Changeset 13119 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess

Timestamp:

11/05/15 07:49:41 (9 years ago)

Author:

gkronber

Message:

#2497: added input scaling also to Gaussian process classification models

Location:

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

Files:

• GaussianProcessBase.cs (modified) (2 diffs)
• GaussianProcessClassificationModelCreator.cs (modified) (2 diffs)

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

@@ -21 +21 @@
 #endregion
 
-using System;
-using System.Linq;
 using HeuristicLab.Algorithms.GradientDescent;
 using HeuristicLab.Common;
@@ -31 +29 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.PluginInfrastructure;
 using HeuristicLab.Problems.DataAnalysis;
 

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

@@ -62 +62 @@
     public override IOperation Apply() {
       try {
-        var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction);
+        var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction, ScaleInputValues);
         ModelParameter.ActualValue = model;
         NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(model.NegativeLogLikelihood);
         HyperparameterGradientsParameter.ActualValue = new RealVector(model.HyperparameterGradients);
         return base.Apply();
-      }
-      catch (ArgumentException) { }
-      catch (alglib.alglibexception) { }
+      } catch (ArgumentException) { } catch (alglib.alglibexception) { }
       NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(1E300);
       HyperparameterGradientsParameter.ActualValue = new RealVector(Hyperparameter.Count());
@@ -75 +73 @@
     }
 
-    public static IGaussianProcessModel Create(IClassificationProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction) {
-      return new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction);
+    public static IGaussianProcessModel Create(IClassificationProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true) {
+      return new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs);
     }
   }