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source: branches/DataAnalysisCSVImport/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RippleFunction.cs @ 8842

Last change on this file since 8842 was 8842, checked in by sforsten, 11 years ago

#1942: merged r8690:8840 from trunk into branch

File size: 3.2 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;
25
26namespace HeuristicLab.Problems.Instances.DataAnalysis {
27  public class RippleFunction : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Vladislavleva-7  F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))"; } }
30    public override string Description {
31      get {
32        return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
33        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
34        + "Function: F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))" + Environment.NewLine
35        + "Training Data: 300 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
36        + "Test Data: 1000 points X1, X2 = Rand(-0.25, 6.35)" + Environment.NewLine
37        + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(x), x^eps, x + eps, x + eps";
38      }
39    }
40    protected override string TargetVariable { get { return "Y"; } }
41    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
42    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
43    protected override int TrainingPartitionStart { get { return 0; } }
44    protected override int TrainingPartitionEnd { get { return 300; } }
45    protected override int TestPartitionStart { get { return 300; } }
46    protected override int TestPartitionEnd { get { return 1300; } }
47
48    protected override List<List<double>> GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
51        data.Add(ValueGenerator.GenerateUniformDistributedValues(TrainingPartitionEnd, 0.05, 6.05).ToList());
52      }
53
54      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
55        data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(TrainingPartitionEnd, -0.25, 6.35));
56      }
57
58      double x1, x2;
59      List<double> results = new List<double>();
60      for (int i = 0; i < data[0].Count; i++) {
61        x1 = data[0][i];
62        x2 = data[1][i];
63        results.Add((x1 - 3) * (x2 - 3) + 2 * Math.Sin((x1 - 4) * (x2 - 4)));
64      }
65      data.Add(results);
66
67      return data;
68    }
69  }
70}
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