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 |
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26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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27 | public class KeijzerFunctionFourteen : ArtificialRegressionDataDescriptor {
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28 |
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29 | public override string Name { get { return "Keijzer 14 f(x, y) = 8 / (2 + x² + y²)"; } }
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30 | public override string Description {
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31 | get {
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32 | return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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33 | + "Authors: Maarten Keijzer" + Environment.NewLine
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34 | + "Function: f(x, y) = 8 / (2 + x² + y²)" + Environment.NewLine
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35 | + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine
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36 | + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
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37 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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38 | }
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39 | }
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40 | protected override string TargetVariable { get { return "F"; } }
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41 | protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
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42 | protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
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43 | protected override int TrainingPartitionStart { get { return 0; } }
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44 | protected override int TrainingPartitionEnd { get { return 20; } }
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45 | protected override int TestPartitionStart { get { return 20; } }
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46 | protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
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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(-3, 3, 0.01m).Select(v => (double) v).ToList();
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51 | List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
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52 |
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53 | var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
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54 |
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55 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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56 | data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
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57 | data[i].AddRange(combinations[i]);
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58 | }
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59 |
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60 | double x, y;
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61 | List<double> results = new List<double>();
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62 | for (int i = 0; i < data[0].Count; i++) {
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63 | x = data[0][i];
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64 | y = data[1][i];
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65 | results.Add(8.0 / (2.0 + x * x + y * y));
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66 | }
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67 | data.Add(results);
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68 |
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69 | return data;
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70 | }
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71 | }
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72 | }
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