Changeset 10489 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovariancePiecewisePolynomial.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/CovariancePiecewisePolynomial.cs (modified) (6 diffs)

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

@@ -45 +45 @@
       get { return (IConstrainedValueParameter<IntValue>)Parameters["V"]; }
     }
+    private bool HasFixedLengthParameter {
+      get { return LengthParameter.Value != null; }
+    }
+    private bool HasFixedScaleParameter {
+      get { return ScaleParameter.Value != null; }
+    }
 
     [StorableConstructor]
@@ -77 +83 @@
     public int GetNumberOfParameters(int numberOfVariables) {
       return
-        (LengthParameter.Value != null ? 0 : 1) +
-        (ScaleParameter.Value != null ? 0 : 1);
+        (HasFixedLengthParameter ? 0 : 1) +
+        (HasFixedScaleParameter ? 0 : 1);
     }
 
@@ -91 +97 @@
       // gather parameter values
       int n = 0;
-      if (LengthParameter.Value != null) {
+      if (HasFixedLengthParameter) {
         length = LengthParameter.Value.Value;
       } else {
@@ -98 +104 @@
       }
 
-      if (ScaleParameter.Value != null) {
+      if (HasFixedScaleParameter) {
         scale = ScaleParameter.Value.Value;
       } else {
@@ -111 +117 @@
       int v = VParameter.Value.Value;
       GetParameterValues(p, out length, out scale);
+      var fixedLength = HasFixedLengthParameter;
+      var fixedScale = HasFixedScaleParameter;
       int exp = (int)Math.Floor(columnIndices.Count() / 2.0) + v + 1;
 
@@ -147 +155 @@
         return scale * Math.Pow(Math.Max(1 - k, 0), exp + v) * f(k);
       };
-      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, length, scale, v, exp, f, df, columnIndices);
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, length, scale, v, exp, f, df, columnIndices, fixedLength, fixedScale);
       return cov;
     }
 
-    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) {
+    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,
+      bool fixedLength, bool fixedScale) {
       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);
+      if (!fixedLength) 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));
+      if (!fixedScale) yield return 2.0 * scale * Math.Pow(Math.Max(1 - k, 0), exp + v) * f(k);
     }
   }