#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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 System.Text;
using HeuristicLab.Core;
using HeuristicLab.Operators;
using HeuristicLab.Random;
using HeuristicLab.Data;
using HeuristicLab.Constraints;
using System.Diagnostics;

namespace HeuristicLab.GP {
  /// <summary>
  /// Implementation of a size fair crossover operator as described in: 
  /// William B. Langdon 
  /// Size Fair and Homologous Tree Genetic Programming Crossovers, 
  /// Genetic Programming and Evolvable Machines, Vol. 1, Number 1/2, pp. 95-119, April 2000
  /// </summary>
  public class SizeFairCrossOver : OperatorBase {
    private const int MAX_RECOMBINATION_TRIES = 20;
    public override string Description {
      get {
        return @"";
      }
    }
    public SizeFairCrossOver()
      : base() {
      AddVariableInfo(new VariableInfo("Random", "Pseudo random number generator", typeof(MersenneTwister), VariableKind.In));
      AddVariableInfo(new VariableInfo("OperatorLibrary", "The operator library containing all available operators", typeof(GPOperatorLibrary), VariableKind.In));
      AddVariableInfo(new VariableInfo("MaxTreeHeight", "The maximal allowed height of the tree", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("MaxTreeSize", "The maximal allowed size (number of nodes) of the tree", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("FunctionTree", "The tree to mutate", typeof(IFunctionTree), VariableKind.In | VariableKind.New));
      AddVariableInfo(new VariableInfo("TreeSize", "The size (number of nodes) of the tree", typeof(IntData), VariableKind.New));
      AddVariableInfo(new VariableInfo("TreeHeight", "The height of the tree", typeof(IntData), VariableKind.New));
    }

    public override IOperation Apply(IScope scope) {
      MersenneTwister random = GetVariableValue<MersenneTwister>("Random", scope, true);
      GPOperatorLibrary opLibrary = GetVariableValue<GPOperatorLibrary>("OperatorLibrary", scope, true);
      int maxTreeHeight = GetVariableValue<IntData>("MaxTreeHeight", scope, true).Data;
      int maxTreeSize = GetVariableValue<IntData>("MaxTreeSize", scope, true).Data;

      TreeGardener gardener = new TreeGardener(random, opLibrary);

      if ((scope.SubScopes.Count % 2) != 0)
        throw new InvalidOperationException("Number of parents is not even");

      int children = scope.SubScopes.Count / 2;
      for (int i = 0; i < children; i++) {
        IScope parent1 = scope.SubScopes[0];
        scope.RemoveSubScope(parent1);
        IScope parent2 = scope.SubScopes[0];
        scope.RemoveSubScope(parent2);
        IScope child = new Scope(i.ToString());
        Cross(gardener, maxTreeSize, maxTreeHeight, scope, random, parent1, parent2, child);
        scope.AddSubScope(child);
      }

      return null;
    }

    private void Cross(TreeGardener gardener, int maxTreeSize, int maxTreeHeight,
      IScope scope, MersenneTwister random, IScope parent1, IScope parent2, IScope child) {
      IFunctionTree newTree = Cross(gardener, parent1, parent2,
        random, maxTreeSize, maxTreeHeight);

      child.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("FunctionTree"), newTree));
      child.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TreeSize"), new IntData(newTree.Size)));
      child.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TreeHeight"), new IntData(newTree.Height)));

      // check if the new tree is valid and the height and size are still in the allowed bounds
      Debug.Assert(gardener.IsValidTree(newTree) && newTree.Height <= maxTreeHeight && newTree.Size <= maxTreeSize);
    }


    private IFunctionTree Cross(TreeGardener gardener, IScope f, IScope g, MersenneTwister random, int maxTreeSize, int maxTreeHeight) {
      IFunctionTree tree0 = f.GetVariableValue<IFunctionTree>("FunctionTree", false);
      int tree0Height = f.GetVariableValue<IntData>("TreeHeight", false).Data;
      int tree0Size = f.GetVariableValue<IntData>("TreeSize", false).Data;

      IFunctionTree tree1 = g.GetVariableValue<IFunctionTree>("FunctionTree", false);
      int tree1Height = g.GetVariableValue<IntData>("TreeHeight", false).Data;
      int tree1Size = g.GetVariableValue<IntData>("TreeSize", false).Data;

      // we are going to insert tree1 into tree0 at a random place so we have to make sure that tree0 is not a terminal
      // in case both trees are higher than 1 we swap the trees with probability 50%
      if (tree0Height == 1 || (tree1Height > 1 && random.Next(2) == 0)) {
        IFunctionTree tmp = tree0; tree0 = tree1; tree1 = tmp;
        int tmpHeight = tree0Height; tree0Height = tree1Height; tree1Height = tmpHeight;
        int tmpSize = tree0Size; tree0Size = tree1Size; tree1Size = tmpSize;
      }

