Common Lisp Alan Dipert, [email protected] Orange Combinator 2018-12-17 Lisp History
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
How Lisp Systems Look Different in Proceedings of European Conference on Software Maintenance and Reengineering (CSMR 2008)
How Lisp Systems Look Different In Proceedings of European Conference on Software Maintenance and Reengineering (CSMR 2008) Adrian Dozsa Tudor Gˆırba Radu Marinescu Politehnica University of Timis¸oara University of Berne Politehnica University of Timis¸oara Romania Switzerland Romania [email protected] [email protected] [email protected] Abstract rently used in a variety of domains, like bio-informatics (BioBike), data mining (PEPITe), knowledge-based en- Many reverse engineering approaches have been devel- gineering (Cycorp or Genworks), video games (Naughty oped to analyze software systems written in different lan- Dog), flight scheduling (ITA Software), natural language guages like C/C++ or Java. These approaches typically processing (SRI International), CAD (ICAD or OneSpace), rely on a meta-model, that is either specific for the language financial applications (American Express), web program- at hand or language independent (e.g. UML). However, one ming (Yahoo! Store or reddit.com), telecom (AT&T, British language that was hardly addressed is Lisp. While at first Telecom Labs or France Telecom R&D), electronic design sight it can be accommodated by current language inde- automation (AMD or American Microsystems) or planning pendent meta-models, Lisp has some unique features (e.g. systems (NASA’s Mars Pathfinder spacecraft mission) [16]. macros, CLOS entities) that are crucial for reverse engi- neering Lisp systems. In this paper we propose a suite of Why Lisp is Different. In spite of its almost fifty-year new visualizations that reveal the special traits of the Lisp history, and of the fact that other programming languages language and thus help in understanding complex Lisp sys- borrowed concepts from it, Lisp still presents some unique tems. -
An Evaluation of Go and Clojure
An evaluation of Go and Clojure A thesis submitted in partial satisfaction of the requirements for the degree Bachelors of Science in Computer Science Fall 2010 Robert Stimpfling Department of Computer Science University of Colorado, Boulder Advisor: Kenneth M. Anderson, PhD Department of Computer Science University of Colorado, Boulder 1. Introduction Concurrent programming languages are not new, but they have been getting a lot of attention more recently due to their potential with multiple processors. Processors have gone from growing exponentially in terms of speed, to growing in terms of quantity. This means processes that are completely serial in execution will soon be seeing a plateau in performance gains since they can only rely on one processor. A popular approach to using these extra processors is to make programs multi-threaded. The threads can execute in parallel and use shared memory to speed up execution times. These multithreaded processes can significantly speed up performance, as long as the number of dependencies remains low. Amdahl‘s law states that these performance gains can only be relative to the amount of processing that can be parallelized [1]. However, the performance gains are significant enough to be looked into. These gains not only come from the processing being divvied up into sections that run in parallel, but from the inherent gains from sharing memory and data structures. Passing new threads a copy of a data structure can be demanding on the processor because it requires the processor to delve into memory and make an exact copy in a new location in memory. Indeed some studies have shown that the problem with optimizing concurrent threads is not in utilizing the processors optimally, but in the need for technical improvements in memory performance [2]. -
Clojure, Given the Pun on Closure, Representing Anything Specific
dynamic, functional programming for the JVM “It (the logo) was designed by my brother, Tom Hickey. “It I wanted to involve c (c#), l (lisp) and j (java). I don't think we ever really discussed the colors Once I came up with Clojure, given the pun on closure, representing anything specific. I always vaguely the available domains and vast emptiness of the thought of them as earth and sky.” - Rich Hickey googlespace, it was an easy decision..” - Rich Hickey Mark Volkmann [email protected] Functional Programming (FP) In the spirit of saying OO is is ... encapsulation, inheritance and polymorphism ... • Pure Functions • produce results that only depend on inputs, not any global state • do not have side effects such as Real applications need some changing global state, file I/O or database updates side effects, but they should be clearly identified and isolated. • First Class Functions • can be held in variables • can be passed to and returned from other functions • Higher Order Functions • functions that do one or both of these: • accept other functions as arguments and execute them zero or more times • return another function 2 ... FP is ... Closures • main use is to pass • special functions that retain access to variables a block of code that were in their scope when the closure was created to a function • Partial Application • ability to create new functions from existing ones that take fewer arguments • Currying • transforming a function of n arguments into a chain of n one argument functions • Continuations ability to save execution state and return to it later think browser • back button 3 .. -
Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of -
Download This Ebook for Free
