#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 System.Collections.Generic;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [Item("SucsessMap", "A map of models of models of models")]
  [StorableType("3880BA82-4CB0-4838-A17A-823E91BC046C")]
  public class EMMSucsessMap : EMMMapBase<ISymbolicExpressionTree> {
    [Storable]
    public SelfConfiguration SelfConfigurationMechanism { get; private set; }
    #region constructors
    [StorableConstructor]
    protected EMMSucsessMap(StorableConstructorFlag _) : base(_) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMSucsessMap(this, cloner);
    }
    public EMMSucsessMap() : base() {
      ModelSet = new List<ISymbolicExpressionTree>();
      SelfConfigurationMechanism = new SelfConfiguration();
    }
    public EMMSucsessMap(EMMSucsessMap original, Cloner cloner) : base(original, cloner) {
      SelfConfigurationMechanism = new SelfConfiguration(original.SelfConfigurationMechanism, cloner);
    }
    #endregion
    #region Map Creation
    override public void CreateMap(IRandom random) {
      if (Map != null) {
        Map.Clear();
      }
      Map.Add(new List<int>());
      MapSizeCheck(ModelSet.Count);
      ApplySucsessMapCreationAlgorithm(random, ModelSet.Count);
    }
    override public void MapRead(IEnumerable<ISymbolicExpressionTree> trees) {
      base.MapRead(trees);
      string fileName = ("Map" + DistanceParametr + ".txt");
      SelfConfigurationMechanism.ReadFromFile(ModelSet.Count, fileName);
    }
    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[1];
      for (int i = 0; i < Map.Count; i++) {
        s[0] = "";
        s[0] += SelfConfigurationMechanism.Probabilities[i].ToString();
      }
      return s;
    }
    private void ApplySucsessMapCreationAlgorithm(IRandom random, int mapSize) {
      for (int t = 0; t < mapSize; t++) {
        Map[t].Add(t);
      }
      SelfConfigurationMechanism.Initialization(mapSize);
    }
    public override void MapUpDate(Dictionary<ISymbolicExpressionTree, double> population) {
      SelfConfigurationMechanism.UpDate(population);
    }
    #endregion
    #region Map Apply Functions
    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
      treeNumber = Map[SelfConfigurationMechanism.Aplay(random)][0];
      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
    }
    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
      return NewModelForInizializtion(random, out treeNumber);
    }
    #endregion
  }
}