Declare an Array of Hashmaps
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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. -
The Hitchhiker's Guide to Data Structures
The Hitchhiker’s Guide to Data Structures University of Rochester Nathan Contino March 2, 2017 Abstract Introductory computer science students often have considerable difficulty extracting infor- mation from data structures textbooks with rather dense writing styles. These textbooks are usually intended to cover subjects comprehensively, but seldom focus on keeping content comprehensible. While this commitment to data preservation is admirable at heart, the author of this paper feels that we can do a better job for introductory students by attempting to balance comprehensibility and comprehensiveness. The Hitchhiker’s Guide to Data Structures is an attempt to present data structures to introductory computer science students in this more friendly manner. Contents 1 Forward 2 2 Introduction 2 2.1 A Note on Running Time . .3 2.2 A Note on Pointers and Values. .5 3 The Proto-Structures 6 3.1 Nodes . .6 3.2 Linked Lists . .8 3.3 Arrays . 13 3.4 Strings . 16 4 Where Things Start to Get Complicated 18 4.1 Doubly-Linked Lists . 18 4.2 Stacks . 22 4.3 Queues . 25 4.4 Trees . 28 4.5 Binary Search Trees . 33 5 Advanced Data Structures 37 5.1 AVL Trees . 37 5.2 Binary Heaps . 40 5.3 Graphs . 42 5.4 Hash Tables . 45 6 Summary 47 1 1 Forward While the purpose of this work seems simple, it has actually turned out to be rather difficult to convey. So we’ll try to illustrate it clearly here in a single brief sentence: this paper is designed specifically as a guide to data structures for keen, interested students at the introductory level of computer science. -
4 Hash Tables and Associative Arrays
4 FREE Hash Tables and Associative Arrays If you want to get a book from the central library of the University of Karlsruhe, you have to order the book in advance. The library personnel fetch the book from the stacks and deliver it to a room with 100 shelves. You find your book on a shelf numbered with the last two digits of your library card. Why the last digits and not the leading digits? Probably because this distributes the books more evenly among the shelves. The library cards are numbered consecutively as students sign up, and the University of Karlsruhe was founded in 1825. Therefore, the students enrolled at the same time are likely to have the same leading digits in their card number, and only a few shelves would be in use if the leadingCOPY digits were used. The subject of this chapter is the robust and efficient implementation of the above “delivery shelf data structure”. In computer science, this data structure is known as a hash1 table. Hash tables are one implementation of associative arrays, or dictio- naries. The other implementation is the tree data structures which we shall study in Chap. 7. An associative array is an array with a potentially infinite or at least very large index set, out of which only a small number of indices are actually in use. For example, the potential indices may be all strings, and the indices in use may be all identifiers used in a particular C++ program.Or the potential indices may be all ways of placing chess pieces on a chess board, and the indices in use may be the place- ments required in the analysis of a particular game. -
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: .. -
JS and the DOM
Back to the client side: JS and the DOM 1 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Q What are the DOM and the BOM? 2 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento JS and the DOM When a web page is loaded, the browser creates a Document Object Model of the page, which is as a tree of Objects. Every element in a document—the document as a whole, the head, tables within the document, table headers, text within the table cells—is part of its DOM, so they can all be accessed and manipulated using the DOM and a scripting language like JavaScript. § With the object model, JavaScript can: § change all the HTML [elements, attributes, styles] in the page § add or remove existing HTML elements and attributes § react to HTML events in the page § create new HTML events in the page 3 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Using languages implementations of the DOM can be built for any language: e.g. Javascript Java Python … But Javascript is the only one that can work client-side. 4 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Q What are the fundamental objects in DOM/BOM? 5 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Fundamental datatypes - 1 § Node Every object located within a document is a node. In an HTML document, an object can be an element node but also a text node or attribute node. § Document (is-a Node) § the root document object itself. -
Project 0: Implementing a Hash Table
