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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Nca/NcaModel.cs @ 13921

Last change on this file since 13921 was 13921, checked in by bburlacu, 8 years ago

#2604: Revert changes to DataAnalysisSolution and IDataAnalysisSolution and implement the desired properties in model classes that implement IDataAnalysisModel, IRegressionModel and IClassificationModel.

File size: 4.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27using HeuristicLab.Problems.DataAnalysis;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [Item("NCA Model", "")]
31  [StorableClass]
32  public class NcaModel : NamedItem, INcaModel {
33    public IEnumerable<string> VariablesUsedForPrediction {
34      get { return allowedInputVariables; }
35    }
36
37    public string TargetVariable {
38      get { return targetVariable; }
39    }
40
41    [Storable]
42    private double[,] transformationMatrix;
43    public double[,] TransformationMatrix {
44      get { return (double[,])transformationMatrix.Clone(); }
45    }
46    [Storable]
47    private string[] allowedInputVariables;
48    [Storable]
49    private string targetVariable;
50    [Storable]
51    private INearestNeighbourModel nnModel;
52    [Storable]
53    private double[] classValues;
54
55    [StorableConstructor]
56    protected NcaModel(bool deserializing) : base(deserializing) { }
57    protected NcaModel(NcaModel original, Cloner cloner)
58      : base(original, cloner) {
59      this.transformationMatrix = (double[,])original.transformationMatrix.Clone();
60      this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
61      this.targetVariable = original.targetVariable;
62      this.nnModel = cloner.Clone(original.nnModel);
63      this.classValues = (double[])original.classValues.Clone();
64    }
65    public NcaModel(int k, double[,] transformationMatrix, IDataset dataset, IEnumerable<int> rows, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues) {
66      Name = ItemName;
67      Description = ItemDescription;
68      this.transformationMatrix = (double[,])transformationMatrix.Clone();
69      this.allowedInputVariables = allowedInputVariables.ToArray();
70      this.targetVariable = targetVariable;
71      this.classValues = (double[])classValues.Clone();
72
73      var ds = ReduceDataset(dataset, rows);
74      nnModel = new NearestNeighbourModel(ds, Enumerable.Range(0, ds.Rows), k, ds.VariableNames.Last(), ds.VariableNames.Take(transformationMatrix.GetLength(1)), classValues);
75    }
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new NcaModel(this, cloner);
79    }
80
81    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
82      var ds = ReduceDataset(dataset, rows);
83      return nnModel.GetEstimatedClassValues(ds, Enumerable.Range(0, ds.Rows));
84    }
85
86    public INcaClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
87      return new NcaClassificationSolution(new ClassificationProblemData(problemData), this);
88    }
89
90    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
91      return CreateClassificationSolution(problemData);
92    }
93
94    public double[,] Reduce(IDataset dataset, IEnumerable<int> rows) {
95      var data = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
96
97      var targets = dataset.GetDoubleValues(targetVariable, rows).ToArray();
98      var result = new double[data.GetLength(0), transformationMatrix.GetLength(1) + 1];
99      for (int i = 0; i < data.GetLength(0); i++)
100        for (int j = 0; j < data.GetLength(1); j++) {
101          for (int x = 0; x < transformationMatrix.GetLength(1); x++) {
102            result[i, x] += data[i, j] * transformationMatrix[j, x];
103          }
104          result[i, transformationMatrix.GetLength(1)] = targets[i];
105        }
106      return result;
107    }
108
109    public Dataset ReduceDataset(IDataset dataset, IEnumerable<int> rows) {
110      return new Dataset(Enumerable
111          .Range(0, transformationMatrix.GetLength(1))
112          .Select(x => "X" + x.ToString())
113          .Concat(targetVariable.ToEnumerable()),
114        Reduce(dataset, rows));
115    }
116  }
117}
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