/*
* Encog(tm) Core Unit Tests 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.
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package org.encog.neural.networks;
import junit.framework.Assert;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.mathutil.randomize.NguyenWidrowRandomizer;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.layers.BasicLayer;
public class NetworkUtil {
public static BasicNetwork createXORNetworkUntrained()
{
// random matrix data. However, it provides a constant starting point
// for the unit tests.
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,4));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
network.getStructure().finalizeStructure();
(new ConsistentRandomizer(-1,1)).randomize(network);
return network;
}
public static BasicNetwork createXORNetworknNguyenWidrowUntrained()
{
// random matrix data. However, it provides a constant starting point
// for the unit tests.
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,3));
network.addLayer(new BasicLayer(null,false,1));
network.getStructure().finalizeStructure();
(new NguyenWidrowRandomizer(-1,1)).randomize( network );
return network;
}
public static void testTraining(MLTrain train, double requiredImprove)
{
train.iteration();
double error1 = train.getError();
for(int i=0;i<10;i++)
train.iteration();
double error2 = train.getError();
double improve = (error1-error2)/error1;
Assert.assertTrue("Improve rate too low for " + train.getClass().getSimpleName() +
",Improve="+improve+",Needed="+requiredImprove, improve>=requiredImprove);
}
}