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source: trunk/sources/HeuristicLab.Encodings.RealVectorEncoding/3.3/Moves/StochasticNormalMultiMoveGenerator.cs @ 9386

Last change on this file since 9386 was 7259, checked in by swagner, 13 years ago

Updated year of copyrights to 2012 (#1716)

File size: 3.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Optimization;
26using HeuristicLab.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28using HeuristicLab.Random;
29
30namespace HeuristicLab.Encodings.RealVectorEncoding {
31  [Item("StochasticNormalMultiMoveGenerator", "Generates normal distributed moves from a given real vector.")]
32  [StorableClass]
33  public class StochasticNormalMultiMoveGenerator : AdditiveMoveGenerator, IMultiMoveGenerator {
34    public IValueLookupParameter<DoubleValue> SigmaParameter {
35      get { return (IValueLookupParameter<DoubleValue>)Parameters["Sigma"]; }
36    }
37    public IValueLookupParameter<IntValue> SampleSizeParameter {
38      get { return (IValueLookupParameter<IntValue>)Parameters["SampleSize"]; }
39    }
40
41    [StorableConstructor]
42    protected StochasticNormalMultiMoveGenerator(bool deserializing) : base(deserializing) { }
43    protected StochasticNormalMultiMoveGenerator(StochasticNormalMultiMoveGenerator original, Cloner cloner) : base(original, cloner) { }
44    public StochasticNormalMultiMoveGenerator()
45      : base() {
46      Parameters.Add(new ValueLookupParameter<DoubleValue>("Sigma", "The standard deviation of the normal distribution.", new DoubleValue(1)));
47      Parameters.Add(new ValueLookupParameter<IntValue>("SampleSize", "The number of moves that should be generated."));
48    }
49
50    public override IDeepCloneable Clone(Cloner cloner) {
51      return new StochasticNormalMultiMoveGenerator(this, cloner);
52    }
53
54    public static AdditiveMove[] Apply(IRandom random, RealVector vector, double sigma, int sampleSize, DoubleMatrix bounds) {
55      AdditiveMove[] moves = new AdditiveMove[sampleSize];
56      NormalDistributedRandom N = new NormalDistributedRandom(random, 0, sigma);
57      for (int i = 0; i < sampleSize; i++) {
58        int index = random.Next(vector.Length);
59        double strength = 0, min = bounds[index % bounds.Rows, 0], max = bounds[index % bounds.Rows, 1];
60        do {
61          strength = N.NextDouble();
62        } while (vector[index] + strength < min || vector[index] + strength > max);
63        moves[i] = new AdditiveMove(index, strength);
64      }
65      return moves;
66    }
67
68    protected override AdditiveMove[] GenerateMoves(IRandom random, RealVector realVector, DoubleMatrix bounds) {
69      return Apply(random, realVector, SigmaParameter.ActualValue.Value, SampleSizeParameter.ActualValue.Value, bounds);
70    }
71  }
72}
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