Package weka.classifiers.bayes

Class 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: 15233 $
    Author:
    Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • 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

      • 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

      • 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

      • 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