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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionNine.cs @ 10614

Last change on this file since 10614 was 9456, checked in by swagner, 12 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 2.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 KeijzerFunctionNine : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Keijzer 9 f(x) = arcsinh(x)  i.e. ln(x + sqrt(x² + 1))"; } }
30    public override string Description {
31      get {
32        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
33        + "Authors: Maarten Keijzer" + Environment.NewLine
34        + "Function: f(x) = arcsinh(x)  i.e. ln(x + sqrt(x² + 1))" + Environment.NewLine
35        + "range(train): x = [0:1:100]" + Environment.NewLine
36        + "range(test): x = [0:0.1:100]" + Environment.NewLine
37        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
38      }
39    }
40    protected override string TargetVariable { get { return "F"; } }
41    protected override string[] VariableNames { get { return new string[] { "X", "F" }; } }
42    protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
43    protected override int TrainingPartitionStart { get { return 0; } }
44    protected override int TrainingPartitionEnd { get { return 101; } }
45    protected override int TestPartitionStart { get { return 101; } }
46    protected override int TestPartitionEnd { get { return 1102; } }
47
48    protected override List<List<double>> GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50      data.Add(ValueGenerator.GenerateSteps(0, 100, 1).ToList());
51      data[0].AddRange(ValueGenerator.GenerateSteps(0, 100, 0.1));
52
53      double x;
54      List<double> results = new List<double>();
55      for (int i = 0; i < data[0].Count; i++) {
56        x = data[0][i];
57        results.Add(Math.Log(x + Math.Sqrt(x * x + 1)));
58      }
59      data.Add(results);
60
61      return data;
62    }
63  }
64}
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