lisp #lisp Table of Contents About 1 Chapter 1: Getting started with lisp 2 Remarks 2 Examples 2 Installation or Setup 2 Dialects of Lisp and their implementations 2 Lisp Resources 3 Credits 4 About You can share this PDF with anyone you feel could benefit from it, downloaded the latest version from: lisp It is an unofficial and free lisp ebook created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow. It is neither affiliated with Stack Overflow nor official lisp. The content is released under Creative Commons BY-SA, and the list of contributors to each chapter are provided in the credits section at the end of this book. Images may be copyright of their respective owners unless otherwise specified. All trademarks and registered trademarks are the property of their respective company owners. Use the content presented in this book at your own risk; it is not guaranteed to be correct nor accurate, please send your feedback and corrections to [email protected] https://riptutorial.com/ 1 Chapter 1: Getting started with lisp Remarks This section provides an overview of what lisp is, and why a developer might want to use it. It should also mention any large subjects within lisp, and link out to the related topics. Since the Documentation for lisp is new, you may need to create initial versions of those related topics. Examples Installation or Setup Probably the two most popular free implementations of Common Lisp are Clozure Common Lisp (CCL) and Steel Bank Common Lisp (SBCL). -
Mixing R with Other Languages JOHN D
Mixing R with other languages JOHN D. COOK, PHD SINGULAR VALUE CONSULTING Why R? Libraries, libraries, libraries De facto standard for statistical research Nice language, as far as statistical languages go “Quirky, flawed, and an enormous success.” Why mix languages? Improve performance of R code Execution speed (e.g. loops) Memory management Raid R’s libraries How to optimize R Vectorize Rewrite not using R A few R quirks Everything is a vector Everything can be null or NA Unit-offset vectors Zero index legal but strange Negative indices remove elements Matrices filled by column by default $ acts like dot, dot not special C package interface Must manage low-level details of R object model and memory Requires Rtools on Windows Lots of macros like REALSXP, PROTECT, and UNPROTECT Use C++ (Rcpp) instead “I do not recommend using C for writing new high-performance code. Instead write C++ with Rcpp.” – Hadley Wickham Rcpp The most widely used extension method for R Call C, C++, or Fortran from R Companion project RInside to call R from C++ Extensive support even for advanced C++ Create R packages or inline code http://rcpp.org Dirk Eddelbuettel’s book Simple Rcpp example library(Rcpp) cppFunction('int add(int x, int y, int z) { int sum = x + y + z; return sum; }') add(1, 2, 3) .NET RDCOM http://sunsite.univie.ac.at/rcom/ F# type provider for R http://bluemountaincapital.github.io/FSharpRProvider/ R.NET https://rdotnet.codeplex.com/ SQL Server 2016 execute sp_execute_external_script @language = N'R' , @script = -
CSC 407/507 Programming Assignment #1: Common Lisp Due Date: Friday, February 2
CSC 407/507 Programming Assignment #1: Common Lisp Due Date: Friday, February 2 Remember you can work in groups of up to three students. Download a version of Common Lisp. Two free (but trial limited) versions are available at Franz Common Lisp (http://www.franz.com/downloads/) and LispWorks (http://www.lispworks.com/). Another version is called Steel Bank Common Lisp which does not come with an IDE, just a command line interface (https://common-lisp.net/downloads/). A GNU’s Common Lisp is available for both Unix/Linux (https://www.gnu.org/software/gcl/) and Windows (https://www.cs.utexas.edu/users/novak/gclwin.html). LispWorks is recommended as the easiest and least fuss. In this program, you will write multiple Lisp functions which support (or implement wholly) sorting algorithms. Some functions may serve multiple sort algorithms if you can work it out although this is not necessary. You should aim to build helper functions so that you are not implementing any single sort in a single function. Some algorithms will require helper functions (e.g., mergesort which has at least the recursive mergesort function and the merge function). You are to implement five of the following seven sort algorithms: bubblesort, heapsort, insertionsort, mergesort, quicksort, radixsort and selectionsort. You must do at least one of mergesort or quicksort among the five you implement. You are to use recursion for the mergesort/quicksort functions and are encouraged to use recursion as much as possible in the remainder of your functions whether they are helper functions or the sort functions. Use lists to store the values to be sorted (they will all be integer numbers) rather than arrays. -
A Quick Introduction to Common Lisp
CSC 244/444 Notes last updated Feb 3, 2020 A Quick Introduction to Common Lisp Lisp is a functional language well-suited to sym- bolic AI, based on the λ-calculus and with list structures as a very flexible basic data type; pro- grams are themselves list structures, and thus can be created and manipulated just like other data. This introduction isn't intended to give you all the details, but just enough to get across the essential ideas, and to let you get under way quickly. The best way to make use of it is in front of a computer, trying out all the things mentioned. Basic characteristics of Lisp and Common Lisp: • Lisp is primarily intended for manipulating symbolic data in the form of list struc- tures, e.g., (THREE (BLIND MICE) SEE (HOW (THEY RUN)) !) • Arithmetic is included, e.g., (SQRT (+ (* 3 3) (* 4 4))) or (sqrt (+ (* 3 3) (* 4 4))) (Common Lisp is case-insensitive, except in strings between quotes " ... ", specially delimited symbols like |Very Unusual Symbol!|, and for characters prefixed with \\"). • Common Lisp (unlike John McCarthy's original \pure" Lisp) has arrays and record structures; this leads to more understandable and efficient code, much as in typed languages, but there is no obligatory typing. • All computation consists of expression evaluation. For instance, typing in the above (SQRT ...) expression and hitting the enter key immediately gives 5. • Lisp is a functional language based on Alonzo Church's λ-calculus (which like Turing machines provides a complete basis for all computable functions). For instance, a function for the square root of the sum of two squares can be expressed as (lambda (x y) (sqrt (+ (* x x) (* y y)))). -
