1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System.IO;
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23 | using System.Linq;
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24 | using HeuristicLab.Algorithms.DataAnalysis;
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25 | using HeuristicLab.Persistence.Default.Xml;
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26 | using HeuristicLab.Problems.DataAnalysis;
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27 | using HeuristicLab.Problems.Instances.DataAnalysis;
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28 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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29 |
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30 | namespace HeuristicLab.Tests {
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31 | /// <summary>
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32 | /// Summary description for GaussianProcessRegressionSampleTest
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33 | /// </summary>
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34 | [TestClass]
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35 | public class GaussianProcessRegressionSampleTest {
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36 | private const string samplesDirectory = SamplesUtils.Directory;
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37 | [ClassInitialize]
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38 | public static void MyClassInitialize(TestContext testContext) {
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39 | if (!Directory.Exists(samplesDirectory))
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40 | Directory.CreateDirectory(samplesDirectory);
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41 | }
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42 |
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43 | [TestMethod]
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44 | [TestCategory("Samples.Create")]
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45 | [TestProperty("Time", "medium")]
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46 | public void CreateGaussianProcessRegressionSampleTest() {
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47 | var gpr = CreateGaussianProcessRegressionSample();
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48 | XmlGenerator.Serialize(gpr, @"Samples\GPR.hl");
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49 | }
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50 | [TestMethod]
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51 | [TestCategory("Samples.Execute")]
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52 | [TestProperty("Time", "long")]
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53 | public void RunGaussianProcessRegressionSample() {
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54 | var gpr = CreateGaussianProcessRegressionSample();
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55 | gpr.SetSeedRandomly = false;
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56 | gpr.Seed = 1618551877;
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57 | SamplesUtils.RunAlgorithm(gpr);
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58 | Assert.AreEqual(-940.39914958616748, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood"));
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59 | Assert.AreEqual(0.995614091354263, SamplesUtils.GetDoubleResult(gpr, "Training R²"));
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60 | }
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61 |
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62 | private GaussianProcessRegression CreateGaussianProcessRegressionSample() {
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63 | var gpr = new GaussianProcessRegression();
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64 | var provider = new VariousInstanceProvider();
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65 | var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Spatial co-evolution")).Single();
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66 | var regProblem = new RegressionProblem();
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67 | regProblem.Load(provider.LoadData(instance));
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68 | #region Algorithm Configuration
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69 | gpr.Name = "Gaussian Process Regression";
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70 | gpr.Description = "A Gaussian process regression algorithm which solves the spatial co-evolution benchmark problem";
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71 | gpr.Problem = regProblem;
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72 |
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73 | gpr.CovarianceFunction = new CovarianceSquaredExponentialIso();
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74 | gpr.MeanFunction = new MeanConst();
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75 | gpr.MinimizationIterations = 20;
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76 | gpr.Seed = 0;
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77 | gpr.SetSeedRandomly = true;
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78 | #endregion
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79 | gpr.Engine = new ParallelEngine.ParallelEngine();
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80 | return gpr;
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81 | }
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82 | }
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83 | }
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