| java.lang.Object
 weka.classifiers.Classifier
weka.classifiers.bayes.NaiveBayesSimple
| NaiveBayesSimple | | public class NaiveBayesSimple extends Classifier implements TechnicalInformationHandler(Code) | |
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.
For more information, see
Richard Duda, Peter Hart (1973). Pattern Classification and Scene Analysis. Wiley, New York.
BibTeX:
@book{Duda1973,
address = {New York},
author = {Richard Duda and Peter Hart},
publisher = {Wiley},
title = {Pattern Classification and Scene Analysis},
year = {1973}
}
Valid options are:
-D
If set, classifier is run in debug mode and
may output additional info to the console
author:
Eibe Frank (eibe@cs.waikato.ac.nz)
version:
$Revision: 1.19 $ |
| Field Summary | |
| protected static double | NORM_CONST
Constant for normal distribution. | | protected double[][][] | m_Counts
All the counts for nominal attributes. | | protected double[][] | m_Devs
The standard deviations for numeric attributes. | | protected Instances | m_Instances
The instances used for training. | | protected double[][] | m_Means
The means for numeric attributes. | | protected double[] | m_Priors
The prior probabilities of the classes. | | final static long | serialVersionUID
|
| NORM_CONST | | protected static double NORM_CONST(Code) | | Constant for normal distribution.
|
| m_Counts | | protected double[][][] m_Counts(Code) | | All the counts for nominal attributes.
|
| m_Devs | | protected double[][] m_Devs(Code) | | The standard deviations for numeric attributes.
|
| m_Instances | | protected Instances m_Instances(Code) | | The instances used for training.
|
| m_Means | | protected double[][] m_Means(Code) | | The means for numeric attributes.
|
| m_Priors | | protected double[] m_Priors(Code) | | The prior probabilities of the classes.
|
| serialVersionUID | | final static long serialVersionUID(Code) | | for serialization
|
| buildClassifier | | public void buildClassifier(Instances instances) throws Exception(Code) | | Generates the classifier.
Parameters:
instances - set of instances serving as training data
exception:
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
exception:
Exception - if distribution can't be computed |
| 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 |
| main | | public static void main(String[] argv)(Code) | | Main method for testing this class.
Parameters:
argv - the options |
| normalDens | | protected double normalDens(double x, double mean, double stdDev)(Code) | | Density function of normal distribution.
Parameters:
x - the value to get the density for
Parameters:
mean - the mean
Parameters:
stdDev - the standard deviation the density |
| toString | | public String toString()(Code) | | Returns a description of the classifier.
a description of the classifier as a string. |
| Fields inherited from weka.classifiers.Classifier | protected boolean m_Debug(Code)(Java Doc)
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