[13861] | 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 HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.Encodings.PermutationEncoding;
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| 26 | using HeuristicLab.Optimization;
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| 27 | using HeuristicLab.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.QuadraticAssignment;
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| 30 | using HeuristicLab.Random;
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| 31 | using System;
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| 32 | using System.Collections.Generic;
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| 33 | using System.Linq;
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[14690] | 34 | using System.Threading;
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[13861] | 35 |
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[14678] | 36 | namespace HeuristicLab.Analysis.FitnessLandscape {
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[13861] | 37 | [Item("Directed Walk (QAP-specific)", "")]
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| 38 | [StorableClass]
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| 39 | public class QAPDirectedWalk : CharacteristicCalculator {
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| 40 |
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| 41 | public IFixedValueParameter<IntValue> PathsParameter {
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| 42 | get { return (IFixedValueParameter<IntValue>)Parameters["Paths"]; }
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| 43 | }
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| 44 |
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| 45 | public IFixedValueParameter<BoolValue> BestImprovementParameter {
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| 46 | get { return (IFixedValueParameter<BoolValue>)Parameters["BestImprovement"]; }
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| 47 | }
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| 48 |
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| 49 | public IValueParameter<IntValue> SeedParameter {
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| 50 | get { return (IValueParameter<IntValue>)Parameters["Seed"]; }
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| 51 | }
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| 52 |
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[14690] | 53 | public IFixedValueParameter<BoolValue> LocalOptimaParameter {
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| 54 | get { return (IFixedValueParameter<BoolValue>)Parameters["LocalOptima"]; }
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| 55 | }
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| 56 |
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[13861] | 57 | public int Paths {
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| 58 | get { return PathsParameter.Value.Value; }
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| 59 | set { PathsParameter.Value.Value = value; }
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| 60 | }
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| 61 |
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| 62 | public bool BestImprovement {
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| 63 | get { return BestImprovementParameter.Value.Value; }
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| 64 | set { BestImprovementParameter.Value.Value = value; }
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| 65 | }
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| 66 |
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| 67 | public int? Seed {
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| 68 | get { return SeedParameter.Value != null ? SeedParameter.Value.Value : (int?)null; }
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| 69 | set { SeedParameter.Value = value.HasValue ? new IntValue(value.Value) : null; }
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| 70 | }
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| 71 |
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[14690] | 72 | public bool LocalOptima {
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| 73 | get { return LocalOptimaParameter.Value.Value; }
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| 74 | set { LocalOptimaParameter.Value.Value = value; }
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| 75 | }
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| 76 |
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[13861] | 77 | [StorableConstructor]
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| 78 | private QAPDirectedWalk(bool deserializing) : base(deserializing) { }
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| 79 | private QAPDirectedWalk(QAPDirectedWalk original, Cloner cloner) : base(original, cloner) { }
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| 80 | public QAPDirectedWalk() {
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| 81 | characteristics.AddRange(new[] { "Swap2.Sharpness", "Swap2.Bumpiness", "Swap2.Flatness", "Swap2.Steadiness" }
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| 82 | .Select(x => new StringValue(x)).ToList());
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| 83 | Parameters.Add(new FixedValueParameter<IntValue>("Paths", "The number of paths to explore (a path is a set of solutions that connect two randomly chosen solutions).", new IntValue(50)));
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| 84 | Parameters.Add(new FixedValueParameter<BoolValue>("BestImprovement", "Whether the best of all alternatives should be chosen for each step in the path or just the first improving (least degrading) move should be made.", new BoolValue(true)));
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| 85 | Parameters.Add(new OptionalValueParameter<IntValue>("Seed", "The seed for the random number generator."));
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[14690] | 86 | Parameters.Add(new FixedValueParameter<BoolValue>("LocalOptima", "Whether to perform walks between local optima.", new BoolValue(false)));
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[13861] | 87 | }
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| 88 |
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| 89 | public override IDeepCloneable Clone(Cloner cloner) {
