/*
* 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.neural.hyperneat;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationSteepenedSigmoid;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLData;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.ea.codec.GeneticCODEC;
import org.encog.ml.ea.genome.Genome;
import org.encog.ml.genetic.GeneticError;
import org.encog.neural.hyperneat.substrate.Substrate;
import org.encog.neural.hyperneat.substrate.SubstrateLink;
import org.encog.neural.hyperneat.substrate.SubstrateNode;
import org.encog.neural.neat.NEATCODEC;
import org.encog.neural.neat.NEATLink;
import org.encog.neural.neat.NEATNetwork;
import org.encog.neural.neat.NEATPopulation;
public class HyperNEATCODEC implements GeneticCODEC {
private double minWeight = 0.2;
private double maxWeight = 5.0;
/**
* {@inheritDoc}
*/
@Override
public MLMethod decode(final Genome genome) {
final NEATPopulation pop = (NEATPopulation) genome.getPopulation();
final Substrate substrate = pop.getSubstrate();
return decode(pop, substrate, genome);
}
public MLMethod decode(final NEATPopulation pop, final Substrate substrate,
final Genome genome) {
// obtain the CPPN
final NEATCODEC neatCodec = new NEATCODEC();
final NEATNetwork cppn = (NEATNetwork) neatCodec.decode(genome);
final List<NEATLink> linkList = new ArrayList<NEATLink>();
final ActivationFunction[] afs = new ActivationFunction[substrate
.getNodeCount()];
final ActivationFunction af = new ActivationSteepenedSigmoid();
// all activation functions are the same
for (int i = 0; i < afs.length; i++) {
afs[i] = af;
}
final double c = this.maxWeight / (1.0 - this.minWeight);
final MLData input = new BasicMLData(cppn.getInputCount());
// First create all of the non-bias links.
for (final SubstrateLink link : substrate.getLinks()) {
final SubstrateNode source = link.getSource();
final SubstrateNode target = link.getTarget();
int index = 0;
for (final double d : source.getLocation()) {
input.setData(index++, d);
}
for (final double d : target.getLocation()) {
input.setData(index++, d);
}
final MLData output = cppn.compute(input);
double weight = output.getData(0);
if (Math.abs(weight) > this.minWeight) {
weight = (Math.abs(weight) - this.minWeight) * c
* Math.signum(weight);
linkList.add(new NEATLink(source.getId(), target.getId(),
weight));
}
}
// now create biased links
input.clear();
final int d = substrate.getDimensions();
final List<SubstrateNode> biasedNodes = substrate.getBiasedNodes();
for (final SubstrateNode target : biasedNodes) {
for (int i = 0; i < d; i++) {
input.setData(d + i, target.getLocation()[i]);
}
final MLData output = cppn.compute(input);
double biasWeight = output.getData(1);
if (Math.abs(biasWeight) > this.minWeight) {
biasWeight = (Math.abs(biasWeight) - this.minWeight) * c
* Math.signum(biasWeight);
linkList.add(new NEATLink(0, target.getId(), biasWeight));
}
}
// check for invalid neural network
if (linkList.size() == 0) {
return null;
}
Collections.sort(linkList);
final NEATNetwork network = new NEATNetwork(substrate.getInputCount(),
substrate.getOutputCount(), linkList, afs);
network.setActivationCycles(substrate.getActivationCycles());
return network;
}
@Override
public Genome encode(final MLMethod phenotype) {
throw new GeneticError(
"Encoding of a HyperNEAT network is not supported.");
}
/**
* @return the maxWeight
*/
public double getMaxWeight() {
return this.maxWeight;
}
/**
* @return the minWeight
*/
public double getMinWeight() {
return this.minWeight;
}
/**
* @param maxWeight
* the maxWeight to set
*/
public void setMaxWeight(final double maxWeight) {
this.maxWeight = maxWeight;
}
/**
* @param minWeight
* the minWeight to set
*/
public void setMinWeight(final double minWeight) {
this.minWeight = minWeight;
}
}