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source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionDemo.cs @ 10662

Last change on this file since 10662 was 9622, checked in by gkronber, 11 years ago

#1967: fixed generation of GPR problem instances (sampling from Gaussian processes) to work together with the current trunk version

File size: 3.1 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 GaussianProcessRegressionDemo : ArtificialRegressionDataDescriptor {
30
31    public override string Name {
32      get { return "Gaussian Process Demo " + name; }
33    }
34    public override string Description {
35      get { return ""; }
36    }
37    protected override string TargetVariable { get { return "Y"; } }
38    protected override string[] VariableNames { get { return new string[] { "X1","Y" }; } }
39    protected override string[] AllowedInputVariables { get { return new string[] { "X1"}; } }
40    protected override int TrainingPartitionStart { get { return 0; } }
41    protected override int TrainingPartitionEnd { get { return 30; } }
42    protected override int TestPartitionStart { get { return 30; } }
43    protected override int TestPartitionEnd { get { return 230; } }
44
45    private ParameterizedCovarianceFunction cov;
46    private string name;
47
48    public GaussianProcessRegressionDemo(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
49      this.name = name;
50      cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, new int[] { 0 });
51    }
52
53    protected override List<List<double>> GenerateValues() {
54      List<List<double>> trainingData = new List<List<double>>() {
55        ValueGenerator.GenerateUniformDistributedValues(30,0, 1).ToList(),
56      };
57
58      List<List<double>> testData = new List<List<double>>() {
59        ValueGenerator.GenerateSteps(-0.1, 1.1, 1.2 / 200).ToList(),
60      };
61
62      var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList<IEnumerable<double>>();
63      var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
64
65      List<List<double>> data = new List<List<double>>();
66      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
67        data.Add(trainingComb[i].ToList());
68        data[i].AddRange(testComb[i]);
69      }
70
71      var mt = new MersenneTwister();
72
73      var target = Util.SampleGaussianProcess(mt, cov, data);
74      data.Add(target);
75
76      return data;
77    }
78  }
79}
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