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CS 240: Data Structures. Thursday, August 2nd Trees – Traversals, Representations, Recursion and Helpers. Submissions. If you need a Project 1 grade, you need to schedule an appointment with me after class. Traversals. Traversals are how we extract data from the tree – or any data structure.
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CS 240: Data Structures Thursday, August 2nd Trees – Traversals, Representations, Recursion and Helpers
Submissions • If you need a Project 1 grade, you need to schedule an appointment with me after class.
Traversals • Traversals are how we extract data from the tree – or any data structure. • Queue and Stack look at the data before moving to the next data. • NLR - However, there is no R in this case. • The three major orders: • Preorder: NLR • Inorder: LNR • Postorder: LRN
Tree Traversals • Preorder: NLR • Inorder: LNR • Postorder: LRN • N means Node. More specifically, look at the node. • L means Left and R means Right. • This is the order we examine data. • You may find it helpful to cross out each thing you do during a traversal. _ _ _
Tree Traversals • Lets look at some examples. • This means we have to create some trees.
Nodes: Data is allocated on each insert and deallocated on each removal Traversals are done using left/right Arrays/Vectors Data allocation only occurs if the insertion is out of range. Deallocation only occurs if sufficient space can be recovered. Traversals are done using index arithmetic Representations
Representations • Nodes have a left and right pointer. • Vector has to solve for left and right. • Starting at location 0: • Left is location*2 + 1 • Right is (location+1)*2 • With vectors it is easier to find the parent node, just reverse the operation. • Drawing time!
Tree ADT • Any node in a Tree is also a Tree. • This also applies to list, any node in a list is also a list. • This is why recursive code works on either of these. • Tree x; • x.insert(15); //calls insert(15,root); • x.insert(30); //calls insert(30,root); • Since root is full, we compare 30 to 15. It is larger, so insert on the right subtree: • insert(30,root->right); 15 30
Private vs Public • Many of our methods are public so that the user can use them. • There are some methods that we don’t want to give the user access to. • insert(T value); vs insert(T value, Node<T> * insertat); • This allows us to create methods for our own use that the user won’t have access to.
Tree Terms • So far, we really only talked about “root” which is equivalent to “first” from our linked lists. • Root also refers to the top-most node in the tree we are looking at. • All nodes are root relative to themselves. • A leaf node is a node who has no children. • Left = Right = NULL; • If a node is not a leaf node, it is a parent node. • A child is a node with a parent. • Level refers to the depth of the node relative to the root we are looking at.
Trees and Graphs • Trees are a subset of Graph. • We can apply searching methods from Graph to Tree: • Breadth-first search: Provides a level-by-level output of the Tree • Depth-first search: If we take the left sub-tree first: we get preorder or inorder traversal. • If we take the right sub-tree first: we get postorder or inorder traversal.
How do we? • Insert: Traverse until you find NULL – going left if we are small, going right if we are larger. At NULL, create a new node, return location to caller – who must now point to the new node. • Remove: Traverse until you find the value you want to remove. • If it is a leaf node, remove it, parent points to NULL. • If it has 1 child, parent points to the child. • If it has 2 children, find smallest value on right sub-tree. Copy that value into the current node. Then, remove(current->data,current->right); • Empty: Starting at root, if NULL return. Otherwise, empty(target->left);, empty(target->right);, delete target;
Problems • First, let us compare a Binary Search Tree with a Binary Tree – we have only really talked about Binary Search Trees so far. • We use Binary Search Trees because of the improved search performance. • However, it can degenerate!
Balancing • Therefore, we need to balance our trees. • Search time is slow when the BST is “skinny”. Therefore, we like our BSTs to be “beefy”.
Self-Balancing Trees • AVL: A self-balancing tree that uses a “balance” factor to determine if an adjustment is needed. • Each node is created with the balance factor set to 0. • Balance is equal to the difference between the height/depth of a node’s sub-trees. • Rebalance occurs whenever one of the 4 cases exists.
AVL Cases • Left-Left: Parent = -2, Left Child – 1 • Rotate Right around Parent • Left-Right: Parent = -2, Left Child = +1 • Rotate left around child, right around Parent • Right-Right: Parent = +2, Right Child = +1 • Rotate Left around Parent • Right-Left: Parent = +2, Right Child = -1 • Rotate right around child, left around Parent.
AVL • This works out rather well. • However, almost half of insertions and removal require rotation.
More trees • Huffman Trees • This is an algorithm for generating codes such as morse code. • For any given set of data and values. • Pair up the two smallest (replace them with a tree containing) • Root = sum of the two values • Left child = one of the data • Right child = the other
Various Tidbits • Activation Stack • Recursion and evaluation • Array vs pointer indexing • Use of “this” • Proving algorithms • Shallow vs Deep copy
