag[] TAGS_KERNELTYPE
```
    the different kernel types
- Constructor Detail
  - LibSVM
```
public LibSVM()
```
- 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 RandomizableClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Sets the classifier options
    Valid options are:
```
 -S <int>
  Set type of SVM (default: 0)
    0 = C-SVC
    1 = nu-SVC
    2 = one-class SVM
    3 = epsilon-SVR
    4 = nu-SVR
 
```
```
 -K <int>
  Set type of kernel function (default: 2)
    0 = linear: u'*v
    1 = polynomial: (gamma*u'*v + coef0)^degree
    2 = radial basis function: exp(-gamma*|u-v|^2)
    3 = sigmoid: tanh(gamma*u'*v + coef0)
 
```
```
 -D <int>
  Set degree in kernel function (default: 3)
 
```
```
 -G <double>
  Set gamma in kernel function (default: 1/k)
 
```
```
 -R <double>
  Set coef0 in kernel function (default: 0)
 
```
```
 -C <double>
  Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   (default: 1)
 
```
```
 -N <double>
  Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
   (default: 0.5)
 
```
```
 -Z
  Turns on normalization of input data (default: off)
 
```
```
 -J
  Turn off nominal to binary conversion.
  WARNING: use only if your data is all numeric!
 
```
```
 -V
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
 
```
```
 -P <double>
  Set the epsilon in loss function of epsilon-SVR (default: 0.1)
 
```
```
 -M <double>
  Set cache memory size in MB (default: 40)
 
```
```
 -E <double>
  Set tolerance of termination criterion (default: 0.001)
 
```
```
 -H
  Turns the shrinking heuristics off (default: on)
 
```
```
 -W <double>
  Set the parameters C of class i to weight[i]*C, for C-SVC
  E.g., for a 3-class problem, you could use "1 1 1" for equally
  weighted classes.
  (default: 1 for all classes)
 
```
```
 -B
  Generate probability estimates for classification
 
```
```
 -seed <num>
  Random seed
  (default = 1)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClassifier
    
    Parameters:
    
    options - the options to parse
    
    Throws:
    
    java.lang.Exception - if parsing fails
  - getOptions
```
public java.lang.String[] getOptions()
```
    Returns the current options
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClassifier
    
    Returns:
    
    the current setup
  - isPresent
```
public static boolean isPresent()
```
    returns whether the libsvm classes are present or not, i.e. whether the classes are in the classpath or not
    
    Returns:
    
    whether the libsvm classes are available
  - setSVMType
```
public void setSVMType(SelectedTag value)
```
    Sets type of SVM (default SVMTYPE_C_SVC)
    
    Parameters:
    
    value - the type of the SVM
  - getSVMType
```
public SelectedTag getSVMType()
```
    Gets type of SVM
    
    Returns:
    
    the type of the SVM
  - SVMTypeTipText
```
public java.lang.String SVMTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setKernelType
```
public void setKernelType(SelectedTag value)
```
    Sets type of kernel function (default KERNELTYPE_RBF)
    
    Parameters:
    
    value - the kernel type
  - getKernelType
```
public SelectedTag getKernelType()
```
    Gets type of kernel function
    
    Returns:
    
    the kernel type
  - kernelTypeTipText
```
public java.lang.String kernelTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDegree
```
public void setDegree(int value)
```
    Sets the degree of the kernel
    
    Parameters:
    
    value - the degree of the kernel
  - getDegree
```
public int getDegree()
```
    Gets the degree of the kernel
    
    Returns:
    
    the degree of the kernel
  - degreeTipText
```
public java.lang.String degreeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setGamma
```
public void setGamma(double value)
```
    Sets gamma (default = 1/no of attributes)
    
    Parameters:
    
    value - the gamma value
  - getGamma
```
public double getGamma()
```
    Gets gamma
    
    Returns:
    
    the current gamma
  - gammaTipText
```
public java.lang.String gammaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCoef0
```
public void setCoef0(double value)
```
    Sets coef (default 0)
    
    Parameters:
    
    value - the coef
  - getCoef0
```
public double getCoef0()
```
    Gets coef
    
    Returns:
    
    the coef
  - coef0TipText
```
public java.lang.String coef0TipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNu
```
public void setNu(double value)
```
    Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
    
    Parameters:
    
    value - the new nu value
  - getNu
```
public double getNu()
```
    Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
    
    Returns:
    
    the current nu value
  - nuTipText
```
public java.lang.String nuTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCacheSize
```
public void setCacheSize(double value)
```
    Sets cache memory size in MB (default 40)
    
    Parameters:
    
    value - the memory size in MB
  - getCacheSize
```
public double getCacheSize()
```
    Gets cache memory size in MB
    
    Returns:
    
    the memory size in MB
  - cacheSizeTipText
```
public java.lang.String cacheSizeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCost
```
public void setCost(double value)
```
    Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
    
    Parameters:
    
    value - the cost value
  - getCost
```
public double getCost()
```
    Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
    
    Returns:
    
    the cost value
  - costTipText
```
public java.lang.String costTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEps
```
public void setEps(double value)
```
    Sets tolerance of termination criterion (default 0.001)
    
