#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;

namespace HeuristicLab.Algorithms.VOffspringSelectionGeneticAlgorithm {
  [Item("PopDivOffspringSelector", "")]
  [StorableClass]
  public class PopDivOffspringSelector : InstrumentedOperator, IOffspringSelector {

    private const string SuccessRatioChart = "SuccessRatioChart";
    private const string SuccessRatioDataRow = "SuccessRatio";


    public ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
    }
    public ValueLookupParameter<DoubleValue> SuccessRatioParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
    }
    public LookupParameter<DoubleValue> SelectionPressureParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["SelectionPressure"]; }
    }
    public LookupParameter<DoubleValue> CurrentSuccessRatioParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["CurrentSuccessRatio"]; }
    }
    public LookupParameter<ItemList<IScope>> OffspringPopulationParameter {
      get { return (LookupParameter<ItemList<IScope>>)Parameters["OffspringPopulation"]; }
    }
    public LookupParameter<IntValue> OffspringPopulationWinnersParameter {
      get { return (LookupParameter<IntValue>)Parameters["OffspringPopulationWinners"]; }
    }
    public ScopeTreeLookupParameter<BoolValue> SuccessfulOffspringParameter {
      get { return (ScopeTreeLookupParameter<BoolValue>)Parameters["SuccessfulOffspring"]; }
    }
    public IValueLookupParameter<BoolValue> FillPopulationWithParentsParameter {
      get { return (IValueLookupParameter<BoolValue>)Parameters["FillPopulationWithParents"]; }
    }
    public ILookupParameter<BoolValue> EnoughChildrenGeneratedParameter {
      get { return (ILookupParameter<BoolValue>)Parameters["EnoughChildrenGenerated"]; }
    }
    public IValueLookupParameter<ISolutionSimilarityCalculator> SimilarityCalculatorParameter {
      get { return (IValueLookupParameter<ISolutionSimilarityCalculator>)Parameters["SimilarityCalculator"]; }
    }
    public ValueLookupParameter<ResultCollection> ResultsParameter {
      get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
    }

    public ValueLookupParameter<DoubleValue> DiversityComparisonFactorParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["DiversityComparisonFactor"]; }
    }
    private ValueLookupParameter<DoubleValue> ComparisonFactorLowerBoundParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["DiversityComparisonFactorLowerBound"]; }
    }
    private ValueLookupParameter<DoubleValue> ComparisonFactorUpperBoundParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["DiversityComparisonFactorUpperBound"]; }
    }

    public IConstrainedValueParameter<IDiscreteDoubleValueModifier> ComparisonFactorModifierParameter {
      get { return (IConstrainedValueParameter<IDiscreteDoubleValueModifier>)Parameters["ComparisonFactorModifier"]; }
    }

    [StorableConstructor]
    protected PopDivOffspringSelector(bool deserializing) : base(deserializing) { }
    protected PopDivOffspringSelector(PopDivOffspringSelector original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new PopDivOffspringSelector(this, cloner);
    }
    public PopDivOffspringSelector()
      : base() {
      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure which prematurely terminates the offspring selection step."));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful offspring that has to be produced."));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("SelectionPressure", "The amount of selection pressure currently necessary to fulfill the success ratio."));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio indicates how much of the successful offspring have already been generated."));
      Parameters.Add(new LookupParameter<ItemList<IScope>>("OffspringPopulation", "Temporary store of the offspring population."));
      Parameters.Add(new LookupParameter<IntValue>("OffspringPopulationWinners", "Temporary store the number of successful offspring in the offspring population."));
      Parameters.Add(new ScopeTreeLookupParameter<BoolValue>("SuccessfulOffspring", "True if the offspring was more successful than its parents.", 2));
      Parameters.Add(new ValueLookupParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded."));
      Parameters.Add(new LookupParameter<BoolValue>("EnoughChildrenGenerated", "True if enough children have been generated, otherwise false."));
      Parameters.Add(new ValueLookupParameter<ISolutionSimilarityCalculator>("SimilarityCalculator", "The similarity calculator that should be used to calculate solution similarity."));
      Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));

