Package libai.nn.supervised

Class Adaline

java.lang.Object

libai.nn.NeuralNetwork

libai.nn.supervised.SupervisedLearning

libai.nn.supervised.Perceptron

libai.nn.supervised.Adaline

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

RBF

public class Adaline
extends Perceptron

Adaptative Linear neural network. Is a special case of the single layer Perceptron. Uses a identity as exit function. The only difference between the training algorithms is the 2*alpha multiplication. Because of this, the Adaline implementation is a subclass of Perceptron single layer.

See Also:

Serialized Form

Field Summary

Fields inherited from class libai.nn.NeuralNetwork

plotter, progress, random

Fields inherited from class libai.nn.supervised.Perceptron

b, ins, outs, signum, W

Constructor Summary

Constructors

Constructor	Description
Adaline(int ins, int outs)	Constructor.
Adaline(int ins, int outs, java.util.Random rand)	Constructor.

Method Summary

Modifier and Type	Method	Description
void	simulate(Column pattern, Column result)	Calculate the output for the pattern and left the result on result.
void	train(Column[] patterns, Column[] answers, double alpha, int epochs, int offset, int length, double minerror)	Alias of super.train(patterns, answers, 2*alpha, epochs, offset, length, minerror);

Methods inherited from class libai.nn.NeuralNetwork

error, error, euclideanDistance2, euclideanDistance2, gaussian, getDefaultRandomGenerator, getPlotter, getProgressBar, initializeProgressBar, open, open, open, save, setPlotter, setProgressBar, train, train

Methods inherited from class java.lang.Object

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

Methods inherited from class libai.nn.supervised.Perceptron

getWeights, simulate

Methods inherited from class libai.nn.supervised.SupervisedLearning

validatePreconditions

Constructor Detail

Adaline

public Adaline(int ins,
               int outs)

Constructor.

Parameters:

ins - Number of inputs for the network = number of elements in the patterns.

outs - Number of outputs for the network.

Adaline

public Adaline(int ins,
               int outs,
               java.util.Random rand)

Constructor.

Parameters:

ins - Number of inputs for the network = number of elements in the patterns.

outs - Number of outputs for the network.

rand - Random generator used for creating matrices

Method Detail

train

public void train(Column[] patterns,
                  Column[] answers,
                  double alpha,
                  int epochs,
                  int offset,
                  int length,
                  double minerror)

Alias of super.train(patterns, answers, 2*alpha, epochs, offset, length, minerror);

Overrides:

train in class Perceptron

Parameters:

patterns - The patterns to be learned.

answers - The expected answers.

alpha - The learning rate.

epochs - The maximum number of iterations

offset - The first pattern position

length - How many patterns will be used.

minerror - The minimal error expected.

simulate

public void simulate(Column pattern,
                     Column result)

Calculate the output for the pattern and left the result on result. result = W * pattern + b

Overrides:

simulate in class Perceptron

Parameters:

pattern - The input pattern

result - The output result.