Changeset 8494

Timestamp:

08/16/12 16:33:50 (12 years ago)

Author:

gkronber

Message:

#1902 changed mean and covariance function parameters to simple value parameters instead of constrained value parameters and changed the solution creator so that it doesn't throw an exception if no model could be found.

Location:

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

Files:

• GaussianProcessRegression.cs (modified) (4 diffs)
• GaussianProcessRegressionSolutionCreator.cs (modified) (1 diff)

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

@@ -22 +22 @@
 
 using System;
-using System.Collections.Generic;
-using System.Linq;
 using HeuristicLab.Algorithms.GradientDescent;
 using HeuristicLab.Common;
@@ -32 +30 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.PluginInfrastructure;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -59 +56 @@
 
     #region parameter properties
-    public IConstrainedValueParameter<IMeanFunction> MeanFunctionParameter {
+    public IValueParameter<IMeanFunction> MeanFunctionParameter {
       get { return (IConstrainedValueParameter<IMeanFunction>)Parameters[MeanFunctionParameterName]; }
     }
-    public IConstrainedValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
+    public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
       get { return (IConstrainedValueParameter<ICovarianceFunction>)Parameters[CovarianceFunctionParameterName]; }
     }
@@ -104 +101 @@
       Problem = new RegressionProblem();
 
-      List<IMeanFunction> meanFunctions = ApplicationManager.Manager.GetInstances<IMeanFunction>().ToList();
-      List<ICovarianceFunction> covFunctions = ApplicationManager.Manager.GetInstances<ICovarianceFunction>().ToList();
-
-      Parameters.Add(new ConstrainedValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.",
-        new ItemSet<IMeanFunction>(meanFunctions), meanFunctions.OfType<MeanConst>().First()));
-      Parameters.Add(new ConstrainedValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.",
-        new ItemSet<ICovarianceFunction>(covFunctions), covFunctions.OfType<CovarianceSEiso>().First()));
+      Parameters.Add(new ValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.", new MeanConst()));
+      Parameters.Add(new ValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSEiso()));
       Parameters.Add(new ValueParameter<IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
       Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));

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

@@ -75 +75 @@
 
     public override IOperation Apply() {
-      var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
-      var data = (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone();
-      var s = new GaussianProcessRegressionSolution(m, data);
+      if (ModelParameter.ActualValue != null) {
+        var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
+        var data = (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone();
+        var s = new GaussianProcessRegressionSolution(m, data);
 
 
-      SolutionParameter.ActualValue = s;
-      var results = ResultsParameter.ActualValue;
-      if (!results.ContainsKey(SolutionParameterName)) {
-        results.Add(new Result(SolutionParameterName, "The Gaussian process regression solution", s));
-        results.Add(new Result(TrainingRSquaredResultName, "The Pearson's R² of the Gaussian process solution on the training partition.", new DoubleValue(s.TrainingRSquared)));
-        results.Add(new Result(TestRSquaredResultName, "The Pearson's R² of the Gaussian process solution on the test partition.", new DoubleValue(s.TestRSquared)));
-      } else {
-        results[SolutionParameterName].Value = s;
-        results[TrainingRSquaredResultName].Value = new DoubleValue(s.TrainingRSquared);
-        results[TestRSquaredResultName].Value = new DoubleValue(s.TestRSquared);
+        SolutionParameter.ActualValue = s;
+        var results = ResultsParameter.ActualValue;
+        if (!results.ContainsKey(SolutionParameterName)) {
+          results.Add(new Result(SolutionParameterName, "The Gaussian process regression solution", s));
+          results.Add(new Result(TrainingRSquaredResultName,
+                                 "The Pearson's R² of the Gaussian process solution on the training partition.",
+                                 new DoubleValue(s.TrainingRSquared)));
+          results.Add(new Result(TestRSquaredResultName,
+                                 "The Pearson's R² of the Gaussian process solution on the test partition.",
+                                 new DoubleValue(s.TestRSquared)));
+        } else {
+          results[SolutionParameterName].Value = s;
+          results[TrainingRSquaredResultName].Value = new DoubleValue(s.TrainingRSquared);
+          results[TestRSquaredResultName].Value = new DoubleValue(s.TestRSquared);
+        }
       }
       return base.Apply();