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

Timestamp:

02/20/14 17:45:06 (11 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/CovarianceSquaredExponentialArd.cs (modified) (6 diffs)

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

@@ -40 +40 @@
       get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
     }
+    private bool HasFixedInverseLengthParameter {
+      get { return InverseLengthParameter.Value != null; }
+    }
+    private bool HasFixedScaleParameter {
+      get { return ScaleParameter.Value != null; }
+    }
 
     [StorableConstructor]
@@ -61 +67 @@
     public int GetNumberOfParameters(int numberOfVariables) {
       return
-        (ScaleParameter.Value != null ? 0 : 1) +
-        (InverseLengthParameter.Value != null ? 0 : numberOfVariables);
+        (HasFixedScaleParameter ? 0 : 1) +
+        (HasFixedInverseLengthParameter ? 0 : numberOfVariables);
     }
 
@@ -76 +82 @@
       int c = 0;
       // gather parameter values
-      if (InverseLengthParameter.Value != null) {
+      if (HasFixedInverseLengthParameter) {
         inverseLength = InverseLengthParameter.Value.ToArray();
       } else {
         int length = p.Length;
-        if (ScaleParameter.Value == null) length--;
+        if (HasFixedScaleParameter) length--;
         inverseLength = p.Select(e => 1.0 / Math.Exp(e)).Take(length).ToArray();
         c += inverseLength.Length;
       }
-      if (ScaleParameter.Value != null) {
+      if (HasFixedScaleParameter) {
         scale = ScaleParameter.Value.Value;
       } else {
@@ -97 +103 @@
       double[] inverseLength;
       GetParameterValues(p, out scale, out inverseLength);
+      var fixedInverseLength = HasFixedInverseLengthParameter;
+      var fixedScale = HasFixedScaleParameter;
       // create functions
       var cov = new ParameterizedCovarianceFunction();
@@ -109 +117 @@
         return scale * Math.Exp(-d / 2.0);
       };
-      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, inverseLength);
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, inverseLength, fixedInverseLength, fixedScale);
       return cov;
     }
 
     // order of returned gradients must match the order in GetParameterValues!
-    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, double[] inverseLength) {
+    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, double[] inverseLength,
+      bool fixedInverseLength, bool fixedScale) {
       double d = i == j
                    ? 0.0
@@ -120 +129 @@
 
       int k = 0;
-      foreach (var columnIndex in columnIndices) {
-        double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
-        yield return scale * Math.Exp(-d / 2.0) * sqrDist;
-        k++;
+      if (!fixedInverseLength) {
+        foreach (var columnIndex in columnIndices) {
+          double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
+          yield return scale * Math.Exp(-d / 2.0) * sqrDist;
+          k++;
+        }
       }
-      yield return 2.0 * scale * Math.Exp(-d / 2.0);
+      if (!fixedScale) yield return 2.0 * scale * Math.Exp(-d / 2.0);
     }
   }