Programming Languages — Fortran — Coroutines and Iterators
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Armadillo C++ Library
Armadillo: a template-based C++ library for linear algebra Conrad Sanderson and Ryan Curtin Abstract The C++ language is often used for implementing functionality that is performance and/or resource sensitive. While the standard C++ library provides many useful algorithms (such as sorting), in its current form it does not provide direct handling of linear algebra (matrix maths). Armadillo is an open source linear algebra library for the C++ language, aiming towards a good balance between speed and ease of use. Its high-level application programming interface (function syntax) is deliberately similar to the widely used Matlab and Octave languages [4], so that mathematical operations can be expressed in a familiar and natural manner. The library is useful for algorithm development directly in C++, or relatively quick conversion of research code into production environments. Armadillo provides efficient objects for vectors, matrices and cubes (third order tensors), as well as over 200 associated functions for manipulating data stored in the objects. Integer, floating point and complex numbers are supported, as well as dense and sparse storage formats. Various matrix factorisations are provided through integration with LAPACK [3], or one of its high performance drop-in replacements such as Intel MKL [6] or OpenBLAS [9]. It is also possible to use Armadillo in conjunction with NVBLAS to obtain GPU-accelerated matrix multiplication [7]. Armadillo is used as a base for other open source projects, such as MLPACK, a C++ library for machine learning and pattern recognition [2], and RcppArmadillo, a bridge between the R language and C++ in order to speed up computations [5]. -
Scala Tutorial
Scala Tutorial SCALA TUTORIAL Simply Easy Learning by tutorialspoint.com tutorialspoint.com i ABOUT THE TUTORIAL Scala Tutorial Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Scala has been created by Martin Odersky and he released the first version in 2003. Scala smoothly integrates features of object-oriented and functional languages. This tutorial gives a great understanding on Scala. Audience This tutorial has been prepared for the beginners to help them understand programming Language Scala in simple and easy steps. After completing this tutorial, you will find yourself at a moderate level of expertise in using Scala from where you can take yourself to next levels. Prerequisites Scala Programming is based on Java, so if you are aware of Java syntax, then it's pretty easy to learn Scala. Further if you do not have expertise in Java but you know any other programming language like C, C++ or Python, then it will also help in grasping Scala concepts very quickly. Copyright & Disclaimer Notice All the content and graphics on this tutorial are the property of tutorialspoint.com. Any content from tutorialspoint.com or this tutorial may not be redistributed or reproduced in any way, shape, or form without the written permission of tutorialspoint.com. Failure to do so is a violation of copyright laws. This tutorial may contain inaccuracies or errors and tutorialspoint provides no guarantee regarding the accuracy of the site or its contents including this tutorial. If you discover that the tutorialspoint.com site or this tutorial content contains some errors, please contact us at [email protected] TUTORIALS POINT Simply Easy Learning Table of Content Scala Tutorial .......................................................................... -
Cmsc 132: Object-Oriented Programming Ii
© Department of Computer Science UMD CMSC 132: OBJECT-ORIENTED PROGRAMMING II Iterator, Marker, Observer Design Patterns Department of Computer Science University of Maryland, College Park © Department of Computer Science UMD Design Patterns • Descriptions of reusable solutions to common software design problems (e.g, Iterator pattern) • Captures the experience of experts • Goals • Solve common programming challenges • Improve reliability of solution • Aid rapid software development • Useful for real-world applications • Design patterns are like recipes – generic solutions to expected situations • Design patterns are language independent • Recognizing when and where to use design patterns requires familiarity & experience • Design pattern libraries serve as a glossary of idioms for understanding common, but complex solutions • Design patterns are used throughout the Java Class Libraries © Department of Computer Science UMD Iterator Pattern • Definition • Move through collection of objects without knowing its internal representation • Where to use & benefits • Use a standard interface to represent data objects • Uses standard iterator built in each standard collection, like List • Need to distinguish variations in the traversal of an aggregate • Example • Iterator for collection • Original • Examine elements of collection directly • Using pattern • Collection provides Iterator class for examining elements in collection © Department of Computer Science UMD Iterator Example public interface Iterator<V> { bool hasNext(); V next(); void remove(); -
Iterators in C++ C 2016 All Rights Reserved
