#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis { /// /// Represents a symbolic time-series prognosis model /// [StorableType("6D0C2490-28ED-43BB-A9A7-B741885B08B2")] [Item(Name = "Symbolic Time-Series Prognosis Model", Description = "Represents a symbolic time series prognosis model.")] public class SymbolicTimeSeriesPrognosisModel : SymbolicRegressionModel, ISymbolicTimeSeriesPrognosisModel { public new ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter Interpreter { get { return (ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter)base.Interpreter; } } [StorableConstructor] protected SymbolicTimeSeriesPrognosisModel(bool deserializing) : base(deserializing) { } protected SymbolicTimeSeriesPrognosisModel(SymbolicTimeSeriesPrognosisModel original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicTimeSeriesPrognosisModel(this, cloner); } public SymbolicTimeSeriesPrognosisModel(ISymbolicExpressionTree tree, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double lowerLimit = double.MinValue, double upperLimit = double.MaxValue) : base(tree, interpreter, lowerLimit, upperLimit) { } public IEnumerable> GetPrognosedValues(IDataset dataset, IEnumerable rows, IEnumerable horizons) { var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows, horizons); return estimatedValues.Select(predictionPerRow => predictionPerRow.LimitToRange(LowerEstimationLimit, UpperEstimationLimit)); } public ISymbolicTimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) { return new SymbolicTimeSeriesPrognosisSolution(this, new TimeSeriesPrognosisProblemData(problemData)); } ITimeSeriesPrognosisSolution ITimeSeriesPrognosisModel.CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) { return CreateTimeSeriesPrognosisSolution(problemData); } } }