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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SalutowiczFunctionOneDimensional.cs @ 15656

Last change on this file since 15656 was 15583, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers

File size: 3.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 SalutowiczFunctionOneDimensional : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Vladislavleva-2 F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)"; } }
31    public override string Description {
32      get {
33        return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
34        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
35        + "Function: F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)" + Environment.NewLine
36        + "Training Data: 100 points X = (0.05:0.1:10)" + Environment.NewLine
37        + "Test Data: 221 points X = (-0.5:0.05:10.5)" + Environment.NewLine
38        + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(x), x^eps, x + eps, x + eps";
39      }
40    }
41    protected override string TargetVariable { get { return "Y"; } }
42    protected override string[] VariableNames { get { return new string[] { "X", "Y" }; } }
43    protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
44    protected override int TrainingPartitionStart { get { return 0; } }
45    protected override int TrainingPartitionEnd { get { return 100; } }
46    protected override int TestPartitionStart { get { return 100; } }
47    protected override int TestPartitionEnd { get { return 321; } }
48
49    protected override List<List<double>> GenerateValues() {
50      List<List<double>> data = new List<List<double>>();
51      data.Add(SequenceGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList());
52      data[0].AddRange(SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v));
53
54      double x;
55      List<double> results = new List<double>();
56      for (int i = 0; i < data[0].Count; i++) {
57        x = data[0][i];
58        results.Add(Math.Exp(-x) * Math.Pow(x, 3) * Math.Cos(x) * Math.Sin(x) * (Math.Cos(x) * Math.Pow(Math.Sin(x), 2) - 1));
59      }
60      data.Add(results);
61
62      return data;
63    }
64  }
65}
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