meka.classifiers.multilabel.neurofuzzy

Class ARAMNetwork

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass

meka.classifiers.multilabel.neurofuzzy.ARAMNetwork

All Implemented Interfaces:

Serializable, Cloneable, IncrementalMultiLabelClassifier, MultiLabelClassifier, MultiLabelClassifierThreaded, weka.classifiers.Classifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler

public class ARAMNetwork
extends ARAMNetworkClass

****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION. Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

For more information on Naive Bayes classifiers, see

George H. John and Pat Langley (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp. 338-345. Morgan Kaufmann, San Mateo.

Valid options are:

-K
Use kernel estimation for modelling numeric attributes rather than a single normal distribution.

-D
Use supervised discretization to process numeric attributes.

Version:

$Revision: 1.16 $ Modified by Rushi for use as a CN710 template

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Rushi Bhatt (rushi@cns.bu.edu)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
int	numClasses
int	numFeatures
double	threshold

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 and Description
ARAMNetwork()
ARAMNetwork(int fnumFeatures, int fnumClasses, double fro, double fthreshold)

Method Summary

Modifier and Type	Method and Description
double[]	ARAMm_Ranking2Class(double[] rankings)
void	buildClassifier(weka.core.Instances D) Generates the classifier.
double	classifyInstance(weka.core.Instance instance) Classifies the given test instance.
String	debugTipText() Returns the tip text for this property
double[]	distributionForInstance(weka.core.Instance instance) Calculates the class membership probabilities for the given test instance.
double[][]	distributionForInstanceM(weka.core.Instances i)
weka.core.Capabilities	getCapabilities()
boolean	getDebug() Get whether debugging is turned on.
String	getModel() Returns a string representation of the model.
int[]	getneuronsactivated()
double[]	getneuronsactivity()
String[]	getOptions() Gets the current settings of the classifier.
String	globalInfo() Returns a string describing this classifier
boolean	isThreaded()
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv)
void	setDebug(boolean debug) Set debugging mode.
void	setOptions(String[] options) Parses a given list of options.
void	setThreaded(boolean setv)
String	toString() Returns a description of the classifier.
void	updateClassifier(weka.core.Instance instance) Updates the classifier with the given instance.

Methods inherited from class meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass

testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, defaultClassifierString, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Field Detail

numFeatures

public int numFeatures

numClasses

public int numClasses

threshold

public double threshold

Constructor Detail

ARAMNetwork

public ARAMNetwork(int fnumFeatures,
                   int fnumClasses,
                   double fro,
                   double fthreshold)

ARAMNetwork

public ARAMNetwork()

Method Detail

getneuronsactivated

public int[] getneuronsactivated()

getneuronsactivity

public double[] getneuronsactivity()

globalInfo

public String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui. ****MODIFY WITH CORRECT INFORMATION****

buildClassifier

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

Generates the classifier.

Parameters:

instances - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

updateClassifier

public void updateClassifier(weka.core.Instance instance)
                      throws Exception

Updates the classifier with the given instance.

Parameters:

instance - the new training instance to include in the model

Throws:

Exception - if the instance could not be incorporated in the model.

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.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

Exception - if there is a problem generating the prediction

ARAMm_Ranking2Class

public double[] ARAMm_Ranking2Class(double[] rankings)

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. Note that a classifier MUST implement either this or distributionForInstance().

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

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.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:

-K
Use kernel estimation for modelling numeric attributes rather than a single normal distribution.

-D
Use supervised discretization to process numeric attributes.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

toString

public String toString()

Returns a description of the classifier.

Overrides:

toString in class Object

Returns:

a description of the classifier as a string.

main

public static void main(String[] argv)

getModel

public String getModel()

Description copied from interface: MultiLabelClassifier

Returns a string representation of the model.

Returns:

the model

isThreaded

public boolean isThreaded()

setThreaded

public void setThreaded(boolean setv)

distributionForInstanceM

public double[][] distributionForInstanceM(weka.core.Instances i)
                                    throws Exception

Throws:

Exception

setDebug

public void setDebug(boolean debug)

Description copied from interface: MultiLabelClassifier

Set debugging mode.

Specified by:

setDebug in interface MultiLabelClassifier

Overrides:

setDebug in class weka.classifiers.AbstractClassifier

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Description copied from interface: MultiLabelClassifier

Get whether debugging is turned on.

Specified by:

getDebug in interface MultiLabelClassifier

Overrides:

getDebug in class weka.classifiers.AbstractClassifier

Returns:

true if debugging output is on

debugTipText

public String debugTipText()

Description copied from interface: MultiLabelClassifier

Returns the tip text for this property

Specified by:

debugTipText in interface MultiLabelClassifier

Overrides:

debugTipText in class weka.classifiers.AbstractClassifier

Returns:

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

getCapabilities

public weka.core.Capabilities getCapabilities()

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.SingleClassifierEnhancer