資料型態(Data Type)-程式語言的變數所能表 示的資料種類 *基本型態(Primitive Type):

Presentation on theme: "資料型態(Data Type)-程式語言的變數所能表 示的資料種類 *基本型態(Primitive Type):"— Presentation transcript:

integer, real, boolean, character(byte) *結構化型態(Structured Type): string, array, record/structure

int i, j; char c; float r; int i1[10], j1[10]; float r1[10]; i1[0]=20; i2[1]=90;

*堆疊是一個有序串列，所有的insertion和 deletion動作均在頂端(top)進行 *具有後進先出(LIFO-last in first out)特性 *佇列是一個有序串列，所有的insertion和 deletion是發生在串列的不同端，加入的一端 稱為尾端(rear)，刪除的一端稱為前端(front) *具有先進先出(FIFO-first in first out)特性

Example of a stack and a queue
B A top A B C D E front rear stack queue

Representation of a stack:
*a one-dimensional array: stack[0:n-1] *a variable: top *linked list *node: data, link top E D C B A data link

Representation of a queue:
*a one-dimensional array: q[0:n-1] *two variables: front & rear *linked list *node: data, link * two variables: front & rear data link A B C D front rear

Circular Queue [4] [4] J4 [3] [3] [n-4] [n-4] J3 J1 [2] J2 [n-3] [2]
J4 [3] [3] [n-4] [n-4] J3 J1 [2] J2 [n-3] [2] [n-3] J1 J2 [1] [1] J4 J3 [n-2] [n-2] [0] [n-1] [0] [n-1] front = 0 ; rear = 4 front = n-4 ; rear = 0 Circular queue of capacity n-1 containing four elements J1, J2, J3, and J4

Trees *A tree is a finite set of one or more nodes *樹是一個或多個節點(node)所組成的有限集合 *有一個特殊的節點稱為樹根(root) *每一個節點底下有零個或一個以上的子樹 (subtree): T1, T2, …, Tn n0 *節點(node) *分支(branch, edge) *樹根(root) *子點(children) *父點(parent) *終端節點(leaf, terminal nodes)

Trees *非終端節點(nonterminal nodes) *兄弟(siblings) *祖先(ancestor of a node): all the nodes along the path from the root to that node *分支度(degree): the number of subtrees of a node *樹的分支度(degree of a tree): the maximum degree of the nodes in the tree *階度(level): initially let the root be at level one

a Sample Tree level 1 A 2 B C D E F G H I J 3 K L 4 M

Trees *高度(height, depth): the maximum level of any node in the tree *A forest is a set of n0 disjoint trees *若一樹有n個nodes，則必有且唯有n-1個 edges *A graph without a cycle

Representation of a Tree:
*linked list *node: data, link, tag *when tag=1, data field contains a pointer to a list rather than a data item A B F C G D I J E K L H M The tag field of a node is one if it has a down-pointing arrow; otherwise it is zero.

Binary Trees : *Any node can have at most two children *二元樹上每個節點的degree2 *左子樹(left subtree) & 右子樹(right subtree) *二元樹可以是空集合(empty) *The maximum number of nodes on leve i of a binary tree is 2i-1 *The maximum number of nodes in a binary tree of depth k is 2k-1, k>0. *For any non-empty binary tree, if n0 is the number of terminal nodes and n2 is the number of nodes of degree 2, then n0=n2+1.

Representation of a Binary Tree:
*a one-dimensional array: tree[1:n] the node numbered i is stored in tree[i] 1. parent(i) is at i/2 if i1. If i=1, i is the root and has no parent. 2. lchild(i) is at 2i if 2in. If 2i>n, i has no left child. 3. rchild(i) is at 2i+1 if 2i+1n. If 2i+1>n, i has no right child. *優點:處理簡單，若為full binary tree，則相當節省 空間。 *缺點:若為skewed binary tree，則相當浪費空間。 不容易處理Insertion or deletion

Binary Trees level A A 1 B B C 2 D E F G C 3 H I D 4 E 5 (a) (b)

Sequential Representation
B - C D E F G H I (1) (2) (3) (4) (5) (6) (7) (8) (9) (16) Tree Tree Sequential representation of the binary trees

Representation of a Binary Tree:
*linked list node: lchild, data, rchild a variable: tree *優點:插入與刪除一個節點相當容易。 *缺點:缺點:很難找到該節點的父點。

tree tree A A B B C C D E F G D H I E (a) (b) Linked representation of the binary trees

Binary Search Trees *A binary search tree is a binary tree. 1. All the keys are distinct. 2. The keys in the left subtree are smaller than the key in the root. 3. The keys in the right subtree are larger than 4. The left and right subtrees are also binary search trees. *Search by key value *Node: lchild, rchild and data

Binary trees 20 30 60 15 25 5 40 70 12 10 22 2 65 80 (b) (c) (a)

Binary Search Trees *Search by rank(find the kth-smallest element) *Node: lchild, rchild, data and leftsize *Leftsize: one plus the number of elements in the left subtree of the node

Representation of a Priority Queue:
*Any data structure that supports the operations of search max, insert, and delete max is called a priority queue.

