| java.lang.Object
 weka.classifiers.Classifier
weka.classifiers.bayes.NaiveBayesMultinomial
All known Subclasses: weka.classifiers.bayes.NaiveBayesMultinomialUpdateable,
| NaiveBayesMultinomial | | public class NaiveBayesMultinomial extends Classifier implements WeightedInstancesHandler,TechnicalInformationHandler(Code) | |
Class for building and using a multinomial Naive Bayes classifier. 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.
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}
}
Valid options are:
-D
If set, classifier is run in debug mode and
may output additional info to the console
author:
Andrew Golightly (acg4@cs.waikato.ac.nz)
author:
Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
version:
$Revision: 1.15 $ |
| m_headerInfo | | protected Instances m_headerInfo(Code) | | copy of header information for use in toString method
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| m_lnFactorialCache | | protected double[] m_lnFactorialCache(Code) | | cache lnFactorial computations
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| m_numAttributes | | protected int m_numAttributes(Code) | | number of unique words
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| m_numClasses | | protected int m_numClasses(Code) | | number of class values
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| m_probOfClass | | protected double[] m_probOfClass(Code) | | the probability of a class (i.e. Pr[H])
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| m_probOfWordGivenClass | | protected double[][] m_probOfWordGivenClass(Code) | | probability that a word (w) exists in a class (H) (i.e. Pr[w|H])
The matrix is in the this format: probOfWordGivenClass[class][wordAttribute]
NOTE: the values are actually the log of Pr[w|H]
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| serialVersionUID | | final static long serialVersionUID(Code) | | for serialization
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| buildClassifier | | public void buildClassifier(Instances instances) throws Exception(Code) | | Generates the classifier.
Parameters:
instances - set of instances serving as training data
throws:
Exception - if the classifier has not been generated successfully |
| distributionForInstance | | public double[] distributionForInstance(Instance instance) throws Exception(Code) | | Calculates the class membership probabilities for the given test
instance.
Parameters:
instance - the instance to be classified predicted class probability distribution
throws:
Exception - if there is a problem generating the prediction |
| getCapabilities | | public Capabilities getCapabilities()(Code) | | Returns default capabilities of the classifier.
the capabilities of this classifier |
| getTechnicalInformation | | public TechnicalInformation getTechnicalInformation()(Code) | | Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
the technical information about this class |
| globalInfo | | public String globalInfo()(Code) | | Returns a string describing this classifier
a description of the classifier suitable fordisplaying in the explorer/experimenter gui |
| lnFactorial | | public double lnFactorial(int n)(Code) | | 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 ln(n!) |
| main | | public static void main(String[] argv)(Code) | | Main method for testing this class.
Parameters:
argv - the options |
| toString | | public String toString()(Code) | | Returns a string representation of the classifier.
a string representation of the classifier |
| Fields inherited from weka.classifiers.Classifier | protected boolean m_Debug(Code)(Java Doc)
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