Changeset 15369

Timestamp:

09/19/17 07:15:37 (7 years ago)

Author:

gkronber

Message:

#2789 added penalized regression splines as implemented in alglib.

Location:

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

Files:

• GAM.cs (modified) (1 diff)
• Splines.cs (modified) (2 diffs)

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

@@ -151 +151 @@
       if (problemData.Dataset.VariableHasType<double>(inputVar)) {
         // Umständlich!
-        return Splines.CalculateSmoothingSplineReinschWithAutomaticTolerance(
+        return Splines.CalculatePenalizedRegressionSpline(
           problemData.Dataset.GetDoubleValues(inputVar, problemData.TrainingIndices).ToArray(),
-          (double[])target.Clone(),
-          new string[] { inputVar },
-          targetVar: problemData.TargetVariable);
+          (double[])target.Clone(), lambda,
+          problemData.TargetVariable, new string[] { inputVar }
+          );
       } else return new ConstantModel(target.Average(), problemData.TargetVariable);
     }

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

@@ -72 +72 @@
           "Smoothing Spline (Reinsch)",
           "Smoothing Spline (Reinsch with automatic tolerance determination using CV)",
-          "B-Spline Smoothing"
+          "B-Spline Smoothing",
+          "Penalized Regression Spline (alglib)"
       }.Select(s => new StringValue(s)));
 
@@ -158 +159 @@
               break;
             }
+          case "Penalized Regression Spline (alglib)":
+          {
+              var lambda = ((IValueParameter<DoubleValue>)Parameters["Lambda"]).Value.Value; // controls extent of smoothing
+              var model = CalculatePenalizedRegressionSpline(x, y, lambda, Problem.ProblemData.TargetVariable, inputVars);
+              var targetVar = Problem.ProblemData.TargetVariable;
+              var problemData = (IRegressionProblemData)Problem.ProblemData.Clone();
+              Results.Add(new Result("Solution", model.CreateRegressionSolution(problemData)));
+
+              break;
+          }
           default: throw new NotSupportedException();
         }
 
       }
+    }
+
+
+
+    public static IRegressionModel CalculatePenalizedRegressionSpline(double[] x, double[] y, double lambda, string targetVar, string[] inputVars)
+    {
+      int info;
+      alglib.spline1dinterpolant interpolant;
+      alglib.spline1dfitreport rep;
+      int n = x.Length;
+      n = (int)Math.Max(50, 3 * Math.Sqrt(n));
+
+      alglib.spline1dfitpenalized(x, y, n, lambda, out info, out interpolant, out rep);
+      return new Spline1dModel(interpolant, targetVar, inputVars);
     }