/*
 * 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.neural.neat;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.ml.ea.codec.GeneticCODEC;
import org.encog.ml.ea.genome.Genome;
import org.encog.ml.genetic.GeneticError;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.neat.training.NEATGenome;
import org.encog.neural.neat.training.NEATLinkGene;
import org.encog.neural.neat.training.NEATNeuronGene;

/**
 * This CODEC is used to create phenomes (NEATNetwork) objects using a genome
 * (NEATGenome). Conversion is only one direction. You are allowed to transform
 * a NEAT genome into a NEATNetwork, but you cannot transform a NEAT phenome
 * back into a genome. The main reason is I have not found a great deal of need
 * to go the other direction. If someone ever does find a need and creates a
 * encode method, please consider contributing it to the Encog project. :)
 * 
 * -----------------------------------------------------------------------------
 * http://www.cs.ucf.edu/~kstanley/
 * Encog's NEAT implementation was drawn from the following three Journal
 * Articles. For more complete BibTeX sources, see NEATNetwork.java.
 * 
 * Evolving Neural Networks Through Augmenting Topologies
 * 
 * Generating Large-Scale Neural Networks Through Discovering Geometric
 * Regularities
 * 
 * Automatic feature selection in neuroevolution
 */
public class NEATCODEC implements GeneticCODEC, Serializable {

	/**
	 * The serial ID.
	 */
	private static final long serialVersionUID = 1L;

	/**
	 * {@inheritDoc}
	 */
	@Override
	public MLMethod decode(final Genome genome) {
		final NEATGenome neatGenome = (NEATGenome) genome;
		final NEATPopulation pop = (NEATPopulation) neatGenome.getPopulation();
		final List neuronsChromosome = neatGenome
				.getNeuronsChromosome();
		final List linksChromosome = neatGenome
				.getLinksChromosome();

		if (neuronsChromosome.get(0).getNeuronType() != NEATNeuronType.Bias) {
			throw new NeuralNetworkError(
					"The first neuron must be the bias neuron, this genome is invalid.");
		}

		final List links = new ArrayList();
		final ActivationFunction[] afs = new ActivationFunction[neuronsChromosome
				.size()];

		for (int i = 0; i < afs.length; i++) {
			afs[i] = neuronsChromosome.get(i).getActivationFunction();
		}

		final Map lookup = new HashMap();
		for (int i = 0; i < neuronsChromosome.size(); i++) {
			final NEATNeuronGene neuronGene = neuronsChromosome.get(i);
			lookup.put(neuronGene.getId(), i);
		}

		// loop over connections
		for (int i = 0; i < linksChromosome.size(); i++) {
			final NEATLinkGene linkGene = linksChromosome.get(i);
			if (linkGene.isEnabled()) {
				links.add(new NEATLink(lookup.get(linkGene.getFromNeuronID()),
						lookup.get(linkGene.getToNeuronID()), linkGene
								.getWeight()));
			}

		}

		Collections.sort(links);

		final NEATNetwork network = new NEATNetwork(neatGenome.getInputCount(),
				neatGenome.getOutputCount(), links, afs);

		network.setActivationCycles(pop.getActivationCycles());
		return network;
	}

	/**
	 * This method is not currently implemented. If you have need of it, and do
	 * implement a conversion from a NEAT phenotype to a genome, consider
	 * contribution to the Encog project.
	 * @param phenotype Not used.
	 * @return Not used.
	 */
	@Override
	public Genome encode(final MLMethod phenotype) {
		throw new GeneticError(
				"Encoding of a NEAT network is not supported.");
	}

}