#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2013 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;
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;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.SlidingWindow {
  [StorableClass]
  [Item("Sliding Window Visualizer", "Visualizes the actual sliding window position.")]
  public sealed class SlidingWindowVisualizer : SymbolicDataAnalysisAnalyzer {
    private const string ProblemDataParameterName = "ProblemData";
    private const string FitnessCalculationPartitionParameterName = "FitnessCalculationPartition";

    private const string SlidingWindowResultName = "Sliding Window";
    private const string SlidingWindowDataResultName = "Sliding Window Data";
    private const string BestTrainingSolutionResultName = "Best training solution";

    #region parameter properties
    public IValueLookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
      get { return (IValueLookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
    }
    public ILookupParameter<IntRange> FitnessCalculationPartitionParameter {
      get { return (ILookupParameter<IntRange>)Parameters[FitnessCalculationPartitionParameterName]; }
    }
    #endregion

    [StorableConstructor]
    private SlidingWindowVisualizer(bool deserializing) : base(deserializing) { }
    private SlidingWindowVisualizer(SlidingWindowVisualizer original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SlidingWindowVisualizer(this, cloner);
    }

    public SlidingWindowVisualizer()
      : base() {
      Parameters.Add(new ValueLookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
      Parameters.Add(new LookupParameter<IntRange>(FitnessCalculationPartitionParameterName, ""));
      ProblemDataParameter.Hidden = true;
    }

    public override IOperation Apply() {
      //create and update result
      var results = ResultCollectionParameter.ActualValue;

      IntRange slidingWindow;
      if (!results.ContainsKey(SlidingWindowResultName)) {
        slidingWindow = new IntRange();
        results.Add(new Result(SlidingWindowResultName, slidingWindow));
      } else slidingWindow = (IntRange)results[SlidingWindowResultName].Value;
      slidingWindow.Start = FitnessCalculationPartitionParameter.ActualValue.Start;
      slidingWindow.End = FitnessCalculationPartitionParameter.ActualValue.End;

      SlidingWindowData slidingWindowData;
      if (!results.ContainsKey(SlidingWindowDataResultName)) {
        string targetVariable;
        var classificationProblemData = ProblemDataParameter.ActualValue as IClassificationProblemData;
        var regressionProblemData = ProblemDataParameter.ActualValue as IRegressionProblemData;
        if (classificationProblemData != null) targetVariable = classificationProblemData.TargetVariable;
        else if (regressionProblemData != null) targetVariable = regressionProblemData.TargetVariable;
        else throw new NotSupportedException();

        var targetData = ProblemDataParameter.ActualValue.Dataset.GetDoubleValues(targetVariable, ProblemDataParameter.ActualValue.TrainingIndices);
        slidingWindowData = new SlidingWindowData(FitnessCalculationPartitionParameter.ActualValue, targetData);
        results.Add(new Result(SlidingWindowDataResultName, slidingWindowData));
      } else slidingWindowData = (SlidingWindowData)results[SlidingWindowDataResultName].Value;

      IEnumerable<double> estimatedValues = Enumerable.Empty<double>();
      if (results.ContainsKey(BestTrainingSolutionResultName)) {
        var trainingSolution = results[BestTrainingSolutionResultName].Value;
        var regressionSolution = trainingSolution as IRegressionSolution;
        var classificationSolution = trainingSolution as IClassificationSolution;

        if (regressionSolution != null) estimatedValues = regressionSolution.EstimatedTrainingValues;
        if (classificationSolution != null) estimatedValues = classificationSolution.EstimatedTrainingClassValues;
      }

      slidingWindowData.SlidingWindowPosition.Start = FitnessCalculationPartitionParameter.ActualValue.Start;
      slidingWindowData.SlidingWindowPosition.End = FitnessCalculationPartitionParameter.ActualValue.End;
      slidingWindowData.EstimatedValues = estimatedValues;
      return base.Apply();
    }
  }
}