Changeset 10489 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovariancePolynomial.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/CovariancePolynomial.cs (modified) (5 diffs)

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

@@ -45 +45 @@
       get { return (IValueParameter<IntValue>)Parameters["Degree"]; }
     }
+    private bool HasFixedConstParameter {
+      get { return ConstParameter.Value != null; }
+    }
+    private bool HasFixedScaleParameter {
+      get { return ScaleParameter.Value != null; }
+    }
 
     [StorableConstructor]
@@ -71 +77 @@
     public int GetNumberOfParameters(int numberOfVariables) {
       return
-        (ConstParameter.Value != null ? 0 : 1) +
-        (ScaleParameter.Value != null ? 0 : 1);
+        (HasFixedConstParameter ? 0 : 1) +
+        (HasFixedScaleParameter ? 0 : 1);
     }
 
@@ -85 +91 @@
       // gather parameter values
       int n = 0;
-      if (ConstParameter.Value != null) {
+      if (HasFixedConstParameter) {
         @const = ConstParameter.Value.Value;
       } else {
@@ -92 +98 @@
       }
 
-      if (ScaleParameter.Value != null) {
+      if (HasFixedScaleParameter) {
         scale = ScaleParameter.Value.Value;
       } else {
@@ -106 +112 @@
       if (degree <= 0) throw new ArgumentException("The degree parameter for CovariancePolynomial must be greater than zero.");
       GetParameterValues(p, out @const, out scale);
+      var fixedConst = HasFixedConstParameter;
+      var fixedScale = HasFixedScaleParameter;
       // create functions
       var cov = new ParameterizedCovarianceFunction();
       cov.Covariance = (x, i, j) => scale * Math.Pow(@const + Util.ScalarProd(x, i, j, 1.0, columnIndices), degree);
       cov.CrossCovariance = (x, xt, i, j) => scale * Math.Pow(@const + Util.ScalarProd(x, i, xt, j, 1.0, columnIndices), degree);
-      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, @const, scale, degree, columnIndices);
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, @const, scale, degree, columnIndices, fixedConst, fixedScale);
       return cov;
     }
 
-    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double c, double scale, int degree, IEnumerable<int> columnIndices) {
+    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double c, double scale, int degree, IEnumerable<int> columnIndices,
+      bool fixedConst, bool fixedScale) {
       double s = Util.ScalarProd(x, i, j, 1.0, columnIndices);
-      yield return c * degree * scale * Math.Pow(c + s, degree - 1);
-      yield return 2 * scale * Math.Pow(c + s, degree);
+      if (!fixedConst) yield return c * degree * scale * Math.Pow(c + s, degree - 1);
+      if (!fixedScale) yield return 2 * scale * Math.Pow(c + s, degree);
     }
   }