Changeset 9616 for branches/HivePerformance/sources/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

06/12/13 15:02:47 (12 years ago)

Author:

ascheibe

Message:

#2030 merged trunk into hive performance branch

Location:

branches/HivePerformance/sources

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNeuralNetwork.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNoise.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovariancePiecewisePolynomial.cs (modified) (2 diffs)

branches/HivePerformance/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNeuralNetwork.cs

@@ -120 +120 @@
       AutoDiff.Term s1 = 1;
       AutoDiff.Term s2 = 1;
-      foreach (var k in columnIndices) {
+      for (int k = 0; k < columnIndices.Count(); k++) {
         x1[k] = new AutoDiff.Variable();
         x2[k] = new AutoDiff.Variable();

branches/HivePerformance/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNoise.cs

@@ -87 +87 @@
       var cov = new ParameterizedCovarianceFunction();
       cov.Covariance = (x, i, j) => i == j ? scale : 0.0;
-      cov.CrossCovariance = (x, xt, i, j) => 0.0;
+      cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, 1.0, columnIndices) < 1e-9 ? scale : 0.0;
       cov.CovarianceGradient = (x, i, j) => Enumerable.Repeat(i == j ? 2.0 * scale : 0.0, 1);
       return cov;

branches/HivePerformance/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovariancePiecewisePolynomial.cs

@@ -140 +140 @@
       var cov = new ParameterizedCovarianceFunction();
       cov.Covariance = (x, i, j) => {
-        double k = Math.Sqrt(Util.SqrDist(x, i, x, j, 1.0 / length));
+        double k = Math.Sqrt(Util.SqrDist(x, i, x, j, 1.0 / length, columnIndices));
         return scale * Math.Pow(Math.Max(1 - k, 0), exp + v) * f(k);
       };
       cov.CrossCovariance = (x, xt, i, j) => {
-        double k = Math.Sqrt(Util.SqrDist(x, i, xt, j, 1.0 / length));
+        double k = Math.Sqrt(Util.SqrDist(x, i, xt, j, 1.0 / length, columnIndices));
         return scale * Math.Pow(Math.Max(1 - k, 0), exp + v) * f(k);
       };
@@ -152 +152 @@
 
     private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double length, double scale, int v, double exp, Func<double, double> f, Func<double, double> df, IEnumerable<int> columnIndices) {
-      double k = Math.Sqrt(Util.SqrDist(x, i, x, j, 1.0 / length));
+      double k = Math.Sqrt(Util.SqrDist(x, i, x, j, 1.0 / length, columnIndices));
       yield return scale * Math.Pow(Math.Max(1.0 - k, 0), exp + v - 1) * k * ((exp + v) * f(k) - Math.Max(1 - k, 0) * df(k));
       yield return 2.0 * scale * Math.Pow(Math.Max(1 - k, 0), exp + v) * f(k);