Changeset 8982 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceConst.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/CovarianceConst.cs (modified) (5 diffs)

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

@@ -25 +25 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
@@ -32 +33 @@
     Description = "Constant covariance function for Gaussian processes.")]
   public sealed class CovarianceConst : ParameterizedNamedItem, ICovarianceFunction {
-
-    [Storable]
-    private double scale;
-    [Storable]
-    private readonly HyperParameter<DoubleValue> scaleParameter;
     public IValueParameter<DoubleValue> ScaleParameter {
-      get { return scaleParameter; }
+      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
     }
 
@@ -48 +44 @@
     private CovarianceConst(CovarianceConst original, Cloner cloner)
       : base(original, cloner) {
-      this.scaleParameter = cloner.Clone(original.scaleParameter);
-      this.scale = original.scale;
-
-      RegisterEvents();
     }
 
@@ -59 +51 @@
       Description = ItemDescription;
 
-      scaleParameter = new HyperParameter<DoubleValue>("Scale", "The scale of the constant covariance function.");
-      Parameters.Add(scaleParameter);
-      RegisterEvents();
+      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of the constant covariance function."));
     }
-
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      RegisterEvents();
-    }
-
-    // caching
-    private void RegisterEvents() {
-      Util.AttachValueChangeHandler<DoubleValue, double>(scaleParameter, () => { scale = scaleParameter.Value.Value; });
-    }
-
 
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -80 +59 @@
 
     public int GetNumberOfParameters(int numberOfVariables) {
-      return scaleParameter.Fixed ? 0 : 1;
+      return ScaleParameter.Value != null ? 0 : 1;
     }
 
-    public void SetParameter(double[] hyp) {
-      if (!scaleParameter.Fixed && hyp.Length == 1) {
-        scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[0])));
-      } else {
-        throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "hyp");
-      }
+    public void SetParameter(double[] p) {
+      double scale;
+      GetParameterValues(p, out scale);
+      ScaleParameter.Value = new DoubleValue(scale);
     }
 
-    public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      return scale;
+    private void GetParameterValues(double[] p, out double scale) {
+      int c = 0;
+      // gather parameter values
+      if (ScaleParameter.Value != null) {
+        scale = ScaleParameter.Value.Value;
+      } else {
+        scale = Math.Exp(2 * p[c]);
+        c++;
+      }
+      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "p");
     }
 
-    public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      yield return 2.0 * scale;
+    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
+      double scale;
+      GetParameterValues(p, out scale);
+      // create functions
+      var cov = new ParameterizedCovarianceFunction();
+      cov.Covariance = (x, i, j) => scale;
+      cov.CrossCovariance = (x, xt, i, j) => scale;
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, columnIndices);
+      return cov;
     }
 
-    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
-      return scale;
+    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double scale, IEnumerable<int> columnIndices) {
+      yield return 2.0 * scale;
     }
   }