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source: branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs

Last change on this file was 17246, checked in by gkronber, 5 years ago

#2925: merged r17037:17242 from trunk to branch

File size: 3.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HEAL.Attic;
25using HeuristicLab.Problems.DataAnalysis;
26using HeuristicLab.Optimization;
27using HeuristicLab.Data;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  /// <summary>
31  /// Represents a k-Means clustering solution for a clustering problem which can be visualized in the GUI.
32  /// </summary>
33  [Item("k-Means clustering solution", "Represents a k-Means solution for a clustering problem which can be visualized in the GUI.")]
34  [StorableType("E46AD906-04C4-4BEB-977D-BBC80E97C874")]
35  public sealed class KMeansClusteringSolution : ClusteringSolution {
36    private const string TrainingIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (training)";
37    private const string TestIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (test)";
38    public new KMeansClusteringModel Model {
39      get { return (KMeansClusteringModel)base.Model; }
40      set { base.Model = value; }
41    }
42
43    [StorableConstructor]
44    private KMeansClusteringSolution(StorableConstructorFlag _) : base(_) { }
45    private KMeansClusteringSolution(KMeansClusteringSolution original, Cloner cloner)
46      : base(original, cloner) {
47    }
48    public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
49      : base(model, problemData) {
50      double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
51      double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
52      this.Add(new Result(TrainingIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the training partition to the cluster center (is minimized by k-Means).", new DoubleValue(trainingIntraClusterSumOfSquares)));
53      this.Add(new Result(TestIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the test partition to the cluster center (is minimized by k-Means).", new DoubleValue(testIntraClusterSumOfSquares)));
54    }
55
56    public override IDeepCloneable Clone(Cloner cloner) {
57      return new KMeansClusteringSolution(this, cloner);
58    }
59  }
60}
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