1 Pattern Matching- Regular Expressions
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Pattern Matching Using Similarity Measures
Pattern matching using similarity measures Patroonvergelijking met behulp van gelijkenismaten (met een samenvatting in het Nederlands) PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van Rector Magnificus, Prof. Dr. H. O. Voorma, ingevolge het besluit van het College voor Promoties in het openbaar te verdedigen op maandag 18 september 2000 des morgens te 10:30 uur door Michiel Hagedoorn geboren op 13 juli 1972, te Renkum promotor: Prof. Dr. M. H. Overmars Faculteit Wiskunde & Informatica co-promotor: Dr. R. C. Veltkamp Faculteit Wiskunde & Informatica ISBN 90-393-2460-3 PHILIPS '$ The&&% research% described in this thesis has been made possible by financial support from Philips Research Laboratories. The work in this thesis has been carried out in the graduate school ASCI. Contents 1 Introduction 1 1.1Patternmatching.......................... 1 1.2Applications............................. 4 1.3Obtaininggeometricpatterns................... 7 1.4 Paradigms in geometric pattern matching . 8 1.5Similaritymeasurebasedpatternmatching........... 11 1.6Overviewofthisthesis....................... 16 2 A theory of similarity measures 21 2.1Pseudometricspaces........................ 22 2.2Pseudometricpatternspaces................... 30 2.3Embeddingpatternsinafunctionspace............. 40 2.4TheHausdorffmetric........................ 46 2.5Thevolumeofsymmetricdifference............... 54 2.6 Reflection visibility based distances . 60 2.7Summary.............................. 71 2.8Experimentalresults....................... -
Pattern Matching Using Fuzzy Methods David Bell and Lynn Palmer, State of California: Genetic Disease Branch
Pattern Matching Using Fuzzy Methods David Bell and Lynn Palmer, State of California: Genetic Disease Branch ABSTRACT The formula is : Two major methods of detecting similarities Euclidean Distance and 2 a "fuzzy Hamming distance" presented in a paper entitled "F %%y Dij : ; ( <3/i - yj 4 Hamming Distance: A New Dissimilarity Measure" by Bookstein, Klein, and Raita, will be compared using entropy calculations to Euclidean distance is especially useful for comparing determine their abilities to detect patterns in data and match data matching whole word object elements of 2 vectors. The records. .e find that both means of measuring distance are useful code in Java is: depending on the context. .hile fuzzy Hamming distance outperforms in supervised learning situations such as searches, the Listing 1: Euclidean Distance Euclidean distance measure is more useful in unsupervised pattern matching and clustering of data. /** * EuclideanDistance.java INTRODUCTION * * Pattern matching is becoming more of a necessity given the needs for * Created: Fri Oct 07 08:46:40 2002 such methods for detecting similarities in records, epidemiological * * @author David Bell: DHS-GENETICS patterns, genetics and even in the emerging fields of criminal * @version 1.0 behavioral pattern analysis and disease outbreak analysis due to */ possible terrorist activity. 0nfortunately, traditional methods for /** Abstact Distance class linking or matching records, data fields, etc. rely on exact data * @param None matches rather than looking for close matches or patterns. 1f course * @return Distance Template proximity pattern matches are often necessary when dealing with **/ messy data, data that has inexact values and/or data with missing key abstract class distance{ values. -
Bbedit 13.5 User Manual
