source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs @ 14185

Last change on this file since 14185 was 14185, checked in by swagner, 3 years ago

#2526: Updated year of copyrights in license headers

File size: 4.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Optimization;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis;
31
32namespace HeuristicLab.Algorithms.DataAnalysis {
33  /// <summary>
34  /// Nearest neighbour classification data analysis algorithm.
35  /// </summary>
36  [Item("Nearest Neighbour Classification (kNN)", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]
37  [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 150)]
38  [StorableClass]
39  public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
40    private const string KParameterName = "K";
41    private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";
42
43    #region parameter properties
44    public IFixedValueParameter<IntValue> KParameter {
45      get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
46    }
47    #endregion
48    #region properties
49    public int K {
50      get { return KParameter.Value.Value; }
51      set {
52        if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
53        else KParameter.Value.Value = value;
54      }
55    }
56    #endregion
57
58    [StorableConstructor]
59    private NearestNeighbourClassification(bool deserializing) : base(deserializing) { }
60    private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)
61      : base(original, cloner) {
62    }
63    public NearestNeighbourClassification()
64      : base() {
65      Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
66      Problem = new ClassificationProblem();
67    }
68    [StorableHook(HookType.AfterDeserialization)]
69    private void AfterDeserialization() { }
70
71    public override IDeepCloneable Clone(Cloner cloner) {
72      return new NearestNeighbourClassification(this, cloner);
73    }
74
75    #region nearest neighbour
76    protected override void Run() {
77      var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K);
78      Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));
79    }
80
81    public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k) {
82      var problemDataClone = (IClassificationProblemData)problemData.Clone();
83      return new NearestNeighbourClassificationSolution(Train(problemDataClone, k), problemDataClone);
84    }
85
86    public static INearestNeighbourModel Train(IClassificationProblemData problemData, int k) {
87      return new NearestNeighbourModel(problemData.Dataset,
88        problemData.TrainingIndices,
89        k,
90        problemData.TargetVariable,
91        problemData.AllowedInputVariables,
92        problemData.ClassValues.ToArray());
93    }
94    #endregion
95  }
96}
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