P4 Sec 4.1.1, 4.1.2, 4.1.3) Abstraction, Algorithms and ADT's
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Encapsulation and Data Abstraction
Data abstraction l Data abstraction is the main organizing principle for building complex software systems l Without data abstraction, computing technology would stop dead in its tracks l We will study what data abstraction is and how it is supported by the programming language l The first step toward data abstraction is called encapsulation l Data abstraction is supported by language concepts such as higher-order programming, static scoping, and explicit state Encapsulation l The first step toward data abstraction, which is the basic organizing principle for large programs, is encapsulation l Assume your television set is not enclosed in a box l All the interior circuitry is exposed to the outside l It’s lighter and takes up less space, so it’s good, right? NO! l It’s dangerous for you: if you touch the circuitry, you can get an electric shock l It’s bad for the television set: if you spill a cup of coffee inside it, you can provoke a short-circuit l If you like electronics, you may be tempted to tweak the insides, to “improve” the television’s performance l So it can be a good idea to put the television in an enclosing box l A box that protects the television against damage and that only authorizes proper interaction (on/off, channel selection, volume) Encapsulation in a program l Assume your program uses a stack with the following implementation: fun {NewStack} nil end fun {Push S X} X|S end fun {Pop S X} X=S.1 S.2 end fun {IsEmpty S} S==nil end l This implementation is not encapsulated! l It has the same problems as a television set -
9. Abstract Data Types
COMPUTER SCIENCE COMPUTER SCIENCE SEDGEWICK/WAYNE SEDGEWICK/WAYNE 9. Abstract Data Types •Overview 9. Abstract Data Types •Color •Image processing •String processing Section 3.1 http://introcs.cs.princeton.edu http://introcs.cs.princeton.edu Abstract data types Object-oriented programming (OOP) A data type is a set of values and a set of operations on those values. Object-oriented programming (OOP). • Create your own data types (sets of values and ops on them). An object holds a data type value. Primitive types • Use them in your programs (manipulate objects). Variable names refer to objects. • values immediately map to machine representations data type set of values examples of operations • operations immediately map to Color three 8-bit integers get red component, brighten machine instructions. Picture 2D array of colors get/set color of pixel (i, j) String sequence of characters length, substring, compare We want to write programs that process other types of data. • Colors, pictures, strings, An abstract data type is a data type whose representation is hidden from the user. • Complex numbers, vectors, matrices, • ... Impact: We can use ADTs without knowing implementation details. • This lecture: how to write client programs for several useful ADTs • Next lecture: how to implement your own ADTs An abstract data type is a data type whose representation is hidden from the user. 3 4 Sound Strings We have already been using ADTs! We have already been using ADTs! A String is a sequence of Unicode characters. defined in terms of its ADT values (typical) Java's String ADT allows us to write Java programs that manipulate strings. -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
Abstract Data Types (Adts)
Data abstraction: Abstract Data Types (ADTs) CSE 331 University of Washington Michael Ernst Outline 1. What is an abstract data type (ADT)? 2. How to specify an ADT – immutable – mutable 3. The ADT design methodology Procedural and data abstraction Recall procedural abstraction Abstracts from the details of procedures A specification mechanism Reasoning connects implementation to specification Data abstraction (Abstract Data Type, or ADT): Abstracts from the details of data representation A specification mechanism + a way of thinking about programs and designs Next lecture: ADT implementations Representation invariants (RI), abstraction functions (AF) Why we need Abstract Data Types Organizing and manipulating data is pervasive Inventing and describing algorithms is rare Start your design by designing data structures Write code to access and manipulate data Potential problems with choosing a data structure: Decisions about data structures are made too early Duplication of effort in creating derived data Very hard to change key data structures An ADT is a set of operations ADT abstracts from the organization to meaning of data ADT abstracts from structure to use Representation does not matter; this choice is irrelevant: class RightTriangle { class RightTriangle { float base, altitude; float base, hypot, angle; } } Instead, think of a type as a set of operations create, getBase, getAltitude, getBottomAngle, ... Force clients (users) to call operations to access data Are these classes the same or different? class Point { class Point { public float x; public float r; public float y; public float theta; }} Different: can't replace one with the other Same: both classes implement the concept "2-d point" Goal of ADT methodology is to express the sameness Clients depend only on the concept "2-d point" Good because: Delay decisions Fix bugs Change algorithms (e.g., performance optimizations) Concept of 2-d point, as an ADT class Point { // A 2-d point exists somewhere in the plane, .. -
