Changeset 10489 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceScale.cs

Timestamp:

02/20/14 17:45:06 (10 years ago)

Author:

gkronber

Message:

#2125 fixed the bug that covariance functions returned the full gradient vector even when parameters are partially fixed.
changed the calculation of NN covariance and gradient to direct calculation (instead of AutoDiff)

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceScale.cs (modified) (5 diffs)

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

@@ -37 +37 @@
       get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
     }
+    private bool HasFixedScaleParameter {
+      get { return ScaleParameter.Value != null; }
+    }
 
     public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
@@ -65 +68 @@
 
     public int GetNumberOfParameters(int numberOfVariables) {
-      return (ScaleParameter.Value != null ? 0 : 1) + CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
+      return (HasFixedScaleParameter ? 0 : 1) + CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
     }
 
@@ -77 +80 @@
     private void GetParameterValues(double[] p, out double scale) {
       // gather parameter values
-      if (ScaleParameter.Value != null) {
+      if (HasFixedScaleParameter) {
         scale = ScaleParameter.Value.Value;
       } else {
@@ -87 +90 @@
       double scale;
       GetParameterValues(p, out scale);
+      var fixedScale = HasFixedScaleParameter;
       var subCov = CovarianceFunctionParameter.Value.GetParameterizedCovarianceFunction(p.Skip(1).ToArray(), columnIndices);
       // create functions
@@ -92 +96 @@
       cov.Covariance = (x, i, j) => scale * subCov.Covariance(x, i, j);
       cov.CrossCovariance = (x, xt, i, j) => scale * subCov.CrossCovariance(x, xt, i, j);
-      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, subCov);
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, subCov, fixedScale);
       return cov;
     }
 
-    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, ParameterizedCovarianceFunction cov) {
-      yield return 2 * scale * cov.Covariance(x, i, j);
+    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, ParameterizedCovarianceFunction cov,
+      bool fixedScale) {
+      if (!fixedScale) {
+        yield return 2 * scale * cov.Covariance(x, i, j);
+      }
       foreach (var g in cov.CovarianceGradient(x, i, j))
         yield return scale * g;