Regular Expressions
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Theory of Computer Science
Theory of Computer Science MODULE-1 Alphabet A finite set of symbols is denoted by ∑. Language A language is defined as a set of strings of symbols over an alphabet. Language of a Machine Set of all accepted strings of a machine is called language of a machine. Grammar Grammar is the set of rules that generates language. CHOMSKY HIERARCHY OF LANGUAGES Type 0-Language:-Unrestricted Language, Accepter:-Turing Machine, Generetor:-Unrestricted Grammar Type 1- Language:-Context Sensitive Language, Accepter:- Linear Bounded Automata, Generetor:-Context Sensitive Grammar Type 2- Language:-Context Free Language, Accepter:-Push Down Automata, Generetor:- Context Free Grammar Type 3- Language:-Regular Language, Accepter:- Finite Automata, Generetor:-Regular Grammar Finite Automata Finite Automata is generally of two types :(i)Deterministic Finite Automata (DFA)(ii)Non Deterministic Finite Automata(NFA) DFA DFA is represented formally by a 5-tuple (Q,Σ,δ,q0,F), where: Q is a finite set of states. Σ is a finite set of symbols, called the alphabet of the automaton. δ is the transition function, that is, δ: Q × Σ → Q. q0 is the start state, that is, the state of the automaton before any input has been processed, where q0 Q. F is a set of states of Q (i.e. F Q) called accept states or Final States 1. Construct a DFA that accepts set of all strings over ∑={0,1}, ending with 00 ? 1 0 A B S State/Input 0 1 1 A B A 0 B C A 1 * C C A C 0 2. Construct a DFA that accepts set of all strings over ∑={0,1}, not containing 101 as a substring ? 0 1 1 A B State/Input 0 1 S *A A B 0 *B C B 0 0,1 *C A R C R R R R 1 NFA NFA is represented formally by a 5-tuple (Q,Σ,δ,q0,F), where: Q is a finite set of states. -
Cs 61A/Cs 98-52
CS 61A/CS 98-52 Mehrdad Niknami University of California, Berkeley Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 1 / 23 Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Something like this? (Is this good?) Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 Motivation How would you find a substring inside a string? Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ -
PHP 8.0.2 - Phpinfo() 2021-02-23 14:53
PHP 8.0.2 - phpinfo() 2021-02-23 14:53 PHP Version 8.0.2 System Linux effa5f35b7e3 5.4.83-v7l+ #1379 SMP Mon Dec 14 13:11:54 GMT 2020 armv7l Build Date Feb 9 2021 12:01:16 Build System Linux 96bc8a22765c 4.15.0-129-generic #132-Ubuntu SMP Thu Dec 10 14:07:05 UTC 2020 armv8l GNU/Linux Configure Command './configure' '--build=arm-linux-gnueabihf' '--with-config-file-path=/usr/local/etc/php' '--with-config-file-scan- dir=/usr/local/etc/php/conf.d' '--enable-option-checking=fatal' '--with-mhash' '--with-pic' '--enable-ftp' '--enable- mbstring' '--enable-mysqlnd' '--with-password-argon2' '--with-sodium=shared' '--with-pdo-sqlite=/usr' '--with- sqlite3=/usr' '--with-curl' '--with-libedit' '--with-openssl' '--with-zlib' '--with-pear' '--with-libdir=lib/arm-linux-gnueabihf' '- -with-apxs2' '--disable-cgi' 'build_alias=arm-linux-gnueabihf' Server API Apache 2.0 Handler Virtual Directory Support disabled Configuration File (php.ini) Path /usr/local/etc/php Loaded Configuration File /usr/local/etc/php/php.ini Scan this dir for additional .ini files /usr/local/etc/php/conf.d Additional .ini files parsed /usr/local/etc/php/conf.d/docker-php-ext-gd.ini, /usr/local/etc/php/conf.d/docker-php-ext-mysqli.ini, /usr/local/etc/php/conf.d/docker-php-ext-pdo_mysql.ini, /usr/local/etc/php/conf.d/docker-php-ext-sodium.ini, /usr/local/etc/php/conf.d/docker-php-ext-zip.ini PHP API 20200930 PHP Extension 20200930 Zend Extension 420200930 Zend Extension Build API420200930,NTS PHP Extension Build API20200930,NTS Debug Build no Thread Safety disabled Zend Signal Handling enabled -
Regular Languages and Finite Automata for Part IA of the Computer Science Tripos
N Lecture Notes on Regular Languages and Finite Automata for Part IA of the Computer Science Tripos Prof. Andrew M. Pitts Cambridge University Computer Laboratory c 2012 A. M. Pitts Contents Learning Guide ii 1 Regular Expressions 1 1.1 Alphabets,strings,andlanguages . .......... 1 1.2 Patternmatching................................. .... 4 1.3 Somequestionsaboutlanguages . ....... 6 1.4 Exercises....................................... .. 8 2 Finite State Machines 11 2.1 Finiteautomata .................................. ... 11 2.2 Determinism, non-determinism, and ε-transitions. .. .. .. .. .. .. .. .. .. 14 2.3 Asubsetconstruction . .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..... 17 2.4 Summary ........................................ 