[7789] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16057] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7789] | 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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[8304] | 23 | using System.Linq;
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[13005] | 24 | using System.Threading.Tasks;
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[7789] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[12085] | 27 | using HeuristicLab.Data;
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[8299] | 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[7789] | 29 |
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| 30 | namespace HeuristicLab.Optimization.Operators {
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| 31 | /// <summary>
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[8086] | 32 | /// A base class for items that perform similarity calculation between two solutions.
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[7789] | 33 | /// </summary>
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[8086] | 34 | [Item("SimilarityCalculator", "A base class for items that perform similarity calculation between two solutions.")]
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[8299] | 35 | [StorableClass]
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[8322] | 36 | public abstract class SolutionSimilarityCalculator : Item, ISolutionSimilarityCalculator {
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[12070] | 37 | protected abstract bool IsCommutative { get; }
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| 38 |
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[12085] | 39 | #region Properties
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| 40 | [Storable]
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| 41 | public string SolutionVariableName { get; set; }
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| 42 | [Storable]
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| 43 | public string QualityVariableName { get; set; }
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[13005] | 44 | [Storable]
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| 45 | public bool ExecuteInParallel { get; set; }
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| 46 | [Storable]
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| 47 | public int MaxDegreeOfParallelism { get; set; }
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[12085] | 48 | #endregion
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| 49 |
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[8299] | 50 | [StorableConstructor]
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[8319] | 51 | protected SolutionSimilarityCalculator(bool deserializing) : base(deserializing) { }
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[12085] | 52 |
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| 53 | protected SolutionSimilarityCalculator(SolutionSimilarityCalculator original, Cloner cloner)
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| 54 | : base(original, cloner) {
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[13005] | 55 | SolutionVariableName = original.SolutionVariableName;
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| 56 | QualityVariableName = original.QualityVariableName;
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| 57 | ExecuteInParallel = original.ExecuteInParallel;
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| 58 | MaxDegreeOfParallelism = original.MaxDegreeOfParallelism;
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[12085] | 59 | }
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[7789] | 60 |
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[13005] | 61 | protected SolutionSimilarityCalculator() : base() {
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| 62 | ExecuteInParallel = false;
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| 63 | MaxDegreeOfParallelism = -1;
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| 64 | }
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| 65 |
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| 66 | [StorableHook(HookType.AfterDeserialization)]
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| 67 | private void AfterDeserialization() {
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| 68 | if (MaxDegreeOfParallelism == 0) {
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| 69 | ExecuteInParallel = false;
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| 70 | MaxDegreeOfParallelism = -1;
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| 71 | }
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| 72 | }
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| 73 |
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[8319] | 74 | public double[][] CalculateSolutionCrowdSimilarity(IScope leftSolutionCrowd, IScope rightSolutionCrowd) {
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| 75 | if (leftSolutionCrowd == null || rightSolutionCrowd == null)
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[8304] | 76 | throw new ArgumentException("Cannot calculate similarity because one of the provided crowds or both are null.");
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[7789] | 77 |
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[8319] | 78 | var leftIndividuals = leftSolutionCrowd.SubScopes;
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| 79 | var rightIndividuals = rightSolutionCrowd.SubScopes;
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[8304] | 80 |
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| 81 | if (!leftIndividuals.Any() || !rightIndividuals.Any())
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| 82 | throw new ArgumentException("Cannot calculate similarity because one of the provided crowds or both are empty.");
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| 83 |
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| 84 | var similarityMatrix = new double[leftIndividuals.Count][];
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| 85 | for (int i = 0; i < leftIndividuals.Count; i++) {
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| 86 | similarityMatrix[i] = new double[rightIndividuals.Count];
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| 87 | for (int j = 0; j < rightIndividuals.Count; j++) {
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[8319] | 88 | similarityMatrix[i][j] = CalculateSolutionSimilarity(leftIndividuals[i], rightIndividuals[j]);
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[8304] | 89 | }
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| 90 | }
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| 91 |
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| 92 | return similarityMatrix;
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[7789] | 93 | }
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| 94 |
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[8319] | 95 | public double[][] CalculateSolutionCrowdSimilarity(IScope solutionCrowd) {
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[13005] | 96 | if (solutionCrowd == null) {
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[8319] | 97 | throw new ArgumentException("Cannot calculate similarity because the provided crowd is null.");
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[13005] | 98 | }
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[8319] | 99 | var individuals = solutionCrowd.SubScopes;
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| 100 |
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[13005] | 101 | if (!individuals.Any()) {
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[8319] | 102 | throw new ArgumentException("Cannot calculate similarity because the provided crowd is empty.");
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[13005] | 103 | }
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[8319] | 104 |
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| 105 | var similarityMatrix = new double[individuals.Count][];
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[13005] | 106 | for (int i = 0; i < individuals.Count; i++) {
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| 107 | similarityMatrix[i] = new double[individuals.Count];
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| 108 | }
