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source: branches/HeuristicLab.Analysis.AlgorithmBehavior/HeuristicLab.Analysis.AlgorithmBehavior.Analyzers/3.3/HyperHull.cs @ 10429

Last change on this file since 10429 was 10224, checked in by ascheibe, 11 years ago

#1886

  • added more performance tests
  • tried to mute qhull
File size: 2.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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.Collections.Generic;
23using System.Linq;
24
25
26namespace HeuristicLab.Analysis.AlgorithmBehavior.Analyzers {
27  /// <summary>
28  /// Based on the idea from:
29  /// http://stackoverflow.com/questions/4901959/find-if-a-point-is-inside-a-convex-hull-for-a-set-of-points-without-computing-th
30  /// </summary>
31  public class HyperHull {
32    public static List<double[]> Calculate(double[][] inputs) {
33      int[] labels = new int[inputs.Count()];
34      List<double[]> result = new List<double[]>();
35
36      for (int i = 0; i < inputs.Length; i++) {
37        labels[i] = -1;
38      }
39      labels[0] = 1;
40
41      for (int i = 0; i < labels.Count(); i++) {
42        if (i > 0) {
43          labels[i - 1] = -1;
44          labels[i] = 1;
45        }
46
47        KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new LinearKernel(), inputs[0].Length);
48        SequentialMinimalOptimization smo = new SequentialMinimalOptimization(machine, inputs, labels);
49        smo.Complexity = 1.0;
50        smo.Epsilon = 0.0000000000001;
51        //smo.Tolerance = 0.0001;
52
53        double error = smo.Run();
54
55        bool isConvexHullPoint = true;
56        for (int j = 0; j < inputs.Length; j++) {
57          int decision = System.Math.Sign(machine.Compute(inputs[j]));
58          if (decision != labels[j]) {
59            isConvexHullPoint = false;
60            break;
61          }
62        }
63        if (isConvexHullPoint) {
64          result.Add(inputs[i]);
65        }
66      }
67      return result;
68    }
69  }
70}
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