Changeset 10553 for branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.cs

Timestamp:

03/05/14 17:30:38 (10 years ago)

Author:

mkommend

Message:

#1998: Updated classification model comparision branch with trunk changes.

File:

• branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.cs (modified) (5 diffs)

branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -37 +37 @@
       get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
     }
+    private bool HasFixedInverseLengthParameter {
+      get { return InverseLengthParameter.Value != null; }
+    }
 
     [StorableConstructor]
@@ -57 +60 @@
 
     public int GetNumberOfParameters(int numberOfVariables) {
-      if (InverseLengthParameter.Value == null)
+      if (HasFixedInverseLengthParameter)
+        return 0;
+      else
         return numberOfVariables;
-      else
-        return 0;
     }
 
@@ -71 +74 @@
     private void GetParameterValues(double[] p, out double[] inverseLength) {
       // gather parameter values
-      if (InverseLengthParameter.Value != null) {
+      if (HasFixedInverseLengthParameter) {
         inverseLength = InverseLengthParameter.Value.ToArray();
       } else {
@@ -81 +84 @@
       double[] inverseLength;
       GetParameterValues(p, out inverseLength);
+      var fixedInverseLength = HasFixedInverseLengthParameter;
       // create functions
       var cov = new ParameterizedCovarianceFunction();
       cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, inverseLength, columnIndices);
       cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices);
-      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, inverseLength, columnIndices);
+      if (fixedInverseLength)
+        cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
+      else
+        cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, inverseLength, columnIndices);
       return cov;
     }
 
     private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double[] inverseLength, IEnumerable<int> columnIndices) {
-      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
-
       int k = 0;
       foreach (int columnIndex in columnIndices) {