moa.classifiers.bayes

Class NaiveBayesMultinomial

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.bayes.NaiveBayesMultinomial

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class NaiveBayesMultinomial
extends AbstractClassifier

Class for building and using a multinomial Naive Bayes classifier. Performs text classic bayesian prediction while making naive assumption that all inputs are independent. For more information see,

Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.

The core equation for this classifier:

P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)

where Ci is class i and D is a document.

Incremental version of the algorithm.

* BibTeX:

 @inproceedings{Mccallum1998,
    author = {Andrew Mccallum and Kamal Nigam},
    booktitle = {AAAI-98 Workshop on 'Learning for Text Categorization'},
    title = {A Comparison of Event Models for Naive Bayes Text Classification},
    year = {1998}
 }
 

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
FloatOption	laplaceCorrectionOption
protected double[]	m_classTotals sum of weight_of_instance * word_count_of_instance for each class
protected weka.core.Instances	m_headerInfo copy of header information for use in toString method
protected int	m_numClasses number of class values
protected double[]	m_probOfClass the probability of a class (i.e.
protected DoubleVector[]	m_wordTotalForClass probability that a word (w) exists in a class (H) (i.e.
protected boolean	reset

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
NaiveBayesMultinomial()

Method Summary

Methods

Modifier and Type	Method and Description
void	getModelDescription(StringBuilder result, 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 instance) Calculates the class membership probabilities for the given test instance.
boolean	isRandomizable() Gets whether this classifier needs a random seed.
void	resetLearningImpl() Resets this classifier.
double	totalSize(weka.core.Instance instance)
void	trainOnInstanceImpl(weka.core.Instance inst) Trains the classifier with the given instance.

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

laplaceCorrectionOption

public FloatOption laplaceCorrectionOption

m_classTotals

protected double[] m_classTotals

sum of weight_of_instance * word_count_of_instance for each class

m_headerInfo

protected weka.core.Instances m_headerInfo

copy of header information for use in toString method

m_numClasses

protected int m_numClasses

number of class values

m_probOfClass

protected double[] m_probOfClass

the probability of a class (i.e. Pr[H])

m_wordTotalForClass

protected DoubleVector[] m_wordTotalForClass

probability that a word (w) exists in a class (H) (i.e. Pr[w|H]) The matrix is in the this format: m_wordTotalForClass[wordAttribute][class]

reset

protected boolean reset

Constructor Detail

NaiveBayesMultinomial

public NaiveBayesMultinomial()

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)

Trains the classifier with the given instance.

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

instance - the new training instance to include in the model

getVotesForInstance

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

Calculates the class membership probabilities for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

totalSize

public double totalSize(weka.core.Instance instance)

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 result,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

result - 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.