/**
* Create a NEAT population.
*
* @param architecture The architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The population.
*/
public MLMethod create(final String architecture, final int input, final int output) {
if (input <= 0) {
throw new EncogError("Must have at least one input for NEAT.");
}
if (output <= 0) {
throw new EncogError("Must have at least one output for NEAT.");
}
final Map<String, String> args = ArchitectureParse.parseParams(architecture);
final ParamsHolder holder = new ParamsHolder(args);
final int populationSize = holder.getInt(MLMethodFactory.PROPERTY_POPULATION_SIZE, false, 1000);
final int cycles =
holder.getInt(MLMethodFactory.PROPERTY_CYCLES, false, NEATPopulation.DEFAULT_CYCLES);
ActivationFunction af =
this.factory.create(
holder.getString(MLMethodFactory.PROPERTY_AF, false, MLActivationFactory.AF_SSIGMOID));
NEATPopulation pop = new NEATPopulation(input, output, populationSize);
pop.reset();
pop.setActivationCycles(cycles);
pop.setNEATActivationFunction(af);
return pop;
}
/** {@inheritDoc} */
@Override
public void mutateWeight(
final Random rnd, final NEATLinkGene linkGene, final double weightRange) {
final double delta = rnd.nextGaussian() * this.sigma;
double w = linkGene.getWeight() + delta;
w = NEATPopulation.clampWeight(w, weightRange);
linkGene.setWeight(w);
}
@Override
public MLMethod decode(final Genome genome) {
final NEATPopulation pop = (NEATPopulation) genome.getPopulation();
final Substrate substrate = pop.getSubstrate();
return decode(pop, substrate, genome);
}
