#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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.Analysis.AlgorithmBehavior.Analyzers {
  [Item("CrossoverPerformanceAnalyzer", "An operator that analyzes the performance of crossovers.")]
  [StorableClass]
  public class CrossoverPerformanceAnalyzer : InitializableOperator, IStatefulItem {
    private const string ResultsParameterName = "Results";
    private const string GenerationsParameterName = "Generations";
    private const string SuccessfullCrossoversRowName = "Successfull Crossovers per Generation with CompFact ";

    #region Parameter properties
    public IValueLookupParameter<BoolValue> MaximizationParameter {
      get { return (IValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
    }
    public IValueLookupParameter<ItemCollection<DoubleValue>> ComparisonFactorParameter {
      get { return (IValueLookupParameter<ItemCollection<DoubleValue>>)Parameters["ComparisonFactor"]; }
    }
    public ILookupParameter<ResultCollection> ResultsParameter {
      get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
    }
    public ILookupParameter<IntValue> GenerationsParameter {
      get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
    }
    public ILookupParameter<ItemArray<DoubleValue>> ParentsQualityParameter {
      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["ParentsQuality"]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public IValueParameter<ISingleObjectiveSolutionSimilarityCalculator> SimilarityCalculatorParameter {
      get { return (IValueParameter<ISingleObjectiveSolutionSimilarityCalculator>)Parameters["SimilarityCalculator"]; }
    }
    public IValueParameter<IUnwantedMutationAnalyzer> UnwantedMutationAnalyzerParameter {
      get { return (IValueParameter<IUnwantedMutationAnalyzer>)Parameters["UnwantedMutationAnalyzer"]; }
    }
    public ILookupParameter<ItemCollection<IItem>> OperatorsParameter {
      get { return (ILookupParameter<ItemCollection<IItem>>)Parameters["Operators"]; }
    }
    #endregion

    #region Properties
    public ResultCollection Results {
      get { return ResultsParameter.ActualValue; }
    }

    [Storable]
    private ScatterPlotHelper worseParentCrossoverPerformancePlot, betterParentCrossoverPerformancePlot, childDiversityToWorseParentHelper, childDiversityToBetterParentHelper, parentDiversityHelper, parentQualityHelper, unwantedMutationsHelper;
    [Storable]
    private DataTableHelper successHelper, equalParentsHelper;
    [Storable]
    private int cnt = 0;
    [Storable]
    private int[] success;
    [Storable]
    private int lastGeneration = 0;
    [Storable]
    private int equalParents = 0;
    #endregion


    [StorableConstructor]
    private CrossoverPerformanceAnalyzer(bool deserializing) : base(deserializing) { }
    private CrossoverPerformanceAnalyzer(CrossoverPerformanceAnalyzer original, Cloner cloner)
      : base(original, cloner) {
      cnt = original.cnt;
      success = (int[])original.success.Clone();
      lastGeneration = original.lastGeneration;
      equalParents = original.equalParents;
      worseParentCrossoverPerformancePlot = (ScatterPlotHelper)original.worseParentCrossoverPerformancePlot.Clone(cloner);
      betterParentCrossoverPerformancePlot = (ScatterPlotHelper)original.betterParentCrossoverPerformancePlot.Clone(cloner);
      childDiversityToWorseParentHelper = (ScatterPlotHelper)original.childDiversityToWorseParentHelper.Clone(cloner);
      childDiversityToBetterParentHelper = (ScatterPlotHelper)original.childDiversityToBetterParentHelper.Clone(cloner);
      parentDiversityHelper = (ScatterPlotHelper)original.parentDiversityHelper.Clone(cloner);
      parentQualityHelper = (ScatterPlotHelper)original.parentQualityHelper.Clone(cloner);
      unwantedMutationsHelper = (ScatterPlotHelper)original.unwantedMutationsHelper.Clone(cloner);
      successHelper = (DataTableHelper)original.successHelper.Clone(cloner);
      equalParentsHelper = (DataTableHelper)original.equalParentsHelper.Clone(cloner);
    }

    public CrossoverPerformanceAnalyzer()
      : base() {
      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The results collection where the analysis values should be stored."));
      Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "Nr of generations."));

      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ParentsQuality", "The quality of the parent solutions."));
      ParentsQualityParameter.ActualName = "TSPTourLength";

      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The evaluated quality of the child solution."));
      QualityParameter.ActualName = "TSPTourLength";