Software Design Lecture Notes Prof. Stewart Weiss Iterators in C++ c 2016 All rights reserved. Iterators in C++ 1 Introduction When a program needs to visit all of the elements of a vector named myvec starting with the rst and ending with the last, you could use an iterative loop such as the following: for ( int i = 0; i < myvec.size(); i++ ) // the visit, i.e., do something with myvec[i] This code works and is a perfectly ne solution. This same type of iterative loop can visit each character in a C++ string. But what if you need code that visits all elements of a C++ list object? Is there a way to use an iterative loop to do this? To be clear, an iterative loop is one that has the form for var = start value; var compared to end value; update var; {do something with node at specic index} In other words, an iterative loop is a counting loop, as opposed to one that tests an arbitrary condition, like a while loop. The short answer to the question is that, in C++, without iterators, you cannot do this. Iterators make it possible to iterate through arbitrary containers. This set of notes answers the question, What is an iterator, and how do you use it? Iterators are a generalization of pointers in C++ and have similar semantics. They allow a program to navigate through dierent types of containers in a uniform manner. Just as pointers can be used to traverse a linked list or a binary tree, and subscripts can be used to traverse the elements of a vector, iterators can be used to sequence through the elements of any standard C++ container class. -
Generic Programming
Generic Programming July 21, 1998 A Dagstuhl Seminar on the topic of Generic Programming was held April 27– May 1, 1998, with forty seven participants from ten countries. During the meeting there were thirty seven lectures, a panel session, and several problem sessions. The outcomes of the meeting include • A collection of abstracts of the lectures, made publicly available via this booklet and a web site at http://www-ca.informatik.uni-tuebingen.de/dagstuhl/gpdag.html. • Plans for a proceedings volume of papers submitted after the seminar that present (possibly extended) discussions of the topics covered in the lectures, problem sessions, and the panel session. • A list of generic programming projects and open problems, which will be maintained publicly on the World Wide Web at http://www-ca.informatik.uni-tuebingen.de/people/musser/gp/pop/index.html http://www.cs.rpi.edu/˜musser/gp/pop/index.html. 1 Contents 1 Motivation 3 2 Standards Panel 4 3 Lectures 4 3.1 Foundations and Methodology Comparisons ........ 4 Fundamentals of Generic Programming.................. 4 Jim Dehnert and Alex Stepanov Automatic Program Specialization by Partial Evaluation........ 4 Robert Gl¨uck Evaluating Generic Programming in Practice............... 6 Mehdi Jazayeri Polytypic Programming........................... 6 Johan Jeuring Recasting Algorithms As Objects: AnAlternativetoIterators . 7 Murali Sitaraman Using Genericity to Improve OO Designs................. 8 Karsten Weihe Inheritance, Genericity, and Class Hierarchies.............. 8 Wolf Zimmermann 3.2 Programming Methodology ................... 9 Hierarchical Iterators and Algorithms................... 9 Matt Austern Generic Programming in C++: Matrix Case Study........... 9 Krzysztof Czarnecki Generative Programming: Beyond Generic Programming........ 10 Ulrich Eisenecker Generic Programming Using Adaptive and Aspect-Oriented Programming . -
Generators & Coroutines Part 1: Iterators
Generators & Coroutines Part 1: Iterators Edited Version of Slides by David Beazely http://www.dabeaz.comPart I Introduction to Iterators and Generators Monday, May 16, 2011 Monday, May 16, 2011 Copyright (C) 2008, http://www.dabeaz.com 1- 11 Iteration Iterating over a Dict As you know, Python has a "for" statement • • If you loop over a dictionary you get keys You use it to loop over a collection of items • >>> prices = { 'GOOG' : 490.10, >>> for x in [1,4,5,10]: ... 'AAPL' : 145.23, ... print x, ... 'YHOO' : 21.71 } ... ... 1 4 5 10 >>> for key in prices: >>> ... print key ... And, as you have probably noticed, you can YHOO • GOOG iterate over many different kinds of objects AAPL (not just lists) >>> Copyright (C) 2008, http://www.dabeaz.com 1- 12 Copyright (C) 2008, http://www.dabeaz.com 1- 13 Iterating over a String • If you loop over a string, you get characters >>> s = "Yow!" >>> for c in s: ... print c ... Y o w ! >>> Copyright (C) 2008, http://www.dabeaz.com 1- 14 Iterating over a Dict • If you loop over a dictionary you get keys >>> prices = { 'GOOG' : 490.10, ... 'AAPL' : 145.23, ... 'YHOO' : 21.71 } ... >>> for key in prices: ... print key ... YHOO GOOG AAPL >>> Copyright (C) 2008, http://www.dabeaz.com 1- 13 Iterating over a String Iterating over a File • If you loop over a file you get lines If you loop over a string, you get characters >>> for line in open("real.txt"): • ... print line, ... >>> s = "Yow!" Iterating over a File Real Programmers write in FORTRAN >>> for c in s: .. -
An Overview of the Scala Programming Language
