public static double[] TrainNeuralNetworkRoulette(TrainingExample[] trainingSet, uint[] config, Func <int, int> rng, double threshold)
{
Genetics.Individual[] individuals = new int[30].Select(_ => {
int nFeatures = (int)Neural.NWeightsFromConfig(config);
Genetics.Gene[] genesA = Genetics.Generate(rng, 48334).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Gene[] genesB = Genetics.Generate(rng, 94353).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Chromosome cA = new Genetics.Chromosome(genesA);
Genetics.Chromosome cB = new Genetics.Chromosome(genesB);
return(new Genetics.Individual(cA, cB));
}).ToArray();
Func <Genetics.Individual, double> individualCost = individual => {
double[] wData = individual.Express(Genetics.DefaultGeneExpression);
double[][][] wsss = Neural.FoldExpression(wData, config);
return(Cost(trainingSet, input => Neural.Network(Neural.Sigmoid, wsss, input).ToArray()));
};
Func <Genetics.Gene, Genetics.Gene> mutator = Genetics.CreateDefaultGeneMutator(546794);
int runs = 0;
Tuple <Genetics.Individual, double>[] currentGen =
individuals.Select(ind => Tuple.Create(ind, individualCost(ind))).ToArray();
Genetics.Individual solution = null;
while (solution == null)
{
runs++;
{
int _rn = 9479238;
Func <int> _rng = () => {
_rn = Genetics.Hash(_rn);
return(_rn);
};
Random r = new Random();
Func <int> __rng = () => {
return(r.Next());
};
currentGen = new bool[currentGen.Count()].AsParallel().Select(_ => {
Genetics.Individual mommy = Roulette(currentGen, __rng);
Genetics.Individual daddy = Roulette(currentGen, __rng);
//Genetics.Individual daddy;
//for (int i = 0; ; i++) {
// daddy = Roulette(currentGen, __rng);
// if (mommy != daddy) break;
//}
Genetics.Individual child = mommy.Mate(__rng, daddy, mutator);
return(Tuple.Create(child, individualCost(child)));
}).OrderBy(c => c.Item2).ToArray();
}
for (int i = 0; i < Math.Min(currentGen.Length, 10); i++)
{
Console.Write($"{string.Format("{0:0.000}", currentGen[i].Item2).PadRight(10)}");
}
Console.WriteLine();
solution = currentGen[0].Item2 <= threshold ? currentGen[0].Item1 : null;
}
Console.WriteLine($"runs: {runs}");
return(solution.Express(Genetics.DefaultGeneExpression));
}
public static double[] TrainNeuralNetworkSelectiveBreeding(TrainingExample[] trainingSet, uint[] config, Func <int, int> rng, double threshold)
{
Genetics.Individual[] individuals = new int[30].Select(_ => {
int nFeatures = (int)Neural.NWeightsFromConfig(config);
Genetics.Gene[] genesA = Genetics.Generate(rng, 48334).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Gene[] genesB = Genetics.Generate(rng, 94353).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Chromosome cA = new Genetics.Chromosome(genesA);
Genetics.Chromosome cB = new Genetics.Chromosome(genesB);
return(new Genetics.Individual(cA, cB));
}).ToArray();
Func <Genetics.Individual, double> individualCost = individual => {
double[] wData = individual.Express(Genetics.DefaultGeneExpression);
double[][][] wsss = Neural.FoldExpression(wData, config);
return(Cost(trainingSet, input => Neural.Network(Neural.Sigmoid, wsss, input).ToArray()));
};
Func <Genetics.Gene, Genetics.Gene> mutator = Genetics.CreateDefaultGeneMutator(546794);
Tuple <Genetics.Individual, double>[] currentGen =
individuals.Select(ind => Tuple.Create(ind, individualCost(ind))).OrderBy(c => c.Item2).ToArray();
