[8425] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[8425] | 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 |
|
---|
| 22 | using System.Linq;
|
---|
| 23 | using HeuristicLab.Common;
|
---|
| 24 | using HeuristicLab.Core;
|
---|
| 25 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 26 | using HeuristicLab.Problems.DataAnalysis;
|
---|
| 27 |
|
---|
[8471] | 28 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
[8425] | 29 | [Item("LDA", "Initializes the matrix by performing a linear discriminant analysis.")]
|
---|
| 30 | [StorableClass]
|
---|
[9270] | 31 | public class LdaInitializer : NcaInitializer {
|
---|
[8425] | 32 |
|
---|
| 33 | [StorableConstructor]
|
---|
[9270] | 34 | protected LdaInitializer(bool deserializing) : base(deserializing) { }
|
---|
| 35 | protected LdaInitializer(LdaInitializer original, Cloner cloner) : base(original, cloner) { }
|
---|
| 36 | public LdaInitializer() : base() { }
|
---|
[8425] | 37 |
|
---|
| 38 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
[9270] | 39 | return new LdaInitializer(this, cloner);
|
---|
[8425] | 40 | }
|
---|
| 41 |
|
---|
[9272] | 42 | public override double[,] Initialize(IClassificationProblemData data, int dimensions) {
|
---|
[8425] | 43 | var instances = data.TrainingIndices.Count();
|
---|
| 44 | var attributes = data.AllowedInputVariables.Count();
|
---|
| 45 |
|
---|
[9272] | 46 | var ldaDs = AlglibUtil.PrepareInputMatrix(data.Dataset,
|
---|
| 47 | data.AllowedInputVariables.Concat(data.TargetVariable.ToEnumerable()),
|
---|
| 48 | data.TrainingIndices);
|
---|
[8425] | 49 |
|
---|
[9451] | 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);
|
---|
[9270] | 55 |
|
---|
[9451] | 56 | for (int row = 0; row < instances; row++)
|
---|
| 57 | ldaDs[row, attributes] = uniqueClasses[ldaDs[row, attributes]];
|
---|
| 58 |
|
---|
[8425] | 59 | int info;
|
---|
| 60 | double[,] matrix;
|
---|
[9451] | 61 | alglib.fisherldan(ldaDs, instances, attributes, uniqueClasses.Count, out info, out matrix);
|
---|
[8425] | 62 |
|
---|
[9270] | 63 | return matrix;
|
---|
[8425] | 64 | }
|
---|
| 65 |
|
---|
| 66 | }
|
---|
| 67 | }
|
---|