KohonenSOMTrainer (JMSL Numerical Library)

java.lang.Object
- com.imsl.datamining.KohonenSOMTrainer

All Implemented Interfaces:

Serializable
```
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()

Constructors
Constructor and Description
`KohonenSOMTrainer()`

Method Summary

Methods
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
  - getIterations
```
public int getIterations()
```
    Returns the number of iterations used for training.
    
    Returns:
    An int scalar containing the number of iterations used for training.
  - getLearningCoefficient
```
public abstract double getLearningCoefficient(int t)
```
    Returns the learning coefficient. The monotonically decreasing learning coefficient function is a scalar factor that defines the size of the update correction. The value of 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.
  - getNeighborhoodValue
```
public abstract double getNeighborhoodValue(int t,
                          double d)
```
    Returns the neighborhood function value. In the simplest form, the neighborhood function is 1 for all nodes closest to the BMU and 0 for others, but a Gaussian function is also commonly used. For example:
    $hleft( {d,t} right) = exp(-d^2/2r^2)$ 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.
  - 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.
  - 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.
  - 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.

Class KohonenSOMTrainer

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

KohonenSOMTrainer

Method Detail

getIterations

getLearningCoefficient

getNeighborhoodValue

getNumberOfThreads

setIterations

setNumberOfThreads

train