User Manual BBEdit™ Professional Code and Text Editor for the Macintosh Bare Bones Software, Inc. ™ BBEdit 13.5 Product Design Jim Correia, Rich Siegel, Steve Kalkwarf, Patrick Woolsey Product Engineering Jim Correia, Seth Dillingham, Matt Henderson, Jon Hueras, Steve Kalkwarf, Rich Siegel, Steve Sisak Engineers Emeritus Chris Borton, Tom Emerson, Pete Gontier, Jamie McCarthy, John Norstad, Jon Pugh, Mark Romano, Eric Slosser, Rob Vaterlaus Documentation Fritz Anderson, Philip Borenstein, Stephen Chernicoff, John Gruber, Jeff Mattson, Jerry Kindall, Caroline Rose, Allan Rouselle, Rich Siegel, Vicky Wong, Patrick Woolsey Additional Engineering Polaschek Computing Icon Design Bryan Bell Factory Color Schemes Luke Andrews Additional Color Schemes Toothpaste by Cat Noon, and Xcode Dark by Andrew Carter. Used by permission. Additional Icons By icons8. Used under license Additional Artwork By Jonathan Hunt PHP keyword lists Contributed by Ted Stresen-Reuter. Previous versions by Carsten Blüm Published by: Bare Bones Software, Inc. 73 Princeton Street, Suite 206 North Chelmsford, MA 01863 USA (978) 251-0500 main (978) 251-0525 fax https://www.barebones.com/ Sales & customer service: [email protected] Technical support: [email protected] BBEdit and the BBEdit User Manual are copyright ©1992-2020 Bare Bones Software, Inc. All rights reserved. Produced/published in USA. Copyrights, Licenses & Trademarks cmark ©2014 by John MacFarlane. Used under license; part of the CommonMark project LibNcFTP Used under license from and copyright © 1996-2010 Mike Gleason & NcFTP Software Exuberant ctags ©1996-2004 Darren Hiebert (source code here) PCRE2 Library Written by Philip Hazel and Zoltán Herczeg ©1997-2018 University of Cambridge, England Info-ZIP Library ©1990-2009 Info-ZIP. -
DFDL WG Stephen M Hanson, IBM [email protected] September 2014
GFD-P-R.207 (OBSOLETED by GFD-P-R.240) Michael J Beckerle, Tresys Technology OGF DFDL WG Stephen M Hanson, IBM [email protected] September 2014 Data Format Description Language (DFDL) v1.0 Specification Status of This Document Grid Final Draft (GFD) Obsoletes This document obsoletes GFD-P-R.174 dated January 2011 [OBSOLETE_DFDL]. Copyright Notice Copyright © Global Grid Forum (2004-2006). Some Rights Reserved. Distribution is unlimited. Copyright © Open Grid Forum (2006-2014). Some Rights Reserved. Distribution is unlimited Abstract This document is OBSOLETE. It is superceded by GFD-P-R.240. This document provides a definition of a standard Data Format Description Language (DFDL). This language allows description of text, dense binary, and legacy data formats in a vendor- neutral declarative manner. DFDL is an extension to the XML Schema Description Language (XSDL). GFD-P-R.207 (OBSOLETED by GFD-P-R.240) September 2014 Contents Data Format Description Language (DFDL) v1.0 Specification ...................................................... 1 1. Introduction ............................................................................................................................... 9 1.1 Why is DFDL Needed? ................................................................................................... 10 1.2 What is DFDL? ................................................................................................................ 10 Simple Example ...................................................................................................... -
CSCI 2041: Pattern Matching Basics
CSCI 2041: Pattern Matching Basics Chris Kauffman Last Updated: Fri Sep 28 08:52:58 CDT 2018 1 Logistics Reading Assignment 2 I OCaml System Manual: Ch I Demo in lecture 1.4 - 1.5 I Post today/tomorrow I Practical OCaml: Ch 4 Next Week Goals I Mon: Review I Code patterns I Wed: Exam 1 I Pattern Matching I Fri: Lecture 2 Consider: Summing Adjacent Elements 1 (* match_basics.ml: basic demo of pattern matching *) 2 3 (* Create a list comprised of the sum of adjacent pairs of 4 elements in list. The last element in an odd-length list is 5 part of the return as is. *) 6 let rec sum_adj_ie list = 7 if list = [] then (* CASE of empty list *) 8 [] (* base case *) 9 else 10 let a = List.hd list in (* DESTRUCTURE list *) 11 let atail = List.tl list in (* bind names *) 12 if atail = [] then (* CASE of 1 elem left *) 13 [a] (* base case *) 14 else (* CASE of 2 or more elems left *) 15 let b = List.hd atail in (* destructure list *) 16 let tail = List.tl atail in (* bind names *) 17 (a+b) :: (sum_adj_ie tail) (* recursive case *) The above function follows a common paradigm: I Select between Cases during a computation I Cases are based on structure of data I Data is Destructured to bind names to parts of it 3 Pattern Matching in Programming Languages I Pattern Matching as a programming language feature checks that data matches a certain structure the executes if so I Can take many forms such as processing lines of input files that match a regular expression I Pattern Matching in OCaml/ML combines I Case analysis: does the data match a certain structure I Destructure Binding: bind names to parts of the data I Pattern Matching gives OCaml/ML a certain "cool" factor I Associated with the match/with syntax as follows match something with | pattern1 -> result1 (* pattern1 gives result1 *) | pattern2 -> (* pattern 2.. -
