source: branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel/Univariate/UnivariateSolutionModel.cs @ 14487

Last change on this file since 14487 was 14487, checked in by sraggl, 3 years ago

#2701 Merged with trunk to get new version of LinearLinkageEncoding
Improved MoveGenerator

File size: 4.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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 HeuristicLab.Algorithms.MemPR.Interfaces;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.MemPR.LinearLinkage.SolutionModel.Univariate {
31  [Item("Univariate solution model (linear linkage)", "")]
32  [StorableClass]
33  public sealed class UnivariateModel : Item, ISolutionModel<Encodings.LinearLinkageEncoding.LinearLinkage> {
34    [Storable]
35    public IntMatrix Frequencies { get; set; }
36    [Storable]
37    public IRandom Random { get; set; }
38    [Storable]
39    public IntValue Maximum { get; set; }
40
41    [StorableConstructor]
42    private UnivariateModel(bool deserializing) : base(deserializing) { }
43    private UnivariateModel(UnivariateModel original, Cloner cloner)
44      : base(original, cloner) {
45      Frequencies = cloner.Clone(original.Frequencies);
46      Random = cloner.Clone(original.Random);
47    }
48    public UnivariateModel(IRandom random, int[,] frequencies, int max) {
49      Frequencies = new IntMatrix(frequencies);
50      Random = random;
51      Maximum = new IntValue(max);
52    }
53    public UnivariateModel(IRandom random, IntMatrix frequencies, int max) {
54      Frequencies = frequencies;
55      Random = random;
56      Maximum = new IntValue(max);
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new UnivariateModel(this, cloner);
61    }
62
63    public Encodings.LinearLinkageEncoding.LinearLinkage Sample() {
64      var N = Frequencies.Rows;
65      var centroid = Encodings.LinearLinkageEncoding.LinearLinkage.SingleElementGroups(N);
66      var dict = new Dictionary<int, int>();
67      for (var i = N - 1; i >= 0; i--) {
68        centroid[i] = i; // default be a cluster of your own
69        for (var j = i + 1; j < N; j++) {
70          // try to find a suitable link
71          if (Maximum.Value * Random.NextDouble() < Frequencies[i, j]) {
72            int pred;
73            if (dict.TryGetValue(j, out pred)) {
74              int tmp, k = pred;
75              while (dict.TryGetValue(k, out tmp)) {
76                if (k == tmp) break;
77                k = tmp;
78              }
79              centroid[i] = k;
80            } else centroid[i] = j;
81            dict[centroid[i]] = i;
82            break;
83          }
84        }
85      }
86      return centroid;
87    }
88
89    public static ISolutionModel<Encodings.LinearLinkageEncoding.LinearLinkage> Create(IRandom random, IEnumerable<Encodings.LinearLinkageEncoding.LinearLinkage> population) {
90      var iter = population.GetEnumerator();
91      if (!iter.MoveNext()) throw new ArgumentException("Cannot create solution model from empty population.");
92      var popSize = 1;
93      var N = iter.Current.Length;
94      var freq = new int[N, N];
95      do {
96        var current = iter.Current;
97        popSize++;
98        foreach (var g in current.GetGroups()) {
99          for (var i = 0; i < g.Count - 1; i++)
100            for (var j = i + 1; j < g.Count; j++) {
101              freq[g[i], g[j]]++;
102              freq[g[j], g[i]]++;
103            }
104        }
105      } while (iter.MoveNext());
106      return new UnivariateModel(random, freq, popSize);
107    }
108  }
109}
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