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

Last change on this file since 14869 was 14869, checked in by gkronber, 3 years ago

#2699: merged changesets from trunk to branch

File size: 4.6 KB
RevLine 
[8412]1#region License Information
2/* HeuristicLab
[14185]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8412]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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27using HeuristicLab.Problems.DataAnalysis;
28
[8471]29namespace HeuristicLab.Algorithms.DataAnalysis {
[8466]30  [Item("NCA Model", "")]
[8412]31  [StorableClass]
[13941]32  public class NcaModel : ClassificationModel, INcaModel {
33    public override IEnumerable<string> VariablesUsedForPrediction {
[13921]34      get { return allowedInputVariables; }
35    }
[8412]36
37    [Storable]
38    private double[,] transformationMatrix;
39    public double[,] TransformationMatrix {
40      get { return (double[,])transformationMatrix.Clone(); }
41    }
42    [Storable]
[8454]43    private string[] allowedInputVariables;
[8441]44    [Storable]
[8454]45    private INearestNeighbourModel nnModel;
46    [Storable]
[8466]47    private double[] classValues;
[8412]48
49    [StorableConstructor]
[8466]50    protected NcaModel(bool deserializing) : base(deserializing) { }
51    protected NcaModel(NcaModel original, Cloner cloner)
[8412]52      : base(original, cloner) {
[8454]53      this.transformationMatrix = (double[,])original.transformationMatrix.Clone();
54      this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
55      this.nnModel = cloner.Clone(original.nnModel);
[8466]56      this.classValues = (double[])original.classValues.Clone();
[8412]57    }
[13941]58    public NcaModel(int k, double[,] transformationMatrix, IDataset dataset, IEnumerable<int> rows, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
59      : base(targetVariable) {
[8454]60      Name = ItemName;
61      Description = ItemDescription;
[8466]62      this.transformationMatrix = (double[,])transformationMatrix.Clone();
[8454]63      this.allowedInputVariables = allowedInputVariables.ToArray();
[8466]64      this.classValues = (double[])classValues.Clone();
[8454]65
[8466]66      var ds = ReduceDataset(dataset, rows);
67      nnModel = new NearestNeighbourModel(ds, Enumerable.Range(0, ds.Rows), k, ds.VariableNames.Last(), ds.VariableNames.Take(transformationMatrix.GetLength(1)), classValues);
[8412]68    }
69
70    public override IDeepCloneable Clone(Cloner cloner) {
[8466]71      return new NcaModel(this, cloner);
[8412]72    }
73
[13941]74    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
[8466]75      var ds = ReduceDataset(dataset, rows);
76      return nnModel.GetEstimatedClassValues(ds, Enumerable.Range(0, ds.Rows));
[8412]77    }
[8454]78
[13941]79    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
80      return new NcaClassificationSolution(this, new ClassificationProblemData(problemData));
[8412]81    }
[8454]82
[13941]83    INcaClassificationSolution INcaModel.CreateClassificationSolution(IClassificationProblemData problemData) {
84      return new NcaClassificationSolution(this, new ClassificationProblemData(problemData));
[8412]85    }
[8437]86
[12509]87    public double[,] Reduce(IDataset dataset, IEnumerable<int> rows) {
[14869]88      var data = dataset.ToArray(allowedInputVariables, rows);
[9272]89
[13941]90      var targets = dataset.GetDoubleValues(TargetVariable, rows).ToArray();
[9272]91      var result = new double[data.GetLength(0), transformationMatrix.GetLength(1) + 1];
92      for (int i = 0; i < data.GetLength(0); i++)
93        for (int j = 0; j < data.GetLength(1); j++) {
[8454]94          for (int x = 0; x < transformationMatrix.GetLength(1); x++) {
[9272]95            result[i, x] += data[i, j] * transformationMatrix[j, x];
[8454]96          }
[8466]97          result[i, transformationMatrix.GetLength(1)] = targets[i];
98        }
[8437]99      return result;
100    }
[8454]101
[12509]102    public Dataset ReduceDataset(IDataset dataset, IEnumerable<int> rows) {
[8466]103      return new Dataset(Enumerable
104          .Range(0, transformationMatrix.GetLength(1))
105          .Select(x => "X" + x.ToString())
[13941]106          .Concat(TargetVariable.ToEnumerable()),
[8466]107        Reduce(dataset, rows));
[8454]108    }
[8412]109  }
110}
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