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source: branches/DataAnalysisCSVImport/3.3/Regression/Nguyen/NguyenFunctionTwo.cs @ 8695

Last change on this file since 8695 was 8224, checked in by gkronber, 12 years ago

#1784: fixed uniform sampling. Adapted Nguyen instances.

File size: 2.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 NguyenFunctionTwo : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Nguyen F2 = x^4 + x³ + x² + x"; } }
30    public override string Description {
31      get {
32        return "Paper: Semantically-based Crossover in Genetic Programming: Application to Real-valued Symbolic Regression" + Environment.NewLine
33        + "Authors: Nguyen Quang Uy · Nguyen Xuan Hoai · Michael O’Neill · R.I. McKay · Edgar Galvan-Lopez" + Environment.NewLine
34        + "Function: F2 = x^4 + x³ + x² + x" + Environment.NewLine
35        + "Fitcases: 20 random points in [-1, 1]" + Environment.NewLine
36        + "Non-terminals: +, -, *, % (protected division), sin, cos, exp, ln(|x|) (protected log)" + Environment.NewLine
37        + "Terminals: only variables (no random constants)";
38      }
39    }
40    protected override string TargetVariable { get { return "Y"; } }
41    protected override string[] InputVariables { get { return new string[] { "X", "Y" }; } }
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 20; } }
45    protected override int TestPartitionStart { get { return 250; } }
46    protected override int TestPartitionEnd { get { return 350; } }
47
48    protected override List<List<double>> GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50      data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
51
52      double x;
53      List<double> results = new List<double>();
54      for (int i = 0; i < data[0].Count; i++) {
55        x = data[0][i];
56        results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2) + x);
57      }
58      data.Add(results);
59
60      return data;
61    }
62  }
63}
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