weka.classifiers.bayes
Class NaiveBayesMultinomial

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.bayes.NaiveBayesMultinomial

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class NaiveBayesMultinomial

extends Classifier

implements WeightedInstancesHandler

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

See Also:

Serialized Form

Constructor Summary

NaiveBayesMultinomial()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String	globalInfo() Returns a string describing this classifier
double	lnFactorial(int n) Fast computation of ln(n!) for non-negative ints negative ints are passed on to the general gamma-function based version in weka.core.SpecialFunctions if the current n value is higher than any previous one, the cache is extended and filled to cover it the common case is reduced to a simple array lookup
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString()

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

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

Constructor Detail

NaiveBayesMultinomial

public NaiveBayesMultinomial()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if there is a problem generating the prediction

lnFactorial

public double lnFactorial(int n)

Fast computation of ln(n!) for non-negative ints negative ints are passed on to the general gamma-function based version in weka.core.SpecialFunctions if the current n value is higher than any previous one, the cache is extended and filled to cover it the common case is reduced to a simple array lookup

Parameters:

n - the integer

Returns:

ln(n!)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options