#region License Information
/* HeuristicLab
* Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
namespace HeuristicLab.Encodings.RealVectorEncoding {
[Item("StochasticNormalMultiMoveGenerator", "Generates normal distributed moves from a given real vector.")]
[StorableClass]
public class StochasticNormalMultiMoveGenerator : AdditiveMoveGenerator, IMultiMoveGenerator {
public IValueLookupParameter SigmaParameter {
get { return (IValueLookupParameter)Parameters["Sigma"]; }
}
public IValueLookupParameter SampleSizeParameter {
get { return (IValueLookupParameter)Parameters["SampleSize"]; }
}
public StochasticNormalMultiMoveGenerator()
: base() {
Parameters.Add(new ValueLookupParameter("Sigma", "The standard deviation of the normal distribution.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter("SampleSize", "The number of moves that should be generated."));
}
public static AdditiveMove[] Apply(IRandom random, RealVector realVector, double sigma, int sampleSize) {
AdditiveMove[] moves = new AdditiveMove[sampleSize];
NormalDistributedRandom N = new NormalDistributedRandom(random, 0, sigma);
for (int i = 0; i < sampleSize; i++) {
int index = random.Next(realVector.Length);
moves[i] = new AdditiveMove(index, N.NextDouble());
}
return moves;
}
protected override AdditiveMove[] GenerateMoves(IRandom random, RealVector realVector) {
return Apply(random, realVector, SigmaParameter.ActualValue.Value, SampleSizeParameter.ActualValue.Value);
}
}
}