Free cookie consent management tool by TermsFeed Policy Generator

source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso6.cs @ 8879

Last change on this file since 8879 was 8879, checked in by gkronber, 10 years ago

#1967 implemented possible benchmark instances for covariance evolution

File size: 3.3 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Algorithms.DataAnalysis;
25using HeuristicLab.Data;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class GaussianProcessSEIso6 : ArtificialRegressionDataDescriptor {
30
31    public override string Name {
32      get {
33        return "Gaussian Process SEiso 6";
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", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
41    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
42    protected override int TrainingPartitionStart { get { return 0; } }
43    protected override int TrainingPartitionEnd { get { return 50; } }
44    protected override int TestPartitionStart { get { return 50; } }
45    protected override int TestPartitionEnd { get { return 100; } }
46
47    protected override List<List<double>> GenerateValues() {
48      List<List<double>> data = new List<List<double>>();
49      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
50        data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList());
51      }
52
53
54      var hyp = new double[]
55        {
56          0.0, 0.0, // SEiso
57          0.0, 0.0,
58          0.0, 0.0,
59          0.0, 0.0,
60          -6.0      // noise
61        };
62
63
64      var covFun = new CovarianceSum();
65      var m1 = new CovarianceMask();
66      m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0, 1 });
67
68      var m2 = new CovarianceMask();
69      m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 2, 3 });
70
71      var m3 = new CovarianceMask();
72      m3.SelectedDimensionsParameter.Value = new IntArray(new int[] { 4, 5 });
73
74      var m4 = new CovarianceMask();
75      m4.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0, 6, 8 });
76
77      covFun.Terms.Add(m1);
78      covFun.Terms.Add(m2);
79      covFun.Terms.Add(m3);
80      covFun.Terms.Add(m4);
81      covFun.Terms.Add(new CovarianceNoise());
82      covFun.SetParameter(hyp.ToArray());
83
84      var mt = new MersenneTwister();
85      var target = Util.SampleGaussianProcess(mt, covFun, data);
86      data.Add(target);
87
88      return data;
89    }
90  }
91}
Note: See TracBrowser for help on using the repository browser.