/*
* Encog(tm) Unit Tests v2.5 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2010 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 org.encog.neural.data.NeuralData;
import org.encog.neural.data.basic.BasicNeuralData;
import org.encog.neural.networks.structure.FlatUpdateNeeded;
import junit.framework.Assert;
import junit.framework.TestCase;
public class TestFlatIntegration extends TestCase {
public void testNetworkOutput()
{
BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
network1.getStructure().finalizeStructure();
Assert.assertNotNull(network1.getStructure().getFlat());
Assert.assertEquals(network1.getStructure().getFlatUpdate(),FlatUpdateNeeded.None);
double[] inputArray = {1.0,1.0};
NeuralData input = new BasicNeuralData(inputArray);
// using a holder will cause the network to calculate without the flat network,
// should calculate to exactly the same number, with or without flat.
NeuralOutputHolder holder = new NeuralOutputHolder();
NeuralData output1 = network1.compute(input);
NeuralData output2 = network1.compute(input,holder);
int i1 = (int)(output1.getData(0) * 10000);
int i2 = (int)(output2.getData(0) * 10000);
Assert.assertEquals(i1, i2);
}
}