Changeset 13941 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineModel.cs

Timestamp:

06/28/16 13:33:17 (8 years ago)

Author:

mkommend

Message:

#2604:

• Base classes for data analysis, classification, and regression models
• Added target variable to classification and regression models
• Switched parameter order in data analysis solutions (model, problemdata)

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineModel.cs (modified) (6 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineModel.cs

@@ -37 +37 @@
   [StorableClass]
   [Item("SupportVectorMachineModel", "Represents a support vector machine model.")]
-  public sealed class SupportVectorMachineModel : NamedItem, ISupportVectorMachineModel {
-    public IEnumerable<string> VariablesUsedForPrediction {
+  public sealed class SupportVectorMachineModel : ClassificationModel, ISupportVectorMachineModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
       get { return allowedInputVariables; }
     }
 
-    public string TargetVariable {
-      get { return targetVariable; }
-    }
 
     private svm_model model;
@@ -90 +87 @@
 
     [Storable]
-    private string targetVariable;
-    [Storable]
     private string[] allowedInputVariables;
     [Storable]
@@ -103 +98 @@
       this.model = original.model;
       this.rangeTransform = original.rangeTransform;
-      this.targetVariable = original.targetVariable;
       this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
       if (original.classValues != null)
@@ -113 +107 @@
     }
     public SupportVectorMachineModel(svm_model model, RangeTransform rangeTransform, string targetVariable, IEnumerable<string> allowedInputVariables)
-      : base() {
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.model = model;
       this.rangeTransform = rangeTransform;
-      this.targetVariable = targetVariable;
       this.allowedInputVariables = allowedInputVariables.ToArray();
     }
@@ -130 +123 @@
       return GetEstimatedValuesHelper(dataset, rows);
     }
-    public SupportVectorRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       return new SupportVectorRegressionSolution(this, new RegressionProblemData(problemData));
     }
-    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
-      return CreateRegressionSolution(problemData);
-    }
     #endregion
 
     #region IClassificationModel Members
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       if (classValues == null) throw new NotSupportedException();
       // return the original class value instead of the predicted value of the model
@@ -159 +149 @@
     }
 
-    public SupportVectorClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
       return new SupportVectorClassificationSolution(this, new ClassificationProblemData(problemData));
-    }
-    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
-      return CreateClassificationSolution(problemData);
     }
     #endregion
     private IEnumerable<double> GetEstimatedValuesHelper(IDataset dataset, IEnumerable<int> rows) {
       // calculate predictions for the currently requested rows
-      svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
+      svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, TargetVariable, allowedInputVariables, rows);
       svm_problem scaledProblem = rangeTransform.Scale(problem);