Changeset 4027 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3

Timestamp:

07/12/10 18:46:59 (14 years ago)

Author:

gkronber

Message:

Moved code for calculation of covariance from the scaled MSE evaluator into a separate online evaluator. #1081

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionScaledMeanSquaredErrorEvaluator.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionScaledMeanSquaredErrorEvaluator.cs

@@ -114 +114 @@
       IEnumerator<double> originalEnumerator = original.GetEnumerator();
       IEnumerator<double> estimatedEnumerator = estimated.GetEnumerator();
-
+      OnlineMeanAndVarianceCalculator yVarianceCalculator = new OnlineMeanAndVarianceCalculator();
+      OnlineMeanAndVarianceCalculator tMeanCalculator = new OnlineMeanAndVarianceCalculator();
+      OnlineCovarianceEvaluator ytCovarianceEvaluator = new OnlineCovarianceEvaluator();
       int cnt = 0;
-      double tMean = 0;
-      double yMean = 0;
-      double Cn = 0;
-      double M2 = 0;
 
       while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
@@ -125 +123 @@
         double t = originalEnumerator.Current;
         if (IsValidValue(t) && IsValidValue(y)) {
+          tMeanCalculator.Add(t);
+          yVarianceCalculator.Add(y);
+          ytCovarianceEvaluator.Add(y, t);
+
           cnt++;
-          // online calculation of tMean
-          tMean = tMean + (t - tMean) / cnt;
-          double delta = y - yMean; // delta = (y - yMean(n-1))
-          yMean = yMean + delta / cnt;
-          
-          // online calculation of variance 
-          M2 = M2 + delta * (y - yMean); // M2(n) = M2(n-1) + (y - yMean(n-1)) (y - yMean(n))
-
-          // online calculation of covariance
-          Cn = Cn + delta * (t - tMean); // C(n) = C(n-1) + (y - yMean(n-1)) (t - tMean(n)) 
         }
       }
@@ -145 +137 @@
         beta = 1;
       } else {
-        // yVariance = M2 / cnt;
-        // ytCovariance = Cn / cnt;
-
-        if (M2.IsAlmost(0.0))
+        if (yVarianceCalculator.Variance.IsAlmost(0.0))
           beta = 1;
         else
-          beta = Cn / M2; // omit division by cnt
+          beta = ytCovarianceEvaluator.Covariance / yVarianceCalculator.Variance;
 
-        alpha = tMean - beta * yMean;
+        alpha = tMeanCalculator.Mean - beta * yVarianceCalculator.Mean;
       }
     }