source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis/3.3/Symbolic/SymbolicTimeSeriesPrognosisSolution.cs @ 4401

#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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Symbols;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
using System.Linq;
using System.Drawing;
using System;

namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic {
  [StorableClass]
  [Item("SymbolicTimeSeriesPrognosisSolution", "Represents a solution for time series prognosis.")]
  public class SymbolicTimeSeriesPrognosisSolution : NamedItem, IMultiVariateDataAnalysisSolution {
    [Storable]
    private MultiVariateDataAnalysisProblemData problemData;
    [Storable]
    private SymbolicTimeSeriesPrognosisModel model;
    [Storable]
    private int horizon;

    [StorableConstructor]
    protected SymbolicTimeSeriesPrognosisSolution(bool deserializing) : base(deserializing) { }

    public SymbolicTimeSeriesPrognosisSolution() {
      horizon = 1;
    }

    public SymbolicTimeSeriesPrognosisSolution(MultiVariateDataAnalysisProblemData problemData, SymbolicTimeSeriesPrognosisModel model, int horizon)
      : this() {
      this.problemData = problemData;
      this.model = model;
      this.horizon = horizon;
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void Initialize() {
      if (problemData != null)
        RegisterProblemDataEvents();
    }

    public override Image ItemImage {
      get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
    }

    public int Horizon {
      get { return horizon; }
      set {
        if (value <= 0) throw new ArgumentException();
        horizon = value;
      }
    }

    public SymbolicTimeSeriesPrognosisModel Model {
      get { return model; }
      set {
        if (model != value) {
          if (value == null) throw new ArgumentNullException();
          model = value;
          RaiseModelChanged();
        }
      }
    }


    public IEnumerable<double[]> GetPrognosis(int t) {
      return model.GetEstimatedValues(problemData, t, t + 1);
    }

    #region IMultiVariateDataAnalysisSolution Members

    public MultiVariateDataAnalysisProblemData ProblemData {
      get { return problemData; }
      set {
        if (problemData != value) {
          if (value == null) throw new ArgumentNullException();
          if (model != null && problemData != null &&
            !(problemData.InputVariables
              .Select(c => c.Value)
              .SequenceEqual(value.InputVariables
                             .Select(c => c.Value)) &&
             problemData.TargetVariables
             .Select(c => c.Value)
             .SequenceEqual(value.TargetVariables
                            .Select(c => c.Value)))) {
            throw new ArgumentException("Could not set new problem data with different structure");
          }

          if (problemData != null) DeregisterProblemDataEvents();
          problemData = value;
          RaiseProblemDataChanged();
          RegisterProblemDataEvents();
        }
      }
    }

    IMultiVariateDataAnalysisModel IMultiVariateDataAnalysisSolution.Model {
      get { return model; }
    }

    public IEnumerable<double[]> EstimatedValues {
      get {
        return model.GetEstimatedValues(problemData, 0, problemData.Dataset.Rows);
      }
    }

    public IEnumerable<double[]> EstimatedTrainingValues {
      get {
        return model.GetEstimatedValues(problemData, problemData.TrainingSamplesStart.Value, problemData.TrainingSamplesEnd.Value);
      }
    }

    public IEnumerable<double[]> EstimatedTestValues {
      get {
        return model.GetEstimatedValues(problemData, problemData.TestSamplesStart.Value, problemData.TestSamplesEnd.Value);
      }
    }

    #endregion


    #region Events
    protected virtual void RegisterProblemDataEvents() {
      ProblemData.ProblemDataChanged += new EventHandler(ProblemData_Changed);
    }
    protected virtual void DeregisterProblemDataEvents() {
      ProblemData.ProblemDataChanged += new EventHandler(ProblemData_Changed);
    }
    private void ProblemData_Changed(object sender, EventArgs e) {
      RaiseProblemDataChanged();
    }

    public event EventHandler ProblemDataChanged;
    protected virtual void RaiseProblemDataChanged() {
      var listeners = ProblemDataChanged;
      if (listeners != null)
        listeners(this, EventArgs.Empty);
    }

    public event EventHandler ModelChanged;
    protected virtual void RaiseModelChanged() {
      EventHandler handler = ModelChanged;
      if (handler != null)
        handler(this, EventArgs.Empty);
    }

    public event EventHandler EstimatedValuesChanged;
    protected virtual void RaiseEstimatedValuesChanged() {
      var listeners = EstimatedValuesChanged;
      if (listeners != null)
        listeners(this, EventArgs.Empty);
    }
    #endregion


    public override IDeepCloneable Clone(Cloner cloner) {
      SymbolicTimeSeriesPrognosisSolution clone = (SymbolicTimeSeriesPrognosisSolution)base.Clone(cloner);
      clone.problemData = (MultiVariateDataAnalysisProblemData)cloner.Clone(problemData);
      clone.model = (SymbolicTimeSeriesPrognosisModel)cloner.Clone(model);
      clone.horizon = horizon;
      return clone;
    }
  }
}