jsat.classifiers.boosting

Class Wagging

java.lang.Object

jsat.classifiers.boosting.Wagging

All Implemented Interfaces:

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

Direct Known Subclasses:

WaggingNormal

public class Wagging
extends java.lang.Object
implements Classifier, Regressor, Parameterized

Wagging is a meta-classifier that is related to Bagging. Instead training on re-sampled data sets, it trains on randomly re-weighted data sets. The weight of each point is selected at random from a specified distribution, and set to zero if negative.

See: Bauer, E.,&Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 38(1998), 1–38.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Wagging(ContinuousDistribution dist, Classifier weakL, int iterations) Creates a new Wagging classifier
Wagging(ContinuousDistribution dist, Regressor weakR, int iterations) Creates a new Wagging regressor

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
Wagging	clone()
ContinuousDistribution	getDistribution() Returns the distribution used for weight sampling
int	getIterations() Returns the number of iterations to create weak learners
Classifier	getWeakClassifier() Returns the weak learner used for classification.
Regressor	getWeakRegressor() Returns the weak learner used for regression
double	regress(DataPoint data)
void	setDistribution(ContinuousDistribution dist) Sets the distribution to select the random weights from
void	setIterations(int iterations) Sets the number of iterations to create weak learners
void	setWeakLearner(Classifier weakL) Sets the weak learner used for classification.
void	setWeakLearner(Regressor weakR) Sets the weak learner used for regressions .
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.
void	train(RegressionDataSet dataSet, boolean parallel)

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.regression.Regressor

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

Wagging

public Wagging(ContinuousDistribution dist,
               Classifier weakL,
               int iterations)

Creates a new Wagging classifier

Parameters:

dist - the distribution to select weights from

weakL - the weak learner to use

iterations - the number of iterations to perform

Wagging

public Wagging(ContinuousDistribution dist,
               Regressor weakR,
               int iterations)

Creates a new Wagging regressor

Parameters:

dist - the distribution to select weights from

weakR - the weak learner to use

iterations - the number of iterations to perform

Method Detail

setWeakLearner

public void setWeakLearner(Classifier weakL)

Sets the weak learner used for classification. If it also supports regressions that will be set as well.

Parameters:

weakL - the weak learner to use

getWeakClassifier

public Classifier getWeakClassifier()

Returns the weak learner used for classification.

Returns:

the weak learner used for classification.

setWeakLearner

public void setWeakLearner(Regressor weakR)

Sets the weak learner used for regressions . If it also supports classification that will be set as well.

Parameters:

weakR - the weak learner to use

getWeakRegressor

public Regressor getWeakRegressor()

Returns the weak learner used for regression

Returns:

the weak learner used for regression

setIterations

public void setIterations(int iterations)

Sets the number of iterations to create weak learners

Parameters:

iterations - the number of iterations to perform

getIterations

public int getIterations()

Returns the number of iterations to create weak learners

Returns:

the number of iterations to perform

setDistribution

public void setDistribution(ContinuousDistribution dist)

Sets the distribution to select the random weights from

Parameters:

dist - the distribution to use

getDistribution

public ContinuousDistribution getDistribution()

Returns the distribution used for weight sampling

Returns:

the distribution used

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

Specified by:

supportsWeightedData in interface Regressor

Returns:

true if the model supports weighted data, false otherwise

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

Specified by:

train in interface Regressor

clone

public Wagging clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class java.lang.Object