[13672] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16565] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[13672] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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[13562] | 23 | using System.Collections.Generic;
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[13620] | 24 | using System.Linq;
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[13562] | 25 |
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[14111] | 26 | namespace HeuristicLab.Problems.TestFunctions.MultiObjective {
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[13562] | 27 |
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[13988] | 28 | /// <summary>
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| 29 | /// Crowding distance d(x,A) is usually defined between a point x and a set of points A
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| 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
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| 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
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[14030] | 32 | /// C(A) = mean(d(x,A)) where x in A and d(x,A) is not infinite
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| 33 | /// Beware that Crowding is not normalized for the number of dimensions. A higher number of dimensions normlly indicated higher expected Crowding values
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[13988] | 34 | /// </summary>
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[14018] | 35 | public static class Crowding {
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[13620] | 36 |
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[13988] | 37 | public static double Calculate(IEnumerable<double[]> front, double[,] bounds) {
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[14030] | 38 | return GetForFront(front, bounds).Where(d => !double.IsPositiveInfinity(d)).DefaultIfEmpty(double.PositiveInfinity).Average();
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| 39 | }
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[14018] | 40 |
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[14030] | 41 | public static IEnumerable<double> GetForFront(IEnumerable<double[]> front, double[,] bounds) {
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| 42 | if (front == null) throw new ArgumentException("Fronts must not be null");
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| 43 | if (!front.Any()) throw new ArgumentException("Fronts must not be empty");
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| 44 | if (bounds == null) throw new ArgumentException("Bounds must not be null");
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[13988] | 45 | double[] pointsums = new double[front.Count()];
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[13620] | 46 |
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[13988] | 47 | for (int dim = 0; dim < front.First().Length; dim++) {
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[14018] | 48 |
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[13988] | 49 | double[] arr = front.Select(x => x[dim]).ToArray();
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| 50 | Array.Sort(arr);
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[14018] | 51 |
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[13988] | 52 | double fmax = bounds[dim % bounds.GetLength(0), 1];
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| 53 | double fmin = bounds[dim % bounds.GetLength(0), 0];
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[14018] | 54 |
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[13988] | 55 | int pointIdx = 0;
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| 56 | foreach (double[] point in front) {
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| 57 | double d = 0;
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| 58 | int pos = Array.BinarySearch(arr, point[dim]);
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| 59 | if (pos != 0 && pos != arr.Count() - 1) {
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| 60 | d = (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin);
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| 61 | pointsums[pointIdx++] += d;
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[14030] | 62 | } else {
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| 63 | pointsums[pointIdx++] = Double.PositiveInfinity;
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[13988] | 64 | }
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| 65 | }
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| 66 | }
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[14030] | 67 | return pointsums;
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| 68 | }
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[13936] | 69 |
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[14030] | 70 | public static double GetForSinglePoints(IEnumerable<double[]> front, double[,] bounds, int pointIndex) {
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| 71 | if (front == null) throw new ArgumentException("Fronts must not be null");
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| 72 | if (!front.Any()) throw new ArgumentException("Fronts must not be empty");
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| 73 | if (bounds == null) throw new ArgumentException("Bounds must not be null");
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| 74 | if (pointIndex < 0 || front.Count() <= pointIndex) throw new ArgumentException("PointIndex is not valid ");
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| 75 | double pointsum = 0;
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| 76 | double[] point = front.ElementAt(pointIndex);
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| 77 | for (int dim = 0; dim < front.First().Length; dim++) {
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| 78 |
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| 79 | double[] arr = front.Select(x => x[dim]).ToArray();
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| 80 | Array.Sort(arr);
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| 81 |
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| 82 | double fmax = bounds[dim % bounds.GetLength(0), 1];
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| 83 | double fmin = bounds[dim % bounds.GetLength(0), 0];
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| 84 |
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| 85 | int pointIdx = pointIndex;
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| 86 |
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| 87 | int pos = Array.BinarySearch(arr, point[dim]);
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| 88 | if (pos != 0 && pos != arr.Count() - 1) {
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| 89 | double d = (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin);
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| 90 | pointsum += d;
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[13672] | 91 | }
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[14030] | 92 |
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[13988] | 93 | }
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[14030] | 94 | return pointsum;
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[13988] | 95 | }
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[13562] | 96 |
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[13988] | 97 | }
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[13562] | 98 | }
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