Package weka.classifiers.functions

Class PLSRegressor

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.RandomizableClassifier

weka.classifiers.functions.PLSRegressor

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler

public class PLSRegressor
extends weka.classifiers.RandomizableClassifier

A wrapper classifier for the PLS filter, utilizing the filter's ability to perform predictions.

Valid options are:

 -algorithm <algorithm specification>
  The PLS algorithm to use. Full classname of algorithm to include,  followed by scheme options.
  (default: adams.data.instancesanalysis.pls.PLS1)

 -S <num>
  Random number seed.
  (default 1)

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).

 -batch-size
  The desired batch size for batch prediction  (default 100).

Author:

fracpete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
protected PLS	m_ActualFilter	the actual filter to use
protected AbstractPLS	m_Algorithm	the PLS algorithm

Fields inherited from class weka.classifiers.RandomizableClassifier

m_Seed

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
PLSRegressor()

Method Summary

Modifier and Type	Method	Description
String	algorithmTipText()	Returns the tip text for this property
void	buildClassifier(weka.core.Instances data)	builds the classifier
double	classifyInstance(weka.core.Instance instance)	Classifies the given test instance.
AbstractPLS	getAlgorithm()	Returns the PLS algorithm to use.
weka.core.Capabilities	getCapabilities()	Returns default capabilities of the classifier.
protected AbstractPLS	getDefaultAlgorithm()	Returns the default algorithm.
String[]	getOptions()	returns the options of the current setup
String	getRevision()	Returns the revision string.
String	globalInfo()	Returns a string describing classifier
Enumeration<weka.core.Option>	listOptions()	Gets an enumeration describing the available options.
static void	main(String[] args)	Main method for running this classifier from commandline.
void	setAlgorithm(AbstractPLS value)	Sets the PLS algorithm to use.
void	setOptions(String[] options)	Parses the options for this object.
String	toString()	returns a string representation of the classifier

Methods inherited from class weka.classifiers.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Algorithm

protected AbstractPLS m_Algorithm

the PLS algorithm

m_ActualFilter

protected PLS m_ActualFilter

the actual filter to use

Constructor Detail

PLSRegressor

public PLSRegressor()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public Enumeration<weka.core.Option> listOptions()

Gets an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.RandomizableClassifier

Returns:

an enumeration of all the available options.

getOptions

public String[] getOptions()

returns the options of the current setup

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.RandomizableClassifier

Returns:

the current options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses the options for this object.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.RandomizableClassifier

Parameters:

options - the options to use

Throws:

Exception - if setting of options fails

getDefaultAlgorithm

protected AbstractPLS getDefaultAlgorithm()

Returns the default algorithm.

Returns:

the default

setAlgorithm

public void setAlgorithm(AbstractPLS value)

Sets the PLS algorithm to use.

Parameters:

value - the algorithm

getAlgorithm

public AbstractPLS getAlgorithm()

Returns the PLS algorithm to use.

Returns:

the algorithm

algorithmTipText

public String algorithmTipText()

Returns the tip text for this property

Returns:

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

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws Exception

builds the classifier

Parameters:

data - the training instances

Throws:

Exception - if something goes wrong

classifyInstance

public double classifyInstance(weka.core.Instance instance)
                        throws Exception

Classifies the given test instance. The instance has to belong to a dataset when it's being classified.

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Utils.missingValue() if no prediction is made

Throws:

Exception - if an error occurred during the prediction

toString

public String toString()

returns a string representation of the classifier

Overrides:

toString in class Object

Returns:

a string representation of the classifier

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

public static void main(String[] args)

Main method for running this classifier from commandline.

Parameters:

args - the options