LibLINEAR (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, 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
`protected static String`	`CLASS_FEATURENODE` the svm_node classname
`protected static String`	`CLASS_LINEAR` the svm classname
`protected static String`	`CLASS_MODEL` the svm_model classname
`protected static String`	`CLASS_PARAMETER` the svm_parameter classname
`protected static String`	`CLASS_PROBLEM` the svm_problem classname
`protected static String`	`CLASS_SOLVERTYPE` the svm_parameter classname
`protected double`	`m_Bias` bias term value
`protected double`	`m_Cost` cost Parameter C
`protected double`	`m_eps` stopping criteria
`protected Filter`	`m_Filter` for normalizing the data
`protected Object`	`m_Model` LibLINEAR Model
`protected NominalToBinary`	`m_NominalToBinary` The filter used to make attributes numeric.
`protected boolean`	`m_Normalize` normalize input data
`protected static boolean`	`m_Present` whether the liblinear classes are in the Classpath
`protected boolean`	`m_ProbabilityEstimates` whether to generate probability estimates instead of +1/-1 in case of classification problems
`protected ReplaceMissingValues`	`m_ReplaceMissingValues` The filter used to get rid of missing values.
`protected int`	`m_SVMType` the SVM solver type
`protected double[]`	`m_Weight`
`protected int[]`	`m_WeightLabel`
`protected static long`	`serialVersionUID` serial UID
`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

Fields inherited from class weka.classifiers.Classifier
m_Debug

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
`String`	`biasTipText()` Returns the tip text for this property
`void`	`buildClassifier(Instances insts)` builds the classifier
`String`	`convertNominalToBinaryTipText()` Returns the tip text for this property
`String`	`costTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Computes the distribution for a given instance.
`String`	`doNotReplaceMissingValuesTipText()` Returns the tip text for this property
`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
`protected Object`	`getField(Object o, String name)` returns the current value of the specified field
`Object`	`getModel()`
`boolean`	`getNormalize()` whether to normalize input data
`String[]`	`getOptions()` Returns the current options
`protected Object`	`getParameters()` transfers the local variables into a svm_parameter object
`boolean`	`getProbabilityEstimates()` Sets whether to generate probability estimates instead of -1/+1 for classification problems.
`protected Object`	`getProblem(List<Object> vx, List<Integer> vy, int max_index)` returns the svm_problem
`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.
`String`	`getWeights()` Gets the parameters C of class i to weight[i]*C (default 1).
`String`	`globalInfo()` Returns a string describing classifier
`protected Object`	`instanceToArray(Instance instance)` returns an instance into a sparse liblinear array
`protected Object`	`invokeMethod(Object o, String name, Class[] paramClasses, Object[] paramValues)` executes the specified method and returns the result, if any
`static boolean`	`isPresent()` returns whether the liblinear classes are present or not, i.e.
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] args)` Main method for testing this class.
`protected void`	`newArray(Object o, String name, Class type, int length)` sets a new array for the field
`protected void`	`newArray(Object o, String name, Class type, int[] dimensions)` sets a new array for the field
`String`	`normalizeTipText()` Returns the tip text for this property
`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)
`protected void`	`setField(Object o, String name, int index, Object value)` sets the specified field in an array
`protected void`	`setField(Object o, String name, Object value)` sets the specified field
`void`	`setNormalize(boolean value)` whether to normalize input data
`void`	`setOptions(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(String weightsStr)` Sets the parameters C of class i to weight[i]*C (default 1).
`String`	`SVMTypeTipText()` Returns the tip text for this property
`String`	`toString()` returns a string representation
`String`	`weightsTipText()` Returns the tip text for this property

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

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

- Field Detail
  - CLASS_LINEAR
```
protected static final String CLASS_LINEAR
```
    the svm classname
    
    See Also:
    
    Constant Field Values
  - CLASS_MODEL
```
protected static final String CLASS_MODEL
```
    the svm_model classname
    
    See Also:
    
    Constant Field Values
  - CLASS_PROBLEM
```
protected static final String CLASS_PROBLEM
```
    the svm_problem classname
    
    See Also:
    
    Constant Field Values
  - CLASS_PARAMETER
```
protected static final String CLASS_PARAMETER
```
    the svm_parameter classname
    
    See Also:
    
    Constant Field Values
  - CLASS_SOLVERTYPE
```
protected static final String CLASS_SOLVERTYPE
```
    the svm_parameter classname
    
    See Also:
    
    Constant Field Values
  - CLASS_FEATURENODE
```
protected static final String CLASS_FEATURENODE
```
    the svm_node classname
    
    See Also:
    
    Constant Field Values
  - serialVersionUID
```
protected static final long serialVersionUID
```
    serial UID
    
    See Also:
    
