Changeset 8639

Timestamp:

09/13/12 09:48:24 (12 years ago)

Author:

mkommend

Message:

#1951: Changed symbolic regression evaluator and scale method in the symbolic regression model to ignore invalid (NaN & infinite) values during calculation of the scaling parameters.

Location:

trunk/sources

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionModel.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs

@@ -50 +50 @@
 
     protected override void UpdateModel(ISymbolicExpressionTree tree) {
-      var model = new SymbolicRegressionModel(tree, Content.Model.Interpreter);
+      var model = new SymbolicRegressionModel(tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit);
       SymbolicRegressionModel.Scale(model, Content.ProblemData);
       Content.Model = model;

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs

@@ -78 +78 @@
         double estimated = estimatedValuesEnumerator.Current;
         cache[i] = estimated;
-        linearScalingCalculator.Add(estimated, target);
+        if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
+          linearScalingCalculator.Add(estimated, target);
         i++;
       }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionModel.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using HeuristicLab.Common;
@@ -76 +77 @@
       var estimatedValues = model.Interpreter.GetSymbolicExpressionTreeValues(model.SymbolicExpressionTree, dataset, rows);
       var targetValues = dataset.GetDoubleValues(targetVariable, rows);
-      double alpha;
-      double beta;
-      OnlineCalculatorError errorState;
-      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta, out errorState);
-      if (errorState != OnlineCalculatorError.None) return;
+
+      var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();
+      var targetValuesEnumerator = targetValues.GetEnumerator();
+      var estimatedValuesEnumerator = estimatedValues.GetEnumerator();
+      while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) {
+        double target = targetValuesEnumerator.Current;
+        double estimated = estimatedValuesEnumerator.Current;
+        if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
+          linearScalingCalculator.Add(estimated, target);
+      }
+      if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
+        throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
+
+      double alpha = linearScalingCalculator.Alpha;
+      double beta = linearScalingCalculator.Beta;
+      if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) return;
 
       ConstantTreeNode alphaTreeNode = null;