Changeset 8982 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanSum.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/MeanFunctions/MeanSum.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanSum.cs

@@ -19 +19 @@
  */
 #endregion
+
+using System.Collections.Generic;
 using System.Linq;
 using HeuristicLab.Common;
@@ -57 +59 @@
     }
 
-    public void SetParameter(double[] hyp) {
+    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[] GetMean(double[,] x) {
-      var res = terms.First().GetMean(x);
-      foreach (var t in terms.Skip(1)) {
-        var a = t.GetMean(x);
-        for (int i = 0; i < res.Length; i++) res[i] += a[i];
+    public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
+      var termMf = new List<ParameterizedMeanFunction>();
+      int totalNumberOfParameters = GetNumberOfParameters(numberOfVariables);
+      int[] termIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the correct mean-term
+      int[] hyperParameterIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the l-th parameter of the correct mean-term
+      int c = 0;
+      // get the parameterized mean function for each term
+      for (int termIndex = 0; termIndex < terms.Count; termIndex++) {
+        var numberOfParameters = terms[termIndex].GetNumberOfParameters(numberOfVariables);
+        termMf.Add(terms[termIndex].GetParameterizedMeanFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
+        p = p.Skip(numberOfParameters).ToArray();
+
+        for (int hyperParameterIndex = 0; hyperParameterIndex < numberOfParameters; hyperParameterIndex++) {
+          termIndexMap[c] = termIndex;
+          hyperParameterIndexMap[c] = hyperParameterIndex;
+          c++;
+        }
       }
-      return res;
-    }
 
-    public double[] GetGradients(int k, double[,] x) {
-      int i = 0;
-      while (k >= terms[i].GetNumberOfParameters(numberOfVariables)) {
-        k -= terms[i].GetNumberOfParameters(numberOfVariables);
-        i++;
-      }
-      return terms[i].GetGradients(k, x);
+      var mf = new ParameterizedMeanFunction();
+      mf.Mean = (x, i) => termMf.Select(t => t.Mean(x, i)).Sum();
+      mf.Gradient = (x, i, k) => {
+        return termMf[termIndexMap[k]].Gradient(x, i, hyperParameterIndexMap[k]);
+      };
+      return mf;
     }
   }