LibSVM

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

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class LibSVM
extends weka.classifiers.RandomizableClassifier
implements weka.core.TechnicalInformationHandler

A wrapper class for the libsvm library. This wrapper supports the classifiers implemented in the libsvm library, including one-class SVMs.
Note: To be consistent with other SVMs in WEKA, the target attribute is now normalized before " SVM regression is performed, if normalization is turned on.

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
  Trains a SVC model instead of a SVR one (default: SVR)

 -model <file>
  Specifies the filename to save the libsvm-internal model to.
  Gets ignored if a directory is provided.

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -seed <num>
  Seed for the random number generator when -B is used.
  (default = 1)

Version:: $Revision: 13288 $
Author:: Yasser EL-Manzalawy, FracPete (fracpete at waikato dot ac dot nz), Eibe Frank
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 weka.core.Tag[]`	`TAGS_KERNELTYPE` the different kernel types.
`static weka.core.Tag[]`	`TAGS_SVMTYPE` SVM types.

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

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(weka.core.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(weka.core.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.
`weka.core.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.
`weka.core.SelectedTag`	`getKernelType()` Gets type of kernel function.
`double`	`getLoss()` Gets the epsilon in loss function of epsilon-SVR.
`java.io.File`	`getModelFile()` Returns the file to save the libsvm-internal model to.
`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()` Returns 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.
`weka.core.SelectedTag`	`getSVMType()` Gets type of SVM.
`weka.core.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.
`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`	`modelFileTipText()` Returns the tip text for this property.
`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(weka.core.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`	`setModelFile(java.io.File value)` Sets the file to save the libsvm-internal model to.
`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)` Sets whether probability estimates are generated instead of -1/+1 for classification problems.
`void`	`setShrinking(boolean value)` whether to use the shrinking heuristics.
`void`	`setSVMType(weka.core.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.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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
  - 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 weka.core.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 weka.core.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 weka.core.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 weka.core.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 weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.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
  Trains a SVC model instead of a SVR one (default: SVR)
```
```
 -model <file>
  Specifies the filename to save the libsvm-internal model to.
  Gets ignored if a directory is provided.
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -seed <num>
  Seed for the random number generator when -B is used.
  (default = 1)
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.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 weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.RandomizableClassifier
    
    Returns:
    
    the current setup
  - setSVMType
```
public void setSVMType(weka.core.SelectedTag value)
```
    Sets type of SVM (default SVMTYPE_C_SVC).
    
    Parameters:
    
    value - the type of the SVM
  - getSVMType
```
public weka.core.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(weka.core.SelectedTag value)
```
    Sets type of kernel function (default KERNELTYPE_RBF).
    
    Parameters:
    
    value - the kernel type
  - getKernelType
```
public weka.core.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)
```
    Sets whether probability estimates are generated instead of -1/+1 for classification problems.
    
    Parameters:
    
    value - whether to predict probabilities
  - getProbabilityEstimates
```
public boolean getProbabilityEstimates()
```
    Returns 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
  - setModelFile
```
public void setModelFile(java.io.File value)
```
    Sets the file to save the libsvm-internal model to. No model is saved if pointing to a directory.
    
    Parameters:
    
    value - the filename/directory
  - getModelFile
```
public java.io.File getModelFile()
```
    Returns the file to save the libsvm-internal model to. No model is saved if pointing to a directory.
    
    Returns:
    
    the file object
  - modelFileTipText
```
public java.lang.String modelFileTipText()
```
    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(weka.core.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.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    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 weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances insts)
                     throws java.lang.Exception
```
    builds the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    insts - the training instances
    
    Throws:
    
    java.lang.Exception - if 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 weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    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

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

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

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

setModelFile

getModelFile

modelFileTipText

distributionForInstance

getCapabilities

buildClassifier