Compiling Pattern Matching to Good Decision Trees
Submitted to ML’08 Compiling Pattern Matching to good Decision Trees Luc Maranget INRIA Luc.marangetinria.fr Abstract In this paper we study compilation to decision tree, whose We address the issue of compiling ML pattern matching to efficient primary advantage is never testing a given subterm of the subject decisions trees. Traditionally, compilation to decision trees is op- value more than once (and whose primary drawback is potential timized by (1) implementing decision trees as dags with maximal code size explosion). Our aim is to refine naive compilation to sharing; (2) guiding a simple compiler with heuristics. We first de- decision trees, and to compare the output of such an optimizing sign new heuristics that are inspired by necessity, a notion from compiler with optimized backtracking automata. lazy pattern matching that we rephrase in terms of decision tree se- Compilation to decision can be very sensitive to the testing mantics. Thereby, we simplify previous semantical frameworks and order of subject value subterms. The situation can be explained demonstrate a direct connection between necessity and decision by the example of an human programmer attempting to translate a ML program into a lower-level language without pattern matching. tree runtime efficiency. We complete our study by experiments, 1 showing that optimized compilation to decision trees is competi- Let f be the following function defined on triples of booleans : tive. We also suggest some heuristics precisely. l e t f x y z = match x,y,z with | _,F,T -> 1 Categories and Subject Descriptors D 3. 3 [Programming Lan- | F,T,_ -> 2 guages]: Language Constructs and Features—Patterns | _,_,F -> 3 | _,_,T -> 4 General Terms Design, Performance, Sequentiality. -
Combinatorial Pattern Matching
Combinatorial Pattern Matching 1 A Recurring Problem Finding patterns within sequences Variants on this idea Finding repeated motifs amoungst a set of strings What are the most frequent k-mers How many time does a specific k-mer appear Fundamental problem: Pattern Matching Find all positions of a particular substring in given sequence? 2 Pattern Matching Goal: Find all occurrences of a pattern in a text Input: Pattern p = p1, p2, … pn and text t = t1, t2, … tm Output: All positions 1 < i < (m – n + 1) such that the n-letter substring of t starting at i matches p def bruteForcePatternMatching(p, t): locations = [] for i in xrange(0, len(t)-len(p)+1): if t[i:i+len(p)] == p: locations.append(i) return locations print bruteForcePatternMatching("ssi", "imissmissmississippi") [11, 14] 3 Pattern Matching Performance Performance: m - length of the text t n - the length of the pattern p Search Loop - executed O(m) times Comparison - O(n) symbols compared Total cost - O(mn) per pattern In practice, most comparisons terminate early Worst-case: p = "AAAT" t = "AAAAAAAAAAAAAAAAAAAAAAAT" 4 We can do better! If we preprocess our pattern we can search more effciently (O(n)) Example: imissmissmississippi 1. s 2. s 3. s 4. SSi 5. s 6. SSi 7. s 8. SSI - match at 11 9. SSI - match at 14 10. s 11. s 12. s At steps 4 and 6 after finding the mismatch i ≠ m we can skip over all positions tested because we know that the suffix "sm" is not a prefix of our pattern "ssi" Even works for our worst-case example "AAAAT" in "AAAAAAAAAAAAAAT" by recognizing the shared prefixes ("AAA" in "AAAA"). -
Integrating Pattern Matching Within String Scanning a Thesis
