Declare a Generic Array in Java
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Programming for Engineers Pointers in C Programming: Part 02
Programming For Engineers Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff1, Ahmad Fakhri Ab. Nasir2 Faculty of Manufacturing Engineering [email protected], [email protected] PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 0.0 Chapter’s Information • Expected Outcomes – To further use pointers in C programming • Contents 1.0 Pointer and Array 2.0 Pointer and String 3.0 Pointer and dynamic memory allocation PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • We will review array data type first and later we will relate array with pointer. • Previously, we learn about basic data types such as integer, character and floating numbers. In C programming language, if we have 5 test scores and would like to average the scores, we may code in the following way. PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • This program is manageable if the scores are only 5. What should we do if we have 100,000 scores? In such case, we need an efficient way to represent a collection of similar data type1. In C programming, we usually use array. • Array is a fixed-size sequence of elements of the same data type.1 • In C programming, we declare an array like the following statement: PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. -
C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms An introduction to elementary programming concepts in C Jack Straub, Instructor Version 2.07 DRAFT C Programming: Data Structures and Algorithms, Version 2.07 DRAFT C Programming: Data Structures and Algorithms Version 2.07 DRAFT Copyright © 1996 through 2006 by Jack Straub ii 08/12/08 C Programming: Data Structures and Algorithms, Version 2.07 DRAFT Table of Contents COURSE OVERVIEW ........................................................................................ IX 1. BASICS.................................................................................................... 13 1.1 Objectives ...................................................................................................................................... 13 1.2 Typedef .......................................................................................................................................... 13 1.2.1 Typedef and Portability ............................................................................................................. 13 1.2.2 Typedef and Structures .............................................................................................................. 14 1.2.3 Typedef and Functions .............................................................................................................. 14 1.3 Pointers and Arrays ..................................................................................................................... 16 1.4 Dynamic Memory Allocation ..................................................................................................... -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
Programming the Capabilities of the PC Have Changed Greatly Since the Introduction of Electronic Computers
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in INTRODUCTION TO PROGRAMMING LANGUAGE Topic Objective: At the end of this topic the student will be able to understand: History of Computer Programming C++ Definition/Overview: Overview: A personal computer (PC) is any general-purpose computer whose original sales price, size, and capabilities make it useful for individuals, and which is intended to be operated directly by an end user, with no intervening computer operator. Today a PC may be a desktop computer, a laptop computer or a tablet computer. The most common operating systems are Microsoft Windows, Mac OS X and Linux, while the most common microprocessors are x86-compatible CPUs, ARM architecture CPUs and PowerPC CPUs. Software applications for personal computers include word processing, spreadsheets, databases, games, and myriad of personal productivity and special-purpose software. Modern personal computers often have high-speed or dial-up connections to the Internet, allowing access to the World Wide Web and a wide range of other resources. Key Points: 1. History of ComputeWWW.BSSVE.INr Programming The capabilities of the PC have changed greatly since the introduction of electronic computers. By the early 1970s, people in academic or research institutions had the opportunity for single-person use of a computer system in interactive mode for extended durations, although these systems would still have been too expensive to be owned by a single person. The introduction of the microprocessor, a single chip with all the circuitry that formerly occupied large cabinets, led to the proliferation of personal computers after about 1975. Early personal computers - generally called microcomputers - were sold often in Electronic kit form and in limited volumes, and were of interest mostly to hobbyists and technicians. -
G22.2110-003 Programming Languages - Fall 2012 Week 14 - Part 1
G22.2110-003 Programming Languages - Fall 2012 Week 14 - Part 1 Thomas Wies New York University Review Last lecture I Exceptions Outline Today: I Generic Programming Sources for today's lecture: I PLP, ch. 8.4 I Programming in Scala, ch. 19, 20.6 Generic programming Subroutines provide a way to abstract over values. Generic programming lets us abstract over types. Examples: I A sorting algorithm has the same structure, regardless of the types being sorted I Stack primitives have the same semantics, regardless of the objects stored on the stack. One common use: I algorithms on containers: updating, iteration, search Language models: I C: macros (textual substitution) or unsafe casts I Ada: generic units and instantiations I C++, Java, C#, Scala: generics (also called templates) I ML: parametric polymorphism, functors Parameterizing software components Construct Parameter(s): array bounds, element type subprogram values (arguments) Ada generic package values, types, packages Ada generic subprogram values, types C++ class template values, types C++ function template values, types Java generic classes Scala generic types (and implicit values) ML function values (including other functions) ML type constructor types ML functor values, types, structures Templates in C++ template <typename T> class Array { public : explicit Array (size_t); // constructor T& operator[] (size_t); // subscript operator ... // other operations private : ... // a size and a pointer to an array }; Array<int> V1(100); // instantiation Array<int> V2; // use default constructor -
