LibLINEAR

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

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.RevisionHandler, weka.core.TechnicalInformationHandler

public class LibLINEAR
extends weka.classifiers.AbstractClassifier
implements weka.core.TechnicalInformationHandler

A wrapper class for the liblinear classifier.
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.

BibTeX:

 @misc{Fan2008,
    author = {Rong-En Fan and Kai-Wei Chang and Cho-Jui Hsieh and Xiang-Rui Wang and Chih-Jen Lin},
    note = {The Weka classifier works with version 1.33 of LIBLINEAR},
    title = {LIBLINEAR - A Library for Large Linear Classification},
    year = {2008},
    URL = {http://www.csie.ntu.edu.tw/\~cjlin/liblinear/}
 }

Valid options are:

 -S <int>
  Set type of solver (default: 1)
   for multi-class classification
     0 -- L2-regularized logistic regression (primal)
     1 -- L2-regularized L2-loss support vector classification (dual)
     2 -- L2-regularized L2-loss support vector classification (primal)
     3 -- L2-regularized L1-loss support vector classification (dual)
     4 -- support vector classification by Crammer and Singer
     5 -- L1-regularized L2-loss support vector classification
     6 -- L1-regularized logistic regression
     7 -- L2-regularized logistic regression (dual)
  for regression
    11 -- L2-regularized L2-loss support vector regression (primal)
    12 -- L2-regularized L2-loss support vector regression (dual)
    13 -- L2-regularized L1-loss support vector regression (dual)

 -C <double>
  Set the cost parameter C
   (default: 1)

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

 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 0.1)

 -I <int>
  The maximum number of iterations to perform.
  (default 0.1)

 -P
  Use probability estimation (default: off)
 currently for L2-regularized logistic regression only!

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

 -W <double>
  Set the parameters C of class i to weight[i]*C
   (default: 1)

 -B <double>
  Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)

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

Version:: 1.9.0
Author:: Benedikt Waldvogel (mail at bwaldvogel.de)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static java.lang.String REVISION

static weka.core.Tag[] TAGS_SVMTYPE
SVM solver types
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields
Modifier and Type	Field and Description
`static java.lang.String`	`REVISION`
`static weka.core.Tag[]`	`TAGS_SVMTYPE` SVM solver types

Constructor Summary

Constructors
Constructor and Description

LibLINEAR()

Constructors
Constructor and Description
`LibLINEAR()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`biasTipText()` Returns the tip text for this property
`void`	`buildClassifier(weka.core.Instances insts)` builds the classifier
`java.lang.String`	`costTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(weka.core.Instance instance)` Computes the distribution for a given instance.
`java.lang.String`	`epsilonParameterTipText()` Returns the tip text for this property
`java.lang.String`	`epsTipText()` Returns the tip text for this property
`double`	`getBias()` Returns bias term value (default 1) No bias term is added if value < 0
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getCost()` Returns the cost parameter C
`double`	`getEps()` Gets tolerance of termination criterion
`double`	`getEpsilonParameter()` Get the value of epsilon parameter of the epsilon insensitive loss function.
`int`	`getMaximumNumberOfIterations()` Get the number of iterations to perform.
`de.bwaldvogel.liblinear.Model`	`getModel()`
`boolean`	`getNormalize()` whether to normalize input data
`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.
`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 (default 1).
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method for testing this class.
`java.lang.String`	`maximumNumberOfIterationsTipText()` Returns the tip text for this property
`java.lang.String`	`normalizeTipText()` Returns the tip text for this property
`java.lang.String`	`probabilityEstimatesTipText()` Returns the tip text for this property
`void`	`setBias(double value)` Sets bias term value (default 1) No bias term is added if value < 0
`void`	`setCost(double value)` Sets the cost parameter C (default 1)
`void`	`setEps(double value)` Sets tolerance of termination criterion (default 0.001)
`void`	`setEpsilonParameter(double v)` Set the value of epsilon parameter of the epsilon insensitive loss function.
`void`	`setMaximumNumberOfIterations(int v)` Set the number of iterations to perform.
`void`	`setNormalize(boolean value)` whether to normalize input data
`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`	`setSVMType(weka.core.SelectedTag value)` Sets type of SVM (default SVMTYPE_L2)
`void`	`setWeights(java.lang.String weightsStr)` Sets the parameters C of class i to weight[i]*C (default 1).
`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.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
  - REVISION
```
public static final java.lang.String REVISION
```
    See Also:
    
