Package weka.classifiers.functions

Class GeneticAlgorithm

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.functions.GeneticAlgorithm

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.RevisionHandler

public class GeneticAlgorithm
extends weka.classifiers.AbstractClassifier

Applies the specified genetic algorithm to the training data and uses the best setup for the final model.

Valid options are:

 -algorithm <classname + options>
  The genetic algorithm.
  (default: adams.genetic.Hermione)

 -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).

Version:

$Revision$

Author:

fracpete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
protected AbstractClassifierBasedGeneticAlgorithm	m_Algorithm	the genetic algorithm.
protected weka.classifiers.Classifier	m_Model	the final model.

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
GeneticAlgorithm()

Method Summary

Modifier and Type	Method	Description
String	algorithmTipText()	Returns the tip text for this property.
void	buildClassifier(weka.core.Instances data)	Generates a classifier.
double	classifyInstance(weka.core.Instance instance)	Classifies the given test instance.
double[]	distributionForInstance(weka.core.Instance instance)	Predicts the class memberships for a given instance.
AbstractClassifierBasedGeneticAlgorithm	getAlgorithm()	Returns the seed value for the random values.
weka.core.Capabilities	getCapabilities()	Returns the Capabilities of this classifier.
String[]	getOptions()	returns the options of the current setup
String	getRevision()	Returns the revision string.
String	globalInfo()	Returns a string describing classifier
Enumeration	listOptions()	Gets an enumeration describing the available options.
static void	main(String[] args)	Main method for running this class.
void	setAlgorithm(AbstractClassifierBasedGeneticAlgorithm value)	Sets the genetic algorithm to use.
void	setOptions(String[] options)	Parses the options for this object.
String	toString()	Returns a string representation of the built model.

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, 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 AbstractClassifierBasedGeneticAlgorithm m_Algorithm

the genetic algorithm.

m_Model

protected weka.classifiers.Classifier m_Model

the final model.

Constructor Detail

GeneticAlgorithm

public GeneticAlgorithm()

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 listOptions()

Gets an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.AbstractClassifier

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.AbstractClassifier

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.AbstractClassifier

Parameters:

options - the options to use

Throws:

Exception - if setting of options fails

setAlgorithm

public void setAlgorithm(AbstractClassifierBasedGeneticAlgorithm value)

Sets the genetic algorithm to use.

Parameters:

value - the algorithm

getAlgorithm

public AbstractClassifierBasedGeneticAlgorithm getAlgorithm()

Returns the seed value for the random values.

Returns:

the seed

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 the Capabilities of this classifier. Maximally permissive capabilities are allowed by default. Derived classifiers should override this method and first disable all capabilities and then enable just those capabilities that make sense for the scheme.

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 object

See Also:

Capabilities

buildClassifier

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

Generates a classifier.

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

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 Instance.missingValue() if no prediction is made

Throws:

Exception - if an error occurred during the prediction

distributionForInstance

public double[] distributionForInstance(weka.core.Instance instance)
                                 throws Exception

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero. If the class is numeric, the array must consist of only one element, which contains the predicted value.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

Exception - if distribution could not be computed successfully

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

toString

public String toString()

Returns a string representation of the built model.

Overrides:

toString in class Object

Returns:

the model string

main

public static void main(String[] args)

Main method for running this class.

Parameters:

args - the commandline parameters