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| 90 | return new QAPDirectedWalk(this, cloner);
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| 91 | }
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| 92 |
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| 93 | public override bool CanCalculate() {
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| 94 | return Problem is QuadraticAssignmentProblem;
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| 95 | }
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| 96 |
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| 97 | public override IEnumerable<IResult> Calculate() {
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[14429] | 98 | IRandom random = Seed.HasValue ? new MersenneTwister((uint)Seed.Value) : new MersenneTwister();
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| 99 | var qap = (QuadraticAssignmentProblem)Problem;
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[13861] | 100 | var pathCount = Paths;
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| 101 |
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[14429] | 102 | var perm = new Permutation(PermutationTypes.Absolute, qap.Weights.Rows, random);
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[14690] | 103 | if (LocalOptima) {
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| 104 | var fit = new DoubleValue(QAPEvaluator.Apply(perm, qap.Weights, qap.Distances));
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| 105 | QAPExhaustiveSwap2LocalImprovement.ImproveFast(perm, qap.Weights, qap.Distances, fit, new IntValue(0), new IntValue(0), qap.Maximization.Value, int.MaxValue, CancellationToken.None);
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| 106 | }
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[14429] | 107 | var permutations = new List<Permutation> { perm };
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| 108 | while (permutations.Count < pathCount + 1) {
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| 109 | perm = (Permutation)permutations.Last().Clone();
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| 110 | BiasedShuffle(perm, random);
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[14690] | 111 | if (LocalOptima) {
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| 112 | var fit = new DoubleValue(QAPEvaluator.Apply(perm, qap.Weights, qap.Distances));
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| 113 | QAPExhaustiveSwap2LocalImprovement.ImproveFast(perm, qap.Weights, qap.Distances, fit, new IntValue(0), new IntValue(0), qap.Maximization.Value, int.MaxValue, CancellationToken.None);
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| 114 | }
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| 115 | if (HammingSimilarityCalculator.CalculateSimilarity(permutations.Last(), perm) < 0.75)
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| 116 | permutations.Add(perm);
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[14429] | 117 | }
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[13861] | 118 |
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[14429] | 119 | var trajectories = Run(random, (QuadraticAssignmentProblem)Problem, permutations, BestImprovement).ToList();
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| 120 | var firstDerivatives = trajectories.Select(path => ApproximateDerivative(path).ToList()).ToList();
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| 121 | var secondDerivatives = firstDerivatives.Select(d1 => ApproximateDerivative(d1).ToList()).ToList();
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| 122 |
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| 123 | var props = GetCharacteristics(trajectories, firstDerivatives, secondDerivatives).ToDictionary(x => x.Item1, x => x.Item2);
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| 124 | foreach (var chara in characteristics.CheckedItems.Select(x => x.Value.Value)) {
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| 125 | if (chara == "Swap2.Sharpness") yield return new Result("Swap2.Sharpness", new DoubleValue(props["Sharpness"]));
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| 126 | if (chara == "Swap2.Bumpiness") yield return new Result("Swap2.Bumpiness", new DoubleValue(props["Bumpiness"]));
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| 127 | if (chara == "Swap2.Flatness") yield return new Result("Swap2.Flatness", new DoubleValue(props["Flatness"]));
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| 128 | if (chara == "Swap2.Steadiness") yield return new Result("Swap2.Steadiness", new DoubleValue(props["Steadiness"]));
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| 129 | }
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| 130 | }
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[13861] | 131 |
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[14678] | 132 | public static IEnumerable<IResult> Calculate(List<List<Tuple<Permutation, double>>> trajectories) {
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| 133 | var firstDerivatives = trajectories.Select(path => ApproximateDerivative(path).ToList()).ToList();
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| 134 | var secondDerivatives = firstDerivatives.Select(d1 => ApproximateDerivative(d1).ToList()).ToList();
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| 135 |
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| 136 | var props = GetCharacteristics(trajectories, firstDerivatives, secondDerivatives).ToDictionary(x => x.Item1, x => x.Item2);
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| 137 | yield return new Result("Swap2.Sharpness", new DoubleValue(props["Sharpness"]));
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| 138 | yield return new Result("Swap2.Bumpiness", new DoubleValue(props["Bumpiness"]));
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| 139 | yield return new Result("Swap2.Flatness", new DoubleValue(props["Flatness"]));
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| 140 | yield return new Result("Swap2.Steadiness", new DoubleValue(props["Steadiness"]));
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| 141 | }
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| 142 |
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[14429] | 143 | public static IEnumerable<List<Tuple<Permutation, double>>> Run(IRandom random, QuadraticAssignmentProblem qap, IEnumerable<Permutation> permutations, bool bestImprovement = true) {
