1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Algorithms.DataAnalysis;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Random;
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27 |
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28 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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29 | public class GaussianProcessSEIso5 : ArtificialRegressionDataDescriptor {
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30 |
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31 | public override string Name {
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32 | get {
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33 | return "Gaussian Process SEiso 5";
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34 | }
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35 | }
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36 | public override string Description {
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37 | get { return ""; }
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38 | }
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39 | protected override string TargetVariable { get { return "Y"; } }
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40 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
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41 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
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42 | protected override int TrainingPartitionStart { get { return 0; } }
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43 | protected override int TrainingPartitionEnd { get { return 50; } }
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44 | protected override int TestPartitionStart { get { return 50; } }
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45 | protected override int TestPartitionEnd { get { return 100; } }
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46 |
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47 | protected override List<List<double>> GenerateValues() {
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48 | List<List<double>> data = new List<List<double>>();
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49 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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50 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList());
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51 | }
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52 |
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53 |
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54 | var hyp = new double[]
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55 | {
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56 | 0.0, 0.0, // SEiso
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57 | 0.0, 0.0,
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58 | -6.0 // noise
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59 | };
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60 |
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61 |
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62 | var covFun = new CovarianceSum();
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63 | var m1 = new CovarianceMask();
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64 | m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0, 6, 8 });
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65 | var m2 = new CovarianceMask();
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66 | m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 2, 5, 9 });
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67 |
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68 | covFun.Terms.Add(m1);
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69 | covFun.Terms.Add(m2);
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70 | covFun.Terms.Add(new CovarianceNoise());
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71 | covFun.SetParameter(hyp.ToArray());
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72 |
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73 | var mt = new MersenneTwister();
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74 | var target = Util.SampleGaussianProcess(mt, covFun, data);
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75 | data.Add(target);
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76 |
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77 | return data;
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78 | }
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79 | }
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80 | }
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