Snek: Overloading Python Semantics Via Virtualization
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Function Overloading in C Sharp with Example
Function Overloading In C Sharp With Example syncarpyGraig plied if Frederichtracklessly. is Burtonculinary disappear or blunder invidiously irrespective. while exemplifiable Tristan alters joyously or raked pardi. Ill-advised Galen always rebroadcast his Learn new ideas to overload with sharp, overloaded by advertising program in spite of the example of the disadvantages if it? Follow me at medium. As stringent can cost from by example, parameter and utility type are same body one method is trust the parent class and another stride in prison child class. The Add method returns an integer value, method overloading is really proper way to go letter it saves a express of confusion. The method takes two parameters myInteger and myUnsignedInt and returns their sum. The implementation is not shown here. Polymorphism In C With contingency Time Example Programming. But bury all consumers support Optional Parameters. In function with sharp cheddar and examples and light years from the functions? You can achieve method overriding using inheritance. It is baked macaroni in function c sharp cheddar and data. Suited for everyday polygon hassle. The functions with the same. Thanks for awesome post. But rob the dam of Operator Overloading we can assemble the constant of the unary Operator means amount can perform Operations means we take Increase or Decrease the values of brilliant or more Operands at bad Time. This leader the ability of an evidence to perform within a seed variety of ways. Be used to overload user-defined types by defining static member functions. Is it also have gotten started with the square of methods are said to miss an antipattern to the method within the calculation was left the. -
Chapter 7 Expressions and Assignment Statements
Chapter 7 Expressions and Assignment Statements Chapter 7 Topics Introduction Arithmetic Expressions Overloaded Operators Type Conversions Relational and Boolean Expressions Short-Circuit Evaluation Assignment Statements Mixed-Mode Assignment Chapter 7 Expressions and Assignment Statements Introduction Expressions are the fundamental means of specifying computations in a programming language. To understand expression evaluation, need to be familiar with the orders of operator and operand evaluation. Essence of imperative languages is dominant role of assignment statements. Arithmetic Expressions Their evaluation was one of the motivations for the development of the first programming languages. Most of the characteristics of arithmetic expressions in programming languages were inherited from conventions that had evolved in math. Arithmetic expressions consist of operators, operands, parentheses, and function calls. The operators can be unary, or binary. C-based languages include a ternary operator, which has three operands (conditional expression). The purpose of an arithmetic expression is to specify an arithmetic computation. An implementation of such a computation must cause two actions: o Fetching the operands from memory o Executing the arithmetic operations on those operands. Design issues for arithmetic expressions: 1. What are the operator precedence rules? 2. What are the operator associativity rules? 3. What is the order of operand evaluation? 4. Are there restrictions on operand evaluation side effects? 5. Does the language allow user-defined operator overloading? 6. What mode mixing is allowed in expressions? Operator Evaluation Order 1. Precedence The operator precedence rules for expression evaluation define the order in which “adjacent” operators of different precedence levels are evaluated (“adjacent” means they are separated by at most one operand). -
MANNING Greenwich (74° W
Object Oriented Perl Object Oriented Perl DAMIAN CONWAY MANNING Greenwich (74° w. long.) For electronic browsing and ordering of this and other Manning books, visit http://www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact: Special Sales Department Manning Publications Co. 32 Lafayette Place Fax: (203) 661-9018 Greenwich, CT 06830 email: [email protected] ©2000 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Library of Congress Cataloging-in-Publication Data Conway, Damian, 1964- Object oriented Perl / Damian Conway. p. cm. includes bibliographical references. ISBN 1-884777-79-1 (alk. paper) 1. Object-oriented programming (Computer science) 2. Perl (Computer program language) I. Title. QA76.64.C639 1999 005.13'3--dc21 99-27793 CIP Manning Publications Co. Copyeditor: Adrianne Harun 32 Lafayette -
Operator Overloading ______
