Changeset 13823 for branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

05/03/16 09:47:26 (9 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/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

• GaussianProcess/GaussianProcessModel.cs (modified) (1 diff)
• GaussianProcess/GaussianProcessRegressionSolution.cs (modified) (2 diffs)
• GaussianProcess/StudentTProcessModel.cs (modified) (1 diff)
• Interfaces/IGaussianProcessModel.cs (modified) (3 diffs)

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();
   }