20 2.5 Exercises....................................... .. 20 3 Regular Languages, I 23 3.1 Finiteautomatafromregularexpressions . ............ 23 3.2 Decidabilityofmatching . ...... 28 3.3 Exercises....................................... .. 30 4 Regular Languages, II 31 4.1 Regularexpressionsfromfiniteautomata . ........... 31 4.2 Anexample ....................................... 32 4.3 Complementand intersectionof regularlanguages . .............. 34 4.4 Exercises....................................... .. 36 5 The Pumping Lemma 39 5.1 ProvingthePumpingLemma . .... 40 5.2 UsingthePumpingLemma . ... 41 5.3 Decidabilityoflanguageequivalence . ........... 44 5.4 Exercises....................................... .. 45 6 Grammars 47 6.1 Context-freegrammars . ..... 47 6.2 Backus-NaurForm ................................ -
MC-1200 Series Linux Software User's Manual
MC-1200 Series Linux Software User’s Manual Version 1.0, November 2020 www.moxa.com/product © 2020 Moxa Inc. All rights reserved. MC-1200 Series Linux Software User’s Manual The software described in this manual is furnished under a license agreement and may be used only in accordance with the terms of that agreement. Copyright Notice © 2020 Moxa Inc. All rights reserved. Trademarks The MOXA logo is a registered trademark of Moxa Inc. All other trademarks or registered marks in this manual belong to their respective manufacturers. Disclaimer Information in this document is subject to change without notice and does not represent a commitment on the part of Moxa. Moxa provides this document as is, without warranty of any kind, either expressed or implied, including, but not limited to, its particular purpose. Moxa reserves the right to make improvements and/or changes to this manual, or to the products and/or the programs described in this manual, at any time. Information provided in this manual is intended to be accurate and reliable. However, Moxa assumes no responsibility for its use, or for any infringements on the rights of third parties that may result from its use. This product might include unintentional technical or typographical errors. Changes are periodically made to the information herein to correct such errors, and these changes are incorporated into new editions of the publication. Technical Support Contact Information www.moxa.com/support Moxa Americas Moxa China (Shanghai office) Toll-free: 1-888-669-2872 Toll-free: 800-820-5036 Tel: +1-714-528-6777 Tel: +86-21-5258-9955 Fax: +1-714-528-6778 Fax: +86-21-5258-5505 Moxa Europe Moxa Asia-Pacific Tel: +49-89-3 70 03 99-0 Tel: +886-2-8919-1230 Fax: +49-89-3 70 03 99-99 Fax: +886-2-8919-1231 Moxa India Tel: +91-80-4172-9088 Fax: +91-80-4132-1045 Table of Contents 1. -
Implementation of the Programming Language Dino – a Case Study in Dynamic Language Performance
Implementation of the Programming Language Dino – A Case Study in Dynamic Language Performance Vladimir N. Makarov Red Hat [email protected] Abstract design of the language, its type system and particular features such The article gives a brief overview of the current state of program- as multithreading, heterogeneous extensible arrays, array slices, ming language Dino in order to see where its stands between other associative tables, first-class functions, pattern-matching, as well dynamic programming languages. Then it describes the current im- as Dino’s unique approach to class inheritance via the ‘use’ class plementation, used tools and major implementation decisions in- composition operator. cluding how to implement a stable, portable and simple JIT com- The second part of the article describes Dino’s implementation. piler. We outline the overall structure of the Dino interpreter and just- We study the effect of major implementation decisions on the in-time compiler (JIT) and the design of the byte code and major performance of Dino on x86-64, AARCH64, and Powerpc64. In optimizations. We also describe implementation details such as brief, the performance of some model benchmark on x86-64 was the garbage collection system, the algorithms underlying Dino’s improved by 3.1 times after moving from a stack based virtual data structures, Dino’s built-in profiling system, and the various machine to a register-transfer architecture, a further 1.5 times by tools and libraries used in the implementation. Our goal is to give adding byte code combining, a further 2.3 times through the use an overview of the major implementation decisions involved in of JIT, and a further 4.4 times by performing type inference with a dynamic language, including how to implement a stable and byte code specialization, with a resulting overall performance im- portable JIT. -
