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source: branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Problems.Instances.Regression/3.4/Vladislavleva/KotanchekFunction.cs @ 7666

Last change on this file since 7666 was 7666, checked in by sforsten, 12 years ago

#1784:

  • deleted obsolete project Problems.Instances.Regression.Views
  • added TrentMcConaghy and Various problem instances (zip file from TrentMcConaghy is rather big)
File size: 3.3 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.Regression {
27  public class KotanchekFunction : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Vladislavleva Kotanchek"; } }
30    public override string Description {
31      get {
32        return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
33        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
34        + "Function: F1(X1, X2) = e^-(X1 - 1)^2 / (1.2 + (X2 -2.5)^2" + Environment.NewLine
35        + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
36        + "Test Data: 2026 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
37        + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x";
38      }
39    }
40    protected override string TargetVariable { get { return "Y"; } }
41    protected override IEnumerable<string> InputVariables { get { return new List<string>() { "X1", "X2", "Y" }; } }
42    protected override IEnumerable<string> AllowedInputVariables { get { return new List<string>() { "X1", "X2" }; } }
43    protected override int TrainingPartitionStart { get { return 0; } }
44    protected override int TrainingPartitionEnd { get { return 100; } }
45    protected override int TestPartitionStart { get { return 1000; } }
46    protected override int TestPartitionEnd { get { return 3025; } }
47
48    protected override double[,] GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50
51      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-0.2, 4.2, 0.1);
52      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
53      testData = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData);
54      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
55        data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0.3, 4));
56        data[i].AddRange(testData[i]);
57      }
58
59      double x1, x2;
60      List<double> results = new List<double>();
61      for (int i = 0; i < data[0].Count; i++) {
62        x1 = data[0][i];
63        x2 = data[1][i];
64        results.Add(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2)));
65      }
66      data.Add(results);
67
68      return ValueGenerator.Transformation(data);
69    }
70  }
71}
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