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

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

@@ -22 +22 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
@@ -32 +34 @@
     Description = "Noise covariance function for Gaussian processes.")]
   public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {
-
-
-    [Storable]
-    private double sf2;
-    [Storable]
-    private readonly HyperParameter<DoubleValue> scaleParameter;
     public IValueParameter<DoubleValue> ScaleParameter {
-      get { return scaleParameter; }
+      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
     }
 
@@ -49 +45 @@
     private CovarianceNoise(CovarianceNoise original, Cloner cloner)
       : base(original, cloner) {
-      this.scaleParameter = cloner.Clone(original.scaleParameter);
-      this.sf2 = original.sf2;
-      RegisterEvents();
     }
 
@@ -59 +52 @@
       Description = ItemDescription;
 
-      this.scaleParameter = new HyperParameter<DoubleValue>("Scale", "The scale of noise.");
-      Parameters.Add(this.scaleParameter);
-
-      RegisterEvents();
+      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of noise."));
     }
 
@@ -69 +59 @@
     }
 
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      RegisterEvents();
+    public int GetNumberOfParameters(int numberOfVariables) {
+      return ScaleParameter.Value != null ? 0 : 1;
     }
 
-    private void RegisterEvents() {
-      Util.AttachValueChangeHandler<DoubleValue, double>(scaleParameter, () => { sf2 = scaleParameter.Value.Value; });
+    public void SetParameter(double[] p) {
+      double scale;
+      GetParameterValues(p, out scale);
+      ScaleParameter.Value = new DoubleValue(scale);
     }
 
-    public int GetNumberOfParameters(int numberOfVariables) {
-      return scaleParameter.Fixed ? 0 : 1;
+    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 CovarianceNoise", "p");
     }
 
-    public void SetParameter(double[] hyp) {
-      if (!scaleParameter.Fixed) {
-        scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[0])));
-      } else {
-        if (hyp.Length > 0) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceNoise", "hyp");
-      }
-    }
-
-    public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      return i == j ? sf2 : 0.0;
-    }
-
-    public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      yield return i == j ? 2 * sf2 : 0.0;
-    }
-
-    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
-      return 0.0;
+    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) => i == j ? scale : 0.0;
+      cov.CrossCovariance = (x, xt, i, j) => 0.0;
+      cov.CovarianceGradient = (x, i, j) => Enumerable.Repeat(i == j ? 2.0 * scale : 0.0, 1);
+      return cov;
     }
   }