      Parameters.Add(new ValueParameter<ISingleObjectiveSolutionSimilarityCalculator>("SimilarityCalculator"));
      Parameters.Add(new ValueParameter<IUnwantedMutationAnalyzer>("UnwantedMutationAnalyzer"));
      Parameters.Add(new LookupParameter<ItemCollection<IItem>>("Operators", "The operators and items that the problem provides to the algorithms."));
      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, false otherwise"));
      Parameters.Add(new ValueLookupParameter<ItemCollection<DoubleValue>>("ComparisonFactor", "Determines if the quality should be compared to the better parent (1.0), to the worse (0.0) or to any linearly interpolated value between them."));


      worseParentCrossoverPerformancePlot = new ScatterPlotHelper(false, true);
      betterParentCrossoverPerformancePlot = new ScatterPlotHelper(false, true);
      childDiversityToWorseParentHelper = new ScatterPlotHelper(false, true);
      childDiversityToBetterParentHelper = new ScatterPlotHelper(false, true);
      unwantedMutationsHelper = new ScatterPlotHelper(false, true);
      parentDiversityHelper = new ScatterPlotHelper(false, true);
      parentQualityHelper = new ScatterPlotHelper(false, true);
      successHelper = new DataTableHelper();
      equalParentsHelper = new DataTableHelper();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new CrossoverPerformanceAnalyzer(this, cloner);
    }

    protected override void InitializeAction() {
      if (SimilarityCalculatorParameter.Value == null) {
        SimilarityCalculatorParameter.Value = OperatorsParameter.ActualValue.OfType<ISingleObjectiveSolutionSimilarityCalculator>().FirstOrDefault();
      }

      if (ComparisonFactorParameter.ActualValue == null) {
        ComparisonFactorParameter.Value = new ItemCollection<DoubleValue>();
        ComparisonFactorParameter.Value.Add(new DoubleValue(0));
        ComparisonFactorParameter.Value.Add(new DoubleValue(0.5));
        ComparisonFactorParameter.Value.Add(new DoubleValue(1));
        ComparisonFactorParameter.Value.Add(new DoubleValue(1.5));
      }

      if (success == null) {
        success = new int[ComparisonFactorParameter.Value.Count];
      }

      worseParentCrossoverPerformancePlot.InitializePlot(Results, "Crossover Performance compared to worse parent", "Solution Index", "Absolut Quality Difference");
      betterParentCrossoverPerformancePlot.InitializePlot(Results, "Crossover Performance compared to better parent", "Solution Index", "Absolut Quality Difference");
      childDiversityToWorseParentHelper.InitializePlot(Results, "Child Diversity to Worse Parent", "Solution Index", "Diversity");
      childDiversityToBetterParentHelper.InitializePlot(Results, "Child Diversity to Better Parent", "Solution Index", "Diversity");
      parentDiversityHelper.InitializePlot(Results, "Parent Diversity", "Solution Index", "Diversity");
      parentQualityHelper.InitializePlot(Results, "Parent Quality Difference", "Solution Index", "Absolut Quality Difference");
      equalParentsHelper.InitializeChart(Results, "Number of equal parents", new string[] { "Absolut number of equal parents" });
      unwantedMutationsHelper.InitializePlot(Results, "Unwanted Mutations", "Solution Index", "Unwanted Mutation");

      List<string> successfullCXRowNames = new List<string>();
      foreach (var value in ComparisonFactorParameter.Value) {
        successfullCXRowNames.Add(SuccessfullCrossoversRowName + value.Value.ToString());
      }
      successHelper.InitializeChart(Results, "Successfull Crossovers", successfullCXRowNames.ToArray());
      Reset();
    }

    public override IOperation Apply() {
      Initialize();

      Point2D<double> worseQualityPoint, betterQualityPoint, childDiversityToWorseParent, childDiversityToBetterParent, diversityPointParent, qualityPointParent, unwantedMutationPoint;
      var qualityParent1 = ParentsQualityParameter.ActualValue.First().Value;
      var qualityParent2 = ParentsQualityParameter.ActualValue.Last().Value;
      var qualityChild = QualityParameter.ActualValue.Value;

      var parentDiversity = SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope.SubScopes.First(), ExecutionContext.Scope.SubScopes.Last());
      string curGenStr = GenerationsParameter.ActualValue.Value.ToString();

