de.jungblut.classification.meta

Class Voter<A extends Classifier>

java.lang.Object

de.jungblut.classification.AbstractPredictor

de.jungblut.classification.AbstractClassifier

de.jungblut.classification.meta.Voter<A>

All Implemented Interfaces:

Classifier, Predictor

public final class Voter<A extends Classifier>
extends AbstractClassifier

Implementation of vote ensembling. This features multithreading, different combination and selection techniques.

Author:

thomas.jungblut

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Voter.CombiningType
static class	Voter.SelectionType

Method Summary

Modifier and Type	Method and Description
static <K extends Classifier> Voter<K>	create(int numClassifiers, Voter.CombiningType type, ClassifierFactory<K> classifierFactory) Creates a new voting classificator.
static <K extends Classifier> Voter<K>	fromTrainedModels(List<K> classifier) Creates a voter from the given trained models for prediction purposes.
Classifier[]	getClassifier()
Voter<A>	numThreads(int threads)
de.jungblut.math.DoubleVector	predict(de.jungblut.math.DoubleVector features) Classifies the given features.
Voter<A>	selectionType(Voter.SelectionType type)
Voter<A>	setCombiningType(Voter.CombiningType type)
void	train(de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome) Trains this classifier with the given features and the outcome.
Voter<A>	verbose()
Voter<A>	verbose(boolean verb)

Methods inherited from class de.jungblut.classification.AbstractClassifier

train

Methods inherited from class de.jungblut.classification.AbstractPredictor

extractPredictedClass, extractPredictedClass, predictedClass, predictedClass, predictProbability

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jungblut.classification.Predictor

asClassifier, extractPredictedClass, extractPredictedClass, predictedClass, predictedClass, predictProbability

Method Detail

train

public void train(de.jungblut.math.DoubleVector[] features,
                  de.jungblut.math.DoubleVector[] outcome)

Description copied from interface: Classifier

Trains this classifier with the given features and the outcome.

Specified by:

train in interface Classifier

Overrides:

train in class AbstractClassifier

outcome - the outcome must have classes labeled as doubles. E.G. in the binary case you have a single element and decide between 0d and 1d. In higher dimensional cases you have each of these single elements mapped to a dimension.

predict

public de.jungblut.math.DoubleVector predict(de.jungblut.math.DoubleVector features)

Description copied from interface: Predictor

Classifies the given features.

Returns:

the vector that contains an indicator at the index of the class. Usually zero or 1, in some cases it is a probability or activation value.

getClassifier

public Classifier[] getClassifier()

verbose

public Voter<A> verbose()

Returns:

sets this instance to verbose and returns it.

verbose

public Voter<A> verbose(boolean verb)

Returns:

sets this instance to verbose and returns it.

selectionType

public Voter<A> selectionType(Voter.SelectionType type)

Returns:

sets the selection type and returns this instance.

numThreads

public Voter<A> numThreads(int threads)

Returns:

sets the number of threads and returns this instance.

setCombiningType

public Voter<A> setCombiningType(Voter.CombiningType type)

Returns:

sets the used combination type in this instance and returns it.

create

public static <K extends Classifier> Voter<K> create(int numClassifiers,
                                                     Voter.CombiningType type,
                                                     ClassifierFactory<K> classifierFactory)

Creates a new voting classificator. The training is single threaded, no shuffling or bagging takes place.

Parameters:

numClassifiers - the number of base classifiers to use.

type - the combining type to use.

classifierFactory - the classifier factory to create base classifiers.

Returns:

a new Voter.

fromTrainedModels

public static <K extends Classifier> Voter<K> fromTrainedModels(List<K> classifier)

Creates a voter from the given trained models for prediction purposes.

Parameters:

classifier -

Returns: