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

Class Perceptron

java.lang.Object

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

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, LearningAlgorithm, OnlineLearningAlgorithm

Direct Known Subclasses:

KernelizedPerceptron, LinearPerceptron

public abstract class Perceptron
extends Object
implements ClassificationLearningAlgorithm, OnlineLearningAlgorithm, BinaryLearningAlgorithm

The perceptron learning algorithm algorithm for classification tasks. 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

Modifier and Type	Field and Description
protected float	alpha
protected BinaryClassifier	classifier
protected Label	label
protected float	margin
protected boolean	unbiased

Constructor Summary

Constructors

Constructor and Description
Perceptron()

Method Summary

Methods

Modifier and Type	Method and Description
float	getAlpha() Returns the learning rate, i.e.
Label	getLabel()
List<Label>	getLabels() Returns the labels representing the concept to be learned.
float	getMargin() Returns the desired margin, i.e.
BinaryClassifier	getPredictionFunction() Returns the classifier learned during the training process
boolean	isUnbiased() Returns whether the bias, i.e.
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
BinaryMarginClassifierOutput	learn(Example example) Applies the learning process on a single example, updating its current model
void	reset() Resets all the learning process, returning to the default state.
void	setAlpha(float alpha) Sets the learning rate, i.e.
void	setLabel(Label label)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.
void	setMargin(float margin) Sets the desired margin, i.e.
void	setUnbiased(boolean unbiased) Sets whether the bias, i.e.

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

duplicate

Field Detail

classifier

protected BinaryClassifier classifier

label

protected Label label

alpha

protected float alpha

margin

protected float margin

unbiased

protected boolean unbiased

Constructor Detail

Perceptron

public Perceptron()

Method Detail

getAlpha

public float getAlpha()

Returns the learning rate, i.e. the weight associated to misclassified examples during the learning process

Returns:

the learning rate

setAlpha

public void setAlpha(float alpha)

Sets the learning rate, i.e. the weight associated to misclassified examples during the learning process

Parameters:

alpha - the learning rate to set

getMargin

public float getMargin()

Returns the desired margin, i.e. the minimum distance from the hyperplane that an example must have in order to be not considered misclassified

Returns:

the margin

setMargin

public void setMargin(float margin)

Sets the desired margin, i.e. the minimum distance from the hyperplane that an example must have in order to be not considered misclassified

Parameters:

margin - the margin to set

isUnbiased

public boolean isUnbiased()

Returns whether the bias, i.e. the constant term of the hyperplane, is always 0, or can be modified during the learning process

Returns:

the unbiased

setUnbiased

public void setUnbiased(boolean unbiased)

Sets whether the bias, i.e. the constant term of the hyperplane, is always 0, or can be modified during the learning process

Parameters:

unbiased - the unbiased to set

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

learn

public BinaryMarginClassifierOutput learn(Example example)

Description copied from interface: OnlineLearningAlgorithm

Applies the learning process on a single example, updating its current model

Specified by:

learn in interface OnlineLearningAlgorithm

Parameters:

example - the instance to be exploited in the learning process

Returns:

the Prediction associated to example before the updating step

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 BinaryClassifier 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

Returns:

the predictionFunction learned during the training process

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