Changeset 13209 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4

Timestamp:

11/17/15 13:17:49 (8 years ago)

Author:

mkommend

Message:

#1967: Implemented IStatefulItem in GaussianProcessCovarianceOptimizationProblem to clear the temporary parameters correctly.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessCovarianceOptimizationProblem.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessCovarianceOptimizationProblem.cs

@@ -37 +37 @@
   [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 300)]
   [StorableClass]
-  public sealed class GaussianProcessCovarianceOptimizationProblem : SymbolicExpressionTreeProblem, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
+  public sealed class GaussianProcessCovarianceOptimizationProblem : SymbolicExpressionTreeProblem, IStatefulItem, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
     #region static variables and ctor
     private static readonly CovarianceMaternIso maternIso1;
@@ -140 +140 @@
 
     // problem stores a few variables for information exchange from Evaluate() to Analyze()
-    private object problemStateLocker = new object();
+    private readonly object problemStateLocker = new object();
     [Storable]
     private double bestQ;
@@ -184 +184 @@
     }
 
-    protected override void OnReset() {
-      base.OnReset();
+    public void InitializeState() { ClearState(); }
+    public void ClearState() {
       meanFunc = null;
       covFunc = null;
@@ -193 +193 @@
 
     public override double Evaluate(ISymbolicExpressionTree tree, IRandom random) {
-
       var meanFunction = new MeanConst();
       var problemData = ProblemData;
@@ -322 +321 @@
         var gradients = model.HyperparameterGradients;
         Array.Copy(gradients, grad, gradients.Length);
-      } catch (ArgumentException) {
+      }
+      catch (ArgumentException) {
         // building the GaussianProcessModel might fail, in this case we return the worst possible objective value
         func = 1.0E+300;