#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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 System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Creators;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Manipulators {
  [StorableClass]
  [Item("ReplaceBranchManipulation", "Selects a branch of the tree randomly and replaces it with a newly initialized branch (using PTC2).")]
  public sealed class ReplaceBranchManipulation : SymbolicExpressionTreeManipulator {
    [StorableConstructor]
    private ReplaceBranchManipulation(bool deserializing) : base(deserializing) { }
    private ReplaceBranchManipulation(ReplaceBranchManipulation original, Cloner cloner) : base(original, cloner) { }
    public ReplaceBranchManipulation() : base() { }

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

    protected override void Manipulate(IRandom random, SymbolicExpressionTree symbolicExpressionTree, ISymbolicExpressionGrammar grammar, IntValue maxTreeSize, IntValue maxTreeHeight, out bool success) {
      ReplaceRandomBranch(random, symbolicExpressionTree, grammar, maxTreeSize.Value, maxTreeHeight.Value, out success);
    }

    public static void ReplaceRandomBranch(IRandom random, SymbolicExpressionTree symbolicExpressionTree, ISymbolicExpressionGrammar grammar, int maxTreeSize, int maxTreeHeight, out bool success) {
      success = false;
      // select any node as parent (except the root node)
      var manipulationPoint = (from parent in symbolicExpressionTree.Root.IterateNodesPrefix().Skip(1)
                               from subtree in parent.SubTrees
                               select new { Parent = parent, Node = subtree, Index = parent.SubTrees.IndexOf(subtree) }).SelectRandom(random);

      int maxSize = maxTreeSize - symbolicExpressionTree.Size + manipulationPoint.Node.GetSize();
      int maxHeight = maxTreeHeight - symbolicExpressionTree.Height + manipulationPoint.Node.GetHeight();
      // find possible symbols for the node (also considering the existing branches below it)
      var allowedSymbols = from symbol in manipulationPoint.Parent.GetAllowedSymbols(manipulationPoint.Index)
                           where manipulationPoint.Node.Grammar.GetMinExpressionDepth(symbol) <= maxHeight
                           where manipulationPoint.Node.Grammar.GetMinExpressionLength(symbol) <= maxSize
                           select symbol;
      if (allowedSymbols.Count() <= 1) return;

      var seedSymbol = SelectRandomSymbol(random, allowedSymbols);  // replace the old node with the new node
      var seedNode = seedSymbol.CreateTreeNode();
      if (seedNode.HasLocalParameters)
        seedNode.ResetLocalParameters(random);

      manipulationPoint.Parent.RemoveSubTree(manipulationPoint.Index);
      manipulationPoint.Parent.InsertSubTree(manipulationPoint.Index, seedNode);
      seedNode = ProbabilisticTreeCreator.PTC2(random, seedNode, maxSize, maxHeight, 0, 0);
      success = true;
    }

    private static Symbol SelectRandomSymbol(IRandom random, IEnumerable<Symbol> symbols) {
      var symbolList = symbols.ToList();
      var ticketsSum = symbolList.Select(x => x.InitialFrequency).Sum();
      if (ticketsSum == 0.0) throw new ArgumentException("The initial frequency of all allowed symbols is zero.");
      var r = random.NextDouble() * ticketsSum;
      double aggregatedTickets = 0;
      for (int i = 0; i < symbolList.Count; i++) {
        aggregatedTickets += symbolList[i].InitialFrequency;
        if (aggregatedTickets > r) {
          return symbolList[i];
        }
      }
      // this should never happen
      throw new ArgumentException("There is a problem with the initial frequency setting of allowed symbols.");
    }
  }
}