it.uniroma2.sag.kelp.predictionfunction.classifier

Class BinaryLinearClassifier

java.lang.Object

it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier

it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier

All Implemented Interfaces:

Classifier, PredictionFunction

public class BinaryLinearClassifier
extends BinaryClassifier

It linear binary classifier operating directly on an explicit vector space

Author:

Simone Filice

Field Summary

Fields inherited from class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier

positiveClass

Constructor Summary

Constructors

Constructor and Description
BinaryLinearClassifier()

Method Summary

Methods

Modifier and Type	Method and Description
BinaryLinearModel	getModel() Returns the model
BinaryMarginClassifierOutput	predict(Example example)
void	reset() Resets all the predictor parameters to the default state.
void	setModel(Model model) Sets the model

Methods inherited from class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier

getLabel, getLabels, setLabel, setLabels

Methods inherited from class java.lang.Object

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

Constructor Detail

BinaryLinearClassifier

public BinaryLinearClassifier()

Method Detail

getModel

public BinaryLinearModel getModel()

Description copied from interface: PredictionFunction

Returns the model

Specified by:

getModel in interface PredictionFunction

Specified by:

getModel in class BinaryClassifier

Returns:

the model

setModel

public void setModel(Model model)

Description copied from interface: PredictionFunction

Sets the model

Parameters:

model - the model to set

predict

public BinaryMarginClassifierOutput predict(Example example)

Specified by:

predict in interface Classifier

Specified by:

predict in interface PredictionFunction

Specified by:

predict in class BinaryClassifier

reset

public void reset()

Description copied from interface: PredictionFunction

Resets all the predictor parameters to the default state. All the learned process is forgotten