source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMMutators.cs @ 16752

#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("EMMChangeNodeTypeManipulation", "Selects a random tree node and changes the symbol.")]
  [StorableType("990D3946-7F06-48B4-B8DB-F8E308D6304D")]
  public sealed class EMMMutators : SymbolicExpressionTreeManipulator {
    private const int MAX_TRIES = 100;

    private const string ModelSetParameterName = "Models";
    private const string ClusterNumberParameterName = "ClusterNumber";
    private const string MapParameterName = "Map";

    public ILookupParameter<ItemList<ISymbolicExpressionTree>> ModelSetParameter {
      get { return (ILookupParameter<ItemList<ISymbolicExpressionTree>>)Parameters[ModelSetParameterName]; }
    }

    public ILookupParameter<ItemList<IntValue>> ClusterNumberParameter {
      get { return (ILookupParameter<ItemList<IntValue>>)Parameters[ClusterNumberParameterName]; }
    }

    public ILookupParameter<ItemList<ItemList<IntValue>>> MapParameter {
      get { return (ILookupParameter<ItemList<ItemList<IntValue>>>)Parameters[MapParameterName]; }
    }

    public ItemList<ISymbolicExpressionTree> ModelSet => ModelSetParameter.ActualValue;
    public ItemList<IntValue> ClusterNumber => ClusterNumberParameter.ActualValue;
    public ItemList<ItemList<IntValue>> Map => MapParameter.ActualValue;

    [StorableConstructor]
    private EMMMutators(StorableConstructorFlag _) : base(_) { }
    private EMMMutators(EMMMutators original, Cloner cloner) : base(original, cloner) { }
    public EMMMutators() : base() {
      Parameters.Add(new LookupParameter<ItemList<ISymbolicExpressionTree>>(ModelSetParameterName));
      Parameters.Add(new LookupParameter<ItemList<IntValue>>(ClusterNumberParameterName));
      Parameters.Add(new LookupParameter<ItemList<ItemList<IntValue>>>(MapParameterName));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMMutators(this, cloner);
    }

    protected override void Manipulate(IRandom random, ISymbolicExpressionTree symbolicExpressionTree) {
      //EMMMapTreeModel map= (EMMMapTreeModel)ExecutionContext.Scope.Variables["TreeModelMap"].Value;
      EMMMutatorsPart(random, symbolicExpressionTree, ModelSet, ClusterNumber, Map);
    }
    public static void EMMMutatorsPart(IRandom random, ISymbolicExpressionTree symbolicExpressionTree, ItemList<ISymbolicExpressionTree> modelSet, ItemList<IntValue> clusterNumber, ItemList<ItemList<IntValue>> map) {
      List<ISymbol> allowedSymbols = new List<ISymbol>();
      ISymbolicExpressionTreeNode parent;
      int childIndex;
      ISymbolicExpressionTreeNode child;
      // repeat until a fitting parent and child are found (MAX_TRIES times)
      int tries = 0;
      do {

#pragma warning disable 612, 618
        parent = symbolicExpressionTree.Root.IterateNodesPrefix().Skip(1).Where(n => n.SubtreeCount > 0).SelectRandom(random);
#pragma warning restore 612, 618

        childIndex = random.Next(parent.SubtreeCount);

        child = parent.GetSubtree(childIndex);
        int existingSubtreeCount = child.SubtreeCount;
        allowedSymbols.Clear();
        foreach (var symbol in parent.Grammar.GetAllowedChildSymbols(parent.Symbol, childIndex)) {
          // check basic properties that the new symbol must have
          if (symbol.Name != child.Symbol.Name &&
            symbol.InitialFrequency > 0 &&
            existingSubtreeCount <= parent.Grammar.GetMinimumSubtreeCount(symbol) &&
            existingSubtreeCount >= parent.Grammar.GetMaximumSubtreeCount(symbol)) {
            // check that all existing subtrees are also allowed for the new symbol
            bool allExistingSubtreesAllowed = true;
            for (int existingSubtreeIndex = 0; existingSubtreeIndex < existingSubtreeCount && allExistingSubtreesAllowed; existingSubtreeIndex++) {
              var existingSubtree = child.GetSubtree(existingSubtreeIndex);
              allExistingSubtreesAllowed &= parent.Grammar.IsAllowedChildSymbol(symbol, existingSubtree.Symbol, existingSubtreeIndex);
            }
            if (allExistingSubtreesAllowed) {
              allowedSymbols.Add(symbol);
            }
          }
        }
        tries++;
      } while (tries < MAX_TRIES && allowedSymbols.Count == 0);

      if (tries < MAX_TRIES) {
        var weights = allowedSymbols.Select(s => s.InitialFrequency).ToList();
#pragma warning disable 612, 618
        var newSymbol = allowedSymbols.SelectRandom(weights, random);
#pragma warning restore 612, 618

        // replace the old node with the new node

        var newNode = newSymbol.CreateTreeNode();
        if (newNode.HasLocalParameters)
          if (newNode is TreeModelTreeNode treeNode) {
            int p = random.Next(map.Count);
            if (child is TreeModelTreeNode chNode) // in real life never bacame true. It need some cheking
            { p = chNode.ClusterNumer; }
            treeNode.TreeNumber = map[p].SampleRandom(random).Value;
            treeNode.Tree = (ISymbolicExpressionTree)modelSet[treeNode.TreeNumber].Clone();
            treeNode.SetLocalParameters(random, 0.5);
          } else
            newNode.ResetLocalParameters(random);
        foreach (var subtree in child.Subtrees)
          newNode.AddSubtree(subtree);
        parent.RemoveSubtree(childIndex);
        parent.InsertSubtree(childIndex, newNode);
      }
    }
  }
}