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| 144 | var iter = permutations.GetEnumerator();
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| 145 | if (!iter.MoveNext()) yield break;
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[13861] | 146 |
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[14678] | 147 | var min = qap.LowerBound.Value;
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| 148 | var max = qap.AverageQuality.Value;
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| 149 |
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[14429] | 150 | var start = iter.Current;
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| 151 | while (iter.MoveNext()) {
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| 152 | var end = iter.Current;
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[13861] | 153 |
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[14429] | 154 | var walk = (bestImprovement ? BestDirectedWalk(qap, start, end) : FirstDirectedWalk(random, qap, start, end)).ToList();
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[14678] | 155 | yield return walk.Select(x => Tuple.Create(x.Item1, (x.Item2 - min) / (max - min))).ToList();
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[14429] | 156 | start = end;
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| 157 | } // end paths
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| 158 | }
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[13861] | 159 |
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[14678] | 160 | private static IEnumerable<Tuple<string, double>> GetCharacteristics(List<List<Tuple<Permutation, double>>> f, List<List<Tuple<Permutation, double>>> f1, List<List<Tuple<Permutation, double>>> f2) {
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[14429] | 161 | var sharpness = f2.Average(x => Area(x));
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| 162 | var bumpiness = 0.0;
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| 163 | var flatness = 0.0;
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| 164 | var downPointing = f1.Where(x => x.Min(y => y.Item2) < 0).ToList();
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| 165 |
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| 166 | var steadiness = 0.0;
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| 167 | foreach (var path in downPointing) {
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| 168 | steadiness += ComBelowZero(path);
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| 169 | }
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| 170 | if (downPointing.Count > 0) steadiness /= downPointing.Count;
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| 171 |
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| 172 | for (var p = 0; p < f2.Count; p++) {
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[14690] | 173 | if (f2[p].Count <= 2) continue;
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[14429] | 174 | var bump = 0;
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| 175 | var flat = 0;
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| 176 | for (var i = 0; i < f2[p].Count - 1; i++) {
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| 177 | if ((f2[p][i].Item2 > 0 && f2[p][i + 1].Item2 < 0) || (f2[p][i].Item2 < 0 && f2[p][i + 1].Item2 > 0)) {
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| 178 | bump++;
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| 179 | } else if (f2[p][i].Item2 == 0) {
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| 180 | flat++;
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[13861] | 181 | }
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| 182 | }
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[14429] | 183 | bumpiness += bump / (f2[p].Count - 1.0);
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| 184 | flatness += flat / (f2[p].Count - 1.0);
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[13861] | 185 | }
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[14429] | 186 | bumpiness /= f2.Count;
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| 187 | flatness /= f2.Count;
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| 188 | return new[] {
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| 189 | Tuple.Create("Sharpness", sharpness),
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| 190 | Tuple.Create("Bumpiness", bumpiness),
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| 191 | Tuple.Create("Flatness", flatness),
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| 192 | Tuple.Create("Steadiness", steadiness)
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| 193 | };
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[13861] | 194 | }
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| 195 |
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[14429] | 196 | public static IEnumerable<Tuple<Permutation, double>> BestDirectedWalk(QuadraticAssignmentProblem qap, Permutation start, Permutation end) {
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[13861] | 197 | var N = qap.Weights.Rows;
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| 198 | var sol = start;
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[14429] | 199 | var fitness = QAPEvaluator.Apply(sol, qap.Weights, qap.Distances);
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| 200 | yield return Tuple.Create(sol, fitness);
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| 201 |
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[13861] | 202 | var invSol = GetInverse(sol);
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| 203 | // we require at most N-1 steps to move from one permutation to another
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| 204 | for (var step = 0; step < N - 1; step++) {
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| 205 | var bestFitness = double.MaxValue;
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| 206 | var bestIndex = -1;
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| 207 | sol = (Permutation)sol.Clone();
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| 208 | for (var index = 0; index < N; index++) {
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| 209 | if (sol[index] == end[index]) continue;
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| 210 | var fit = QAPSwap2MoveEvaluator.Apply(sol, new Swap2Move(index, invSol[end[index]]), qap.Weights, qap.Distances);
