/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.factory.method;
import java.util.Map;
import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.ml.factory.MLActivationFactory;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.neural.neat.NEATPopulation;
import org.encog.util.ParamsHolder;
/**
* A factor to create feedforward networks.
*
*/
public class NEATFactory {
/**
* The activation function factory to use.
*/
private MLActivationFactory factory = new MLActivationFactory();
/**
* 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;
}
}