source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/HelpFunctions.cs @ 17505

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

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  class HelpFunctions {
    public static int OneElementFromListProportionalSelection(IRandom random, List<double> list) {
      double selectedQuality = random.NextDouble() * list.Sum();
      int index = 0;
      double currentQuality = list[index];
      while ((currentQuality < selectedQuality) && (index < list.Count)) {
        index++;
        currentQuality += list[index];
      }
      return index;
    }
    public static int ChooseMinElementIndex(List<double> distances, int currentElement, List<int> previousNumbers) {
      double minValue = 100;
      int minElementNumber = 0;
      int temp = 0, i = 0;
      while (temp == 0) {
        if ((currentElement != i) && (!CheckNumberIsInList(i, previousNumbers))) {
          minValue = distances[i];
          minElementNumber = i;
          temp = i;
        }
        i++;
      }
      for (i = 0; i < distances.Count(); i++) {
        if ((distances[i] < minValue) && (currentElement != i) && (!CheckNumberIsInList(i, previousNumbers))) {
          minValue = distances[i];
          minElementNumber = i;
        }
      }
      return minElementNumber;
    }

    public static bool CheckNumberIsInList(int number, List<int> priviousNumber) {
      foreach (var pNum in priviousNumber) {
        if (number == pNum)
          return true;
      }
      return false;
    }
    public static int ChooseMinElementIndex(List<double> averageClusterDistance) {
      double minValue = averageClusterDistance[0];
      int minElementNumber = 0;
      for (int i = 1; i < averageClusterDistance.Count(); i++) {
        if (averageClusterDistance[i] < minValue) {
          minValue = averageClusterDistance[i];
          minElementNumber = i;
        }
      }
      return minElementNumber;
    }
    public static int ChooseMaxElementIndex(List<double> averageClusterDistance) {
      double maxValue = averageClusterDistance[0];
      int maxElementNumber = 0;
      for (int i = 1; i < averageClusterDistance.Count(); i++) {
        if (averageClusterDistance[i] > maxValue) {
          maxValue = averageClusterDistance[i];
          maxElementNumber = i;
        }
      }
      return maxElementNumber;
    }

    public static void SetLocalParametersForTree(IRandom random, double shakingFactor, ISymbolicExpressionTree tree) {
      foreach (var node in tree.IterateNodesPrefix().Where(x => x.HasLocalParameters)) {
        if (node is VariableTreeNode variableTreeNode) {
          var symbol = variableTreeNode.Symbol;
          variableTreeNode.Weight = NormalDistributedRandom.NextDouble(random, symbol.WeightManipulatorMu, symbol.WeightManipulatorSigma);
        } else {
          node.ResetLocalParameters(random);
        }
      }
    }
  }
}