#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.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Random;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [Item("IslandMap", "A map of models of models of models")]
  [StorableType("E4AB04B9-FD5D-47EE-949D-243660754F3A")]
  public class EMMIslandMap : EMMMapBase<ISymbolicExpressionTree> {

    [Storable]
    public List<int> ClusterNumber { get; set; }  // May be only Island Map really need it
    public double[] AverageDistance { get; private set; }
    private const string ClusterNumbersParameterName = "ClusterNumbers";
    private const string ClusterNumbersShowParameterName = "ClusterNumbersShow";
    public IValueParameter<IntValue> ClusterNumbersParameter {
      get { return (IValueParameter<IntValue>)Parameters[ClusterNumbersParameterName]; }
    }
    public IValueParameter<IntValue> ClusterNumbersShowParameter {
      get { return (IValueParameter<IntValue>)Parameters[ClusterNumbersShowParameterName]; }
    }
    public IntValue ClusterNumbers {
      get { return ClusterNumbersParameter.Value; }
      set { ClusterNumbersParameter.Value = value; }
    }
    public IntValue ClusterNumbersShow {
      get { return ClusterNumbersShowParameter.Value; }
      set { ClusterNumbersShowParameter.Value = value; }
    }
    #region constructors
    [StorableConstructor]
    protected EMMIslandMap(StorableConstructorFlag _) : base(_) { }
    public EMMIslandMap() {
      Parameters.Add(new ValueParameter<IntValue>(ClusterNumbersParameterName, "The number of clusters for model Map.", new IntValue(10)));
      Parameters.Add(new ValueParameter<IntValue>(ClusterNumbersShowParameterName, "The number of clusters for model Map.", new IntValue(10)));
      ModelSet = new List<ISymbolicExpressionTree>();
      ClusterNumber = new List<int>();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMIslandMap(this, cloner);
    }
    public EMMIslandMap(EMMIslandMap original, Cloner cloner) : base(original, cloner) {
      if (original.ClusterNumber != null) {
        ClusterNumber = original.ClusterNumber.ToList();
      }
    }
    #endregion
    #region Map Apply Functions
    override public void CreateMap(IRandom random) {
      var totalDistance = ModelSetPreparation.CalculateDistances(ModelSet); //structure distances
      CreateMap(random, totalDistance);
    }
    override public void CreateMap(IRandom random, ISymbolicDataAnalysisSingleObjectiveProblem problem) {
      CreateMap(random, ModelSetPreparation.TotalDistanceMatrixCalculation(random, problem, ModelSet, DistanceParametr));
    }
    override public void CreateMap(IRandom random, double[,] totalDistance) {
      if (Map != null) {
        Map.Clear();
      }
      ClusterNumbersShow.Value = KMeansClusterizationAlgorithm.ApplyClusteringAlgorithm(random, totalDistance, ClusterNumber, ClusterNumbers.Value);
      MapSizeCheck(ClusterNumbersShow.Value);
      for (int i = 0; i < ModelSet.Count; i++) {
        Map[ClusterNumber[i]].Add(i);
      }
      AverageDistanceInClusterCalculation(totalDistance, ClusterNumbersShow.Value);
    }
    override public void MapRead(IEnumerable<ISymbolicExpressionTree> trees) {
      base.MapRead(trees);
      string fileName = ("Map" + DistanceParametr + ".txt");
      Map = FileComuncations.IntMatrixFromFileRead(fileName);
      ClusterNumbers.Value = Map.Count;
      ClusterNumbersShow.Value = ClusterNumbers.Value;
      ClusterNumbersCalculate();
      AverageDistanceInClusterCalculation(ModelSetPreparation.CalculateDistances(ModelSet), Map.Count);
    }
    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
      return NewModelForInizializtion(random, out treeNumber);
    }
    private void AverageDistanceInClusterCalculation(double[,] distances, int k) {
      AverageDistance = new double[k];
      var temp = new List<double>();
      for (int i = 0; i < k; i++) {
        KMeansClusterizationAlgorithm.AverageClusterDistanceCalculation(temp, distances, ClusterNumber, ClusterNumber.Count, i);
        var number = HelpFunctions.ChooseMinElementIndex(temp);
        AverageDistance[i] = temp[number] / Map[i].Count;
        temp.Clear();
      }
    }
    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
      if (parentTreeNumber == -10) {
        treeNumber = random.Next(ModelSet.Count);
      } else {
        treeNumber = Map[ClusterNumber[parentTreeNumber]].SampleRandom(random);
      }
      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
    }
    public void ClusterNumbersCalculate() {
      for (int i = 0; i < Map.Count; i++) {
        for (int j = 0; j < Map[i].Count; j++) {
          ClusterNumber.Add(0);
        }
      }
      for (int i = 0; i < Map.Count; i++) {
        for (int j = 0; j < Map[i].Count; j++) {
          ClusterNumber[Map[i][j]] = i;
        }
      }
    }
    #endregion

  }
}