LibLINEAR

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

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

public class LibLINEAR
extends Classifier
implements TechnicalInformationHandler

A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this 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)
    0 = L2-regularized logistic regression
    1 = L2-loss support vector machines (dual)
    2 = L2-loss support vector machines (primal)
    3 = L1-loss support vector machines (dual)
    4 = multi-class support vector machines by Crammer and Singer

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

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

 -N
  Turn on nominal to binary conversion.

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

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

 -E <double>
  Set tolerance of termination criterion (default: 0.01)

 -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:: $Revision: 5917 $
Author:: Benedikt Waldvogel (mail at bwaldvogel.de)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`SVMTYPE_L1LOSS_SVM_DUAL` SVM solver type L1-loss support vector machines (dual)
`static int`	`SVMTYPE_L2_LR` SVM solver type L2-regularized logistic regression
`static int`	`SVMTYPE_L2LOSS_SVM` SVM solver type L2-loss support vector machines (primal)
`static int`	`SVMTYPE_L2LOSS_SVM_DUAL` SVM solver type L2-loss support vector machines (dual)
`static int`	`SVMTYPE_MCSVM_CS` SVM solver type multi-class support vector machines by Crammer and Singer
`static 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(Instances insts)` builds the classifier
`java.lang.String`	`convertNominalToBinaryTipText()` Returns the tip text for this property
`java.lang.String`	`costTipText()` 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
`double`	`getBias()` Returns bias term value (default 1) No bias term is added if value < 0
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getConvertNominalToBinary()` Gets whether conversion of nominal to binary is turned on.
`double`	`getCost()` Returns the cost parameter C
`boolean`	`getDoNotReplaceMissingValues()` Gets whether automatic replacement of missing values is disabled.
`double`	`getEps()` Gets tolerance of termination criterion
`java.lang.Object`	`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.
`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 (default 1).
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`static boolean`	`isPresent()` returns whether the liblinear classes are present or not, i.e.
`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`	`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`	`setConvertNominalToBinary(boolean b)` Whether to turn on conversion of nominal attributes to binary.
`void`	`setCost(double value)` Sets the cost parameter C (default 1)
`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`	`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(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.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_L2_LR
```
public static final int SVMTYPE_L2_LR
```
    SVM solver type L2-regularized logistic regression
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_L2LOSS_SVM_DUAL
```
public static final int SVMTYPE_L2LOSS_SVM_DUAL
```
    SVM solver type L2-loss support vector machines (dual)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_L2LOSS_SVM
```
public static final int SVMTYPE_L2LOSS_SVM
```
    SVM solver type L2-loss support vector machines (primal)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_L1LOSS_SVM_DUAL
```
public static final int SVMTYPE_L1LOSS_SVM_DUAL
```
    SVM solver type L1-loss support vector machines (dual)
    
    See Also:
    
    Constant Field Values
  - SVMTYPE_MCSVM_CS
```
public static final int SVMTYPE_MCSVM_CS
```
    SVM solver type multi-class support vector machines by Crammer and Singer
    
    See Also:
    
    Constant Field Values
  - TAGS_SVMTYPE
```
public static final Tag[] TAGS_SVMTYPE
```
    SVM solver types
- Constructor Detail
  - LibLINEAR
```
public LibLINEAR()
```
- Method Detail
  - getModel
```
public java.lang.Object 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 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 Classifier
    
    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)
    0 = L2-regularized logistic regression
    1 = L2-loss support vector machines (dual)
    2 = L2-loss support vector machines (primal)
    3 = L1-loss support vector machines (dual)
    4 = multi-class support vector machines by Crammer and Singer
```
```
 -C <double>
  Set the cost parameter C
   (default: 1)
```
```
 -Z
  Turn on normalization of input data (default: off)
```
```
 -N
  Turn on nominal to binary conversion.
```
```
 -M
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
```
```
 -P
  Use probability estimation (default: off)
 currently for L2-regularized logistic regression only! 
```
```
 -E <double>
  Set tolerance of termination criterion (default: 0.01)
```
```
 -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 OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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 Classifier
    
    Returns:
    
    the current setup
  - isPresent
```
public static boolean isPresent()
```
    returns whether the liblinear classes are present or not, i.e. whether the classes are in the classpath or not
    
    Returns:
    
    whether the liblinear classes are available
  - setSVMType
```
public void setSVMType(SelectedTag value)
```
    Sets type of SVM (default SVMTYPE_L2)
    
    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
  - 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
  - convertNominalToBinaryTipText
```
public java.lang.String convertNominalToBinaryTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setConvertNominalToBinary
```
public void setConvertNominalToBinary(boolean b)
```
    Whether to turn on conversion of nominal attributes to binary.
    
    Parameters:
    
    b - true if nominal to binary conversion is to be turned on
  - getConvertNominalToBinary
```
public boolean getConvertNominalToBinary()
```
    Gets whether conversion of nominal to binary is turned on.
    
    Returns:
    
    true if nominal to binary conversion is turned on.
  - 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 (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(Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance.
    
    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 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 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 LibLINEAR

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

SVMTYPE_L2_LR

SVMTYPE_L2LOSS_SVM_DUAL

SVMTYPE_L2LOSS_SVM

SVMTYPE_L1LOSS_SVM_DUAL

SVMTYPE_MCSVM_CS

TAGS_SVMTYPE

Constructor Detail

LibLINEAR

Method Detail

getModel

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

isPresent

setSVMType

getSVMType

SVMTypeTipText

setCost

getCost

costTipText

setEps

getEps

epsTipText

setBias

getBias

biasTipText

normalizeTipText

setNormalize

getNormalize

convertNominalToBinaryTipText

setConvertNominalToBinary

getConvertNominalToBinary

doNotReplaceMissingValuesTipText

setDoNotReplaceMissingValues

getDoNotReplaceMissingValues

setWeights

getWeights

weightsTipText

setProbabilityEstimates

getProbabilityEstimates

probabilityEstimatesTipText

distributionForInstance

getCapabilities

buildClassifier

toString

getRevision

main