    Constant Field Values
  - m_Model
```
protected Object m_Model
```
    LibLINEAR Model
  - m_Filter
```
protected Filter m_Filter
```
    for normalizing the data
  - m_Normalize
```
protected boolean m_Normalize
```
    normalize input data
  - 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
  - m_SVMType
```
protected int m_SVMType
```
    the SVM solver type
  - m_eps
```
protected double m_eps
```
    stopping criteria
  - m_Cost
```
protected double m_Cost
```
    cost Parameter C
  - m_Bias
```
protected double m_Bias
```
    bias term value
  - m_WeightLabel
```
protected int[] m_WeightLabel
```
  - m_Weight
```
protected double[] m_Weight
```
  - m_ProbabilityEstimates
```
protected boolean m_ProbabilityEstimates
```
    whether to generate probability estimates instead of +1/-1 in case of classification problems
  - m_ReplaceMissingValues
```
protected ReplaceMissingValues m_ReplaceMissingValues
```
    The filter used to get rid of missing values.
  - m_NominalToBinary
```
protected NominalToBinary m_NominalToBinary
```
    The filter used to make attributes numeric.
  - m_Present
```
protected static boolean m_Present
```
    whether the liblinear classes are in the Classpath
- Constructor Detail
  - LibLINEAR
```
public LibLINEAR()
```
- Method Detail
  - getModel
```
public Object getModel()
```
  - globalInfo
```
public 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 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(String[] options)
                throws 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:
    
    Exception - if parsing fails
  - getOptions
```
public 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 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 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 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 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 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 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 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(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 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 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 String probabilityEstimatesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setField
```
protected void setField(Object o,
                        String name,
                        Object value)
```
    sets the specified field
    
    Parameters:
    
    o - the object to set the field for
    
    name - the name of the field
    
    value - the new value of the field
  - setField
```
protected void setField(Object o,
                        String name,
                        int index,
                        Object value)
```
    sets the specified field in an array
    
    Parameters:
    
    o - the object to set the field for
    
    name - the name of the field
    
    index - the index in the array
    
    value - the new value of the field
  - getField
```
protected Object getField(Object o,
                          String name)
```
    returns the current value of the specified field
    
    Parameters:
    
    o - the object the field is member of
    
    name - the name of the field
    
    Returns:
    
    the value
  - newArray
```
protected void newArray(Object o,
                        String name,
                        Class type,
                        int length)
```
    sets a new array for the field
    
    Parameters:
    
    o - the object to set the array for
    
    name - the name of the field
    
    type - the type of the array
    
    length - the length of the one-dimensional array
  - newArray
```
protected void newArray(Object o,
                        String name,
                        Class type,
                        int[] dimensions)
```
    sets a new array for the field
    
    Parameters:
    
    o - the object to set the array for
    
    name - the name of the field
    
    type - the type of the array
    
    dimensions - the dimensions of the array
  - invokeMethod
```
protected Object invokeMethod(Object o,
                              String name,
                              Class[] paramClasses,
                              Object[] paramValues)
```
    executes the specified method and returns the result, if any
    
    Parameters:
    
    o - the object the method should be called from
    
    name - the name of the method
    
    paramClasses - the classes of the parameters
    
    paramValues - the values of the parameters
    
    Returns:
    
    the return value of the method, if any (in that case null)
  - getParameters
```
protected Object getParameters()
```
    transfers the local variables into a svm_parameter object
    
    Returns:
    
    the configured svm_parameter object
  - getProblem
```
protected Object getProblem(List<Object> vx,
                            List<Integer> vy,
                            int max_index)
```
    returns the svm_problem
    
    Parameters:
    
    vx - the x values
    
    vy - the y values
    
    max_index -
    
    Returns:
    
    the Problem object
  - instanceToArray
```
protected Object instanceToArray(Instance instance)
                          throws Exception
```
    returns an instance into a sparse liblinear array
    
    Parameters:
    
    instance - the instance to work on
    
    Returns:
    
    the liblinear array
    
    Throws:
    
    Exception - if setup of array fails
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws 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:
    
    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 Exception
```
    builds the classifier
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    insts - the training instances
    
    Throws:
    
    Exception - if liblinear classes not in classpath or liblinear encountered a problem
  - toString
```
public String toString()
```
    returns a string representation
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a string representation
  - getRevision
```
public 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(String[] args)
```
    Main method for testing this class.
    
    Parameters:
    
    args - the options

Class LibLINEAR

Field Summary

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

CLASS_LINEAR

CLASS_MODEL

CLASS_PROBLEM

CLASS_PARAMETER

CLASS_SOLVERTYPE

CLASS_FEATURENODE

serialVersionUID

m_Model

m_Filter

m_Normalize

SVMTYPE_L2_LR

SVMTYPE_L2LOSS_SVM_DUAL

SVMTYPE_L2LOSS_SVM

SVMTYPE_L1LOSS_SVM_DUAL

SVMTYPE_MCSVM_CS

TAGS_SVMTYPE

m_SVMType

m_eps

m_Cost

m_Bias

m_WeightLabel

m_Weight

m_ProbabilityEstimates

m_ReplaceMissingValues

m_NominalToBinary

m_Present

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

setField

setField

getField

newArray

newArray

invokeMethod

getParameters

getProblem

instanceToArray