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.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Random;
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26 |
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27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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28 | public class FriedmanOne : ArtificialRegressionDataDescriptor {
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29 |
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30 | public override string Name { get { return "Friedman - I"; } }
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31 | public override string Description {
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32 | get {
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33 | return "Paper: Multivariate Adaptive Regression Splines" + Environment.NewLine
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34 | + "Authors: Jerome H. Friedman";
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35 | }
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36 | }
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37 | protected override string TargetVariable { get { return "Y"; } }
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38 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
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39 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
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40 | protected override int TrainingPartitionStart { get { return 0; } }
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41 | protected override int TrainingPartitionEnd { get { return 5000; } }
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42 | protected override int TestPartitionStart { get { return 5000; } }
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43 | protected override int TestPartitionEnd { get { return 10000; } }
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44 | public int Seed { get; private set; }
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45 |
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46 | public FriedmanOne() : this((int)DateTime.Now.Ticks) { }
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47 |
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48 | public FriedmanOne(int seed) : base() {
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49 | Seed = seed;
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50 | }
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51 |
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52 | protected override List<List<double>> GenerateValues() {
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53 | List<List<double>> data = new List<List<double>>();
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54 | var rand = new MersenneTwister((uint)Seed);
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55 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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56 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 10000, 0, 1).ToList());
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57 | }
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58 |
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59 | double x1, x2, x3, x4, x5;
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60 | double f;
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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 | x1 = data[0][i];
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64 | x2 = data[1][i];
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65 | x3 = data[2][i];
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66 | x4 = data[3][i];
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67 | x5 = data[4][i];
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68 |
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69 | f = 0.1 * Math.Exp(4 * x1) + 4 / (1 + Math.Exp(-20 * (x2 - 0.5))) + 3 * x3 + 2 * x4 + x5;
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70 |
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71 | results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, 1));
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72 | }
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73 | data.Add(results);
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74 |
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75 | return data;
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76 | }
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77 | }
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78 | }
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