source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/Maps/EMMNetworkMap.cs @ 17607

#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;
using System.Collections.Generic;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [Item("NetworkMap", "A map of models of models of models")]
  [StorableType("C200ECC2-6D33-4468-A538-580B07D75B3C")]
  public class EMMNetworkMap : EMMMapBase<ISymbolicExpressionTree> {
    private const string NegbourTypeParameterName = "NegbourType";
    private const string NegbourNumberParameterName = "NegbourNumber";
    public IFixedValueParameter<StringValue> NegbourTypeParameter {
      get { return (IFixedValueParameter<StringValue>)Parameters[NegbourTypeParameterName]; }
    }
    public IValueParameter<IntValue> NegbourNumberParameter {
      get { return (IValueParameter<IntValue>)Parameters[NegbourNumberParameterName]; }
    }
    public StringValue NegbourType {
      get { return NegbourTypeParameter.Value; }
      set { NegbourTypeParameter.Value.Value = value.Value; }
    }
    public IntValue NegbourNumber {
      get { return NegbourNumberParameter.Value; }
      set { NegbourNumberParameter.Value.Value = value.Value; }
    }
    #region constructors
    [StorableConstructor]
    protected EMMNetworkMap(StorableConstructorFlag _) : base(_) { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMNetworkMap(this, cloner);
    }
    public EMMNetworkMap() : base() {
      Parameters.Add(new ValueParameter<IntValue>(NegbourNumberParameterName, "The parameter for FullMap type of map creation algorithm. Use one from: 10, 20.", new IntValue(10)));
      Parameters.Add(new FixedValueParameter<StringValue>(NegbourTypeParameterName, "The parameter for FullMap type of map creation algorithm. Use one from: Percent, Number.", new StringValue("Number")));
      MapParameterUpdate();
      ModelSet = new List<ISymbolicExpressionTree>();
    }
    public EMMNetworkMap(EMMNetworkMap original, Cloner cloner) : base(original, cloner) { NegbourNumber = original.NegbourNumber; }
    #endregion
    #region Map Transformation
    override public void CreateMap(IRandom random) {
      MapParameterUpdate();
      if (Map != null) {
        Map.Clear();
      }
      ApplyNetworkMapCreationAlgorithm(random, ModelSetPreparation.CalculateDistances(ModelSet), Map, NegbourNumber.Value);
    }
    override public void CreateMap(IRandom random, ISymbolicDataAnalysisSingleObjectiveProblem problem) {
      MapParameterUpdate();
      if (Map != null) {
        Map.Clear();
      }
      ApplyNetworkMapCreationAlgorithm(random, ModelSetPreparation.DistanceMatrixCalculation(ModelSet, DistanceParametr, problem), Map, NegbourNumber.Value);
    }
    override public void MapRead(IEnumerable<ISymbolicExpressionTree> trees) {
      base.MapRead(trees);
      string fileName = ("Map" + DistanceParametr + ".txt");
      Map = FileComuncations.IntMatrixFromFileRead(fileName);
      NegbourNumber.Value = Map[0].Count;
    }
    public static void ApplyNetworkMapCreationAlgorithm(IRandom random, double[,] distances, List<List<int>> map, int neghboorNumber = 10) {
      int mapSize = distances.GetLength(0);
      List<double> currentList = new List<double>();
      for (int i = 0; i < mapSize; i++) {
        map.Add(new List<int>());
        for (int j = 0; j < mapSize; j++) {
          currentList.Add(distances[i, j]);
        }
        map[i].Add(HelpFunctions.ChooseMinElementIndex(currentList));
        while (map[i].Count < neghboorNumber) {
          map[i].Add(HelpFunctions.ChooseMinElementIndex(currentList, i, map[i]));
        }
        currentList.Clear();
      }
    }
    protected void MapParameterUpdate() {
      switch (NegbourType.Value) {
        case "Percent": NegbourNumber.Value = Convert.ToInt32((Convert.ToDouble(ModelSet.Count)) * (Convert.ToDouble(NegbourNumber.Value)) / 100.0); break;
        case "Number": NegbourNumber.Value = NegbourNumber.Value; break;
        default: NegbourNumber.Value = NegbourNumber.Value; break;
      }
    }
    #endregion
    #region Dialog with surroundings
    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
      treeNumber = Map[parentTreeNumber].SampleRandom(random);
      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
    }
    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
      var newTree = NewModelForInizializtion(random, out treeNumber);
      return newTree;
    }
    #endregion
  }
}