weka.classifiers
Class DistributionClassifier

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier

All Implemented Interfaces:

java.lang.Cloneable, java.io.Serializable

Direct Known Subclasses:

AdaBoostM1, ADTree, AttributeSelectedClassifier, Bagging, ClassificationViaRegression, DecisionStump, DecisionTable, DistributionMetaClassifier, FilteredClassifier, HyperPipes, IBk, Id3, J48, KernelDensity, KStar, Logistic, LogitBoost, MultiClassClassifier, NaiveBayes, NaiveBayesSimple, NeuralNetwork, PART, SMO, ThresholdSelector, UserClassifier, VFI, VotedPerceptron, ZeroR

public abstract class DistributionClassifier

extends Classifier

Abstract classification model that produces (for each test instance) an estimate of the membership in each class (ie. a probability distribution).

Author:

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

See Also:

Serialized Form

Constructor Summary

DistributionClassifier()

Method Summary

double	classifyInstance(Instance instance) Classifies the given test instance.
abstract double[]	distributionForInstance(Instance instance) Predicts the class memberships for a given instance.

Methods inherited from class weka.classifiers.Classifier

buildClassifier, forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DistributionClassifier

public DistributionClassifier()

Method Detail

distributionForInstance

public abstract double[] distributionForInstance(Instance instance)
                                          throws java.lang.Exception

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero. If the class is numeric, the array must consist of only one element, which contains the predicted value.

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class (this should sum to at most 1)

Throws:

java.lang.Exception - if distribution could not be computed successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies the given test instance. The instance has to belong to a dataset when it's being classified.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Instance.missingValue() if no prediction is made

Throws:

java.lang.Exception - if an error occurred during the prediction