Changeset 17246 for branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs
- Timestamp:
- 09/11/19 14:06:25 (5 years ago)
- Location:
- branches/2925_AutoDiffForDynamicalModels
- Files:
-
- 4 edited
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branches/2925_AutoDiffForDynamicalModels
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branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Algorithms.DataAnalysis
- Property svn:mergeinfo changed
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branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Algorithms.DataAnalysis/3.4
- Property svn:mergeinfo changed
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branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs
r17035 r17246 1 1 #region License Information 2 2 /* HeuristicLab 3 * Copyright (C) 2002-2019Heuristic and Evolutionary Algorithms Laboratory (HEAL)3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL) 4 4 * and the BEACON Center for the Study of Evolution in Action. 5 5 * … … 196 196 Parameters.Add(new FixedValueParameter<EnumValue<ModelCreation>>(ModelCreationParameterName, "Defines the results produced at the end of the run (Surrogate => Less disk space, lazy recalculation of model)", new EnumValue<ModelCreation>(value))); 197 197 Parameters[ModelCreationParameterName].Hidden = true; 198 } else if (!Parameters.ContainsKey(ModelCreationParameterName)) { 199 // very old version contains neither ModelCreationParameter nor CreateSolutionParameter 200 Parameters.Add(new FixedValueParameter<EnumValue<ModelCreation>>(ModelCreationParameterName, "Defines the results produced at the end of the run (Surrogate => Less disk space, lazy recalculation of model)", new EnumValue<ModelCreation>(ModelCreation.Model))); 201 Parameters[ModelCreationParameterName].Hidden = true; 198 202 } 199 203 #endregion … … 269 273 270 274 if (ModelCreation == ModelCreation.SurrogateModel) { 271 model = new GradientBoostedTreesModelSurrogate( problemData, (uint)Seed, lossFunction, Iterations, MaxSize, R, M, Nu, (GradientBoostedTreesModel)model);275 model = new GradientBoostedTreesModelSurrogate((GradientBoostedTreesModel)model, problemData, (uint)Seed, lossFunction, Iterations, MaxSize, R, M, Nu); 272 276 } 273 277
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