LWL (weka-dev 3.7.6-SNAPSHOT API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.lazy.LWL

All Implemented Interfaces:: Serializable, Cloneable, Classifier, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class LWL
extends SingleClassifierEnhancer
implements UpdateableClassifier, WeightedInstancesHandler, TechnicalInformationHandler

Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).

For more info, see

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.

C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..

BibTeX:

 @inproceedings{Frank2003,
    author = {Eibe Frank and Mark Hall and Bernhard Pfahringer},
    booktitle = {19th Conference in Uncertainty in Artificial Intelligence},
    pages = {249-256},
    publisher = {Morgan Kaufmann},
    title = {Locally Weighted Naive Bayes},
    year = {2003}
 }
 
 @article{Atkeson1996,
    author = {C. Atkeson and A. Moore and S. Schaal},
    journal = {AI Review},
    title = {Locally weighted learning},
    year = {1996}
 }

Valid options are:

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).

 -K <number of neighbours>
  Set the number of neighbours used to set the kernel bandwidth.
  (default all)

 -U <number of weighting method>
  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
  2=Tricube, 3=Inverse, 4=Gaussian.
  (default 0 = Linear)

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

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:

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

Version:: $Revision: 8034 $
Author:: Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`CONSTANT`
`static int`	`EPANECHNIKOV`
`static int`	`GAUSS`
`static int`	`INVERSE`
`static int`	`LINEAR` The available kernel weighting methods.
`static int`	`TRICUBE`

Constructor Summary

Constructors
Constructor and Description

LWL()
Constructor.

Constructors
Constructor and Description
`LWL()` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names produced by the neighbour search algorithm.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`int`	`getKNN()` Gets the number of neighbours used for kernel bandwidth setting.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure from the neighbour search algorithm.
`NearestNeighbourSearch`	`getNearestNeighbourSearchAlgorithm()` Returns the current nearestNeighbourSearch algorithm in use.
`String[]`	`getOptions()` Gets the current settings of the classifier.
`String`	`getRevision()` Returns the revision string.
`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.
`int`	`getWeightingKernel()` Gets the kernel weighting method to use.
`String`	`globalInfo()` Returns a string describing classifier.
`String`	`KNNTipText()` Returns the tip text for this property.
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`String`	`nearestNeighbourSearchAlgorithmTipText()` Returns the tip text for this property.
`void`	`setKNN(int knn)` Sets the number of neighbours used for kernel bandwidth setting.
`void`	`setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)` Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setWeightingKernel(int kernel)` Sets the kernel weighting method to use.
`String`	`toString()` Returns a description of this classifier.
`void`	`updateClassifier(Instance instance)` Adds the supplied instance to the training set.
`String`	`weightingKernelTipText()` Returns the tip text for this property.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier

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

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

- Field Detail
  - LINEAR
```
public static final int LINEAR
```
    The available kernel weighting methods.
    
    See Also:
    Constant Field Values
  - EPANECHNIKOV
```
public static final int EPANECHNIKOV
```
    See Also:
    Constant Field Values
  - TRICUBE
```
public static final int TRICUBE
```
    See Also:
    Constant Field Values
  - INVERSE
```
public static final int INVERSE
```
    See Also:
    Constant Field Values
  - GAUSS
```
public static final int GAUSS
```
    See Also:
    Constant Field Values
  - CONSTANT
```
public static final int CONSTANT
```
    See Also:
    Constant Field Values
- Constructor Detail
  - LWL
```
public LWL()
```
    Constructor.
- Method Detail
  - 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
  - enumerateMeasures
```
public Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names produced by the neighbour search algorithm.
    
    Returns:
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(String additionalMeasureName)
```
    Returns the value of the named measure from the neighbour search algorithm.
    
    Parameters:
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    the value of the named measure
    
    Throws:
    
    IllegalArgumentException - if the named measure is not supported
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class SingleClassifierEnhancer
    
    Returns:
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 
```
```
 -K <number of neighbours>
  Set the number of neighbours used to set the kernel bandwidth.
  (default all)
```
```
 -U <number of weighting method>
  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
  2=Tricube, 3=Inverse, 4=Gaussian.
  (default 0 = Linear)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
```
```
 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
```
```
 -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 SingleClassifierEnhancer
    
    Parameters:
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class SingleClassifierEnhancer
    
    Returns:
    an array of strings suitable for passing to setOptions
  - KNNTipText
```
public String KNNTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setKNN
```
public void setKNN(int knn)
```
    Sets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.
    
    Parameters:
    knn - the number of neighbours included inside the kernel bandwidth, or 0 to specify using all neighbors.
  - getKNN
```
public int getKNN()
```
    Gets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.
    
    Returns:
    the number of neighbours included inside the kernel bandwidth, or 0 for all neighbours
  - weightingKernelTipText
```
public String weightingKernelTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setWeightingKernel
```
public void setWeightingKernel(int kernel)
```
    Sets the kernel weighting method to use. Must be one of LINEAR, EPANECHNIKOV, TRICUBE, INVERSE, GAUSS or CONSTANT, other values are ignored.
    
    Parameters:
    kernel - the new kernel method to use. Must be one of LINEAR, EPANECHNIKOV, TRICUBE, INVERSE, GAUSS or CONSTANT.
  - getWeightingKernel
```
public int getWeightingKernel()
```
    Gets the kernel weighting method to use.
    
    Returns:
    the new kernel method to use. Will be one of LINEAR, EPANECHNIKOV, TRICUBE, INVERSE, GAUSS or CONSTANT.
  - nearestNeighbourSearchAlgorithmTipText
```
public String nearestNeighbourSearchAlgorithmTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNearestNeighbourSearchAlgorithm
```
public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm()
```
    Returns the current nearestNeighbourSearch algorithm in use.
    
    Returns:
    the NearestNeighbourSearch algorithm currently in use.
  - setNearestNeighbourSearchAlgorithm
```
public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)
```
    Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
    
    Parameters:
    nearestNeighbourSearchAlgorithm - - The NearestNeighbourSearch class.
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class SingleClassifierEnhancer
    
    Returns:
    the capabilities of this classifier
    See Also:
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    instances - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws Exception
```
    Adds the supplied instance to the training set.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    instance - the instance to add
    
    Throws:
    
    Exception - if instance could not be incorporated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    instance - the instance to be classified
    
    Returns:
    preedicted class probability distribution
    
    Throws:
    
    Exception - if distribution can't be computed successfully
  - toString
```
public String toString()
```
    Returns a description of this classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    a description of this classifier as a string.
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    argv - the options

Class LWL

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

LINEAR

EPANECHNIKOV

TRICUBE

INVERSE

GAUSS

CONSTANT

Constructor Detail

LWL

Method Detail

globalInfo

getTechnicalInformation

enumerateMeasures

getMeasure

listOptions

setOptions

getOptions

KNNTipText

setKNN

getKNN

weightingKernelTipText

setWeightingKernel

getWeightingKernel

nearestNeighbourSearchAlgorithmTipText

getNearestNeighbourSearchAlgorithm

setNearestNeighbourSearchAlgorithm

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

toString

getRevision

main