Priority Queues *unordered linear list insert time: (1) delete time: (n) n-element unordered list *ordered linear list insert time: O(n) delete time: (1) n-element ordered list *heap insert time: O(log n) delete time: O(log n)

MAX Heap *a complete binary tree *the value at each node is at least as large as the value at its children(任何一父點的值必大於 *The largest number is in root node *A max heap can be implemented using an array a[]. *Insertion into a heap *Deletion from a heap

Action of Inset inserting 90 into an existing heap
Insertion into a Heap 80 80 45 70 45 90 40 35 50 90 40 35 50 70 90 45 80 40 35 50 70 Action of Inset inserting 90 into an existing heap

Heap sort 1)建立一binary tree 2)將binary tree轉成Heap (Heapify) 3)Output: Removing the largest number and restoring the heap (Adjust)

100,119,118,171,112,151,32,40,80,35,90,45,50,70

Forming a heap from the set{40,80,35,90,45,50,70}
(b) (c) 80 90 90 40 35 80 35 80 35 90 40 40 45 (d) (e) 90 90 90 90 80 35 80 50 80 50 80 70 40 45 50 40 45 35 40 45 35 70 40 45 35 50 (f) (g) Forming a heap from the set{40,80,35,90,45,50,70}

Heapify 100 100 119 118 119 151 171 112 151 132 171 112 118 132 (a) (b) 100 171 171 151 119 151 119 112 118 132 100 112 118 132 (c) (d) Action of Heapify(a,7) on the data (100, 119, 118, 171, 112, 151, and 132)

Sets and Disjoint Set Union
*The sets are assumed to be pairwise disjoint. *Each set is represented as a tree. *Link the nodes from the children to the parent Ex: S1={1, 7, 8, 9}, S2={2, 5, 10}, S3={3, 4, 6} Set Operations: *Disjoint set union Make one of the trees a subtree of the other *Find(i)

Representations of Union
1 5 7 8 9 2 10 S1 S2 1 5 7 8 9 5 1 2 10 2 10 7 8 9 or S1∪S2 S1∪S2 Possible representations of S1∪S2

Weighting rule for Union(i, j):
If the number of nodes in the tree with root i is less than the number in the tree with root j, then make j the parent of i; otherwise make i the parent of j. *Maintain a count in the p field of the roots as a negative number. *Count field: the number of nodes in that tree.

Lemma 2.3 Assume that we start with a forest of trees, each having one node. Let T be a tree with m nodes created as a result of a sequence of unions each performed using WeightedUnion. The height of T is no greater than log m+1.

Collapsing rule: If j is a node on the path from i to its root and p[i]root[i], then set p[j] to root[i].

Graph A graph G(V,E) is a structure which consists of 1. a finite nonempty set V(G) of vertices (points, nodes) 2. a (possible empty) set E(G) of edges (lines) V(G): vertex set of G E(G): edge set of G

Sample Graphs Three sample graphs 1 1 1 2 2 3 2 3 4 5 6 7 4 3 (a) G1
(b) G2 (c) G3 Three sample graphs

Undirected Graph(無方向圖形): (u,v)=(v,u)
*假如(u,v)E(G)，則u和v是adjacent vertices， 且(u,v)是incident on u和v *Degree of a vertex: the number of edges incident to that vertex G has n vertices and e edges, *A subgraph of G is a graph G'(V',E')，V'V 且 E'E *Path:假如(u,i1), (i1,i2), ..., (ik,v)E，則u與v有一條 Path(路徑)存在。 *Path Length: the number of edges on the path *Simple path:路徑上除了起點和終點可能相同外，其它 的頂點都是不同的

Undirected Graph(無方向圖形): (u,v)=(v,u)
*Cycle: a simple path且此路徑上的起點和終點相同 且(u,v)是incident on u和v *In G, two vertices u and v are said to be connected iff there is a path in G from u to v. *Connected graph:圖上每個頂點都有路徑通到其它 頂點 *Connected component: a maximal connected subgraph (圖上相連在一起的最大子圖) *Complete graph: if |V|=n then |E|=n(n-1)/2 *A tree is a connected acyclic(no cycles) graph. *Self-edge(self-loop): an edge from a vertex back to itself *Multigraph: have multiple occurrences of the same edge

A graph with two connected components
5 1 6 7 3 2 8 4 G4 A graph with two connected components

