1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022019 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 


22  using System;


23  using HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Optimization;


27  using HeuristicLab.Parameters;


28  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


29  using HeuristicLab.Random;


30 


31  namespace HeuristicLab.Encodings.RealVectorEncoding {


32  /// <summary>


33  /// An operator which creates a new random real vector with each element normally distributed in a specified range.


34  /// </summary>


35  [Item("NormalDistributedRealVectorCreator", "An operator which creates a new random real vector with each element normally distributed in a specified range.")]


36  [StorableClass]


37  public class NormalDistributedRealVectorCreator : RealVectorCreator, IStrategyParameterCreator {


38 


39  public IValueLookupParameter<RealVector> MeanParameter {


40  get { return (IValueLookupParameter<RealVector>)Parameters["Mean"]; }


41  }


42 


43  public IValueLookupParameter<DoubleArray> SigmaParameter {


44  get { return (IValueLookupParameter<DoubleArray>)Parameters["Sigma"]; }


45  }


46 


47  public IValueParameter<IntValue> MaximumTriesParameter {


48  get { return (IValueParameter<IntValue>)Parameters["MaximumTries"]; }


49  }


50 


51  [StorableConstructor]


52  protected NormalDistributedRealVectorCreator(bool deserializing) : base(deserializing) { }


53  protected NormalDistributedRealVectorCreator(NormalDistributedRealVectorCreator original, Cloner cloner) : base(original, cloner) { }


54  public NormalDistributedRealVectorCreator()


55  : base() {


56  Parameters.Add(new ValueLookupParameter<RealVector>("Mean", "The mean vector around which the points will be sampled."));


57  Parameters.Add(new ValueLookupParameter<DoubleArray>("Sigma", "The standard deviations for all or for each dimension."));


58  Parameters.Add(new ValueParameter<IntValue>("MaximumTries", "The maximum number of tries to sample within the specified bounds.", new IntValue(1000)));


59  }


60 


61  public override IDeepCloneable Clone(Cloner cloner) {


62  return new NormalDistributedRealVectorCreator(this, cloner);


63  }


64 


65  [StorableHook(HookType.AfterDeserialization)]


66  private void AfterDeserialization() {


67  if (!Parameters.ContainsKey("MaximumTries"))


68  Parameters.Add(new ValueParameter<IntValue>("MaximumTries", "The maximum number of tries to sample within the specified bounds.", new IntValue(1000)));


69  }


70 


71  /// <summary>


72  /// Generates a new random real vector normally distributed around the given mean with the given <paramref name="length"/> and in the interval [min,max).


73  /// </summary>


74  /// <exception cref="ArgumentException">


75  /// Thrown when <paramref name="random"/> is null.<br />


76  /// Thrown when <paramref name="mean"/> is null or of length 0.<br />


77  /// Thrown when <paramref name="sigma"/> is null or of length 0.<br />


78  /// </exception>


79  /// <remarks>


80  /// If no bounds are given the bounds will be set to (double.MinValue;double.MaxValue).


81  ///


82  /// If dimensions of the mean do not lie within the given bounds they're set to either to the min or max of the bounds depending on whether the given dimension


83  /// for the mean is smaller or larger than the bounds. If min and max for a certain dimension are almost the same the resulting value will be set to min.


84  ///


85  /// However, please consider that such static bounds are not really meaningful to optimize.


86  ///


87  /// The sigma vector can contain 0 values in which case the dimension will be exactly the same as the given mean.


88  /// </remarks>


89  /// <param name="random">The random number generator.</param>


90  /// <param name="means">The mean vector around which the resulting vector is sampled.</param>


91  /// <param name="sigmas">The vector of standard deviations, must have at least one row.</param>


92  /// <param name="bounds">The lower and upper bound (1st and 2nd column) of the positions in the vector. If there are less rows than dimensions, the rows are cycled.</param>


93  /// <param name="maximumTries">The maximum number of tries to sample a value inside the bounds for each dimension. If a valid value cannot be obtained, the mean will be used.</param>


94  /// <returns>The newly created real vector.</returns>


95  public static RealVector Apply(IntValue lengthValue, IRandom random, RealVector means, DoubleArray sigmas, DoubleMatrix bounds, int maximumTries = 1000) {


96  if (lengthValue == null  lengthValue.Value == 0) throw new ArgumentException("Length is not defined or zero");


97  if (random == null) throw new ArgumentNullException("Random is not defined", "random");


98  if (means == null  means.Length == 0) throw new ArgumentNullException("Mean is not defined", "mean");


99  if (sigmas == null  sigmas.Length == 0) throw new ArgumentNullException("Sigma is not defined.", "sigma");


100  if (bounds == null  bounds.Rows == 0) bounds = new DoubleMatrix(new[,] { { double.MinValue, double.MaxValue } });


101  var length = lengthValue.Value;


102  var nd = new NormalDistributedRandom(random, 0, 1);


103  var result = new RealVector(length);


104  for (int i = 0; i < result.Length; i++) {


105  var min = bounds[i % bounds.Rows, 0];


106  var max = bounds[i % bounds.Rows, 1];


107  var mean = means[i % means.Length];


108  var sigma = sigmas[i % sigmas.Length];


109  if (min.IsAlmost(max)  mean < min) result[i] = min;


110  else if (mean > max) result[i] = max;


111  else {


112  int count = 0;


113  bool inRange;


114  do {


115  result[i] = mean + sigma * nd.NextDouble();


116  inRange = result[i] >= min && result[i] < max;


117  count++;


118  } while (count < maximumTries && !inRange);


119  if (count == maximumTries && !inRange)


120  result[i] = mean;


121  }


122  }


123  return result;


124  }


125 


126  /// <summary>


127  /// Forwards the call to <see cref="Apply(IRandom, RealVector, DoubleArray, DoubleMatrix)"/>.


128  /// </summary>


129  /// <param name="random">The pseudo random number generator to use.</param>


130  /// <param name="length">The length of the real vector.</param>


131  /// <param name="bounds">The lower and upper bound (1st and 2nd column) of the positions in the vector. If there are less rows than dimensions, the rows are cycled.</param>


132  /// <returns>The newly created real vector.</returns>


133  protected override RealVector Create(IRandom random, IntValue length, DoubleMatrix bounds) {


134  return Apply(length, random, MeanParameter.ActualValue, SigmaParameter.ActualValue, bounds, MaximumTriesParameter.Value.Value);


135  }


136  }


137  }

