Changeset 5649 for branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs

Timestamp:

03/10/11 10:00:09 (13 years ago)

Author:

gkronber

Message:

#1418 Implemented classes for classification based on a discriminant function and thresholds and implemented interfaces and base classes for clustering.

File:

• branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs (modified) (4 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs

@@ -88 +88 @@
     public SupportVectorClassification()
       : base() {
+      Problem = new ClassificationProblem();
+
       List<StringValue> svrTypes = (from type in new List<string> { "NU_SVC", "EPSILON_SVC" }
                                     select new StringValue(type).AsReadOnly())
@@ -95 +97 @@
                                        select new StringValue(type).AsReadOnly())
                                    .ToList();
-      ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(svrTypes);
+      ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(kernelTypes);
       Parameters.Add(new ConstrainedValueParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", svrTypeSet, svrTypes[0]));
       Parameters.Add(new ConstrainedValueParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", kernelTypeSet, kernelTypes[3]));
@@ -112 +114 @@
     protected override void Run() {
       IClassificationProblemData problemData = Problem.ProblemData;
-      IEnumerable<string> selectedInputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value);
+      IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables;
       var solution = CreateSupportVectorClassificationSolution(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value);
 
@@ -140 +142 @@
       SVM.RangeTransform rangeTransform = SVM.RangeTransform.Compute(problem);
       SVM.Problem scaledProblem = SVM.Scaling.Scale(rangeTransform, problem);
-      var model = new SupportVectorMachineModel();
-      model.Model = SVM.Training.Train(scaledProblem, parameter);
-      model.RangeTransform = rangeTransform;
+      var model = new SupportVectorMachineModel(SVM.Training.Train(scaledProblem, parameter), rangeTransform, targetVariable, allowedInputVariables);
 
-      return new SupportVectorClassificationSolution(model, problemData, allowedInputVariables, double.NegativeInfinity, double.PositiveInfinity);
+      return new SupportVectorClassificationSolution(model, problemData);
     }
     #endregion