Csci 658-01: Software Language Engineering Python 3 Reflexive
CSci 658-01: Software Language Engineering Python 3 Reflexive Metaprogramming Chapter 3 H. Conrad Cunningham 4 May 2018 Contents 3 Decorators and Metaclasses 2 3.1 Basic Function-Level Debugging . .2 3.1.1 Motivating example . .2 3.1.2 Abstraction Principle, staying DRY . .3 3.1.3 Function decorators . .3 3.1.4 Constructing a debug decorator . .4 3.1.5 Using the debug decorator . .6 3.1.6 Case study review . .7 3.1.7 Variations . .7 3.1.7.1 Logging . .7 3.1.7.2 Optional disable . .8 3.2 Extended Function-Level Debugging . .8 3.2.1 Motivating example . .8 3.2.2 Decorators with arguments . .9 3.2.3 Prefix decorator . .9 3.2.4 Reformulated prefix decorator . 10 3.3 Class-Level Debugging . 12 3.3.1 Motivating example . 12 3.3.2 Class-level debugger . 12 3.3.3 Variation: Attribute access debugging . 14 3.4 Class Hierarchy Debugging . 16 3.4.1 Motivating example . 16 3.4.2 Review of objects and types . 17 3.4.3 Class definition process . 18 3.4.4 Changing the metaclass . 20 3.4.5 Debugging using a metaclass . 21 3.4.6 Why metaclasses? . 22 3.5 Chapter Summary . 23 1 3.6 Exercises . 23 3.7 Acknowledgements . 23 3.8 References . 24 3.9 Terms and Concepts . 24 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Note: This chapter adapts David Beazley’s debugly example presentation from his Python 3 Metaprogramming tutorial at PyCon’2013 [Beazley 2013a]. -
Binary Search Tree
ADT Binary Search Tree! Ellen Walker! CPSC 201 Data Structures! Hiram College! Binary Search Tree! •" Value-based storage of information! –" Data is stored in order! –" Data can be retrieved by value efficiently! •" Is a binary tree! –" Everything in left subtree is < root! –" Everything in right subtree is >root! –" Both left and right subtrees are also BST#s! Operations on BST! •" Some can be inherited from binary tree! –" Constructor (for empty tree)! –" Inorder, Preorder, and Postorder traversal! •" Some must be defined ! –" Insert item! –" Delete item! –" Retrieve item! The Node<E> Class! •" Just as for a linked list, a node consists of a data part and links to successor nodes! •" The data part is a reference to type E! •" A binary tree node must have links to both its left and right subtrees! The BinaryTree<E> Class! The BinaryTree<E> Class (continued)! Overview of a Binary Search Tree! •" Binary search tree definition! –" A set of nodes T is a binary search tree if either of the following is true! •" T is empty! •" Its root has two subtrees such that each is a binary search tree and the value in the root is greater than all values of the left subtree but less than all values in the right subtree! Overview of a Binary Search Tree (continued)! Searching a Binary Tree! Class TreeSet and Interface Search Tree! BinarySearchTree Class! BST Algorithms! •" Search! •" Insert! •" Delete! •" Print values in order! –" We already know this, it#s inorder traversal! –" That#s why it#s called “in order”! Searching the Binary Tree! •" If the tree is -
Binary Search Trees (BST)
CSCI-UA 102 Joanna Klukowska Lecture 7: Binary Search Trees [email protected] Lecture 7: Binary Search Trees (BST) Reading materials Goodrich, Tamassia, Goldwasser: Chapter 8 OpenDSA: Chapter 18 and 12 Binary search trees visualizations: http://visualgo.net/bst.html http://www.cs.usfca.edu/~galles/visualization/BST.html Topics Covered 1 Binary Search Trees (BST) 2 2 Binary Search Tree Node 3 3 Searching for an Item in a Binary Search Tree3 3.1 Binary Search Algorithm..............................................4 3.1.1 Recursive Approach............................................4 3.1.2 Iterative Approach.............................................4 3.2 contains() Method for a Binary Search Tree..................................5 4 Inserting a Node into a Binary Search Tree5 4.1 Order of Insertions.................................................6 5 Removing a Node from a Binary Search Tree6 5.1 Removing a Leaf Node...............................................7 5.2 Removing a Node with One Child.........................................7 5.3 Removing a Node with Two Children.......................................8 5.4 Recursive Implementation.............................................9 6 Balanced Binary Search Trees 10 6.1 Adding a balance() Method........................................... 