using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.MetaOptimization {
  [Item("AlgorithmRunsAnalyzer", "")]
  [StorableClass]
  public class AlgorithmRunsAnalyzer : SingleSuccessorOperator {

    #region Parameter properties
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public LookupParameter<IntValue> GenerationsParameter {
      get { return (LookupParameter<IntValue>)Parameters["Generations"]; }
    }
    public LookupParameter<IntValue> RepetitionsParameter {
      get { return (LookupParameter<IntValue>)Parameters[MetaOptimizationProblem.RepetitionsParameterName]; }
    }
    public ILookupParameter<ParameterConfigurationTree> ParameterConfigurationParameter {
      get { return (ILookupParameter<ParameterConfigurationTree>)Parameters["ParameterConfigurationTree"]; }
    }
    public ILookupParameter<IItemList<IProblem>> ProblemsParameter {
      get { return (ILookupParameter<IItemList<IProblem>>)Parameters[MetaOptimizationProblem.ProblemsParameterName]; }
    }
    public LookupParameter<DoubleArray> ReferenceQualityAveragesParameter {
      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceQualityAverages"]; }
    }
    public LookupParameter<DoubleArray> ReferenceQualityDeviationsParameter {
      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceQualityDeviations"]; }
    }
    public LookupParameter<DoubleArray> ReferenceEvaluatedSolutionAveragesParameter {
      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceEvaluatedSolutionAverages"]; }
    }
    public LookupParameter<ResultCollection> ResultsParameter {
      get { return (LookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    public ScopeTreeLookupParameter<IAlgorithm> AlgorithmParameter {
      get { return (ScopeTreeLookupParameter<IAlgorithm>)Parameters["Algorithm"]; }
    }
    public ScopeTreeLookupParameter<IntValue> ProblemIndexParameter {
      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["ProblemIndex"]; }
    }
    public ScopeTreeLookupParameter<IntValue> RepetitionIndexParameter {
      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["RepetitionIndex"]; }
    }
    public LookupParameter<BoolValue> MaximizationParameter {
      get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
    }
    public LookupParameter<DoubleValue> QualityWeightParameter {
      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.QualityWeightParameterName]; }
    }
    public LookupParameter<DoubleValue> StandardDeviationWeightParameter {
      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.StandardDeviationWeightParameterName]; }
    }
    public LookupParameter<DoubleValue> EvaluatedSolutionsWeightParameter {
      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.EvaluatedSolutionsWeightParameterName]; }
    }
    private ScopeParameter CurrentScopeParameter {
      get { return (ScopeParameter)Parameters["CurrentScope"]; }
    }
    public IScope CurrentScope {
      get { return CurrentScopeParameter.ActualValue; }
    }
    public LookupParameter<StringValue> QualityMeasureNameParameter {
      get { return (LookupParameter<StringValue>)Parameters[MetaOptimizationProblem.QualityMeasureNameName]; }
    }
    #endregion

    [StorableConstructor]
    protected AlgorithmRunsAnalyzer(bool deserializing) : base(deserializing) { }
    public AlgorithmRunsAnalyzer()
      : base() {
      Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used to initialize the new random permutation."));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The evaluated quality of the ParameterVector."));
      Parameters.Add(new LookupParameter<IntValue>("Generations", ""));
      Parameters.Add(new LookupParameter<IntValue>(MetaOptimizationProblem.RepetitionsParameterName, "Number of evaluations on one problem."));
      Parameters.Add(new LookupParameter<ParameterConfigurationTree>("ParameterConfigurationTree", ""));
      Parameters.Add(new LookupParameter<IItemList<IProblem>>(MetaOptimizationProblem.ProblemsParameterName, ""));
      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceQualityAverages", ""));
      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceQualityDeviations", ""));
      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceEvaluatedSolutionAverages", ""));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", ""));
      Parameters.Add(new ScopeTreeLookupParameter<IAlgorithm>("Algorithm", "The finished algorithms containing Runs."));
      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("ProblemIndex", "The index of the problem an algorithm was executed with."));
      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("RepetitionIndex", "The index of the repetition"));
      Parameters.Add(new LookupParameter<BoolValue>("Maximization", "Set to false if the problem should be minimized."));
      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.QualityWeightParameterName));
      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.StandardDeviationWeightParameterName));
      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.EvaluatedSolutionsWeightParameterName));
      Parameters.Add(new LookupParameter<StringValue>(MetaOptimizationProblem.QualityMeasureNameName));
      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope whose sub-scopes represent the parents."));
    }
    protected AlgorithmRunsAnalyzer(AlgorithmRunsAnalyzer original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new AlgorithmRunsAnalyzer(this, cloner);
    }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (!Parameters.ContainsKey("CurrentScope")) Parameters.Add(new ScopeParameter("CurrentScope", "The current scope whose sub-scopes represent the parents.")); // backwards compatibility
      if (!Parameters.ContainsKey(MetaOptimizationProblem.QualityMeasureNameName)) Parameters.Add(new LookupParameter<StringValue>(MetaOptimizationProblem.QualityMeasureNameName)); // backwards compatibility
    }