      diversityPointParent = new Point2D<double>(cnt, parentDiversity);
      qualityPointParent = new Point2D<double>(cnt, Math.Abs(qualityParent1 - qualityParent2));
      if (UnwantedMutationAnalyzerParameter.Value != null) {
        unwantedMutationPoint = new Point2D<double>(cnt, UnwantedMutationAnalyzerParameter.Value.AnalyzeUnwantedMutations(ExecutionContext.Scope.SubScopes.First(), ExecutionContext.Scope.SubScopes.Last(), ExecutionContext.Scope));
      } else {
        //TODO: this is not a good solution
        unwantedMutationPoint = new Point2D<double>(cnt, 0.0);
      }

      double worseQuality, betterQuality;

      if (qualityParent1 > qualityParent2) {
        if (MaximizationParameter.ActualValue.Value) {
          childDiversityToWorseParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.Last()));
          childDiversityToBetterParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.First()));
          worseQuality = qualityParent2;
          betterQuality = qualityParent1;
        } else {
          childDiversityToWorseParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.First()));
          childDiversityToBetterParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.Last()));
          worseQuality = qualityParent1;
          betterQuality = qualityParent2;
        }
      } else {
        if (MaximizationParameter.ActualValue.Value) {
          childDiversityToWorseParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.First()));
          childDiversityToBetterParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.Last()));
          worseQuality = qualityParent1;
          betterQuality = qualityParent2;
        } else {
          childDiversityToWorseParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.Last()));
          childDiversityToBetterParent = new Point2D<double>(cnt, SimilarityCalculatorParameter.Value.CalculateSolutionSimilarity(ExecutionContext.Scope, ExecutionContext.Scope.SubScopes.First()));
          worseQuality = qualityParent2;
          betterQuality = qualityParent1;
        }
      }

      worseQualityPoint = new Point2D<double>(cnt, Math.Abs(worseQuality - qualityChild));
      betterQualityPoint = new Point2D<double>(cnt++, Math.Abs(betterQuality - qualityChild));

      if (GenerationsParameter.ActualValue.Value == lastGeneration) {
        CountSuccess(qualityChild, qualityParent1, qualityParent2);

        if (parentDiversity == 1.0) {
          equalParents++;
        }
      }

      if (GenerationsParameter.ActualValue.Value != 0) {
        if (GenerationsParameter.ActualValue.Value > lastGeneration) {
          equalParentsHelper.AddPoint(equalParents);

          for (int i = 0; i < ComparisonFactorParameter.Value.Count; i++) {
            successHelper.AddPoint(SuccessfullCrossoversRowName + ComparisonFactorParameter.Value.ElementAt(i).Value.ToString(), success[i]);
          }

          Reset();

          CountSuccess(qualityChild, qualityParent1, qualityParent2);
          if (parentDiversity == 1.0) {
            equalParents++;
          }
        }

        betterParentCrossoverPerformancePlot.AddPoint(curGenStr, betterQualityPoint);
        worseParentCrossoverPerformancePlot.AddPoint(curGenStr, worseQualityPoint);
        childDiversityToWorseParentHelper.AddPoint(curGenStr, childDiversityToWorseParent);
        childDiversityToBetterParentHelper.AddPoint(curGenStr, childDiversityToBetterParent);
        parentDiversityHelper.AddPoint(curGenStr, diversityPointParent);
        parentQualityHelper.AddPoint(curGenStr, qualityPointParent);
        unwantedMutationsHelper.AddPoint(curGenStr, unwantedMutationPoint);
      }

      return base.Apply();
    }

    private void CountSuccess(double qualityChild, double qualityParent1, double qualityParent2) {
      //track successfull cx
      var compFactsArray = ComparisonFactorParameter.Value.ToArray();
      for (int i = 0; i < compFactsArray.Length; i++) {
        if (WeightedParentsQualityComparer.Compare(qualityChild, qualityParent1, qualityParent2, compFactsArray[i].Value, MaximizationParameter.ActualValue.Value)) {
          success[i]++;
        }
      }
    }

    private void Reset() {
      cnt = 0;
      success = new int[ComparisonFactorParameter.Value.Count];
      lastGeneration = GenerationsParameter.ActualValue.Value;
      equalParents = 0;
    }

    public override void ClearState() {
      betterParentCrossoverPerformancePlot.CleanUp();
      worseParentCrossoverPerformancePlot.CleanUp();
      childDiversityToWorseParentHelper.CleanUp();
      childDiversityToBetterParentHelper.CleanUp();
      parentDiversityHelper.CleanUp();
      parentQualityHelper.CleanUp();
      unwantedMutationsHelper.CleanUp();
    }
  }
}