jsat.classifiers.boosting

Class WaggingNormal

java.lang.Object

jsat.classifiers.boosting.Wagging

jsat.classifiers.boosting.WaggingNormal

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor

public class WaggingNormal
extends Wagging

Wagging using the Normal distribution.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Modifier	Constructor and Description
	WaggingNormal(Classifier weakLearner, int interations) Creates a new Wagging classifier
	WaggingNormal(Regressor weakLearner, int interations) Creates a new Wagging regressor
protected	WaggingNormal(Wagging clone) Copy constructor

Method Summary

Modifier and Type	Method and Description
WaggingNormal	clone()
ContinuousDistribution	getDistribution() Returns the distribution used for weight sampling
double	getMean() Returns the mean value used for the normal distribution
double	getStandardDeviations() Returns the standard deviation used for the normal distribution
void	setDistribution(ContinuousDistribution dist) Sets the distribution to select the random weights from
void	setMean(double mean) Sets the mean value used for the normal distribution
void	setStandardDeviations(double devs) Sets the standard deviations used for the normal distribution

Methods inherited from class jsat.classifiers.boosting.Wagging

classify, getIterations, getParameter, getParameters, getWeakClassifier, getWeakRegressor, regress, setIterations, setWeakLearner, setWeakLearner, supportsWeightedData, train, train, trainC, trainC

Methods inherited from class java.lang.Object

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

Constructor Detail

WaggingNormal

public WaggingNormal(Classifier weakLearner,
                     int interations)

Creates a new Wagging classifier

Parameters:

weakLearner - the weak learner to use

interations - the number of iterations to perform

WaggingNormal

public WaggingNormal(Regressor weakLearner,
                     int interations)

Creates a new Wagging regressor

Parameters:

weakLearner - the weak learner to use

interations - the number of iterations to perform

WaggingNormal

protected WaggingNormal(Wagging clone)

Copy constructor

Parameters:

clone - to copy

Method Detail

getDistribution

public ContinuousDistribution getDistribution()

Description copied from class: Wagging

Returns the distribution used for weight sampling

Overrides:

getDistribution in class Wagging

Returns:

the distribution used

setDistribution

public void setDistribution(ContinuousDistribution dist)

Description copied from class: Wagging

Sets the distribution to select the random weights from

Overrides:

setDistribution in class Wagging

Parameters:

dist - the distribution to use

setMean

public void setMean(double mean)

Sets the mean value used for the normal distribution

Parameters:

mean - the new mean value

getMean

public double getMean()

Returns the mean value used for the normal distribution

Returns:

the mean value used

setStandardDeviations

public void setStandardDeviations(double devs)

Sets the standard deviations used for the normal distribution

Parameters:

devs - the standard deviations to set

getStandardDeviations

public double getStandardDeviations()

Returns the standard deviation used for the normal distribution

Returns:

the standard deviation used

clone

public WaggingNormal clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class Wagging