Changeset 16692 for branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs
- Timestamp:
- 03/18/19 17:24:30 (5 years ago)
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- branches/2521_ProblemRefactoring
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- 4 edited
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branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis
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branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4
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/stable/HeuristicLab.Algorithms.DataAnalysis/3.4 merged eligible /branches/1721-RandomForestPersistence/HeuristicLab.Algorithms.DataAnalysis/3.4 10321-10322 /branches/Async/HeuristicLab.Algorithms.DataAnalysis/3.4 13329-15286 /branches/Benchmarking/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 6917-7005 /branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4 9070-13099 /branches/CloningRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4 4656-4721 /branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4 5471-5808 /branches/DataAnalysis SolutionEnsembles/HeuristicLab.Algorithms.DataAnalysis/3.4 5815-6180 /branches/DataAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.4 4458-4459,4462,4464 /branches/DataPreprocessing/HeuristicLab.Algorithms.DataAnalysis/3.4 10085-11101 /branches/GP.Grammar.Editor/HeuristicLab.Algorithms.DataAnalysis/3.4 6284-6795 /branches/GP.Symbols (TimeLag, Diff, Integral)/HeuristicLab.Algorithms.DataAnalysis/3.4 5060 /branches/HeuristicLab.DatasetRefactor/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 11570-12508 /branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Algorithms.DataAnalysis/3.4 11130-12721 /branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4 13819-14091 /branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4 8116-8789 /branches/LogResidualEvaluator/HeuristicLab.Algorithms.DataAnalysis/3.4 10202-10483 /branches/NET40/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 5138-5162 /branches/ParallelEngine/HeuristicLab.Algorithms.DataAnalysis/3.4 5175-5192 /branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Algorithms.DataAnalysis/3.4 7773-7810 /branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4 6350-6627 /branches/Restructure trunk solution/HeuristicLab.Algorithms.DataAnalysis/3.4 6828 /branches/SpectralKernelForGaussianProcesses/HeuristicLab.Algorithms.DataAnalysis/3.4 10204-10479 /branches/SuccessProgressAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.4 5370-5682 /branches/Trunk/HeuristicLab.Algorithms.DataAnalysis/3.4 6829-6865 /branches/VNS/HeuristicLab.Algorithms.DataAnalysis/3.4 5594-5752 /branches/Weighted TSNE/3.4 15451-15531 /branches/histogram/HeuristicLab.Algorithms.DataAnalysis/3.4 5959-6341 /branches/symbreg-factors-2650/HeuristicLab.Algorithms.DataAnalysis/3.4 14232-14825 /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 13331-15681
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branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs
r13157 r16692 1 1 #region License Information 2 2 /* HeuristicLab 3 * Copyright (C) 2002-201 5Heuristic and Evolutionary Algorithms Laboratory (HEAL)3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL) 4 4 * and the BEACON Center for the Study of Evolution in Action. 5 5 * … … 96 96 weights = new List<double>(); 97 97 // add constant model 98 models.Add(new ConstantModel(f0 ));98 models.Add(new ConstantModel(f0, problemData.TargetVariable)); 99 99 weights.Add(1.0); 100 100 } … … 148 148 // for custom stepping & termination 149 149 public static IGbmState CreateGbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize = 3, double r = 0.66, double m = 0.5, double nu = 0.01) { 150 // check input variables. Only double variables are allowed. 151 var invalidInputs = 152 problemData.AllowedInputVariables.Where(name => !problemData.Dataset.VariableHasType<double>(name)); 153 if (invalidInputs.Any()) 154 throw new NotSupportedException("Gradient tree boosting only supports real-valued variables. Unsupported inputs: " + string.Join(", ", invalidInputs)); 155 150 156 return new GbmState(problemData, lossFunction, randSeed, maxSize, r, m, nu); 151 157 }
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