Red Hat Virtualization 4.4 Package Manifest
Red Hat Virtualization 4.4 Package Manifest Package listing for Red Hat Virtualization 4.4 Last Updated: 2021-09-09 Red Hat Virtualization 4.4 Package Manifest Package listing for Red Hat Virtualization 4.4 Red Hat Virtualization Documentation Team Red Hat Customer Content Services [email protected] Legal Notice Copyright © 2021 Red Hat, Inc. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/ . In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. -
Neural Edit Operations for Biological Sequences
Neural Edit Operations for Biological Sequences Satoshi Koide Keisuke Kawano Toyota Central R&D Labs. Toyota Central R&D Labs. [email protected] [email protected] Takuro Kutsuna Toyota Central R&D Labs. [email protected] Abstract The evolution of biological sequences, such as proteins or DNAs, is driven by the three basic edit operations: substitution, insertion, and deletion. Motivated by the recent progress of neural network models for biological tasks, we implement two neural network architectures that can treat such edit operations. The first proposal is the edit invariant neural networks, based on differentiable Needleman-Wunsch algorithms. The second is the use of deep CNNs with concatenations. Our analysis shows that CNNs can recognize regular expressions without Kleene star, and that deeper CNNs can recognize more complex regular expressions including the insertion/deletion of characters. The experimental results for the protein secondary structure prediction task suggest the importance of insertion/deletion. The test accuracy on the widely-used CB513 dataset is 71.5%, which is 1.2-points better than the current best result on non-ensemble models. 1 Introduction Neural networks are now used in many applications, not limited to classical fields such as image processing, speech recognition, and natural language processing. Bioinformatics is becoming an important application field of neural networks. These biological applications are often implemented as a supervised learning model that takes a biological string (such as DNA or protein) as an input, and outputs the corresponding label(s), such as a protein secondary structure [13, 14, 15, 18, 19, 23, 24, 26], protein contact maps [4, 8], and genome accessibility [12]. -
Context-Free Grammars
Chapter 3 Context-Free Grammars By Dr Zalmiyah Zakaria •Context-Free Grammars and Languages •Regular Grammars Formal Definition of Context-Free Grammars (CFG) A CFG can be formally defined by a quadruple of (V, , P, S) where: – V is a finite set of variables (non-terminal) – (the alphabet) is a finite set of terminal symbols , where V = – P is a finite set of rules (production rules) written as: A for A V, (v )*. – S is the start symbol, S V 2 Formal Definition of CFG • We can give a formal description to a particular CFG by specifying each of its four components, for example, G = ({S, A}, {0, 1}, P, S) where P consists of three rules: S → 0S1 S → A A → Sept2011 Theory of Computer Science 3 Context-Free Grammars • Terminal symbols – elements of the alphabet • Variables or non-terminals – additional symbols used in production rules • Variable S (start symbol) initiates the process of generating acceptable strings. 4 Terminal or Variable ? • S → (S) | S + S | S × S | A • A → 1 | 2 | 3 • The terminal symbols are { (, ), +, ×, 1, 2, 3} • The variable symbols are S and A Sept2011 Theory of Computer Science 5 Context-Free Grammars • A rule is an element of the set V (V )*. • An A rule: [A, w] or A w • A null rule or lambda rule: A 6 Context-Free Grammars • Grammars are used to generate strings of a language. • An A rule can be applied to the variable A whenever and wherever it occurs. • No limitation on applicability of a rule – it is context free 8 Context-Free Grammars • CFG have no restrictions on the right-hand side of production rules. -
Theory of Computation
