it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron

Class LinearPerceptron

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron

it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, LearningAlgorithm, LinearMethod, OnlineLearningAlgorithm

public class LinearPerceptron
extends Perceptron
implements LinearMethod

The perceptron learning algorithm algorithm for classification tasks (linear version). Reference:

[Rosenblatt1957] F. Rosenblatt. The Perceptron – a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory (1957)

Author:

Simone Filice

Field Summary

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron

alpha, classifier, label, margin, unbiased

Constructor Summary

Constructors

Constructor and Description
LinearPerceptron()
LinearPerceptron(float alpha, float margin, boolean unbiased, String representation, Label label)

Method Summary

Methods

Modifier and Type	Method and Description
LinearPerceptron	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
BinaryLinearClassifier	getPredictionFunction() Returns the classifier 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.classification.perceptron.Perceptron

getAlpha, getLabel, getLabels, getMargin, isUnbiased, learn, learn, reset, setAlpha, setLabel, setLabels, setMargin, setUnbiased

Methods inherited from class java.lang.Object

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

Constructor Detail

LinearPerceptron

public LinearPerceptron()

LinearPerceptron

public LinearPerceptron(float alpha,
                float margin,
                boolean unbiased,
                String representation,
                Label label)

Method Detail

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

duplicate

public LinearPerceptron 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

getPredictionFunction

public BinaryLinearClassifier getPredictionFunction()

Description copied from interface: ClassificationLearningAlgorithm

Returns the classifier learned during the training process

Specified by:

getPredictionFunction in interface ClassificationLearningAlgorithm

Specified by:

getPredictionFunction in interface LearningAlgorithm

Overrides:

getPredictionFunction in class Perceptron

Returns:

the predictionFunction 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