it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive

Class LinearPassiveAggressiveRegression

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression

All Implemented Interfaces:

BinaryLearningAlgorithm, LearningAlgorithm, LinearMethod, OnlineLearningAlgorithm, RegressionLearningAlgorithm

public class LinearPassiveAggressiveRegression
extends PassiveAggressiveRegression
implements LinearMethod

Online Passive-Aggressive Learning Algorithm for regression tasks (linear version). reference:

[CrammerJLMR2006] Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer Online Passive-Aggressive Algorithms. Journal of Machine Learning Research (2006)

Author:

Simone Filice

Nested Class Summary

Nested classes/interfaces inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

PassiveAggressive.Policy

Field Summary

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

epsilon, regressor

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

c, label, policy

Constructor Summary

Constructors

Constructor and Description
LinearPassiveAggressiveRegression()
LinearPassiveAggressiveRegression(float aggressiveness, float epsilon, PassiveAggressive.Policy policy, String representation, Label label)

Method Summary

Methods

Modifier and Type	Method and Description
LinearPassiveAggressiveRegression	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
UnivariateLinearRegressionFunction	getPredictionFunction() Returns the regressor learned during the training process
String	getRepresentation() Returns the representation this learning algorithm exploits
void	setPredictionFunction(PredictionFunction predictionFunction) Sets the predictionFunction learned during the training process.
void	setRepresentation(String representation) Sets the representation this learning algorithm will exploit

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

getEpsilon, learn, learn, setEpsilon

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

computeWeight, getC, getLabel, getLabels, getPolicy, reset, setC, setLabel, setLabels, setPolicy

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.LearningAlgorithm

getLabels, reset, setLabels

Constructor Detail

LinearPassiveAggressiveRegression

public LinearPassiveAggressiveRegression()

LinearPassiveAggressiveRegression

public LinearPassiveAggressiveRegression(float aggressiveness,
                                 float epsilon,
                                 PassiveAggressive.Policy policy,
                                 String representation,
                                 Label label)

Method Detail

duplicate

public LinearPassiveAggressiveRegression duplicate()

Description copied from interface: LearningAlgorithm

Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied

Specified by:

duplicate in interface LearningAlgorithm

getRepresentation

public String getRepresentation()

Description copied from interface: LinearMethod

Returns the representation this learning algorithm exploits

Specified by:

getRepresentation in interface LinearMethod

Returns:

the representation

setRepresentation

public void setRepresentation(String representation)

Description copied from interface: LinearMethod

Sets the representation this learning algorithm will exploit

Specified by:

setRepresentation in interface LinearMethod

Parameters:

representation - the representation to set

getPredictionFunction

public UnivariateLinearRegressionFunction getPredictionFunction()

Description copied from interface: RegressionLearningAlgorithm

Returns the regressor learned during the training process

Specified by:

getPredictionFunction in interface LearningAlgorithm

Specified by:

getPredictionFunction in interface RegressionLearningAlgorithm

Overrides:

getPredictionFunction in class PassiveAggressiveRegression

Returns:

the regressor learned during the training process

setPredictionFunction

public void setPredictionFunction(PredictionFunction predictionFunction)

Description copied from interface: LearningAlgorithm

Sets the predictionFunction learned during the training process. It can be used to change the default prediction function, or to set an already trained one.

Specified by:

setPredictionFunction in interface LearningAlgorithm