10 7 Self-Balancing Binary Search Trees 11 7.1 AVL Tree...................................................... 11 7.1.1 When balancing is necessary........................................ 12 7.2 Red-Black Trees.................................................. 15 1 CSCI-UA 102 Joanna Klukowska Lecture 7: Binary Search Trees [email protected] 1 Binary Search Trees (BST) A binary search tree is a binary tree with additional properties: • the value stored in a node is greater than or equal to the value stored in its left child and all its descendants (or left subtree), and • the value stored in a node is smaller than the value stored in its right child and all its descendants (or its right subtree). -
A Set Intersection Algorithm Via X-Fast Trie
Journal of Computers A Set Intersection Algorithm Via x-Fast Trie Bangyu Ye* National Engineering Laboratory for Information Security Technologies, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100091, China. * Corresponding author. Tel.:+86-10-82546714; email: [email protected] Manuscript submitted February 9, 2015; accepted May 10, 2015. doi: 10.17706/jcp.11.2.91-98 Abstract: This paper proposes a simple intersection algorithm for two sorted integer sequences . Our algorithm is designed based on x-fast trie since it provides efficient find and successor operators. We present that our algorithm outperforms skip list based algorithm when one of the sets to be intersected is relatively ‘dense’ while the other one is (relatively) ‘sparse’. Finally, we propose some possible approaches which may optimize our algorithm further. Key words: Set intersection, algorithm, x-fast trie. 1. Introduction Fast set intersection is a key operation in the context of several fields when it comes to big data era [1], [2]. For example, modern search engines use the set intersection for inverted posting list which is a standard data structure in information retrieval to return relevant documents. So it has been studied in many domains and fields [3]-[8]. One of the most typical situations is Boolean query which is required to retrieval the documents that contains all the terms in the query. Besides, set intersection is naturally a key component of calculating the Jaccard coefficient of two sets, which is defined by the fraction of size of intersection and union. In this paper, we propose a new algorithm via x-fast trie which is a kind of advanced data structure. -
Objectives Objectives Searching a Simple Searching Problem A
Python Programming: Objectives An Introduction to To understand the basic techniques for Computer Science analyzing the efficiency of algorithms. To know what searching is and understand the algorithms for linear and binary Chapter 13 search. Algorithm Design and Recursion To understand the basic principles of recursive definitions and functions and be able to write simple recursive functions. Python Programming, 1/e 1 Python Programming, 1/e 2 Objectives Searching To understand sorting in depth and Searching is the process of looking for a know the algorithms for selection sort particular value in a collection. and merge sort. For example, a program that maintains To appreciate how the analysis of a membership list for a club might need algorithms can demonstrate that some to look up information for a particular problems are intractable and others are member œ this involves some sort of unsolvable. search process. Python Programming, 1/e 3 Python Programming, 1/e 4 A simple Searching Problem A Simple Searching Problem Here is the specification of a simple In the first example, the function searching function: returns the index where 4 appears in def search(x, nums): the list. # nums is a list of numbers and x is a number # Returns the position in the list where x occurs # or -1 if x is not in the list. In the second example, the return value -1 indicates that 7 is not in the list. Here are some sample interactions: >>> search(4, [3, 1, 4, 2, 5]) 2 Python includes a number of built-in >>> search(7, [3, 1, 4, 2, 5]) -1 search-related methods! Python Programming, 1/e 5 Python Programming, 1/e 6 Python Programming, 1/e 1 A Simple Searching Problem A Simple Searching Problem We can test to see if a value appears in The only difference between our a sequence using in. -
Prefix Hash Tree an Indexing Data Structure Over Distributed Hash
