[9129] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2013 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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[9303] | 22 | using System;
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[9129] | 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Encodings.RealVectorEncoding;
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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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[9291] | 31 | using HeuristicLab.Random;
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[9129] | 32 |
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| 33 | namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
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| 34 | [Item("CMAMutator", "Mutates the solution vector according to the CMA-ES scheme.")]
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| 35 | [StorableClass]
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| 36 | public sealed class CMAMutator : SingleSuccessorOperator, IStochasticOperator, ICMAManipulator, IIterationBasedOperator {
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| 37 |
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| 38 | public Type CMAType {
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| 39 | get { return typeof(CMAParameters); }
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| 40 | }
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| 41 |
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| 42 | #region Parameter Properties
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| 43 | public ILookupParameter<IRandom> RandomParameter {
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| 44 | get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
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| 45 | }
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| 46 |
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| 47 | public ILookupParameter<IntValue> PopulationSizeParameter {
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| 48 | get { return (ILookupParameter<IntValue>)Parameters["PopulationSize"]; }
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| 49 | }
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| 50 |
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| 51 | public ILookupParameter<IntValue> IterationsParameter {
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| 52 | get { return (ILookupParameter<IntValue>)Parameters["Iterations"]; }
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| 53 | }
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| 54 |
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| 55 | public IValueLookupParameter<IntValue> MaximumIterationsParameter {
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| 56 | get { return (IValueLookupParameter<IntValue>)Parameters["MaximumIterations"]; }
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| 57 | }
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| 58 |
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| 59 | public ILookupParameter<RealVector> MeanParameter {
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| 60 | get { return (ILookupParameter<RealVector>)Parameters["Mean"]; }
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| 61 | }
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| 62 |
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| 63 | public IScopeTreeLookupParameter<RealVector> RealVectorParameter {
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| 64 | get { return (IScopeTreeLookupParameter<RealVector>)Parameters["RealVector"]; }
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| 65 | }
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| 66 |
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| 67 | public IValueLookupParameter<DoubleMatrix> BoundsParameter {
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| 68 | get { return (IValueLookupParameter<DoubleMatrix>)Parameters["Bounds"]; }
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| 69 | }
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| 70 |
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| 71 | public ILookupParameter<CMAParameters> StrategyParametersParameter {
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| 72 | get { return (ILookupParameter<CMAParameters>)Parameters["StrategyParameters"]; }
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| 73 | }
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| 74 |
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| 75 | public IValueParameter<IntValue> MaxTriesParameter {
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| 76 | get { return (IValueParameter<IntValue>)Parameters["MaxTries"]; }
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| 77 | }
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| 78 | #endregion
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| 79 |
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| 80 | [StorableConstructor]
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| 81 | private CMAMutator(bool deserializing) : base(deserializing) { }
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| 82 | private CMAMutator(CMAMutator original, Cloner cloner) : base(original, cloner) { }
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| 83 | public CMAMutator()
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| 84 | : base() {
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| 85 | Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
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| 86 | Parameters.Add(new LookupParameter<IntValue>("PopulationSize", "The population size (lambda) determines how many offspring should be created."));
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| 87 | Parameters.Add(new LookupParameter<IntValue>("Iterations", "The current iteration that is being processed."));
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| 88 | Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of iterations to be processed."));
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| 89 | Parameters.Add(new LookupParameter<RealVector>("Mean", "The current mean solution."));
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| 90 | Parameters.Add(new ScopeTreeLookupParameter<RealVector>("RealVector", "The solution vector of real values."));
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| 91 | Parameters.Add(new ValueLookupParameter<DoubleMatrix>("Bounds", "The bounds for the dimensions."));
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| 92 | Parameters.Add(new LookupParameter<CMAParameters>("StrategyParameters", "The CMA-ES strategy parameters used for mutation."));
