Changeset 2043 for trunk/sources/HeuristicLab.SupportVectorMachines/3.2/VariableEvaluationImpactCalculator.cs

Timestamp:

06/15/09 16:46:25 (15 years ago)

Author:

gkronber

Message:

Added variable impact calculation operators for support vector machines. #644 (Variable impact of CEDMA models should be calculated and stored in the result DB)

File:

• trunk/sources/HeuristicLab.SupportVectorMachines/3.2/VariableEvaluationImpactCalculator.cs (copied) (copied from trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/VariableEvaluationImpactCalculator.cs) (1 diff)

trunk/sources/HeuristicLab.SupportVectorMachines/3.2/VariableEvaluationImpactCalculator.cs

@@ -29 +29 @@
 using System.Linq;
 
-namespace HeuristicLab.GP.StructureIdentification {
+namespace HeuristicLab.SupportVectorMachines {
   public class VariableEvaluationImpactCalculator : HeuristicLab.Modeling.VariableEvaluationImpactCalculator {
 
     public VariableEvaluationImpactCalculator()
       : base() {
-      AddVariableInfo(new VariableInfo("TreeEvaluator", "The evaluator that should be used to evaluate the expression tree", typeof(ITreeEvaluator), VariableKind.In));
-      AddVariableInfo(new VariableInfo("FunctionTree", "The function tree that should be evaluated", typeof(IFunctionTree), VariableKind.In));
-      AddVariableInfo(new VariableInfo("TreeSize", "Size (number of nodes) of the tree to evaluate", typeof(IntData), VariableKind.In));
-      AddVariableInfo(new VariableInfo("PunishmentFactor", "Punishment factor for invalid estimations", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("SVMModel", "The model that should be evaluated", typeof(SVMModel), VariableKind.In));
     }
 
 
-    protected override double[] GetOutputs(IScope scope, Dataset dataset, int targetVariable, int start, int end) {
-      ITreeEvaluator evaluator = GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true);
-      IFunctionTree tree = GetVariableValue<IFunctionTree>("FunctionTree", scope, true);
-      double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
-      evaluator.PrepareForEvaluation(dataset, targetVariable, start, end, punishmentFactor, tree);
+    protected override double[] GetOutputs(IScope scope, Dataset dataset, int targetVariable, ItemList<IntData> allowedFeatures, int start, int end) {
+      SVMModel model = GetVariableValue<SVMModel>("SVMModel", scope, true);
+      SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
+      SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, model.RangeTransform);
 
-      double[] result = new double[end - start];
-      for (int i = start; i < end; i++) {
-        result[i - start] = evaluator.Evaluate(i);
+      double[] values = new double[end - start];
+      for (int i = 0; i < end - start; i++) {
+        values[i] = SVM.Prediction.Predict(model.Model, scaledProblem.X[i]);
       }
-
-      return result;
+      return values;
     }
   }