source: branches/2520_PersistenceReintegration/HeuristicLab.Problems.TestFunctions.MultiObjective/3.3/Calculators/Crowding.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 2 years ago

#2520: updated year of copyrights

File size: 4.1 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 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 System;
23using System.Collections.Generic;
24using System.Linq;
25
26namespace HeuristicLab.Problems.TestFunctions.MultiObjective {
27
28  /// <summary>
29  /// Crowding distance d(x,A) is usually defined between a point x and a set of points A
30  /// d(x,A) is then a weighted sum over all dimensions where for each dimension the next larger and the next smaller Point to x are subtracted
31  /// I extended the concept and defined the Crowding distance of a front A as the mean of the crowding distances of every point x in A
32  /// C(A) = mean(d(x,A)) where x in A  and d(x,A) is not infinite
33  /// Beware that Crowding is not normalized for the number of dimensions. A higher number of dimensions normlly indicated higher expected Crowding values
34  /// </summary>
35  public static class Crowding {
36
37    public static double Calculate(IEnumerable<double[]> front, double[,] bounds) {
38      return GetForFront(front, bounds).Where(d => !double.IsPositiveInfinity(d)).DefaultIfEmpty(double.PositiveInfinity).Average();
39    }
40
41    public static IEnumerable<double> GetForFront(IEnumerable<double[]> front, double[,] bounds) {
42      if (front == null) throw new ArgumentException("Fronts must not be null");
43      if (!front.Any()) throw new ArgumentException("Fronts must not be empty");
44      if (bounds == null) throw new ArgumentException("Bounds must not be null");
45      double[] pointsums = new double[front.Count()];
46
47      for (int dim = 0; dim < front.First().Length; dim++) {
48
49        double[] arr = front.Select(x => x[dim]).ToArray();
50        Array.Sort(arr);
51
52        double fmax = bounds[dim % bounds.GetLength(0), 1];
53        double fmin = bounds[dim % bounds.GetLength(0), 0];
54
55        int pointIdx = 0;
56        foreach (double[] point in front) {
57          double d = 0;
58          int pos = Array.BinarySearch(arr, point[dim]);
59          if (pos != 0 && pos != arr.Count() - 1) {
60            d = (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin);
61            pointsums[pointIdx++] += d;
62          } else {
63            pointsums[pointIdx++] = Double.PositiveInfinity;
64          }
65        }
66      }
67      return pointsums;
68    }
69
70    public static double GetForSinglePoints(IEnumerable<double[]> front, double[,] bounds, int pointIndex) {
71      if (front == null) throw new ArgumentException("Fronts must not be null");
72      if (!front.Any()) throw new ArgumentException("Fronts must not be empty");
73      if (bounds == null) throw new ArgumentException("Bounds must not be null");
74      if (pointIndex < 0 || front.Count() <= pointIndex) throw new ArgumentException("PointIndex is not valid ");
75      double pointsum = 0;
76      double[] point = front.ElementAt(pointIndex);
77      for (int dim = 0; dim < front.First().Length; dim++) {
78
79        double[] arr = front.Select(x => x[dim]).ToArray();
80        Array.Sort(arr);
81
82        double fmax = bounds[dim % bounds.GetLength(0), 1];
83        double fmin = bounds[dim % bounds.GetLength(0), 0];
84
85        int pointIdx = pointIndex;
86
87        int pos = Array.BinarySearch(arr, point[dim]);
88        if (pos != 0 && pos != arr.Count() - 1) {
89          double d = (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin);
90          pointsum += d;
91        }
92
93      }
94      return pointsum;
95    }
96
97  }
98}
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