Integrating Pattern Matching Within String Scanning A Thesis Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science with a Major in Computer Science in the College of Graduate Studies University of Idaho by John H. Goettsche Major Professor: Clinton Jeffery, Ph.D. Committee Members: Robert Heckendorn, Ph.D.; Robert Rinker, Ph.D. Department Administrator: Frederick Sheldon, Ph.D. July 2015 ii Authorization to Submit Thesis This Thesis of John H. Goettsche, submitted for the degree of Master of Science with a Major in Computer Science and titled \Integrating Pattern Matching Within String Scanning," has been reviewed in final form. Permission, as indicated by the signatures and dates below, is now granted to submit final copies to the College of Graduate Studies for approval. Major Professor: Date: Clinton Jeffery, Ph.D. Committee Members: Date: Robert Heckendorn, Ph.D. Date: Robert Rinker, Ph.D. Department Administrator: Date: Frederick Sheldon, Ph.D. iii Abstract A SNOBOL4 like pattern data type and pattern matching operation were introduced to the Unicon language in 2005, but patterns were not integrated with the Unicon string scanning control structure and hence, the SNOBOL style patterns were not adopted as part of the language at that time. The goal of this project is to make the pattern data type accessible to the Unicon string scanning control structure and vice versa; and also make the pattern operators and functions lexically consistent with Unicon. To accomplish these goals, a Unicon string matching operator was changed to allow the execution of a pattern match in the anchored mode, pattern matching unevaluated expressions were revised to handle complex string scanning functions, and the pattern matching lexemes were revised to be more consistent with the Unicon language. -
Practical Authenticated Pattern Matching with Optimal Proof Size
Practical Authenticated Pattern Matching with Optimal Proof Size Dimitrios Papadopoulos Charalampos Papamanthou Boston University University of Maryland [email protected] [email protected] Roberto Tamassia Nikos Triandopoulos Brown University RSA Laboratories & Boston University [email protected] [email protected] ABSTRACT otherwise. More elaborate models for pattern matching involve We address the problem of authenticating pattern matching queries queries expressed as regular expressions over Σ or returning multi- over textual data that is outsourced to an untrusted cloud server. By ple occurrences of p, and databases allowing search over multiple employing cryptographic accumulators in a novel optimal integrity- texts or other (semi-)structured data (e.g., XML data). This core checking tool built directly over a suffix tree, we design the first data-processing problem has numerous applications in a wide range authenticated data structure for verifiable answers to pattern match- of topics including intrusion detection, spam filtering, web search ing queries featuring fast generation of constant-size proofs. We engines, molecular biology and natural language processing. present two main applications of our new construction to authen- Previous works on authenticated pattern matching include the ticate: (i) pattern matching queries over text documents, and (ii) schemes by Martel et al. [28] for text pattern matching, and by De- exact path queries over XML documents. Answers to queries are vanbu et al. [16] and Bertino et al. [10] for XML search. In essence, verified by proofs of size at most 500 bytes for text pattern match- these works adopt the same general framework: First, by hierarchi- ing, and at most 243 bytes for exact path XML search, indepen- cally applying a cryptographic hash function (e.g., SHA-2) over the dently of the document or answer size. -
Notetab User Manual
NoteTab User Manual Copyright © 1995-2016, FOOKES Holding Ltd, Switzerland NoteTab® Tame Your Text with NoteTab by FOOKES Holding Ltd A leading-edge text and HTML editor. Handle a stack of huge files with ease, format text, use a spell-checker, and perform system-wide searches and multi-line global replacements. Build document templates, convert text to HTML on the fly, and take charge of your code with a bunch of handy HTML tools. Use a power-packed scripting language to create anything from a text macro to a mini-application. Winner of top industry awards since 1998. “NoteTab” and “Fookes” are registered trademarks of Fookes Holding Ltd. All other trademarks and service