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source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionInstance2D.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: 3.3 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Algorithms.DataAnalysis;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class GaussianProcessRegressionInstance2D : ArtificialRegressionDataDescriptor {
30
31    private const int STEPS = 20;
32    public override string Name {
33      get { return "Gaussian Process " + name; }
34    }
35    public override string Description {
36      get { return ""; }
37    }
38    protected override string TargetVariable { get { return "Y"; } }
39    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
40    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
41    protected override int TrainingPartitionStart { get { return 0; } }
42    protected override int TrainingPartitionEnd { get { return (STEPS+1) * (STEPS+1); } }
43    protected override int TestPartitionStart { get { return TrainingPartitionEnd; } }
44    protected override int TestPartitionEnd { get { return 2 * (STEPS + 1) * (STEPS + 1); } }
45
46    private ParameterizedCovarianceFunction cov;
47    private string name;
48
49    public GaussianProcessRegressionInstance2D(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
50      this.name = name;
51      cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 2));
52    }
53
54    protected override List<List<double>> GenerateValues() {
55      List<List<double>> trainingData = new List<List<double>>() {
56        ValueGenerator.GenerateSteps(0, 1, 1.0 / STEPS).ToList(),
57        ValueGenerator.GenerateSteps(0, 1, 1.0 / STEPS).ToList()
58      };
59
60      List<List<double>> testData = new List<List<double>>() {
61        ValueGenerator.GenerateSteps(-0.1, 1.1, 1.2 / STEPS).ToList(),
62        ValueGenerator.GenerateSteps(-0.1, 1.1, 1.2 / STEPS).ToList()
63      };
64
65      var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList<IEnumerable<double>>();
66      var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
67
68      List<List<double>> data = new List<List<double>>();
69      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
70        data.Add(trainingComb[i].ToList());
71        data[i].AddRange(testComb[i]);
72      }
73
74      var mt = new MersenneTwister();
75
76      var target = Util.SampleGaussianProcess(mt, cov, data);
77      data.Add(target);
78
79      return data;
80    }
81  }
82}
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