    public override IOperation Apply() {
      ParameterConfigurationTree parameterConfiguration = ParameterConfigurationParameter.ActualValue;
      ItemArray<IAlgorithm> algorithms = AlgorithmParameter.ActualValue;
      ItemArray<IntValue> problemIndices = ProblemIndexParameter.ActualValue;
      ItemArray<IntValue> repetitionIndices = RepetitionIndexParameter.ActualValue;
      IEnumerable<string> parameterNames = parameterConfiguration.GetOptimizedParameterNames();
      IItemList<IProblem> problems = ProblemsParameter.ActualValue;
      bool maximization = MaximizationParameter.ActualValue.Value;
      int repetitions = RepetitionsParameter.ActualValue.Value;
      double qualityWeight = QualityWeightParameter.ActualValue.Value;
      double standardDeviationWeight = StandardDeviationWeightParameter.ActualValue.Value;
      double evaluatedSolutionsWeight = EvaluatedSolutionsWeightParameter.ActualValue.Value;
      string qualityMeasureName = QualityMeasureNameParameter.ActualValue.Value;
      var resultNames = new List<string> { qualityMeasureName, "Execution Time", "EvaluatedSolutions" };
      int currentGeneration = GenerationsParameter.ActualValue != null ? GenerationsParameter.ActualValue.Value : 0;
      double[] referenceQualityAverages;
      double[] referenceQualityDeviations;
      double[] referenceEvaluatedSolutionAverages;
      GetReferenceValues(problems.Count, out referenceQualityAverages, out referenceQualityDeviations, out referenceEvaluatedSolutionAverages);

      ResultCollection results = ResultsParameter.ActualValue;

      if (algorithms.All(x => x.Runs.Count == 1)) {
        var runs = new RunCollection();
        var qualities = new double[problems.Count][];
        var executionTimes = new TimeSpan[problems.Count][];
        var evaluatedSolutions = new int[problems.Count][];

        for (int i = 0; i < problems.Count; i++) {
          qualities[i] = new double[repetitions];
          evaluatedSolutions[i] = new int[repetitions];
          executionTimes[i] = new TimeSpan[repetitions];
        }

        for (int i = 0; i < algorithms.Length; i++) {
          int problemIndex = problemIndices[i].Value;
          int repetitionIndex = repetitionIndices[i].Value;

          IRun run = (IRun)algorithms[i].Runs.Single().Clone();
          MetaOptimizationUtil.ClearResults(run, resultNames);
          MetaOptimizationUtil.ClearParameters(run, parameterNames);
          run.Results.Add("Meta-FromCache", new BoolValue(false));
          run.Results.Add("Meta-Generation", new IntValue(currentGeneration));
          run.Results.Add("Meta-ProblemIndex", new IntValue(problemIndex));
          run.Name = string.Format("{0} Problem {1} Run {2}", parameterConfiguration.ParameterInfoString, problemIndex, repetitionIndex);
          
          qualities[problemIndex][repetitionIndex] = GetResultValue<DoubleValue>(run.Results, qualityMeasureName).Value; 
          executionTimes[problemIndex][repetitionIndex] = (((TimeSpanValue)run.Results["Execution Time"]).Value);
          evaluatedSolutions[problemIndex][repetitionIndex] = (((IntValue)run.Results["EvaluatedSolutions"]).Value);
          runs.Add(run);
        }

        parameterConfiguration.AverageExecutionTimes = new ItemList<TimeSpanValue>(executionTimes.Select(t => new TimeSpanValue(TimeSpan.FromMilliseconds(t.Average(ts => ts.TotalMilliseconds)))));
        parameterConfiguration.AverageEvaluatedSolutions = new DoubleArray(evaluatedSolutions.Select(x => x.Average()).ToArray());
        parameterConfiguration.Repetitions = new IntValue(repetitions);
        parameterConfiguration.AverageQualities = new DoubleArray(qualities.Select(q => q.Average()).ToArray());

