Changeset 13145 for stable

Timestamp:

11/13/15 20:47:03 (9 years ago)

Author:

gkronber

Message:

#2502 merged r13121 from trunk to stable branch

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs (modified) (7 diffs)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs (modified) (1 diff)

stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs

@@ -70 +70 @@
         var mean = Content.EstimatedTrainingValues.ToArray();
         var s2 = Content.EstimatedTrainingVariance.ToArray();
-        var lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-        var upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+        var lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+        var upper = mean.Zip(s2, GetUpperConfBound).ToArray();
         this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndices.ToArray(), lower, upper);
         this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME]);
@@ -83 +83 @@
         mean = Content.EstimatedTestValues.ToArray();
         s2 = Content.EstimatedTestVariance.ToArray();
-        lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-        upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+        lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+        upper = mean.Zip(s2, GetUpperConfBound).ToArray();
         this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndices.ToArray(), lower, upper);
         this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME]);
@@ -93 +93 @@
         mean = Content.EstimatedValues.ToArray();
         s2 = Content.EstimatedVariance.ToArray();
-        lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-        upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+        lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+        upper = mean.Zip(s2, GetUpperConfBound).ToArray();
         List<double> allLower = allIndices.Select(index => lower[index]).ToList();
         List<double> allUpper = allIndices.Select(index => upper[index]).ToList();
@@ -265 +265 @@
             mean = Content.EstimatedValues.ToArray();
             s2 = Content.EstimatedVariance.ToArray();
-            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+            lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+            upper = mean.Zip(s2, GetUpperConfBound).ToArray();
             lower = indices.Select(index => lower[index]).ToArray();
             upper = indices.Select(index => upper[index]).ToArray();
@@ -274 +274 @@
             mean = Content.EstimatedTrainingValues.ToArray();
             s2 = Content.EstimatedTrainingVariance.ToArray();
-            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+            lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+            upper = mean.Zip(s2, GetUpperConfBound).ToArray();
             break;
           case ESTIMATEDVALUES_TEST_SERIES_NAME:
@@ -281 +281 @@
             mean = Content.EstimatedTestValues.ToArray();
             s2 = Content.EstimatedTestVariance.ToArray();
-            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
-            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
+            lower = mean.Zip(s2, GetLowerConfBound).ToArray();
+            upper = mean.Zip(s2, GetUpperConfBound).ToArray();
             break;
         }
@@ -295 +295 @@
     }
 
+    private double GetLowerConfBound(double m, double s) {
+      return m - 1.96 * Math.Sqrt(s);
+    }
+
+
+    private double GetUpperConfBound(double m, double s) {
+      return m + 1.96 * Math.Sqrt(s);
+    }
+
     // workaround as per http://stackoverflow.com/questions/5744930/datapointcollection-clear-performance
     private static void ClearPointsQuick(DataPointCollection points) {

stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs

@@ -355 +355 @@
       for (int i = 0; i < newN; i++) {
         var sumV = Util.ScalarProd(Util.GetCol(sWKs, i), Util.GetCol(sWKs, i));
+        kss[i] += sqrSigmaNoise; // kss is V(f), add noise variance of predictive distibution to get V(y)
         kss[i] -= sumV;
         if (kss[i] < 0) kss[i] = 0;