1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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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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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Linq.Expressions;
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26 | using System.Threading.Tasks;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Problems.DataAnalysis;
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30 | using LibSVM;
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31 |
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32 | namespace HeuristicLab.Algorithms.DataAnalysis {
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33 | public class SupportVectorMachineUtil {
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34 | /// <summary>
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35 | /// Transforms <paramref name="problemData"/> into a data structure as needed by libSVM.
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36 | /// </summary>
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37 | /// <param name="problemData">The problem data to transform</param>
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38 | /// <param name="rowIndices">The rows of the dataset that should be contained in the resulting SVM-problem</param>
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39 | /// <returns>A problem data type that can be used to train a support vector machine.</returns>
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40 | public static svm_problem CreateSvmProblem(Dataset dataset, string targetVariable, IEnumerable<string> inputVariables, IEnumerable<int> rowIndices) {
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41 | double[] targetVector = dataset.GetDoubleValues(targetVariable, rowIndices).ToArray();
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42 | svm_node[][] nodes = new svm_node[targetVector.Length][];
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43 | int maxNodeIndex = 0;
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44 | int svmProblemRowIndex = 0;
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45 | List<string> inputVariablesList = inputVariables.ToList();
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46 | foreach (int row in rowIndices) {
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47 | List<svm_node> tempRow = new List<svm_node>();
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48 | int colIndex = 1; // make sure the smallest node index for SVM = 1
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49 | foreach (var inputVariable in inputVariablesList) {
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50 | double value = dataset.GetDoubleValue(inputVariable, row);
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51 | // SVM also works with missing values
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52 | // => don't add NaN values in the dataset to the sparse SVM matrix representation
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53 | if (!double.IsNaN(value)) {
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54 | tempRow.Add(new svm_node() { index = colIndex, value = value }); // nodes must be sorted in ascending ordered by column index
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55 | if (colIndex > maxNodeIndex) maxNodeIndex = colIndex;
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56 | }
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57 | colIndex++;
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58 | }
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59 | nodes[svmProblemRowIndex++] = tempRow.ToArray();
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60 | }
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61 | return new svm_problem { l = targetVector.Length, y = targetVector, x = nodes };
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62 | }
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63 |
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64 | /// <summary>
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65 | /// Instantiate and return a svm_parameter object with default values.
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66 | /// </summary>
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67 | /// <returns>A svm_parameter object with default values</returns>
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68 | public static svm_parameter DefaultParameters() {
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69 | svm_parameter parameter = new svm_parameter();
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70 | parameter.svm_type = svm_parameter.NU_SVR;
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71 | parameter.kernel_type = svm_parameter.RBF;
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72 | parameter.C = 1;
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73 | parameter.nu = 0.5;
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74 | parameter.gamma = 1;
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75 | parameter.p = 1;
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76 | parameter.cache_size = 500;
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77 | parameter.probability = 0;
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78 | parameter.eps = 0.001;
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79 | parameter.degree = 3;
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80 | parameter.shrinking = 1;
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81 | parameter.coef0 = 0;
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82 |
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83 | return parameter;
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84 | }
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85 |
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86 | /// <summary>
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87 | /// Generate a collection of row sequences corresponding to folds in the data (used for crossvalidation)
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88 | /// </summary>
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89 | /// <remarks>This method is aimed to be lightweight and as such does not clone the dataset.</remarks>
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90 | /// <param name="problemData">The problem data</param>
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91 | /// <param name="numberOfFolds">The number of folds to generate</param>
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92 | /// <returns>A sequence of folds representing each a sequence of row numbers</returns>
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93 | public static IEnumerable<IEnumerable<int>> GenerateFolds(IDataAnalysisProblemData problemData, int numberOfFolds) {
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94 | int size = problemData.TrainingPartition.Size;
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95 | int f = size / numberOfFolds, r = size % numberOfFolds; // number of folds rounded to integer and remainder
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96 | int start = 0, end = f;
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97 | for (int i = 0; i < numberOfFolds; ++i) {
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98 | if (r > 0) { ++end; --r; }
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99 | yield return problemData.TrainingIndices.Skip(start).Take(end - start);
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100 | start = end;
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101 | end += f;
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102 | }
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103 | }
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104 |
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105 | private static Tuple<svm_problem, svm_problem>[] GenerateSvmPartitions(IDataAnalysisProblemData problemData, int numberOfFolds) {
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106 | var folds = GenerateFolds(problemData, numberOfFolds).ToList();