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| 211 | if (fit < bestFitness) { // QAP is minimization
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| 212 | bestFitness = fit;
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| 213 | bestIndex = index;
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| 214 | }
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| 215 | }
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| 216 | if (bestIndex >= 0) {
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| 217 | var prev = sol[bestIndex];
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| 218 | Swap2Manipulator.Apply(sol, bestIndex, invSol[end[bestIndex]]);
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| 219 | fitness += bestFitness;
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| 220 | yield return Tuple.Create(sol, fitness);
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| 221 | invSol[prev] = invSol[end[bestIndex]];
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| 222 | invSol[sol[bestIndex]] = bestIndex;
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| 223 | } else break;
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| 224 | }
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| 225 | }
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| 226 |
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[14429] | 227 | public static IEnumerable<Tuple<Permutation, double>> FirstDirectedWalk(IRandom random, QuadraticAssignmentProblem qap, Permutation start, Permutation end) {
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[13861] | 228 | var N = qap.Weights.Rows;
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| 229 | var sol = start;
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[14429] | 230 | var fitness = QAPEvaluator.Apply(sol, qap.Weights, qap.Distances);
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| 231 | yield return Tuple.Create(sol, fitness);
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| 232 |
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[13861] | 233 | var invSol = GetInverse(sol);
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| 234 | // randomize the order in which improvements are tried
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| 235 | var order = Enumerable.Range(0, N).Shuffle(random).ToArray();
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| 236 | // we require at most N-1 steps to move from one permutation to another
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| 237 | for (var step = 0; step < N - 1; step++) {
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| 238 | var bestFitness = double.MaxValue;
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| 239 | var bestIndex = -1;
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| 240 | sol = (Permutation)sol.Clone();
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| 241 | for (var i = 0; i < N; i++) {
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| 242 | var index = order[i];
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| 243 | if (sol[index] == end[index]) continue;
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| 244 | var fit = QAPSwap2MoveEvaluator.Apply(sol, new Swap2Move(index, invSol[end[index]]), qap.Weights, qap.Distances);
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| 245 | if (fit < bestFitness) { // QAP is minimization
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| 246 | bestFitness = fit;
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| 247 | bestIndex = index;
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| 248 | if (bestFitness < 0) break;
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| 249 | }
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| 250 | }
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| 251 | if (bestIndex >= 0) {
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| 252 | var prev = sol[bestIndex];
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| 253 | Swap2Manipulator.Apply(sol, bestIndex, invSol[end[bestIndex]]);
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| 254 | fitness += bestFitness;
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| 255 | yield return Tuple.Create(sol, fitness);
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| 256 | invSol[prev] = invSol[end[bestIndex]];
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| 257 | invSol[sol[bestIndex]] = bestIndex;
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| 258 | } else break;
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| 259 | }
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| 260 | }
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| 261 |
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[14429] | 262 | private static double Area(IEnumerable<Tuple<Permutation, double>> path) {
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| 263 | var iter = path.GetEnumerator();
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| 264 | if (!iter.MoveNext()) return 0.0;
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| 265 | var area = 0.0;
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| 266 | var prev = iter.Current;
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| 267 | while (iter.MoveNext()) {
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| 268 | area += TrapezoidArea(prev, iter.Current);
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| 269 | prev = iter.Current;
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| 270 | }
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| 271 | return area;
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| 272 | }
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| 273 |
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| 274 | private static double TrapezoidArea(Tuple<Permutation, double> a, Tuple<Permutation, double> b) {
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| 275 | var area = 0.0;
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| 276 | var dist = Dist(a.Item1, b.Item1);
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| 277 | if ((a.Item2 <= 0 && b.Item2 <= 0) || (a.Item2 >= 0 && b.Item2 >= 0))
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| 278 | area += dist * (Math.Abs(a.Item2) + Math.Abs(b.Item2)) / 2.0;
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| 279 | else {
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| 280 | var k = (b.Item2 - a.Item2) / dist;
