static Generation Train(IGenTrainer trainer, DataSet seed, Run run, int generationNum, Action <int, int, int> onGenerationProgress, MixtureInfo[] pop)
{
var gen = new Generation(run, generationNum);
trainer.Train(seed, gen, pop.Select(mi => new MixtureInfo(new Mixture(gen, mi.Parents).Id, mi.Chromosomes)).ToArray(),
progress => onGenerationProgress.Fire(generationNum, progress.Completed, progress.Total));
return(gen);
}
public static Generation MakeInitialGeneration(DataSet seed, Run run, IGenTrainer trainer)
{
var gen = new Generation(run, 0);
Func <int, NetworkType, int, Chromosome[]> makeChromosomes = (n, type, orderOffset) =>
Lists.Repeat(n, i => MakeRandomChromosome(type, run.ProtoRun.ProtoChromosome, orderOffset + i)).ToArray();
Func <Chromosome[]> makeMixtureChromosomes = () =>
makeChromosomes(run.ProtoRun.RnnPerMixture, NetworkType.Rnn, 0).Concat(
makeChromosomes(run.ProtoRun.RbfPerMixture, NetworkType.Rbf, run.ProtoRun.RnnPerMixture)).ToArray();
Func <Mixture> makeMixture = () => new Mixture(gen, new Mixture[0]);
Func <Mixture, MixtureInfo> makeMixtureInfo = m => new MixtureInfo(m.Id, makeMixtureChromosomes());
var pop = Lists.Repeat(run.ProtoRun.MixturesPerGeneration, _ => makeMixture()).Select(makeMixtureInfo);
trainer.Train(seed, gen, pop, progress => Console.WriteLine("Initialized {0} of {1}", progress.Completed, progress.Total));
return(gen);
}
public static Run Evolve(ProtoRun protoRun, IGenTrainer trainer, DataSet trainingSet, DataSet validationSet,
string symbol = null, DateTime startDate = default(DateTime), DateTime endDate = default(DateTime), double validationPct = 0,
Action <int, int, int> onGenerationProgress = null,
Action <Generation> onGenerationComplete = null)
{
var run = new Run(protoRun, protoRun.ProtoChromosome, null, startDate, endDate, validationPct);
var gen = Initialization.MakeInitialGeneration(trainingSet, run, trainer);
while (true)
{
var evaluated = Evaluate(gen, validationSet);
onGenerationComplete(gen);
if (gen.Order == protoRun.NumGenerations - 1)
{
return(run);
}
gen = Train(trainer, trainingSet, run, gen.Order + 1, onGenerationProgress,
Mutate(run,
Combine(protoRun.MixturesPerGeneration,
Select(protoRun.SelectionSize,
evaluated))));
}
}
