Changeset 15469 for branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs

Timestamp:

11/10/17 09:05:38 (6 years ago)

Author:

gkronber

Message:

#2789 trying to get SBART to work correctly.

File:

• branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs (modified) (3 diffs)

branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs

@@ -223 +223 @@
           product = product.Zip(problemData.Dataset.GetDoubleValues(inputVars[i], problemData.TrainingIndices), (pi, vi) => pi * vi).ToArray();
         }
-        CubicSplineGCV.CubGcvReport report;
-        return CubicSplineGCV.CalculateCubicSpline(
-          product,
-          (double[])target.Clone(), 
-          problemData.TargetVariable, inputVars, out report
-          );
-
-        double optTolerance; double cvRMSE;
+        // CubicSplineGCV.CubGcvReport report;
+        // return CubicSplineGCV.CalculateCubicSpline(
+        //   product,
+        //   (double[])target.Clone(), 
+        //   problemData.TargetVariable, inputVars, out report
+        //   );
+        // 
+        // double optTolerance; double cvRMSE;
         // find tolerance
         // var ensemble = Splines.CalculateSmoothingSplineReinsch(product, (double[])target.Clone(), inputVars, problemData.TargetVariable, out optTolerance, out cvRMSE);
@@ -239 +239 @@
         // find tolerance
         //var bestLambda = double.NaN;
-        double bestCVRMSE = target.StandardDeviation();
-        double avgTrainRMSE = double.PositiveInfinity;
-        double[] bestPredictions = new double[target.Length]; // zero
+        // double bestCVRMSE = target.StandardDeviation();
+        // double avgTrainRMSE = double.PositiveInfinity;
+        // double[] bestPredictions = new double[target.Length]; // zero
 
 
@@ -269 +269 @@
         // return Splines.CalculatePenalizedRegressionSpline(product, (double[])target.Clone(), lambda, problemData.TargetVariable, inputVars, out avgTrainRMSE, out cvRMSE, out bestPredictions);
         SBART.SBART_Report rep;
-        var model = SBART.CalculateSBART(product, (double[])target.Clone(), problemData.TargetVariable, inputVars, out rep);
+        var w = product.Select(_ => 1.0).ToArray();
+        var model = SBART.CalculateSBART(product, (double[])target.Clone(), w, 10, problemData.TargetVariable, inputVars, out rep);
         Console.WriteLine("{0} {1:N5} {2:N5} {3:N5} {4:N5}", string.Join(",", inputVars), rep.gcv, rep.leverage.Sum(), product.StandardDeviation(), target.StandardDeviation());
         return model;