Changeset 5010 for branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic

Timestamp:

12/02/10 15:25:55 (14 years ago)

Author:

gkronber

Message:

commit of local changes in data-analysis feature exploration branch. #1142

Location:

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic

Files:

• Analyzers/OverfittingAnalyzer.cs (modified) (5 diffs)
• SymbolicRegressionSolution.cs (modified) (1 diff)

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/OverfittingAnalyzer.cs

@@ -119 +119 @@
       get { return (ILookupParameter<DoubleValue>)Parameters["InitialTrainingQuality"]; }
     }
-    public ILookupParameter<DoubleMatrix> TrainingAndValidationQualitiesParameter {
-      get { return (ILookupParameter<DoubleMatrix>)Parameters["TrainingAndValidationQualities"]; }
+    public ILookupParameter<ItemList<DoubleMatrix>> TrainingAndValidationQualitiesParameter {
+      get { return (ILookupParameter<ItemList<DoubleMatrix>>)Parameters["TrainingAndValidationQualities"]; }
     }
     public IValueLookupParameter<DoubleValue> PercentileParameter {
@@ -187 +187 @@
       Parameters.Add(new LookupParameter<ResultCollection>("Results"));
       Parameters.Add(new LookupParameter<DoubleValue>("InitialTrainingQuality"));
-      Parameters.Add(new LookupParameter<DoubleMatrix>("TrainingAndValidationQualities"));
+      Parameters.Add(new LookupParameter<ItemList<DoubleMatrix>>("TrainingAndValidationQualities"));
       Parameters.Add(new ValueLookupParameter<DoubleValue>("Percentile", new DoubleValue(1)));
 
@@ -207 +207 @@
       //}
       if (!Parameters.ContainsKey("TrainingAndValidationQualities")) {
-        Parameters.Add(new LookupParameter<DoubleMatrix>("TrainingAndValidationQualities"));
+        Parameters.Add(new LookupParameter<ItemList<DoubleMatrix>>("TrainingAndValidationQualities"));
       }
       if (!Parameters.ContainsKey("Percentile")) {
@@ -290 +290 @@
       double[] validationArr = new double[n];
       double[] trainingArr = new double[n];
-      //double[,] qualitiesArr = new double[n, 2];
+      double[,] qualitiesArr = new double[n, 2];
       for (int i = 0; i < n; i++) {
         validationArr[i] = orderedDistinctPairs[i].Validation;
         trainingArr[i] = orderedDistinctPairs[i].Training;
 
-        //qualitiesArr[i, 0] = trainingArr[i];
-        //qualitiesArr[i, 1] = validationArr[i];
+        qualitiesArr[i, 0] = trainingArr[i];
+        qualitiesArr[i, 1] = validationArr[i];
       }
       double r = alglib.correlation.spearmanrankcorrelation(trainingArr, validationArr, n);
@@ -309 +309 @@
 
       OverfittingParameter.ActualValue = new BoolValue(overfitting);
-      //TrainingAndValidationQualitiesParameter.ActualValue = new DoubleMatrix(qualitiesArr);
+      ItemList<DoubleMatrix> list = TrainingAndValidationQualitiesParameter.ActualValue;
+      if (list == null) {
+        TrainingAndValidationQualitiesParameter.ActualValue = new ItemList<DoubleMatrix>();
+      }
+      TrainingAndValidationQualitiesParameter.ActualValue.Add(new DoubleMatrix(qualitiesArr));
       return base.Apply();
     }

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionSolution.cs

@@ -61 +61 @@
           let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, x))
           select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX;
-      estimatedValues = Enumerable.Repeat(double.NaN, Math.Abs(minLag)).Concat(calculatedValues).ToList();
+      estimatedValues = Enumerable.Repeat(UpperEstimationLimit, Math.Abs(minLag)).Concat(calculatedValues).ToList();
       OnEstimatedValuesChanged();
     }