/*
* 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.training.opp;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.ml.ea.genome.Genome;
import org.encog.ml.ea.opp.EvolutionaryOperator;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.neural.neat.NEATNeuronType;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.training.NEATGenome;
import org.encog.neural.neat.training.NEATInnovation;
import org.encog.neural.neat.training.NEATLinkGene;
import org.encog.neural.neat.training.NEATNeuronGene;
import java.io.Serializable;
/**
* This class represents a NEAT mutation. NEAT supports several different types
* of mutations. This class provides common utility needed by any sort of a NEAT
* mutation. This class is abstract and cannot be used by itself.
*
* -----------------------------------------------------------------------------
* 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 abstract class NEATMutation implements EvolutionaryOperator, Serializable {
/**
* The trainer that owns this class.
*/
private EvolutionaryAlgorithm owner;
/**
* Choose a random neuron.
*
* @param target
* The target genome. Should the input and bias neurons be
* included.
* @param choosingFrom
* True if we are chosing from all neurons, false if we exclude
* the input and bias.
* @return The random neuron.
*/
public NEATNeuronGene chooseRandomNeuron(final NEATGenome target,
final boolean choosingFrom) {
int start;
if (choosingFrom) {
start = 0;
} else {
start = target.getInputCount() + 1;
}
// if this network will not "cycle" then output neurons cannot be source
// neurons
if (!choosingFrom) {
final int ac = ((NEATPopulation) target.getPopulation())
.getActivationCycles();
if (ac == 1) {
start += target.getOutputCount();
}
}
final int end = target.getNeuronsChromosome().size() - 1;
// no neurons to pick!
if (start > end) {
return null;
}
final int neuronPos = RangeRandomizer.randomInt(start, end);
final NEATNeuronGene neuronGene = target.getNeuronsChromosome().get(
neuronPos);
return neuronGene;
}
/**
* Create a link between two neuron id's. Create or find any necessary
* innovation records.
*
* @param target
* The target genome.
* @param neuron1ID
* The id of the source neuron.
* @param neuron2ID
* The id of the target neuron.
* @param weight
* The weight of this new link.
*/
public void createLink(final NEATGenome target, final long neuron1ID,
final long neuron2ID, final double weight) {
// first, does this link exist? (and if so, hopefully disabled,
// otherwise we have a problem)
for (final NEATLinkGene linkGene : target.getLinksChromosome()) {
if ((linkGene.getFromNeuronID() == neuron1ID)
&& (linkGene.getToNeuronID() == neuron2ID)) {
// bring the link back, at the new weight
linkGene.setEnabled(true);
linkGene.setWeight(weight);
return;
}
}
// check to see if this innovation has already been tried
final NEATInnovation innovation = ((NEATPopulation) target
.getPopulation()).getInnovations().findInnovation(neuron1ID,
neuron2ID);
// now create this link
final NEATLinkGene linkGene = new NEATLinkGene(neuron1ID, neuron2ID,
true, innovation.getInnovationID(), weight);
target.getLinksChromosome().add(linkGene);
}
/**
* Get the specified neuron's index.
* @param target The target genome.
* @param neuronID
* The neuron id to check for.
* @return The index.
*/
public int getElementPos(final NEATGenome target, final long neuronID) {
for (int i = 0; i < target.getNeuronsChromosome().size(); i++) {
final NEATNeuronGene neuronGene = target.getNeuronsChromosome()
.get(i);
if (neuronGene.getId() == neuronID) {
return i;
}
}
return -1;
}
/**
* @return the owner
*/
public EvolutionaryAlgorithm getOwner() {
return this.owner;
}
/**
* {@inheritDoc}
*/
@Override
public void init(final EvolutionaryAlgorithm theOwner) {
this.owner = theOwner;
}
/**
* Determine if this is a duplicate link.
* @param target The target.
* @param fromNeuronID
* The from neuron id.
* @param toNeuronID
* The to neuron id.
* @return True if this is a duplicate link.
*/
public boolean isDuplicateLink(final NEATGenome target,
final long fromNeuronID, final long toNeuronID) {
for (final NEATLinkGene linkGene : target.getLinksChromosome()) {
if ((linkGene.isEnabled())
&& (linkGene.getFromNeuronID() == fromNeuronID)
&& (linkGene.getToNeuronID() == toNeuronID)) {
return true;
}
}
return false;
}
/**
* Determines if a neuron is still needed. If all links to/from a neuron
* have been removed, then the neuron is no longer needed.
*
* @param target
* The target genome.
* @param neuronID
* The neuron id to check for.
* @return Returns true, if the neuron is still needed.
*/
public boolean isNeuronNeeded(final NEATGenome target, final long neuronID) {
// do not remove bias or input neurons or output
for (final NEATNeuronGene gene : target.getNeuronsChromosome()) {
if (gene.getId() == neuronID) {
final NEATNeuronGene neuron = gene;
if ((neuron.getNeuronType() == NEATNeuronType.Input)
|| (neuron.getNeuronType() == NEATNeuronType.Bias)
|| (neuron.getNeuronType() == NEATNeuronType.Output)) {
return true;
}
}
}
// Now check to see if the neuron is used in any links
for (final NEATLinkGene gene : target.getLinksChromosome()) {
final NEATLinkGene linkGene = gene;
if (linkGene.getFromNeuronID() == neuronID) {
return true;
}
if (linkGene.getToNeuronID() == neuronID) {
return true;
}
}
return false;
}
/**
* Obtain the NEATGenome that we will mutate. NEAT mutates the genomes in
* place. So the parent and child genome must be the same literal object.
* Throw an exception, if this is not the case.
*
* @param parents
* The parents.
* @param parentIndex
* The parent index.
* @param offspring
* The offspring.
* @param offspringIndex
* The offspring index.
* @return The genome that we will mutate.
*/
public NEATGenome obtainGenome(final Genome[] parents,
final int parentIndex, final Genome[] offspring,
final int offspringIndex) {
offspring[offspringIndex] = this.getOwner().getPopulation()
.getGenomeFactory().factor(parents[0]);
return (NEATGenome) offspring[offspringIndex];
}
/**
* @return Returns 1, as NEAT mutations only produce one child.
*/
@Override
public int offspringProduced() {
return 1;
}
/**
* @return Returns 1, as mutations typically are asexual and only require a
* single parent.
*/
@Override
public int parentsNeeded() {
return 1;
}
/**
* Remove the specified neuron.
*
* @param target
* The target genome.
* @param neuronID
* The neuron to remove.
*/
public void removeNeuron(final NEATGenome target, final long neuronID) {
for (final NEATNeuronGene gene : target.getNeuronsChromosome()) {
if (gene.getId() == neuronID) {
target.getNeuronsChromosome().remove(gene);
return;
}
}
}
}
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