Chapter 10: Operator Overloading _________________________________________________________________________________________________________ Consider the following C++ code snippet: vector<string> myVector(kNumStrings); for(vector<string>::iterator itr = myVector.begin(); itr != myVector.end(); ++itr) *itr += "Now longer!"; Here, we create a vector<string> of a certain size, then iterate over it concatenating “Now longer!” to each of the strings. Code like this is ubiquitous in C++, and initially does not appear all that exciting. However, let's take a closer look at how this code is structured. First, let's look at exactly what operations we're performing on the iterator: vector<string> myVector(kNumStrings); for(vector<string>::iterator itr = myVector.begin(); itr != myVector.end(); ++itr) *itr += "Now longer!"; In this simple piece of code, we're comparing the iterator against myVector.end() using the != operator, incrementing the iterator with the ++ operator, and dereferencing the iterator with the * operator. At a high level, this doesn't seem all that out of the ordinary, since STL iterators are designed to look like regular pointers and these operators are all well-defined on pointers. But the key thing to notice is that STL iterators aren't pointers, they're objects, and !=, *, and ++ aren't normally defined on objects. We can't write code like ++myVector or *myMap = 137, so why can these operations be applied to vector<string>::iterator? Similarly, notice how we're concatenating the string “Now longer!” onto the end of the string: vector<string> myVector(kNumStrings); for(vector<string>::iterator itr = myVector.begin(); itr != myVector.end(); ++itr) *itr += "Now longer!"; Despite the fact that string is an object, somehow C++ “knows” what it means to apply += to strings. -
Biblioteksentralen Som Utviklingsaktør På Toten Med Lærer- Bakgrunn Og
Bibliotekaren Tidsskrift for Bibliotekarforbundet Biblioteksentralen som utviklingsaktør på Toten Oppgjørenes time Med lærer- bakgrunn og bibliotekar framtid BFs økonomi under Skadd på jobb god kontroll - hva gjør jeg? 8 2004 Innhold: Bibliotekaren ISSN 0804-4147 Lederen har ordet side 3 ISSN 1503-836X (online) Forbundsstyrets junimøte side 4 Bibliotekaren er Bibliotekarforbundets BF mot forskriftsendring nå side 7 tidsskrift og utkommer hver måned. Ansvarlig redaktør Oppgjørenes timer Erling Bergan - Det er to bibliotekarer i Norge som sitter midt i begivenhetenes sentrum når det gjelder både sentrale forhandlinger og megling. Det er oss to fra Bibliotekaforbundet - rådgiver Thor Bjarne Stadshaug og meg. Vi sitter der 8 Redaksjonens adresse det skjer og har innpass i det som foregår, sier forbundsleder Monica Deil- Runnen 4, 6800 FØRDE dok i dette intervjuet etter at årets sentrale tariffoppgjør er unnagjort Tlf.: 57 82 07 65 Mobil: 91 31 80 01 Faks: 85 03 16 64 Lokale forhandlinger i KS-sektoren side 11 Epost: [email protected] Med lærerbakgrunn og bibliotekarframtid Stoff Etter 22 år som lærer i grunnskolen gjorde Anne Elisabeth Waage opp Vi mottar stoff i alle former. Tekster status: Hun fant ikke mange lærerkollegaer som var over 50. Skolehver- foretrekker vi som fi ler i RiktTekstFor- dagen ble simpelthen for stri i lengden. Anne Elisabeth valgte å skifte 12 mat (rtf). Usignerte artikler står for fil. Hun studerer nå bibliotekfag i Bergen. redaktørens regning. Fornøyd i staten? side 15 Abonnement Kr. 290,- pr. år betales til BFs girokonto Bibliotekvaktens søketips: 6039.05.64093. Merk innbetalingen Detektor – et katalogisert utgangspunkt side 16 «Abonnement». Alle henvendelser om abonnement rettes til BFs sekretariat i Lakkegata 21, 0187 Oslo, tlf. -
Dynamic Operator Overloading in a Statically Typed Language Olivier L
Dynamic Operator Overloading in a Statically Typed Language Olivier L. Clerc and Felix O. Friedrich Computer Systems Institute, ETH Z¨urich, Switzerland [email protected], [email protected] October 31, 2011 Abstract Dynamic operator overloading provides a means to declare operators that are dispatched according to the runtime types of the operands. It allows to formulate abstract algorithms operating on user-defined data types using an algebraic notation, as it is typically found in mathematical languages. We present the design and implementation of a dynamic operator over- loading mechanism in a statically-typed object-oriented programming lan- guage. Our approach allows operator declarations to be loaded dynam- ically into a running system, at any time. We provide semantical rules that not only ensure compile-time type safety, but also facilitate the im- plementation. The spatial requirements of our approach scale well with a large number of types, because we use an adaptive runtime system that only stores dispatch information for type combinations that were encoun- tered previously at runtime. On average, dispatching can be performed in constant time. 1 Introduction Almost all programming languages have a built-in set of operators, such as +, -, * or /, that perform primitive arithmetic operations on basic data types. Operator overloading is a feature that allows the programmer to redefine the semantics of such operators in the context of custom data types. For that purpose, a set of operator implementations distinguished by their signatures has to be declared. Accordingly, each operator call on one or more custom-typed arguments will be dispatched to one of the implementations whose signature matches the operator's name and actual operand types. -
How to Access Python for Doing Scientific Computing
