Changeset 2161 for trunk/sources/HeuristicLab.SupportVectorMachines/3.2/SupportVectorRegression.cs

Timestamp:

07/15/09 16:23:29 (15 years ago)

Author:

gkronber

Message:

Implemented #704 (Shuffling of training data (only) in (non-timeseries) modeling algorithms)

File:

• trunk/sources/HeuristicLab.SupportVectorMachines/3.2/SupportVectorRegression.cs (modified) (4 diffs)

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

@@ -142 +142 @@
       seq.Name = "Initialization";
       seq.AddSubOperator(CreateGlobalInjector());
-      seq.AddSubOperator(new ProblemInjector());
+      ProblemInjector probInjector = new ProblemInjector();
+      probInjector.GetVariableInfo("MaxNumberOfTrainingSamples").Local = true;
+      probInjector.AddVariable(new Variable("MaxNumberOfTrainingSamples", new IntData(5000)));
+      seq.AddSubOperator(probInjector);
       seq.AddSubOperator(new RandomInjector());
+
+      DatasetShuffler shuffler = new DatasetShuffler();
+      shuffler.GetVariableInfo("ShuffleStart").ActualName = "TrainingSamplesStart";
+      shuffler.GetVariableInfo("ShuffleEnd").ActualName = "TrainingSamplesEnd";
+      seq.AddSubOperator(shuffler);
       return seq;
     }
@@ -246 +254 @@
 
       SupportVectorCreator modelCreator = new SupportVectorCreator();
-      modelCreator.GetVariableInfo("SamplesStart").ActualName = "TrainingSamplesStart";
-      modelCreator.GetVariableInfo("SamplesEnd").ActualName = "TrainingSamplesEnd";
+      modelCreator.GetVariableInfo("SamplesStart").ActualName = "ActualTrainingSamplesStart";
+      modelCreator.GetVariableInfo("SamplesEnd").ActualName = "ActualTrainingSamplesEnd";
       modelCreator.GetVariableInfo("SVMCost").ActualName = "Cost";
       modelCreator.GetVariableInfo("SVMGamma").ActualName = "Gamma";
@@ -256 +264 @@
 
       modelProcessor.AddSubOperator(modelCreator);
-      CombinedOperator trainingEvaluator = (CombinedOperator)CreateEvaluator("Training");
+      CombinedOperator trainingEvaluator = (CombinedOperator)CreateEvaluator("ActualTraining");
       trainingEvaluator.OperatorGraph.InitialOperator.SubOperators[1].GetVariableInfo("MSE").ActualName = "Quality";
       modelProcessor.AddSubOperator(trainingEvaluator);
@@ -415 +423 @@
       model.ValidationMeanSquaredError = bestModelScope.GetVariableValue<DoubleData>("ValidationQuality", false).Data;
       model.TestMeanSquaredError = bestModelScope.GetVariableValue<DoubleData>("TestQuality", false).Data;
-      model.TrainingCoefficientOfDetermination = bestModelScope.GetVariableValue<DoubleData>("TrainingR2", false).Data;
+      model.TrainingCoefficientOfDetermination = bestModelScope.GetVariableValue<DoubleData>("ActualTrainingR2", false).Data;
       model.ValidationCoefficientOfDetermination = bestModelScope.GetVariableValue<DoubleData>("ValidationR2", false).Data;
       model.TestCoefficientOfDetermination = bestModelScope.GetVariableValue<DoubleData>("TestR2", false).Data;
-      model.TrainingMeanAbsolutePercentageError = bestModelScope.GetVariableValue<DoubleData>("TrainingMAPE", false).Data;
+      model.TrainingMeanAbsolutePercentageError = bestModelScope.GetVariableValue<DoubleData>("ActualTrainingMAPE", false).Data;
       model.ValidationMeanAbsolutePercentageError = bestModelScope.GetVariableValue<DoubleData>("ValidationMAPE", false).Data;
       model.TestMeanAbsolutePercentageError = bestModelScope.GetVariableValue<DoubleData>("TestMAPE", false).Data;
-      model.TrainingMeanAbsolutePercentageOfRangeError = bestModelScope.GetVariableValue<DoubleData>("TrainingMAPRE", false).Data;
+      model.TrainingMeanAbsolutePercentageOfRangeError = bestModelScope.GetVariableValue<DoubleData>("ActualTrainingMAPRE", false).Data;
       model.ValidationMeanAbsolutePercentageOfRangeError = bestModelScope.GetVariableValue<DoubleData>("ValidationMAPRE", false).Data;
       model.TestMeanAbsolutePercentageOfRangeError = bestModelScope.GetVariableValue<DoubleData>("TestMAPRE", false).Data;
-      model.TrainingVarianceAccountedFor = bestModelScope.GetVariableValue<DoubleData>("TrainingVAF", false).Data;
+      model.TrainingVarianceAccountedFor = bestModelScope.GetVariableValue<DoubleData>("ActualTrainingVAF", false).Data;
       model.ValidationVarianceAccountedFor = bestModelScope.GetVariableValue<DoubleData>("ValidationVAF", false).Data;
       model.TestVarianceAccountedFor = bestModelScope.GetVariableValue<DoubleData>("TestVAF", false).Data;