#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 . */ #endregion using System; using System.Collections.Generic; using System.Text; using HeuristicLab.Core; using HeuristicLab.Constraints; using System.Diagnostics; using HeuristicLab.Data; using System.Linq; using HeuristicLab.Random; using HeuristicLab.Operators; using System.Collections; using HeuristicLab.Selection; namespace HeuristicLab.GP { internal class TreeGardener { private IRandom random; private GPOperatorLibrary funLibrary; private List functions; private List terminals; internal IList Terminals { get { return terminals; } } private List allFunctions; internal IList AllFunctions { get { return allFunctions; } } #region constructors internal TreeGardener(IRandom random, GPOperatorLibrary funLibrary) { this.random = random; this.funLibrary = funLibrary; this.allFunctions = new List(); terminals = new List(); functions = new List(); // init functions and terminals based on constraints foreach(IFunction fun in funLibrary.Group.Operators) { if(fun.MaxArity == 0) { terminals.Add(fun); allFunctions.Add(fun); } else { functions.Add(fun); allFunctions.Add(fun); } } } #endregion #region random initialization /// /// Creates a random balanced tree with a maximal size and height. When the max-height or max-size are 1 it will return a random terminal. /// In other cases it will return either a terminal (tree of size 1) or any other tree with a function in it's root (at least height 2). /// /// Maximal size of the tree (number of nodes). /// Maximal height of the tree. /// internal IFunctionTree CreateBalancedRandomTree(int maxTreeSize, int maxTreeHeight) { IFunction rootFunction = GetRandomRoot(maxTreeSize, maxTreeHeight); IFunctionTree tree = MakeBalancedTree(rootFunction, maxTreeHeight - 1); return tree; } /// /// Creates a random (unbalanced) tree with a maximal size and height. When the max-height or max-size are 1 it will return a random terminal. /// In other cases it will return either a terminal (tree of size 1) or any other tree with a function in it's root (at least height 2). /// /// Maximal size of the tree (number of nodes). /// Maximal height of the tree. /// internal IFunctionTree CreateUnbalancedRandomTree(int maxTreeSize, int maxTreeHeight) { IFunction rootFunction = GetRandomRoot(maxTreeSize, maxTreeHeight); IFunctionTree tree = MakeUnbalancedTree(rootFunction, maxTreeHeight - 1); return tree; } internal IFunctionTree PTC2(IRandom random, int size, int maxDepth) { return PTC2(random, GetRandomRoot(size, maxDepth), size, maxDepth); } internal IFunctionTree PTC2(IRandom random, IFunction rootF, int size, int maxDepth) { IFunctionTree root = rootF.GetTreeNode(); if(size <= 1 || maxDepth <= 1) return root; List list = new List(); int currentSize = 1; int totalListMinSize = 0; int minArity = root.Function.MinArity; int maxArity = root.Function.MaxArity; if(maxArity >= size) { maxArity = size; } int actualArity = random.Next(minArity, maxArity + 1); totalListMinSize += GetMinimalTreeSize(root.Function) - 1; for(int i = 0; i < actualArity; i++) { // insert a dummy sub-tree and add the pending extension to the list root.AddSubTree(null); list.Add(new object[] { root, i, 2 }); } while(list.Count > 0 && totalListMinSize + currentSize < size) { int randomIndex = random.Next(list.Count); object[] nextExtension = list[randomIndex]; list.RemoveAt(randomIndex); IFunctionTree parent = (IFunctionTree)nextExtension[0]; int a = (int)nextExtension[1]; int d = (int)nextExtension[2]; if(d == maxDepth) { parent.RemoveSubTree(a); IFunctionTree branch = CreateRandomTree(GetAllowedSubFunctions(parent.Function, a), 1, 1); parent.InsertSubTree(a, branch); // insert a smallest possible tree currentSize += branch.Size; totalListMinSize -= branch.Size; } else { IFunction selectedFunction = RandomSelect(GetAllowedSubFunctions(parent.Function, a).Where( f => !IsTerminal(f) && GetMinimalTreeHeight(f) + (d - 1) <= maxDepth).ToArray()); IFunctionTree newTree = selectedFunction.GetTreeNode(); parent.RemoveSubTree(a); parent.InsertSubTree(a, newTree); currentSize++; totalListMinSize--; minArity = selectedFunction.MinArity; maxArity = selectedFunction.MaxArity; if(maxArity >= size) { maxArity = size; } actualArity = random.Next(minArity, maxArity + 1); for(int i = 0; i < actualArity; i++) { // insert a dummy sub-tree and add the pending extension to the list newTree.AddSubTree(null); list.Add(new object[] { newTree, i, d + 1 }); } totalListMinSize += GetMinimalTreeSize(newTree.Function) - 1; } } while(list.Count > 0) { int randomIndex = random.Next(list.Count); object[] nextExtension = list[randomIndex]; list.RemoveAt(randomIndex); IFunctionTree parent = (IFunctionTree)nextExtension[0]; int a = (int)nextExtension[1]; int d = (int)nextExtension[2]; parent.RemoveSubTree(a); parent.InsertSubTree(a, CreateRandomTree(GetAllowedSubFunctions(parent.Function, a), 1, 1)); // append a tree with minimal possible height } return root; } /// /// selects a random function from allowedFunctions and creates a random (unbalanced) tree with maximal size and height. /// /// Set of allowed functions. /// Maximal size of the tree (number of nodes). /// Maximal height of the tree. /// New random unbalanced tree internal IFunctionTree CreateRandomTree(ICollection allowedFunctions, int maxTreeSize, int maxTreeHeight) { // get the minimal needed height based on allowed functions and extend the max-height if necessary int minTreeHeight = allowedFunctions.Select(f => GetMinimalTreeHeight(f)).Min(); if(minTreeHeight > maxTreeHeight) maxTreeHeight = minTreeHeight; // get the minimal needed size based on allowed functions and extend the max-size if necessary int minTreeSize = allowedFunctions.Select(f => GetMinimalTreeSize(f)).Min(); if(minTreeSize > maxTreeSize) maxTreeSize = minTreeSize; // select a random value for the size and height int treeHeight = random.Next(minTreeHeight, maxTreeHeight + 1); int treeSize = random.Next(minTreeSize, maxTreeSize + 1); // filter the set of allowed functions and select only from those that fit into the given maximal size and height limits IFunction[] possibleFunctions = allowedFunctions.Where(f => GetMinimalTreeHeight(f) <= treeHeight && GetMinimalTreeSize(f) <= treeSize).ToArray(); IFunction selectedFunction = RandomSelect(possibleFunctions); // build the tree IFunctionTree root; root = PTC2(random, selectedFunction, maxTreeSize, maxTreeHeight); return root; } internal CompositeOperation CreateInitializationOperation(ICollection trees, IScope scope) { // needed for the parameter shaking operation CompositeOperation initializationOperation = new CompositeOperation(); Scope tempScope = new Scope("Temp. initialization scope"); var parametricTrees = trees.Where(t => t.Function.GetVariable(FunctionBase.INITIALIZATION) != null); foreach(IFunctionTree tree in parametricTrees) { // enqueue an initialization operation for each operator with local variables IOperator initialization = (IOperator)tree.Function.GetVariable(FunctionBase.INITIALIZATION).Value; Scope initScope = new Scope(); // copy the local variables into a temporary scope used for initialization foreach(IVariable variable in tree.LocalVariables) { initScope.AddVariable(variable); } tempScope.AddSubScope(initScope); initializationOperation.AddOperation(new AtomicOperation(initialization, initScope)); } Scope backupScope = new Scope("backup"); foreach(Scope subScope in scope.SubScopes) { backupScope.AddSubScope(subScope); } scope.AddSubScope(tempScope); scope.AddSubScope(backupScope); // add an operation to remove the temporary scopes initializationOperation.AddOperation(new AtomicOperation(new RightReducer(), scope)); return initializationOperation; } #endregion #region tree information gathering internal IFunctionTree GetRandomParentNode(IFunctionTree tree) { List parentNodes = new List(); // add null for the parent of the root node parentNodes.Add(null); TreeForEach(tree, delegate(IFunctionTree possibleParentNode) { if(possibleParentNode.SubTrees.Count > 0) { parentNodes.Add(possibleParentNode); } }); return parentNodes[random.Next(parentNodes.Count)]; } internal ICollection GetAllSubTrees(IFunctionTree root) { List allTrees = new List(); TreeForEach(root, t => { allTrees.Add(t); }); return allTrees; } /// /// returns the height level of branch in the tree /// if the branch == tree => 1 /// if branch is in the sub-trees of tree => 2 /// ... /// if branch is not found => -1 /// /// root of the function tree to process /// branch that is searched in the tree /// internal int GetBranchLevel(IFunctionTree tree, IFunctionTree branch) { return GetBranchLevelHelper(tree, branch, 1); } // 'tail-recursive' helper private int GetBranchLevelHelper(IFunctionTree tree, IFunctionTree branch, int level) { if(branch == tree) return level; foreach(IFunctionTree subTree in tree.SubTrees) { int result = GetBranchLevelHelper(subTree, branch, level + 1); if(result != -1) return result; } return -1; } internal bool IsValidTree(IFunctionTree tree) { for(int i = 0; i < tree.SubTrees.Count; i++) { if(!tree.Function.AllowedSubFunctions(i).Contains(tree.SubTrees[i].Function)) return false; } if(tree.SubTrees.Count < tree.Function.MinArity || tree.SubTrees.Count > tree.Function.MaxArity) return false; foreach(IFunctionTree subTree in tree.SubTrees) { if(!IsValidTree(subTree)) return false; } return true; } // returns a random branch from the specified level in the tree internal IFunctionTree GetRandomBranch(IFunctionTree tree, int level) { if(level == 0) return tree; List branches = new List(); GetBranchesAtLevel(tree, level, branches); return branches[random.Next(branches.Count)]; } #endregion #region function information (arity, allowed childs and parents) internal ICollection GetPossibleParents(List list) { List result = new List(); foreach(IFunction f in functions) { if(IsPossibleParent(f, list)) { result.Add(f); } } return result; } private bool IsPossibleParent(IFunction f, List children) { int minArity = f.MinArity; int maxArity = f.MaxArity; // note: we can't assume that the operators in the children list have different types! // when the maxArity of this function is smaller than the list of operators that // should be included as sub-operators then it can't be a parent if(maxArity < children.Count()) { return false; } int nSlots = Math.Max(minArity, children.Count); List> slotSets = new List>(); // we iterate through all slots for sub-trees and calculate the set of // allowed functions for this slot. // we only count those slots that can hold at least one of the children that we should combine for(int slot = 0; slot < nSlots; slot++) { HashSet functionSet = new HashSet(f.AllowedSubFunctions(slot)); if(functionSet.Count() > 0) { slotSets.Add(functionSet); } } // ok at the end of this operation we know how many slots of the parent can actually // hold one of our children. // if the number of slots is smaller than the number of children we can be sure that // we can never combine all children as sub-trees of the function and thus the function // can't be a parent. if(slotSets.Count() < children.Count()) { return false; } // finally we sort the sets by size and beginning from the first set select one // function for the slot and thus remove it as possible sub-tree from the remaining sets. // when we can successfully assign all available children to a slot the function is a valid parent // when only a subset of all children can be assigned to slots the function is no valid parent slotSets.Sort((p, q) => p.Count() - q.Count()); int assignments = 0; for(int i = 0; i < slotSets.Count() - 1; i++) { if(slotSets[i].Count > 0) { IFunction selected = slotSets[i].ElementAt(0); assignments++; for(int j = i + 1; j < slotSets.Count(); j++) { slotSets[j].Remove(selected); } } } // sanity check if(assignments > children.Count) throw new InvalidProgramException(); return assignments == children.Count - 1; } internal IList GetAllowedParents(IFunction child, int childIndex) { List parents = new List(); foreach(IFunction function in functions) { ICollection allowedSubFunctions = GetAllowedSubFunctions(function, childIndex); if(allowedSubFunctions.Contains(child)) { parents.Add(function); } } return parents; } internal bool IsTerminal(IFunction f) { return f.MinArity == 0 && f.MaxArity == 0; } internal IList GetAllowedSubFunctions(IFunction f, int index) { if(f == null) { return allFunctions; } else { return f.AllowedSubFunctions(index); } } #endregion #region private utility methods private IFunction GetRandomRoot(int maxTreeSize, int maxTreeHeight) { if(maxTreeHeight == 1 || maxTreeSize == 1) { IFunction selectedTerminal = RandomSelect(terminals); return selectedTerminal; } else { IFunction[] possibleFunctions = functions.Where(f => GetMinimalTreeHeight(f) <= maxTreeHeight && GetMinimalTreeSize(f) <= maxTreeSize).ToArray(); IFunction selectedFunction = RandomSelect(possibleFunctions); return selectedFunction; } } private IFunctionTree MakeUnbalancedTree(IFunction parent, int maxTreeHeight) { if(maxTreeHeight == 0) return parent.GetTreeNode(); int minArity = parent.MinArity; int maxArity = parent.MaxArity; int actualArity = random.Next(minArity, maxArity + 1); if(actualArity > 0) { IFunctionTree parentTree = parent.GetTreeNode(); for(int i = 0; i < actualArity; i++) { IFunction[] possibleFunctions = GetAllowedSubFunctions(parent, i).Where(f => GetMinimalTreeHeight(f) <= maxTreeHeight).ToArray(); IFunction selectedFunction = RandomSelect(possibleFunctions); IFunctionTree newSubTree = MakeUnbalancedTree(selectedFunction, maxTreeHeight - 1); parentTree.InsertSubTree(i, newSubTree); } return parentTree; } return parent.GetTreeNode(); } // NOTE: this method doesn't build fully balanced trees because we have constraints on the // types of possible sub-functions which can indirectly impose a limit for the depth of a given sub-tree private IFunctionTree MakeBalancedTree(IFunction parent, int maxTreeHeight) { if(maxTreeHeight == 0) return parent.GetTreeNode(); int minArity = parent.MinArity; int maxArity = parent.MaxArity; int actualArity = random.Next(minArity, maxArity + 1); if(actualArity > 0) { IFunctionTree parentTree = parent.GetTreeNode(); for(int i = 0; i < actualArity; i++) { // first try to find a function that fits into the maxHeight limit IFunction[] possibleFunctions = GetAllowedSubFunctions(parent, i).Where(f => GetMinimalTreeHeight(f) <= maxTreeHeight && !IsTerminal(f)).ToArray(); // no possible function found => extend function set to terminals if(possibleFunctions.Length == 0) { possibleFunctions = GetAllowedSubFunctions(parent, i).Where(f => IsTerminal(f)).ToArray(); IFunction selectedTerminal = RandomSelect(possibleFunctions); IFunctionTree newTree = selectedTerminal.GetTreeNode(); parentTree.InsertSubTree(i, newTree); } else { IFunction selectedFunction = RandomSelect(possibleFunctions); IFunctionTree newTree = MakeBalancedTree(selectedFunction, maxTreeHeight - 1); parentTree.InsertSubTree(i, newTree); } } return parentTree; } return parent.GetTreeNode(); } private int GetMinimalTreeHeight(IOperator op) { return ((IntData)op.GetVariable(GPOperatorLibrary.MIN_TREE_HEIGHT).Value).Data; } private int GetMinimalTreeSize(IOperator op) { return ((IntData)op.GetVariable(GPOperatorLibrary.MIN_TREE_SIZE).Value).Data; } private void TreeForEach(IFunctionTree tree, Action action) { action(tree); foreach(IFunctionTree subTree in tree.SubTrees) { TreeForEach(subTree, action); } } private void GetBranchesAtLevel(IFunctionTree tree, int level, List result) { if(level == 1) result.AddRange(tree.SubTrees); foreach(IFunctionTree subTree in tree.SubTrees) { if(subTree.Height >= level - 1) GetBranchesAtLevel(subTree, level - 1, result); } } private IFunction RandomSelect(IList functionSet) { double[] accumulatedTickets = new double[functionSet.Count]; double ticketAccumulator = 0; int i = 0; // precalculate the slot-sizes foreach(IFunction function in functionSet) { ticketAccumulator += ((DoubleData)function.GetVariable(GPOperatorLibrary.TICKETS).Value).Data; accumulatedTickets[i] = ticketAccumulator; i++; } // throw ball double r = random.NextDouble() * ticketAccumulator; // find the slot that has been hit for(i = 0; i < accumulatedTickets.Length; i++) { if(r < accumulatedTickets[i]) return functionSet[i]; } // sanity check throw new InvalidProgramException(); // should never happen } #endregion } }