[4420] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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.Linq;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Encodings.PermutationEncoding;
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| 27 | using HeuristicLab.Operators;
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| 28 | using HeuristicLab.Optimization;
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| 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 |
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| 32 | namespace HeuristicLab.Problems.TravelingSalesman {
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| 33 | /// <summary>
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| 34 | /// An operator for analyzing the diversity of a population of solutions for a Traveling Salesman Problems given in path representation using city coordinates.
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| 35 | /// </summary>
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| 36 | [Item("TSPPopulationDiversityAnalyzer", "An operator for analyzing the diversity of a population of solutions for a Traveling Salesman Problems given in path representation using city coordinates.")]
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| 37 | [StorableClass]
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| 38 | public sealed class TSPPopulationDiversityAnalyzer : SingleSuccessorOperator, IAnalyzer {
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| 39 |
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| 40 | // TODO:
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| 41 | // - iterations sampling
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| 42 | // - view
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| 43 |
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| 44 | public ScopeTreeLookupParameter<Permutation> PermutationParameter {
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| 45 | get { return (ScopeTreeLookupParameter<Permutation>)Parameters["Permutation"]; }
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| 46 | }
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| 47 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 48 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 49 | }
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[4432] | 50 | public ValueParameter<BoolValue> StoreCompleteHistoryParameter {
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| 51 | get { return (ValueParameter<BoolValue>)Parameters["StoreCompleteHistory"]; }
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[4420] | 52 | }
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[4432] | 53 | public ValueParameter<ItemList<DoubleMatrix>> SimilaritiesParameter {
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| 54 | get { return (ValueParameter<ItemList<DoubleMatrix>>)Parameters["Similarities"]; }
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[4420] | 55 | }
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[4432] | 56 | public ValueParameter<ItemList<DoubleArray>> MaximumSimilaritiesParameter {
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| 57 | get { return (ValueParameter<ItemList<DoubleArray>>)Parameters["MaximumSimilarities"]; }
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[4420] | 58 | }
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[4432] | 59 | public ValueParameter<ItemList<DoubleValue>> AverageMaximumSimilaritiesParameter {
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| 60 | get { return (ValueParameter<ItemList<DoubleValue>>)Parameters["AverageMaximumSimilarities"]; }
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[4420] | 61 | }
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[4432] | 62 | public ValueParameter<ItemList<DoubleValue>> AverageSimilaritiesParameter {
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| 63 | get { return (ValueParameter<ItemList<DoubleValue>>)Parameters["AverageSimilarities"]; }
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| 64 | }
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[4420] | 65 |
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| 66 | public TSPPopulationDiversityAnalyzer()
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| 67 | : base() {
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| 68 | Parameters.Add(new ScopeTreeLookupParameter<Permutation>("Permutation", "The TSP solutions given in path representation from which the best solution should be analyzed."));
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| 69 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the TSP solutions which should be analyzed."));
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[4432] | 70 | Parameters.Add(new ValueParameter<BoolValue>("StoreCompleteHistory", "Flag that denotes whether the complete history of similarity values shall be stored.", new BoolValue(true)));
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| 71 | Parameters.Add(new ValueParameter<ItemList<DoubleMatrix>>("Similarities", "The similarities of the TSP solutions which should be analyzed."));
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| 72 | Parameters.Add(new ValueParameter<ItemList<DoubleArray>>("MaximumSimilarities", "The maximum similarities of the TSP solutions which should be analyzed."));
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| 73 | Parameters.Add(new ValueParameter<ItemList<DoubleValue>>("AverageMaximumSimilarities", "The average maximum similarities of the TSP solutions which should be analyzed."));
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| 74 | Parameters.Add(new ValueParameter<ItemList<DoubleValue>>("AverageSimilarities", "The average similarities of the TSP solutions which should be analyzed."));
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[4420] | 75 | }
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| 76 |
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| 77 | public override IOperation Apply() {
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| 78 |
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[4432] | 79 | #region testing
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| 80 | /*
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| 81 | Permutation permutationA = new Permutation(PermutationTypes.Absolute, new int[] { 0, 5, 4, 3, 2, 1 });
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| 82 | Permutation permutationB = new Permutation(PermutationTypes.Absolute, new int[] { 0, 3, 2, 4, 5, 1 });
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| 83 | Permutation permutationC = new Permutation(PermutationTypes.Absolute, new int[] { 3, 2, 4, 5, 1, 0 });
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| 84 | Permutation permutationD = new Permutation(PermutationTypes.Absolute, new int[] { 3, 2, 4, 5, 0, 1 });
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| 85 | int[] edgesA = CalculateEdgesVector(permutationA);
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| 86 | int[] edgesB = CalculateEdgesVector(permutationB);
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| 87 | int[] edgesC = CalculateEdgesVector(permutationC);
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| 88 | int[] edgesD = CalculateEdgesVector(permutationD);
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| 89 | double s = CalculateSimilarity(edgesA, edgesB);
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| 90 | s = CalculateSimilarity(edgesA, edgesA);
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| 91 | s = CalculateSimilarity(edgesB, edgesB);
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| 92 | s = CalculateSimilarity(edgesC, edgesC);
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| 93 | s = CalculateSimilarity(edgesD, edgesD);
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| 94 | s = CalculateSimilarity(edgesB, edgesC);
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| 95 | s = CalculateSimilarity(edgesC, edgesD);
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| 96 | */
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| 97 | #endregion
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| 98 |
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[4420] | 99 | ItemArray<Permutation> permutations = PermutationParameter.ActualValue;
