A Brief Overview of Functional Programming Languages
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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. -
Akka Java Documentation Release 2.2.5
Akka Java Documentation Release 2.2.5 Typesafe Inc February 19, 2015 CONTENTS 1 Introduction 1 1.1 What is Akka?............................................1 1.2 Why Akka?..............................................3 1.3 Getting Started............................................3 1.4 The Obligatory Hello World.....................................7 1.5 Use-case and Deployment Scenarios.................................8 1.6 Examples of use-cases for Akka...................................9 2 General 10 2.1 Terminology, Concepts........................................ 10 2.2 Actor Systems............................................ 12 2.3 What is an Actor?.......................................... 14 2.4 Supervision and Monitoring..................................... 16 2.5 Actor References, Paths and Addresses............................... 19 2.6 Location Transparency........................................ 25 2.7 Akka and the Java Memory Model.................................. 26 2.8 Message Delivery Guarantees.................................... 28 2.9 Configuration............................................. 33 3 Actors 65 3.1 Actors................................................ 65 3.2 Typed Actors............................................. 84 3.3 Fault Tolerance............................................ 88 3.4 Dispatchers.............................................. 103 3.5 Mailboxes.............................................. 106 3.6 Routing................................................ 111 3.7 Building Finite State Machine -
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. -
Pattern Matching
Functional Programming Steven Lau March 2015 before function programming... https://www.youtube.com/watch?v=92WHN-pAFCs Models of computation ● Turing machine ○ invented by Alan Turing in 1936 ● Lambda calculus ○ invented by Alonzo Church in 1930 ● more... Turing machine ● A machine operates on an infinite tape (memory) and execute a program stored ● It may ○ read a symbol ○ write a symbol ○ move to the left cell ○ move to the right cell ○ change the machine’s state ○ halt Turing machine Have some fun http://www.google.com/logos/2012/turing-doodle-static.html http://www.ioi2012.org/wp-content/uploads/2011/12/Odometer.pdf http://wcipeg.com/problems/desc/ioi1211 Turing machine incrementer state symbol action next_state _____ state 0 __1__ state 1 0 _ or 0 write 1 1 _10__ state 2 __1__ state 1 0 1 write 0 2 _10__ state 0 __1__ state 0 _00__ state 2 1 _ left 0 __0__ state 2 _00__ state 0 __0__ state 0 1 0 or 1 right 1 100__ state 1 _10__ state 1 2 0 left 0 100__ state 1 _10__ state 1 100__ state 1 _10__ state 1 100__ state 1 _10__ state 0 100__ state 0 _11__ state 1 101__ state 1 _11__ state 1 101__ state 1 _11__ state 0 101__ state 0 λ-calculus Beware! ● think mathematical, not C++/Pascal ● (parentheses) are for grouping ● variables cannot be mutated ○ x = 1 OK ○ x = 2 NO ○ x = x + 1 NO λ-calculus Simplification 1 of 2: ● Only anonymous functions are used ○ f(x) = x2+1 f(1) = 12+1 = 2 is written as ○ (λx.x2+1)(1) = 12+1 = 2 note that f = λx.x2+1 λ-calculus Simplification 2 of 2: ● Only unary functions are used ○ a binary function can be written as a unary function that return another unary function ○ (λ(x,y).x+y)(1,2) = 1+2 = 3 is written as [(λx.(λy.x+y))(1)](2) = [(λy.1+y)](2) = 1+2 = 3 ○ this technique is known as Currying Haskell Curry λ-calculus ● A lambda term has 3 forms: ○ x ○ λx.A ○ AB where x is a variable, A and B are lambda terms. -
Lambda Calculus and Functional Programming
Global Journal of Researches in Engineering Vol. 10 Issue 2 (Ver 1.0) June 2010 P a g e | 47 Lambda Calculus and Functional Programming Anahid Bassiri1Mohammad Reza. Malek2 GJRE Classification (FOR) 080299, 010199, 010203, Pouria Amirian3 010109 Abstract-The lambda calculus can be thought of as an idealized, Basis concept of a Turing machine is the present day Von minimalistic programming language. It is capable of expressing Neumann computers. Conceptually these are Turing any algorithm, and it is this fact that makes the model of machines with random access registers. Imperative functional programming an important one. This paper is programming languages such as FORTRAN, Pascal etcetera focused on introducing lambda calculus and its application. As as well as all the assembler languages are based on the way an application dikjestra algorithm is implemented using a Turing machine is instructed by a sequence of statements. lambda calculus. As program shows algorithm is more understandable using lambda calculus in comparison with In addition functional programming languages, like other imperative languages. Miranda, ML etcetera, are based on the lambda calculus. Functional programming is a programming paradigm that I. INTRODUCTION treats computation as the evaluation of mathematical ambda calculus (λ-calculus) is a useful device to make functions and avoids state and mutable data. It emphasizes L the theories realizable. Lambda calculus, introduced by the application of functions, in contrast with the imperative Alonzo Church and Stephen Cole Kleene in the 1930s is a programming style that emphasizes changes in state. formal system designed to investigate function definition, Lambda calculus provides a theoretical framework for function application and recursion in mathematical logic and describing functions and their evaluation. -
