#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 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 HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using System;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [Item("RankMap", "A map of models of models of models")]
  [StorableType("1D4DD90E-553A-46DB-B0CD-6A899AA0B6D0")]
  public class EMMRankMap : EMMMapBase<ISymbolicExpressionTree> { // it do not work absolutely
    [Storable]
    public List<List<double>> Probabilities { get; set; }
    #region constructors
    [StorableConstructor]
    protected EMMRankMap(StorableConstructorFlag _) : base(_) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMRankMap(this, cloner);
    }
    public EMMRankMap() : base() {
      ModelSet = new List<ISymbolicExpressionTree>();
      Probabilities = new List<List<double>>();
    }
    public EMMRankMap(EMMRankMap original, Cloner cloner) : base(original, cloner) {
      if (original.Probabilities != null) {
        Probabilities = original.Probabilities.Select(x => x.ToList()).ToList();
      }
    }
    #endregion
    #region MapCreation
    override public void CreateMap(IRandom random) {
      MapSizeCheck(ModelSet.Count);
      ApplyRankMapCreationAlgorithm(ModelSetPreparation.CalculateDistances(ModelSet), Map, Probabilities);
    }
    override public void CreateMap(IRandom random, ISymbolicDataAnalysisSingleObjectiveProblem problem) {
      MapSizeCheck(ModelSet.Count);
      ApplyRankMapCreationAlgorithm(ModelSetPreparation.DistanceMatrixCalculation(ModelSet, DistanceParametr, problem), Map, Probabilities);
    }
    override public void MapRead(IEnumerable<ISymbolicExpressionTree> trees) {
      base.MapRead(trees);
      MapFullment(trees.Count());
      string fileName = ("Map" + DistanceParametr + ".txt");
      Probabilities = FileComuncations.DoubleMatrixFromFileRead(fileName);
    }
    protected void MapFullment(int mapSize) {
      if (Map != null) {
        Map.Clear();
      }
      for (int t = 0; t < mapSize; t++) {
        for (int i = 0; i < mapSize; i++) {
          if (i == t)
            continue;
          Map[t].Add(i);
        }
      }
    }
    override public string[] MapToStoreInFile() { // Function that prepare Map to printing in .txt File: create a set of strings for future reading by computer
      string[] s;
      s = new string[Map.Count];
      for (int i = 0; i < Map.Count; i++) {
        s[i] = "";
        for (int j = 0; j < (Map.Count - 1); j++) {
          s[i] += Probabilities[i][j].ToString();
          if (j != (Map.Count - 2)) { s[i] += " "; }
        }
      }
      return s;
    }
    public static void ApplyRankMapCreationAlgorithm(double[,] distances, List<List<int>> map, List<List<double>> probabilities) {
      int mapSize = distances.GetLength(0);
      double tempSum = 0;
      for (int i = 0; i < mapSize; i++) {
        tempSum += i;
      }
      List<List<double>> currentList = new List<List<double>>();
      for (int t = 0; t < mapSize; t++) {
        for (int i = 0; i < mapSize; i++) {
          if (distances[i, t].IsAlmost(0))
            continue;
          currentList.Add(new List<double>());
          currentList[i].Add(i);
          currentList[i].Add(distances[i, t]);
        }
        currentList.Sort((a, b) => a[1].CompareTo(b[1])); ///workable sorting
        for (int i = 0; i < currentList.Count; i++) {
          currentList[i].Add(currentList.Count - i);
        }
        probabilities.Add(new List<double>());
        for (int i = 0; i < mapSize; i++) {
          if (distances[i, t].IsAlmost(0))
            continue;
          map[t].Add(Convert.ToInt32(currentList[i][0]));
          probabilities[t].Add(currentList[i][2] / tempSum);
        }
        currentList.Clear();
      }
    }
    #endregion
    #region Map Apply Functions
    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
      treeNumber = Map[parentTreeNumber][HelpFunctions.OneElementFromListProportionalSelection(random, Probabilities[parentTreeNumber])];
      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
    }
    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
      return NewModelForInizializtion(random, out treeNumber);
    }
    #endregion
  }
}