    Constant Field Values
  - TAGS_SVMTYPE
```
public static final weka.core.Tag[] TAGS_SVMTYPE
```
    SVM solver types
- Constructor Detail
  - LibLINEAR
```
public LibLINEAR()
```
- Method Detail
  - getModel
```
public de.bwaldvogel.liblinear.Model getModel()
```
  - 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.AbstractClassifier
    
    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 solver (default: 1)
   for multi-class classification
     0 -- L2-regularized logistic regression (primal)
     1 -- L2-regularized L2-loss support vector classification (dual)
     2 -- L2-regularized L2-loss support vector classification (primal)
     3 -- L2-regularized L1-loss support vector classification (dual)
     4 -- support vector classification by Crammer and Singer
     5 -- L1-regularized L2-loss support vector classification
     6 -- L1-regularized logistic regression
     7 -- L2-regularized logistic regression (dual)
  for regression
    11 -- L2-regularized L2-loss support vector regression (primal)
    12 -- L2-regularized L2-loss support vector regression (dual)
    13 -- L2-regularized L1-loss support vector regression (dual)
```
```
 -C <double>
  Set the cost parameter C
   (default: 1)
```
```
 -Z
  Turn on normalization of input data (default: off)
```
```
 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 0.1)
 
```
```
 -I <int>
  The maximum number of iterations to perform.
  (default 0.1)
 
```
```
 -P
  Use probability estimation (default: off)
 currently for L2-regularized logistic regression only! 
```
```
 -E <double>
  Set tolerance of termination criterion (default: 0.001)
```
```
 -W <double>
  Set the parameters C of class i to weight[i]*C
   (default: 1)
```
```
 -B <double>
  Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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.AbstractClassifier
    
    Returns:
    
    the current setup
  - setSVMType
```
public void setSVMType(weka.core.SelectedTag value)
```
    Sets type of SVM (default SVMTYPE_L2)
    
    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
  - getEpsilonParameter
```
public double getEpsilonParameter()
```
    Get the value of epsilon parameter of the epsilon insensitive loss function.
    
    Returns:
    
    Value of epsilon parameter.
  - setEpsilonParameter
```
public void setEpsilonParameter(double v)
```
    Set the value of epsilon parameter of the epsilon insensitive loss function.
    
    Parameters:
    
    v - Value to assign to epsilon parameter.
  - epsilonParameterTipText
```
public java.lang.String epsilonParameterTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMaximumNumberOfIterations
```
public int getMaximumNumberOfIterations()
```
    Get the number of iterations to perform.
    
    Returns:
    
    maximum number of iterations to perform.
  - setMaximumNumberOfIterations
```
public void setMaximumNumberOfIterations(int v)
```
    Set the number of iterations to perform.
    
    Parameters:
    
    v - the number of iterations to perform.
  - maximumNumberOfIterationsTipText
```
public java.lang.String maximumNumberOfIterationsTipText()
```
    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 cost parameter C (default 1)
    
    Parameters:
    
    value - the cost value
  - getCost
```
public double getCost()
```
    Returns the cost parameter C
    
    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
  - setBias
```
public void setBias(double value)
```
    Sets bias term value (default 1) No bias term is added if value < 0
    
    Parameters:
    
    value - the bias term value
  - getBias
```
public double getBias()
```
    Returns bias term value (default 1) No bias term is added if value < 0
    
    Returns:
    
    the bias term value
  - biasTipText
```
public java.lang.String biasTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - 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
  - 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
  - setWeights
```
public void setWeights(java.lang.String weightsStr)
```
    Sets the parameters C of class i to weight[i]*C (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 (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(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance.
    
    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 liblinear classes not in classpath or liblinear 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 LibLINEAR

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

REVISION

TAGS_SVMTYPE

Constructor Detail

LibLINEAR

Method Detail

getModel

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

setSVMType

getSVMType

SVMTypeTipText

getEpsilonParameter

setEpsilonParameter

epsilonParameterTipText

getMaximumNumberOfIterations

setMaximumNumberOfIterations

maximumNumberOfIterationsTipText

setCost

getCost

costTipText

setEps

getEps

epsTipText

setBias

getBias

biasTipText

normalizeTipText

setNormalize

getNormalize

setWeights

getWeights

weightsTipText

setProbabilityEstimates

getProbabilityEstimates

probabilityEstimatesTipText

distributionForInstance

getCapabilities

buildClassifier

toString

getRevision

main