Changeset 7460 for branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs
- Timestamp:
- 02/06/12 17:50:17 (13 years ago)
- Location:
- branches/HeuristicLab.TimeSeries
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- 2 edited
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branches/HeuristicLab.TimeSeries
- Property svn:ignore
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old new 3 3 *.resharper 4 4 *.suo 5 *.user 5 6 *.vsp 6 7 Doxygen 8 FxCopResults.txt 7 9 Google.ProtocolBuffers-0.9.1.dll 8 10 HeuristicLab 3.3.5.1.ReSharper.user
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- Property svn:mergeinfo changed
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branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs
r7268 r7460 121 121 IRegressionProblemData problemData = Problem.ProblemData; 122 122 IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables; 123 var solution = CreateSupportVectorRegressionSolution(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value); 123 double trainR2, testR2; 124 int nSv; 125 var solution = CreateSupportVectorRegressionSolution(problemData, selectedInputVariables, SvmType.Value, 126 KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value, 127 out trainR2, out testR2, out nSv); 124 128 125 129 Results.Add(new Result("Support vector regression solution", "The support vector regression solution.", solution)); 130 Results.Add(new Result("Training R²", "The Pearson's R² of the SVR solution on the training partition.", new DoubleValue(trainR2))); 131 Results.Add(new Result("Test R²", "The Pearson's R² of the SVR solution on the test partition.", new DoubleValue(testR2))); 132 Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.", new IntValue(nSv))); 126 133 } 127 134 128 135 public static SupportVectorRegressionSolution CreateSupportVectorRegressionSolution(IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables, 129 string svmType, string kernelType, double cost, double nu, double gamma, double epsilon) { 136 string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, 137 out double trainingR2, out double testR2, out int nSv) { 130 138 Dataset dataset = problemData.Dataset; 131 139 string targetVariable = problemData.TargetVariable; … … 146 154 SVM.RangeTransform rangeTransform = SVM.RangeTransform.Compute(problem); 147 155 SVM.Problem scaledProblem = SVM.Scaling.Scale(rangeTransform, problem); 148 var model = new SupportVectorMachineModel(SVM.Training.Train(scaledProblem, parameter), rangeTransform, targetVariable, allowedInputVariables); 149 return new SupportVectorRegressionSolution(model, (IRegressionProblemData)problemData.Clone()); 156 var svmModel = SVM.Training.Train(scaledProblem, parameter); 157 nSv = svmModel.SupportVectorCount; 158 var model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables); 159 var solution = new SupportVectorRegressionSolution(model, (IRegressionProblemData)problemData.Clone()); 160 trainingR2 = solution.TrainingRSquared; 161 testR2 = solution.TestRSquared; 162 return solution; 150 163 } 151 164 #endregion
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