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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionOne.cs @ 16819

Last change on this file since 16819 was 15584, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers on stable

File size: 2.8 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 KeijzerFunctionOne : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Keijzer 1 f(x) = 0.3 * x *sin(2 * PI * x); Interval [-1, 1]"; } }
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) = 0.3 * x *sin(2 * PI * x)" + Environment.NewLine
36        + "range(train): x = [-1:0.1:1]" + Environment.NewLine
37        + "range(test): x = [-1:0.001:1]" + 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", "F" }; } }
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 21; } }
46    protected override int TestPartitionStart { get { return 21; } }
47    protected override int TestPartitionEnd { get { return 2022; } }
48
49    protected override List<List<double>> GenerateValues() {
50      List<List<double>> data = new List<List<double>>();
51      data.Add(SequenceGenerator.GenerateSteps(-1, 1, 0.1m).Select(v => (double)v).ToList());
52      data[0].AddRange(SequenceGenerator.GenerateSteps(-1, 1, 0.001m).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(0.3 * x * Math.Sin(2 * Math.PI * x));
59      }
60      data.Add(results);
61
62      return data;
63    }
64  }
65}
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