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source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIsoDependentNoise.cs @ 8826

Last change on this file since 8826 was 8826, checked in by gkronber, 10 years ago

#1967: added benchmark instances for GPR tuning

File size: 2.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Algorithms.DataAnalysis;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class GaussianProcessSEIsoDependentNoise : ArtificialRegressionDataDescriptor {
30
31    public override string Name {
32      get {
33        return "Gaussian Process SE iso with dependent noise";
34      }
35    }
36    public override string Description {
37      get { return ""; }
38    }
39    protected override string TargetVariable { get { return "Y"; } }
40    protected override string[] VariableNames { get { return new string[] { "X1", "Y" }; } }
41    protected override string[] AllowedInputVariables { get { return new string[] { "X1" }; } }
42    protected override int TrainingPartitionStart { get { return 0; } }
43    protected override int TrainingPartitionEnd { get { return 100; } }
44    protected override int TestPartitionStart { get { return 100; } }
45    protected override int TestPartitionEnd { get { return 200; } }
46
47    protected override List<List<double>> GenerateValues() {
48
49      List<List<double>> data = new List<List<double>>();
50      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
51        data.Add(ValueGenerator.GenerateSteps(0, 0.99, 0.01).ToList());
52        data[i].AddRange(ValueGenerator.GenerateSteps(0.005, 1, 0.01).ToList());
53      }
54
55      var covarianceFunction = new CovarianceSum();
56      covarianceFunction.Terms.Add(new CovarianceSquaredExponentialIso());
57      var prod = new CovarianceProduct();
58      prod.Factors.Add(new CovarianceSquaredExponentialIso());
59      prod.Factors.Add(new CovarianceNoise());
60      covarianceFunction.Terms.Add(prod);
61      covarianceFunction.Terms.Add(new CovarianceNoise());
62      covarianceFunction.SetParameter(new double[] { Math.Log(0.1), Math.Log(Math.Sqrt(1)), Math.Log(0.5), Math.Log(Math.Sqrt(1)), Math.Log(Math.Sqrt(0.1)), Math.Log(Math.Sqrt(0.01)) });
63
64      var mt = new MersenneTwister(31415);
65      var target = Util.SampleGaussianProcess(mt, covarianceFunction, data);
66      data.Add(target);
67
68      return data;
69    }
70  }
71}
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