Package weka.classifiers.lazy
Class LWL
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.lazy.LWL
- All Implemented Interfaces:
Serializable,Cloneable,Classifier,UpdateableClassifier,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,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:
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: 10141 $
- 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:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final intstatic final intstatic final intstatic final intstatic final intThe available kernel weighting methods.static final intFields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoidbuildClassifier(Instances instances) Generates the classifier.double[]distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.Returns an enumeration of the additional measure names produced by the neighbour search algorithm.Returns default capabilities of the classifier.intgetKNN()Gets the number of neighbours used for kernel bandwidth setting.doublegetMeasure(String additionalMeasureName) Returns the value of the named measure from the neighbour search algorithm.Returns the current nearestNeighbourSearch algorithm in use.String[]Gets the current settings of the classifier.Returns the revision string.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.intGets the kernel weighting method to use.Returns a string describing classifier.Returns the tip text for this property.Returns an enumeration describing the available options.static voidMain method for testing this class.Returns the tip text for this property.voidsetKNN(int knn) Sets the number of neighbours used for kernel bandwidth setting.voidsetNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm) Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).voidsetOptions(String[] options) Parses a given list of options.voidsetWeightingKernel(int kernel) Sets the kernel weighting method to use.toString()Returns a description of this classifier.voidupdateClassifier(Instance instance) Adds the supplied instance to the training set.Returns the tip text for this property.Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifierMethods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
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Field Details
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LINEAR
public static final int LINEARThe available kernel weighting methods.- See Also:
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EPANECHNIKOV
public static final int EPANECHNIKOV- See Also:
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TRICUBE
public static final int TRICUBE- See Also:
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INVERSE
public static final int INVERSE- See Also:
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GAUSS
public static final int GAUSS- See Also:
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CONSTANT
public static final int CONSTANT- See Also:
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Constructor Details
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LWL
public LWL()Constructor.
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Method Details
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globalInfo
Returns a string describing classifier.- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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:
getTechnicalInformationin interfaceTechnicalInformationHandler- Returns:
- the technical information about this class
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enumerateMeasures
Returns an enumeration of the additional measure names produced by the neighbour search algorithm.- Returns:
- an enumeration of the measure names
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getMeasure
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
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classSingleClassifierEnhancer- Returns:
- an enumeration of all the available options.
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setOptions
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:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classSingleClassifierEnhancer- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classSingleClassifierEnhancer- Returns:
- an array of strings suitable for passing to setOptions
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KNNTipText
Returns the tip text for this property.- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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.
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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
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weightingKernelTipText
Returns the tip text for this property.- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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.
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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.
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nearestNeighbourSearchAlgorithmTipText
Returns the tip text for this property.- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNearestNeighbourSearchAlgorithm
Returns the current nearestNeighbourSearch algorithm in use.- Returns:
- the NearestNeighbourSearch algorithm currently in use.
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setNearestNeighbourSearchAlgorithm
public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm) Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).- Parameters:
nearestNeighbourSearchAlgorithm- - The NearestNeighbourSearch class.
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilitiesin interfaceCapabilitiesHandler- Specified by:
getCapabilitiesin interfaceClassifier- Overrides:
getCapabilitiesin classSingleClassifierEnhancer- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Generates the classifier.- Specified by:
buildClassifierin interfaceClassifier- Parameters:
instances- set of instances serving as training data- Throws:
Exception- if the classifier has not been generated successfully
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updateClassifier
Adds the supplied instance to the training set.- Specified by:
updateClassifierin interfaceUpdateableClassifier- Parameters:
instance- the instance to add- Throws:
Exception- if instance could not be incorporated successfully
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- Specified by:
distributionForInstancein interfaceClassifier- Overrides:
distributionForInstancein classAbstractClassifier- Parameters:
instance- the instance to be classified- Returns:
- preedicted class probability distribution
- Throws:
Exception- if distribution can't be computed successfully
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toString
Returns a description of this classifier. -
getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv- the options
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