/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.examples.neural.lunar;
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.mathutil.randomize.FanInRandomizer;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing;
import org.encog.neural.networks.training.genetic.NeuralGeneticAlgorithm;
import org.encog.neural.pattern.FeedForwardPattern;
public class LunarLander {
public static BasicNetwork createNetwork()
{
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.setInputNeurons(3);
pattern.addHiddenLayer(50);
pattern.setOutputNeurons(1);
pattern.setActivationFunction(new ActivationTANH());
BasicNetwork network = (BasicNetwork)pattern.generate();
network.reset();
return network;
}
public static void main(String args[])
{
BasicNetwork network = createNetwork();
MLTrain train;
if( args.length>0 && args[0].equalsIgnoreCase("anneal"))
{
train = new NeuralSimulatedAnnealing(
network, new PilotScore(), 10, 2, 100);
}
else
{
train = new NeuralGeneticAlgorithm(
network, new FanInRandomizer(),
new PilotScore(),500, 0.1, 0.25);
}
int epoch = 1;
for(int i=0;i<50;i++) {
train.iteration();
System.out
.println("Epoch #" + epoch + " Score:" + train.getError());
epoch++;
}
System.out.println("\nHow the winning network landed:");
network = (BasicNetwork)train.getMethod();
NeuralPilot pilot = new NeuralPilot(network,true);
System.out.println(pilot.scorePilot());
Encog.getInstance().shutdown();
}
}