#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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.Linq;
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.PermutationProblems {
  [Item("Linear Ordering Problem (LOP)", "Represents a Linear Ordering Problem")]
  [Creatable(CreatableAttribute.Categories.CombinatorialProblems)]
  [StorableClass]
  public sealed class LinearOrderingProblem : SingleObjectiveBasicProblem<PermutationEncoding>, IProblemInstanceConsumer<LOPData>, IProblemInstanceExporter<LOPData>, IStorableContent {
    private static readonly LOPData DefaultInstance = new LOPData() {
      Name = "Linaer Ordering Problem (LOP)",
      Description = "The default instance of the LOP in HeuristicLab",
      Dimension = 4,
      Matrix = new double[,] {
                {0 ,3, 6 ,6},
                {2 ,0, 8 ,4},
                {4 ,2, 0 ,4},
                {5 ,3, 8 ,0}
            }
    };

    public OptionalValueParameter<Permutation> BestKnownSolutionParameter
    {
      get { return (OptionalValueParameter<Permutation>)Parameters["BestKnownSolution"]; }
    }
    public Permutation BestKnownSolution
    {
      get { return BestKnownSolutionParameter.Value; }
      set
      {
        BestKnownSolutionParameter.Value = value;
      }
    }

    public ValueParameter<DoubleMatrix> MatrixParameter
    {
      get { return (ValueParameter<DoubleMatrix>)Parameters["Matrix"]; }
    }
    public DoubleMatrix Matrix
    {
      get { return MatrixParameter.Value; }
      set { MatrixParameter.Value = value; }
    }

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

    [StorableConstructor]
    private LinearOrderingProblem(bool deserializing) : base(deserializing) { }
    private LinearOrderingProblem(LinearOrderingProblem original, Cloner cloner) : base(original, cloner) { }
    public LinearOrderingProblem() {
      Parameters.Add(new OptionalValueParameter<Permutation>("BestKnownSolution", "The best known solution of this LOP instance."));
      Parameters.Add(new ValueParameter<DoubleMatrix>("Matrix", "The matrix which contains the corresponding LOP-values"));

      Load(DefaultInstance);
      EvaluatorParameter.GetsCollected = false;
      EvaluatorParameter.Hidden = true;

      Evaluator.QualityParameter.ActualName = "Superdiagonal";
    }

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

    public void Load(LOPData data) {
      if (data.Matrix.GetLength(0) != data.Matrix.GetLength(1)) {
        throw new ArgumentException("Matrix must be square");
      }
      if (data.BestKnownQuality.HasValue) {
        BestKnownQuality = data.BestKnownQuality.Value;
      }
      Name = data.Name;
      Description = data.Description;
      Matrix = new DoubleMatrix(data.Matrix);
      Encoding.Length = Matrix.Columns;

      if (data.BestKnownPermutation != null) {
        int[] permut = data.BestKnownPermutation;
        //Clean up if the first index = 1
        if (!permut.Contains(0)) { permut = permut.Select(v => v - 1).ToArray(); }

        BestKnownSolution = new Permutation(PermutationTypes.Absolute, permut);
        BestKnownQuality = Evaluate(new Permutation(PermutationTypes.Absolute, permut), Matrix);
      }
    }

    public LOPData Export() {
      var result = new LOPData {
        Name = Name,
        Description = Description,
        BestKnownQuality = BestKnownQuality,
        BestKnownPermutation = BestKnownSolution.ToArray(),
        Dimension = Matrix.Rows,
        Matrix = Matrix.CloneAsMatrix()
      };

      return result;
    }

    public override double Evaluate(Individual individual, IRandom random) {
      return Evaluate(individual.Permutation(), Matrix);
    }
    private double Evaluate(Permutation permutation, DoubleMatrix matrix) {
      double sum = 0;
      for (int i = 1; i < matrix.Columns; i++) {
        for (int j = 0; j < i; j++) {
          sum += matrix[permutation[j], permutation[i]];
        }
      }

      return sum;
    }
  }
}