[14450] | 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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[14496] | 22 | using System;
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[14450] | 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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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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[15255] | 31 | using HeuristicLab.Optimization;
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[14450] | 32 |
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| 33 | namespace HeuristicLab.Algorithms.MemPR.Permutation.SolutionModel.Univariate {
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| 34 | [Item("Univariate solution model (Permutation.Relative)", "")]
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| 35 | [StorableClass]
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| 36 | public sealed class UnivariateRelativeModel : Item, ISolutionModel<Encodings.PermutationEncoding.Permutation> {
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| 37 | [Storable]
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[14496] | 38 | public DoubleMatrix Probabilities { get; set; }
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[14450] | 39 |
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| 40 | [Storable]
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| 41 | public IRandom Random { get; set; }
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| 42 |
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| 43 | [Storable]
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| 44 | public PermutationTypes PermutationType { get; set; }
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| 45 |
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| 46 | [StorableConstructor]
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| 47 | private UnivariateRelativeModel(bool deserializing) : base(deserializing) { }
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| 48 | private UnivariateRelativeModel(UnivariateRelativeModel original, Cloner cloner)
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| 49 | : base(original, cloner) {
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| 50 | Probabilities = cloner.Clone(original.Probabilities);
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| 51 | Random = cloner.Clone(original.Random);
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| 52 | PermutationType = original.PermutationType;
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| 53 | }
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[14496] | 54 | public UnivariateRelativeModel(IRandom random, double[,] probabilities, PermutationTypes permutationType) {
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| 55 | Probabilities = new DoubleMatrix(probabilities);
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[14450] | 56 | Random = random;
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| 57 | PermutationType = permutationType;
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| 58 | }
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[14496] | 59 | public UnivariateRelativeModel(IRandom random, DoubleMatrix probabilties, PermutationTypes permutationType) {
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[14450] | 60 | Probabilities = probabilties;
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| 61 | Random = random;
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| 62 | PermutationType = permutationType;
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| 63 | }
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| 64 |
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| 65 | public override IDeepCloneable Clone(Cloner cloner) {
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| 66 | return new UnivariateRelativeModel(this, cloner);
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| 67 | }
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| 68 |
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| 69 | public Encodings.PermutationEncoding.Permutation Sample() {
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| 70 | var N = Probabilities.Rows;
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| 71 | var next = Random.Next(N);
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| 72 | var child = new Encodings.PermutationEncoding.Permutation(PermutationType, N);
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| 73 | child[0] = next;
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| 74 | var open = Enumerable.Range(0, N).Where(x => x != next).Shuffle(Random).ToList();
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| 75 | for (var i = 1; i < N - 1; i++) {
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| 76 | var total = 0.0;
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| 77 | for (var j = 0; j < open.Count; j++) {
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| 78 | total += Probabilities[next, open[j]] + 1.0 / N;
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| 79 | }
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| 80 | var ball = Random.NextDouble() * total;
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| 81 | for (var j = 0; j < open.Count; j++) {
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| 82 | ball -= Probabilities[next, open[j]] + 1.0 / N;
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| 83 | if (ball <= 0.0) {
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| 84 | child[i] = open[j];
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| 85 | next = open[j];
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| 86 | open.RemoveAt(j);
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| 87 | break;
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| 88 | }
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| 89 | }
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| 90 | }
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| 91 | child[N - 1] = open[0];
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| 92 | return child;
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| 93 | }
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| 94 |
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| 95 | public static UnivariateRelativeModel CreateDirected(IRandom random, IList<Encodings.PermutationEncoding.Permutation> pop, int N) {
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[14496] | 96 | var model = new double[N, N];
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[14450] | 97 | for (var i = 0; i < pop.Count; i++) {
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| 98 | for (var j = 0; j < N - 1; j++) {
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| 99 | for (var k = j + 1; k < N; k++) {
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| 100 | model[pop[i][j], pop[i][k]]++;
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| 101 | }
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| 102 | model[pop[i][N - 1], pop[i][0]]++;
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| 103 | }
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| 104 | }
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| 105 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeDirected);
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| 106 | }
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| 107 |
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[14496] | 108 | public static UnivariateRelativeModel CreateDirectedWithRankBias(IRandom random, bool maximization, IList<Encodings.PermutationEncoding.Permutation> population, IEnumerable<double> qualities, int N) {
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| 109 | var popSize = 0;
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| 110 | var model = new double[N, N];
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| 111 |
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| 112 | var pop = population.Zip(qualities, (b, q) => new { Solution = b, Fitness = q });
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| 113 | foreach (var ind in maximization ? pop.OrderBy(x => x.Fitness) : pop.OrderByDescending(x => x.Fitness)) {
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| 114 | // from worst to best, worst solution has 1 vote, best solution N votes
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| 115 | popSize++;
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| 116 | for (var j = 0; j < N - 1; j++) {
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| 117 | for (var k = j + 1; k < N; k++) {
