Changeset 15449 for branches/MathNetNumerics-Exploration-2789/Main

Timestamp:

11/03/17 20:28:37 (7 years ago)

Author:

gkronber

Message:

#2789 created x64 build of CUBGCV algorithm, fixed some bugs

Location:

branches/MathNetNumerics-Exploration-2789/Main

Files:

• Main.csproj (modified) (2 diffs)
• Program.cs (modified) (1 diff)

branches/MathNetNumerics-Exploration-2789/Main/Main.csproj

@@ -23 +23 @@
     <ErrorReport>prompt</ErrorReport>
     <WarningLevel>4</WarningLevel>
+    <Prefer32Bit>false</Prefer32Bit>
   </PropertyGroup>
   <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
@@ -40 +41 @@
       <HintPath>..\..\..\trunk\sources\bin\HeuristicLab.Common-3.3.dll</HintPath>
     </Reference>
+    <Reference Include="HeuristicLab.Core-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec" />
     <Reference Include="HeuristicLab.Problems.DataAnalysis-3.4, Version=3.4.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
       <SpecificVersion>False</SpecificVersion>

branches/MathNetNumerics-Exploration-2789/Main/Program.cs

@@ -13 +13 @@
     static void Main(string[] args) {
       var xs = HeuristicLab.Common.SequenceGenerator.GenerateSteps(-3.5, 3.5, 0.1, includeEnd: true).ToList();
-      var ys = xs.Select(xi => 1.0 / Math.Sqrt(2 *Math.PI) * Math.Exp(-0.5 * xi*xi)).ToArray(); // 1.0 / (Math.Sqrt(2 * Math.PI) * Math.Exp(-0.5 * xi * xi))).ToArray();
+      var ys = xs.Select(xi => 1.0 / Math.Sqrt(2 * Math.PI) * Math.Exp(-0.5 * xi * xi)).ToArray(); // 1.0 / (Math.Sqrt(2 * Math.PI) * Math.Exp(-0.5 * xi * xi))).ToArray();
 
+      int n = xs.Count();
       alglib.hqrndstate state;
       alglib.hqrndseed(1234, 5678, out state);
       var ys_noise = ys.Select(yi => yi + alglib.hqrndnormal(state) * 0.1).ToList();
 
-      int n = xs.Count;
-      double[] ys_smoothed = new double[n];
-      double[] df = Enumerable.Repeat(1.0, n).ToArray();   // set each df if actual standard deviation of data points is not known
-      double[,] c = new double[n - 1, 3];
-      double var = -99.0;
-      int ic = n - 1;
-      int job = 1; // calc estimates of standard errors in points
-      double[] se = new double[n]; // standard errors in points
-      double[,] wk = new double[7, (n + 2)]; // work array;
-      int ier = -99;
-      if (Environment.Is64BitProcess) {
-        // CubicSplineGCV.cubgcv_x64(xs.ToArray(), f, df, ref n, ys_noise.ToArray(), c, ref ic, ref var, ref job, se, wk);
-        Console.WriteLine("x64 version not supported");
-      } else {
-        CubicSplineGCV.cubgcv_x86(xs.ToArray(), ys_noise.ToArray(), df, ref n, ys_smoothed,
-          c, ref ic, ref var, ref job, se, wk, ref ier);
+      CubicSplineGCV.CubGcvReport report;
+      var model = CubicSplineGCV.CalculateCubicSpline(
+        xs.ToArray(), ys_noise.ToArray(), "y", new string[] { "x" }, out report);
 
+      Console.WriteLine("Smoothing Parameter (= RHO/(RHO + 1) {0}", report.smoothingParameter);
+      Console.WriteLine("Estimated DOF of RSS {0}", report.estimatedRSSDegreesOfFreedom);
+      Console.WriteLine("GCV {0}", report.generalizedCrossValidation);
+      Console.WriteLine("Mean squared residual {0}", report.meanSquareResiudal);
+      Console.WriteLine("Estimate of true MSE at data points {0}", report.estimatedTrueMeanSquaredErrorAtDataPoints);
+      Console.WriteLine("Estimate of error variance {0}", report.estimatedErrorVariance);
+      Console.WriteLine("Mean square value of DF(I) {0}", report.meanSquareOfDf);
 
-        //                           WK(1) = SMOOTHING PARAMETER (= RHO/(RHO + 1))
-        //                           WK(2) = ESTIMATE OF THE NUMBER OF DEGREES OF
-        //                                   FREEDOM OF THE RESIDUAL SUM OF SQUARES
-        //                           WK(3) = GENERALIZED CROSS VALIDATION
-        //                           WK(4) = MEAN SQUARE RESIDUAL
-        //                           WK(5) = ESTIMATE OF THE TRUE MEAN SQUARE ERROR
-        //                                   AT THE DATA POINTS
-        //                           WK(6) = ESTIMATE OF THE ERROR VARIANCE
-        //                           WK(7) = MEAN SQUARE VALUE OF THE DF(I)
+      OnlineCalculatorError error;
+      var ys_smoothed = xs.Select(xi => model.GetEstimatedValue(xi)).ToArray();
+      var mse = OnlineMeanSquaredErrorCalculator.Calculate(ys, ys_smoothed, out error);
+      Console.WriteLine("MSE(ys, ys_smooth) = {0}", mse);
+      mse = OnlineMeanSquaredErrorCalculator.Calculate(ys, ys_noise, out error);
+      Console.WriteLine("MSE(ys, ys_noise) = {0}", mse);
 
-        Console.WriteLine("Smoothing Parameter (= RHO/(RHO + 1) {0}", wk[0, 0]);
-        Console.WriteLine("Estimated DOF of RSS {0}", wk[0, 1]);
-        Console.WriteLine("GCV {0}", wk[0, 2]);
-        Console.WriteLine("Mean squared residual {0}", wk[0, 3]);
-        Console.WriteLine("Estimate of true MSE at data points {0}", wk[0, 4]);
-        Console.WriteLine("Estimate of error variance {0}", wk[0, 5]);
-        Console.WriteLine("Mean square value of DF(I) {0}", wk[0, 6]);
+      Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
 
-        OnlineCalculatorError error;
-        var mse = OnlineMeanSquaredErrorCalculator.Calculate(ys, ys_smoothed, out error);
-        Console.WriteLine("MSE(ys, ys_smooth) = {0}", mse);
-        mse = OnlineMeanSquaredErrorCalculator.Calculate(ys, ys_noise, out error);
-        Console.WriteLine("MSE(ys, ys_noise) = {0}", mse);
-
-        Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
-
-        for (int i=0;i<n;i++) {
-          Console.WriteLine("{0}\t{1}\t{2}\t{3}\t{4}\t{5}", 
-            xs[i], ys[i], ys_smoothed[i], ys_noise[i], ys_smoothed[i] + 1.96 * se[i], ys_smoothed[i] - 1.96 * se[i]);
-        }
+      for (int i = 0; i < n; i++) {
+        Console.WriteLine("{0}\t{1}\t{2}\t{3}\t{4}\t{5}",
+          xs[i], ys[i], ys_smoothed[i], ys_noise[i], ys_smoothed[i] + 1.96 * report.se[i], ys_smoothed[i] - 1.96 * report.se[i]);
       }
     }