Changeset 8982 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialIso.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/CovarianceSquaredExponentialIso.cs (modified) (6 diffs)

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

@@ -22 +22 @@
 using System;
 using System.Collections.Generic;
+using System.Linq.Expressions;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
@@ -32 +34 @@
     Description = "Isotropic squared exponential covariance function for Gaussian processes.")]
   public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction {
-    [Storable]
-    private double sf2;
-    [Storable]
-    private readonly HyperParameter<DoubleValue> scaleParameter;
-    public IValueParameter<DoubleValue> ScaleParameter { get { return scaleParameter; } }
+    public IValueParameter<DoubleValue> ScaleParameter {
+      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
+    }
 
-    [Storable]
-    private double inverseLength;
-    [Storable]
-    private readonly HyperParameter<DoubleValue> inverseLengthParameter;
-    public IValueParameter<DoubleValue> InverseLengthParameter { get { return inverseLengthParameter; } }
+    public IValueParameter<DoubleValue> InverseLengthParameter {
+      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
+    }
 
     [StorableConstructor]
@@ -51 +49 @@
     private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner)
       : base(original, cloner) {
-      this.sf2 = original.sf2;
-      this.scaleParameter = cloner.Clone(original.scaleParameter);
-
-      this.inverseLength = original.inverseLength;
-      this.inverseLengthParameter = cloner.Clone(original.inverseLengthParameter);
-
-      RegisterEvents();
     }
 
@@ -65 +56 @@
       Description = ItemDescription;
 
-      this.scaleParameter = new HyperParameter<DoubleValue>("Scale", "The scale parameter of the isometric squared exponential covariance function.");
-      this.inverseLengthParameter = new HyperParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function.");
-
-      Parameters.Add(scaleParameter);
-      Parameters.Add(inverseLengthParameter);
-
-      RegisterEvents();
+      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric squared exponential covariance function."));
+      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function."));
     }
 
@@ -78 +64 @@
     }
 
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      RegisterEvents();
+    public int GetNumberOfParameters(int numberOfVariables) {
+      return
+        (ScaleParameter.Value != null ? 0 : 1) +
+        (InverseLengthParameter.Value != null ? 0 : 1);
     }
 
-    private void RegisterEvents() {
-      Util.AttachValueChangeHandler<DoubleValue, double>(scaleParameter, () => { sf2 = scaleParameter.Value.Value; });
-      Util.AttachValueChangeHandler<DoubleValue, double>(inverseLengthParameter, () => { inverseLength = inverseLengthParameter.Value.Value; });
-    }
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      return
-        (scaleParameter.Fixed ? 0 : 1) +
-        (inverseLengthParameter.Fixed ? 0 : 1);
-    }
-
-    public void SetParameter(double[] hyp) {
-      int i = 0;
-      if (!inverseLengthParameter.Fixed) {
-        inverseLengthParameter.SetValue(new DoubleValue(1.0 / Math.Exp(hyp[i])));
-        i++;
-      }
-      if (!scaleParameter.Fixed) {
-        scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[i])));
-        i++;
-      }
-      if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "hyp");
+    public void SetParameter(double[] p) {
+      double scale, inverseLength;
+      GetParameterValues(p, out scale, out inverseLength);
+      ScaleParameter.Value = new DoubleValue(scale);
+      InverseLengthParameter.Value = new DoubleValue(inverseLength);
     }
 
 
-    public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
-      double d = i == j
-                   ? 0.0
-                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
-      return sf2 * Math.Exp(-d / 2.0);
+    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
+      // gather parameter values
+      int c = 0;
+      if (InverseLengthParameter.Value != null) {
+        inverseLength = InverseLengthParameter.Value.Value;
+      } else {
+        inverseLength = 1.0 / Math.Exp(p[c]);
+        c++;
+      }
+
+      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 CovarianceSquaredExponentialIso", "p");
     }
 
-    public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
+    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
+      double inverseLength, scale;
+      GetParameterValues(p, out scale, out inverseLength);
+      // create functions
+      var cov = new ParameterizedCovarianceFunction();
+      cov.Covariance = (x, i, j) => {
+        double d = i == j
+                ? 0.0
+                : Util.SqrDist(x, i, j, inverseLength, columnIndices);
+        return scale * Math.Exp(-d / 2.0);
+      };
+      cov.CrossCovariance = (x, xt, i, j) => {
+        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
+        return scale * Math.Exp(-d / 2.0);
+      };
+      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, inverseLength, columnIndices);
+      return cov;
+    }
+
+    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, IEnumerable<int> columnIndices) {
       double d = i == j
                    ? 0.0
@@ -123 +124 @@
       yield return 2.0 * sf2 * g;
     }
-
-    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
-      double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
-      return sf2 * Math.Exp(-d / 2.0);
-    }
   }
 }