1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 |
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27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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28 | public class SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor {
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29 |
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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)"; } }
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31 | public override string Description {
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32 | get {
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33 | return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
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34 | + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
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35 | + "Function: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine
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36 | + "Training Data: 600 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
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37 | + "Test Data: 221 * 23 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
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38 | + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(x), x^eps, x + eps, x + eps";
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39 | }
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40 | }
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41 | protected override string TargetVariable { get { return "Y"; } }
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42 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
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43 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
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44 | protected override int TrainingPartitionStart { get { return 0; } }
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45 | protected override int TrainingPartitionEnd { get { return 600; } }
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46 | protected override int TestPartitionStart { get { return 600; } }
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47 | protected override int TestPartitionEnd { get { return 600 + (221 * 23); } }
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48 |
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49 | protected override List<List<double>> GenerateValues() {
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50 | List<List<double>> data = new List<List<double>>();
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51 | List<List<double>> trainingData = new List<List<double>>() {
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52 | SequenceGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList(),
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53 | SequenceGenerator.GenerateSteps(0.05m, 10.05m, 2).Select(v => (double)v).ToList()
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54 | };
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55 |
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56 | List<List<double>> testData = new List<List<double>>() {
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57 | SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v).ToList(),
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58 | SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.5m).Select(v => (double)v).ToList()
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59 | };
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60 |
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61 | var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList<IEnumerable<double>>();
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62 | var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
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63 |
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64 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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65 | data.Add(trainingComb[i].ToList());
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66 | data[i].AddRange(testComb[i]);
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67 | }
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68 |
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69 | double x1, x2;
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70 | List<double> results = new List<double>();
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71 | for (int i = 0; i < data[0].Count; i++) {
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72 | x1 = data[0][i];
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73 | x2 = data[1][i];
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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));
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75 | }
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76 | data.Add(results);
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77 |
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78 | return data;
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79 | }
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80 | }
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81 | }
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