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/branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification (added)	merged: 12204,12209-12211,12216,12218,12302,12309,12311,12409,12416-12418,12448-12449,12486-12487,13401
/trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification	merged: 16763,16788

branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4.csproj

r16662	r16892
126	126	<Compile Include="ModelCreators\NormalDistributedThresholdsModelCreator.cs" />
127	127	<Compile Include="MultiObjective\SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs" />
	128	<Compile Include="SingleObjective\SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs" />
	129	<Compile Include="SingleObjective\SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator.cs" />
128	130	<Compile Include="SymbolicClassificationPhenotypicDiversityAnalyzer.cs" />
129	131	<Compile Include="SymbolicClassificationPruningAnalyzer.cs" />

r16662	r16892
81	81	var classificationProblemData = problemData as IClassificationProblemData;
82	82	if (classificationProblemData == null)
83		throw new ArgumentException("The problem data is not ~~a regression problem data~~. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
	83	throw new ArgumentException("The problem data is not compatible with this classification model. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
84	84	return IsProblemDataCompatible(classificationProblemData, out errorMessage);
85	85	}

-                      r16662
+                      r16892
     public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+      var estimatedValues = GetEstimatedValues(dataset, rows);
+      return GetEstimatedClassValues(estimatedValues);
+    }
+    public IEnumerable<double> GetEstimatedClassValues(IEnumerable<double> estimatedValues) {
       if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model.");
       foreach (var x in GetEstimatedValues(dataset, rows)) {
+      foreach (var x in estimatedValues) {
         int classIndex = 0;
         // find first threshold value which is larger than x => class index = threshold index + 1
 …
+      }
+    }
     public override ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {

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