it.uniroma2.sag.kelp.learningalgorithm

Class PassiveAggressive

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

All Implemented Interfaces:

BinaryLearningAlgorithm, LearningAlgorithm, OnlineLearningAlgorithm

Direct Known Subclasses:

PassiveAggressiveClassification, PassiveAggressiveRegression

public abstract class PassiveAggressive
extends Object
implements OnlineLearningAlgorithm, BinaryLearningAlgorithm

It is an online learning algorithms that implements the Passive Aggressive algorithms described in [Crammer, JMLR2006] K. Crammer, O. Dekel, J. Keshet and S. Shalev-Shwartz. Online passive-aggressive algorithms. Journal of Machine Learning Research 7:551–585, 2006.

Author:

Simone Filice

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PassiveAggressive.Policy It is the updating policy applied by the Passive Aggressive Algorithm when a miss-prediction occurs

Field Summary

Fields

Modifier and Type	Field and Description
protected float	c
protected Label	label
protected PassiveAggressive.Policy	policy

Constructor Summary

Constructors

Constructor and Description
PassiveAggressive()

Method Summary

Methods

Modifier and Type	Method and Description
protected float	computeWeight(Example example, float lossValue, float exampleSquaredNorm, float aggressiveness)
float	getC()
Label	getLabel()
List<Label>	getLabels() Returns the labels representing the concept to be learned.
PassiveAggressive.Policy	getPolicy()
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
void	reset() Resets all the learning process, returning to the default state.
void	setC(float c)
void	setLabel(Label label)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.
void	setPolicy(PassiveAggressive.Policy policy)

Methods inherited from class java.lang.Object

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

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.OnlineLearningAlgorithm

learn

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm

duplicate, getPredictionFunction

Field Detail

label

protected Label label

policy

protected PassiveAggressive.Policy policy

c

protected float c

Constructor Detail

PassiveAggressive

public PassiveAggressive()

Method Detail

reset

public void reset()

Description copied from interface: LearningAlgorithm

Resets all the learning process, returning to the default state. All the learned process is forgotten

Specified by:

reset in interface LearningAlgorithm

getPolicy

public PassiveAggressive.Policy getPolicy()

Returns:

the updating policy

setPolicy

public void setPolicy(PassiveAggressive.Policy policy)

Parameters:

policy - the updating policy to set

getC

public float getC()

Returns:

the aggressiveness parameter

setC

public void setC(float c)

Parameters:

c - the aggressiveness to set

computeWeight

protected float computeWeight(Example example,
                  float lossValue,
                  float exampleSquaredNorm,
                  float aggressiveness)

setLabels

public void setLabels(List<Label> labels)

Description copied from interface: LearningAlgorithm

Sets the labels representing the concept to be learned.

Specified by:

setLabels in interface BinaryLearningAlgorithm

Specified by:

setLabels in interface LearningAlgorithm

Parameters:

labels - the labels representing the concept to be learned

getLabels

public List<Label> getLabels()

Description copied from interface: LearningAlgorithm

Returns the labels representing the concept to be learned.

Specified by:

getLabels in interface BinaryLearningAlgorithm

Specified by:

getLabels in interface LearningAlgorithm

Returns:

the labels representing the concept to be learned

learn

public void learn(Dataset dataset)

Description copied from interface: LearningAlgorithm

It starts the training process exploiting the provided dataset

Specified by:

learn in interface LearningAlgorithm

Parameters:

dataset - the training data

getLabel

public Label getLabel()

Specified by:

getLabel in interface BinaryLearningAlgorithm

Returns:

the label associated to the positive class

setLabel

public void setLabel(Label label)

Specified by:

setLabel in interface BinaryLearningAlgorithm