1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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.Optimization;
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26 | using HeuristicLab.Parameters;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Random;
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29 |
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30 | namespace HeuristicLab.Encodings.RealVectorEncoding {
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31 | [Item("StochasticNormalMultiMoveGenerator", "Generates normal distributed moves from a given real vector.")]
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32 | [StorableType("F8D689F6-6CF2-4896-9109-E9A6CCDF9AF4")]
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33 | public class StochasticNormalMultiMoveGenerator : AdditiveMoveGenerator, IMultiMoveGenerator {
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34 | public IValueLookupParameter<DoubleValue> SigmaParameter {
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35 | get { return (IValueLookupParameter<DoubleValue>)Parameters["Sigma"]; }
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36 | }
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37 | public IValueLookupParameter<IntValue> SampleSizeParameter {
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38 | get { return (IValueLookupParameter<IntValue>)Parameters["SampleSize"]; }
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39 | }
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40 |
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41 | [StorableConstructor]
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42 | protected StochasticNormalMultiMoveGenerator(bool deserializing) : base(deserializing) { }
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43 | protected StochasticNormalMultiMoveGenerator(StochasticNormalMultiMoveGenerator original, Cloner cloner) : base(original, cloner) { }
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44 | public StochasticNormalMultiMoveGenerator()
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45 | : base() {
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46 | Parameters.Add(new ValueLookupParameter<DoubleValue>("Sigma", "The standard deviation of the normal distribution.", new DoubleValue(1)));
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47 | Parameters.Add(new ValueLookupParameter<IntValue>("SampleSize", "The number of moves that should be generated."));
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48 | }
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49 |
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50 | public override IDeepCloneable Clone(Cloner cloner) {
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51 | return new StochasticNormalMultiMoveGenerator(this, cloner);
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52 | }
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53 |
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54 | public static AdditiveMove[] Apply(IRandom random, RealVector vector, double sigma, int sampleSize, DoubleMatrix bounds) {
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55 | AdditiveMove[] moves = new AdditiveMove[sampleSize];
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56 | NormalDistributedRandom N = new NormalDistributedRandom(random, 0, sigma);
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57 | for (int i = 0; i < sampleSize; i++) {
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58 | int index = random.Next(vector.Length);
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59 | double strength = 0, min = bounds[index % bounds.Rows, 0], max = bounds[index % bounds.Rows, 1];
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60 | do {
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61 | strength = N.NextDouble();
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62 | } while (vector[index] + strength < min || vector[index] + strength > max);
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63 | moves[i] = new AdditiveMove(index, strength);
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64 | }
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65 | return moves;
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66 | }
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67 |
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68 | protected override AdditiveMove[] GenerateMoves(IRandom random, RealVector realVector, DoubleMatrix bounds) {
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69 | return Apply(random, realVector, SigmaParameter.ActualValue.Value, SampleSizeParameter.ActualValue.Value, bounds);
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70 | }
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71 | }
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72 | }
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