Package libai.nn.supervised

Class LVQ

java.lang.Object

libai.nn.NeuralNetwork

libai.nn.supervised.SupervisedLearning

libai.nn.supervised.LVQ

All Implemented Interfaces:

java.io.Serializable

public class LVQ
extends SupervisedLearning

Learning Vector Quantization or LVQ. Is an hybrid neural network with 3 layers (1-input, 1-hidden, 1-output). The first set of weights are trained using a competitive approach, and the second set of weights are trained using a supervised approach. Therefore the first steps are taken from the competitive network. This network was proposed by Teuvo Kohonen as alternative to the standard competitive. networks.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
protected int	ins
protected int	outs
protected int	subclasses
protected Matrix	W
protected Matrix	W2

Fields inherited from class libai.nn.NeuralNetwork

plotter, progress, random

Constructor Summary

Constructors

Constructor	Description
LVQ(int in, int subclass, int out)	Constructor.
LVQ(int in, int subclass, int out, java.util.Random rand)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	error(Column[] patterns, Column[] answers, int offset, int length)	Calculates the number of incorrect answers over the total.
Matrix[]	getWeights()
Column	simulate(Column pattern)	Calculates the output for the pattern.
void	simulate(Column pattern, Column result)	Calculate the output for the pattern and left the result in result.
protected int	simulateNoChange(Matrix pattern)
void	train(Column[] patterns, Column[] answers, double alpha, int epochs, int offset, int length, double minerror)	Train the network using a hybrid scheme.

Methods inherited from class libai.nn.NeuralNetwork

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.SupervisedLearning

validatePreconditions

Field Detail

W

protected Matrix W

W2

protected Matrix W2

ins

protected int ins

outs

protected int outs

subclasses

protected int subclasses

Constructor Detail

LVQ

public LVQ(int in,
           int subclass,
           int out)

Constructor. Number of inputs, number of subclasses and number of outputs.

Parameters:

in - Number of input to the network.

subclass - Number of subclasses for output class. Greater subdivision provides better classification.

out - Number of outputs for the network

LVQ

public LVQ(int in,
           int subclass,
           int out,
           java.util.Random rand)

Constructor. Number of inputs, number of subclasses and number of outputs.

Parameters:

in - Number of input to the network.

subclass - Number of subclasses for output class. Greater subdivision provides better classification.

out - 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)

Train the network using a hybrid scheme. Uses the "winner takes all" rule.

Specified by:

train in class NeuralNetwork

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 Column simulate(Column pattern)

Description copied from class: NeuralNetwork

Calculates the output for the pattern.

Specified by:

simulate in class NeuralNetwork

Parameters:

pattern - Pattern to use as input.

Returns:

The output for the neural network.

simulate

public void simulate(Column pattern,
                     Column result)

Calculate the output for the pattern and left the result in result. The result will be a row matrix fill with 0 except for the winner position.

Specified by:

simulate in class NeuralNetwork

Parameters:

pattern - Pattern to use as input.

result - The output for the input.

simulateNoChange

protected int simulateNoChange(Matrix pattern)

error

public double error(Column[] patterns,
                    Column[] answers,
                    int offset,
                    int length)

Calculates the number of incorrect answers over the total.

Overrides:

error in class NeuralNetwork

Parameters:

patterns - The array with the patterns to test

answers - The array with the expected answers for the patterns.

offset - The initial position inside the array.

length - How many patterns must be taken from the offset.

Returns:

The relation between the incorrect answers and the total number of answers.

getWeights

public Matrix[] getWeights()