Theory of Computation Alexandre Duret-Lutz [email protected] September 10, 2010 ADL Theory of Computation 1 / 121 References Introduction to the Theory of Computation (Michael Sipser, 2005). Lecture notes from Pierre Wolper's course at http://www.montefiore.ulg.ac.be/~pw/cours/calc.html (The page is in French, but the lecture notes labelled chapitre 1 to chapitre 8 are in English). Elements of Automata Theory (Jacques Sakarovitch, 2009). Compilers: Principles, Techniques, and Tools (A. Aho, R. Sethi, J. Ullman, 2006). ADL Theory of Computation 2 / 121 Introduction What would be your reaction if someone came at you to explain he has invented a perpetual motion machine (i.e. a device that can sustain continuous motion without losing energy or matter)? You would probably laugh. Without looking at the machine, you know outright that such the device cannot sustain perpetual motion. Indeed the laws of thermodynamics demonstrate that perpetual motion devices cannot be created. We know beforehand, from scientic knowledge, that building such a machine is impossible. The ultimate goal of this course is to develop similar knowledge for computer programs. ADL Theory of Computation 3 / 121 Theory of Computation Theory of computation studies whether and how eciently problems can be solved using a program on a model of computation (abstractions of a computer). Computability theory deals with the whether, i.e., is a problem solvable for a given model. For instance a strong result we will learn is that the halting problem is not solvable by a Turing machine. Complexity theory deals with the how eciently. -
A Representation-Based Approach to Connect Regular Grammar and Deep Learning
The Pennsylvania State University The Graduate School A REPRESENTATION-BASED APPROACH TO CONNECT REGULAR GRAMMAR AND DEEP LEARNING A Dissertation in Information Sciences and Technology by Kaixuan Zhang ©2021 Kaixuan Zhang Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2021 The dissertation of Kaixuan Zhang was reviewed and approved by the following: C. Lee Giles Professor of College of Information Sciences and Technology Dissertation Adviser Chair of Committee Kenneth Huang Assistant Professor of College of Information Sciences and Technology Shomir Wilson Assistant Professor of College of Information Sciences and Technology Daniel Kifer Associate Professor of Department of Computer Science and Engineering Mary Beth Rosson Professor of Information Sciences and Technology Director of Graduate Programs, College of Information Sciences and Technology ii Abstract Formal language theory has brought amazing breakthroughs in many traditional areas, in- cluding control systems, compiler design, and model verification, and continues promoting these research directions. As recent years have witnessed that deep learning research brings the long-buried power of neural networks to the surface and has brought amazing break- throughs, it is crucial to revisit formal language theory from a new perspective. Specifically, investigation of the theoretical foundation, rather than a practical application of the con- necting point obviously warrants attention. On the other hand, as the spread of deep neural networks (DNN) continues to reach multifarious branches of research, it has been found that the mystery of these powerful models is equally impressive as their capability in learning tasks. Recent work has demonstrated the vulnerability of DNN classifiers constructed for many different learning tasks, which opens the discussion of adversarial machine learning and explainable artificial intelligence. -
6.035 Lecture 2, Specifying Languages with Regular Expressions and Context-Free Grammars
MIT 6.035 Specifying Languages with Regular Expressions and Context-Free Grammars Martin Rinard Laboratory for Computer Science Massachusetts Institute of Technology Language Definition Problem • How to precisely define language • Layered struc ture of langua ge defifinitiition • Start with a set of letters in language • Lexical structurestructure - identifies “words ” in language (each word is a sequence of letters) • Syntactic sstructuretructure - identifies “sentences ” in language (each sentence is a sequence of words) • Semantics - meaning of program (specifies what result should be for each input) • Today’s topic: lexical and syntactic structures Specifying Formal Languages • Huge Triumph of Computer Science • Beautiful Theoretical Results • Practical Techniques and Applications • Two Dual Notions • Generative approach ((grammar or regular expression) • Recognition approach (automaton) • Lots of theorems about convertingconverting oonene approach automatically to another Specifying Lexical Structure Using Regular ExpressionsExpressions • Have some alphabet ∑ = set of letters • R egular expressions are bu ilt from: • ε -empty string • AAnyn yl lettere tte rf fromr oma alphabetlp ha be t ∑ •r1r2 – regular expression r1 followed by r2 (sequence) •r1| r2 – either regular expression r1 or r2 (choice) • r* - iterated sequence and choice ε | r | rr | … • Parentheses to indicate grouping/precedence Concept of Regular Expression Generating a StringString Rewrite regular expression until have only a sequence of letters (string) left Genera