Pattern Matching with Regular Expressions
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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. -
Use Perl Regular Expressions in SAS® Shuguang Zhang, WRDS, Philadelphia, PA
NESUG 2007 Programming Beyond the Basics Use Perl Regular Expressions in SAS® Shuguang Zhang, WRDS, Philadelphia, PA ABSTRACT Regular Expression (Regexp) enhance search and replace operations on text. In SAS®, the INDEX, SCAN and SUBSTR functions along with concatenation (||) can be used for simple search and replace operations on static text. These functions lack flexibility and make searching dynamic text difficult, and involve more function calls. Regexp combines most, if not all, of these steps into one expression. This makes code less error prone, easier to maintain, clearer, and can improve performance. This paper will discuss three ways to use Perl Regular Expression in SAS: 1. Use SAS PRX functions; 2. Use Perl Regular Expression with filename statement through a PIPE such as ‘Filename fileref PIPE 'Perl programm'; 3. Use an X command such as ‘X Perl_program’; Three typical uses of regular expressions will also be discussed and example(s) will be presented for each: 1. Test for a pattern of characters within a string; 2. Replace text; 3. Extract a substring. INTRODUCTION Perl is short for “Practical Extraction and Report Language". Larry Wall Created Perl in mid-1980s when he was trying to produce some reports from a Usenet-Nes-like hierarchy of files. Perl tries to fill the gap between low-level programming and high-level programming and it is easy, nearly unlimited, and fast. A regular expression, often called a pattern in Perl, is a template that either matches or does not match a given string. That is, there are an infinite number of possible text strings. -
Lecture 18: Theory of Computation Regular Expressions and Dfas
Introduction to Theoretical CS Lecture 18: Theory of Computation Two fundamental questions. ! What can a computer do? ! What can a computer do with limited resources? General approach. Pentium IV running Linux kernel 2.4.22 ! Don't talk about specific machines or problems. ! Consider minimal abstract machines. ! Consider general classes of problems. COS126: General Computer Science • http://www.cs.Princeton.EDU/~cos126 2 Why Learn Theory In theory . Regular Expressions and DFAs ! Deeper understanding of what is a computer and computing. ! Foundation of all modern computers. ! Pure science. ! Philosophical implications. a* | (a*ba*ba*ba*)* In practice . ! Web search: theory of pattern matching. ! Sequential circuits: theory of finite state automata. a a a ! Compilers: theory of context free grammars. b b ! Cryptography: theory of computational complexity. 0 1 2 ! Data compression: theory of information. b "In theory there is no difference between theory and practice. In practice there is." -Yogi Berra 3 4 Pattern Matching Applications Regular Expressions: Basic Operations Test if a string matches some pattern. Regular expression. Notation to specify a set of strings. ! Process natural language. ! Scan for virus signatures. ! Search for information using Google. Operation Regular Expression Yes No ! Access information in digital libraries. ! Retrieve information from Lexis/Nexis. Concatenation aabaab aabaab every other string ! Search-and-replace in a word processors. cumulus succubus Wildcard .u.u.u. ! Filter text (spam, NetNanny, Carnivore, malware). jugulum tumultuous ! Validate data-entry fields (dates, email, URL, credit card). aa Union aa | baab baab every other string ! Search for markers in human genome using PROSITE patterns. aa ab Closure ab*a abbba ababa Parse text files. -
SQL Wildcard Operators
SSQQLL -- WWIILLDDCCAARRDD OOPPEERRAATTOORRSS http://www.tutorialspoint.com/sql/sql-wildcards.htm Copyright © tutorialspoint.com We already have discussed SQL LIKE operator, which is used to compare a value to similar values using wildcard operators. SQL supports following two wildcard operators in conjunction with the LIKE operator: Wildcards Description The percent sign Matches one or more characters. Note that MS Access uses the asterisk ∗ wildcard character instead of the percent sign wildcard character. The underscore _ Matches one character. Note that MS Access uses a question mark ? instead of the underscore _ to match any one character. The percent sign represents zero, one, or multiple characters. The underscore represents a single number or character. The symbols can be used in combinations. Syntax: The basic syntax of '%' and '_' is as follows: SELECT FROM table_name WHERE column LIKE 'XXXX%' or SELECT FROM table_name WHERE column LIKE '%XXXX%' or SELECT FROM table_name WHERE column LIKE 'XXXX_' or SELECT FROM table_name WHERE column LIKE '_XXXX' or SELECT FROM table_name WHERE column LIKE '_XXXX_' You can combine N number of conditions using AND or OR operators. Here, XXXX could be any numeric or string value. Example: Here are number of examples showing WHERE part having different LIKE clause with '%' and '_' operators: Statement Description WHERE SALARY LIKE Finds any values that start with 200 '200%' WHERE SALARY LIKE Finds any values that have 200 in any position '%200%' WHERE SALARY LIKE Finds any values that have 00 -
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.. -