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[8413] | 109 |
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[13005] | 110 | if (ExecuteInParallel) {
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| 111 | var parallelOptions = new ParallelOptions { MaxDegreeOfParallelism = MaxDegreeOfParallelism };
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| 112 | if (IsCommutative) {
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| 113 | Parallel.For(0, individuals.Count, parallelOptions, i => {
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| 114 | for (int j = i; j < individuals.Count; j++) {
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| 115 | similarityMatrix[i][j] =
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| 116 | similarityMatrix[j][i] = CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 117 | }
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| 118 | });
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| 119 | } else {
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| 120 | Parallel.For(0, individuals.Count, parallelOptions, i => {
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| 121 | for (int j = i; j < individuals.Count; j++) {
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| 122 | similarityMatrix[i][j] = CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 123 | if (i == j) continue;
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| 124 | similarityMatrix[j][i] = CalculateSolutionSimilarity(individuals[j], individuals[i]);
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| 125 | }
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| 126 | });
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[8319] | 127 | }
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[12070] | 128 | } else {
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[13005] | 129 | if (IsCommutative) {
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| 130 | for (int i = 0; i < individuals.Count; i++) {
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| 131 | for (int j = i; j < individuals.Count; j++) {
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| 132 | similarityMatrix[i][j] =
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| 133 | similarityMatrix[j][i] = CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 134 | }
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[12070] | 135 | }
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[13005] | 136 | } else {
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| 137 | for (int i = 0; i < individuals.Count; i++) {
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| 138 | for (int j = i; j < individuals.Count; j++) {
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| 139 | similarityMatrix[i][j] = CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 140 | if (i == j) continue;
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| 141 | similarityMatrix[j][i] = CalculateSolutionSimilarity(individuals[j], individuals[i]);
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| 142 | }
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| 143 | }
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[12070] | 144 | }
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[8319] | 145 | }
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| 146 |
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| 147 | return similarityMatrix;
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| 148 | }
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| 149 |
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| 150 | public abstract double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution);
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[12085] | 151 |
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| 152 | public virtual bool Equals(IScope x, IScope y) {
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| 153 | if (ReferenceEquals(x, y)) return true;
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| 154 | if (x == null || y == null) return false;
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| 155 |
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[12126] | 156 | var q1 = x.Variables[QualityVariableName].Value;
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| 157 | var q2 = y.Variables[QualityVariableName].Value;
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[12085] | 158 |
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| 159 | return CheckQualityEquality(q1, q2) && CalculateSolutionSimilarity(x, y).IsAlmost(1.0);
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| 160 | }
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| 161 |
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| 162 | public virtual int GetHashCode(IScope scope) {
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| 163 | var quality = scope.Variables[QualityVariableName].Value;
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| 164 | var dv = quality as DoubleValue;
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| 165 | if (dv != null)
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| 166 | return dv.Value.GetHashCode();
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| 167 |
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| 168 | var da = quality as DoubleArray;
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| 169 | if (da != null) {
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| 170 | int hash = 17;
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| 171 | unchecked {
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| 172 | for (int i = 0; i < da.Length; ++i) {
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| 173 | hash += hash * 23 + da[i].GetHashCode();
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| 174 | }
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| 175 | return hash;
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| 176 | }
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| 177 | }
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[12126] | 178 | return 0;
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[12085] | 179 | }
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| 180 |
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| 181 | private static bool CheckQualityEquality(IItem q1, IItem q2) {
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| 182 | var d1 = q1 as DoubleValue;
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| 183 | var d2 = q2 as DoubleValue;
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| 184 |
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| 185 | if (d1 != null && d2 != null)
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| 186 | return d1.Value.IsAlmost(d2.Value);
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| 187 |
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| 188 | var da1 = q1 as DoubleArray;
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| 189 | var da2 = q2 as DoubleArray;
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| 190 |
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[12129] | 191 | if (da1 != null && da2 != null) {
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| 192 | if (da1.Length != da2.Length)
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| 193 | throw new ArgumentException("The quality arrays must have the same length.");
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[12085] | 194 |
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[12129] | 195 | for (int i = 0; i < da1.Length; ++i) {
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| 196 | if (!da1[i].IsAlmost(da2[i]))
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| 197 | return false;
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| 198 | }
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| 199 |
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| 200 | return true;
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| 201 | }
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| 202 |
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[12085] | 203 | throw new ArgumentException("Could not determine quality equality.");
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| 204 | }
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[7789] | 205 | }
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| 206 | }
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