Prefix Hash Tree An Indexing Data Structure over Distributed Hash Tables Sriram Ramabhadran ∗ Sylvia Ratnasamy University of California, San Diego Intel Research, Berkeley Joseph M. Hellerstein Scott Shenker University of California, Berkeley International Comp. Science Institute, Berkeley and and Intel Research, Berkeley University of California, Berkeley ABSTRACT this lookup interface has allowed a wide variety of Distributed Hash Tables are scalable, robust, and system to be built on top DHTs, including file sys- self-organizing peer-to-peer systems that support tems [9, 27], indirection services [30], event notifi- exact match lookups. This paper describes the de- cation [6], content distribution networks [10] and sign and implementation of a Prefix Hash Tree - many others. a distributed data structure that enables more so- phisticated queries over a DHT. The Prefix Hash DHTs were designed in the Internet style: scala- Tree uses the lookup interface of a DHT to con- bility and ease of deployment triumph over strict struct a trie-based structure that is both efficient semantics. In particular, DHTs are self-organizing, (updates are doubly logarithmic in the size of the requiring no centralized authority or manual con- domain being indexed), and resilient (the failure figuration. They are robust against node failures of any given node in the Prefix Hash Tree does and easily accommodate new nodes. Most impor- not affect the availability of data stored at other tantly, they are scalable in the sense that both la- nodes). tency (in terms of the number of hops per lookup) and the local state required typically grow loga- Categories and Subject Descriptors rithmically in the number of nodes; this is crucial since many of the envisioned scenarios for DHTs C.2.4 [Comp. -
On the Interaction of Object-Oriented Design Patterns and Programming
On the Interaction of Object-Oriented Design Patterns and Programming Languages Gerald Baumgartner∗ Konstantin L¨aufer∗∗ Vincent F. Russo∗∗∗ ∗ Department of Computer and Information Science The Ohio State University 395 Dreese Lab., 2015 Neil Ave. Columbus, OH 43210–1277, USA [email protected] ∗∗ Department of Mathematical and Computer Sciences Loyola University Chicago 6525 N. Sheridan Rd. Chicago, IL 60626, USA [email protected] ∗∗∗ Lycos, Inc. 400–2 Totten Pond Rd. Waltham, MA 02154, USA [email protected] February 29, 1996 Abstract Design patterns are distilled from many real systems to catalog common programming practice. However, some object-oriented design patterns are distorted or overly complicated because of the lack of supporting programming language constructs or mechanisms. For this paper, we have analyzed several published design patterns looking for idiomatic ways of working around constraints of the implemen- tation language. From this analysis, we lay a groundwork of general-purpose language constructs and mechanisms that, if provided by a statically typed, object-oriented language, would better support the arXiv:1905.13674v1 [cs.PL] 31 May 2019 implementation of design patterns and, transitively, benefit the construction of many real systems. In particular, our catalog of language constructs includes subtyping separate from inheritance, lexically scoped closure objects independent of classes, and multimethod dispatch. The proposed constructs and mechanisms are not radically new, but rather are adopted from a variety of languages and programming language research and combined in a new, orthogonal manner. We argue that by describing design pat- terns in terms of the proposed constructs and mechanisms, pattern descriptions become simpler and, therefore, accessible to a larger number of language communities. -
Software II: Principles of Programming Languages
Software II: Principles of Programming Languages Lecture 6 – Data Types Some Basic Definitions • A data type defines a collection of data objects and a set of predefined operations on those objects • A descriptor is the collection of the attributes of a variable • An object represents an instance of a user- defined (abstract data) type • One design issue for all data types: What operations are defined and how are they specified? Primitive Data Types • Almost all programming languages provide a set of primitive data types • Primitive data types: Those not defined in terms of other data types • Some primitive data types are merely reflections of the hardware • Others require only a little non-hardware support for their implementation The Integer Data Type • Almost always an exact reflection of the hardware so the mapping is trivial • There may be as many as eight different integer types in a language • Java’s signed integer sizes: byte , short , int , long The Floating Point Data Type • Model real numbers, but only as approximations • Languages for scientific use support at least two floating-point types (e.g., float and double ; sometimes more • Usually exactly like the hardware, but not always • IEEE Floating-Point Standard 754 Complex Data Type • Some languages support a complex type, e.g., C99, Fortran, and Python • Each value consists of two floats, the real part and the imaginary part • Literal form real component – (in Fortran: (7, 3) imaginary – (in Python): (7 + 3j) component The Decimal Data Type • For business applications (money)