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| 100 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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| 101 | Permutation[] permutationsArray = permutations.ToArray();
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| 102 | DoubleValue[] qualitiesArray = qualities.ToArray();
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| 103 | int cities = permutationsArray.Length;
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| 104 | #region sort permutations array
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| 105 | for (int i = 0; i < cities; i++) {
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| 106 | int minIndex = i;
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| 107 | for (int j = i + 1; j < cities; j++) {
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| 108 | if (qualitiesArray[j].Value < qualitiesArray[minIndex].Value)
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| 109 | minIndex = j;
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| 110 | }
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| 111 | if (minIndex != i) {
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| 112 | Permutation p = permutationsArray[i];
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| 113 | permutationsArray[i] = permutationsArray[minIndex];
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| 114 | permutationsArray[minIndex] = p;
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| 115 | DoubleValue d = qualitiesArray[i];
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| 116 | qualitiesArray[i] = qualitiesArray[minIndex];
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| 117 | qualitiesArray[minIndex] = d;
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| 118 | }
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| 119 | }
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| 120 | #endregion
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| 121 |
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[4432] | 122 | int[][] edges = new int[cities][];
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| 123 | for (int i = 0; i < cities; i++)
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| 124 | edges[i] = CalculateEdgesVector(permutationsArray[i]);
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| 125 |
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[4420] | 126 | DoubleMatrix similarities = new DoubleMatrix(cities, cities);
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| 127 | DoubleArray maxSimilarities = new DoubleArray(cities);
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| 128 | double avgSimilarity = 0;
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| 129 | int n = 0;
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| 130 | for (int i = 0; i < cities; i++) {
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| 131 | similarities[i, i] = 1;
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[4432] | 132 | for (int j = (i + 1); j < cities; j++) {
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| 133 | double similarity = CalculateSimilarity(edges[i], edges[j]);
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[4420] | 134 | avgSimilarity += similarity;
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| 135 | n++;
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| 136 | similarities[i, j] = similarity;
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| 137 | similarities[j, i] = similarity;
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| 138 | if (maxSimilarities[i] < similarity)
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| 139 | maxSimilarities[i] = similarity;
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| 140 | if (maxSimilarities[j] < similarity)
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| 141 | maxSimilarities[j] = similarity;
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| 142 | }
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| 143 | }
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| 144 | DoubleValue averageMaximumSimilarity = new DoubleValue(maxSimilarities.Average());
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| 145 | DoubleValue averageSimilarity = new DoubleValue(avgSimilarity / n);
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| 146 |
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[4432] | 147 | if (SimilaritiesParameter.Value == null) {
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| 148 | SimilaritiesParameter.Value = new ItemList<DoubleMatrix>();
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| 149 | MaximumSimilaritiesParameter.Value = new ItemList<DoubleArray>();
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| 150 | AverageMaximumSimilaritiesParameter.Value = new ItemList<DoubleValue>();
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| 151 | AverageSimilaritiesParameter.Value = new ItemList<DoubleValue>();
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[4420] | 152 | }
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[4432] | 153 | if (!StoreCompleteHistoryParameter.Value.Value && SimilaritiesParameter.Value.Count > 0) {
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| 154 | SimilaritiesParameter.Value[SimilaritiesParameter.Value.Count - 1] = null;
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| 155 | MaximumSimilaritiesParameter.Value[MaximumSimilaritiesParameter.Value.Count - 1] = null;
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| 156 | }
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| 157 | SimilaritiesParameter.Value.Add(similarities);
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| 158 | MaximumSimilaritiesParameter.Value.Add(maxSimilarities);
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| 159 | AverageMaximumSimilaritiesParameter.Value.Add(averageMaximumSimilarity);
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| 160 | AverageSimilaritiesParameter.Value.Add(averageSimilarity);
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[4420] | 161 |
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| 162 | return base.Apply();
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| 163 | }
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| 164 |
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[4432] | 165 | private static int[] CalculateEdgesVector(Permutation permutation) {
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| 166 | int cities = permutation.Length;
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| 167 | int[] edgesVector = new int[cities];
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| 168 | for (int i = 0; i < (cities - 1); i++)
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| 169 | edgesVector[permutation[i]] = permutation[i + 1];
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| 170 | edgesVector[permutation[cities - 1]] = permutation[0];
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| 171 | return edgesVector;
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| 172 | }
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[4420] | 173 |
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[4432] | 174 | private double CalculateSimilarity(int[] edgesA, int[] edgesB) {
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| 175 | if (edgesA.Length != edgesB.Length)
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| 176 | throw new InvalidOperationException("ERROR in " + Name + ": Similarity can only be calculated between instances of an equal number of cities");
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| 177 | int cities = edgesA.Length;
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[4420] | 178 | int similarEdges = 0;
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[4432] | 179 | for (int i = 0; i < edgesA.Length; i++) {
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| 180 | if (edgesA[i] == edgesB[i] || edgesA[edgesB[i]] == i)
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[4420] | 181 | similarEdges++;
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| 182 | }
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[4432] | 183 | return (double)(similarEdges) / cities;
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[4420] | 184 | }
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| 185 |
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| 186 | }
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| 187 | }
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