using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Algorithms.SAPBA.Strategies {
  public class GenerationalStrategy : StrategyBase {
    public const string NoGenerationsParameterName = "Number of generations";
    public IFixedValueParameter<IntValue> NoGenerationsParameter => Parameters[NoGenerationsParameterName] as IFixedValueParameter<IntValue>;
    public IntValue NoGenerations => NoGenerationsParameter.Value;
    [Storable]
    public int Generations;

    #region Constructors
    private GenerationalStrategy(GenerationalStrategy original, Cloner cloner) : base(original, cloner) {
      Generations = original.Generations;
    }
    public GenerationalStrategy() {
      Parameters.Add(new FixedValueParameter<IntValue>(NoGenerationsParameterName, "The number of generations before a new model is constructed", new IntValue(3)));
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new GenerationalStrategy(this, cloner);
    }
    #endregion

    public override double Evaluate(RealVector r, IRandom random) {
      return EstimateSample(r, random).Item2;
    }

    protected override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, ResultCollection globalResults, IRandom random) {
    }

    protected override void ProcessPopulation(Individual[] individuals, double[] qualities, IRandom random) {
      if (RegressionSolution != null && Generations < NoGenerations.Value) Generations++;
      else {
        foreach (var indi in individuals) EvaluateSample(indi.RealVector(), random);
        BuildRegressionSolution(random);
        Generations = 0;
      }
    }

    protected override void Initialize() {
      Generations = 0;
    }
  }
}