Changeset 5681 for branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

03/15/11 08:25:27 (14 years ago)

Author:

gkronber

Message:

#1418 refactored threshold calculators.

Location:

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis

Files:

• 3.3 (modified) (1 prop)
• 3.4 (modified) (1 prop)
• 3.4/Linear/LinearDiscriminantAnalysis.cs (modified) (1 diff)

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.3

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 obj
+*.vs10x

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 obj
+*.vs10x

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 HeuristicLab.Algorithms.DataAnalysis-3.4.csproj.user
+*.vs10x

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 HeuristicLab.Algorithms.DataAnalysis-3.4.csproj.user
+*.vs10x

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

@@ -120 +120 @@
       IEnumerable<int> rows) {
       string targetVariable = problemData.TargetVariable;
-      List<double> originalClasses = problemData.ClassValues.ToList();
-      int nClasses = problemData.Classes;
-      List<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows).ToList();
-      double maxEstimatedValue = estimatedValues.Max();
-      double minEstimatedValue = estimatedValues.Min();
-      var estimatedTargetValues =
-         (from row in problemData.TrainingIndizes
-          select new { EstimatedValue = estimatedValues[row], TargetValue = problemData.Dataset[targetVariable, row] })
-         .ToList();
+      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
+      var targetClassValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariable, rows);
 
-      Dictionary<double, double> classMean = new Dictionary<double, double>();
-      Dictionary<double, double> classStdDev = new Dictionary<double, double>();
-      // calculate moments per class
-      foreach (var classValue in originalClasses) {
-        var estimatedValuesForClass = from x in estimatedTargetValues
-                                      where x.TargetValue == classValue
-                                      select x.EstimatedValue;
-        double mean, variance;
-        OnlineMeanAndVarianceCalculator.Calculate(estimatedValuesForClass, out mean, out variance);
-        classMean[classValue] = mean;
-        classStdDev[classValue] = Math.Sqrt(variance);
-      }
-      List<double> thresholds = new List<double>();
-      for (int i = 0; i < nClasses - 1; i++) {
-        for (int j = i + 1; j < nClasses; j++) {
-          double x1, x2;
-          double class0 = originalClasses[i];
-          double class1 = originalClasses[j];
-          // calculate all thresholds
-          CalculateCutPoints(classMean[class0], classStdDev[class0], classMean[class1], classStdDev[class1], out x1, out x2);
-          if (!thresholds.Any(x => x.IsAlmost(x1))) thresholds.Add(x1);
-          if (!thresholds.Any(x => x.IsAlmost(x2))) thresholds.Add(x2);
-        }
-      }
-      thresholds.Sort();
-      thresholds.Insert(0, double.NegativeInfinity);
-      thresholds.Add(double.PositiveInfinity);
-      List<double> classValues = new List<double>();
-      for (int i = 0; i < thresholds.Count - 1; i++) {
-        double m;
-        if (double.IsNegativeInfinity(thresholds[i])) {
-          m = thresholds[i + 1] - 1.0;
-        } else if (double.IsPositiveInfinity(thresholds[i + 1])) {
-          m = thresholds[i] + 1.0;
-        } else {
-          m = thresholds[i] + (thresholds[i + 1] - thresholds[i]) / 2.0;
-        }
-
-        double maxDensity = 0;
-        double maxDensityClassValue = -1;
-        foreach (var classValue in originalClasses) {
-          double density = NormalDensity(m, classMean[classValue], classStdDev[classValue]);
-          if (density > maxDensity) {
-            maxDensity = density;
-            maxDensityClassValue = classValue;
-          }
-        }
-        classValues.Add(maxDensityClassValue);
-      }
-      List<double> filteredThresholds = new List<double>();
-      List<double> filteredClassValues = new List<double>();
-      filteredThresholds.Add(thresholds[0]);
-      filteredClassValues.Add(classValues[0]);
-      for (int i = 0; i < classValues.Count - 1; i++) {
-        if (classValues[i] != classValues[i + 1]) {
-          filteredThresholds.Add(thresholds[i + 1]);
-          filteredClassValues.Add(classValues[i + 1]);
-        }
-      }
-      filteredThresholds.Add(double.PositiveInfinity);
-
-      return new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, filteredClassValues, filteredThresholds);
-    }
-
-    private static double NormalDensity(double x, double mu, double sigma) {
-      return (1.0 / Math.Sqrt(2.0 * Math.PI * sigma * sigma)) * Math.Exp(-((x - mu) * (x - mu)) / (2.0 * sigma * sigma));
-    }
-
-    private static void CalculateCutPoints(double m1, double s1, double m2, double s2, out double x1, out double x2) {
-      double a = (s1 * s1 - s2 * s2);
-      x1 = -(-m2 * s1 * s1 + m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a;
-      x2 = (m2 * s1 * s1 - m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a;
+      double[] classValues;
+      double[] thresholds;
+      NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(problemData, estimatedValues, targetClassValues, out classValues, out thresholds);
+      return new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, classValues, thresholds);
     }
   }

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 obj
+*.vs10x

@@ -3 +3 @@
 HeuristicLabAlgorithmsDataAnalysisPlugin.cs
 HeuristicLab.Algorithms.DataAnalysis-3.4.csproj.user
+*.vs10x