moa.classifiers.bayes

Class NaiveBayes

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.bayes.NaiveBayes

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class NaiveBayes
extends AbstractClassifier

Naive Bayes incremental learner.

Performs classic bayesian prediction while making naive assumption that all inputs are independent.
Naive Bayes is a classifier algorithm known for its simplicity and low computational cost. Given n different classes, the trained Naive Bayes classifier predicts for every unlabelled instance I the class C to which it belongs with high accuracy.

Parameters:

• -r : Seed for random behaviour of the classifier

Version:

$Revision: 7 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected AutoExpandVector<AttributeClassObserver>	attributeObservers
protected DoubleVector	observedClassDistribution

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
NaiveBayes()

Method Summary

Methods

Modifier and Type	Method and Description
static double[]	doNaiveBayesPrediction(weka.core.Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> attributeObservers)
static double[]	doNaiveBayesPredictionLog(weka.core.Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> observers, AutoExpandVector<AttributeClassObserver> observers2)
void	getModelDescription(StringBuilder out, int indent) Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl() Gets the current measurements of this classifier. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.
String	getPurposeString() Gets the purpose of this object
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	manageMemory(int currentByteSize, int maxByteSize)
protected AttributeClassObserver	newNominalClassObserver()
protected AttributeClassObserver	newNumericClassObserver()
void	resetLearningImpl() Resets this classifier.
void	trainOnInstanceImpl(weka.core.Instance inst) Trains this classifier incrementally using the given instance. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getSubClassifiers, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

observedClassDistribution

protected DoubleVector observedClassDistribution

attributeObservers

protected AutoExpandVector<AttributeClassObserver> attributeObservers

Constructor Detail

NaiveBayes

public NaiveBayes()

Method Detail

getPurposeString

public String getPurposeString()

Description copied from interface: OptionHandler

Gets the purpose of this object

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class AbstractClassifier

Returns:

the string with the purpose of this object

resetLearningImpl

public void resetLearningImpl()

Description copied from class: AbstractClassifier

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

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

resetLearningImpl in class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.Instance inst)

Description copied from class: AbstractClassifier

Trains this classifier incrementally using the given instance.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

getVotesForInstance

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

Description copied from interface: Classifier

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

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Description copied from class: AbstractClassifier

Gets the current measurements of this classifier.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

isRandomizable

public boolean isRandomizable()

Description copied from interface: Classifier

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.

newNominalClassObserver

protected AttributeClassObserver newNominalClassObserver()

newNumericClassObserver

protected AttributeClassObserver newNumericClassObserver()

doNaiveBayesPrediction

public static double[] doNaiveBayesPrediction(weka.core.Instance inst,
                              DoubleVector observedClassDistribution,
                              AutoExpandVector<AttributeClassObserver> attributeObservers)

doNaiveBayesPredictionLog

public static double[] doNaiveBayesPredictionLog(weka.core.Instance inst,
                                 DoubleVector observedClassDistribution,
                                 AutoExpandVector<AttributeClassObserver> observers,
                                 AutoExpandVector<AttributeClassObserver> observers2)

manageMemory

public void manageMemory(int currentByteSize,
                int maxByteSize)