Changeset 15469 for branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs
- Timestamp:
- 11/10/17 09:05:38 (7 years ago)
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- 1 edited
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branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs
r15468 r15469 223 223 product = product.Zip(problemData.Dataset.GetDoubleValues(inputVars[i], problemData.TrainingIndices), (pi, vi) => pi * vi).ToArray(); 224 224 } 225 CubicSplineGCV.CubGcvReport report;226 return CubicSplineGCV.CalculateCubicSpline(227 product,228 (double[])target.Clone(),229 problemData.TargetVariable, inputVars, out report230 );231 232 double optTolerance; double cvRMSE;225 // CubicSplineGCV.CubGcvReport report; 226 // return CubicSplineGCV.CalculateCubicSpline( 227 // product, 228 // (double[])target.Clone(), 229 // problemData.TargetVariable, inputVars, out report 230 // ); 231 // 232 // double optTolerance; double cvRMSE; 233 233 // find tolerance 234 234 // var ensemble = Splines.CalculateSmoothingSplineReinsch(product, (double[])target.Clone(), inputVars, problemData.TargetVariable, out optTolerance, out cvRMSE); … … 239 239 // find tolerance 240 240 //var bestLambda = double.NaN; 241 double bestCVRMSE = target.StandardDeviation();242 double avgTrainRMSE = double.PositiveInfinity;243 double[] bestPredictions = new double[target.Length]; // zero241 // double bestCVRMSE = target.StandardDeviation(); 242 // double avgTrainRMSE = double.PositiveInfinity; 243 // double[] bestPredictions = new double[target.Length]; // zero 244 244 245 245 … … 269 269 // return Splines.CalculatePenalizedRegressionSpline(product, (double[])target.Clone(), lambda, problemData.TargetVariable, inputVars, out avgTrainRMSE, out cvRMSE, out bestPredictions); 270 270 SBART.SBART_Report rep; 271 var model = SBART.CalculateSBART(product, (double[])target.Clone(), problemData.TargetVariable, inputVars, out rep); 271 var w = product.Select(_ => 1.0).ToArray(); 272 var model = SBART.CalculateSBART(product, (double[])target.Clone(), w, 10, problemData.TargetVariable, inputVars, out rep); 272 273 Console.WriteLine("{0} {1:N5} {2:N5} {3:N5} {4:N5}", string.Join(",", inputVars), rep.gcv, rep.leverage.Sum(), product.StandardDeviation(), target.StandardDeviation()); 273 274 return model;
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