      Parameters.Add(new ValueLookupParameter<DoubleValue>("DiversityComparisonFactor", "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.", new DoubleValue(0.0)));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("DiversityComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0.5)));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("DiversityComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1.0)));
      Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet<IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));


      foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
        ComparisonFactorModifierParameter.ValidValues.Add(modifier);
      IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
      if (linearModifier != null) ComparisonFactorModifierParameter.Value = linearModifier;
      ParameterizeComparisonFactorModifiers();

      this.AfterExecutionOperators.Clear();
      this.AfterExecutionOperators.Add(ComparisonFactorModifierParameter.Value);
      ComparisonFactorModifierParameter.ValueChanged += ComparisonFactorModifierParameter_ValueChanged;
    }

    void ComparisonFactorModifierParameter_ValueChanged(object sender, EventArgs e) {
      this.AfterExecutionOperators.Clear();
      this.AfterExecutionOperators.Add(ComparisonFactorModifierParameter.Value);
    }

    private void ParameterizeComparisonFactorModifiers() {
      //TODO: does not work if Generations parameter names are changed
      foreach (IDiscreteDoubleValueModifier modifier in ComparisonFactorModifierParameter.ValidValues) {
        modifier.IndexParameter.ActualName = "Generations";
        modifier.EndIndexParameter.ActualName = "MaximumGenerations";
        modifier.EndValueParameter.ActualName = ComparisonFactorUpperBoundParameter.Name;
        modifier.StartIndexParameter.Value = new IntValue(0);
        modifier.StartValueParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
        modifier.ValueParameter.ActualName = "DiversityComparisonFactor";
      }
    }

    public override IOperation InstrumentedApply() {
      double maxSelPress = MaximumSelectionPressureParameter.ActualValue.Value;
      double successRatio = SuccessRatioParameter.ActualValue.Value;
      bool fillPopulationWithParents = false;
      if (FillPopulationWithParentsParameter.ActualValue != null)
        fillPopulationWithParents = FillPopulationWithParentsParameter.ActualValue.Value;
      IScope scope = ExecutionContext.Scope;
      IScope parents = scope.SubScopes[0];
      IScope offspring = scope.SubScopes[1];
      int populationSize = parents.SubScopes.Count;

      // retrieve actual selection pressure and success ratio
      DoubleValue selectionPressure = SelectionPressureParameter.ActualValue;
      if (selectionPressure == null) {
        selectionPressure = new DoubleValue(0);
        SelectionPressureParameter.ActualValue = selectionPressure;
      }
      DoubleValue currentSuccessRatio = CurrentSuccessRatioParameter.ActualValue;
      if (currentSuccessRatio == null) {
        currentSuccessRatio = new DoubleValue(0);
        CurrentSuccessRatioParameter.ActualValue = currentSuccessRatio;
      }

      DataTable successRatioDataTable;
      if (ResultsParameter.ActualValue.ContainsKey(SuccessRatioChart)) {
        successRatioDataTable = (DataTable)ResultsParameter.ActualValue[SuccessRatioChart].Value;
      } else {
        successRatioDataTable = new DataTable(SuccessRatioChart);
        successRatioDataTable.Rows.Add(new DataRow(SuccessRatioDataRow));
        ResultsParameter.ActualValue.Add(new Result(SuccessRatioChart, successRatioDataTable));
      }

      // retrieve next population
      ItemList<IScope> population = OffspringPopulationParameter.ActualValue;
      IntValue successfulOffspring;
      if (population == null) {
        population = new ItemList<IScope>();
        OffspringPopulationParameter.ActualValue = population;
        selectionPressure.Value = 0; // initialize selection pressure for this round
        currentSuccessRatio.Value = 0; // initialize current success ratio for this round
        successfulOffspring = new IntValue(0);
        OffspringPopulationWinnersParameter.ActualValue = successfulOffspring;
      } else successfulOffspring = OffspringPopulationWinnersParameter.ActualValue;

      int worseOffspringNeeded = (int)((1 - successRatio) * populationSize) - (population.Count - successfulOffspring.Value);
      int successfulOffspringAdded = 0;

      double avgPopDiv = DiversityComparisonFactorParameter.Value.Value;
      bool takeWorseOffspring = false;

