org.encog.neural.networks.training.pso

Class NeuralPSO

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.neural.networks.training.pso.NeuralPSO

All Implemented Interfaces:

MLTrain

public class NeuralPSO
extends BasicTraining

Iteratively trains a population of neural networks by applying particle swarm optimisation (PSO). Contributed by: Geoffroy Noel https://github.com/goffer-looney References: James Kennedy and Russell C. Eberhart, Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942-1948

Constructor Summary

Constructors

Constructor and Description
NeuralPSO(BasicNetwork network, MLDataSet trainingSet) Construct a PSO using a training set score function, 20 particles and the NguyenWidrowRandomizer randomizer.
NeuralPSO(BasicNetwork network, Randomizer randomizer, CalculateScore calculateScore, int populationSize) Constructor.

Method Summary

Modifier and Type	Method and Description
boolean	canContinue()
double	getC1() Get the cognition coefficient (c1).
double	getC2() Get the social coefficient (c2).
java.lang.String	getDescription() Get a description of all the current settings.
double	getInertiaWeight() Get the inertia weight (w)
double	getMaxPosition() Get the boundary of the search space (Xmax)
double	getMaxVelocity() Get the maximum velocity (Vmax)
MLMethod	getMethod() Get the current best machine learning method from the training.
int	getPopulationSize() Returns the swarm size.
boolean	isMultiThreaded() Get the multi-threaded mode.
void	iteration() Runs one PSO iteration over the whole population of networks.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setC1(double c1) Sets the cognition coefficient (c1).
void	setC2(double c2) Set the social coefficient (c2).
void	setInertiaWeight(double inertiaWeight) Set the inertia weight (w)
void	setInitialPopulation(BasicNetwork[] initialPopulation) Keep a reference to the passed population of networks.
void	setMaxPosition(double maxPosition) Set the boundary of the search space (Xmax)
void	setMaxVelocity(double maxVelocity) Sets the maximum velocity.
void	setPopulationSize(int populationSize) Set the swarm size.

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

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

Constructor Detail

NeuralPSO

public NeuralPSO(BasicNetwork network,
                 Randomizer randomizer,
                 CalculateScore calculateScore,
                 int populationSize)

Constructor.

Parameters:

network - an initialised Encog network. The networks in the swarm will be created with the same topology as this network.

randomizer - any type of Encog network weight initialisation object.

calculateScore - any type of Encog network scoring/fitness object.

populationSize - the swarm size.

NeuralPSO

public NeuralPSO(BasicNetwork network,
                 MLDataSet trainingSet)

Construct a PSO using a training set score function, 20 particles and the NguyenWidrowRandomizer randomizer.

Parameters:

network - The network to train. an initialised Encog network. The networks in the swarm will be created with the same topology as this network.

trainingSet - The training set.

Method Detail

iteration

public void iteration()

Runs one PSO iteration over the whole population of networks.

pause

public TrainingContinuation pause()

Description copied from interface: MLTrain

Pause the training to continue later.

Returns:

A training continuation object.

canContinue

public boolean canContinue()

Returns:

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

resume

public void resume(TrainingContinuation state)

Description copied from interface: MLTrain

Resume training.

Parameters:

state - The training continuation object to use to continue.

setPopulationSize

public void setPopulationSize(int populationSize)

Set the swarm size.

Parameters:

populationSize - the swarm size

getPopulationSize

public int getPopulationSize()

Returns the swarm size.

Returns:

the swarm size.

setMaxVelocity

public void setMaxVelocity(double maxVelocity)

Sets the maximum velocity.

Parameters:

maxVelocity - Maximum velocity / Vmax

getMaxVelocity

public double getMaxVelocity()

Get the maximum velocity (Vmax)

Returns:

maximum velocity (Vmax)

setMaxPosition

public void setMaxPosition(double maxPosition)

Set the boundary of the search space (Xmax)

Parameters:

maxPosition - maximum value for a component (Xmax)

getMaxPosition

public double getMaxPosition()

Get the boundary of the search space (Xmax)

Returns:

the maximum value a component can take (Xmax)

setC1

public void setC1(double c1)

Sets the cognition coefficient (c1).

Parameters:

c1 - cognition coefficient (c1)

getC1

public double getC1()

Get the cognition coefficient (c1).

Returns:

the cognition coefficient (c1)

setC2

public void setC2(double c2)

Set the social coefficient (c2).

Parameters:

c2 - the social coefficient (c2)

getC2

public double getC2()

Get the social coefficient (c2).

Returns:

the social coefficient (c2)

setInertiaWeight

public void setInertiaWeight(double inertiaWeight)

Set the inertia weight (w)

Parameters:

inertiaWeight - the inertia weight (w)

getInertiaWeight

public double getInertiaWeight()

Get the inertia weight (w)

Returns:

the inertia weight (w)

getDescription

public java.lang.String getDescription()

Get a description of all the current settings.

Returns:

a String describing all the current setting in a single line.

getMethod

public MLMethod getMethod()

Description copied from interface: MLTrain

Get the current best machine learning method from the training.

Returns:

The best machine learning method.

setInitialPopulation

public void setInitialPopulation(BasicNetwork[] initialPopulation)

Keep a reference to the passed population of networks. This population is not copied, it will evolve during training.

Parameters:

initialPopulation - the initial population of networks.

isMultiThreaded

public boolean isMultiThreaded()

Get the multi-threaded mode.

Returns:

true if PSO works in multi-threaded mode