Changeset 13823 for branches

Timestamp:

05/03/16 09:47:26 (8 years ago)

Author:

pfleck

Message:

#2597:

• Added new interface IConfidenceBoundRegressionModel and IConfidenceBoundRegressionSolution for regression models/solutions that support confidence information on estimated samples.
• Renamed GetEstimatedVariance to GetEstimatedVariances to be consistent with GetEstimatedValues.

Location:

branches/HeuristicLab.RegressionSolutionGradientView

Files:

• HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionEstimatedValuesView.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionInteractiveRangeEstimatorView.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs (modified) (6 diffs)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessRegressionSolution.cs (modified) (2 diffs)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessModel.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IGaussianProcessModel.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionSolution.cs (modified) (1 diff)

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionEstimatedValuesView.cs

@@ -53 +53 @@
       var testRows = Content.ProblemData.TestIndices;
 
-      var estimated_var_training = Content.GetEstimatedVariance(trainingRows).GetEnumerator();
-      var estimated_var_test = Content.GetEstimatedVariance(testRows).GetEnumerator();
+      var estimated_var_training = Content.GetEstimatedVariances(trainingRows).GetEnumerator();
+      var estimated_var_test = Content.GetEstimatedVariances(testRows).GetEnumerator();
 
       foreach (var row in Content.ProblemData.TrainingIndices) {

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionInteractiveRangeEstimatorView.cs

@@ -114 +114 @@
       var model = Content.Model;
       var means = model.GetEstimatedValues(dataset, Enumerable.Range(0, DrawingSteps)).ToList();
-      var variances = model.GetEstimatedVariance(dataset, Enumerable.Range(0, DrawingSteps)).ToList();
+      var variances = model.GetEstimatedVariances(dataset, Enumerable.Range(0, DrawingSteps)).ToList();
 
       // Charting config

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs

@@ -69 +69 @@
         this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].EmptyPointStyle.Color = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Color;
         var mean = Content.EstimatedTrainingValues.ToArray();
-        var s2 = Content.EstimatedTrainingVariance.ToArray();
+        var s2 = Content.EstimatedTrainingVariances.ToArray();
         var lower = mean.Zip(s2, GetLowerConfBound).ToArray();
         var upper = mean.Zip(s2, GetUpperConfBound).ToArray();
@@ -82 +82 @@
 
         mean = Content.EstimatedTestValues.ToArray();
-        s2 = Content.EstimatedTestVariance.ToArray();
+        s2 = Content.EstimatedTestVariances.ToArray();
         lower = mean.Zip(s2, GetLowerConfBound).ToArray();
         upper = mean.Zip(s2, GetUpperConfBound).ToArray();
@@ -92 +92 @@
         int[] allIndices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray();
         mean = Content.EstimatedValues.ToArray();
-        s2 = Content.EstimatedVariance.ToArray();
+        s2 = Content.EstimatedVariances.ToArray();
         lower = mean.Zip(s2, GetLowerConfBound).ToArray();
         upper = mean.Zip(s2, GetUpperConfBound).ToArray();
@@ -264 +264 @@
             indices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray();
             mean = Content.EstimatedValues.ToArray();
-            s2 = Content.EstimatedVariance.ToArray();
+            s2 = Content.EstimatedVariances.ToArray();
             lower = mean.Zip(s2, GetLowerConfBound).ToArray();
             upper = mean.Zip(s2, GetUpperConfBound).ToArray();
@@ -273 +273 @@
             indices = Content.ProblemData.TrainingIndices.ToArray();
             mean = Content.EstimatedTrainingValues.ToArray();
-            s2 = Content.EstimatedTrainingVariance.ToArray();
+            s2 = Content.EstimatedTrainingVariances.ToArray();
             lower = mean.Zip(s2, GetLowerConfBound).ToArray();
             upper = mean.Zip(s2, GetUpperConfBound).ToArray();
@@ -280 +280 @@
             indices = Content.ProblemData.TestIndices.ToArray();
             mean = Content.EstimatedTestValues.ToArray();
-            s2 = Content.EstimatedTestVariance.ToArray();
+            s2 = Content.EstimatedTestVariances.ToArray();
             lower = mean.Zip(s2, GetLowerConfBound).ToArray();
             upper = mean.Zip(s2, GetUpperConfBound).ToArray();

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs

@@ -346 +346 @@
     }
 
-    public IEnumerable<double> GetEstimatedVariance(IDataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
       try {
         if (x == null) {

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessRegressionSolution.cs

@@ -33 +33 @@
   [Item("GaussianProcessRegressionSolution", "Represents a Gaussian process solution for a regression problem which can be visualized in the GUI.")]
   [StorableClass]
-  public sealed class GaussianProcessRegressionSolution : RegressionSolution, IGaussianProcessSolution {
+  public sealed class GaussianProcessRegressionSolution : RegressionSolution, IGaussianProcessSolution, IConfidenceBoundRegressionSolution {
     private new readonly Dictionary<int, double> evaluationCache;
 
@@ -61 +61 @@
     }
 
-    public IEnumerable<double> EstimatedVariance {
-      get { return GetEstimatedVariance(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
+    public IEnumerable<double> EstimatedVariances {
+      get { return GetEstimatedVariances(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
     }
-    public IEnumerable<double> EstimatedTrainingVariance {
-      get { return GetEstimatedVariance(ProblemData.TrainingIndices); }
+    public IEnumerable<double> EstimatedTrainingVariances {
+      get { return GetEstimatedVariances(ProblemData.TrainingIndices); }
     }
-    public IEnumerable<double> EstimatedTestVariance {
-      get { return GetEstimatedVariance(ProblemData.TestIndices); }
+    public IEnumerable<double> EstimatedTestVariances {
+      get { return GetEstimatedVariances(ProblemData.TestIndices); }
     }
 
-    public IEnumerable<double> GetEstimatedVariance(IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedVariances(IEnumerable<int> rows) {
       var rowsToEvaluate = rows.Except(evaluationCache.Keys);
       var rowsEnumerator = rowsToEvaluate.GetEnumerator();
-      var valuesEnumerator = Model.GetEstimatedVariance(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
+      var valuesEnumerator = Model.GetEstimatedVariances(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
 
       while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessModel.cs

@@ -363 +363 @@
     }
 
-    public IEnumerable<double> GetEstimatedVariance(IDataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
       try {
         if (x == null) {

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IGaussianProcessModel.cs

@@ -20 +20 @@
 #endregion
 
-using System.Collections.Generic;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -27 +26 @@
   /// Interface to represent a Gaussian process posterior
   /// </summary>
-  public interface IGaussianProcessModel : IRegressionModel {
+  public interface IGaussianProcessModel : IConfidenceBoundRegressionModel {
     double NegativeLogLikelihood { get; }
     double SigmaNoise { get; }
@@ -34 +33 @@
     double[] HyperparameterGradients { get; }
 
-    IEnumerable<double> GetEstimatedVariance(IDataset ds, IEnumerable<int> rows);
     void FixParameters();
   }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionModel.cs

@@ -26 +26 @@
     IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData);
   }
+
+  public interface IConfidenceBoundRegressionModel : IRegressionModel {
+    IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows);
+  }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionSolution.cs

@@ -44 +44 @@
     double TestRootMeanSquaredError { get; }
   }
+
+  public interface IConfidenceBoundRegressionSolution : IRegressionSolution {
+    IEnumerable<double> GetEstimatedVariances(IEnumerable<int> rows);
+  }
 }