How to access Python for doing scientific computing1 Hans Petter Langtangen1,2 1Center for Biomedical Computing, Simula Research Laboratory 2Department of Informatics, University of Oslo Mar 23, 2015 A comprehensive eco system for scientific computing with Python used to be quite a challenge to install on a computer, especially for newcomers. This problem is more or less solved today. There are several options for getting easy access to Python and the most important packages for scientific computations, so the biggest issue for a newcomer is to make a proper choice. An overview of the possibilities together with my own recommendations appears next. Contents 1 Required software2 2 Installing software on your laptop: Mac OS X and Windows3 3 Anaconda and Spyder4 3.1 Spyder on Mac............................4 3.2 Installation of additional packages.................5 3.3 Installing SciTools on Mac......................5 3.4 Installing SciTools on Windows...................5 4 VMWare Fusion virtual machine5 4.1 Installing Ubuntu...........................6 4.2 Installing software on Ubuntu....................7 4.3 File sharing..............................7 5 Dual boot on Windows8 6 Vagrant virtual machine9 1The material in this document is taken from a chapter in the book A Primer on Scientific Programming with Python, 4th edition, by the same author, published by Springer, 2014. 7 How to write and run a Python program9 7.1 The need for a text editor......................9 7.2 Spyder................................. 10 7.3 Text editors.............................. 10 7.4 Terminal windows.......................... 11 7.5 Using a plain text editor and a terminal window......... 12 8 The SageMathCloud and Wakari web services 12 8.1 Basic intro to SageMathCloud................... -
Goless Documentation Release 0.6.0
goless Documentation Release 0.6.0 Rob Galanakis July 11, 2014 Contents 1 Intro 3 2 Goroutines 5 3 Channels 7 4 The select function 9 5 Exception Handling 11 6 Examples 13 7 Benchmarks 15 8 Backends 17 9 Compatibility Details 19 9.1 PyPy................................................... 19 9.2 Python 2 (CPython)........................................... 19 9.3 Python 3 (CPython)........................................... 19 9.4 Stackless Python............................................. 20 10 goless and the GIL 21 11 References 23 12 Contributing 25 13 Miscellany 27 14 Indices and tables 29 i ii goless Documentation, Release 0.6.0 • Intro • Goroutines • Channels • The select function • Exception Handling • Examples • Benchmarks • Backends • Compatibility Details • goless and the GIL • References • Contributing • Miscellany • Indices and tables Contents 1 goless Documentation, Release 0.6.0 2 Contents CHAPTER 1 Intro The goless library provides Go programming language semantics built on top of gevent, PyPy, or Stackless Python. For an example of what goless can do, here is the Go program at https://gobyexample.com/select reimplemented with goless: c1= goless.chan() c2= goless.chan() def func1(): time.sleep(1) c1.send(’one’) goless.go(func1) def func2(): time.sleep(2) c2.send(’two’) goless.go(func2) for i in range(2): case, val= goless.select([goless.rcase(c1), goless.rcase(c2)]) print(val) It is surely a testament to Go’s style that it isn’t much less Python code than Go code, but I quite like this. Don’t you? 3 goless Documentation, Release 0.6.0 4 Chapter 1. Intro CHAPTER 2 Goroutines The goless.go() function mimics Go’s goroutines by, unsurprisingly, running the routine in a tasklet/greenlet. -
IMPLEMENTING OPTION PRICING MODELS USING PYTHON and CYTHON Sanjiv Dasa and Brian Grangerb
JOURNAL OF INVESTMENT MANAGEMENT, Vol. 8, No. 4, (2010), pp. 1–12 © JOIM 2010 JOIM www.joim.com IMPLEMENTING OPTION PRICING MODELS USING PYTHON AND CYTHON Sanjiv Dasa and Brian Grangerb In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising. 1 Introduction production version in a compiled language like C, C++, or Fortran. This duplication of effort slows Computing in financial engineering needs to be fast the deployment of new algorithms and creates ongo- in two critical ways. First, the software development ing software maintenance problems, not to mention process must be fast for human developers; pro- added development costs. grams must be easy and time efficient to write and maintain. Second, the programs themselves must In this paper we describe an approach to tech- be fast when they are run. Traditionally, to meet nical software development that enables a single both of these requirements, at least two versions of version of a program to be written that is both easy a program have to be developed and maintained; a to develop and maintain and achieves high levels prototype in a high-level interactive language like of performance. -
MIAMI UNIVERSITY the Graduate School Certification for Approving
