moa.classifiers

Interface Classifier

All Superinterfaces:

AWTRenderable, MOAObject, OptionHandler, Serializable

All Known Implementing Classes:

AbstractAMRules, AbstractClassifier, AccuracyUpdatedEnsemble, AccuracyWeightedEnsemble, ActiveClassifier, ADACC, AdaHoeffdingOptionTree, AMRulesRegressor, ASHoeffdingTree, ChangeDetectorLearner, DACC, DecisionStump, DriftDetectionMethodClassifier, FadingTargetMean, FIMTDD, HoeffdingAdaptiveTree, HoeffdingOptionTree, HoeffdingTree, HoeffdingTreeClassifLeaves, kNN, kNNwithPAW, kNNwithPAWandADWIN, LeveragingBag, LimAttClassifier, LimAttHoeffdingTree, MajorityClass, MajorityLabelset, MEKAClassifier, MLOzaBag, MLOzaBagAdwin, MultilabelHoeffdingTree, NaiveBayes, NaiveBayesMultinomial, NoChange, OCBoost, OnlineAccuracyUpdatedEnsemble, OnlineSmoothBoost, ORTO, OzaBag, OzaBagAdwin, OzaBagASHT, OzaBoost, OzaBoostAdwin, Perceptron, Perceptron, RandomHoeffdingTree, RandomRules, RuleClassifier, RuleClassifierNBayes, SGD, SGDMultiClass, SingleClassifierDrift, SPegasos, TargetMean, TemporallyAugmentedClassifier, WeightedMajorityAlgorithm, WEKAClassifier

public interface Classifier
extends MOAObject, OptionHandler, AWTRenderable

Classifier interface for incremental classification models.

Version:

$Revision: 7 $

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz)

Method Summary

Methods

Modifier and Type	Method and Description
Classifier	copy() Produces a copy of this classifier.
boolean	correctlyClassifies(weka.core.Instance inst) Gets whether this classifier correctly classifies an instance.
InstancesHeader	getModelContext() Gets the reference to the header of the data stream.
Measurement[]	getModelMeasurements() Gets the current measurements of this classifier.
Classifier[]	getSubClassifiers() Gets the classifiers of this ensemble.
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
boolean	isRandomizable() Gets whether this classifier needs a random seed.
void	resetLearning() Resets this classifier.
void	setModelContext(InstancesHeader ih) Sets the reference to the header of the data stream.
void	setRandomSeed(int s) Sets the seed for random number generation.
boolean	trainingHasStarted() Gets whether training has started.
double	trainingWeightSeenByModel() Gets the sum of the weights of the instances that have been used by this classifier during the training in trainOnInstance
void	trainOnInstance(weka.core.Instance inst) Trains this classifier incrementally using the given instance.

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, getPurposeString, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

getDescription, measureByteSize

Methods inherited from interface moa.gui.AWTRenderable

getAWTRenderer

Method Detail

setModelContext

void setModelContext(InstancesHeader ih)

Sets the reference to the header of the data stream. The header of the data stream is extended from WEKA Instances. This header is needed to know the number of classes and attributes

Parameters:

ih - the reference to the data stream header

getModelContext

InstancesHeader getModelContext()

Gets the reference to the header of the data stream. The header of the data stream is extended from WEKA Instances. This header is needed to know the number of classes and attributes

Returns:

the reference to the data stream header

isRandomizable

boolean isRandomizable()

Gets whether this classifier needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Returns:

true if the classifier needs a random seed.

setRandomSeed

void setRandomSeed(int s)

Sets the seed for random number generation.

Parameters:

s - the seed

trainingHasStarted

boolean trainingHasStarted()

Gets whether training has started.

Returns:

true if training has started

trainingWeightSeenByModel

double trainingWeightSeenByModel()

Gets the sum of the weights of the instances that have been used by this classifier during the training in trainOnInstance

Returns:

the weight of the instances that have been used training

resetLearning

void resetLearning()

Resets this classifier. It must be similar to starting a new classifier from scratch.

trainOnInstance

void trainOnInstance(weka.core.Instance inst)

Trains this classifier incrementally using the given instance.

Parameters:

inst - the instance to be used for training

getVotesForInstance

double[] getVotesForInstance(weka.core.Instance inst)

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.

Parameters:

inst - the instance to be classified

Returns:

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

correctlyClassifies

boolean correctlyClassifies(weka.core.Instance inst)

Gets whether this classifier correctly classifies an instance. Uses getVotesForInstance to obtain the prediction and the instance to obtain its true class.

Parameters:

inst - the instance to be classified

Returns:

true if the instance is correctly classified

getModelMeasurements

Measurement[] getModelMeasurements()

Gets the current measurements of this classifier.

Returns:

an array of measurements to be used in evaluation tasks

getSubClassifiers

Classifier[] getSubClassifiers()

Gets the classifiers of this ensemble. Returns null if this classifier is a single classifier.

Returns:

an array of the classifiers of the ensemble

copy

Classifier copy()

Produces a copy of this classifier.

Specified by:

copy in interface MOAObject

Specified by:

copy in interface OptionHandler

Returns:

the copy of this classifier