using HeuristicLab.Algorithms.CMAEvolutionStrategy;
using HeuristicLab.Algorithms.LocalSearch;
using HeuristicLab.Algorithms.ParameterlessPopulationPyramid;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Core.Networks;
using HeuristicLab.Data;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Networks.IntegratedOptimization {
  [Item("TtpNetwork4", "An optimization network for the TTP.")]
  [Creatable("Optimization Networks")]
  [StorableClass]
  // standard ttp
  // cmaes variegates tsp coordinates
  // 1) init cmaes (length = ksp.values.length)
  // 2) start cmaes
  // 3) evaluate vector as follows:
  // 4) change tsp coordinates (mult by factor in vector)
  // 5) best tsp
  // 6) best ksp
  // 7) return ttp quality
  public sealed class TtpNetwork4 : Network {
    [StorableConstructor]
    private TtpNetwork4(bool deserializing) : base(deserializing) { }
    private TtpNetwork4(TtpNetwork4 original, Cloner cloner) : base(original, cloner) { }
    public TtpNetwork4() : base("TtpNetwork4") {
      var orchestratorNode = new TtpOrchestratorNode4();
      Nodes.Add(orchestratorNode);

      var metaSolverNode = new OrchestratedAlgorithmNode("MetaSolver");
      var cmaes = new CMAEvolutionStrategy();
      cmaes.MaximumEvaluatedSolutions = 3000;
      cmaes.Engine = new ParallelEngine.ParallelEngine();
      metaSolverNode.Algorithm = cmaes;
      orchestratorNode.MetaSolverOrchestrationPort.ConnectedPort = metaSolverNode.OrchestrationPort;
      Nodes.Add(metaSolverNode);

      var tspSolverNode = new OrchestratedAlgorithmNode("TspSolver") { CloneAlgorithm = true };
      var ls = new LocalSearch();
      ls.Problem = orchestratorNode.TspParameter.Value;
      ls.MaximumIterations.Value = 100;
      tspSolverNode.Algorithm = ls;
      orchestratorNode.TspSolverOrchestrationPort.ConnectedPort = tspSolverNode.OrchestrationPort;
      Nodes.Add(tspSolverNode);

      var kspSolverNode = new OrchestratedAlgorithmNode("KspSolver") { CloneAlgorithm = true };
      var p3 = new ParameterlessPopulationPyramid();
      p3.Problem = orchestratorNode.KspParameter.Value;
      p3.MaximumRuntime = 3;
      kspSolverNode.Algorithm = p3;
      orchestratorNode.KspSolverOrchestrationPort.ConnectedPort = kspSolverNode.OrchestrationPort;
      Nodes.Add(kspSolverNode);

      #region Import
      DoubleMatrix tspCoordinates;
      IntValue kspCapacity; IntArray kspItemWeights; IntArray kspItemValues;
      IntArray ttpAvailability; DoubleValue ttpMinSpeed; DoubleValue ttpMaxSpeed; DoubleValue ttpRentingRatio;
      TtpImporter.ImportTtpInstance(@"ttp-instances\berlin52-ttp\berlin52_n51_uncorr_01.ttp",
          out tspCoordinates,
          out kspCapacity, out kspItemValues, out kspItemWeights,
          out ttpAvailability, out ttpMinSpeed, out ttpMaxSpeed, out ttpRentingRatio);

      var tsp = orchestratorNode.TspParameter.Value;
      tsp.Coordinates = tspCoordinates;

      var ksp = orchestratorNode.KspParameter.Value;
      ksp.KnapsackCapacity = kspCapacity;
      ksp.Encoding.Length = kspItemValues.Length;
      ksp.Values = kspItemValues;
      ksp.Weights = kspItemWeights;

      orchestratorNode.AvailabilityParameter.Value = ttpAvailability;
      orchestratorNode.MinSpeedParameter.Value = ttpMinSpeed;
      orchestratorNode.MaxSpeedParameter.Value = ttpMaxSpeed;
      orchestratorNode.RentingRatioParameter.Value = ttpRentingRatio;
      #endregion
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new TtpNetwork4(this, cloner);
    }
  }
}