1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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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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23 | using System.Collections.Generic;
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24 | using System.Drawing;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 | using HeuristicLab.Problems.DataAnalysis;
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30 | using HeuristicLab.Optimization;
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31 | using HeuristicLab.Data;
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32 |
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33 | namespace HeuristicLab.Algorithms.DataAnalysis {
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34 | /// <summary>
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35 | /// Represents a k-Means clustering solution for a clustering problem which can be visualized in the GUI.
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36 | /// </summary>
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37 | [Item("k-Means clustering solution", "Represents a k-Means solution for a clustering problem which can be visualized in the GUI.")]
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38 | [StorableClass]
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39 | public sealed class KMeansClusteringSolution : ClusteringSolution {
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40 | private const string TrainingIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (training)";
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41 | private const string TestIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (test)";
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42 | public new KMeansClusteringModel Model {
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43 | get { return (KMeansClusteringModel)base.Model; }
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44 | set { base.Model = value; }
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45 | }
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46 |
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47 | [StorableConstructor]
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48 | private KMeansClusteringSolution(bool deserializing) : base(deserializing) { }
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49 | private KMeansClusteringSolution(KMeansClusteringSolution original, Cloner cloner)
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50 | : base(original, cloner) {
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51 | }
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52 | public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
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53 | : base(model, problemData) {
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54 | double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
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55 | double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
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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)));
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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)));
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58 | }
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59 |
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60 | public override IDeepCloneable Clone(Cloner cloner) {
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61 | return new KMeansClusteringSolution(this, cloner);
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62 | }
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63 | }
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64 | }
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