#region License Information /* HeuristicLab * Copyright (C) 2002-2014 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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [StorableClass] [Item("SlidingWindowBestRegressionSolutionsCollection", "A collection of best sliding window solutions for symbolic regression.")] public class SlidingWindowBestRegressionSolutionsCollection : SlidingWindowBestSolutionsCollection { public new IRegressionProblemData ProblemData { get { return (IRegressionProblemData)base.ProblemData; } set { base.ProblemData = value; } } [StorableConstructor] protected SlidingWindowBestRegressionSolutionsCollection(bool deserializing) : base(deserializing) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { } public SlidingWindowBestRegressionSolutionsCollection(SlidingWindowBestRegressionSolutionsCollection original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SlidingWindowBestRegressionSolutionsCollection(this, cloner); } public SlidingWindowBestRegressionSolutionsCollection() { } public override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) { return new SymbolicRegressionModel(tree, interpreter, lowerEstimationLimit, upperEstimationLimit); } public override ISymbolicDataAnalysisSolution CreateSolution(ISymbolicDataAnalysisModel model, IDataAnalysisProblemData problemData) { return new SymbolicRegressionSolution((ISymbolicRegressionModel)model, (IRegressionProblemData)problemData); } } }