An Overview of the Scala Programming Language Second Edition Martin Odersky, Philippe Altherr, Vincent Cremet, Iulian Dragos Gilles Dubochet, Burak Emir, Sean McDirmid, Stéphane Micheloud, Nikolay Mihaylov, Michel Schinz, Erik Stenman, Lex Spoon, Matthias Zenger École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne, Switzerland Technical Report LAMP-REPORT-2006-001 Abstract guage for component software needs to be scalable in the sense that the same concepts can describe small as well as Scala fuses object-oriented and functional programming in large parts. Therefore, we concentrate on mechanisms for a statically typed programming language. It is aimed at the abstraction, composition, and decomposition rather than construction of components and component systems. This adding a large set of primitives which might be useful for paper gives an overview of the Scala language for readers components at some level of scale, but not at other lev- who are familar with programming methods and program- els. Second, we postulate that scalable support for compo- ming language design. nents can be provided by a programming language which unies and generalizes object-oriented and functional pro- gramming. For statically typed languages, of which Scala 1 Introduction is an instance, these two paradigms were up to now largely separate. True component systems have been an elusive goal of the To validate our hypotheses, Scala needs to be applied software industry. Ideally, software should be assembled in the design of components and component systems. Only from libraries of pre-written components, just as hardware is serious application by a user community can tell whether the assembled from pre-fabricated chips. -
Cmsc 132: Object-Oriented Programming Ii
© Department of Computer Science UMD CMSC 132: OBJECT-ORIENTED PROGRAMMING II Object-Oriented Programming Intro Department of Computer Science University of Maryland, College Park © Department of Computer Science UMD Object-Oriented Programming (OOP) • Approach to improving software • View software as a collection of objects (entities) • OOP takes advantage of two techniques • Abstraction • Encapsulation © Department of Computer Science UMD Techniques – Abstraction • Abstraction • Provide high-level model of activity or data • Don’t worry about the details. What does it do, not how • Example from outside of CS: Microwave Oven • Procedural abstraction • Specify what actions should be performed • Hide algorithms • Example: Sort numbers in an array (is it Bubble sort? Quicksort? etc.) • Data abstraction • Specify data objects for problem • Hide representation • Example: List of names • Abstract Data Type (ADT) • Implementation independent of interfaces • Example: The ADT is a map (also called a dictionary). We know it should associate a key with a value. Can be implemented in different ways: binary search tree, hash table, or a list. © Department of Computer Science UMD Techniques – Encapsulation • Encapsulation • Definition: A design technique that calls for hiding implementation details while providing an interface (methods) for data access • Example: use the keyword private when designing Java classes • Allow us to use code without having to know its implementation (supports the concept of abstraction) • Simplifies the process of code -
Concurrency Patterns for Easier Robotic Coordination
Concurrency Patterns for Easier Robotic Coordination Andrey Rusakov∗ Jiwon Shin∗ Bertrand Meyer∗† ∗Chair of Software Engineering, Department of Computer Science, ETH Zurich,¨ Switzerland †Software Engineering Lab, Innopolis University, Kazan, Russia fandrey.rusakov, jiwon.shin, [email protected] Abstract— Software design patterns are reusable solutions to Guarded suspension – are chosen for their concurrent nature, commonly occurring problems in software development. Grow- applicability to robotic coordination, and evidence of use ing complexity of robotics software increases the importance of in existing robotics frameworks. The paper aims to help applying proper software engineering principles and methods such as design patterns to robotics. Concurrency design patterns programmers who are not yet experts in concurrency to are particularly interesting to robotics because robots often have identify and then to apply one of the common concurrency- many components that can operate in parallel. However, there based solutions for their applications on robotic coordination. has not yet been any established set of reusable concurrency The rest of the paper is organized as follows: After design patterns for robotics. For this purpose, we choose six presenting related work in Section II, it proceeds with a known concurrency patterns – Future, Periodic timer, Invoke later, Active object, Cooperative cancellation, and Guarded sus- general description of concurrency approach for robotics pension. We demonstrate how these patterns could be used for in Section III. Then the paper presents and describes in solving common robotic coordination tasks. We also discuss detail a number of concurrency patterns related to robotic advantages and disadvantages of the patterns and how existing coordination in Section IV. -
CSCI 104 C++ STL; Iterators, Maps, Sets
