source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/KotanchekFunction.cs @ 13644

Last change on this file since 13644 was 13644, checked in by gkronber, 5 years ago

#2526 fixed a typo in the name & description of a benchmark problem

File size: 3.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 KotanchekFunction : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Vladislavleva-1 F1(X1,X2) = exp(- (X1 - 1)²) / (1.2 + (X2 - 2.5)²)"; } }
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: F1(X1, X2) = exp(- (X1 - 1)²) / (1.2 + (X2 - 2.5)²)" + Environment.NewLine
36        + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
37        + "Test Data: 45*45 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
38        + "Function Set: +, -, *, /, square, e^x, e^-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[] { "X1", "X2", "Y" }; } }
43    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
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 100 + (45 * 45); } }
48
49    protected override List<List<double>> GenerateValues() {
50      List<List<double>> data = new List<List<double>>();
51
52      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.2m, 4.2m, 0.1m).Select(v => (double)v).ToList();
53      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
54      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
55      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
56        data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0.3, 4).ToList());
57        data[i].AddRange(combinations[i]);
58      }
59
60      double x1, x2;
61      List<double> results = new List<double>();
62      for (int i = 0; i < data[0].Count; i++) {
63        x1 = data[0][i];
64        x2 = data[1][i];
65        results.Add(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2)));
66      }
67      data.Add(results);
68
69      return data;
70    }
71  }
72}
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