weka.classifiers

Class AbstractClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

All Implemented Interfaces:

Serializable, Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler

Direct Known Subclasses:

BayesNet, DecisionStump, DecisionTable, GaussianProcesses, IBk, J48, JRip, KStar, LinearRegression, LMT, Logistic, LogisticBase, M5Base, MultilayerPerceptron, MultipleClassifiersCombiner, NaiveBayes, NaiveBayesMultinomial, NaiveBayesMultinomialText, OneR, PART, PMMLClassifier, PreConstructedLinearModel, RandomForest, RandomizableClassifier, RandomTree, REPTree, RuleNode, SerializedClassifier, SimpleLinearRegression, SimpleLogistic, SingleClassifierEnhancer, SMO, SMOreg, VotedPerceptron, ZeroR

public abstract class AbstractClassifier
extends Object
implements Classifier, Cloneable, Serializable, OptionHandler, CapabilitiesHandler, RevisionHandler

Abstract classifier. All schemes for numeric or nominal prediction in Weka extend this class. Note that a classifier MUST either implement distributionForInstance() or classifyInstance().

Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AbstractClassifier()

Method Summary

Methods

Modifier and Type	Method and Description
double	classifyInstance(Instance instance) Classifies the given test instance.
String	debugTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Predicts the class memberships for a given instance.
static Classifier	forName(String classifierName, String[] options) Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
Capabilities	getCapabilities() Returns the Capabilities of this classifier.
boolean	getDebug() Get whether debugging is turned on.
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 Classifier[]	makeCopies(Classifier model, int num) Creates a given number of deep copies of the given classifier using serialization.
static Classifier	makeCopy(Classifier model) Creates a deep copy of the given classifier using serialization.
static void	runClassifier(Classifier classifier, String[] options) runs the classifier instance with the given options.
void	setDebug(boolean debug) Set debugging mode.
void	setOptions(String[] options) Parses a given list of options.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface weka.classifiers.Classifier

buildClassifier

Constructor Detail

AbstractClassifier

public AbstractClassifier()

Method Detail

classifyInstance

public double classifyInstance(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 Classifier

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

distributionForInstance

public double[] distributionForInstance(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. Note that a classifier MUST implement either this or classifyInstance().

Specified by:

distributionForInstance in interface Classifier

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

forName

public static Classifier forName(String classifierName,
                 String[] options)
                          throws Exception

Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method. If the classifier implements OptionHandler and the options parameter is non-null, the classifier will have it's options set.

Parameters:

classifierName - the fully qualified class name of the classifier

options - an array of options suitable for passing to setOptions. May be null.

Returns:

the newly created classifier, ready for use.

Throws:

Exception - if the classifier name is invalid, or the options supplied are not acceptable to the classifier

makeCopy

public static Classifier makeCopy(Classifier model)
                           throws Exception

Creates a deep copy of the given classifier using serialization.

Parameters:

model - the classifier to copy

Returns:

a deep copy of the classifier

Throws:

Exception - if an error occurs

makeCopies

public static Classifier[] makeCopies(Classifier model,
                      int num)
                               throws Exception

Creates a given number of deep copies of the given classifier using serialization.

Parameters:

model - the classifier to copy

num - the number of classifier copies to create.

Returns:

an array of classifiers.

Throws:

Exception - if an error occurs

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

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:

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

Specified by:

setOptions in interface OptionHandler

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 OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setDebug

public void setDebug(boolean debug)

Set debugging mode.

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Get whether debugging is turned on.

Returns:

true if debugging output is on

debugTipText

public String debugTipText()

Returns the tip text for this property

Returns:

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

getCapabilities

public 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 Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision

runClassifier

public static void runClassifier(Classifier classifier,
                 String[] options)

runs the classifier instance with the given options.

Parameters:

classifier - the classifier to run

options - the commandline options