com.imsl.test.example.datamining

Class KohonenSOMEx1

java.lang.Object

com.imsl.datamining.KohonenSOMTrainer

com.imsl.test.example.datamining.KohonenSOMEx1

All Implemented Interfaces:

Serializable

public class KohonenSOMEx1
extends KohonenSOMTrainer

Creates and trains a Kohonen self-organizing map.

This example creates a Kohonen network with 40 x 40 nodes using the class KohonenSOM. Each node has 3 weights, representing the RGB values of a color. This network is trained with 8 colors using 500 iterations. Then, the example prints out a forecast result.

The initial image of the nodes is:

After the training, the image is:

See Also:

Code, Output, Serialized Form

Constructor Summary

Constructors

Constructor and Description
KohonenSOMEx1()

Method Summary

Modifier and Type	Method and Description
double	getLearningCoefficient(int t) Returns the learning coefficient.
double	getNeighborhoodValue(int t, double d) Returns the neighborhood function value.
static void	main(String[] args)

Methods inherited from class com.imsl.datamining.KohonenSOMTrainer

getIterations, getNumberOfThreads, setIterations, setNumberOfThreads, train

Methods inherited from class java.lang.Object

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

Constructor Detail

KohonenSOMEx1

public KohonenSOMEx1()

Method Detail

getNeighborhoodValue

public double getNeighborhoodValue(int t,
                                   double d)

Description copied from class: KohonenSOMTrainer

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

Specified by:

getNeighborhoodValue in class KohonenSOMTrainer

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 double getLearningCoefficient(int t)

Description copied from class: KohonenSOMTrainer

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

Specified by:

getLearningCoefficient in class KohonenSOMTrainer

Parameters:

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

Returns:

A double scalar containing the computed learning coefficient.

main

public static void main(String[] args)