NeuralPSO (encog-java-core 3.4 API)

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

Field Summary

Fields
Modifier and Type	Field and Description
`protected double[]`	`m_bestErrors`
`protected int`	`m_bestVectorIndex`
`protected double[][]`	`m_bestVectors`
`protected double`	`m_c1`
`protected double`	`m_c2`
`protected CalculateScore`	`m_calculateScore`
`protected double`	`m_inertiaWeight`
`protected double`	`m_maxPosition`
`protected double`	`m_maxVelocity`
`protected boolean`	`m_multiThreaded`
`protected BasicNetwork[]`	`m_networks`
`protected int`	`m_populationSize`
`protected Randomizer`	`m_randomizer`
`protected VectorAlgebra`	`m_va`
`protected double[][]`	`m_velocities`

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

All Methods Instance Methods Concrete Methods
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.
`protected double[]`	`getNetworkState(int particleIndex)` Returns the state of a network in the swarm
`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.
`protected void`	`iterationPSO(boolean init)` Internal method for the iteration of the swarm.
`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.
`protected void`	`setNetworkState(int particleIndex, double[] state)` Sets the state of the networks in the swarm
`void`	`setPopulationSize(int populationSize)` Set the swarm size.
`protected void`	`updateGlobalBestPosition()` Update the swarm's best position
`protected void`	`updateParticle(int particleIndex, boolean init)` Update the velocity, position and personal best position of a particle
`protected void`	`updatePersonalBestPosition(int particleIndex, double[] particlePosition)` Update the personal best position of a particle.
`protected void`	`updateVelocity(int particleIndex, double[] particlePosition)` Update the velocity of a particle

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, toString, wait, wait, wait

- Field Detail
  - m_multiThreaded
```
protected boolean m_multiThreaded
```
  - m_va
```
protected VectorAlgebra m_va
```
  - m_calculateScore
```
protected CalculateScore m_calculateScore
```
  - m_randomizer
```
protected Randomizer m_randomizer
```
  - m_networks
```
protected BasicNetwork[] m_networks
```
  - m_velocities
```
protected double[][] m_velocities
```
  - m_bestVectors
```
protected double[][] m_bestVectors
```
  - m_bestErrors
```
protected double[] m_bestErrors
```
  - m_bestVectorIndex
```
protected int m_bestVectorIndex
```
  - m_populationSize
```
protected int m_populationSize
```
  - m_maxPosition
```
protected double m_maxPosition
```
  - m_maxVelocity
```
protected double m_maxVelocity
```
  - m_c1
```
protected double m_c1
```
  - m_c2
```
protected double m_c2
```
  - m_inertiaWeight
```
protected double m_inertiaWeight
```
- 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.
  - iterationPSO
```
protected void iterationPSO(boolean init)
```
    Internal method for the iteration of the swarm.
    
    Parameters:
    
    init - true if this is an initialisation iteration.
  - updateParticle
```
protected void updateParticle(int particleIndex,
                              boolean init)
```
    Update the velocity, position and personal best position of a particle
    
    Parameters:
    
    particleIndex - index of the particle in the swarm
    
    init - if true, the position and velocity will be initialised.
  - updateVelocity
```
protected void updateVelocity(int particleIndex,
                              double[] particlePosition)
```
    Update the velocity of a particle
    
    Parameters:
    
    particleIndex - index of the particle in the swarm
    
    particlePosition - the particle current position vector
  - updatePersonalBestPosition
```
protected void updatePersonalBestPosition(int particleIndex,
                                          double[] particlePosition)
```
    Update the personal best position of a particle.
    
    Parameters:
    
    particleIndex - index of the particle in the swarm
    
    particlePosition - the particle current position vector
  - updateGlobalBestPosition
```
protected void updateGlobalBestPosition()
```
    Update the swarm's best position
  - 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.
  - getNetworkState
```
protected double[] getNetworkState(int particleIndex)
```
    Returns the state of a network in the swarm
    
    Parameters:
    
    particleIndex - index of the network in the swarm
    
    Returns:
    
    an array of weights and biases for the given network
  - setNetworkState
```
protected void setNetworkState(int particleIndex,
                               double[] state)
```
    Sets the state of the networks in the swarm
    
    Parameters:
    
    particleIndex - index of the network in the swarm
    
    state - an array of weights and biases
  - 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

Class NeuralPSO

Field Summary

Constructor Summary

Method Summary

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

Methods inherited from class java.lang.Object

Field Detail

m_multiThreaded

m_va

m_calculateScore

m_randomizer

m_networks

m_velocities

m_bestVectors

m_bestErrors

m_bestVectorIndex

m_populationSize

m_maxPosition

m_maxVelocity

m_c1

m_c2

m_inertiaWeight

Constructor Detail

NeuralPSO

NeuralPSO

Method Detail

iteration

iterationPSO

updateParticle

updateVelocity

updatePersonalBestPosition

updateGlobalBestPosition

pause

canContinue

resume

getNetworkState

setNetworkState

setPopulationSize

getPopulationSize

setMaxVelocity

getMaxVelocity

setMaxPosition

getMaxPosition

setC1

getC1

setC2

getC2

setInertiaWeight

getInertiaWeight

getDescription

getMethod

setInitialPopulation

isMultiThreaded