| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You 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. |
| */ |
| |
| package org.apache.commons.math4.ml.neuralnet.sofm; |
| |
| import org.apache.commons.math4.ml.distance.DistanceMeasure; |
| import org.apache.commons.math4.ml.distance.EuclideanDistance; |
| import org.apache.commons.math4.ml.neuralnet.FeatureInitializer; |
| import org.apache.commons.math4.ml.neuralnet.FeatureInitializerFactory; |
| import org.apache.commons.math4.ml.neuralnet.MapRanking; |
| import org.apache.commons.math4.ml.neuralnet.Network; |
| import org.apache.commons.math4.ml.neuralnet.Neuron; |
| import org.apache.commons.math4.ml.neuralnet.OffsetFeatureInitializer; |
| import org.apache.commons.math4.ml.neuralnet.UpdateAction; |
| import org.apache.commons.math4.ml.neuralnet.oned.NeuronString; |
| import org.junit.Test; |
| import org.junit.Assert; |
| |
| /** |
| * Tests for {@link KohonenUpdateAction} class. |
| */ |
| public class KohonenUpdateActionTest { |
| /* |
| * Test assumes that the network is |
| * |
| * 0-----1-----2 |
| */ |
| @Test |
| public void testUpdate() { |
| final FeatureInitializer init |
| = new OffsetFeatureInitializer(FeatureInitializerFactory.uniform(0, 0.1)); |
| final FeatureInitializer[] initArray = { init }; |
| |
| final int netSize = 3; |
| final Network net = new NeuronString(netSize, false, initArray).getNetwork(); |
| final DistanceMeasure dist = new EuclideanDistance(); |
| final LearningFactorFunction learning |
| = LearningFactorFunctionFactory.exponentialDecay(1, 0.1, 100); |
| final NeighbourhoodSizeFunction neighbourhood |
| = NeighbourhoodSizeFunctionFactory.exponentialDecay(3, 1, 100); |
| final UpdateAction update = new KohonenUpdateAction(dist, learning, neighbourhood); |
| final MapRanking rank = new MapRanking(net, dist); |
| |
| // The following test ensures that, after one "update", |
| // 1. when the initial learning rate equal to 1, the best matching |
| // neuron's features are mapped to the input's features, |
| // 2. when the initial neighbourhood is larger than the network's size, |
| // all neuron's features get closer to the input's features. |
| |
| final double[] features = new double[] { 0.3 }; |
| final double[] distancesBefore = new double[netSize]; |
| int count = 0; |
| for (Neuron n : net) { |
| distancesBefore[count++] = dist.compute(n.getFeatures(), features); |
| } |
| final Neuron bestBefore = rank.rank(features, 1).get(0); |
| |
| // Initial distance from the best match is larger than zero. |
| Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2); |
| |
| update.update(net, features); |
| |
| final double[] distancesAfter = new double[netSize]; |
| count = 0; |
| for (Neuron n : net) { |
| distancesAfter[count++] = dist.compute(n.getFeatures(), features); |
| } |
| final Neuron bestAfter = rank.rank(features, 1).get(0); |
| |
| Assert.assertEquals(bestBefore, bestAfter); |
| // Distance is now zero. |
| Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), 1e-16); |
| |
| for (int i = 0; i < netSize; i++) { |
| // All distances have decreased. |
| Assert.assertTrue(distancesAfter[i] < distancesBefore[i]); |
| } |
| } |
| } |