Purity in Erlang
Purity in Erlang Mihalis Pitidis1 and Konstantinos Sagonas1,2 1 School of Electrical and Computer Engineering, National Technical University of Athens, Greece 2 Department of Information Technology, Uppsala University, Sweden [email protected], [email protected] Abstract. Motivated by a concrete goal, namely to extend Erlang with the abil- ity to employ user-defined guards, we developed a parameterized static analysis tool called PURITY, that classifies functions as referentially transparent (i.e., side- effect free with no dependency on the execution environment and never raising an exception), side-effect free with no dependencies but possibly raising excep- tions, or side-effect free but with possible dependencies and possibly raising ex- ceptions. We have applied PURITY on a large corpus of Erlang code bases and report experimental results showing the percentage of functions that the analysis definitely classifies in each category. Moreover, we discuss how our analysis has been incorporated on a development branch of the Erlang/OTP compiler in order to allow extending the language with user-defined guards. 1 Introduction Purity plays an important role in functional programming languages as it is a corner- stone of referential transparency, namely that the same language expression produces the same value when evaluated twice. Referential transparency helps in writing easy to test, robust and comprehensible code, makes equational reasoning possible, and aids program analysis and optimisation. In pure functional languages like Clean or Haskell, any side-effect or dependency on the state is captured by the type system and is reflected in the types of functions. In a language like ERLANG, which has been developed pri- marily with concurrency in mind, pure functions are not the norm and impure functions can freely be used interchangeably with pure ones. -
Parsing with First-Class Derivatives
Parsing with First-Class Derivatives Jonathan Immanuel Brachthauser¨ Tillmann Rendel Klaus Ostermann rtifact A Comple * t * te n * A * te is W E s A e n C l l L o D C S University of Tubingen,¨ Germany o * * c P u e m s E u O e e n v R t e O o d t a y * s E a * fbrachthaeuser, rendel, [email protected] l u d a e t Abstract often reducing the modularity of their parser because these ad- Brzozowski derivatives, well known in the context of reg- hoc additions are cross-cutting with respect to the otherwise ular expressions, have recently been rediscovered to give a context-free syntactic structure of the language. simplified explanation to parsers of context-free languages. Parser combinators [10, 10, 15, 21, 22] are a parsing We add derivatives as a novel first-class feature to a standard approach that is well-suited for such ad-hoc additions. With parser combinator language. First-class derivatives enable an parser combinators, a parser is described as a first-class inversion of the control flow, allowing to implement modular entity in some host language, and parsers for smaller subsets parsers for languages that previously required separate pre- of a language are combined into parsers of larger subsets processing steps or cross-cutting modifications of the parsers. using built-in or user-defined combinators such as sequence, We show that our framework offers new opportunities for alternative, or iteration. The fact that parsers are first-class reuse and supports a modular definition of interesting use entities at runtime allows an elegant way of structuring cases of layout-sensitive parsing. -
Expert Python Programming Third Edition
Expert Python Programming Third Edition Become a master in Python by learning coding best practices and advanced programming concepts in Python 3.7 Michał Jaworski Tarek Ziadé BIRMINGHAM - MUMBAI Expert Python Programming Third Edition Copyright © 2019 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Kunal Chaudhari Acquisition Editor: Chaitanya Nair Content Development Editor: Zeeyan Pinheiro Technical Editor: Ketan Kamble Copy Editor: Safis Editing Project Coordinator: Vaidehi Sawant Proofreader: Safis Editing Indexer: Priyanka Dhadke Graphics: Alishon Mendonsa Production Coordinator: Shraddha Falebhai First published: September 2008 Second edition: May 2016 Third edition: April 2019 Production reference: 1270419 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78980-889-6 www.packtpub.com To my beloved wife, Oliwia, for her love, inspiration, and her endless patience. -
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). -
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