Advanced-Java.Pdf
Advanced java i Advanced java Advanced java ii Contents 1 How to create and destroy objects 1 1.1 Introduction......................................................1 1.2 Instance Construction.................................................1 1.2.1 Implicit (Generated) Constructor.......................................1 1.2.2 Constructors without Arguments.......................................1 1.2.3 Constructors with Arguments........................................2 1.2.4 Initialization Blocks.............................................2 1.2.5 Construction guarantee............................................3 1.2.6 Visibility...................................................4 1.2.7 Garbage collection..............................................4 1.2.8 Finalizers...................................................5 1.3 Static initialization..................................................5 1.4 Construction Patterns.................................................5 1.4.1 Singleton...................................................6 1.4.2 Utility/Helper Class.............................................7 1.4.3 Factory....................................................7 1.4.4 Dependency Injection............................................8 1.5 Download the Source Code..............................................9 1.6 What’s next......................................................9 2 Using methods common to all objects 10 2.1 Introduction...................................................... 10 2.2 Methods equals and hashCode........................................... -
Java Generics Adoption: How New Features Are Introduced, Championed, Or Ignored
Java Generics Adoption: How New Features are Introduced, Championed, or Ignored Chris Parnin Christian Bird Emerson Murphy-Hill College of Computing Microsoft Research Dept. of Computer Science Georgia Institute of Redmond, Washington North Carolina State Technology [email protected] University Atlanta, Georgia Raleigh, North Carolina [email protected] [email protected] Far too often, greatly heralded claims and visions of new language features fail to hold or persist in practice. Discus- ABSTRACT sions of the costs and benefits of language features can easily devolve into a religious war with both sides armed with little Support for generic programming was added to the Java more than anecdotes [13]. Empirical evidence about the language in 2004, representing perhaps the most significant adoption and use of past language features should inform change to one of the most widely used programming lan- and encourage a more rational discussion when designing guages today. Researchers and language designers antici- language features and considering how they should be de- pated this addition would relieve many long-standing prob- ployed. Collecting this evidence is not just sensible but a lems plaguing developers, but surprisingly, no one has yet responsibility of our community. measured whether generics actually provide such relief. In In this paper, we examine the adoption and use of generics, this paper, we report on the first empirical investigation into which were introduced into the Java language in 2004. When how Java generics have been integrated into open source Sun introduced generics, they claimed that the language software by automatically mining the history of 20 popular feature was \a long-awaited enhancement to the type system" open source Java programs, traversing more than 500 million that \eliminates the drudgery of casting." Sun recommended lines of code in the process. -
Addressing Common Crosscutting Problems with Arcum∗
Addressing Common Crosscutting Problems with Arcum∗ Macneil Shonle William G. Griswold Sorin Lerner Computer Science & Engineering, UC San Diego La Jolla, CA 92093-0404 {mshonle, wgg, lerner}@cs.ucsd.edu ABSTRACT 1. INTRODUCTION Crosscutting is an inherent part of software development and can Arcum is a framework for declaring and performing user-defined typically be managed through modularization: A module’s stable program checks and transformations, with the goal of increasing properties are defined in an interface while its likely-to-change automated refactoring opportunities for the user [21]. By using Ar- properties are encapsulated within the module [19]. The cross- cum, a programmer can view the implementation of a crosscutting cutting of the stable properties, such as class and method names, design idiom as a form of module. Arcum uses a declarative lan- can be mitigated with automated refactoring tools that allow, for guage to describe the idiom’s implementation, where descriptions example, the interface’s elements to be renamed [9, 18]. However, are composed of Arcum interface and Arcum option constructs. An often the crosscutting from design idioms (such as design patterns option describes one possible implementation of a crosscutting de- and coding styles) are so specific to the program’s domain that sign idiom, and a set of options are related to each other when they their crosscutting would not likely have been anticipated by the all implement the same Arcum interface. developers of an automated refactoring system. Arcum’s declarative language uses a Java-like syntax for first- The Arcum plug-in for Eclipse enables programmers to describe order logic predicate statements, including a special pattern nota- the implementation of a crosscutting design idiom as a set of syn- tion for expressing Java code. -
REVERSE GENERICS Parametrization After the Fact
