[3842] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[5445] | 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3842] | 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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[1808] | 22 | using System.Collections.Generic;
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| 23 | using System.Linq;
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| 24 |
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[3842] | 25 | namespace HeuristicLab.Problems.DataAnalysis.SupportVectorMachine {
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| 26 | public class SupportVectorMachineUtil {
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| 27 | /// <summary>
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| 28 | /// Transforms <paramref name="problemData"/> into a data structure as needed by libSVM.
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| 29 | /// </summary>
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| 30 | /// <param name="problemData">The problem data to transform</param>
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[4543] | 31 | /// <param name="rowIndices">The rows of the dataset that should be contained in the resulting SVM-problem</param>
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[3842] | 32 | /// <returns>A problem data type that can be used to train a support vector machine.</returns>
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[4543] | 33 | public static SVM.Problem CreateSvmProblem(DataAnalysisProblemData problemData, IEnumerable<int> rowIndices) {
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| 34 | double[] targetVector =
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| 35 | problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable.Value, rowIndices)
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| 36 | .ToArray();
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[2148] | 37 |
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| 38 | SVM.Node[][] nodes = new SVM.Node[targetVector.Length][];
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[1808] | 39 | List<SVM.Node> tempRow;
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[3842] | 40 | int maxNodeIndex = 0;
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[4543] | 41 | int svmProblemRowIndex = 0;
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| 42 | foreach (int row in rowIndices) {
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[1808] | 43 | tempRow = new List<SVM.Node>();
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[3855] | 44 | foreach (var inputVariable in problemData.InputVariables.CheckedItems) {
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| 45 | int col = problemData.Dataset.GetVariableIndex(inputVariable.Value.Value);
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[4543] | 46 | double value = problemData.Dataset[row, col];
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[3842] | 47 | if (!double.IsNaN(value)) {
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[4543] | 48 | int nodeIndex = col + 1; // make sure the smallest nodeIndex is 1 (libSVM convention)
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[3842] | 49 | tempRow.Add(new SVM.Node(nodeIndex, value));
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| 50 | if (nodeIndex > maxNodeIndex) maxNodeIndex = nodeIndex;
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[2148] | 51 | }
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[1808] | 52 | }
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[4543] | 53 | nodes[svmProblemRowIndex++] = tempRow.OrderBy(x => x.Index).ToArray(); // make sure the values are sorted by node index
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[1808] | 54 | }
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| 55 |
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[3842] | 56 | return new SVM.Problem(targetVector.Length, targetVector, nodes, maxNodeIndex);
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[1808] | 57 | }
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| 58 | }
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| 59 | }
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