org.encog.neural.som.training.basic

Class BasicTrainSOM

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.neural.som.training.basic.BasicTrainSOM

All Implemented Interfaces:

MLTrain, LearningRate

public class BasicTrainSOM
extends BasicTraining
implements LearningRate

This class implements competitive training, which would be used in a winner-take-all neural network, such as the self organizing map (SOM). This is an unsupervised training method, no ideal data is needed on the training set. If ideal data is provided, it will be ignored. Training is done by looping over all of the training elements and calculating a "best matching unit" (BMU). This BMU output neuron is then adjusted to better "learn" this pattern. Additionally, this training may be applied to other "nearby" output neurons. The degree to which nearby neurons are update is defined by the neighborhood function. A neighborhood function is required to determine the degree to which neighboring neurons (to the winning neuron) are updated by each training iteration. Because this is unsupervised training, calculating an error to measure progress by is difficult. The error is defined to be the "worst", or longest, Euclidean distance of any of the BMU's. This value should be minimized, as learning progresses. Because only the BMU neuron and its close neighbors are updated, you can end up with some output neurons that learn nothing. By default these neurons are not forced to win patterns that are not represented well. This spreads out the workload among all output neurons. This feature is not used by default, but can be enabled by setting the "forceWinner" property.

Constructor Summary

Constructors

Constructor and Description
BasicTrainSOM(SOM network, double learningRate, MLDataSet training, NeighborhoodFunction neighborhood) Create an instance of competitive training.

Method Summary

Modifier and Type	Method and Description
void	autoDecay() Should be called each iteration if autodecay is desired.
boolean	canContinue()
void	decay(double d) Called to decay the learning rate and radius by the specified amount.
void	decay(double decayRate, double decayRadius) Decay the learning rate and radius by the specified amount.
int	getInputNeuronCount()
double	getLearningRate()
MLMethod	getMethod() Get the current best machine learning method from the training.
NeighborhoodFunction	getNeighborhood()
int	getOutputNeuronCount()
boolean	isForceWinner()
void	iteration() Perform one training iteration.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setAutoDecay(int plannedIterations, double startRate, double endRate, double startRadius, double endRadius) Setup autodecay.
void	setForceWinner(boolean forceWinner) Determine if a winner is to be forced.
void	setLearningRate(double rate) Set the learning rate.
void	setParams(double rate, double radius) Set the learning rate and radius.
java.lang.String	toString()
void	trainPattern(MLData pattern) Train the specified pattern.

Methods inherited from class org.encog.ml.train.BasicTraining

addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining

Methods inherited from class java.lang.Object

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

Constructor Detail

BasicTrainSOM

public BasicTrainSOM(SOM network,
                     double learningRate,
                     MLDataSet training,
                     NeighborhoodFunction neighborhood)

Create an instance of competitive training.

Parameters:

network - The network to train.

learningRate - The learning rate, how much to apply per iteration.

training - The training set (unsupervised).

neighborhood - The neighborhood function to use.

Method Detail

autoDecay

public void autoDecay()

Should be called each iteration if autodecay is desired.

canContinue

public boolean canContinue()

Specified by:

canContinue in interface MLTrain

Returns:

True if the training can be paused, and later continued.

decay

public void decay(double d)

Called to decay the learning rate and radius by the specified amount.

Parameters:

d - The percent to decay by.

decay

public void decay(double decayRate,
                  double decayRadius)

Decay the learning rate and radius by the specified amount.

Parameters:

decayRate - The percent to decay the learning rate by.

decayRadius - The percent to decay the radius by.

getInputNeuronCount

public int getInputNeuronCount()

Returns:

The input neuron count.

getLearningRate

public double getLearningRate()

Specified by:

getLearningRate in interface LearningRate

Returns:

The learning rate. This was set when the object was created.

getMethod

public MLMethod getMethod()

Get the current best machine learning method from the training.

Specified by:

getMethod in interface MLTrain

Returns:

The best machine learning method.

getNeighborhood

public NeighborhoodFunction getNeighborhood()

Returns:

The network neighborhood function.

getOutputNeuronCount

public int getOutputNeuronCount()

Returns:

The output neuron count.

isForceWinner

public boolean isForceWinner()

Returns:

Is a winner to be forced of neurons that do not learn. See class description for more info.

iteration

public void iteration()

Perform one training iteration.

Specified by:

iteration in interface MLTrain

pause

public TrainingContinuation pause()

Pause the training to continue later.

Specified by:

pause in interface MLTrain

Returns:

A training continuation object.

resume

public void resume(TrainingContinuation state)

Resume training.

Specified by:

resume in interface MLTrain

Parameters:

state - The training continuation object to use to continue.

setAutoDecay

public void setAutoDecay(int plannedIterations,
                         double startRate,
                         double endRate,
                         double startRadius,
                         double endRadius)

Setup autodecay. This will decrease the radius and learning rate from the start values to the end values.

Parameters:

plannedIterations - The number of iterations that are planned. This allows the decay rate to be determined.

startRate - The starting learning rate.

endRate - The ending learning rate.

startRadius - The starting radius.

endRadius - The ending radius.

setForceWinner

public void setForceWinner(boolean forceWinner)

Determine if a winner is to be forced. See class description for more info.

Parameters:

forceWinner - True if a winner is to be forced.

setLearningRate

public void setLearningRate(double rate)

Set the learning rate. This is the rate at which the weights are changed.

Specified by:

setLearningRate in interface LearningRate

Parameters:

rate - The learning rate.

setParams

public void setParams(double rate,
                      double radius)

Set the learning rate and radius.

Parameters:

rate - The new learning rate.

radius - The new radius.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

trainPattern

public void trainPattern(MLData pattern)

Train the specified pattern. Find a winning neuron and adjust all neurons according to the neighborhood function.

Parameters:

pattern - The pattern to train.