1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 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;
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24 | using System.Collections.Generic;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Problems.DataAnalysis;
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29 | using HeuristicLab.Random;
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30 | using MathNet.Numerics.Statistics;
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31 | using DoubleVector = MathNet.Numerics.LinearAlgebra.Vector<double>;
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32 |
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33 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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34 | public class AzzaliKorns5 : ArtificialRegressionDataDescriptor {
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35 | public override string Name { get { return "Azzali Korns5 K5 = 3.0 + 2.23 * log(X4)"; } }
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36 | public override string Description { get { return "I. Azzali, L. Vanneschi, S. Silva, I. Bakurov, and M. Giacobini, “A Vectorial Approach to Genetic Programming,” EuroGP, pp. 213–227, 2019."; } }
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37 |
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38 | protected override string TargetVariable { get { return "K5"; } }
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39 | protected override string[] VariableNames { get { return AllowedInputVariables.Concat(new[] { TargetVariable }).ToArray(); } }
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40 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4" }; } }
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41 | protected override int TrainingPartitionStart { get { return 0; } }
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42 | protected override int TrainingPartitionEnd { get { return 70; } }
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43 | protected override int TestPartitionStart { get { return 70; } }
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44 | protected override int TestPartitionEnd { get { return 100; } }
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45 |
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46 | public int Seed { get; private set; }
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47 |
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48 | public AzzaliKorns5()
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49 | : this((int)DateTime.Now.Ticks) { }
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50 | public AzzaliKorns5(int seed)
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51 | : base() {
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52 | Seed = seed;
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53 | }
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54 |
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55 |
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56 | protected override List<List<double>> GenerateValues() { return null; }
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57 | protected override List<IList> GenerateValuesExtended() {
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58 | var rand = new MersenneTwister((uint)Seed);
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59 |
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60 | var x1Column = new List<double>(100);
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61 | var x2Column = new List<double>(100);
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62 | var x3Column = new List<double>(100);
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63 | var x4Column = new List<DoubleVector>(100);
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64 | var k5Column = new List<DoubleVector>(100);
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65 |
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66 | for (int i = 0; i < 100; i++) {
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67 | var x1 = rand.NextDouble(-50, +50);
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68 | var x2 = rand.NextDouble(-50, +50);
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69 | var x3 = rand.NextDouble(-50, +50);
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70 | var x4 = rand.NextDoubleVector(-50, +50, 10);
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71 |
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72 | var k5 = 3 + 2.13 * x4.PointwiseLog();
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73 |
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74 | x1Column.Add(x1);
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75 | x2Column.Add(x2);
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76 | x3Column.Add(x3);
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77 | x4Column.Add(x4);
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78 | k5Column.Add(k5);
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79 | }
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80 |
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81 | return new List<IList> { x1Column, x2Column, x3Column, x4Column, k5Column };
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82 | }
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83 | }
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84 | } |
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