#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis { /// /// Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity /// [StorableType("b095c9b2-7c8d-4483-8eeb-490bb4dcc1f4")] [Item(Name = "SymbolicTimeSeriesPrognosisSolution", Description = "Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity.")] public sealed class SymbolicTimeSeriesPrognosisSolution : TimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSolution { private const string ModelLengthResultName = "Model Length"; private const string ModelDepthResultName = "Model Depth"; public new ISymbolicTimeSeriesPrognosisModel Model { get { return (ISymbolicTimeSeriesPrognosisModel)base.Model; } set { base.Model = value; } } ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model { get { return (ISymbolicDataAnalysisModel)base.Model; } } public int ModelLength { get { return ((IntValue)this[ModelLengthResultName].Value).Value; } private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; } } public int ModelDepth { get { return ((IntValue)this[ModelDepthResultName].Value).Value; } private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; } } [StorableConstructor] private SymbolicTimeSeriesPrognosisSolution(StorableConstructorFlag deserializing) : base(deserializing) { } private SymbolicTimeSeriesPrognosisSolution(SymbolicTimeSeriesPrognosisSolution original, Cloner cloner) : base(original, cloner) { } public SymbolicTimeSeriesPrognosisSolution(ISymbolicTimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData) : base(model, problemData) { Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue())); Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue())); CalculateResults(); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicTimeSeriesPrognosisSolution(this, cloner); } protected override void RecalculateResults() { base.RecalculateResults(); CalculateResults(); } private void CalculateResults() { ModelLength = Model.SymbolicExpressionTree.Length; ModelDepth = Model.SymbolicExpressionTree.Depth; } } }