#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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 System.Text;
using System.Threading.Tasks;
using HeuristicLab.Optimization;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.Instances;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Common;
using HeuristicLab.Parameters;
using HeuristicLab.Data;

namespace HeuristicLab.Problems.PTSP {
  [Item("Probabilistic Traveling Salesman Problem", "Represents a Probabilistic Traveling Salesman Problem.")]
  [StorableClass]
  public abstract class ProbabilisticTravelingSalesmanProblem : SingleObjectiveBasicProblem<PermutationEncoding>, IStorableContent,
  IProblemInstanceConsumer<TSPData> {
    private static readonly int DistanceMatrixSizeLimit = 1000;

    #region Parameter Properties
    public OptionalValueParameter<DoubleMatrix> CoordinatesParameter {
      get { return (OptionalValueParameter<DoubleMatrix>)Parameters["Coordinates"]; }
    }
    public OptionalValueParameter<DistanceMatrix> DistanceMatrixParameter {
      get { return (OptionalValueParameter<DistanceMatrix>)Parameters["DistanceMatrix"]; }
    }
    public ValueParameter<BoolValue> UseDistanceMatrixParameter {
      get { return (ValueParameter<BoolValue>)Parameters["UseDistanceMatrix"]; }
    }
    public OptionalValueParameter<Permutation> BestKnownSolutionParameter {
      get { return (OptionalValueParameter<Permutation>)Parameters["BestKnownSolution"]; }
    }
    public ValueParameter<DoubleArray> ProbabilityMatrixParameter {
      get { return (ValueParameter<DoubleArray>)Parameters["ProbabilityMatrix"]; }
    }
    public ValueParameter<IntValue> SampleSizeParameter {
      get { return (ValueParameter<IntValue>)Parameters["SampleSize"]; }
    }

    #endregion

    #region Properties
    public DoubleMatrix Coordinates {
      get { return CoordinatesParameter.Value; }
      set { CoordinatesParameter.Value = value; }
    }
    public DistanceMatrix DistanceMatrix {
      get { return DistanceMatrixParameter.Value; }
      set { DistanceMatrixParameter.Value = value; }
    }
    public BoolValue UseDistanceMatrix {
      get { return UseDistanceMatrixParameter.Value; }
      set { UseDistanceMatrixParameter.Value = value; }
    }
    public Permutation BestKnownSolution {
      get { return BestKnownSolutionParameter.Value; }
      set { BestKnownSolutionParameter.Value = value; }
    }
    public DoubleArray ProbabilityMatrix {
      get { return ProbabilityMatrixParameter.Value; }
      set { ProbabilityMatrixParameter.Value = value; }
    }
    public IntValue SampleSize {
      get { return SampleSizeParameter.Value; }
      set { SampleSizeParameter.Value = value; }
    }
    #endregion
    

    public override bool Maximization {
      get { return false; }
    }

    [StorableConstructor]
    protected ProbabilisticTravelingSalesmanProblem(bool deserializing) : base(deserializing) { }
    protected ProbabilisticTravelingSalesmanProblem(ProbabilisticTravelingSalesmanProblem original, Cloner cloner)
      : base(original, cloner) {
    }

    public ProbabilisticTravelingSalesmanProblem() {
      Parameters.Add(new OptionalValueParameter<DoubleMatrix>("Coordinates", "The x- and y-Coordinates of the cities."));
      Parameters.Add(new OptionalValueParameter<DistanceMatrix>("DistanceMatrix", "The matrix which contains the distances between the cities."));
      Parameters.Add(new ValueParameter<BoolValue>("UseDistanceMatrix", "True if the coordinates based evaluators should calculate the distance matrix from the coordinates and use it for evaluation similar to the distance matrix evaluator, otherwise false.", new BoolValue(true)));
      Parameters.Add(new OptionalValueParameter<Permutation>("BestKnownSolution", "The best known solution of this TSP instance."));
      Parameters.Add(new ValueParameter<DoubleArray>("ProbabilityMatrix", "The matrix which contains the probabilities of each of the cities."));

      Coordinates = new DoubleMatrix(new double[,] {
        { 100, 100 }, { 100, 200 }, { 100, 300 }, { 100, 400 },
        { 200, 100 }, { 200, 200 }, { 200, 300 }, { 200, 400 },
        { 300, 100 }, { 300, 200 }, { 300, 300 }, { 300, 400 },
        { 400, 100 }, { 400, 200 }, { 400, 300 }, { 400, 400 }
      });

      ProbabilityMatrix = new DoubleArray(new double[] {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
      
    }

    public virtual void Load(TSPData data) {
      if (data.Coordinates == null && data.Distances == null)
        throw new System.IO.InvalidDataException("The given instance specifies neither coordinates nor distances!");
      if (data.Dimension > DistanceMatrixSizeLimit && (data.DistanceMeasure == DistanceMeasure.Att
        || data.DistanceMeasure == DistanceMeasure.Manhattan
        || data.DistanceMeasure == DistanceMeasure.Maximum))
        throw new System.IO.InvalidDataException("The given instance uses an unsupported distance measure and is too large for using a distance matrix.");
      if (data.Coordinates != null && data.Coordinates.GetLength(1) != 2)
        throw new System.IO.InvalidDataException("The coordinates of the given instance are not in the right format, there need to be one row for each customer and two columns for the x and y coordinates.");

      Name = data.Name;
      Description = data.Description;

      bool clearCoordinates = false, clearDistanceMatrix = false;
      if (data.Coordinates != null && data.Coordinates.GetLength(0) > 0)
        Coordinates = new DoubleMatrix(data.Coordinates);
      else clearCoordinates = true;

      // reset them after assigning the evaluator
      if (clearCoordinates) Coordinates = null;
      if (clearDistanceMatrix) DistanceMatrix = null;

      DistanceMatrix = new DistanceMatrix(data.GetDistanceMatrix());
      // Get Probabilities of cities using random with seed from hash function on the Name of the instance
      ProbabilityMatrix = new DoubleArray(data.Dimension);
      Random r = new Random(data.Name.GetHashCode());
      for (int i = 0; i < data.Dimension; i++) {
        ProbabilityMatrix[i] = r.NextDouble();
      }
      Encoding.Length = data.Dimension;

      OnReset();
    }
  }
}