source: branches/RBFRegression/HeuristicLab.Algorithms.DataAnalysis/3.4/Nca/Initialization/PcaInitializer.cs @ 14869

Last change on this file since 14869 was 14869, checked in by gkronber, 2 years ago

#2699: merged changesets from trunk to branch

File size: 2.1 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.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26using HeuristicLab.Problems.DataAnalysis;
27
28namespace HeuristicLab.Algorithms.DataAnalysis {
29  [Item("PCA", "Initializes the matrix by performing a principal components analysis.")]
30  [StorableClass]
31  public sealed class PcaInitializer : NcaInitializer {
32
33    [StorableConstructor]
34    private PcaInitializer(bool deserializing) : base(deserializing) { }
35    private PcaInitializer(PcaInitializer original, Cloner cloner) : base(original, cloner) { }
36    public PcaInitializer() : base() { }
37
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new PcaInitializer(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 pcaDs = data.Dataset.ToArray(data.AllowedInputVariables, data.TrainingIndices);
47
48      int info;
49      double[] varianceValues;
50      double[,] matrix;
51      alglib.pcabuildbasis(pcaDs, instances, attributes, out info, out varianceValues, out matrix);
52
53      return matrix;
54    }
55
56  }
57}
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