marks, both marked and not marked, are the property of their respective ow ners. NoteTab® Copyright © 1995-2016, FOOKES Holding Ltd, Switzerland All rights reserved. No parts of this work may be reproduced in any form or by any means - graphic, electronic, or mechanical, including photocopying, recording, taping, or information storage and retrieval systems - without the written permission of the publisher. “NoteTab” and “Fookes” are registered trademarks of Fookes Holding Ltd. All other trademarks and service marks, both marked and not marked, are the property of their respective owners. While every precaution has been taken in the preparation of this document, the publisher and the author assume no responsibility for errors or omissions, or for damages resulting from the use of information contained in this document or from the use of programs and source code that may accompany it. In no event shall the publisher and the author be liable for any loss of profit or any other commercial damage caused or alleged to have been caused directly or indirectly by this document. -
Sample Chapter 3
108_GILLAM.ch03.fm Page 61 Monday, August 19, 2002 1:58 PM 3 Architecture: Not Just a Pile of Code Charts f you’re used to working with ASCII or other similar encodings designed I for European languages, you’ll find Unicode noticeably different from those other standards. You’ll also find that when you’re dealing with Unicode text, various assumptions you may have made in the past about how you deal with text don’t hold. If you’ve worked with encodings for other languages, at least some characteristics of Unicode will be familiar to you, but even then, some pieces of Unicode will be unfamiliar. Unicode is more than just a big pile of code charts. To be sure, it includes a big pile of code charts, but Unicode goes much further. It doesn’t just take a bunch of character forms and assign numbers to them; it adds a wealth of infor- mation on what those characters mean and how they are used. Unlike virtually all other character encoding standards, Unicode isn’t de- signed for the encoding of a single language or a family of closely related lan- guages. Rather, Unicode is designed for the encoding of all written languages. The current version doesn’t give you a way to encode all written languages (and in fact, this concept is such a slippery thing to define that it probably never will), but it does provide a way to encode an extremely wide variety of lan- guages. The languages vary tremendously in how they are written, so Unicode must be flexible enough to accommodate all of them. -
Pattern Matching
Pattern Matching Document #: P1371R1 Date: 2019-06-17 Project: Programming Language C++ Evolution Reply-to: Sergei Murzin <[email protected]> Michael Park <[email protected]> David Sankel <[email protected]> Dan Sarginson <[email protected]> Contents 1 Revision History 3 2 Introduction 3 3 Motivation and Scope 3 4 Before/After Comparisons4 4.1 Matching Integrals..........................................4 4.2 Matching Strings...........................................4 4.3 Matching Tuples...........................................4 4.4 Matching Variants..........................................5 4.5 Matching Polymorphic Types....................................5 4.6 Evaluating Expression Trees.....................................6 4.7 Patterns In Declarations.......................................8 5 Design Overview 9 5.1 Basic Syntax.............................................9 5.2 Basic Model..............................................9 5.3 Types of Patterns........................................... 10 5.3.1 Primary Patterns....................................... 10 5.3.1.1 Wildcard Pattern................................. 10 5.3.1.2 Identifier Pattern................................. 10 5.3.1.3 Expression Pattern................................ 10 5.3.2 Compound Patterns..................................... 11 5.3.2.1 Structured Binding Pattern............................ 11 5.3.2.2 Alternative Pattern................................ 12 5.3.2.3 Parenthesized Pattern............................... 15 5.3.2.4 Case Pattern...................................