REVERSE GENERICS Parametrization after the Fact Alexandre Bergel PLEIAD Laboratory, Computer Science Department (DCC), University of Chile, Santiago, Chile Lorenzo Bettini Dipartimento di Informatica, Universit`adi Torino, Italy Keywords: Generic programming, Java generics, C++ templates. Abstract: By abstracting over types, generic programming enables one to write code that is independent from specific data type implementation. This style is supported by most mainstream languages, including C++ with tem- plates and Java with generics. If some code is not designed in a generic way from the start, a major effort is required to convert this code to use generic types. This conversion is manually realized which is known to be tedious and error-prone. We propose Reverse Generics, a general linguistic mechanism to define a generic class from a non-generic class. For a given set of types, a generic is formed by unbinding static dependencies contained in these types. This generalization and generic type instantiation may be done incrementally. This paper studies the possible application of this linguistic mechanism to C++ and Java and, in particular, it reviews limitations of Java generics against our proposal. 1 INTRODUCTION more than 100 down-casts and up-casts and 70 uses of instanceof. This examination reveals that in many The concept of generic programming (Dos Reis and places the amount of up-casting subsequent down- J¨arvi, 2005), which has characterized functional pro- casting that is used almost makes the programs be- gramming for several decades, appeared in main- have like dynamically typed code. stream programming object-oriented languages such Note, that the need to make existing code generic as C++, only in the late 80s, where it motivated from may arise also in languages where generic types were the beginning the design of the Standard Template Li- already available. -
C DEFINES and C++ TEMPLATES Professor Ken Birman
Professor Ken Birman C DEFINES AND C++ TEMPLATES CS4414 Lecture 10 CORNELL CS4414 - FALL 2020. 1 COMPILE TIME “COMPUTING” In lecture 9 we learned about const, constexpr and saw that C++ really depends heavily on these Ken’s solution to homework 2 runs about 10% faster with extensive use of these annotations Constexpr underlies the “auto” keyword and can sometimes eliminate entire functions by precomputing their results at compile time. Parallel C++ code would look ugly without normal code structuring. Const and constexpr allow the compiler to see “beyond” that and recognize parallelizable code paths. CORNELL CS4414 - FALL 2020. 2 … BUT HOW FAR CAN WE TAKE THIS IDEA? Today we will look at the concept of programming the compiler using the templating layer of C++ We will see that it is a powerful tool! There are also programmable aspects of Linux, and of the modern hardware we use. By controlling the whole system, we gain speed and predictability while writing elegant, clean code. CORNELL CS4414 - FALL 2020. 3 IDEA MAP FOR TODAY History of generics: #define in C Templates are easy to create, if you stick to basics The big benefit compared to Java is that a template We have seen a number of parameterized is a compile-time construct, whereas in Java a generic types in C++, like std::vector and std::map is a run-time construct. The template language is Turing-complete, but computes These are examples of “templates”. only on types, not data from the program (even when They are like generics in Java constants are provided). -
Lecture Notes)
Sri Vidya College of Engineering & Technology Course Material ( Lecture Notes) Data structures can be classified as · Simple data structure · Compound data structure · Linear data structure · Non linear data structure Simple Data Structure: Simple data structure can be constructed with the help of primitive data structure. A primitive data structure used to represent the standard data types of any one of the computer languages. Variables, arrays, pointers, structures, unions, etc. are examples of primitive data structures. Compound Data structure: Compound data structure can be constructed with the help of any one of the primitive data structure and it is having a specific functionality. It can be designed by user. It can be classified as 1) Linear data structure 2) Non-linear data structure Linear data structure : Collection of nodes which are logically adjacent in which logical adjacency is maintained by pointers (or) Linear data structures can be constructed as a continuous arrangement of data elements in the memory. It can be constructed by using array data type. In the linear Data Structures the relation ship of adjacency is maintained between the Data elements. Operations applied on linear data structure : The following list of operations applied on linear data structures 1. Add an element 2. Delete an element 3. Traverse 4. Sort the list of elements CS8391 – Data Structures - Unit I Page 1 Sri Vidya College of Engineering & Technology Course Material ( Lecture Notes) 5. Search for a data element By applying one or more functionalities to create different types of data structures For example Stack, Queue, Tables, List, and Linked Lists. Non-linear data structure: Non-linear data structure can be constructed as a collection of randomly distributed set of data item joined together by using a special pointer (tag). -
Java (Software Platform) from Wikipedia, the Free Encyclopedia Not to Be Confused with Javascript
Java (software platform) From Wikipedia, the free encyclopedia Not to be confused with JavaScript. This article may require copy editing for grammar, style, cohesion, tone , or spelling. You can assist by editing it. (February 2016) Java (software platform) Dukesource125.gif The Java technology logo Original author(s) James Gosling, Sun Microsystems Developer(s) Oracle Corporation Initial release 23 January 1996; 20 years ago[1][2] Stable release 8 Update 73 (1.8.0_73) (February 5, 2016; 34 days ago) [±][3] Preview release 9 Build b90 (November 2, 2015; 4 months ago) [±][4] Written in Java, C++[5] Operating system Windows, Solaris, Linux, OS X[6] Platform Cross-platform Available in 30+ languages List of languages [show] Type Software platform License Freeware, mostly open-source,[8] with a few proprietary[9] compo nents[10] Website www.java.com Java is a set of computer software and specifications developed by Sun Microsyst ems, later acquired by Oracle Corporation, that provides a system for developing application software and deploying it in a cross-platform computing environment . Java is used in a wide variety of computing platforms from embedded devices an d mobile phones to enterprise servers and supercomputers. While less common, Jav a applets run in secure, sandboxed environments to provide many features of nati ve applications and can be embedded in HTML pages. Writing in the Java programming language is the primary way to produce code that will be deployed as byte code in a Java Virtual Machine (JVM); byte code compil ers are also available for other languages, including Ada, JavaScript, Python, a nd Ruby.