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

Timestamp:

12/01/12 19:02:47 (11 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/CovarianceProduct.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceProduct.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 (factors.Count == 0) throw new ArgumentException("at least one factor is necessary for the product covariance function.");
+    public void SetParameter(double[] p) {
       int offset = 0;
-      foreach (var t in factors) {
-        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
-        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
+      foreach (var f in factors) {
+        var numberOfParameters = f.GetNumberOfParameters(numberOfVariables);
+        f.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
         offset += numberOfParameters;
       }
     }
 
-    public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      return factors.Select(f => f.GetCovariance(x, i, j, columnIndices)).Aggregate((a, b) => a * b);
+    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
+      if (factors.Count == 0) throw new ArgumentException("at least one factor is necessary for the product covariance function.");
+      var functions = new List<ParameterizedCovarianceFunction>();
+      foreach (var f in factors) {
+        int numberOfParameters = f.GetNumberOfParameters(numberOfVariables);
+        functions.Add(f.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
+        p = p.Skip(numberOfParameters).ToArray();
+      }
+
+
+      var product = new ParameterizedCovarianceFunction();
+      product.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Aggregate((a, b) => a * b);
+      product.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Aggregate((a, b) => a * b);
+      product.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, functions);
+      return product;
     }
 
-    public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      var covariances = factors.Select(f => f.GetCovariance(x, i, j, columnIndices)).ToArray();
-      for (int ii = 0; ii < factors.Count; ii++) {
-        foreach (var g in factors[ii].GetGradient(x, i, j, columnIndices)) {
+    public static IEnumerable<double> GetGradient(double[,] x, int i, int j, List<ParameterizedCovarianceFunction> factorFunctions) {
+      var covariances = factorFunctions.Select(f => f.Covariance(x, i, j)).ToArray();
+      for (int ii = 0; ii < factorFunctions.Count; ii++) {
+        foreach (var g in factorFunctions[ii].CovarianceGradient(x, i, j)) {
           double res = g;
           for (int jj = 0; jj < covariances.Length; jj++)
@@ -91 +104 @@
       }
     }
-
-    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
-      return factors.Select(f => f.GetCrossCovariance(x, xt, i, j, columnIndices)).Aggregate((a, b) => a * b);
-    }
   }
 }