/*
* 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.List;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLError;
import org.encog.ml.MLRegression;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.util.EngineArray;
import org.encog.util.simple.EncogUtility;
/**
* NEAT networks relieve the programmer of the need to define the hidden layer
* structure of the neural network.
*
* The output from the neural network can be calculated normally or using a
* snapshot. The snapshot mode is slower, but it can be more accurate. The
* snapshot handles recurrent layers better, as it takes the time to loop
* through the network multiple times to "flush out" the recurrent links.
*
* NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm for the
* generation of evolving artificial neural networks. It was developed by Ken
* Stanley while at The University of Texas at Austin.
*
* http://www.cs.ucf.edu/~kstanley/
*
* The following Journal articles were used to implement NEAT/HyperNEAT in
* Encog. Provided in BibTeX form.
*
* Article{stanley:ec02,title={Evolving Neural Networks Through Augmenting
* Topologies}, author={Kenneth O. Stanley and Risto Miikkulainen}, volume={10},
* journal={Evolutionary Computation}, number={2}, pages={99-127}, url=
* "http://nn.cs.utexas.edu/?stanley:ec02" , year={2002}}
*
* MISC{Gauci_abstractgenerating, author = {Jason Gauci and Kenneth Stanley},
* title = {ABSTRACT Generating Large-Scale Neural Networks Through Discovering
* Geometric Regularities}, year = {}}
*
* INPROCEEDINGS{Whiteson05automaticfeature, author = {Shimon Whiteson and
* Kenneth O. Stanley and Risto Miikkulainen}, title = {Automatic feature
* selection in neuroevolution}, booktitle = {In Genetic and Evolutionary
* Computation Conference}, year = {2005}, pages = {1225--1232}, publisher =
* {ACM Press} }
*/
public class NEATNetwork implements MLRegression, MLError, Serializable {
/**
* The serial ID.
*/
private static final long serialVersionUID = 3660295468309926508L;
/**
* The neuron links.
*/
private final NEATLink[] links;
/**
* The activation functions.
*/
private final ActivationFunction[] activationFunctions;
/**
* The pre-activation values, used to feed the neurons.
*/
private final double[] preActivation;
/**
* The post-activation values, used as the output from the neurons.
*/
private final double[] postActivation;
/**
* The index to the starting location of the output neurons.
*/
private final int outputIndex;
/**
* The input count.
*/
private int inputCount;
/**
* The output count.
*/
private int outputCount;
/**
* The number of activation cycles to use.
*/
private int activationCycles = NEATPopulation.DEFAULT_CYCLES;
/**
* True, if the network has relaxed and values no longer changing. Used when
* activationCycles is set to zero for auto.
*/
private boolean hasRelaxed = false;
/**
* The amount of change allowed before the network is considered to have
* relaxed.
*/
private double relaxationThreshold;
/**
* Construct a NEAT network. The links that are passed in also define the
* neurons.
*
* @param inputNeuronCount
* The input neuron count.
* @param outputNeuronCount
* The output neuron count.
* @param connectionArray
* The links.
* @param theActivationFunctions
* The activation functions.
*/
public NEATNetwork(final int inputNeuronCount, final int outputNeuronCount,
final List connectionArray,
final ActivationFunction[] theActivationFunctions) {
this.links = new NEATLink[connectionArray.size()];
for (int i = 0; i < connectionArray.size(); i++) {
this.links[i] = connectionArray.get(i);
}
this.activationFunctions = theActivationFunctions;
final int neuronCount = this.activationFunctions.length;
this.preActivation = new double[neuronCount];
this.postActivation = new double[neuronCount];
this.inputCount = inputNeuronCount;
this.outputIndex = inputNeuronCount + 1;
this.outputCount = outputNeuronCount;
// bias
this.postActivation[0] = 1.0;
}
/**
* Calculate the error for this neural network.
*
* @param data
* The training set.
* @return The error percentage.
*/
@Override
public double calculateError(final MLDataSet data) {
return EncogUtility.calculateRegressionError(this, data);
}
/**
* Compute the output from this synapse.
*
* @param input
* The input to this synapse.
* @return The output from this synapse.
*/
@Override
public MLData compute(final MLData input) {
final MLData result = new BasicMLData(this.outputCount);
// clear from previous
EngineArray.fill(this.preActivation, 0.0);
EngineArray.fill(this.postActivation, 0.0);
this.postActivation[0] = 1.0;
// copy input
EngineArray.arrayCopy(input.getData(), 0, this.postActivation, 1,
this.inputCount);
// iterate through the network activationCycles times
for (int i = 0; i < this.activationCycles; ++i) {
internalCompute();
}
// copy output
EngineArray.arrayCopy(this.postActivation, this.outputIndex,
result.getData(), 0, this.outputCount);
return result;
}
/**
* @return The number of activation cycles to use.
*/
public int getActivationCycles() {
return this.activationCycles;
}
/**
* @return The activation functions.
*/
public ActivationFunction[] getActivationFunctions() {
return this.activationFunctions;
}
/**
* {@inheritDoc}
*/
@Override
public int getInputCount() {
return this.inputCount;
}
/**
* @return The links in the neural network.
*/
public NEATLink[] getLinks() {
return this.links;
}
/**
* {@inheritDoc}
*/
@Override
public int getOutputCount() {
return this.outputCount;
}
/**
* @return The starting location of the output neurons.
*/
public int getOutputIndex() {
return this.outputIndex;
}
/**
* @return The post-activation values, used as the output from the neurons.
*/
public double[] getPostActivation() {
return this.postActivation;
}
/**
* @return The pre-activation values, used to feed the neurons.
*/
public double[] getPreActivation() {
return this.preActivation;
}
/**
* @return The amount of change allowed before the network is considered to
* have relaxed.
*/
public double getRelaxationThreshold() {
return this.relaxationThreshold;
}
/**
* Perform one activation cycle.
*/
private void internalCompute() {
for (int j = 0; j < this.links.length; j++) {
this.preActivation[this.links[j].getToNeuron()] += this.postActivation[this.links[j]
.getFromNeuron()] * this.links[j].getWeight();
}
for (int j = this.outputIndex; j < this.preActivation.length; j++) {
this.postActivation[j] = this.preActivation[j];
this.activationFunctions[j].activationFunction(this.postActivation,
j, 1);
this.preActivation[j] = 0.0F;
}
}
/**
* @return True, if the network has relaxed and values no longer changing.
* Used when activationCycles is set to zero for auto.
*/
public boolean isHasRelaxed() {
return this.hasRelaxed;
}
/**
* Set the number of activation cycles to use.
*
* @param activationCycles
* The number of activation cycles.
*/
public void setActivationCycles(final int activationCycles) {
this.activationCycles = activationCycles;
}
/**
* Set true, if the network has relaxed and values no longer changing. Used
* when activationCycles is set to zero for auto.
*
* @param hasRelaxed
* True if the network has relaxed.
*/
public void setHasRelaxed(final boolean hasRelaxed) {
this.hasRelaxed = hasRelaxed;
}
/**
* The amount of change allowed before the network is considered to have
* relaxed.
*
* @param relaxationThreshold
* The relaxation threshold.
*/
public void setRelaxationThreshold(final double relaxationThreshold) {
this.relaxationThreshold = relaxationThreshold;
}
}
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