jsat.classifiers

Class OneVSOne

java.lang.Object

jsat.classifiers.OneVSOne

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Parameterized

Direct Known Subclasses:

DDAG

public class OneVSOne
extends java.lang.Object
implements Classifier, Parameterized

A One VS One classifier extends binary decision classifiers into multi-class decision classifiers. This is done by creating a binary-classification problem for every possible pair of classes, and then classifying by taking the result of all possible combinations and choosing the class that got the most results. This allows for a soft decision result, however, it often proves to be a meaningless soft boundary.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
OneVSOne(Classifier baseClassifier) Creates a new One-vs-One classifier
OneVSOne(Classifier baseClassifier, boolean concurrentTrain) Creates a new One-vs-One classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
OneVSOne	clone()
boolean	isConcurrentTraining()
void	setConcurrentTraining(boolean concurrentTrain) Controls whether or not training of the several classifiers occurs concurrently or sequentually.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

OneVSOne

public OneVSOne(Classifier baseClassifier)

Creates a new One-vs-One classifier

Parameters:

baseClassifier - the binary classifier to extend

OneVSOne

public OneVSOne(Classifier baseClassifier,
                boolean concurrentTrain)

Creates a new One-vs-One classifier

Parameters:

baseClassifier - the binary classifier to extend

concurrentTrain - true to have training of individual classifiers occur in parallel, false to have them use their native parallel training method.

Method Detail

setConcurrentTraining

public void setConcurrentTraining(boolean concurrentTrain)

Controls whether or not training of the several classifiers occurs concurrently or sequentually.

Parameters:

concurrentTrain - true to have training of individual classifiers occur in parallel, false to have them use their native parallel training method.

isConcurrentTraining

public boolean isConcurrentTraining()

Returns:

true if training of individual classifiers occur in parallel, false they use their native parallel training method.

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Returns:

true if the model supports weighted data, false otherwise

clone

public OneVSOne clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object