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| 281 | var d = a.Item2;
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| 282 | var x = -d / k;
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| 283 | area += Math.Abs(x * a.Item2 / 2.0);
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| 284 | area += Math.Abs((dist - x) * b.Item2 / 2.0);
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| 285 | }
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| 286 | return area;
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| 287 | }
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| 288 |
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[14690] | 289 | // Center-of-Mass
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[14429] | 290 | private static double ComBelowZero(IEnumerable<Tuple<Permutation, double>> path) {
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| 291 | var area = 0.0;
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| 292 | var com = 0.0;
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| 293 | var nwalkDist = 0.0;
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| 294 | Tuple<Permutation, double> prev = null;
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| 295 | var iter = path.GetEnumerator();
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| 296 | while (iter.MoveNext()) {
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| 297 | var c = iter.Current;
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| 298 | if (prev != null) {
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| 299 | var ndist = Dist(prev.Item1, c.Item1) / (double)c.Item1.Length;
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| 300 | nwalkDist += ndist;
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| 301 | if (prev.Item2 < 0 || c.Item2 < 0) {
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| 302 | var a = TrapezoidArea(prev, c) / (double)c.Item1.Length;
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| 303 | area += a;
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| 304 | com += (nwalkDist - (ndist / 2.0)) * a;
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| 305 | }
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| 306 | }
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| 307 | prev = c;
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| 308 | }
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| 309 | return com / area;
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| 310 | }
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| 311 |
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| 312 | private static IEnumerable<Tuple<Permutation, double>> ApproximateDerivative(IEnumerable<Tuple<Permutation, double>> data) {
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| 313 | Tuple<Permutation, double> prev = null, prev2 = null;
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| 314 | foreach (var d in data) {
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| 315 | if (prev == null) {
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| 316 | prev = d;
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| 317 | continue;
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| 318 | }
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| 319 | if (prev2 == null) {
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| 320 | prev2 = prev;
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| 321 | prev = d;
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| 322 | continue;
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| 323 | }
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| 324 | var dist = Dist(prev2.Item1, d.Item1);
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| 325 | yield return Tuple.Create(prev.Item1, (d.Item2 - prev2.Item2) / (double)dist);
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| 326 | prev2 = prev;
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| 327 | prev = d;
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| 328 | }
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| 329 | }
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| 330 |
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[13861] | 331 | private static double Dist(Permutation a, Permutation b) {
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[14429] | 332 | var dist = 0;
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| 333 | for (var i = 0; i < a.Length; i++)
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| 334 | if (a[i] != b[i]) dist++;
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| 335 | return dist;
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[13861] | 336 | }
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| 337 |
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| 338 | private static int[] GetInverse(Permutation p) {
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| 339 | var inv = new int[p.Length];
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[14429] | 340 | for (var i = 0; i < p.Length; i++) {
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| 341 | inv[p[i]] = i;
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| 342 | }
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[13861] | 343 | return inv;
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| 344 | }
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| 345 |
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[14429] | 346 | // permutation must be strictly different in every position
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| 347 | private static void BiasedShuffle(Permutation p, IRandom random) {
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| 348 | for (var i = p.Length - 1; i > 0; i--) {
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| 349 | // Swap element "i" with a random earlier element (excluding itself)
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| 350 | var swapIndex = random.Next(i);
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| 351 | var h = p[swapIndex];
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| 352 | p[swapIndex] = p[i];
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| 353 | p[i] = h;
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| 354 | }
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[13861] | 355 | }
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| 356 | }
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| 357 | }
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