Subgraphs Some subgraphs 1 2 3 1 1 2 3 4 2 3 4 (i) (ii) (iii) (iv)
(a) Some of the subgraphs of G1 1 1 1 1 2 2 2 3 3 3 (i) (ii) (iii) (iv) (b) Some of the subgraphs of G3 Some subgraphs

Graphlike Structures Examples of graphlike structures 1 2 4 1 2 3 3
(a) Graph with a self edge (b) Multigraph Examples of graphlike structures

Undirected Graph(無方向圖形): (u,v)=(v,u)
*Eulerian walk(cycle):從任何一個頂點開始， 經過每個邊 一次，再回到原出發點 (每個頂點的degree必須是偶數) *Eulerian chain:從任一頂點開始，經過每個邊 一次，不一定要回到原點 (只有兩個頂點的degree是奇數，其它必須是 偶數)

Directed Graph(Digraph,有方向圖形): <u,v><v,u>
*假如<u,v>E(G)，則稱u is the tail and v the head of the edge u是adjacent to v，而v是adjacent from v <u,v>是incident to u和v *in-degree of v: the number of edges for which v is the head *out-degree of v: the number of edges for which v is the tail. *Subgraph:G'=(V', E')， V' V 且 E' E

Directed Graph(Digraph,有方向圖形): <u,v><v,u>
*Directed Path:假如<u,i1>, <i1,i2>, ..., <ik,v>E， 則u與v有一條Path(路徑)存在。 *Path Length:路徑上所包含的有向邊的數目 *Simple directed path:路徑上除了起點和終點可能 相同外，其它的頂點都是不同的 *Directed Cycle(Circuit): A simple directed path 且路徑上的起點和終點相同

Directed Graph(Digraph,有方向圖形): <u,v><v,u>
*Strongly connected graph: for every pair of distinct vertices u and v in V(G), there is a directed path from u to v and also from v to u. *Strongly connected component: a maximal subgraph that is strongly connected (有向圖上緊密相連在一起的最大有向子圖) *Complete digraph: if |V|=n then |E|=n(n-1)

Strongly Connected Components
1 1 2 2 3 3 (a) (b) A graph and its strongly connected components

Graph Representations:

Adjacency Matrix G(V, E): a graph with n vertices A two-dimensional n*n array: a[1:n, 1:n] *a[i, j]=1 iff (i, j)E(G) *Space: O(n2) *The adjacency matrix for an undirected graph is symmetric. Use the upper or lower triangle of the matrix *For an undirected graph the degree of i is its row sum. *For a directed graph the row sum is the out-degree, and the column sum is the in-degree.

Adjacency Matrices 1 1 5 1 3 2 6 7 2 2 3 4 8 4 3 (a) G1 (b) G3 (c) G4

Adjacency List G(V, E): a graph with n vertices and e edges *the n rows of the adjacency matrix are represented as n linked lists *There is one list for each vertex in G node: vertex, link *Each list has a head node *Space: n head nodes and 2e nodes for an undirected graph n head nodes and e nodes for a directed graph Use the upper or lower triangle of the matrix *For an undirected graph the degree of i is to count the number of nodes in its adjacency list. *For a directed graph the out-degree of i is to count the

head nodes vertex link 1 [1] 4 2 3 2 3 [2] 3 4 1 4 [3] 2 4 1 [4] 1 2 3 (a) G1 head nodes 1 vertex link [1] 2 2 [2] 3 1 [3] 3 (b) G3

head nodes vertex link [1] 3 2 [2] 4 1 [3] 1 4 1 5 [4] 2 3 3 2 6 7 [5] 6 4 8 [6] 7 5 [7] 6 8 [8] 7 (c) G4

Adjacency List Inverse adjacency lists – to count the in-degree easily *One list for each vertex *Each list contains a node for each vertex adjacent to the vertex it represents.

1 2 3 [1] 2 [2] 1 [3] 2 Inverse adjacency lists for G3

Array node[1: n+2e+1] to represent a graph G(V, E) with n vertices and e edges - eliminate the use of pointers *The node[i] gives the starting point of the list for vertex i, 1in *node[n+1]:=n+2e+2

Sequential representation
1 5 3 2 6 7 4 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Sequential representation of graph G4：Array node[1：n + 2e + 1]

Each node has four fields and represents one edge. Node structure:
Orthogonal list Each node has four fields and represents one edge. Node structure: tail head column link for head row link for tail

Orthogonal list representation
Head nodes (shown twice) 1 2 3 1 2 3 Orthogonal list representation for G3

*One list for each vertex *node can be shared among several lists
Adjacency Multilist *One list for each vertex *node can be shared among several lists *for each edge there is exactly one node, but the node is in two lists *node strucure m vertex1 vertext2 list1 list2