#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.Collections.Generic;
using System.IO;
using System.Linq;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [StorableType("83CF9650-98FF-454B-9072-82EA4D39C752")]
  public abstract class EMMMapBase<T> : ParameterizedNamedItem where T : class {
    #region data members
    [Storable]
    public List<T> ModelSet { get; protected set; }
    [Storable]
    public List<List<int>> Map { get; set; }
    public string DistanceParametr { get; set; }
    #endregion
    #region constructors
    [StorableConstructor]
    protected EMMMapBase(StorableConstructorFlag _) : base(_) { }
    public EMMMapBase() {
      Map = new List<List<int>>();
      DistanceParametr = "Symbolic";
    }
    public EMMMapBase(EMMMapBase<T> original, Cloner cloner) : base(original, cloner) {
      if (original.ModelSet != null) {
        if (original.ModelSet is List<ISymbolicExpressionTree> originalSet && ModelSet is List<ISymbolicExpressionTree> set)
          set = originalSet.Select(cloner.Clone).ToList();
        else ModelSet = original.ModelSet.ToList(); /// check this if you want to use it
      }
      if (original.Map != null) {
        Map = original.Map.Select(x => x.ToList()).ToList();
      }
      DistanceParametr = original.DistanceParametr;
    }
    #endregion
    #region map creation functions

    public abstract void CreateMap(IRandom random);
    public void MapCreationPrepare(IEnumerable<T> trees) {
      ModelSet = trees.ToList();
    }
    public virtual void CreateMap(IRandom random, ISymbolicDataAnalysisSingleObjectiveProblem problem) {
      if (Map != null) {
        Map.Clear();
      }
      CreateMap(random);
    }
    public virtual void CreateMap(IRandom random, double[,] totalDistance) {
      if (Map != null) {
        Map.Clear();
      }
      CreateMap(random);
    }

    protected void MapSizeCheck(int k) {
      if (Map != null) Map.Clear();
      else Map = new List<List<int>>();
      if (Map.Count != k) {
        if (Map.Count != 0) {
          Map.Clear();
        }
        for (int i = 0; i < k; i++) {
          Map.Add(new List<int>());
        }
      }
    }
    #endregion
    #region map and files
    public virtual void MapRead(IEnumerable<T> trees) {
      ModelSet = trees.ToList();
    }
    public void WriteMapToTxtFile(IRandom random) {
      string s = random.NextDouble().ToString();
      string fileName = "MapToAnalize";
      fileName += s;
      fileName += DistanceParametr;
      fileName += ".txt";
      File.WriteAllLines(fileName, MapToString());
      string fileName2 = "MapToSee";
      fileName2 += s;
      fileName2 += DistanceParametr;
      fileName2 += ".txt";
      File.WriteAllLines(fileName2, MapToSee());
      string fileName3 = "Map";
      fileName3 += DistanceParametr;
      fileName3 += ".txt";
      File.WriteAllLines(fileName3, MapToStoreInFile());
    }
    public string[] MapToString() { // Function that prepare Map to printing in .txt File: create a set of strings for future analyzing
      string[] s;
      s = new string[Map.Count];
      for (int i = 0; i < Map.Count; i++) {
        s[i] = i.ToString() + ": ";
        for (int j = 0; j < Map[i].Count; j++) {
          s[i] += Map[i][j].ToString();
          s[i] += " ";
        }
        if (this is EMMIslandMap island) {
          s[i] += "  Average distance:" + island.AverageDistance[i].ToString();
        }
      }
      return s;
    }
    public virtual 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[i].Count; j++) {
          s[i] += Map[i][j].ToString();
          if (j != (Map[i].Count - 1)) { s[i] += " "; }
        }
      }
      return s;
    }
    public string[] MapToSee() { // Function that prepare Map to printing in .txt File: create a set of strings in human readable view
      var fmt = new InfixExpressionFormatter();
      string[] s;
      s = new string[(ModelSet.Count) + 1];
      s[0] = "ClusterNumber" + "," + "ModelNumber" + "," + "Model";
      for (int i = 1; i < ((ModelSet.Count) + 1); i++) {
        s[i] = "";
        if (this is EMMIslandMap island) {
          s[i] += island.ClusterNumber[i - 1].ToString() + ",";
        }
        s[i] += (i - 1).ToString() + ",";
        if (ModelSet[i - 1] is ISymbolicExpressionTree model) {
          s[i] += fmt.Format(model);
        } else { s[i] += ModelSet[i - 1].ToString(); }
      }
      return s;
    }
    #endregion

    #region map use functions
    public abstract T NewModelForInizializtionNotTree(IRandom random, out int treeNumber);
    public ISymbolicExpressionTree NewModelForInizializtion(IRandom random, out int treeNumber) {
      treeNumber = random.Next(ModelSet.Count);
      if (ModelSet[treeNumber] is ISymbolicExpressionTree model)
        return (ISymbolicExpressionTree)(model.Clone());
      return new SymbolicExpressionTree();
    }
    public void NodeManipulationForInizializtion(IRandom random, TreeModelTreeNode node) {
      node.Tree = NewModelForInizializtion(random, out int treeNumber);
      node.SetLocalParameters(random, 0.5);
      node.TreeNumber = treeNumber;
    }
    public abstract ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber);
    public virtual void NodeForMutationChange(IRandom random, TreeModelTreeNode treeNode) {
      int treeNumber = treeNode.TreeNumber;
      int treeNumber2 = treeNode.TreeNumber;
      treeNode.Tree = new SymbolicExpressionTree(NewModelForMutation(random, out treeNumber, treeNumber2).Root);
      treeNode.TreeNumber = treeNumber;
      HelpFunctions.SetLocalParametersForTree(random, 0.5, treeNode.Tree);
    }
    public virtual void MapUpDate(Dictionary<ISymbolicExpressionTree, double> population) { }
    #endregion
  }
}