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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SalutowiczFunctionTwoDimensional.cs @ 12292

Last change on this file since 12292 was 12292, checked in by pfleck, 9 years ago

#2301 Removed the GenerateSteps from the ValueGenerator and put it into the new SequenceGenerator.
Adapted DataAnalysis-Instances and scripts (samples and unit tests).

File size: 4.0 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 SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Vladislavleva-3 F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)"; } }
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: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine
36        + "Training Data: 600 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
37        + "Test Data: 221 * 23 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
38        + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(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 600; } }
46    protected override int TestPartitionStart { get { return 600; } }
47    protected override int TestPartitionEnd { get { return 600 + (221 * 23); } }
48
49    protected override List<List<double>> GenerateValues() {
50      List<List<double>> data = new List<List<double>>();
51      List<List<double>> trainingData = new List<List<double>>() {
52        SequenceGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList(),
53        SequenceGenerator.GenerateSteps(0.05m, 10.05m, 2).Select(v => (double)v).ToList()
54      };
55
56      List<List<double>> testData = new List<List<double>>() {
57        SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v).ToList(),
58        SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.5m).Select(v => (double)v).ToList()
59      };
60
61      var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList<IEnumerable<double>>();
62      var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
63
64      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
65        data.Add(trainingComb[i].ToList());
66        data[i].AddRange(testComb[i]);
67      }
68
69      double x1, x2;
70      List<double> results = new List<double>();
71      for (int i = 0; i < data[0].Count; i++) {
72        x1 = data[0][i];
73        x2 = data[1][i];
74        results.Add(Math.Exp(-x1) * Math.Pow(x1, 3) * Math.Cos(x1) * Math.Sin(x1) * (Math.Cos(x1) * Math.Pow(Math.Sin(x1), 2) - 1) * (x2 - 5));
75      }
76      data.Add(results);
77
78      return data;
79    }
80  }
81}
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