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

All Implemented Interfaces:: Serializable, Cloneable, weka.classifiers.Classifier, ThreadSafeClassifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class LWLSynchroPrefilter
extends weka.classifiers.lazy.LWL
implements ThreadSafeClassifier

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:

 -no-update
  Suppresses the update of the nearest neighbor search (nns)
  algorithm with the data that is to be classified.
 (default: nns gets updated).

 -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.functions.GaussianProcesses)

 Options specific to classifier weka.classifiers.functions.GaussianProcesses:

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

 -L <double>
  Level of Gaussian Noise wrt transformed target. (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:

 -D
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

 -C <num>
  The size of the cache (a prime number), 0 for full cache and
  -1 to turn it off.
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Note: the build(Instance) needs manual syncing with the original WEKA classifier (distributionForInstance(Instance) method).

Version:: $Revision: 12986 $
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:: LWL.distributionForInstance(Instance), Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`protected weka.filters.unsupervised.attribute.AddID`	`m_addId`
`protected weka.core.Instances`	`m_filtered`
`protected int`	`m_IndexOfID`
`protected weka.core.neighboursearch.NearestNeighbourSearch`	`m_ModifiedSearchMethod`
`protected boolean`	`m_NoUpdate`	whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
`protected weka.core.Instances`	`m_original`
`protected weka.filters.Filter`	`m_PreFilter`
`protected boolean`	`m_run`

Fields inherited from class weka.classifiers.lazy.LWL
CONSTANT, EPANECHNIKOV, GAUSS, INVERSE, LINEAR, m_kNN, m_NNSearch, m_Train, m_UseAllK, m_WeightKernel, m_ZeroR, TRICUBE

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor Description

LWLSynchroPrefilter()
Initializes the classifier.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`protected void`	`build(weka.core.Instance instance)`	Builds the classifier.
`protected String`	`defaultClassifierString()`	Default classifier classname.
`double[]`	`distributionForInstance(weka.core.Instance instance)`	Calculates the class membership probabilities for the given test instance.
`String`	`filterTipText()`
`weka.filters.Filter`	`getFilter()`
`protected String`	`getFilterSpec()`
`boolean`	`getNoUpdate()`	Returns whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
`String[]`	`getOptions()`	Gets the current settings of the classifier.
`String`	`getRevision()`	Returns the revision string.
`Enumeration`	`listOptions()`	Returns an enumeration describing the available options.
`static void`	`main(String[] argv)`	Main method for testing this class.
`String`	`noUpdateTipText()`	Returns the tip text for this property.
`void`	`setFilter(weka.filters.Filter filter)`
`void`	`setNoUpdate(boolean value)`	Sets whether to suppress updating the nearest-neighbor search algorithm when making predictions.
`void`	`setOptions(String[] options)`	Parses a given list of options.
`protected void`	`setUp(weka.core.Instances data)`
`String`	`toString()`	Returns a description of this classifier.

Methods inherited from class weka.classifiers.lazy.LWL
buildClassifier, enumerateMeasures, getCapabilities, getKNN, getMeasure, getNearestNeighbourSearchAlgorithm, getTechnicalInformation, getWeightingKernel, globalInfo, KNNTipText, nearestNeighbourSearchAlgorithmTipText, setKNN, setNearestNeighbourSearchAlgorithm, setWeightingKernel, updateClassifier, weightingKernelTipText

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods 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

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

Methods inherited from interface weka.classifiers.Classifier
buildClassifier, classifyInstance, getCapabilities

Field Detail

m_NoUpdate
```
protected boolean m_NoUpdate
```
whether to suppress the update of the nearest-neighbor search algorithm when making predictions.

m_PreFilter

protected weka.filters.Filter m_PreFilter

m_filtered

protected weka.core.Instances m_filtered

m_original

protected weka.core.Instances m_original

m_run
```
protected boolean m_run
```

m_addId

protected weka.filters.unsupervised.attribute.AddID m_addId

m_ModifiedSearchMethod

protected weka.core.neighboursearch.NearestNeighbourSearch m_ModifiedSearchMethod

m_IndexOfID
```
protected int m_IndexOfID
```

Constructor Detail
- LWLSynchroPrefilter
```
public LWLSynchroPrefilter()
```
  Initializes the classifier.

Method Detail

setUp

protected void setUp(weka.core.Instances data)
              throws Exception

Throws:: Exception

defaultClassifierString
```
protected String defaultClassifierString()
```
Default classifier classname.

Overrides:

defaultClassifierString in class weka.classifiers.lazy.LWL

Returns:

the classname

filterTipText
```
public String filterTipText()
```

setFilter

public void setFilter(weka.filters.Filter filter)

getFilter

public weka.filters.Filter getFilter()

getFilterSpec
```
protected String getFilterSpec()
```

listOptions
```
public Enumeration listOptions()
```
Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.lazy.LWL

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:

 -no-update
  Suppresses the update of the nearest neighbor search (nns)
  algorithm with the data that is to be classified.
 (default: nns gets updated).

 -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.functions.GaussianProcesses)

 Options specific to classifier weka.classifiers.functions.GaussianProcesses:

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

 -L <double>
  Level of Gaussian Noise wrt transformed target. (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:

 -D
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

 -C <num>
  The size of the cache (a prime number), 0 for full cache and
  -1 to turn it off.
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Specified by:: setOptions in interface weka.core.OptionHandler
Overrides:: setOptions in class weka.classifiers.lazy.LWL
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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.lazy.LWL

Returns:

an array of strings suitable for passing to setOptions

noUpdateTipText
```
public String noUpdateTipText()
```
Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNoUpdate
```
public void setNoUpdate(boolean value)
```
Sets whether to suppress updating the nearest-neighbor search algorithm when making predictions.

Parameters:

value - if true then no update happens.

getNoUpdate
```
public boolean getNoUpdate()
```
Returns whether to suppress the update of the nearest-neighbor search algorithm when making predictions.

Returns:

true if the update is suppressed

build
```
protected void build(weka.core.Instance instance)
              throws Exception
```
Builds the classifier.

Note: needs manual syncing with the distributionForInstance method of the original WEKA classifier.

Parameters:

instance - the instance to make prediction for

Throws:

Exception - if build fails

See Also:

LWL.distributionForInstance(Instance)

distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws Exception
```
Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.lazy.LWL

Parameters:

instance - the instance to be classified

Returns:

predicted 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 weka.classifiers.lazy.LWL

Returns:

a description of this classifier as a string.

getRevision
```
public String getRevision()
```
Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.lazy.LWL

Returns:

the revision

main
```
public static void main(String[] argv)
```
Main method for testing this class.

Parameters:

argv - the options

Class LWLSynchroPrefilter

Field Summary

Fields inherited from class weka.classifiers.lazy.LWL

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.lazy.LWL

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Field Detail

m_NoUpdate

m_PreFilter

m_filtered

m_original

m_run

m_addId

m_ModifiedSearchMethod

m_IndexOfID

Constructor Detail

LWLSynchroPrefilter

Method Detail

setUp

defaultClassifierString

filterTipText

setFilter

getFilter

getFilterSpec

listOptions

setOptions

getOptions

noUpdateTipText

setNoUpdate

getNoUpdate

build

distributionForInstance

toString

getRevision

main