#region License Information /* HeuristicLab * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.IO; using System.Linq; using HEAL.Attic; using HeuristicLab.Algorithms.DataAnalysis; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Problems.Instances.DataAnalysis; using Microsoft.VisualStudio.TestTools.UnitTesting; namespace HeuristicLab.Tests { [TestClass] public class GaussianProcessRegressionSampleTest { private const string SampleFileName = "GPR"; private static readonly ProtoBufSerializer serializer = new ProtoBufSerializer(); [TestMethod] [TestCategory("Samples.Create")] [TestProperty("Time", "medium")] public void CreateGaussianProcessRegressionSampleTest() { var gpr = CreateGaussianProcessRegressionSample(); string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension); serializer.Serialize(gpr, path); } [TestMethod] [TestCategory("Samples.Execute")] [TestProperty("Time", "long")] public void RunGaussianProcessRegressionSample() { var gpr = CreateGaussianProcessRegressionSample(); gpr.SetSeedRandomly = false; gpr.Seed = 1618551877; SamplesUtils.RunAlgorithm(gpr); Assert.AreEqual(-940.70700288855619, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood")); Assert.AreEqual(0.99563390794061979, SamplesUtils.GetDoubleResult(gpr, "Training R²")); } private GaussianProcessRegression CreateGaussianProcessRegressionSample() { var gpr = new GaussianProcessRegression(); var provider = new VariousInstanceProvider(); var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Spatial co-evolution")).Single(); var regProblem = new RegressionProblem(); regProblem.Load(provider.LoadData(instance)); #region Algorithm Configuration gpr.Name = "Gaussian Process Regression"; gpr.Description = "A Gaussian process regression algorithm which solves the spatial co-evolution benchmark problem"; gpr.Problem = regProblem; gpr.CovarianceFunction = new CovarianceSquaredExponentialIso(); gpr.MeanFunction = new MeanConst(); gpr.MinimizationIterations = 20; gpr.Seed = 0; gpr.SetSeedRandomly = true; #endregion gpr.Engine = new ParallelEngine.ParallelEngine(); return gpr; } } }