Changeset 13784 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSpectralMixture.cs

Timestamp:

04/22/16 13:47:35 (8 years ago)

Author:

pfleck

Message:

#2591 Made the creation of a GaussianProcessModel faster by avoiding additional iterators during calculation of the hyperparameter gradients.
The gradients of the hyperparameters are now calculated in one sweep and returned as IList, instead of returning an iterator (with yield return).
This avoids a large amount of Move-calls of the iterator, especially for covariance functions with a lot of hyperparameters.
Besides, the signature of the CovarianceGradientFunctionDelegate is changed, to return an IList instead of an IEnumerable to avoid unnececary ToList or ToArray calls.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSpectralMixture.cs (modified) (4 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSpectralMixture.cs

@@ -181 +181 @@
 
     // order of returned gradients must match the order in GetParameterValues!
-    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, int[] columnIndices,
+    private static IList<double> GetGradient(double[,] x, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, int[] columnIndices,
       bool fixedWeight, bool fixedFrequency, bool fixedLengthScale) {
       double[] tau = Util.GetRow(x, i, columnIndices).Zip(Util.GetRow(x, j, columnIndices), (xi, xj) => xi - xj).ToArray();
       int numberOfVariables = lengthScale.Length / maxQ;
 
+      var g = new List<double>((!fixedWeight ? maxQ : 0) + (!fixedFrequency ? maxQ * columnIndices.Length : 0) + (!fixedLengthScale ? maxQ * columnIndices.Length : 0));
       if (!fixedWeight) {
         // weight
@@ -198 +199 @@
             idx++;
           }
-          yield return k;
+          g.Add(k);
         }
       }
@@ -213 +214 @@
                        Math.Sin(2 * Math.PI * tau[idx] * frequency[q * numberOfVariables + c]);
             idx++;
-            yield return weight[q] * k;
+            g.Add(weight[q] * k);
           }
         }
@@ -229 +230 @@
                        f2(tau[idx], frequency[q * numberOfVariables + c]);
             idx++;
-            yield return weight[q] * k;
+            g.Add(weight[q] * k);
           }
         }
       }
+
+      return g;
     }
   }