Changeset 7233

Timestamp:

12/25/11 20:52:38 (12 years ago)

Author:

gkronber

Message:

#1685:

• fixed bug in simplification view for discriminating function classification solutions (incorrect threshold calculation was used)
• fixed bug in linear discriminant analysis algorithm (an additional constant term was added to the model incorrectly)

Location:

trunk/sources

Files:

• HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views/3.4/InteractiveSymbolicDiscriminantFunctionClassificationSolutionSimplifierView.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

@@ -100 +100 @@
       }
 
-      ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
-      cNode.Value = w[w.Length - 1];
-      addition.AddSubtree(cNode);
-
-
       var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
       SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone());

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views/3.4/InteractiveSymbolicDiscriminantFunctionClassificationSolutionSimplifierView.cs

@@ -81 +81 @@
       double[] thresholds;
       // normal distribution cut points are used as thresholds here because they are a lot faster to calculate than the accuracy maximizing thresholds
-      NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(Content.ProblemData, originalOutput, targetClassValues, out classValues, out thresholds);
+      AccuracyMaximizationThresholdCalculator.CalculateThresholds(Content.ProblemData, originalOutput, targetClassValues, out classValues, out thresholds);
       var classifier = new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter);
       classifier.SetThresholdsAndClassValues(thresholds, classValues);
@@ -96 +96 @@
           .LimitToRange(Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit)
           .ToArray();
-        NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(Content.ProblemData, newOutput, targetClassValues, out classValues, out thresholds);
+        AccuracyMaximizationThresholdCalculator.CalculateThresholds(Content.ProblemData, newOutput, targetClassValues, out classValues, out thresholds);
         classifier = new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter);
         classifier.SetThresholdsAndClassValues(thresholds, classValues);