source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SineCosineFunction.cs @ 11171

Last change on this file since 11171 was 11171, checked in by ascheibe, 6 years ago

#2115 merged r11170 (copyright update) into trunk

File size: 3.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 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 SineCosineFunction : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Vladislavleva-6 F6(X1, X2) = 6 * sin(X1) * cos(X2)"; } }
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: F6(X1, X2) = 6 * sin(X1) * cos(X2)" + Environment.NewLine
35        + "Training Data: 30 points X1, X2 = Rand(0.1, 5.9)" + Environment.NewLine
36        + "Test Data: 306*306 points X1, X2 = (-0.05:0.02:6.05)" + Environment.NewLine
37        + "Function Set: +, -, *, /, square, e^x, e^-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 30; } }
45    protected override int TestPartitionStart { get { return 30; } }
46    protected override int TestPartitionEnd { get { return 30 + (306 * 306); } }
47
48    protected override List<List<double>> GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-0.05, 6.05, 0.02).ToList();
51      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
52      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
53
54      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
55        data.Add(ValueGenerator.GenerateUniformDistributedValues(30, 0.1, 5.9).ToList());
56        data[i].AddRange(combinations[i]);
57      }
58
59      double x1, x2;
60      List<double> results = new List<double>();
61      for (int i = 0; i < data[0].Count; i++) {
62        x1 = data[0][i];
63        x2 = data[1][i];
64        results.Add(6 * Math.Sin(x1) * Math.Cos(x2));
65      }
66      data.Add(results);
67
68      return data;
69    }
70  }
71}
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