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| 118 | model[ind.Solution[j], ind.Solution[k]] += popSize;
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| 119 | }
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| 120 | model[ind.Solution[N - 1], ind.Solution[0]] += popSize;
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| 121 | }
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| 122 | }
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| 123 | if (popSize == 0) throw new ArgumentException("Cannot train model from empty population.");
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| 124 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeDirected);
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| 125 | }
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| 126 |
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| 127 | public static UnivariateRelativeModel CreateDirectedWithFitnessBias(IRandom random, bool maximization, IList<Encodings.PermutationEncoding.Permutation> population, IEnumerable<double> qualities, int N) {
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[14666] | 128 | var proportions = Util.Auxiliary.PrepareProportional(qualities, true, !maximization);
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[14496] | 129 | var factor = 1.0 / proportions.Sum();
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| 130 | var model = new double[N, N];
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| 131 |
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| 132 | foreach (var ind in population.Zip(proportions, (p, q) => new { Solution = p, Proportion = q })) {
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| 133 | for (var x = 0; x < model.Length; x++) {
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| 134 | for (var j = 0; j < N - 1; j++) {
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| 135 | for (var k = j + 1; k < N; k++) {
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| 136 | model[ind.Solution[j], ind.Solution[k]] += ind.Proportion * factor;
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| 137 | }
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| 138 | model[ind.Solution[N - 1], ind.Solution[0]] += ind.Proportion * factor;
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| 139 | }
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| 140 | }
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| 141 | }
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| 142 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeDirected);
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| 143 | }
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| 144 |
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[14450] | 145 | public static UnivariateRelativeModel CreateUndirected(IRandom random, IList<Encodings.PermutationEncoding.Permutation> pop, int N) {
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[14496] | 146 | var model = new double[N, N];
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[14450] | 147 | for (var i = 0; i < pop.Count; i++) {
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| 148 | for (var j = 0; j < N - 1; j++) {
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| 149 | for (var k = j + 1; k < N; k++) {
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| 150 | model[pop[i][j], pop[i][k]]++;
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| 151 | model[pop[i][k], pop[i][j]]++;
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| 152 | }
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| 153 | model[pop[i][0], pop[i][N - 1]]++;
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| 154 | model[pop[i][N - 1], pop[i][0]]++;
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| 155 | }
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| 156 | }
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| 157 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeUndirected);
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| 158 | }
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[14496] | 159 |
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| 160 | public static UnivariateRelativeModel CreateUndirectedWithRankBias(IRandom random, bool maximization, IList<Encodings.PermutationEncoding.Permutation> population, IEnumerable<double> qualities, int N) {
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| 161 | var popSize = 0;
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| 162 | var model = new double[N, N];
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| 163 |
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| 164 | var pop = population.Zip(qualities, (b, q) => new { Solution = b, Fitness = q });
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| 165 | foreach (var ind in maximization ? pop.OrderBy(x => x.Fitness) : pop.OrderByDescending(x => x.Fitness)) {
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| 166 | // from worst to best, worst solution has 1 vote, best solution N votes
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| 167 | popSize++;
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| 168 | for (var j = 0; j < N - 1; j++) {
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| 169 | for (var k = j + 1; k < N; k++) {
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| 170 | model[ind.Solution[j], ind.Solution[k]] += popSize;
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| 171 | model[ind.Solution[k], ind.Solution[j]] += popSize;
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| 172 | }
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| 173 | model[ind.Solution[0], ind.Solution[N - 1]] += popSize;
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| 174 | model[ind.Solution[N - 1], ind.Solution[0]] += popSize;
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| 175 | }
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| 176 | }
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| 177 | if (popSize == 0) throw new ArgumentException("Cannot train model from empty population.");
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| 178 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeUndirected);
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| 179 | }
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| 180 |
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| 181 | public static UnivariateRelativeModel CreateUndirectedWithFitnessBias(IRandom random, bool maximization, IList<Encodings.PermutationEncoding.Permutation> population, IEnumerable<double> qualities, int N) {
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[14666] | 182 | var proportions = Util.Auxiliary.PrepareProportional(qualities, true, !maximization);
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[14496] | 183 | var factor = 1.0 / proportions.Sum();
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| 184 | var model = new double[N, N];
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| 185 |
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| 186 | foreach (var ind in population.Zip(proportions, (p, q) => new { Solution = p, Proportion = q })) {
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| 187 | for (var x = 0; x < model.Length; x++) {
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| 188 | for (var j = 0; j < N - 1; j++) {
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| 189 | for (var k = j + 1; k < N; k++) {
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| 190 | model[ind.Solution[j], ind.Solution[k]] += ind.Proportion * factor;
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| 191 | model[ind.Solution[k], ind.Solution[j]] += ind.Proportion * factor;
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| 192 | }
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| 193 | model[ind.Solution[0], ind.Solution[N - 1]] += ind.Proportion * factor;
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| 194 | model[ind.Solution[N - 1], ind.Solution[0]] += ind.Proportion * factor;
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| 195 | }
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| 196 | }
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| 197 | }
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| 198 | return new UnivariateRelativeModel(random, model, PermutationTypes.RelativeUndirected);
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| 199 | }
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[14450] | 200 | }
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| 201 | }
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