#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.Collections.Generic; using System.Drawing; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { /// /// Represents a k-Means clustering model. /// [StorableClass] [Item("KMeansClusteringModel", "Represents a k-Means clustering model.")] public sealed class KMeansClusteringModel : NamedItem, IClusteringModel { public static new Image StaticItemImage { get { return HeuristicLab.Common.Resources.VSImageLibrary.Function; } } public IEnumerable VariablesUsedForPrediction { get { return allowedInputVariables; } } [Storable] private string[] allowedInputVariables; public IEnumerable AllowedInputVariables { get { return allowedInputVariables; } } [Storable] private List centers; public IEnumerable Centers { get { return centers.Select(x => (double[])x.Clone()); } } [StorableConstructor] private KMeansClusteringModel(bool deserializing) : base(deserializing) { } private KMeansClusteringModel(KMeansClusteringModel original, Cloner cloner) : base(original, cloner) { this.allowedInputVariables = (string[])original.allowedInputVariables.Clone(); this.centers = new List(original.Centers); } public KMeansClusteringModel(double[,] centers, IEnumerable allowedInputVariables) : base() { this.name = ItemName; this.description = ItemDescription; // disect center matrix into list of double[] // centers are given as double matrix where number of rows = dimensions and number of columns = clusters // each column is a cluster center this.centers = new List(); for (int i = 0; i < centers.GetLength(1); i++) { double[] c = new double[centers.GetLength(0)]; for (int j = 0; j < c.Length; j++) { c[j] = centers[j, i]; } this.centers.Add(c); } this.allowedInputVariables = allowedInputVariables.ToArray(); } public override IDeepCloneable Clone(Cloner cloner) { return new KMeansClusteringModel(this, cloner); } public IEnumerable GetClusterValues(IDataset dataset, IEnumerable rows) { return KMeansClusteringUtil.FindClosestCenters(centers, dataset, allowedInputVariables, rows); } } }