1 CSCI 104 C++ STL; Iterators, Maps, Sets Mark Redekopp David Kempe 2 Container Classes • ArrayLists, LinkedList, Deques, etc. are classes used simply for storing (or contain) other items • C++ Standard Template Library provides implementations of all of these containers – DynamicArrayList => C++: std::vector<T> – LinkedList => C++: std::list<T> – Deques => C++: std::deque<T> – Sets => C++: std::set<T> – Maps => C++: std::map<K,V> • Question: – Consider the get() method. What is its time complexity for… – ArrayList => O(____) – LinkedList => O(____) – Deques => O(____) 3 Container Classes • ArrayLists, LinkedList, Deques, etc. are classes used simply for storing (or contain) other items • C++ Standard Template Library provides implementations of all of these containers – DynamicArrayList => C++: std::vector<T> – LinkedList => C++: std::list<T> – Deques => C++: std::deque<T> – Sets => C++: std::set<T> – Maps => C++: std::map<K,V> • Question: – Consider the at() method. What is its time complexity for… – ArrayList => O(1) // contiguous memory, so just go to location – LinkedList => O(n) // must traverse the list to location i – Deques => O(1) 4 Iteration • Consider how you iterate over all the ArrayList<int> mylist; ... elements in a list for(int i=0; i < mylist.size(); ++i) – Use a for loop and get() or { cout << mylist.get(i) << endl; operator[] } • For an array list this is fine since each call to at() is O(1) LinkedList<int> mylist; ... • For a linked list, calling get(i) for(int i=0; i < mylist.size(); ++i) requires taking i steps through the { cout << mylist.get(i) << endl; linked list } – 0th call = 0 steps – 1st call = 1 step head – 2nd call = 2 steps 0x148 – 0+1+2+…+n-2+n-1 = O(n2) 0x148 0x1c0 0x3e0 3 0x1c0 9 0x3e0 5 NULL • You are re-walking over the linked list a lot of the time get(0) get(1) get(2) 5 Iteration: A Better Approach iterator head • Solution: Don't use get() Mylist.end() Curr = NULL 0x148 • Use an iterator 0x148 0x1c0 0x3e0 – an internal state variable (i.e. -
Programming Robots for Activities of Everyday Life
Thesis for The Degree of Licentiate of Engineering Programming Robots for Activities of Everyday Life Swaib Dragule Division of Software Engineering Department of Computer Science & Engineering Chalmers University of Technology and Gothenburg University Gothenburg, Sweden, 2021 Programming Robots for Activities of Everyday Life Swaib Dragule Copyright ©2021 Swaib Dragule except where otherwise stated. All rights reserved. Department of Computer Science & Engineering Division of Software Engineering Chalmers University of Technology and Gothenburg University Gothenburg, Sweden This thesis has been prepared using LATEX. Printed by Chalmers Reproservice, Gothenburg, Sweden 2021. ii ”Smiling to someone is an act of charity.” - Prophet Muhammad (P.B.U.H) iv v Abstract Text-based programming remains a challenge to novice programmers in all programming domains, including robotics. The use of robots is gaining considerable traction in several domains since robots are capable of assisting humans in repetitive and hazardous tasks. Soon robots will commonly be used in tasks-of-everyday-life in homes, hotels, airports, and museums. However, robotic missions have been either predefined or programmed using low-level APIs, making mission specification task-specific and error-prone. To harness the full potential of robots, it must be possible to define missions for specific application domains as needed. The specification of missions of robotic applications should be performed via easy-to-use, accessible ways, and at the same time, be accurate and unambiguous. Simplicity and flexibility in programming such robots are important since end-users come from diverse domains, not necessarily with sufficient programming knowledge. The main objective of this licentiate thesis is to empirically understand the state-of-the-art in languages and tools used for specifying robot missions by end-users. -
Functional Programming HOWTO Release 3.3.3
Functional Programming HOWTO Release 3.3.3 Guido van Rossum Fred L. Drake, Jr., editor November 17, 2013 Python Software Foundation Email: [email protected] Contents 1 Introduction ii 1.1 Formal provability.......................................... iii 1.2 Modularity.............................................. iii 1.3 Ease of debugging and testing.................................... iii 1.4 Composability............................................. iii 2 Iterators iv 2.1 Data Types That Support Iterators..................................v 3 Generator expressions and list comprehensions vi 4 Generators vii 4.1 Passing values into a generator.................................... viii 5 Built-in functions ix 6 The itertools module xi 6.1 Creating new iterators......................................... xi 6.2 Calling functions on elements.................................... xii 6.3 Selecting elements.......................................... xii 6.4 Grouping elements.......................................... xiii 7 The functools module xiv 7.1 The operator module......................................... xv 8 Small functions and the lambda expression xv 9 Revision History and Acknowledgements xvi 10 References xvi 10.1 General................................................ xvi 10.2 Python-specific............................................ xvii 10.3 Python documentation........................................ xvii Indexxix Author A. M. Kuchling Release 0.31 In this document, we’ll take a tour of Python’s features suitable for implementing programs