#region License Information /* HeuristicLab * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis { [StorableType("CB105C71-FA47-4FFC-BF56-57B6BB2E0BC7")] [Item("RegressionEnsembleProblemData", "Represents an item containing all data defining a regression problem.")] public sealed class RegressionEnsembleProblemData : RegressionProblemData { public override bool IsTrainingSample(int index) { return index >= 0 && index < Dataset.Rows && TrainingPartition.Start <= index && index < TrainingPartition.End; } public override bool IsTestSample(int index) { return index >= 0 && index < Dataset.Rows && TestPartition.Start <= index && index < TestPartition.End; } private static readonly RegressionEnsembleProblemData emptyProblemData; public new static RegressionEnsembleProblemData EmptyProblemData { get { return emptyProblemData; } } static RegressionEnsembleProblemData() { var problemData = new RegressionEnsembleProblemData(); problemData.Parameters.Clear(); problemData.Name = "Empty Regression ProblemData"; problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded."; problemData.isEmpty = true; problemData.Parameters.Add(new FixedValueParameter(DatasetParameterName, "", new Dataset())); problemData.Parameters.Add(new FixedValueParameter>(InputVariablesParameterName, "")); problemData.Parameters.Add(new FixedValueParameter(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly())); problemData.Parameters.Add(new FixedValueParameter(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly())); problemData.Parameters.Add(new ConstrainedValueParameter(TargetVariableParameterName, new ItemSet())); emptyProblemData = problemData; } [StorableConstructor] private RegressionEnsembleProblemData(StorableConstructorFlag _) : base(_) { } private RegressionEnsembleProblemData(RegressionEnsembleProblemData original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { if (this == emptyProblemData) return emptyProblemData; return new RegressionEnsembleProblemData(this, cloner); } public RegressionEnsembleProblemData() : base() { } public RegressionEnsembleProblemData(IRegressionProblemData regressionProblemData) : base(regressionProblemData.Dataset, regressionProblemData.AllowedInputVariables, regressionProblemData.TargetVariable) { TrainingPartition.Start = regressionProblemData.TrainingPartition.Start; TrainingPartition.End = regressionProblemData.TrainingPartition.End; TestPartition.Start = regressionProblemData.TestPartition.Start; TestPartition.End = regressionProblemData.TestPartition.End; } public RegressionEnsembleProblemData(Dataset dataset, IEnumerable allowedInputVariables, string targetVariable) : base(dataset, allowedInputVariables, targetVariable) { } } }