1. Binary Trees A binary tree is made up of a finite set of nodes that is either empty or consists of a node called the root together with two binary trees called the left and right subtrees which are disjoint from each other and from the root Notation: Node, Children, Edge, Parent, Ancestor, Descendant, Path, Depth, Height, Level, Leaf Node, Internal Node, Subtree. A G FE C D B IH

2. Full and Complete Binary Trees A Full binary tree has each node being either a leaf or internal node with exactly two non-empty children. A Complete binary tree: If the height of the tree is d, then all levels except possibly level d are completely full. The bottom level has all nodes to the left side.

3. Full Binary Tree Theorem Theorem: The number of leaves in a non-empty full binary tree is one more than the number of internal nodes. Proof (by Mathematical Induction) Base Case: A full binary tree with 1 internal node must have two leaf nodes. Induction Hypothesis: Assume any full binary tree T containing n-1 internal nodes has n leaves. Induction Step: Pick an arbitrary leaf node j of T. Make j an internal node by giving it two children. The number of internal nodes has now gone up by 1 to reach n. The number of leaves has also gone up by 1. Corollary: The number of NULL pointers in a non-empty binary tee is one more than the number of nodes in the tree.

4. Traversals Any process for visiting the nodes in some order is called a traversal. Any traversal that lists every node in the tree exactly once is called an enumeration of the tree’s nodes. Preorder traversal: Visit each node before visiting its children. Postorder traversal: Visit each node after visiting its children. Inorder traversal: Visit the left subtree, then the node, then the right subtree. void preorder(BinNode * root) { if (root==NULL) return; visit(root); preorder(root->leftchild()); preorder(root->rightchild()); }

5. Expression Trees Example of (a-b)/((x*y+3)-(6*z)) / - a b - + 6z * yx 3*

6. Binary Search Trees Left means less – right means greater. Find –If item dat then cur=cur->left –Else if item>cur->dat then cur=cur->right –Else found –Repeat while not found and cur not NULL No need for recursion.

7. Find min and max The min will be all the way to the left –While cur->left != NULL, cur=cur->left The max will be all the way to the right –While cur->right !=NULL, cur=cur->right Insert –Like a find, but stop when you would go to a null and insert there.

8. Remove If node to be deleted is a leaf (no children), can remove it and adjust the parent node (must keep track of previous) If the node to be deleted has one child, remove it and have the parent point to that child. If the node to be deleted has 2 children –Replace the data value with the smallest value in the right subtree –Delete the smallest value in the right subtree (it will have no left child, so a trivial delete.

9. Array Implementation For a complete binary tree Parent(x)= Leftchild(x)= Rightchild(x)= Leftsibling(x)= Rightsibling(x)= 0 12 345 6 7891011 (x-1)/2 2x+1 2x+2 x-1 x+1

10. Huffman Coding Trees Each character has exactly 8 bits Goal: to have a message/file take less space Allow some characters to have shorter bit patterns, but some characters can have longer. Will not have any benefit if each character appears with equal probability. English does not have equal distribution of character occurance. If we let the single bit 1 represent the letter E, then no other character can start with a 1. So some will have to be longer.

11. The Tree Look at the left pointer as the way to go for a 0 and the right pointer is the way to go for a 1. Take input string of 1’s and 0’s and follow them until hit null pointer, then the character in that node is the character being held. Example: 0 = E 10 = T 110 = P 111 = F 0110111010=EPFET E T P F

12. Weighted Tree Each time we have to go to another level, takes time. Want to go down as few times as needed. Have the most frequently used items at the top, least frequently items at the bottom. If we have weights or frequencies of nodes, then we want a tree with minimal external path weight.

13. Huffman Example Assume the following characters with their relative frequencies. Z K F C U D L E 2 7 24 32 37 42 42 120 Arrange from smallest to largest. Combine 2 smallest with a parent node with the sum of the 2 frequencies. Replace the 2 values with the sum. Combine the nodes with the 2 smallest values until only one node left.

14. Huffman Tree Construction Z K F C U D L E 2 7 24 32 37 42 42 120 306 186 79107 65 33 9 120 E 37 U 42 D 42 L 32 C 2Z2Z 7K7K 24 F

15. Results LetFreqCodeBitsMess. Len.Old BitsOld Mess. Len. C3211104128396 D4210131263126 E120011203360 F24111115120372 K7111101642321 L4211031263126 U3710031113111 Z211110061236 TOTAL:785918

16. Heap Is a complete binary tree with the heap property –min-heap : All values are less than the child values. –Max-heap: all values are greater than the child values. The values in a heap are partially ordered. There is a relationship between a node’s value and the value of it’s children nodes. Representation: Usually the array based complete binary tree representation.

17. Building the Heap Several ways to build the heap. As we add each node, or create “a” tree as we get all the data and then “heapify” it. More efficient if we wait until all data is in. 1 45 2 67 3 1 42 5 63 7 7 42 5 63 1 7 42 5 13 6

18. Heap ADT class heap{ private: ELEM* Heap; int size; int n; void siftdown(int); public: heap(ELEM*, int, int); int heapsize() const; bool isLeaf(int) const; int leftchild(int) const; int rightchild(int) const; int parent(int) const; void insert(const ELEM); ELEM removemax(); ELEM remove(int); void buildheap();};

19. Siftdown For fast heap construction –Work from high end of array to low end. –Call siftdown for each item. –Don’t need to call siftdown on leaf nodes. void heap::buildheap() {for (int i=n/2-1; i>=0; i--) siftdown(i);} void heap::siftdown(int pos){ assert((pos>=0) && (pos<n)); while (!isleaf(pos)) { int j=leftchild(pos); if ((j<(n-1) && key(Heap[j])<key(Heap[j+1]))) j++; // j now has position of child with greater value if (key(Heap[pos])>=key(Heap[j])) return; swap(Heap[pos], Heap[j]); pos=j; } }

20. Priority Queues A priority queue stores objects and on request, releases the object with the greatest value. Example : scheduling jobs in a multi-tasking operating system. The priority of a job may change, requiring some reordering of the jobs. Implementation: use a heap to store the priority queue. To support priority reordering, delete and re-insert. Need to know index for the object. ELEM heap::remove(int pos) { assert((pos>0) && (pos<n)); swap (Heap[pos], Heap[--n]); if (n!=0) siftdown(pos); return Heap[n]; }