      // select a random suboperator of the 'receiving' tree
      IFunctionTree crossoverPoint = gardener.GetRandomParentNode(tree0);
      int removedBranchIndex;
      IFunctionTree removedBranch;
      IList<IFunction> allowedFunctions;
      if (crossoverPoint == null) {
        removedBranchIndex = 0;
        removedBranch = tree0;
        allowedFunctions = gardener.GetAllowedSubFunctions(null, 0);
      } else {
        removedBranchIndex = random.Next(crossoverPoint.SubTrees.Count);
        removedBranch = crossoverPoint.SubTrees[removedBranchIndex];
        allowedFunctions = gardener.GetAllowedSubFunctions(crossoverPoint.Function, removedBranchIndex);
      }
      int removedBranchSize = removedBranch.Size;
      int maxBranchSize = maxTreeSize - (tree0.Size - removedBranchSize);
      int maxBranchHeight = maxTreeHeight - gardener.GetBranchLevel(tree0, removedBranch) + 1;
      IFunctionTree insertedBranch = GetReplacementBranch(random, gardener, allowedFunctions, tree1, removedBranchSize, maxBranchSize, maxBranchHeight);

      int tries = 0;
      while (insertedBranch == null) {
        if (tries++ > MAX_RECOMBINATION_TRIES) {
          if (random.Next() > 0.5) return tree1;
          else return tree0;
        }

        // retry with a different crossoverPoint        
        crossoverPoint = gardener.GetRandomParentNode(tree0);
        if (crossoverPoint == null) {
          removedBranchIndex = 0;
          removedBranch = tree0;
          allowedFunctions = gardener.GetAllowedSubFunctions(null, 0);
        } else {
          removedBranchIndex = random.Next(crossoverPoint.SubTrees.Count);
          removedBranch = crossoverPoint.SubTrees[removedBranchIndex];
          allowedFunctions = gardener.GetAllowedSubFunctions(crossoverPoint.Function, removedBranchIndex);
        }
        removedBranchSize = removedBranch.Size;
        maxBranchSize = maxTreeSize - (tree0.Size - removedBranchSize);
        maxBranchHeight = maxTreeHeight - gardener.GetBranchLevel(tree0, removedBranch) + 1;
        insertedBranch = GetReplacementBranch(random, gardener, allowedFunctions, tree1, removedBranchSize, maxBranchSize, maxBranchHeight);
      }
      if (crossoverPoint != null) {
        // replace the branch below the crossoverpoint with the selected branch from root1
        crossoverPoint.RemoveSubTree(removedBranchIndex);
        crossoverPoint.InsertSubTree(removedBranchIndex, insertedBranch);
        return tree0;
      } else {
        return insertedBranch;
      }
    }

    private IFunctionTree GetReplacementBranch(IRandom random, TreeGardener gardener, IList<IFunction> allowedFunctions, IFunctionTree tree, int removedBranchSize, int maxBranchSize, int maxBranchHeight) {
      var branches = gardener.GetAllSubTrees(tree).Where(t => allowedFunctions.Contains(t.Function) && t.Size < maxBranchSize && t.Height < maxBranchHeight)
        .Select(t => new { Tree = t, Size = t.Size }).Where(s => s.Size < 2 * removedBranchSize + 1);

      var shorterBranches = branches.Where(t => t.Size < removedBranchSize);
      var longerBranches = branches.Where(t => t.Size > removedBranchSize);
      var equalLengthBranches = branches.Where(t => t.Size == removedBranchSize);

      if (shorterBranches.Count() == 0 || longerBranches.Count() == 0) {
        if (equalLengthBranches.Count() == 0) {
          return null;
        } else {
          return equalLengthBranches.ElementAt(random.Next(equalLengthBranches.Count())).Tree;
        }
      } else {
        // invariant: |shorterBranches| > 0  and |longerBranches| > 0
        double pEqualLength = equalLengthBranches.Count() > 0 ? 1.0 / removedBranchSize : 0.0;
        double pLonger = (1.0 - pEqualLength) / (longerBranches.Count() * (1.0 + longerBranches.Average(t => t.Size) / shorterBranches.Average(t => t.Size)));
        double pShorter = (1.0 - pEqualLength - pLonger);

        double r = random.NextDouble();
        if (r < pLonger) {
          return longerBranches.ElementAt(random.Next(longerBranches.Count())).Tree;
        } else if (r < pLonger + pShorter) {
          return shorterBranches.ElementAt(random.Next(shorterBranches.Count())).Tree;
        } else {
          return equalLengthBranches.ElementAt(random.Next(equalLengthBranches.Count())).Tree;
        }
      }
    }
  }
}