1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.PermutationEncoding;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Random;
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31 |
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32 | namespace HeuristicLab.Algorithms.MemPR.Permutation.SolutionModel.Univariate {
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33 | [Item("Univariate solution model (Permutation.Absolute)", "")]
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34 | [StorableClass]
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35 | public sealed class UnivariateAbsoluteModel : Item, ISolutionModel<Encodings.PermutationEncoding.Permutation> {
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36 | [Storable]
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37 | public IntMatrix Probabilities { get; set; }
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38 | [Storable]
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39 | public IRandom Random { get; set; }
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40 |
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41 | [StorableConstructor]
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42 | private UnivariateAbsoluteModel(bool deserializing) : base(deserializing) { }
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43 | private UnivariateAbsoluteModel(UnivariateAbsoluteModel original, Cloner cloner)
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44 | : base(original, cloner) {
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45 | Probabilities = cloner.Clone(original.Probabilities);
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46 | Random = cloner.Clone(original.Random);
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47 | }
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48 | public UnivariateAbsoluteModel(IRandom random, int[,] probabilities) {
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49 | Probabilities = new IntMatrix(probabilities);
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50 | Random = random;
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51 | }
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52 | public UnivariateAbsoluteModel(IRandom random, IntMatrix probabilties) {
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53 | Probabilities = probabilties;
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54 | Random = random;
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55 | }
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56 |
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57 | public override IDeepCloneable Clone(Cloner cloner) {
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58 | return new UnivariateAbsoluteModel(this, cloner);
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59 | }
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60 |
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61 | public Encodings.PermutationEncoding.Permutation Sample() {
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62 | var N = Probabilities.Rows;
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63 | var child = new Encodings.PermutationEncoding.Permutation(PermutationTypes.Absolute, N);
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64 | var indices = Enumerable.Range(0, N).Shuffle(Random).ToList();
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65 | var values = Enumerable.Range(0, N).Shuffle(Random).ToList();
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66 | for (var i = N - 1; i > 0; i--) {
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67 | var nextIndex = indices[i];
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68 | var total = 0.0;
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69 | for (var v = 0; v < values.Count; v++) {
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70 | total += Probabilities[nextIndex, values[v]] + 1.0 / N;
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71 | }
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72 | var ball = Random.NextDouble() * total;
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73 | for (var v = 0; v < values.Count; v++) {
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74 | ball -= Probabilities[nextIndex, values[v]] + 1.0 / N;
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75 | if (ball <= 0.0) {
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76 | child[nextIndex] = values[v];
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77 | values.RemoveAt(v);
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78 | indices.RemoveAt(i);
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79 | break;
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80 | }
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81 | }
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82 | }
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83 | child[indices[0]] = values[0];
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84 | return child;
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85 | }
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86 |
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87 | public static UnivariateAbsoluteModel Create(IRandom random, IList<Encodings.PermutationEncoding.Permutation> pop, int N) {
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88 | var model = new int[N, N];
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89 | for (var i = 0; i < pop.Count; i++) {
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90 | for (var j = 0; j < N; j++) {
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91 | model[j, pop[i][j]]++;
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92 | }
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93 | }
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94 | return new UnivariateAbsoluteModel(random, model);
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95 | }
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96 | }
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97 | }
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