source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThirteen.cs @ 14186

Last change on this file since 14186 was 14186, checked in by swagner, 3 years ago

#2526: Updated year of copyrights in license headers

File size: 3.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Common;
26
27namespace HeuristicLab.Problems.Instances.DataAnalysis {
28  public class KeijzerFunctionThirteen : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Keijzer 13 f(x, y) = 6 * sin(x) * cos(y)"; } }
31    public override string Description {
32      get {
33        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
34        + "Authors: Maarten Keijzer" + Environment.NewLine
35        + "Function: f(x, y) = 6 * sin(x) * cos(y)" + Environment.NewLine
36        + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine
37        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
38        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
39      }
40    }
41    protected override string TargetVariable { get { return "F"; } }
42    protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
43    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
44    protected override int TrainingPartitionStart { get { return 0; } }
45    protected override int TrainingPartitionEnd { get { return 20; } }
46    protected override int TestPartitionStart { get { return 20; } }
47    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
48
49    protected override List<List<double>> GenerateValues() {
50      List<List<double>> data = new List<List<double>>();
51      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.01m).Select(v => (double)v).ToList();
52      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
53
54      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
55
56      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
57        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
58        data[i].AddRange(combinations[i]);
59      }
60
61      double x, y;
62      List<double> results = new List<double>();
63      for (int i = 0; i < data[0].Count; i++) {
64        x = data[0][i];
65        y = data[1][i];
66        results.Add(6 * Math.Sin(x) * Math.Cos(y));
67      }
68      data.Add(results);
69
70      return data;
71    }
72  }
73}
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