1 | #region License Information |
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2 | /* HeuristicLab |
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3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL) |
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4 | * and the BEACON Center for the Study of Evolution in Action. |
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5 | * |
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6 | * This file is part of HeuristicLab. |
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7 | * |
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8 | * HeuristicLab is free software: you can redistribute it and/or modify |
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9 | * it under the terms of the GNU General Public License as published by |
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10 | * the Free Software Foundation, either version 3 of the License, or |
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11 | * (at your option) any later version. |
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12 | * |
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13 | * HeuristicLab is distributed in the hope that it will be useful, |
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of |
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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16 | * GNU General Public License for more details. |
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17 | * |
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18 | * You should have received a copy of the GNU General Public License |
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>. |
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20 | */ |
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21 | #endregion |
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22 | |
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23 | using System; |
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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.Persistence.Default.CompositeSerializers.Storable; |
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29 | |
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30 | namespace HeuristicLab.Algorithms.DataAnalysis { |
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31 | [StorableClass] |
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32 | [Item("Squared error loss", "")] |
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33 | public sealed class SquaredErrorLoss : Item, ILossFunction { |
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34 | public SquaredErrorLoss() { } |
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35 | |
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36 | public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) { |
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37 | var targetEnum = target.GetEnumerator(); |
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38 | var predEnum = pred.GetEnumerator(); |
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39 | |
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40 | double s = 0; |
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41 | while (targetEnum.MoveNext() & predEnum.MoveNext()) { |
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42 | double res = targetEnum.Current - predEnum.Current; |
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43 | s += res * res; // (res)^2 |
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44 | } |
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45 | if (targetEnum.MoveNext() | predEnum.MoveNext()) |
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46 | throw new ArgumentException("target and pred have different lengths"); |
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47 | |
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48 | return s; |
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49 | } |
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50 | |
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51 | public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred) { |
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52 | var targetEnum = target.GetEnumerator(); |
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53 | var predEnum = pred.GetEnumerator(); |
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54 | |
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55 | while (targetEnum.MoveNext() & predEnum.MoveNext()) { |
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56 | yield return 2.0 * (targetEnum.Current - predEnum.Current); // dL(y, f(x)) / df(x) = 2 * res |
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57 | } |
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58 | if (targetEnum.MoveNext() | predEnum.MoveNext()) |
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59 | throw new ArgumentException("target and pred have different lengths"); |
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60 | } |
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61 | |
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62 | // targetArr and predArr are not changed by LineSearch |
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63 | public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) { |
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64 | if (targetArr.Length != predArr.Length) |
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65 | throw new ArgumentException("target and pred have different lengths"); |
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66 | |
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67 | // line search for squared error loss |
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68 | // for a given partition of rows the optimal constant that should be added to the current prediction values is the average of the residuals |
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69 | double s = 0.0; |
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70 | int n = 0; |
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71 | for (int i = startIdx; i <= endIdx; i++) { |
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72 | int row = idx[i]; |
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73 | s += (targetArr[row] - predArr[row]); |
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74 | n++; |
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75 | } |
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76 | return s / n; |
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77 | } |
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78 | |
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79 | #region item implementation |
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80 | [StorableConstructor] |
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81 | private SquaredErrorLoss(bool deserializing) : base(deserializing) { } |
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82 | |
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83 | private SquaredErrorLoss(SquaredErrorLoss original, Cloner cloner) : base(original, cloner) { } |
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84 | |
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85 | public override IDeepCloneable Clone(Cloner cloner) { |
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86 | return new SquaredErrorLoss(this, cloner); |
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87 | } |
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88 | #endregion |
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89 | } |
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90 | } |
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