private static void VerifyNeuralNetResponseInner(bool enableHardwareAcceleration)
{
var activationFnFactory = new DefaultActivationFunctionFactory <double>(enableHardwareAcceleration);
var metaNeatGenome = new MetaNeatGenome <double>(4, 1, true, activationFnFactory.GetActivationFunction("LeakyReLU"));
// Load test genome.
NeatGenomeLoader <double> loader = NeatGenomeLoaderFactory.CreateLoaderDouble(metaNeatGenome);
NeatGenome <double> genome = loader.Load("TestData/binary-three-multiplexer.genome");
// Decode genome to a neural net.
var genomeDecoder = NeatGenomeDecoderFactory.CreateGenomeDecoderAcyclic();
IBlackBox <double> blackBox = genomeDecoder.Decode(genome);
// Evaluate the neural net.
var evaluator = new BinaryThreeMultiplexerEvaluator();
// Confirm the expected fitness (to a limited amount of precision to allow for small variations of floating point
// results that can occur as a result of platform/environmental variations).
FitnessInfo fitnessInfo = evaluator.Evaluate(blackBox);
Assert.Equal(107.50554956432657, fitnessInfo.PrimaryFitness, 6);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy<NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy<NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
BinaryThreeMultiplexerEvaluator evaluator = new BinaryThreeMultiplexerEvaluator();
// Create genome decoder. Decodes to a neural network packaged with an activation scheme.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator<NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