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| 93 | Parameters.Add(new ValueParameter<IntValue>("MaxTries", "The maximum number of tries a mutation should be performed if it was outside the bounds.", new IntValue(1000)));
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| 94 | }
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| 95 |
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| 96 | public override IDeepCloneable Clone(Cloner cloner) {
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| 97 | return new CMAMutator(this, cloner);
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| 98 | }
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| 99 |
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| 100 | public override IOperation Apply() {
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| 101 | var maxTries = MaxTriesParameter.Value.Value;
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| 102 | var random = RandomParameter.ActualValue;
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| 103 | var lambda = PopulationSizeParameter.ActualValue.Value;
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| 104 | var xmean = MeanParameter.ActualValue;
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| 105 | var arx = RealVectorParameter.ActualValue;
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| 106 | var sp = StrategyParametersParameter.ActualValue;
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| 107 | var iterations = IterationsParameter.ActualValue.Value;
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[9297] | 108 | var initialIterations = sp.InitialIterations;
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[9129] | 109 | var bounds = BoundsParameter.ActualValue;
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| 110 |
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| 111 | if (arx == null || arx.Length == 0) {
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| 112 | arx = new ItemArray<RealVector>(lambda);
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| 113 | for (int i = 0; i < lambda; i++) arx[i] = new RealVector(xmean.Length);
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| 114 | RealVectorParameter.ActualValue = arx;
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| 115 | }
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[9291] | 116 | var nd = new NormalDistributedRandom(random, 0, 1);
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[9129] | 117 |
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[9303] | 118 | var length = arx[0].Length;
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| 119 |
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[9129] | 120 | for (int i = 0; i < lambda; i++) {
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| 121 | int tries = 0;
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[9199] | 122 | bool inRange;
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[9129] | 123 | if (initialIterations > iterations) {
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[9303] | 124 | for (int k = 0; k < length; k++) {
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[9129] | 125 | do {
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[9297] | 126 | arx[i][k] = xmean[k] + sp.Sigma * sp.D[k] * nd.NextDouble();
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[9245] | 127 | inRange = bounds[k % bounds.Rows, 0] <= arx[i][k] && arx[i][k] <= bounds[k % bounds.Rows, 1];
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[9199] | 128 | if (!inRange) tries++;
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| 129 | } while (!inRange && tries < maxTries);
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| 130 | if (!inRange && maxTries > 1) {
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| 131 | if (bounds[k % bounds.Rows, 0] > arx[i][k]) arx[i][k] = bounds[k % bounds.Rows, 0];
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[9244] | 132 | else if (bounds[k % bounds.Rows, 1] < arx[i][k]) arx[i][k] = bounds[k % bounds.Rows, 1];
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[9199] | 133 | }
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[9129] | 134 | }
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| 135 | } else {
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| 136 | var B = sp.B;
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| 137 | do {
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| 138 | tries++;
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| 139 | inRange = true;
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[9303] | 140 | var artmp = new double[length];
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| 141 | for (int k = 0; k < length; ++k) {
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[9297] | 142 | artmp[k] = sp.D[k] * nd.NextDouble();
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[9148] | 143 | }
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[9129] | 144 |
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[9303] | 145 | for (int k = 0; k < length; k++) {
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[9129] | 146 | var sum = 0.0;
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[9303] | 147 | for (int j = 0; j < length; j++)
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[9129] | 148 | sum += B[k, j] * artmp[j];
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[9297] | 149 | arx[i][k] = xmean[k] + sp.Sigma * sum; // m + sig * Normal(0,C)
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[9129] | 150 | if (bounds[k % bounds.Rows, 0] > arx[i][k] || arx[i][k] > bounds[k % bounds.Rows, 1])
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| 151 | inRange = false;
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| 152 | }
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| 153 | } while (!inRange && tries < maxTries);
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[9199] | 154 | if (!inRange && maxTries > 1) {
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[9303] | 155 | for (int k = 0; k < length; k++) {
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[9199] | 156 | if (bounds[k % bounds.Rows, 0] > arx[i][k]) arx[i][k] = bounds[k % bounds.Rows, 0];
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[9244] | 157 | else if (bounds[k % bounds.Rows, 1] < arx[i][k]) arx[i][k] = bounds[k % bounds.Rows, 1];
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[9199] | 158 | }
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| 159 | }
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[9129] | 160 | }
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| 161 | }
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| 162 | return base.Apply();
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| 163 | }
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| 164 | }
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| 165 | } |
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