[8425] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14186] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8425] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Linq;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 26 | using HeuristicLab.Problems.DataAnalysis;
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| 27 |
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[8471] | 28 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[8425] | 29 | [Item("PCA", "Initializes the matrix by performing a principal components analysis.")]
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| 30 | [StorableClass]
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[9270] | 31 | public sealed class PcaInitializer : NcaInitializer {
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[8425] | 32 |
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| 33 | [StorableConstructor]
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[9270] | 34 | private PcaInitializer(bool deserializing) : base(deserializing) { }
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| 35 | private PcaInitializer(PcaInitializer original, Cloner cloner) : base(original, cloner) { }
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| 36 | public PcaInitializer() : base() { }
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[8425] | 37 |
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| 38 | public override IDeepCloneable Clone(Cloner cloner) {
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[9270] | 39 | return new PcaInitializer(this, cloner);
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[8425] | 40 | }
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| 41 |
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[9272] | 42 | public override double[,] Initialize(IClassificationProblemData data, int dimensions) {
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[8425] | 43 | var instances = data.TrainingIndices.Count();
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| 44 | var attributes = data.AllowedInputVariables.Count();
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| 45 |
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[9272] | 46 | var pcaDs = AlglibUtil.PrepareInputMatrix(data.Dataset, data.AllowedInputVariables, data.TrainingIndices);
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[8425] | 47 |
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| 48 | int info;
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| 49 | double[] varianceValues;
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| 50 | double[,] matrix;
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| 51 | alglib.pcabuildbasis(pcaDs, instances, attributes, out info, out varianceValues, out matrix);
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| 52 |
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[9270] | 53 | return matrix;
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[8425] | 54 | }
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| 55 |
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| 56 | }
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| 57 | }
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