Tuple <Genetics.Individual, double>[] solutions =
new Tuple <Genetics.Individual, double> [0];
int runs = 0;
while (solutions.Length == 0)
{
runs++;
Tuple <Genetics.Individual, double> uber1 = currentGen[0];
Tuple <Genetics.Individual, double> uber2 = currentGen[1];
{
int _rn = 9479238;
Func <int> _rng = () => {
_rn = Genetics.Hash(_rn);
return(_rn);
};
Random r = new Random();
Func <int> __rng = () => {
return(r.Next());
};
currentGen = currentGen.AsParallel().Select(c => {
Genetics.Individual child = uber1.Item1.Mate(__rng, uber2.Item1, mutator);
return(Tuple.Create(child, individualCost(child)));
}).OrderBy(c => c.Item2).ToArray();
}
for (int i = 0; i < Math.Min(currentGen.Length, 10); i++)
{
Console.Write($"{string.Format("{0:0.000}", currentGen[i].Item2).PadRight(10)}");
}
Console.WriteLine();
solutions = currentGen.Where(ind => ind.Item2 <= threshold).ToArray();
}
Console.WriteLine($"runs: {runs}");
return(solutions.OrderBy(solution => solution.Item2).FirstOrDefault()?.Item1.Express(Genetics.DefaultGeneExpression));
}
public static double[] TrainNeuralNetworkRuleofTwo(TrainingExample[] trainingSet, uint[] config, Func <int, int> rng, double threshold)
{
Genetics.Individual[] individuals = new int[3].Select(_ => {
int nFeatures = (int)Neural.NWeightsFromConfig(config);
Genetics.Gene[] genesA = Genetics.Generate(rng, 48334).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Gene[] genesB = Genetics.Generate(rng, 94353).Select(x => new Genetics.Gene((short)x)).Take(nFeatures).ToArray();
Genetics.Chromosome cA = new Genetics.Chromosome(genesA);
Genetics.Chromosome cB = new Genetics.Chromosome(genesB);
return(new Genetics.Individual(cA, cB));
}).ToArray();
Func <Genetics.Individual, double> individualCost = individual => {
double[] wData = individual.Express(Genetics.DefaultGeneExpression);
double[][][] wsss = Neural.FoldExpression(wData, config);
return(Cost(trainingSet, input => Neural.Network(Neural.Sigmoid, wsss, input).ToArray()));
};
Func <Genetics.Gene, Genetics.Gene> mutator = Genetics.CreateDefaultGeneMutator(546794);
Tuple <Genetics.Individual, double> daddy = Tuple.Create(individuals[0], individualCost(individuals[0]));
Tuple <Genetics.Individual, double> mommy = Tuple.Create(individuals[1], individualCost(individuals[1]));
Tuple <Genetics.Individual, double> child = Tuple.Create(individuals[2], individualCost(individuals[2]));
int runs = 0;
while (child.Item2 >= threshold)
{
runs++;
if (daddy.Item2 > mommy.Item2) // daddy worse than mommy?
{
daddy = daddy.Item2 > child.Item2 ? child : daddy;
}
else // mommy worse than daddy?
{
mommy = mommy.Item2 > child.Item2 ? child : mommy;
}
{
int _rn = 9479238;
Func <int> _rng = () => {
_rn = Genetics.Hash(_rn);
return(_rn);
};
Random r = new Random();
Func <int> __rng = () => {
return(r.Next());
};
Genetics.Individual c = mommy.Item1.Mate(__rng, daddy.Item1, mutator);
child = Tuple.Create(c, individualCost(c));
}
Console.Write($"mommy: {string.Format("{0:0.000}", mommy.Item2)}; ");
Console.Write($"daddy: {string.Format("{0:0.000}", daddy.Item2)}; ");
Console.Write($"child: {string.Format("{0:0.000}", child.Item2)};");
Console.WriteLine();
}
Console.WriteLine($"runs: {runs}");
return(child.Item1.Express(Genetics.DefaultGeneExpression));
}
