Changeset 8664 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs

Timestamp:

09/17/12 11:18:40 (12 years ago)

Author:

mkommend

Message:

#1951:

• Added linear scaling parameter to data analysis problems.
• Adapted interfaces, evaluators and analyzers accordingly.
• Added OnlineBoundedMeanSquaredErrorCalculator.
• Adapted symbolic regression sample unit test.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs

@@ -22 +22 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Parameters;
@@ -37 +36 @@
     private const string ModelCreatorParameterName = "ModelCreator";
     private const string EstimationLimitsParameterName = "EstimationLimits";
-    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
 
     #region parameter properties
     public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
       get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
-    }
-    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
-      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
     }
     public IValueLookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
@@ -54 +49 @@
     #endregion
 
-    #region properties
-    public BoolValue ApplyLinearScaling {
-      get { return ApplyLinearScalingParameter.Value; }
-    }
-    #endregion
     [StorableConstructor]
     private SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
@@ -65 +55 @@
       : base() {
       Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The loewr and upper limit for the estimated values produced by the symbolic classification model."));
-      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
       Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
     }
@@ -72 +61 @@
     }
 
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      if (!Parameters.ContainsKey(ModelCreatorParameterName))
-        Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
-    }
 
     protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQualities) {
       var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
-      if (ApplyLinearScaling.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
+      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
 
       model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);