[3197] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[7259] | 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3197] | 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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[4722] | 22 | using HeuristicLab.Common;
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[3197] | 23 | using HeuristicLab.Core;
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[4068] | 24 | using HeuristicLab.Data;
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[3197] | 25 | using HeuristicLab.Optimization;
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| 26 | using HeuristicLab.Parameters;
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[4068] | 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[3197] | 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 | [StorableClass]
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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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[4722] | 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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[3197] | 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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[4722] | 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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[5381] | 54 | public static AdditiveMove[] Apply(IRandom random, RealVector vector, double sigma, int sampleSize, DoubleMatrix bounds) {
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[3197] | 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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[5381] | 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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[3197] | 64 | }
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| 65 | return moves;
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| 66 | }
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| 67 |
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[5381] | 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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[3197] | 70 | }
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| 71 | }
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| 72 | }
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