Changeset 8664 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs

Timestamp:

09/17/12 11:18:40 (12 years ago)

Author:

mkommend

Message:

#1951:

• Added linear scaling parameter to data analysis problems.
• Adapted interfaces, evaluators and analyzers accordingly.
• Added OnlineBoundedMeanSquaredErrorCalculator.
• Adapted symbolic regression sample unit test.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanSquaredErrorCalculator.cs) (5 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs

@@ -24 +24 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineMeanSquaredErrorCalculator : IOnlineCalculator {
+  public class OnlineBoundedMeanSquaredErrorCalculator : IOnlineCalculator {
 
-    private double sse;
+    private double errorSum;
     private int n;
-    public double MeanSquaredError {
+    public double BoundedMeanSquaredError {
       get {
-        return n > 0 ? sse / n : 0.0;
+        return n > 0 ? errorSum / n : 0.0;
       }
     }
 
-    public OnlineMeanSquaredErrorCalculator() {
+    public double LowerBound { get; private set; }
+    public double UpperBound { get; private set; }
+
+
+    public OnlineBoundedMeanSquaredErrorCalculator(double lowerBound, double upperbound) {
+      LowerBound = lowerBound;
+      UpperBound = upperbound;
       Reset();
     }
@@ -44 +50 @@
     }
     public double Value {
-      get { return MeanSquaredError; }
+      get { return BoundedMeanSquaredError; }
     }
     public void Reset() {
       n = 0;
-      sse = 0.0;
+      errorSum = 0.0;
       errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
@@ -58 +64 @@
       } else {
         double error = estimated - original;
-        sse += error * error;
+        if (estimated < LowerBound || estimated > UpperBound)
+          errorSum += Math.Abs(error);
+        else
+          errorSum += error * error;
         n++;
         errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
@@ -65 +74 @@
     #endregion
 
-    public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
+    public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, double lowerBound, double upperBound, out OnlineCalculatorError errorState) {
       IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
       IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
-      OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
+      OnlineBoundedMeanSquaredErrorCalculator boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowerBound, upperBound);
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -74 +83 @@
         double original = originalEnumerator.Current;
         double estimated = estimatedEnumerator.Current;
-        mseCalculator.Add(original, estimated);
-        if (mseCalculator.ErrorState != OnlineCalculatorError.None) break;
+        boundedMseCalculator.Add(original, estimated);
+        if (boundedMseCalculator.ErrorState != OnlineCalculatorError.None) break;
       }
 
       // check if both enumerators are at the end to make sure both enumerations have the same length
-      if (mseCalculator.ErrorState == OnlineCalculatorError.None &&
+      if (boundedMseCalculator.ErrorState == OnlineCalculatorError.None &&
          (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
         throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
       } else {
-        errorState = mseCalculator.ErrorState;
-        return mseCalculator.MeanSquaredError;
+        errorState = boundedMseCalculator.ErrorState;
+        return boundedMseCalculator.BoundedMeanSquaredError;
       }
     }