/* * 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.networks.logic; import junit.framework.TestCase; import org.encog.ml.data.MLDataSet; import org.encog.ml.data.basic.BasicMLDataSet; import org.encog.ml.train.MLTrain; import org.encog.neural.networks.BasicNetwork; import org.encog.neural.networks.NetworkUtil; import org.encog.neural.networks.XOR; import org.encog.neural.networks.training.simple.TrainAdaline; import org.encog.neural.pattern.ADALINEPattern; public class TestADALINE extends TestCase { public void testAdalineNet() throws Throwable { ADALINEPattern pattern = new ADALINEPattern(); pattern.setInputNeurons(2); pattern.setOutputNeurons(1); BasicNetwork network = (BasicNetwork)pattern.generate(); // train it MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL); MLTrain train = new TrainAdaline(network,training,0.01); NetworkUtil.testTraining(training,train,0.01); } }