source: branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 12 months ago

#2520: updated year of copyrights

File size: 3.2 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.Drawing;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis;
30using HeuristicLab.Optimization;
31using HeuristicLab.Data;
32
33namespace HeuristicLab.Algorithms.DataAnalysis {
34  /// <summary>
35  /// Represents a k-Means clustering solution for a clustering problem which can be visualized in the GUI.
36  /// </summary>
37  [Item("k-Means clustering solution", "Represents a k-Means solution for a clustering problem which can be visualized in the GUI.")]
38  [StorableClass]
39  public sealed class KMeansClusteringSolution : ClusteringSolution {
40    private const string TrainingIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (training)";
41    private const string TestIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (test)";
42    public new KMeansClusteringModel Model {
43      get { return (KMeansClusteringModel)base.Model; }
44      set { base.Model = value; }
45    }
46
47    [StorableConstructor]
48    private KMeansClusteringSolution(bool deserializing) : base(deserializing) { }
49    private KMeansClusteringSolution(KMeansClusteringSolution original, Cloner cloner)
50      : base(original, cloner) {
51    }
52    public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
53      : base(model, problemData) {
54      double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
55      double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
56      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)));
57      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)));
58    }
59
60    public override IDeepCloneable Clone(Cloner cloner) {
61      return new KMeansClusteringSolution(this, cloner);
62    }
63  }
64}
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