Changeset 16847 for branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/ComplexLeaf.cs

Timestamp:

04/19/19 13:06:11 (5 years ago)

Author:

gkronber

Message:

#2847: made some minor changes while reviewing

File:

• branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/ComplexLeaf.cs (modified) (2 diffs)

branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/ComplexLeaf.cs

@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis;
+using HEAL.Attic;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item("ComplexLeaf", "A leaf type that uses an arbitriary RegressionAlgorithm to create leaf models")]
+  [StorableType("F34A0ED9-2CF6-4DEE-850D-08790663B66D")]
+  [Item("ComplexLeaf", "A leaf type that uses an arbitrary RegressionAlgorithm to create leaf models")]
   public class ComplexLeaf : LeafBase {
     public const string RegressionParameterName = "Regression";
     public IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>> RegressionParameter {
-      get { return Parameters[RegressionParameterName] as IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>; }
+      get { return (IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>)Parameters[RegressionParameterName]; }
     }
     public IDataAnalysisAlgorithm<IRegressionProblem> Regression {
       get { return RegressionParameter.Value; }
+      set { RegressionParameter.Value = value; }
     }
 
     #region Constructors & Cloning
     [StorableConstructor]
-    private ComplexLeaf(bool deserializing) : base(deserializing) { }
+    private ComplexLeaf(StorableConstructorFlag _) : base(_) { }
     private ComplexLeaf(ComplexLeaf original, Cloner cloner) : base(original, cloner) { }
     public ComplexLeaf() {
@@ -61 +62 @@
       if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
       noParameters = pd.Dataset.Rows + 1;
-      Regression.Problem = new RegressionProblem {ProblemData = pd};
+      Regression.Problem = new RegressionProblem { ProblemData = pd };
       var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
       var t = res.Select(x => x.Value).OfType<IRegressionSolution>().FirstOrDefault();