NaiveBayes

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Aggregateable<NaiveBayes>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedAttributesHandler, WeightedInstancesHandler

Direct Known Subclasses:

NaiveBayesUpdateable
```
public class NaiveBayes
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, WeightedAttributesHandler, TechnicalInformationHandler, Aggregateable<NaiveBayes>
```
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: 15520 $

Author:

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

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

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
`NaiveBayes`	`aggregate(NaiveBayes toAggregate)` Aggregate an object with this one
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`java.lang.String`	`displayModelInOldFormatTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`Estimator`	`getClassEstimator()` Get the class estimator.
`Estimator[][]`	`getConditionalEstimators()` Get all the conditional estimators.
`boolean`	`getDisplayModelInOldFormat()` Get whether to display model output in the old, original format.
`Instances`	`getHeader()` Return the header that this classifier was trained with
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.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.
`java.lang.String`	`globalInfo()` Returns a string describing this classifier
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.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(java.lang.String[] options)` Parses a given list of options.
`void`	`setUseKernelEstimator(boolean v)` Sets if kernel estimator is to be used.
`void`	`setUseSupervisedDiscretization(boolean s)` Set whether supervised discretization is to be used.
`java.lang.String`	`toString()` Returns a description of the classifier.
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.
`java.lang.String`	`useKernelEstimatorTipText()` Returns the tip text for this property
`java.lang.String`	`useSupervisedDiscretizationTipText()` Returns the tip text for this property

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - NaiveBayes
```
public NaiveBayes()
```
- 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
  - 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 Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws java.lang.Exception
```
    Updates the classifier with the given instance.
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    java.lang.Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if there is a problem generating the prediction
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.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 AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - toString
```
public java.lang.String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of the classifier as a string.
  - useKernelEstimatorTipText
```
public java.lang.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 java.lang.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 s)
```
    Set whether supervised discretization is to be used.
    
    Parameters:
    
    s - true if supervised discretization is to be used.
  - displayModelInOldFormatTipText
```
public java.lang.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
  - getHeader
```
public Instances getHeader()
```
    Return the header that this classifier was trained with
    
    Returns:
    
    the header that this classifier was trained with
  - getConditionalEstimators
```
public Estimator[][] getConditionalEstimators()
```
    Get all the conditional estimators.
    
    Returns:
    
    all the conditional estimators.
  - getClassEstimator
```
public Estimator getClassEstimator()
```
    Get the class estimator.
    
    Returns:
    
    the class estimator
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - aggregate
```
public NaiveBayes aggregate(NaiveBayes toAggregate)
                     throws java.lang.Exception
```
    Description copied from interface: Aggregateable
    
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<NaiveBayes>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    java.lang.Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws java.lang.Exception
```
    Description copied from interface: Aggregateable
    
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<NaiveBayes>
    
    Throws:
    
    java.lang.Exception - if the aggregation can't be finalized for some reason
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class NaiveBayes

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

NaiveBayes

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

listOptions

setOptions

getOptions

toString

useKernelEstimatorTipText

getUseKernelEstimator

setUseKernelEstimator

useSupervisedDiscretizationTipText

getUseSupervisedDiscretization

setUseSupervisedDiscretization

displayModelInOldFormatTipText

setDisplayModelInOldFormat

getDisplayModelInOldFormat

getHeader

getConditionalEstimators

getClassEstimator

getRevision

aggregate

finalizeAggregation

main