      // implement the ActualValue fetch here - otherwise the parent scope would also be included, given that there may be 1000 or more parents, this is quite unnecessary
      string tname = SuccessfulOffspringParameter.TranslatedName;
      double tmpSelPress = selectionPressure.Value, tmpSelPressInc = 1.0 / populationSize;
      int cnt = 0;
      int stepWidth = offspring.SubScopes.Count / 5;
      for (int i = 0; i < offspring.SubScopes.Count; i++) {
        //calculate population diversity so far
        if (cnt != 0 && cnt % stepWidth == 0) {
          Scope tmpScope = new Scope();
          tmpScope.SubScopes.AddRange(population);
          double popDiversity = SimilarityCalculatorParameter.ActualValue.CalculateSolutionCrowdSimilarity(tmpScope).Average(x => x.Average());
          //this assumes that low-quality solutions are of high diversity
          if (popDiversity > avgPopDiv) {
            takeWorseOffspring = true;
          } else {
            takeWorseOffspring = false;
          }
        }
        cnt++;

        // fetch value
        IVariable tmpVar;
        if (!offspring.SubScopes[i].Variables.TryGetValue(tname, out tmpVar)) throw new InvalidOperationException(Name + ": Could not determine if an offspring was successful or not.");
        BoolValue tmp = (tmpVar.Value as BoolValue);
        if (tmp == null) throw new InvalidOperationException(Name + ": The variable that indicates whether an offspring is successful or not must contain a BoolValue.");

        if (takeWorseOffspring) {
          IScope currentOffspring = offspring.SubScopes[i];
          offspring.SubScopes.Remove(currentOffspring);
          i--;
          population.Add(currentOffspring);
          worseOffspringNeeded--;
        } else if (tmp.Value) {
          IScope currentOffspring = offspring.SubScopes[i];
          offspring.SubScopes.Remove(currentOffspring);
          i--;
          population.Add(currentOffspring);
          successfulOffspringAdded++;
        } else if (worseOffspringNeeded > 0 || tmpSelPress >= maxSelPress) {
          IScope currentOffspring;
          if (!fillPopulationWithParents || worseOffspringNeeded > 0) {
            currentOffspring = offspring.SubScopes[i];
            offspring.SubScopes.Remove(currentOffspring);
            i--;
            worseOffspringNeeded--;
          } else {
            currentOffspring = parents.SubScopes[i];
          }
          population.Add(currentOffspring);
        }
        tmpSelPress += tmpSelPressInc;
        if (population.Count >= populationSize) break;
      }
      successfulOffspring.Value += successfulOffspringAdded;

      // calculate actual selection pressure and success ratio
      selectionPressure.Value = tmpSelPress;
      currentSuccessRatio.Value = successfulOffspring.Value / ((double)populationSize);
      successRatioDataTable.Rows[SuccessRatioDataRow].Values.Add(currentSuccessRatio.Value);

      if (EnoughChildrenGeneratedParameter.ActualValue == null) {
        EnoughChildrenGeneratedParameter.ActualValue = new BoolValue();
      }
      // check if enough children have been generated
      if (((selectionPressure.Value < maxSelPress) &&
        (currentSuccessRatio.Value < successRatio) &&
        (population.Count < populationSize)) ||
          (population.Count < populationSize)) {
        // more children required -> reduce left and start children generation again
        scope.SubScopes.Remove(parents);
        scope.SubScopes.Remove(offspring);
        while (parents.SubScopes.Count > 0) {
          IScope parent = parents.SubScopes[0];
          parents.SubScopes.RemoveAt(0);
          scope.SubScopes.Add(parent);
        }
        EnoughChildrenGeneratedParameter.ActualValue.Value = false;
      } else {
        // enough children generated
        offspring.SubScopes.Clear();
        offspring.SubScopes.AddRange(population);

        scope.Variables.Remove(OffspringPopulationParameter.TranslatedName);
        scope.Variables.Remove(OffspringPopulationWinnersParameter.TranslatedName);
        EnoughChildrenGeneratedParameter.ActualValue.Value = true;
      }

      return base.InstrumentedApply();
    }
  }
}