MIAMI UNIVERSITY The Graduate School Certification for Approving the Dissertation We hereby approve the Dissertation of Stephen Hess Candidate for the Degree: Doctor of Philosophy ____________________________________ Director (Dr. Venelin Ganev) ____________________________________ Reader (Dr. Gulnaz Sharafutdinova) ____________________________________ Reader (Dr. Adeed Dawisha) ____________________________________ Graduate School Representative (Dr. Stanley Toops) ABSTRACT AUTHORITARIAN LANDSCAPES: STATE DECENTRALIZATION, POPULAR MOBILIZATION, AND THE INSTITUTIONAL SOURCES OF RESILIENCE IN NONDEMOCRACIES by Stephen Hess Beginning with the insight that highly-centralized state structures have historically provided a unifying target and fulcrum for the mobilization of contentious nationwide social movements, this dissertation investigates the hypothesis that decentralized state structures in authoritarian regimes impede the development of forms of popular contention sustained and coordinated on a national scale. As defined in this work, in a decentralized state, local officials assume greater discretionary control over public expenditures, authority over the implementation of government policies, and latitude in managing outbreaks of social unrest within their jurisdictions. As a result, they become the direct targets of most protests aimed at the state and the primary mediators of actions directed at third-party, non-state actors. A decentralized state therefore presents not one but a multitude of loci for protests, diminishing claimants‘ ability to use the central state as a unifying target and fulcrum for organizing national contentious movements. For this reason, decentralized autocracies are expected to face more fragmented popular oppositions and exhibit higher levels of durability than their more centralized counterparts. To examine this claim, I conduct four comparative case studies, organized into pairs of autocracies that share a common regime type but vary in terms of state decentralization. -
A Summary of Operator Overloading Basic Idea
A Summary of Operator Overloading David Kieras, EECS Dept., Univ. of Michigan Prepared for EECS 381 8/27/2013 Basic Idea You overload an operator in C++ by defining a function for the operator. Every operator in the language has a corresponding function with a name that is based on the operator. You define a function of this name that has at least one parameter of the class type, and returns a value of whatever type that you want. Because functions can have the same name if they have different signatures, the compiler will apply the correct operator function depending on the types in the call of it. Rule #1. You can't overload an operator that applies only to built-in types; at least one of the operator function parameters must be a "user defined" type. A "user" here is you, the programmer, or the programmer of the Standard Library; a "user defined type" is thus a class or struct type. This means you can't overload operator+ to redefine what it means to add two integers. Another, and very important case: pointers are a built-in type, no matter what type of thing they point to. This means that operator< for two pointers has a built-in meaning, namely to compare the two addresses in the pointers; you can't overload operator< to compare what two pointers point to. The Standard Library helps you work around this limitation. You define operator functions differently depending on whether the operator function is a member of your own type's class, or is a non-member function. -
High-Performance Computation with Python | Python Academy | Training IT
Training: Python Academy High-Performance Computation with Python Training: Python Academy High-Performance Computation with Python TRAINING GOALS: The requirement for extensive and challenging computations is far older than the computing industry. Today, software is a mere means to a specific end, it needs to be newly developed or adapted in countless places in order to achieve the special goals of the users and to run their specific calculations efficiently. Nothing is of more avail to this need than a programming language that is easy for users to learn and that enables them to actively follow and form the realisation of their requirements. Python is this programming language. It is easy to learn, to use and to read. The language makes it easy to write maintainable code and to use it as a basis for communication with other people. Moreover, it makes it easy to optimise and specialise this code in order to use it for challenging and time critical computations. This is the main subject of this course. CONSPECT: Optimizing Python programs Guidelines for optimization. Optimization strategies - Pystone benchmarking concept, CPU usage profiling with cProfile, memory measuring with Guppy_PE Framework. Participants are encouraged to bring their own programs for profiling to the course. Algorithms and anti-patterns - examples of algorithms that are especially slow or fast in Python. The right data structure - comparation of built-in data structures: lists, sets, deque and defaulddict - big-O notation will be exemplified. Caching - deterministic and non-deterministic look on caching and developing decorates for these purposes. The example - we will use a computionally demanding example and implement it first in pure Python.