Changeset 5275 for branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/SupportVectorRegression

Timestamp:

01/11/11 15:03:46 (13 years ago)

Author:

gkronber

Message:

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

Location:

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/SupportVectorRegression

Files:

• BestSupportVectorRegressionSolutionAnalyzer.cs (modified) (3 diffs)
• SupportVectorRegressionSolution.cs (modified) (5 diffs)

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/SupportVectorRegression/BestSupportVectorRegressionSolutionAnalyzer.cs

@@ -22 +22 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
@@ -52 +53 @@
     #endregion
 
+    [StorableConstructor]
+    private BestSupportVectorRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
+    private BestSupportVectorRegressionSolutionAnalyzer(BestSupportVectorRegressionSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
     public BestSupportVectorRegressionSolutionAnalyzer()
       : base() {
       Parameters.Add(new ScopeTreeLookupParameter<SupportVectorMachineModel>(SupportVectorRegressionModelParameterName, "The support vector regression models to analyze."));
       Parameters.Add(new LookupParameter<SupportVectorRegressionSolution>(BestSolutionParameterName, "The best support vector regression solution."));
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new BestSupportVectorRegressionSolutionAnalyzer(this, cloner);
     }
 
@@ -68 +76 @@
                                              where ProblemData.InputVariables.ItemChecked(var)
                                              select var.Value;
-        var solution = new SupportVectorRegressionSolution(ProblemData, SupportVectorMachineModel[i], inputVariables, lowerEstimationLimit, upperEstimationLimit);
+        var solution = new SupportVectorRegressionSolution((DataAnalysisProblemData)ProblemData.Clone(), SupportVectorMachineModel[i], inputVariables, lowerEstimationLimit, upperEstimationLimit);
 
         BestSolutionParameter.ActualValue = solution;

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/SupportVectorRegression/SupportVectorRegressionSolution.cs

@@ -24 +24 @@
 using System.Drawing;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -36 +37 @@
   [StorableClass]
   public sealed class SupportVectorRegressionSolution : DataAnalysisSolution {
-    public SupportVectorRegressionSolution() : base() { }
-    public SupportVectorRegressionSolution(DataAnalysisProblemData problemData, SupportVectorMachineModel model, IEnumerable<string> inputVariables, double lowerEstimationLimit, double upperEstimationLimit)
-      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
-      this.Model = model;
-    }
-
     public override Image ItemImage {
       get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
@@ -53 +48 @@
     public Dataset SupportVectors {
       get { return CalculateSupportVectors(); }
+    }
+
+    private List<double> estimatedValues;
+    public override IEnumerable<double> EstimatedValues {
+      get {
+        if (estimatedValues == null) RecalculateEstimatedValues();
+        return estimatedValues;
+      }
+    }
+
+    public override IEnumerable<double> EstimatedTrainingValues {
+      get {
+        return GetEstimatedValues(ProblemData.TrainingIndizes);
+      }
+    }
+
+    public override IEnumerable<double> EstimatedTestValues {
+      get {
+        return GetEstimatedValues(ProblemData.TestIndizes);
+      }
+    }
+
+    [StorableConstructor]
+    private SupportVectorRegressionSolution(bool deserializing) : base(deserializing) { }
+    private SupportVectorRegressionSolution(SupportVectorRegressionSolution original, Cloner cloner) : base(original, cloner) { }
+    public SupportVectorRegressionSolution() : base() { }
+    public SupportVectorRegressionSolution(DataAnalysisProblemData problemData, SupportVectorMachineModel model, IEnumerable<string> inputVariables, double lowerEstimationLimit, double upperEstimationLimit)
+      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
+      this.Model = model;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SupportVectorRegressionSolution(this, cloner);
     }
 
@@ -73 +101 @@
 
     protected override void RecalculateEstimatedValues() {
-      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(ProblemData, 0, ProblemData.Dataset.Rows);
+      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(ProblemData, Enumerable.Range(0, ProblemData.Dataset.Rows));
       SVM.Problem scaledProblem = Scaling.Scale(Model.RangeTransform, problem);
 
@@ -83 +111 @@
     }
 
-    private List<double> estimatedValues;
-    public override IEnumerable<double> EstimatedValues {
-      get {
-        if (estimatedValues == null) RecalculateEstimatedValues();
-        return estimatedValues.AsEnumerable();
-      }
-    }
 
-    public override IEnumerable<double> EstimatedTrainingValues {
-      get {
-        if (estimatedValues == null) RecalculateEstimatedValues();
-        int start = ProblemData.TrainingSamplesStart.Value;
-        int n = ProblemData.TrainingSamplesEnd.Value - start;
-        return estimatedValues.Skip(start).Take(n).ToList();
-      }
-    }
-
-    public override IEnumerable<double> EstimatedTestValues {
-      get {
-        if (estimatedValues == null) RecalculateEstimatedValues();
-        int start = ProblemData.TestSamplesStart.Value;
-        int n = ProblemData.TestSamplesEnd.Value - start;
-        return estimatedValues.Skip(start).Take(n).ToList();
-      }
+    private IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
+      if (estimatedValues == null) RecalculateEstimatedValues();
+      foreach (int row in rows)
+        yield return estimatedValues[row];
     }
   }