NaiveBayes (weka-stable 3.6.8 API)

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.bayes.NaiveBayes

All Implemented Interfaces:

Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:

NaiveBayesUpdateable
```
public class NaiveBayes
extends Classifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler
```
Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

For more information on Naive Bayes classifiers, see

George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
BibTeX:
```
 @inproceedings{John1995,
    address = {San Mateo},
    author = {George H. John and Pat Langley},
    booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence},
    pages = {338-345},
    publisher = {Morgan Kaufmann},
    title = {Estimating Continuous Distributions in Bayesian Classifiers},
    year = {1995}
 }
 
```
Valid options are:
```
 -K
  Use kernel density estimator rather than normal
  distribution for numeric attributes
```
```
 -D
  Use supervised discretization to process numeric attributes
 
```
```
 -O
  Display model in old format (good when there are many classes)
 
```
Version:

$Revision: 5516 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected static double`	`DEFAULT_NUM_PRECISION` The precision parameter used for numeric attributes
`protected Estimator`	`m_ClassDistribution` The class estimator.
`protected Discretize`	`m_Disc` The discretization filter.
`protected boolean`	`m_displayModelInOldFormat`
`protected Estimator[][]`	`m_Distributions` The attribute estimators.
`protected Instances`	`m_Instances` The dataset header for the purposes of printing out a semi-intelligible model
`protected int`	`m_NumClasses` The number of classes (or 1 for numeric class)
`protected boolean`	`m_UseDiscretization` Whether to use discretization than normal distribution for numeric attributes
`protected boolean`	`m_UseKernelEstimator` Whether to use kernel density estimator rather than normal distribution for numeric attributes

Fields inherited from class weka.classifiers.Classifier
m_Debug

Constructor Summary

Constructors
Constructor and Description

NaiveBayes()

Constructors
Constructor and Description
`NaiveBayes()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`String`	`displayModelInOldFormatTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getDisplayModelInOldFormat()` Get whether to display model output in the old, original format.
`String[]`	`getOptions()` Gets the current settings of the classifier.
`String`	`getRevision()` Returns the revision string.
`TechnicalInformation`	`getTechnicalInformation()` 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.
`boolean`	`getUseKernelEstimator()` Gets if kernel estimator is being used.
`boolean`	`getUseSupervisedDiscretization()` Get whether supervised discretization is to be used.
`String`	`globalInfo()` Returns a string describing this classifier
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`void`	`setDisplayModelInOldFormat(boolean d)` Set whether to display model output in the old, original format.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setUseKernelEstimator(boolean v)` Sets if kernel estimator is to be used.
`void`	`setUseSupervisedDiscretization(boolean newblah)` Set whether supervised discretization is to be used.
`String`	`toString()` Returns a description of the classifier.
`protected String`	`toStringOriginal()` Returns a description of the classifier in the old format.
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.
`String`	`useKernelEstimatorTipText()` Returns the tip text for this property
`String`	`useSupervisedDiscretizationTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Distributions
```
protected Estimator[][] m_Distributions
```
    The attribute estimators.
  - m_ClassDistribution
```
protected Estimator m_ClassDistribution
```
    The class estimator.
  - m_UseKernelEstimator
```
protected boolean m_UseKernelEstimator
```
    Whether to use kernel density estimator rather than normal distribution for numeric attributes
  - m_UseDiscretization
```
protected boolean m_UseDiscretization
```
    Whether to use discretization than normal distribution for numeric attributes
  - m_NumClasses
```
protected int m_NumClasses
```
    The number of classes (or 1 for numeric class)
  - m_Instances
```
protected Instances m_Instances
```
    The dataset header for the purposes of printing out a semi-intelligible model
  - DEFAULT_NUM_PRECISION
```
protected static final double DEFAULT_NUM_PRECISION
```
    The precision parameter used for numeric attributes
    
    See Also:
    
    Constant Field Values
  - m_Disc
```
protected Discretize m_Disc
```
    The discretization filter.
  - m_displayModelInOldFormat
```
protected boolean m_displayModelInOldFormat
```
- Constructor Detail
  - NaiveBayes
```
public NaiveBayes()
```
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this classifier
    
    Returns:
    
    a description of the classifier suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    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.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws Exception
```
    Updates the classifier with the given instance.
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws 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:
    
    Exception - if there is a problem generating the prediction
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -K
  Use kernel density estimator rather than normal
  distribution for numeric attributes
```
```
 -D
  Use supervised discretization to process numeric attributes
 
```
```
 -O
  Display model in old format (good when there are many classes)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - toString
```
public String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string.
  - toStringOriginal
```
protected String toStringOriginal()
```
    Returns a description of the classifier in the old format.
    
    Returns:
    
    a description of the classifier as a string.
  - useKernelEstimatorTipText
```
public String useKernelEstimatorTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUseKernelEstimator
```
public boolean getUseKernelEstimator()
```
    Gets if kernel estimator is being used.
    
    Returns:
    
    Value of m_UseKernelEstimatory.
  - setUseKernelEstimator
```
public void setUseKernelEstimator(boolean v)
```
    Sets if kernel estimator is to be used.
    
    Parameters:
    
    v - Value to assign to m_UseKernelEstimatory.
  - useSupervisedDiscretizationTipText
```
public String useSupervisedDiscretizationTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUseSupervisedDiscretization
```
public boolean getUseSupervisedDiscretization()
```
    Get whether supervised discretization is to be used.
    
    Returns:
    
    true if supervised discretization is to be used.
  - setUseSupervisedDiscretization
```
public void setUseSupervisedDiscretization(boolean newblah)
```
    Set whether supervised discretization is to be used.
    
    Parameters:
    
    newblah - true if supervised discretization is to be used.
  - displayModelInOldFormatTipText
```
public String displayModelInOldFormatTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDisplayModelInOldFormat
```
public void setDisplayModelInOldFormat(boolean d)
```
    Set whether to display model output in the old, original format.
    
    Parameters:
    
    d - true if model ouput is to be shown in the old format
  - getDisplayModelInOldFormat
```
public boolean getDisplayModelInOldFormat()
```
    Get whether to display model output in the old, original format.
    
    Returns:
    
    true if model ouput is to be shown in the old format
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class NaiveBayes

Field Summary

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_Distributions

m_ClassDistribution

m_UseKernelEstimator

m_UseDiscretization

m_NumClasses

m_Instances

DEFAULT_NUM_PRECISION

m_Disc

m_displayModelInOldFormat

Constructor Detail

NaiveBayes

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

listOptions

setOptions

getOptions

toString

toStringOriginal

useKernelEstimatorTipText

getUseKernelEstimator

setUseKernelEstimator

useSupervisedDiscretizationTipText

getUseSupervisedDiscretization

setUseSupervisedDiscretization

displayModelInOldFormatTipText

setDisplayModelInOldFormat

getDisplayModelInOldFormat

getRevision

main