Changeset 2421 for trunk/sources/HeuristicLab.SupportVectorMachines/3.2/PredictorBuilder.cs

Timestamp:

10/08/09 16:22:35 (15 years ago)

Author:

gkronber

Message:

Fixed bugs related to time series prognosis with SVMs. And fixed an exception when trying to save time-series models to the database. #776 (Error when trying to save time-series prognosis predictors to the database)

File:

• trunk/sources/HeuristicLab.SupportVectorMachines/3.2/PredictorBuilder.cs (modified) (2 diffs)

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

@@ -36 +36 @@
       AddVariableInfo(new VariableInfo("SVMModel", "The SVM model", typeof(SVMModel), VariableKind.In));
       AddVariableInfo(new VariableInfo("TargetVariable", "The target variable", typeof(StringData), VariableKind.In));
-      AddVariableInfo(new VariableInfo("InputVariables", "The input variable names", typeof(StringData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("InputVariables", "The input variable names", typeof(ItemList), VariableKind.In));
       AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of the training set", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of the training set", typeof(IntData), VariableKind.In));
@@ -63 +63 @@
       string targetVariableName = ds.GetVariableName(targetVariable);
       ItemList inputVariables = GetVariableValue<ItemList>("InputVariables", scope, true);
-      Dictionary<string, int> variableNames = new Dictionary<string, int>();
-      for (int i = 0; i < ds.Columns; i++) variableNames[ds.GetVariableName(i)] = i;
+      var inputVariableNames = from x in inputVariables
+                               select ((StringData)x).Data;
 
       double mean = ds.GetMean(targetVariable, start, end);
       double range = ds.GetRange(targetVariable, start, end);
 
-      Predictor predictor = new Predictor(model, targetVariableName, variableNames, minTimeOffset, maxTimeOffset);
+      Predictor predictor = new Predictor(model, targetVariableName, inputVariableNames, minTimeOffset, maxTimeOffset);
       predictor.LowerPredictionLimit = mean - punishmentFactor * range;
       predictor.UpperPredictionLimit = mean + punishmentFactor * range;