#region License Information /* HeuristicLab * Copyright (C) 2002-2010 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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.PluginInfrastructure; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Problems.DataAnalysis.Symbolic; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.ArchitectureManipulators; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Manipulators; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Crossovers; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Creators; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Interfaces; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Analyzers; using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Evaluators; using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Analyzers; using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Interfaces; namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic { [Item("Symbolic Time Series Prognosis Problem", "Represents a symbolic time series prognosis problem.")] [Creatable("Problems")] [StorableClass] public class SingleObjectiveSymbolicTimeSeriesPrognosisProblem : SymbolicTimeSeriesPrognosisProblem, ISingleObjectiveProblem { #region Parameter Properties public ValueParameter EvaluatorParameter { get { return (ValueParameter)Parameters["Evaluator"]; } } IParameter IProblem.EvaluatorParameter { get { return EvaluatorParameter; } } public ValueParameter MaximizationParameter { get { return (ValueParameter)Parameters["Maximization"]; } } IParameter ISingleObjectiveProblem.MaximizationParameter { get { return MaximizationParameter; } } public OptionalValueParameter BestKnownQualityParameter { get { return (OptionalValueParameter)Parameters["BestKnownQuality"]; } } IParameter ISingleObjectiveProblem.BestKnownQualityParameter { get { return BestKnownQualityParameter; } } #endregion #region Properties public ISingleObjectiveSymbolicTimeSeriesPrognosisEvaluator Evaluator { get { return EvaluatorParameter.Value; } } ISingleObjectiveEvaluator ISingleObjectiveProblem.Evaluator { get { return Evaluator; } } IEvaluator IProblem.Evaluator { get { return Evaluator; } } public DoubleValue BestKnownQuality { get { return BestKnownQualityParameter.Value; } } #endregion [StorableConstructor] protected SingleObjectiveSymbolicTimeSeriesPrognosisProblem(bool deserializing) : base(deserializing) { } protected SingleObjectiveSymbolicTimeSeriesPrognosisProblem(SingleObjectiveSymbolicTimeSeriesPrognosisProblem original, Cloner cloner) : base(original, cloner) { AttachEventHandlers(); } public SingleObjectiveSymbolicTimeSeriesPrognosisProblem() : base() { var evaluator = new SymbolicTimeSeriesPrognosisScaledNormalizedMseEvaluator(); Parameters.Add(new ValueParameter("Maximization", "Set to false as the error of the time series prognosis model should be minimized.", (BoolValue)new BoolValue(false).AsReadOnly())); Parameters.Add(new ValueParameter("Evaluator", "The operator which should be used to evaluate symbolic time series prognosis solutions.", evaluator)); Parameters.Add(new OptionalValueParameter("BestKnownQuality", "The minimal error value that reached by symbolic time series prognosis solutions for the problem.")); evaluator.QualityParameter.ActualName = "TrainingMeanSquaredError"; ParameterizeEvaluator(); InitializeOperators(); AttachEventHandlers(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { AttachEventHandlers(); } public override IDeepCloneable Clone(Cloner cloner) { return new SingleObjectiveSymbolicTimeSeriesPrognosisProblem(this, cloner); } #region event handling protected override void OnMultiVariateDataAnalysisProblemChanged(EventArgs e) { base.OnMultiVariateDataAnalysisProblemChanged(e); BestKnownQualityParameter.Value = null; // paritions could be changed ParameterizeEvaluator(); ParameterizeAnalyzers(); } protected override void OnSolutionParameterNameChanged(EventArgs e) { ParameterizeEvaluator(); ParameterizeAnalyzers(); } protected virtual void OnEvaluatorChanged(EventArgs e) { ParameterizeEvaluator(); ParameterizeAnalyzers(); RaiseEvaluatorChanged(e); } #endregion #region Helpers private void AttachEventHandlers() { } private void InitializeOperators() { AddOperator(new ValidationBestScaledSymbolicTimeSeriesPrognosisSolutionAnalyzer()); ParameterizeAnalyzers(); } private void ParameterizeEvaluator() { Evaluator.TimeSeriesPrognosisModelParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName; Evaluator.TimeSeriesExpressionInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name; Evaluator.ProblemDataParameter.ActualName = MultiVariateDataAnalysisProblemDataParameter.Name; Evaluator.PredictionHorizonParameter.ActualName = PredictionHorizonParameter.Name; Evaluator.SamplesStartParameter.Value = TrainingSamplesStart; Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd; } private void ParameterizeAnalyzers() { foreach (var analyzer in Analyzers) { var bestValidationSolutionAnalyzer = analyzer as ValidationBestScaledSymbolicTimeSeriesPrognosisSolutionAnalyzer; if (bestValidationSolutionAnalyzer != null) { bestValidationSolutionAnalyzer.ProblemDataParameter.ActualName = MultiVariateDataAnalysisProblemDataParameter.Name; bestValidationSolutionAnalyzer.SymbolicExpressionTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name; bestValidationSolutionAnalyzer.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName; bestValidationSolutionAnalyzer.ValidationSamplesStartParameter.Value = ValidationSamplesStart; bestValidationSolutionAnalyzer.ValidationSamplesEndParameter.Value = ValidationSamplesEnd; bestValidationSolutionAnalyzer.BestKnownQualityParameter.ActualName = BestKnownQualityParameter.Name; bestValidationSolutionAnalyzer.ValidationPredictionHorizonParameter.ActualName = PredictionHorizonParameter.Name; bestValidationSolutionAnalyzer.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameter.Name; bestValidationSolutionAnalyzer.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameter.Name; } } foreach (ISymbolicExpressionTreeAnalyzer analyzer in Operators.OfType()) { analyzer.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName; } } #endregion } }