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107 | var targetVariable = GetTargetVariableName(problemData);
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108 | var partitions = new Tuple<svm_problem, svm_problem>[numberOfFolds];
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109 | for (int i = 0; i < numberOfFolds; ++i) {
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110 | int p = i; // avoid "access to modified closure" warning below
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111 | var trainingRows = folds.SelectMany((par, j) => j != p ? par : Enumerable.Empty<int>());
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112 | var testRows = folds[i];
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113 | var trainingSvmProblem = CreateSvmProblem(problemData.Dataset, targetVariable, problemData.AllowedInputVariables, trainingRows);
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114 | var testSvmProblem = CreateSvmProblem(problemData.Dataset, targetVariable, problemData.AllowedInputVariables, testRows);
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115 | partitions[i] = new Tuple<svm_problem, svm_problem>(trainingSvmProblem, testSvmProblem);
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116 | }
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117 | return partitions;
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118 | }
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119 |
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120 | public static void CrossValidate(IDataAnalysisProblemData problemData, svm_parameter parameters, int numberOfFolds, out double avgTestMse) {
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121 | var partitions = GenerateSvmPartitions(problemData, numberOfFolds);
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122 | CrossValidate(problemData, parameters, partitions, out avgTestMse);
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123 | }
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124 |
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125 | public static void CrossValidate(IDataAnalysisProblemData problemData, svm_parameter parameters, Tuple<svm_problem, svm_problem>[] partitions, out double avgTestMse) {
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126 | avgTestMse = 0;
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127 | var calc = new OnlineMeanSquaredErrorCalculator();
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128 | foreach (Tuple<svm_problem, svm_problem> tuple in partitions) {
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129 | var trainingSvmProblem = tuple.Item1;
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130 | var testSvmProblem = tuple.Item2;
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131 | var model = svm.svm_train(trainingSvmProblem, parameters);
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132 | calc.Reset();
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133 | for (int i = 0; i < testSvmProblem.l; ++i)
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134 | calc.Add(testSvmProblem.y[i], svm.svm_predict(model, testSvmProblem.x[i]));
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135 | avgTestMse += calc.MeanSquaredError;
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136 | }
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137 | avgTestMse /= partitions.Length;
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138 | }
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139 |
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140 | private static Action<svm_parameter, double> GenerateSetter(string fieldName) {
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141 | var targetExp = Expression.Parameter(typeof(svm_parameter));
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142 | var valueExp = Expression.Parameter(typeof(double));
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143 | var fieldExp = Expression.Field(targetExp, fieldName);
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144 | var assignExp = Expression.Assign(fieldExp, Expression.Convert(valueExp, fieldExp.Type));
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145 | var setter = Expression.Lambda<Action<svm_parameter, double>>(assignExp, targetExp, valueExp).Compile();
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146 | return setter;
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147 | }
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148 |
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149 | public static svm_parameter GridSearch(IDataAnalysisProblemData problemData, int numberOfFolds, Dictionary<string, IEnumerable<double>> parameterRanges, int maxDegreeOfParallelism = 1) {
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150 | DoubleValue mse = new DoubleValue(Double.MaxValue);
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151 | var bestParam = DefaultParameters();
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152 | var pNames = parameterRanges.Keys.ToList();
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153 | var pRanges = pNames.Select(x => parameterRanges[x]);
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154 | var crossProduct = pRanges.CartesianProduct();
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155 | var setters = pNames.Select(GenerateSetter).ToList();
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156 | var partitions = GenerateSvmPartitions(problemData, numberOfFolds);
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157 | Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, nuple => {
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158 | var list = nuple.ToList();
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159 | var parameters = DefaultParameters();
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160 | for (int i = 0; i < pNames.Count; ++i) {
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161 | var s = setters[i];
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162 | s(parameters, list[i]);
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163 | }
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164 | double testMse;
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165 | CrossValidate(problemData, parameters, partitions, out testMse);
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166 | if (testMse < mse.Value) {
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167 | lock (mse) { mse.Value = testMse; }
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168 | lock (bestParam) { bestParam = (svm_parameter)parameters.Clone(); }
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169 | }
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170 | });
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171 | return bestParam;
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172 | }
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173 |
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174 | private static string GetTargetVariableName(IDataAnalysisProblemData problemData) {
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175 | var regressionProblemData = problemData as IRegressionProblemData;
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176 | var classificationProblemData = problemData as IClassificationProblemData;
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177 |
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178 | if (regressionProblemData != null)
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179 | return regressionProblemData.TargetVariable;
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180 | if (classificationProblemData != null)
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181 | return classificationProblemData.TargetVariable;
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182 |
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183 | throw new ArgumentException("Problem data is neither regression or classification problem data.");
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184 | }
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185 | }
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186 | }
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