jsat.classifiers.linear.kernelized

Class Forgetron

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.linear.kernelized.Forgetron

All Implemented Interfaces:

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

public class Forgetron
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized

Implementation of the first two Forgetron algorithms. The Forgetron is a kernelized version of the Perceptron that maintains a fixed sized buffer of data instances that it uses to form its decision boundary.

See:
Dekel, O., Shalev-Shwartz, S.,&Singer, Y. (2008). The Forgetron: A kernel-based perceptron on a fixed budget. SIAM Journal on Computing, 37(5), 1342–1372.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Forgetron(KernelTrick kernel, int budget) Creates a new Forgetron

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
Forgetron	clone()
int	getBudget() Returns the current budget
KernelTrick	getKernelTrick() Returns the current kernel trick
double	getScore(DataPoint dp) Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
boolean	isSelfTuned()
void	setBudget(int budget) Sets the new budget, which is the maximum number of data points the Forgetron can use to form its decision boundary.
void	setKernelTrick(KernelTrick K) Sets the kernel trick to use
void	setSelfTurned(boolean selfTurned) Sets whether or not the self-tuned variant of the Forgetron is used, the default is true
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

getEpochs, setEpochs, train, train, trainEpochs

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train, train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

Forgetron

public Forgetron(KernelTrick kernel,
                 int budget)

Creates a new Forgetron

Parameters:

kernel - the kernel function to use

budget - the maximum number of data points to use

Method Detail

setSelfTurned

public void setSelfTurned(boolean selfTurned)

Sets whether or not the self-tuned variant of the Forgetron is used, the default is true

Parameters:

selfTurned - true to use the self-tuned variance, false otherwise.

isSelfTuned

public boolean isSelfTuned()

Returns:

true if the self-tuned variant is used, false otherwise.

setBudget

public void setBudget(int budget)

Sets the new budget, which is the maximum number of data points the Forgetron can use to form its decision boundary.

Parameters:

budget - the maximum number of data points to use

getBudget

public int getBudget()

Returns the current budget

Returns:

the current budget

setKernelTrick

public void setKernelTrick(KernelTrick K)

Sets the kernel trick to use

Parameters:

K - the kernel trick to use

getKernelTrick

public KernelTrick getKernelTrick()

Returns the current kernel trick

Returns:

the current kernel trick

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.

getScore

public double getScore(DataPoint dp)

Description copied from interface: BinaryScoreClassifier

Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.

Specified by:

getScore in interface BinaryScoreClassifier

Parameters:

dp - the data point to predict the class label of

Returns:

the score for the given data point

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 Forgetron clone()

Specified by:

clone in interface BinaryScoreClassifier

Specified by:

clone in interface Classifier

Specified by:

clone in interface UpdateableClassifier

Specified by:

clone in class BaseUpdateableClassifier

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)

Description copied from interface: UpdateableClassifier

Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.

Specified by:

setUp in interface UpdateableClassifier

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

predicting - the information for the target class that will be predicted

update

public void update(DataPoint dataPoint,
                   int targetClass)

Description copied from interface: UpdateableClassifier

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableClassifier

Parameters:

dataPoint - the data point to learn

targetClass - the target class of the data point