CS165: DATA SYSTEMS Project 0: Implementing a Hash Table CS 165, Data Systems, Fall 2019 Goal and Motivation. The goal of Project 0 is to help you develop (or refresh) basic skills at designing and implementing data structures and algorithms. These skills will be essential for following the class. Project 0 is meant to be done prior to the semester or during the early weeks. If you are doing it before the semester feel free to contact the staff for any help or questions by coming to the office hours or posting in Piazza. We expect that for most students Project 0 will take anything between a few days to a couple of weeks, depending in the student’s background on the above areas. If you are having serious trouble navigating Project 0 then you should reconsider the decision to take CS165. You can expect the semester project to be multiple orders of magnitude more work and more complex. How much extra work is this? Project 0 is actually designed as a part of the fourth milestone of the semester project. So after finishing Project 0 you will have refreshed some basic skills and you will have a part of your project as well. Basic Project Description. In many computer applications, it is often necessary to store a collection of key-value pairs. For example, consider a digital movie catalog. In this case, keys are movie names and values are their corresponding movie descriptions. Users of the application look up movie names and expect the program to fetch their corresponding descriptions quickly. -
PAM: Parallel Augmented Maps
PAM: Parallel Augmented Maps Yihan Sun Daniel Ferizovic Guy E. Belloch Carnegie Mellon University Karlsruhe Institute of Technology Carnegie Mellon University [email protected] [email protected] [email protected] Abstract 1 Introduction Ordered (key-value) maps are an important and widely-used The map data type (also called key-value store, dictionary, data type for large-scale data processing frameworks. Beyond table, or associative array) is one of the most important da- simple search, insertion and deletion, more advanced oper- ta types in modern large-scale data analysis, as is indicated ations such as range extraction, filtering, and bulk updates by systems such as F1 [60], Flurry [5], RocksDB [57], Or- form a critical part of these frameworks. acle NoSQL [50], LevelDB [41]. As such, there has been We describe an interface for ordered maps that is augment- significant interest in developing high-performance paral- ed to support fast range queries and sums, and introduce a lel and concurrent algorithms and implementations of maps parallel and concurrent library called PAM (Parallel Augment- (e.g., see Section 2). Beyond simple insertion, deletion, and ed Maps) that implements the interface. The interface includes search, this work has considered “bulk” functions over or- a wide variety of functions on augmented maps ranging from dered maps, such as unions [11, 20, 33], bulk-insertion and basic insertion and deletion to more interesting functions such bulk-deletion [6, 24, 26], and range extraction [7, 14, 55]. as union, intersection, filtering, extracting ranges, splitting, One particularly useful function is to take a “sum” over a and range-sums. -
Chapter 6 : DATA TYPES
Chapter 6 : DATA TYPES •Introduction • Primitive Data Types • Character String Types • User-Defined Ordinal Types • Array Types • Associative Arrays • Record Types • Union Types • Pointer Types Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-1 Introduction •A data type defines a collection of data objects and a set of predefined operations on those objects • Evolution of data types: – FORTRAN I (1957) - INTEGER, REAL, arrays – Ada (1983) - User can create a unique type for every category of variables in the problem space and have the system enforce the types Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-2 Introduction • Design issues for all data types: 1. What is the syntax of references to variables? 2. What operations are defined and how are they specified? Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-3 1 Primitive Data Types • Those not defined in terms of other data types 1. Integer – 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 Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-4 Primitive Data Types 2. Floating Point – Model real numbers, but only as approximations – Languages for scientific use support at least two floating-point types; sometimes more – Usually exactly like the hardware, but not always Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-5 IEEE Floating Point Formats Copyright © 2006 Pearson Addison-Wesley. All rights reserved. 6-6 2 Primitive Data Types 3. Decimal – For business applications (money) – Store a fixed number of decimal digits (coded) – Advantage: accuracy – Disadvantages: limited range, wastes memory Copyright © 2006 Pearson Addison-Wesley.