Changeset 8982 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSum.cs

Timestamp:

12/01/12 19:02:47 (12 years ago)

Author:

gkronber

Message:

#1902: removed class HyperParameter and changed implementations of covariance and mean functions to remove the parameter value caching and event handlers for parameter caching. Instead it is now possible to create the actual covariance and mean functions as Func from templates and specified parameter values. The instances of mean and covariance functions configured in the GUI are actually templates where the structure and fixed parameters can be specified.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSum.cs (modified) (2 diffs)

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

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using System.Linq.Expressions;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -66 +67 @@
     }
 
-    public void SetParameter(double[] hyp) {
-      if (terms.Count == 0) throw new ArgumentException("At least one term is needed for sum covariance function.");
+    public void SetParameter(double[] p) {
       int offset = 0;
       foreach (var t in terms) {
         var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
-        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
+        t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
         offset += numberOfParameters;
       }
     }
 
-    public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      return terms.Select(t => t.GetCovariance(x, i, j, columnIndices)).Sum();
-    }
+    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
+      if (terms.Count == 0) throw new ArgumentException("at least one term is necessary for the product covariance function.");
+      var functions = new List<ParameterizedCovarianceFunction>();
+      foreach (var t in terms) {
+        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
+        functions.Add(t.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
+        p = p.Skip(numberOfParameters).ToArray();
+      }
 
-    public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      return terms.Select(t => t.GetGradient(x, i, j, columnIndices)).Aggregate(Enumerable.Concat);
-    }
-
-    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
-      return terms.Select(t => t.GetCrossCovariance(x, xt, i, j, columnIndices)).Sum();
+      var sum = new ParameterizedCovarianceFunction();
+      sum.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Sum();
+      sum.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Sum();
+      sum.CovarianceGradient = (x, i, j) => functions.Select(e => e.CovarianceGradient(x, i, j)).Aggregate(Enumerable.Concat);
+      return sum;
     }
   }