/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
///
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer, ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate) : base(TrainingImplementationType.Iterative)
{
Genetic = new NeuralGeneticAlgorithmHelper
{
CalculateScore = new GeneticScoreAdapter(calculateScore)
};
IPopulation population = new BasicPopulation(populationSize);
Genetic.MutationPercent = mutationPercent;
Genetic.MatingPopulation = percentToMate * 2;
Genetic.PercentToMate = percentToMate;
Genetic.Crossover = new Splice(network.Structure.CalculateSize() / 3);
Genetic.Mutate = new MutatePerturb(4.0d);
Genetic.Population = population;
for (int i = 0; i < population.PopulationSize; i++)
{
var chromosomeNetwork = (BasicNetwork)(network
.Clone());
randomizer.Randomize(chromosomeNetwork);
var genome = new NeuralGenome(chromosomeNetwork)
{
GA = Genetic
};
Genetic.PerformCalculateScore(genome);
Genetic.Population.Add(genome);
}
population.Sort();
}
/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
///
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer, ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate) : base(TrainingImplementationType.Iterative)
{
Genetic = new NeuralGeneticAlgorithmHelper
{
CalculateScore = new GeneticScoreAdapter(calculateScore)
};
IPopulation population = new BasicPopulation(populationSize);
Genetic.MutationPercent = mutationPercent;
Genetic.MatingPopulation = percentToMate*2;
Genetic.PercentToMate = percentToMate;
Genetic.Crossover = new Splice(network.Structure.CalculateSize()/3);
Genetic.Mutate = new MutatePerturb(4.0d);
Genetic.Population = population;
for (int i = 0; i < population.PopulationSize; i++)
{
var chromosomeNetwork = (BasicNetwork) (network
.Clone());
randomizer.Randomize(chromosomeNetwork);
var genome = new NeuralGenome(chromosomeNetwork) {GA = Genetic};
Genetic.PerformCalculateScore(genome);
Genetic.Population.Add(genome);
}
population.Sort();
}
/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer,
ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate)
{
this.Helper = new NeuralGeneticAlgorithmHelper();
this.Helper.CalculateScore = new GeneticScoreAdapter(calculateScore);
IPopulation population = new BasicPopulation(populationSize);
Helper.MutationPercent = mutationPercent;
Helper.MatingPopulation = (percentToMate * 2);
Helper.PercentToMate = (percentToMate);
Helper.Crossover = (new Splice(network.Structure.CalculateSize() / 3));
Helper.Mutate = (new MutatePerturb(4.0));
Helper.Population = (population);
for (int i = 0; i < population.PopulationSize; i++)
{
BasicNetwork chromosomeNetwork = (BasicNetwork)network
.Clone();
randomizer.Randomize(chromosomeNetwork);
NeuralGenome genome =
new NeuralGenome(this, chromosomeNetwork);
Helper.PerformScoreCalculation(genome);
Helper.Population.Add(genome);
}
population.Sort();
}
/// <summary>
/// Create a new NNThreadSafeNetworkPool
/// </summary>
/// <param name="baseNetwork">The network to use for this object</param>
/// <param name="listStartLength">The default length to start the network list at.</param>
public NNThreadSafeNetworkPool(BasicNetwork baseNetwork, string networkConfigStr, int listStartLength)
{
this.baseNetwork = (BasicNetwork)baseNetwork.Clone();
this.networkConfigStr = networkConfigStr;
for (int i = 0; i < listStartLength; i++)
{
threadSafeNetworksList.Add(new ThreadSafeNetwork((BasicNetwork)baseNetwork.Clone()));
}
//Initialise idleNetworksBitFlags
for (int i = 0; i < listStartLength; i++)
{
idleNetworksBitFlags = idleNetworksBitFlags | (1 << i);
}
}
/// <summary>
/// Update the velocity, position and personal
/// best position of a particle.
/// </summary>
/// <param name="particleIndex">index of the particle in the swarm</param>
/// <param name="init">if true, the position and velocity
/// will be initialised. </param>
public void UpdateParticle(int particleIndex, bool init)
{
int i = particleIndex;
double[] particlePosition = null;
if (init)
{
// Create a new particle with random values.
// Except the first particle which has the same values
// as the network passed to the algorithm.
if (m_networks[i] == null)
{
m_networks[i] = (BasicNetwork)m_bestNetwork.Clone();
if (i > 0)
{
m_randomizer.Randomize(m_networks[i]);
}
}
particlePosition = GetNetworkState(i);
m_bestVectors[i] = particlePosition;
// randomise the velocity
m_va.Randomise(m_velocities[i], m_maxVelocity);
}
else
{
particlePosition = GetNetworkState(i);
UpdateVelocity(i, particlePosition);
// velocity clamping
m_va.ClampComponents(m_velocities[i], m_maxVelocity);
// new position (Xt = Xt-1 + Vt)
m_va.Add(particlePosition, m_velocities[i]);
// pin the particle against the boundary of the search space.
// (only for the components exceeding maxPosition)
m_va.ClampComponents(particlePosition, m_maxPosition);
SetNetworkState(i, particlePosition);
}
UpdatePersonalBestPosition(i, particlePosition);
}
/// <summary>
/// Get a prediction from the network pool using the provided inputs
/// </summary>
/// <param name="inputs"></param>
/// <returns></returns>
public double[] getNetworkPrediction(double[] inputs)
{
int networkInstance = DetermineAvailableInstanceIndex();
if (networkInstance == -1)
{
lock (baseLocker)
{
threadSafeNetworksList.Add(new ThreadSafeNetwork((BasicNetwork)baseNetwork.Clone()));
networkInstance = threadSafeNetworksList.Count - 1;
}
}
double[] outputs = threadSafeNetworksList[networkInstance].getNetworkPrediction(inputs);
//Now set instance available again
lock (baseLocker)
idleNetworksBitFlags = idleNetworksBitFlags | (1 << networkInstance);
//Get next available network in list
//If one is not avaialble add one to the list and then use that
return(outputs);
}