Changeset 7183 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs

Timestamp:

12/13/11 13:52:22 (12 years ago)

Author:

gkronber

Message:

#1081 implemented remaining metrics for time series prognosis solutions, added estimation limits fixed training and validation best solution analyzers, implemented overfitting analyzer.

File:

• branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs (modified) (4 diffs)

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs

@@ -57 +57 @@
     protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
       Dictionary<ISymbolicExpressionTreeNode, double> replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
-      foreach (ISymbolicExpressionTreeNode node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
-        replacementValues[node] = CalculateReplacementValue(node, tree);
-      }
+      foreach (var componentBranch in tree.Root.GetSubtree(0).Subtrees)
+        foreach (ISymbolicExpressionTreeNode node in componentBranch.IterateNodesPrefix()) {
+          replacementValues[node] = CalculateReplacementValue(node, tree);
+        }
       return replacementValues;
     }
@@ -72 +73 @@
         .ToArray();
       int i = 0;
+      int nTargetVariables = Content.ProblemData.TargetVariables.Count();
       foreach (var targetVariable in Content.ProblemData.TargetVariables) {
         List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(i).IterateNodesPostfix().ToList();
         var targetValues = dataset.GetDoubleValues(targetVariable, rows);
         OnlineCalculatorError errorState;
-        double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput.Select(v => v.ElementAt(i)), out errorState);
+        double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput.Skip(i).TakeEvery(nTargetVariables), out errorState);
         if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
 
@@ -85 +87 @@
           SwitchNode(parent, node, replacementNode);
           var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, Content.ProblemData.TargetVariables.ToArray(), rows, 1);
-          double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput.Select(v => v.First().ElementAt(i)), out errorState);
+          double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput.Skip(i).TakeEvery(nTargetVariables), out errorState);
           if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
 
@@ -112 +114 @@
       var rows = Content.ProblemData.TrainingIndizes;
       var allPrognosedValues = interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, Content.ProblemData.TargetVariables.ToArray(), rows, 1);
-      
-      return allPrognosedValues.Select(x=>x.First().First()).Median();
+
+      return allPrognosedValues.Median();
     }