/*
 * Encog(tm) Core v3.4 - Java Version
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-core
 
 * Copyright 2008-2016 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 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;
	}

}