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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineUtil.cs @ 7259

Last change on this file since 7259 was 7259, checked in by swagner, 13 years ago

Updated year of copyrights to 2012 (#1716)

File size: 2.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Problems.DataAnalysis;
25
26namespace HeuristicLab.Algorithms.DataAnalysis {
27  public class SupportVectorMachineUtil {
28    /// <summary>
29    /// Transforms <paramref name="problemData"/> into a data structure as needed by libSVM.
30    /// </summary>
31    /// <param name="problemData">The problem data to transform</param>
32    /// <param name="rowIndices">The rows of the dataset that should be contained in the resulting SVM-problem</param>
33    /// <returns>A problem data type that can be used to train a support vector machine.</returns>
34    public static SVM.Problem CreateSvmProblem(Dataset dataset, string targetVariable, IEnumerable<string> inputVariables, IEnumerable<int> rowIndices) {
35      double[] targetVector =
36        dataset.GetDoubleValues(targetVariable, rowIndices).ToArray();
37
38      SVM.Node[][] nodes = new SVM.Node[targetVector.Length][];
39      List<SVM.Node> tempRow;
40      int maxNodeIndex = 0;
41      int svmProblemRowIndex = 0;
42      List<string> inputVariablesList = inputVariables.ToList();
43      foreach (int row in rowIndices) {
44        tempRow = new List<SVM.Node>();
45        int colIndex = 1; // make sure the smallest node index for SVM = 1
46        foreach (var inputVariable in inputVariablesList) {
47          double value = dataset.GetDoubleValue(inputVariable, row);
48          // SVM also works with missing values
49          // => don't add NaN values in the dataset to the sparse SVM matrix representation
50          if (!double.IsNaN(value)) {
51            tempRow.Add(new SVM.Node(colIndex, value)); // nodes must be sorted in ascending ordered by column index
52            if (colIndex > maxNodeIndex) maxNodeIndex = colIndex;
53          }
54          colIndex++;
55        }
56        nodes[svmProblemRowIndex++] = tempRow.ToArray();
57      }
58
59      return new SVM.Problem(targetVector.Length, targetVector, nodes, maxNodeIndex);
60    }
61  }
62}
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