1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022018 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.Optimization.Operators;


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


27 


28  namespace HeuristicLab.Encodings.RealVectorEncoding {


29  [Item("Euclidean Similarity Calculator for RealVector", "Calculates the solution similarity based on the Euclidean distance and a transformation into (0;1] between two real vectors.")]


30  [StorableClass]


31  public sealed class EuclideanSimilarityCalculator : SingleObjectiveSolutionSimilarityCalculator {


32  protected override bool IsCommutative {


33  get { return true; }


34  }


35 


36  [Storable]


37  private double scaling;


38  /// <summary>


39  /// The higher the scaling, the higher the similarity score for solutions of larger Euclidean distance.


40  /// A value of 1 means no scaling is applied. The function for squashing the numbers into (0;1] is


41  /// 1 / (1 + x / scaling) where x is the Euclidean distance.


42  /// </summary>


43  public double Scaling {


44  get { return scaling; }


45  set { scaling = value; }


46  }


47 


48  [StorableConstructor]


49  private EuclideanSimilarityCalculator(bool deserializing) : base(deserializing) { }


50  private EuclideanSimilarityCalculator(EuclideanSimilarityCalculator original, Cloner cloner)


51  : base(original, cloner) {


52  scaling = original.scaling;


53  }


54  public EuclideanSimilarityCalculator() {


55  scaling = 1;


56  }


57 


58  public override IDeepCloneable Clone(Cloner cloner) {


59  return new EuclideanSimilarityCalculator(this, cloner);


60  }


61 


62  public static double CalculateSimilarity(RealVector left, RealVector right, double scaling = 1.0) {


63  if (left == null  right == null)


64  throw new ArgumentException("Cannot calculate similarity because one or both of the provided solutions is null.");


65  if (left.Length != right.Length)


66  throw new ArgumentException("Cannot calculate similarity because the provided solutions have different lengths.");


67  if (left.Length == 0)


68  throw new ArgumentException("Cannot calculate similarity because solutions are of length 0.");


69  if (scaling <= 0)


70  throw new ArgumentException("Cannot choose a 0 or negative scaling value.");


71  if (ReferenceEquals(left, right)) return 1.0;


72 


73  var distance = 0.0;


74  for (int i = 0; i < left.Length; i++)


75  distance += (left[i]  right[i]) * (left[i]  right[i]);


76  return 1.0 / (1.0 + Math.Sqrt(distance) / scaling);


77 


78  }


79 


80  public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {


81  var left = leftSolution.Variables[SolutionVariableName].Value as RealVector;


82  var right = rightSolution.Variables[SolutionVariableName].Value as RealVector;


83 


84  return CalculateSimilarity(left, right, Scaling);


85  }


86  }


87  }