A History of Clojure
A History of Clojure RICH HICKEY, Cognitect, Inc., USA Shepherd: Mira Mezini, Technische Universität Darmstadt, Germany Clojure was designed to be a general-purpose, practical functional language, suitable for use by professionals wherever its host language, e.g., Java, would be. Initially designed in 2005 and released in 2007, Clojure is a dialect of Lisp, but is not a direct descendant of any prior Lisp. It complements programming with pure functions of immutable data with concurrency-safe state management constructs that support writing correct multithreaded programs without the complexity of mutex locks. Clojure is intentionally hosted, in that it compiles to and runs on the runtime of another language, such as the JVM. This is more than an implementation strategy; numerous features ensure that programs written in Clojure can leverage and interoperate with the libraries of the host language directly and efficiently. In spite of combining two (at the time) rather unpopular ideas, functional programming and Lisp, Clojure has since seen adoption in industries as diverse as finance, climate science, retail, databases, analytics, publishing, healthcare, advertising and genomics, and by consultancies and startups worldwide, much to the career-altering surprise of its author. Most of the ideas in Clojure were not novel, but their combination puts Clojure in a unique spot in language design (functional, hosted, Lisp). This paper recounts the motivation behind the initial development of Clojure and the rationale for various design decisions and language constructs. It then covers its evolution subsequent to release and adoption. CCS Concepts: • Software and its engineering ! General programming languages; • Social and pro- fessional topics ! History of programming languages. -
Programming Clojure Second Edition
Extracted from: Programming Clojure Second Edition This PDF file contains pages extracted from Programming Clojure, published by the Pragmatic Bookshelf. For more information or to purchase a paperback or PDF copy, please visit http://www.pragprog.com. Note: This extract contains some colored text (particularly in code listing). This is available only in online versions of the books. The printed versions are black and white. Pagination might vary between the online and printer versions; the content is otherwise identical. Copyright © 2010 The Pragmatic Programmers, LLC. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form, or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior consent of the publisher. The Pragmatic Bookshelf Dallas, Texas • Raleigh, North Carolina Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and The Pragmatic Programmers, LLC was aware of a trademark claim, the designations have been printed in initial capital letters or in all capitals. The Pragmatic Starter Kit, The Pragmatic Programmer, Pragmatic Programming, Pragmatic Bookshelf, PragProg and the linking g device are trade- marks of The Pragmatic Programmers, LLC. Every precaution was taken in the preparation of this book. However, the publisher assumes no responsibility for errors or omissions, or for damages that may result from the use of information (including program listings) contained herein. Our Pragmatic courses, workshops, and other products can help you and your team create better software and have more fun. -
Steps Towards Teaching the Clojure Programming Language in an Introductory CS Class
Overview of Clojure Technical challenges of teaching Clojure as the first language Approaches to teaching Clojure to beginners Conclusions Steps towards teaching the Clojure programming language in an introductory CS class Elena Machkasova, Stephen J Adams, Joe Einertson University of Minnesota, Morris Trends in Functional Programming in Education (TFPIE) 2013. 1 / 18 Overview of Clojure Technical challenges of teaching Clojure as the first language Approaches to teaching Clojure to beginners Conclusions Outline 1 Overview of Clojure 2 Technical challenges of teaching Clojure as the first language 3 Approaches to teaching Clojure to beginners 4 Conclusions 2 / 18 Overview of Clojure Technical challenges of teaching Clojure as the first language Approaches to teaching Clojure to beginners Conclusions What is Clojure? Clojure is a LISP. Developed by Rich Hickey, released in 2007, rapidly gaining popularity. Designed to support concurrency. Provides multiple immutable persistent data structures (lists, vectors, hash maps, sets, etc.). Runs on the JVM, fully integrated with Java. Provides REPL (Read Eval Print Loop). 3 / 18 Overview of Clojure Technical challenges of teaching Clojure as the first language Approaches to teaching Clojure to beginners Conclusions Why the popularity? Elegant. Efficient (fast bytecode, efficient implementation of data structures). Convenient and safe efficient multi-threading. Integrates with Java. 4 / 18 Overview of Clojure Technical challenges of teaching Clojure as the first language Approaches to teaching Clojure to beginners Conclusions Why Clojure in intro CS courses? It's a real-life language done well. Introduces multiple data structures; abstraction vs implementation. Can be used in later courses (concurrency, interoperability with Java, purely functional data structures).