com.imsl.datamining

Class KohonenSOMTrainer

java.lang.Object

com.imsl.datamining.KohonenSOMTrainer

All Implemented Interfaces:

Serializable

Direct Known Subclasses:

KohonenSOMEx1

public abstract class KohonenSOMTrainer
extends Object
implements Serializable

Trains a Kohonen network.

KohonenSOMTrainer is an abstract class with 2 abstract methods, getNeighborhoodValue and getLearningCoefficient. Therefore, a subclass of KohonenSOMTrainer needs to implement the getNeighborhoodValue and getLearningCoefficient methods.

See Also:

KohonenSOM Example, Serialized Form

Constructor Summary

Constructors

Constructor and Description
KohonenSOMTrainer()

Method Summary

Modifier and Type	Method and Description
int	getIterations() Returns the number of iterations used for training.
abstract double	getLearningCoefficient(int t) Returns the learning coefficient.
abstract double	getNeighborhoodValue(int t, double d) Returns the neighborhood function value.
int	getNumberOfThreads() Returns the number of java.lang.Thread instances used for parallel processing.
void	setIterations(int iterations) Sets the number of iterations to be used for training.
void	setNumberOfThreads(int numberOfThreads) Sets the number of java.lang.Thread instances to be used for parallel processing.
void	train(KohonenSOM kohonen, double[][] data) Trains a Kohonen network.

Methods inherited from class java.lang.Object

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

Constructor Detail

KohonenSOMTrainer

public KohonenSOMTrainer()

Method Detail

getNeighborhoodValue

public abstract double getNeighborhoodValue(int t,
                                            double d)

Returns the neighborhood function value. In the simplest form, the neighborhood function \(h\left( {d,t} \right)\) is 1 for all nodes closest to the BMU and 0 for others, but a Gaussian function is also commonly used. For example:

\(h\left( {d,t} \right) = \exp(-d^2/2r^2)\) where r represents the neighborhood radius at index t

Parameters:

t - An int scalar containing the current iteration of the training.

d - A double scalar containing the lattice distance between the best matching node and the current node.

Returns:

A double scalar containing the computed neighborhood function value.

getLearningCoefficient

public abstract double getLearningCoefficient(int t)

Returns the learning coefficient. The monotonically decreasing learning coefficient function \(\alpha \left( t \right)\) is a scalar factor that defines the size of the update correction. The value of \(\alpha \left( t \right)\) decreases with the step index t. Typical forms are linear, power, and inverse time/step. For example:

power:

$$\alpha \left( t \right) = \alpha_{0} \left( \frac{\alpha_{T}}{\alpha_{0}} \right)^{t/T}$$

where t=t, T=the number of iterations used for training, \(\alpha_{0}\) = initial learning coefficient, \(\alpha_{T}\) = final learning coefficient

inverse time:

$$\alpha \left( t \right) = \frac{A}{t+B}$$

where A and B are user determined constants

Parameters:

t - An int scalar containing the current iteration of the training.

Returns:

A double scalar containing the computed learning coefficient.

setNumberOfThreads

public void setNumberOfThreads(int numberOfThreads)

Sets the number of java.lang.Thread instances to be used for parallel processing.

Parameters:

numberOfThreads - an int specifying the number of java.lang.Thread instances to be used for parallel processing.

Default: numberOfThreads = 1.

getNumberOfThreads

public int getNumberOfThreads()

Returns the number of java.lang.Thread instances used for parallel processing.

Returns:

an int containing the number of java.lang.Thread instances used for parallel processing.

setIterations

public void setIterations(int iterations)

Sets the number of iterations to be used for training.

Parameters:

iterations - An int scalar containing the number of iterations to be used for training. iterations must be greater than zero.

Default: iterations = 100.

getIterations

public int getIterations()

Returns the number of iterations used for training.

Returns:

An int scalar containing the number of iterations used for training.

train

public final void train(KohonenSOM kohonen,
                        double[][] data)

Trains a Kohonen network.

Parameters:

kohonen - A KohonenSOM object to be trained.

data - A double matrix containing the data to be used for training the Kohonen network. data[i].length must be equal to dim, the number of weights for each node, in kohonen.