Changeset 8660 for branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators

Timestamp:

09/14/12 18:58:15 (12 years ago)

Author:

gkronber

Message:

#1847 merged r8205:8635 from trunk into branch

Location:

branches/GP-MoveOperators

Files:

• . (modified) (2 props)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs (modified) (2 diffs)

branches/GP-MoveOperators

@@ -21 +21 @@
 protoc.exe
 _ReSharper.HeuristicLab 3.3 Tests
+Google.ProtocolBuffers-2.4.1.473.dll

@@ -21 +21 @@
 protoc.exe
 _ReSharper.HeuristicLab 3.3 Tests
+Google.ProtocolBuffers-2.4.1.473.dll

branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs

@@ -85 +85 @@
             //all positives
             if (pair.TargetClassValue.IsAlmost(classValues[i - 1])) {
-              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue < actualThreshold)
+              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue <= actualThreshold)
                 //true positive
-                classificationScore += problemData.GetClassificationPenalty(classValues[i - 1], classValues[i - 1]);
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, pair.TargetClassValue);
               else
                 //false negative
-                classificationScore += problemData.GetClassificationPenalty(classValues[i], classValues[i - 1]);
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i]);
             }
               //all negatives
             else {
-              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue < actualThreshold)
-                //false positive
-                classificationScore += problemData.GetClassificationPenalty(classValues[i - 1], classValues[i]);
-              else
-                //true negative, consider only upper class
-                classificationScore += problemData.GetClassificationPenalty(classValues[i], classValues[i]);
+              //false positive
+              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue <= actualThreshold)
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i - 1]);
+              else if (pair.EstimatedValue <= lowerThreshold)
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i - 2]);
+              else if (pair.EstimatedValue > actualThreshold) {
+                if (pair.TargetClassValue < classValues[i - 1]) //negative in wrong class, consider upper class
+                  classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i]);
+                else //true negative, must be optimized by the other thresholds
+                  classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, pair.TargetClassValue);
+              }
             }
           }

branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs

@@ -107 +107 @@
         double maxDensityClassValue = -1;
         foreach (var classValue in originalClasses) {
-          double density = NormalDensity(m, classMean[classValue], classStdDev[classValue]);
+          double density = LogNormalDensity(m, classMean[classValue], classStdDev[classValue]);
           if (density > maxDensity) {
             maxDensity = density;
@@ -139 +139 @@
     }
 
-    private static double NormalDensity(double x, double mu, double sigma) {
-      if (sigma.IsAlmost(0.0)) {
-        if (x.IsAlmost(mu)) return 1.0; else return 0.0;
-      } else {
-        return (1.0 / Math.Sqrt(2.0 * Math.PI * sigma * sigma)) * Math.Exp(-((x - mu) * (x - mu)) / (2.0 * sigma * sigma));
-      }
+    private static double LogNormalDensity(double x, double mu, double sigma) {
+      return -0.5 * Math.Log(2.0 * Math.PI * sigma * sigma) - ((x - mu) * (x - mu)) / (2.0 * sigma * sigma);
     }
 

@@ -21 +21 @@
 protoc.exe
 _ReSharper.HeuristicLab 3.3 Tests
+Google.ProtocolBuffers-2.4.1.473.dll