source: branches/2839_HiveProjectManagement/HeuristicLab.Algorithms.DataAnalysis/3.4/Nca/Initialization/LdaInitializer.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 17 months ago

#2839:

File size: 2.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26using HeuristicLab.Problems.DataAnalysis;
27
28namespace HeuristicLab.Algorithms.DataAnalysis {
29  [Item("LDA", "Initializes the matrix by performing a linear discriminant analysis.")]
30  [StorableClass]
31  public class LdaInitializer : NcaInitializer {
32
33    [StorableConstructor]
34    protected LdaInitializer(bool deserializing) : base(deserializing) { }
35    protected LdaInitializer(LdaInitializer original, Cloner cloner) : base(original, cloner) { }
36    public LdaInitializer() : base() { }
37
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new LdaInitializer(this, cloner);
40    }
41
42    public override double[,] Initialize(IClassificationProblemData data, int dimensions) {
43      var instances = data.TrainingIndices.Count();
44      var attributes = data.AllowedInputVariables.Count();
45
46      var ldaDs = data.Dataset.ToArray(
47                                       data.AllowedInputVariables.Concat(data.TargetVariable.ToEnumerable()),
48                                       data.TrainingIndices);
49
50      // map class values to sequential natural numbers (required by alglib)
51      var uniqueClasses = data.Dataset.GetDoubleValues(data.TargetVariable, data.TrainingIndices)
52                                        .Distinct()
53                                        .Select((v, i) => new { v, i })
54                                        .ToDictionary(x => x.v, x => x.i);
55
56      for (int row = 0; row < instances; row++)
57        ldaDs[row, attributes] = uniqueClasses[ldaDs[row, attributes]];
58
59      int info;
60      double[,] matrix;
61      alglib.fisherldan(ldaDs, instances, attributes, uniqueClasses.Count, out info, out matrix);
62
63      return matrix;
64    }
65
66  }
67}
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