1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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 |
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26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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27 | public class SineCosineFunction : ArtificialRegressionDataDescriptor {
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28 |
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29 | public override string Name { get { return "Vladislavleva SineCosine"; } }
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30 | public override string Description {
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31 | get {
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32 | 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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33 | + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
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34 | + "Function: F6(X1, X2) = 6 * sin(X1) * cos(X2)" + Environment.NewLine
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35 | + "Training Data: 30 points X1, X2 = Rand(0.1, 5.9)" + Environment.NewLine
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36 | + "Test Data: 961 points X1, X2 = (-0.05:0.02:6.05)" + Environment.NewLine
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37 | + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x";
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38 | }
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39 | }
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40 | protected override string TargetVariable { get { return "Y"; } }
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41 | protected override string[] InputVariables { get { return new string[] { "X1", "X2", "Y" }; } }
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42 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
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43 | protected override int TrainingPartitionStart { get { return 0; } }
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44 | protected override int TrainingPartitionEnd { get { return 30; } }
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45 | protected override int TestPartitionStart { get { return 500; } }
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46 | protected override int TestPartitionEnd { get { return 1461; } }
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47 |
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48 | protected override List<List<double>> GenerateValues() {
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49 | List<List<double>> data = new List<List<double>>();
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50 | List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-0.05, 6.05, 0.02).ToList();
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51 | List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
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52 | var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
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53 |
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54 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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55 | data.Add(ValueGenerator.GenerateUniformDistributedValues(500, 0.1, 5.9).ToList());
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56 | data[i].AddRange(combinations[i]);
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57 | }
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58 |
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59 | double x1, x2;
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60 | List<double> results = new List<double>();
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61 | for (int i = 0; i < data[0].Count; i++) {
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62 | x1 = data[0][i];
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63 | x2 = data[1][i];
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64 | results.Add(6 * Math.Sin(x1) * Math.Cos(x2));
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65 | }
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66 | data.Add(results);
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67 |
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68 | return data;
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69 | }
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70 | }
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71 | }
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