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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/SupportVectorMachine/SupportVectorMachineUtil.cs @ 7370

Last change on this file since 7370 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 2.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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;
24
25namespace HeuristicLab.Problems.DataAnalysis.SupportVectorMachine {
26  public class SupportVectorMachineUtil {
27    /// <summary>
28    /// Transforms <paramref name="problemData"/> into a data structure as needed by libSVM.
29    /// </summary>
30    /// <param name="problemData">The problem data to transform</param>
31    /// <param name="rowIndices">The rows of the dataset that should be contained in the resulting SVM-problem</param>
32    /// <returns>A problem data type that can be used to train a support vector machine.</returns>
33    public static SVM.Problem CreateSvmProblem(DataAnalysisProblemData problemData, IEnumerable<int> rowIndices) {
34      double[] targetVector =
35        problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable.Value, rowIndices)
36        .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      foreach (int row in rowIndices) {
43        tempRow = new List<SVM.Node>();
44        foreach (var inputVariable in problemData.InputVariables.CheckedItems) {
45          int col = problemData.Dataset.GetVariableIndex(inputVariable.Value.Value);
46          double value = problemData.Dataset[row, col];
47          if (!double.IsNaN(value)) {
48            int nodeIndex = col + 1; // make sure the smallest nodeIndex is 1 (libSVM convention)
49            tempRow.Add(new SVM.Node(nodeIndex, value));
50            if (nodeIndex > maxNodeIndex) maxNodeIndex = nodeIndex;
51          }
52        }
53        nodes[svmProblemRowIndex++] = tempRow.OrderBy(x => x.Index).ToArray(); // make sure the values are sorted by node index
54      }
55
56      return new SVM.Problem(targetVector.Length, targetVector, nodes, maxNodeIndex);
57    }
58  }
59}
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