#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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.Instances;

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

    #region Parameter Properties
    public OptionalValueParameter<DoubleMatrix> CoordinatesParameter {
      get { return (OptionalValueParameter<DoubleMatrix>)Parameters["Coordinates"]; }
    }
    public OptionalValueParameter<DistanceCalculator> DistanceCalculatorParameter {
      get { return (OptionalValueParameter<DistanceCalculator>)Parameters["DistanceCalculator"]; }
    }
    public OptionalValueParameter<DistanceMatrix> DistanceMatrixParameter {
      get { return (OptionalValueParameter<DistanceMatrix>)Parameters["DistanceMatrix"]; }
    }
    public IFixedValueParameter<BoolValue> UseDistanceMatrixParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters["UseDistanceMatrix"]; }
    }
    public OptionalValueParameter<Permutation> BestKnownSolutionParameter {
      get { return (OptionalValueParameter<Permutation>)Parameters["BestKnownSolution"]; }
    }
    public IValueParameter<DoubleArray> ProbabilitiesParameter {
      get { return (IValueParameter<DoubleArray>)Parameters["Probabilities"]; }
    }
    #endregion

    #region Properties
    public DoubleMatrix Coordinates {
      get { return CoordinatesParameter.Value; }
      set { CoordinatesParameter.Value = value; }
    }
    public DistanceCalculator DistanceCalculator {
      get { return DistanceCalculatorParameter.Value; }
      set { DistanceCalculatorParameter.Value = value; }
    }
    public DistanceMatrix DistanceMatrix {
      get { return DistanceMatrixParameter.Value; }
      set { DistanceMatrixParameter.Value = value; }
    }
    public bool UseDistanceMatrix {
      get { return UseDistanceMatrixParameter.Value.Value; }
      set { UseDistanceMatrixParameter.Value.Value = value; }
    }
    public Permutation BestKnownSolution {
      get { return BestKnownSolutionParameter.Value; }
      set { BestKnownSolutionParameter.Value = value; }
    }
    public DoubleArray Probabilities {
      get { return ProbabilitiesParameter.Value; }
      set { ProbabilitiesParameter.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) { }
    protected ProbabilisticTravelingSalesmanProblem() {
      Parameters.Add(new OptionalValueParameter<DoubleMatrix>("Coordinates", "The x- and y-Coordinates of the cities."));
      Parameters.Add(new OptionalValueParameter<DistanceCalculator>("DistanceCalculator", "Calculates the distance between two rows in the coordinates matrix."));
      Parameters.Add(new OptionalValueParameter<DistanceMatrix>("DistanceMatrix", "The matrix which contains the distances between the cities."));
      Parameters.Add(new FixedValueParameter<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>("Probabilities", "This list describes for each city the probability of appearing in a realized instance."));

      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 }
      });

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

    public override double Evaluate(Individual individual, IRandom random) {
      return Evaluate(individual.Permutation(), random);
    }

    public abstract double Evaluate(Permutation tour, IRandom random);

    public virtual void Load(PTSPData 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.Coordinates == null || data.Coordinates.GetLength(0) != data.Dimension || data.Coordinates.GetLength(1) != 2))
        throw new System.IO.InvalidDataException("The given instance is too large for using a distance matrix and there is a problem with the coordinates.");
      if (data.Coordinates != null && (data.Coordinates.GetLength(0) != data.Dimension || 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 respectively.");

      switch (data.DistanceMeasure) {
        case DistanceMeasure.Direct:
          DistanceCalculator = null;
          if (data.Dimension > DistanceMatrixSizeLimit && Coordinates != null) {
            DistanceCalculator = new EuclideanDistance();
            UseDistanceMatrix = false;
          } else UseDistanceMatrix = true;
          break;
        case DistanceMeasure.Att: DistanceCalculator = new AttDistance(); break;
        case DistanceMeasure.Euclidean: DistanceCalculator = new EuclideanDistance(); break;
        case DistanceMeasure.Geo: DistanceCalculator = new GeoDistance(); break;
        case DistanceMeasure.Manhattan: DistanceCalculator = new ManhattanDistance(); break;
        case DistanceMeasure.Maximum: DistanceCalculator = new MaximumDistance(); break;
        case DistanceMeasure.RoundedEuclidean: DistanceCalculator = new RoundedEuclideanDistance(); break;
        case DistanceMeasure.UpperEuclidean: DistanceCalculator = new UpperEuclideanDistance(); break;
        default: throw new ArgumentException("Distance measure is unknown");
      }

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

      Probabilities = new DoubleArray(data.Probabilities);
      BestKnownSolution = data.BestKnownTour != null ? new Permutation(PermutationTypes.RelativeUndirected, data.BestKnownTour) : null;
      Coordinates = data.Coordinates != null && data.Coordinates.GetLength(0) > 0 ? new DoubleMatrix(data.Coordinates) : null;
      DistanceMatrix = new DistanceMatrix(data.GetDistanceMatrix());

      Encoding.Length = data.Dimension;

      OnReset();
    }
  }
}