Perl Regular Expressions Tip Sheet Functions and Call Routines
– Perl Regular Expressions Tip Sheet Functions and Call Routines Basic Syntax Advanced Syntax regex-id = prxparse(perl-regex) Character Behavior Character Behavior Compile Perl regular expression perl-regex and /…/ Starting and ending regex delimiters non-meta Match character return regex-id to be used by other PRX functions. | Alternation character () Grouping {}[]()^ Metacharacters, to match these pos = prxmatch(regex-id | perl-regex, source) $.|*+?\ characters, override (escape) with \ Search in source and return position of match or zero Wildcards/Character Class Shorthands \ Override (escape) next metacharacter if no match is found. Character Behavior \n Match capture buffer n Match any one character . (?:…) Non-capturing group new-string = prxchange(regex-id | perl-regex, times, \w Match a word character (alphanumeric old-string) plus "_") Lazy Repetition Factors Search and replace times number of times in old- \W Match a non-word character (match minimum number of times possible) string and return modified string in new-string. \s Match a whitespace character Character Behavior \S Match a non-whitespace character *? Match 0 or more times call prxchange(regex-id, times, old-string, new- \d Match a digit character +? Match 1 or more times string, res-length, trunc-value, num-of-changes) Match a non-digit character ?? Match 0 or 1 time Same as prior example and place length of result in \D {n}? Match exactly n times res-length, if result is too long to fit into new-string, Character Classes Match at least n times trunc-value is set to 1, and the number of changes is {n,}? Character Behavior Match at least n but not more than m placed in num-of-changes. -
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. -
Mastering Powershellpowershell
CopyrightCopyright © 2009 BBS Technologies ALL RIGHTS RESERVED. No part of this work covered by the copyright herein may be reproduced, transmitted, stored, or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, information networks, or information storage and retrieval systems except as permitted under Section 107 or 108 of the 1976 United States Copyright Act without the prior written permission of the publisher. For permission to use material from the text please contact Idera at [email protected]. Microsoft® Windows PowerShell® and Microsoft® SQL Server® are registered trademarks of Microsoft Corporation in the United Stated and other countries. All other trademarks are the property of their respective owners. AboutAbout thethe AuthorAuthor Dr. Tobias Weltner is one of the most visible PowerShell MVPs in Europe. He has published more than 80 books on Windows and Scripting Techniques with Microsoft Press and other publishers, is a regular speaker at conferences and road shows and does high level PowerShell and Scripting trainings for companies throughout Europe. He created the powershell.com website and community in an effort to help people adopt and use PowerShell more efficiently. As software architect, he created a number of award-winning scripting tools such as SystemScripter (VBScript), the original PowerShell IDE and PowerShell Plus, a comprehensive integrated PowerShell development system. AcknowledgmentsAcknowledgments First and foremost, I’d like to thank my family who is always a source of inspiration and encouragement. A special thanks to Idera, Rick Pleczko, David Fargo, Richard Giles, Conley Smith and David Twamley for helping to bring this book to the English speaking world. -
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"). -
Issue Planned Despatch & Payment Date* Covermount
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Unicode Regular Expressions Technical Reports
7/1/2019 UTS #18: Unicode Regular Expressions Technical Reports Working Draft for Proposed Update Unicode® Technical Standard #18 UNICODE REGULAR EXPRESSIONS Version 20 Editors Mark Davis, Andy Heninger Date 2019-07-01 This Version http://www.unicode.org/reports/tr18/tr18-20.html Previous Version http://www.unicode.org/reports/tr18/tr18-19.html Latest Version http://www.unicode.org/reports/tr18/ Latest Proposed http://www.unicode.org/reports/tr18/proposed.html Update Revision 20 Summary This document describes guidelines for how to adapt regular expression engines to use Unicode. Status This is a draft document which may be updated, replaced, or superseded by other documents at any time. Publication does not imply endorsement by the Unicode Consortium. This is not a stable document; it is inappropriate to cite this document as other than a work in progress. A Unicode Technical Standard (UTS) is an independent specification. Conformance to the Unicode Standard does not imply conformance to any UTS. Please submit corrigenda and other comments with the online reporting form [Feedback]. Related information that is useful in understanding this document is found in the References. For the latest version of the Unicode Standard, see [Unicode]. For a list of current Unicode Technical Reports, see [Reports]. For more information about versions of the Unicode Standard, see [Versions]. Contents 0 Introduction 0.1 Notation 0.2 Conformance 1 Basic Unicode Support: Level 1 1.1 Hex Notation 1.1.1 Hex Notation and Normalization 1.2 Properties 1.2.1 General