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Discrete Maths. 241-303 , Semester 1 2014-2015. Objective introduce more unusual tree algorithms and techniques you already know about binary (search) trees. 6 . Trees. Overview. 1. Uses of Trees 2. Huffman Codes 3. (Rooted) Tree Terminology 4. Spanning Trees

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## Discrete Maths

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**Discrete Maths**241-303, Semester 1 2014-2015 • Objective • introduce more unusual tree algorithms and techniques • you already know about binary (search) trees 6. Trees**Overview**1. Uses of Trees 2. Huffman Codes 3. (Rooted) Tree Terminology 4. Spanning Trees 5. Minimal Spanning Trees 6. Game Trees 7. More Information**1. Uses of Trees**Davenport S. Williams S. Williams S. Williams Bartoli V. Williams V. Williams Wimbledon Womens Tennis**Organizational Chart**President Vice-President for Academics Vice-President for Admin. Dean of Engineering Dean of Business Planning Officer Purchases Officer . . . . . . . . Head of CoE Head of EE Head of AC. . . . .**Saturated Hydrocarbons**H H H H • Non-rooted (free) trees • a free tree is a graph with no cycles C H H C C C H H C H H H C H H H C H H Isobutane H C H H Butane H**A Computer File System**/ usr bin tmp bin ad spool ls mail who junk ed vi exs opr uucp printer**2. Huffman Codes**• A Huffman code represents characters by variable-length bit strings • there are many different Huffman codes • By comparison, ASCII uses fixed-length codes. • Character ASCII code A 1000001 B 1000010 : : S 1010011 : :**A Huffman Code in Tree Form**Root • Character Code A 1 O 00 R 010 S 0110 T 0111 1 0 A 1 0 O 1 0 1 0 R T S where do these come from?**Huffman uses Less Bits**useful for network comms, text compression, etc. • ASCII coding of "RATS": • 1010010 1000001 1010100 1010011 • 4*7 = 28 bits • Huffman Coding (as in the tree): • 010 1 0111 0110 • 12 bits**Constructing a Huffman Code**• A Huffman code is based on a table giving the frequency of the characters in the 'language'. • The 'language' can be a subset of a full language • e.g. business English, computer language keywords, the months of the year, a language made up of only {A, O, R, S, T}**Letter Frequencies for English**• The most common letters, in full English, in order of frequency, are usually ETAOINSHRDLU. • This varies depending on the data source • e.g. novels, textbooks, cartoons**Example: The Haddock Language**• The Haddock language contains only 5 characters: • !, @, #, $, % • we can make words like: • !!!@@ $%$# @@@@@@@@@ • We must create a frequency table by analysing existing examples of Haddock writing. continued**The frequency table we create:**• Character Frequency ! 2/32 @ 3/32 # 7/32 $ 8/32 % 12/32 • We work with the numerators of the fractions.**Informal Algorithm**• 1. Make a sequence of the frequencies: { 2, 3, 7, 8, 12} • 2. Repeatedly replace the smallest two frequencies by a sum until only 2 elements are left: • {2,3,7,8,12} ==> {2+3, 7, 8, 12}{5,7,8,12} ==> {5+7, 8, 12}{8,12,12} ==> {8+12, 12}{12,20} continued**3. Build a tree by expanding the sequence back to its full**size. • Start with the 2 elements as a 2 branch tree. 1 1 0 1 0 0 ==> ==> ==> 1 0 1 0 1 0 12 20 12 8 12 5 7 8 12 continued**1**0 ==> 1 0 1 0 1 0 7 8 12 • 4. Replace each frequency by the character having that frequency: 2 3 1 0 1 0 1 0 1 0 # $ % ! @ continued**Huffman Code for Haddock**• Character Code ! 111 @ 110 # 10 $ 01 % 00 • The higher frequency letters have shorter Huffman codes.**Another Possible Answer**1 0 • Why? There are two "12" nodes (see slide 12), either of which can be expanded back to "5" and "7". 12 1 0 1 8 0 1 0 7 2 3 continued**Huffman Code for Haddock v.2**• Character Code ! 0011 @ 0010 # 000 $ 01 % 1 • The higher frequency letters have shorter Huffman codes.**Huffman Algorithm**• Tree huffman(FreqSeq f, int size){ if (size == 2){ let f1, f2 be the frequencies in f return baseTree; else { let fi, fj be the smallest freqs in f; replace fi and fj in f with fi+fj; Tree t1 = huffman(f, size-1); Tree t = replace "fi+fj" vertex in t1 with extTree return t; }} 1 0 f1 f2 baseTree 1 0 fi fj extTree call: Tree t = huffman( {2,3,7,8,12}, 5);**3. (Rooted) Tree Terminology**• e.g. Part of the ancient Greek god family: levels 0 Uranus 1 Aphrodite Kronos Atlas Prometheus 2 Eros Zeus Poseidon Hades Ares 3 Apollo Athena Hermes Heracles : :**Some Definitions**• Let T be a tree with root v0. • Suppose that x, y, z are verticies in T. • (v0, v1,..., vn) is a simple path in T (no loops). • a) vn-1 is the parent of vn. • b) v0, ..., vn-1 are ancestors of vn • c) vn is a child of vn-1 continued**d) If x is an ancestor of y, then y is a descendant of x.**• e) If x and y are children of z, then x and y are siblings. • f) If x has no children, then x is a terminal vertex (or a leaf). • g) If x is not a terminal vertex, then x is an internal (or branch) vertex. continued**h) The subtree of T rooted at x is the graph with vertex**set V and edge set E • V contains x and all the descendents of x • E = {e | e is an edge on a simple path from x to some vertex in V} • i) The length of a path is the number of edges it uses, not verticies. continued**j) The level of a vertex x is the length of the simple path**from the root to x. • k) The height of a vertex x is the length of the simple path from x to the farthest leaf • the height of a tree is the height of its root • l) A tree where every internal vertex has exactly m children is called a full m-ary tree.