    Parameters:
    
    value - the tolerance
  - getEps
```
public double getEps()
```
    Gets tolerance of termination criterion
    
    Returns:
    
    the current tolerance
  - epsTipText
```
public java.lang.String epsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLoss
```
public void setLoss(double value)
```
    Sets the epsilon in loss function of epsilon-SVR (default 0.1)
    
    Parameters:
    
    value - the loss epsilon
  - getLoss
```
public double getLoss()
```
    Gets the epsilon in loss function of epsilon-SVR
    
    Returns:
    
    the loss epsilon
  - lossTipText
```
public java.lang.String lossTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setShrinking
```
public void setShrinking(boolean value)
```
    whether to use the shrinking heuristics
    
    Parameters:
    
    value - true uses shrinking
  - getShrinking
```
public boolean getShrinking()
```
    whether to use the shrinking heuristics
    
    Returns:
    
    true, if shrinking is used
  - shrinkingTipText
```
public java.lang.String shrinkingTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNormalize
```
public void setNormalize(boolean value)
```
    whether to normalize input data
    
    Parameters:
    
    value - whether to normalize the data
  - getNormalize
```
public boolean getNormalize()
```
    whether to normalize input data
    
    Returns:
    
    true, if the data is normalized
  - normalizeTipText
```
public java.lang.String normalizeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - doNotReplaceMissingValuesTipText
```
public java.lang.String doNotReplaceMissingValuesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDoNotReplaceMissingValues
```
public void setDoNotReplaceMissingValues(boolean b)
```
    Whether to turn off automatic replacement of missing values. Set to true only if the data does not contain missing values.
    
    Parameters:
    
    b - true if automatic missing values replacement is to be disabled.
  - getDoNotReplaceMissingValues
```
public boolean getDoNotReplaceMissingValues()
```
    Gets whether automatic replacement of missing values is disabled.
    
    Returns:
    
    true if automatic replacement of missing values is disabled.
  - setWeights
```
public void setWeights(java.lang.String weightsStr)
```
    Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1). Blank separated list of doubles.
    
    Parameters:
    
    weightsStr - the weights (doubles, separated by blanks)
  - getWeights
```
public java.lang.String getWeights()
```
    Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1). Blank separated doubles.
    
    Returns:
    
    the weights (doubles separated by blanks)
  - weightsTipText
```
public java.lang.String weightsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setProbabilityEstimates
```
public void setProbabilityEstimates(boolean value)
```
    Returns whether probability estimates are generated instead of -1/+1 for classification problems.
    
    Parameters:
    
    value - whether to predict probabilities
  - getProbabilityEstimates
```
public boolean getProbabilityEstimates()
```
    Sets whether to generate probability estimates instead of -1/+1 for classification problems.
    
    Returns:
    
    true, if probability estimates should be returned
  - probabilityEstimatesTipText
```
public java.lang.String probabilityEstimatesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance. In case of 1-class classification, 1 is returned at index 0 if libsvm returns 1 and NaN (= missing) if libsvm returns -1.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances insts)
                     throws java.lang.Exception
```
    builds the classifier
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    insts - the training instances
    
    Throws:
    
    java.lang.Exception - if libsvm classes not in classpath or libsvm encountered a problem
  - toString
```
public java.lang.String toString()
```
    returns a string representation
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for testing this class.
    
    Parameters:
    
    args - the options

Class LibSVM

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

SVMTYPE_C_SVC

SVMTYPE_NU_SVC

SVMTYPE_ONE_CLASS_SVM

SVMTYPE_EPSILON_SVR

SVMTYPE_NU_SVR

TAGS_SVMTYPE

KERNELTYPE_LINEAR

KERNELTYPE_POLYNOMIAL

KERNELTYPE_RBF

KERNELTYPE_SIGMOID

TAGS_KERNELTYPE

Constructor Detail

LibSVM

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

isPresent

setSVMType

getSVMType

SVMTypeTipText

setKernelType

getKernelType

kernelTypeTipText

setDegree

getDegree

degreeTipText

setGamma

getGamma

gammaTipText

setCoef0

getCoef0

coef0TipText

setNu

getNu

nuTipText

setCacheSize

getCacheSize

cacheSizeTipText

setCost

getCost

costTipText

setEps

getEps

epsTipText

setLoss

getLoss

lossTipText

setShrinking

getShrinking

shrinkingTipText

setNormalize

getNormalize

normalizeTipText

doNotReplaceMissingValuesTipText

setDoNotReplaceMissingValues

getDoNotReplaceMissingValues

setWeights

getWeights

weightsTipText

setProbabilityEstimates

getProbabilityEstimates

probabilityEstimatesTipText

distributionForInstance

getCapabilities

buildClassifier

toString

getRevision

main