        if (maximization)
          parameterConfiguration.BestQualities = new DoubleArray(qualities.Select(q => q.Max()).ToArray());
        else
          parameterConfiguration.BestQualities = new DoubleArray(qualities.Select(q => q.Min()).ToArray());

        if (maximization)
          parameterConfiguration.WorstQualities = new DoubleArray(qualities.Select(q => q.Min()).ToArray());
        else
          parameterConfiguration.WorstQualities = new DoubleArray(qualities.Select(q => q.Max()).ToArray());

        parameterConfiguration.QualityVariances = new DoubleArray(qualities.Select(q => q.Variance()).ToArray());
        parameterConfiguration.QualityStandardDeviations = new DoubleArray(qualities.Select(q => q.StandardDeviation()).ToArray());
        parameterConfiguration.Runs = runs;

        this.QualityParameter.ActualValue = new DoubleValue(MetaOptimizationUtil.Normalize(parameterConfiguration, referenceQualityAverages, referenceQualityDeviations, referenceEvaluatedSolutionAverages, qualityWeight, standardDeviationWeight, evaluatedSolutionsWeight, maximization));
      } else {
        // something terrible happened -> most probably due to invalid parameters.
        // penalty with worst quality from latest generation!
        double penaltyValue;
        if (maximization)
          penaltyValue = results.ContainsKey("CurrentWorstQuality") ? ((DoubleValue)results["CurrentWorstQuality"].Value).Value : referenceQualityAverages.Min();
        else
          penaltyValue = results.ContainsKey("CurrentWorstQuality") ? ((DoubleValue)results["CurrentWorstQuality"].Value).Value : referenceQualityAverages.Max();

        this.QualityParameter.ActualValue = new DoubleValue(penaltyValue);
        parameterConfiguration.Quality = new DoubleValue(penaltyValue);

        parameterConfiguration.AverageExecutionTimes = new ItemList<TimeSpanValue>(Enumerable.Repeat(new TimeSpanValue(TimeSpan.Zero), problems.Count));
        parameterConfiguration.AverageEvaluatedSolutions = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.Repetitions = new IntValue(repetitions);
        parameterConfiguration.AverageQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.BestQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.WorstQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.QualityVariances = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.QualityStandardDeviations = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
        parameterConfiguration.Runs = null;
      }

      // in OSGA there are more subscopes, so be careful which to delete
      CurrentScope.SubScopes.RemoveAll(x => x.Variables.Count((v) => (v.Name == "RepetitionCount")) == 1);
      
      return base.Apply();
    }

    private T1 GetResultValue<T1>(IDictionary<string, IItem> results, string resultName) {
      return (T1)results[resultName];

      //string separator = ".";
      //string[] tokens = resultName.Split(separator.ToCharArray());

      //IDictionary<string, IItem> currentResults = results;
      //IItem currentResult = null;
      //for (int i = 0; i < tokens.Length; i++) {
      //  if(currentResults == null)
      //    throw new KeyNotFoundException("Result value " + resultName + " was not found");
      //  if (currentResults.ContainsKey(tokens[i])) {
      //    currentResult = currentResults[tokens[i]];
      //    currentResults = currentResult as IDictionary<string, IItem>;
      //  } else {
      //    throw new KeyNotFoundException("Result value " + resultName + " was not found");
      //  }
      //}
      //return (T1)currentResult;
    }

    private void GetReferenceValues(int problemsCount, out double[] referenceQualityAverages, out double[] referenceQualityDeviations, out double[] referenceEvaluatedSolutionAverages) {
      if (ReferenceQualityAveragesParameter.ActualValue == null) {
        // this is generation zero. no reference qualities for normalization have been calculated yet. in this special case the ReferenceQualityAnalyzer will do the normalization
        referenceQualityAverages = new double[problemsCount];
        referenceQualityDeviations = new double[problemsCount];
        referenceEvaluatedSolutionAverages = new double[problemsCount];
        for (int i = 0; i < referenceQualityAverages.Length; i++) {
          referenceQualityAverages[i] = 1;
          referenceQualityDeviations[i] = 1;
          referenceEvaluatedSolutionAverages[i] = 1;
        }
      } else {
        referenceQualityAverages = ReferenceQualityAveragesParameter.ActualValue.ToArray();
        referenceQualityDeviations = ReferenceQualityDeviationsParameter.ActualValue.ToArray();
        referenceEvaluatedSolutionAverages = ReferenceEvaluatedSolutionAveragesParameter.ActualValue.ToArray();
      }
    }
  }
}