#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Optimization;
using HeuristicLab.Data;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Represents a k-Means clustering solution for a clustering problem which can be visualized in the GUI.
///
[Item("k-Means clustering solution", "Represents a k-Means solution for a clustering problem which can be visualized in the GUI.")]
[StorableClass]
public sealed class KMeansClusteringSolution : ClusteringSolution {
private const string TrainingIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (training)";
private const string TestIntraClusterSumOfSquaresResultName = "Intra-cluster sum of squares (test)";
public new KMeansClusteringModel Model {
get { return (KMeansClusteringModel)base.Model; }
set { base.Model = value; }
}
[StorableConstructor]
private KMeansClusteringSolution(bool deserializing) : base(deserializing) { }
private KMeansClusteringSolution(KMeansClusteringSolution original, Cloner cloner)
: base(original, cloner) {
}
public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
: base(model, problemData) {
double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
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)));
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)));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new KMeansClusteringSolution(this, cloner);
}
}
}