it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear

Class LibLinearRegression

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression

All Implemented Interfaces:

BinaryLearningAlgorithm, LearningAlgorithm, LinearMethod, RegressionLearningAlgorithm

public class LibLinearRegression
extends Object
implements LinearMethod, RegressionLearningAlgorithm, BinaryLearningAlgorithm

This class implements linear SVM regression trained using a coordinate descent algorithm [Fan et al, 2008]. It operates in an explicit feature space (i.e. it does not relies on any kernel). This code has been adapted from the Java port of the original LIBLINEAR C++ sources.

Further details can be found in:

[Fan et al, 2008] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. Software available at

The original LIBLINEAR code: http://www.csie.ntu.edu.tw/~cjlin/liblinear

The original JAVA porting (v 1.94): http://liblinear.bwaldvogel.de

Author:

Danilo Croce

Constructor Summary

Constructors

Constructor and Description
LibLinearRegression()
LibLinearRegression(double c, double p, String representationName)
LibLinearRegression(Label label, double c, double p, String representationName)

Method Summary

Methods

Modifier and Type	Method and Description
LibLinearRegression	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
double	getC()
Label	getLabel()
List<Label>	getLabels() Returns the labels representing the concept to be learned.
double	getP()
UnivariateLinearRegressionFunction	getPredictionFunction() Returns the regressor learned during the training process
String	getRepresentation() Returns the representation this learning algorithm exploits
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(double c)
void	setLabel(Label label)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.
void	setP(double p)
void	setRepresentation(String representation) Sets the representation this learning algorithm will exploit

Methods inherited from class java.lang.Object

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

Constructor Detail

LibLinearRegression

public LibLinearRegression(Label label,
                   double c,
                   double p,
                   String representationName)

Parameters:

label - The regression property to be learned

c - The regularization parameter

p - The The epsilon in loss function of SVR

representationName - The identifier of the representation to be considered for the training step

LibLinearRegression

public LibLinearRegression(double c,
                   double p,
                   String representationName)

Parameters:

c - The regularization parameter

representationName - The identifier of the representation to be considered for the training step

LibLinearRegression

public LibLinearRegression()

Method Detail

getC

public double getC()

Returns:

the regularization parameter

setC

public void setC(double c)

Parameters:

c - the regularization parameter

getP

public double getP()

Returns:

the epsilon in loss function

setP

public void setP(double p)

Parameters:

p - the epsilon in loss function

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

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

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

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

duplicate

public LibLinearRegression 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

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

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

Returns:

the regressor learned during the training process