public TicTacToeGenetic(FeedforwardNetwork network,
bool reset, int populationSize,
double mutationPercent, double percentToMate,
Type opponent)
{
this.setOpponent(opponent);
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.Chromosomes = new TicTacToeChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TicTacToeChromosome c = new TicTacToeChromosome(this,
chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
/// <summary>
/// Construct a genetic algorithm for a neural network that uses training sets.
/// </summary>
/// <param name="network">The neural network.</param>
/// <param name="reset">Should each neural network be reset to random values.</param>
/// <param name="input">The input training set.</param>
/// <param name="ideal">The ideal values for the input training set.</param>
/// <param name="populationSize">The initial population size.</param>
/// <param name="mutationPercent">The mutation percentage.</param>
/// <param name="percentToMate">The percentage of the population allowed to mate.</param>
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network,
bool reset, double[][] input,
double[][] ideal, int populationSize,
double mutationPercent, double percentToMate)
{
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.input = input;
this.ideal = ideal;
this.Chromosomes = new TrainingSetNeuralChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TrainingSetNeuralChromosome c = new TrainingSetNeuralChromosome(
this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
/// <summary>
/// Construct a genetic algorithm for a neural network that uses training sets.
/// </summary>
/// <param name="network">The neural network.</param>
/// <param name="reset">Should each neural network be reset to random values.</param>
/// <param name="input">The input training set.</param>
/// <param name="ideal">The ideal values for the input training set.</param>
/// <param name="populationSize">The initial population size.</param>
/// <param name="mutationPercent">The mutation percentage.</param>
/// <param name="percentToMate">The percentage of the population allowed to mate.</param>
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network,
bool reset, double[][] input,
double[][] ideal, int populationSize,
double mutationPercent, double percentToMate)
{
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.input = input;
this.ideal = ideal;
this.Chromosomes = new TrainingSetNeuralChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TrainingSetNeuralChromosome c = new TrainingSetNeuralChromosome(
this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network, bool reset, double[][] input, double[][] ideal,
int populationSize, double mutationPercent, double allowedPercentageToMate)
{
MutationPercent = mutationPercent;
MatingPopulation = allowedPercentageToMate * 2;
PopulationSize = populationSize;
PercentToMate = allowedPercentageToMate;
Input = input;
Ideal = ideal;
Chromosomes = new TrainingSetNeuralChromosome[PopulationSize];
for (var i = 0; i < Chromosomes.Length; i++)
{
var chromosomeNetwork = (FeedforwardNetwork)network.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
var c = new TrainingSetNeuralChromosome(this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
public TicTacToeGenetic(FeedforwardNetwork network,
bool reset, int populationSize,
double mutationPercent, double percentToMate,
Type opponent)
{
this.setOpponent(opponent);
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.Chromosomes = new TicTacToeChromosome[this.PopulationSize];
var rangePartitioner = Partitioner.Create(0, this.Chromosomes.Length);
//Parallel.ForEach(rangePartitioner, (range, loopState) =>
{
for (int i = 0; i < Chromosomes.Length; i++)
{
Console.WriteLine("step: " + i);
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TicTacToeChromosome c = new TicTacToeChromosome(this,
chromosomeNetwork);
c.UpdateGenes();
lock (_locker)
{
SetChromosome(i, c);
}
}
};
Console.WriteLine("PreEpoch # Error:" + getScore());
SortChromosomes();
}
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network, bool reset, double[][] input, double[][] ideal,
int populationSize, double mutationPercent, double allowedPercentageToMate)
{
MutationPercent = mutationPercent;
MatingPopulation = allowedPercentageToMate * 2;
PopulationSize = populationSize;
PercentToMate = allowedPercentageToMate;
Input = input;
Ideal = ideal;
Chromosomes = new TrainingSetNeuralChromosome[PopulationSize];
for (var i = 0; i < Chromosomes.Length; i++)
{
var chromosomeNetwork = (FeedforwardNetwork)network.Clone();
if (reset)
chromosomeNetwork.Reset();
var c = new TrainingSetNeuralChromosome(this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