**Applied to the Example**• The root is Uranus. • A simple path is {Uranus, Aphrodite, Eros} • The parent of Eros is Aphrodite. • The ancestors of Hermes are Zeus, Kronos, and Uranus. • The children of Zeus are Apollo, Athena, Hermes, and Heracles. continued**The descendants of Kronos are Zeus, Poseidon, Hades, Ares,**Apollo, Athena, Hermes, and Heracles. • The leaves (terminal verticies) are Eros, Apollo, Athena, Hermes, Heracles, Poseidon, Hades, Ares, Atlas, and Prometheus. • The branches (internal verticies) are Uranus, Aphrodite, Kronos, and Zeus. continued**The subtree rooted at Kronos:**Kronos Zeus Poseidon Hades Ares Apollo Athena Hermes Heracles continued**The length of the path {Uranus, Aphrodite, Eros} is 2 (not**3). • The level of Ares is 2. • The height of the tree is 3.**Some Properties of Trees**• 1) A tree with n verticies has n-1 edges. • e.g. • Proof: • ignore the root; • pair each vertex with the edge above it; • so n-1 verticies use n-1 edges (all of them)**2) A full m-ary tree with i internal verticies has a total**of m*i + 1 verticies. • e.g. A full 2-ary tree (binary tree) 4 internal verticies A total of 9 verticies continued**Proof**• ignore the root; • every remaining vertex is the child of an internal vertex; • each internal vertex has m children; • there are i internal verticies, so there is a total of m*i verticies in the tree; • add back the root, to get m*i + 1**4. Spanning Trees**• A spanning tree T is a subgraph of a graph G which contains all the verticies of G. • Example graph G: a b c d e f h g continued**One possible spanning tree:**a b the tree is drawn with thick lines c d e f h g**4.1. Example: IP Multicasting**• A network of computers and routers: source computer router continued**How can a packet (message) be sent from the source computer**to every other computer? • The inefficient way is to use broadcasting • send a copy along every link, and have each router do the same • each router and computer will receive many copies of the same packet • loops may mean the packet never disappears! continued**IP multicasting is an efficient solution**• send a single packet to one router • have the router send it to 1 or more routers in such a way that a computer never receives the packet more than once • This behaviour can be represented by a spanning tree. continued**The spanning tree for the network:**source computer the tree is drawn with thick lines router**4.2. Finding a Spanning Tree**• There are two main types of algorithms: • breadth-first search • depth-first search**4.3. Breadth-first Search**• Process all the verticies at a given level before moving to the next level. • Example graph G (again): a b c d e f h g**Informal Algorithm**• 1) Put the verticies into an ordering • e.g. {a, b, c, d, e, f, g, h} • 2) Select a vertex, add it to the spanning tree T: e.g. a • 3) Add to T all edges (a,X) and X verticies that do not create a cycle in T • i.e. (a,b), (a,c), (a,g) T = {a, b, c, g} a g b c continued**a**level 1 g b c • Repeat step 3 on the verticies just added, these are on level 1 • i.e. b: add (b,d) c: add (c,e) g: nothing T = {a,b,c,d,e} • Repeat step 3 on the verticies just added, these are on level 2 • i.e. d: add (d,f) e: nothing T = {a,b,c,d,e,f} d e a g b c level 2 d e f continued**a**g • Repeat step 3 on the verticies just added, these are on level 3 • i.e. f: add (f,h) T = {a,b,c,d,e,f,h} • Repeat step 3 on the verticies just added, these are on level 4 • i.e. h: nothing, so stop b c d e level 3 f h continued**Resulting spanning tree:**a b a different spanning tree from the earlier solution c d e f h g**Breadth-first Algorithm**• Tree bfs(Verts vsG, Edges edsG)// input ordered verts and edges of graph G{ Vert v1 = firstVert(VsG); // first vert in G Seq levelVs = ( v1 ); // verts at curr level Verts vsT = { v1 }; // verts in tree Edges edsT = {}; // edges in tree while (1) { for each x in levelVs for each y in vsG - vsT if (x,y) is an edge in edsG add (x,y) to edsT; add y to vsT; if no edges added to vsT return tree made from vsT and edsT; levelVs = childrenOf( levelVs ); }} continued**Typical call:**Verts vsG = {a, b, c, d, e, f, g, h};Edges edsG = { (a,b), (a,c), (a,g), (b,d), (b,g), (c,d), (c,e), (d,f), (e,g), (f,h) };Tree spanTree = bfs( vsG, edsG ); :**4.4. Depth-first Search**• Process all the verticies down one path, then backtrack (go back) to verticies along other paths. • Example graph G (again): a b c d e f h g**Informal Algorithm**• 1) Put the verticies into an ordering • e.g. {a, b, c, d, e, f, g, h} • 2) Select a vertex, add it to the spanning tree T: e.g. a • 3) Add the edge (a,X) where X is the smallest vertex in the ordering, and does not make a cycle in T • i.e. (a,b), T = {a, b} a b continued**a**b d • 4) Repeat step 3 with the new vertex, until there is no possible new vertex • i.e. add the edges (b,d) (d,c) (c,e) (e,f) (f,h) T = {a,b,d,c,e,f,h} • 5) At this point, there is no (h,X), so backtrack to a vertex that does have another edge: • parent of h == fbut there is no new (f,X) to add, so backtrack c e f h continued**a**b • parent of f == e • there is an (e,g) to add, so repeat step 3 with e • 6) After g is added, there are no further verticies to add, so stop. d c e g f h continued

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