Regexing in SAS for Pattern Matching and Replacement
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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....................... -
Express Yourself! Regular Expressions Vs SAS Text String Functions Spencer Childress, Rho®, Inc., Chapel Hill, NC
PharmaSUG 2014 - Paper BB08 Express Yourself! Regular Expressions vs SAS Text String Functions Spencer Childress, Rho®, Inc., Chapel Hill, NC ABSTRACT ® SAS and Perl regular expression functions offer a powerful alternative and complement to typical SAS text string functions. By harnessing the power of regular expressions, SAS functions such as PRXMATCH and PRXCHANGE not only overlap functionality with functions such as INDEX and TRANWRD, they also eclipse them. With the addition of the modifier argument to such functions as COMPRESS, SCAN, and FINDC, some of the regular expression syntax already exists for programmers familiar with SAS 9.2 and later versions. We look at different methods that solve the same problem, with detailed explanations of how each method works. Problems range from simple searches to complex search and replaces. Programmers should expect an improved grasp of the regular expression and how it can complement their portfolio of code. The techniques presented herein offer a good overview of basic data step text string manipulation appropriate for all levels of SAS capability. While this article targets a clinical computing audience, the techniques apply to a broad range of computing scenarios. INTRODUCTION This article focuses on the added capability of Perl regular expressions to a SAS programmer’s skillset. A regular expression (regex) forms a search pattern, which SAS uses to scan through a text string to detect matches. An extensive library of metacharacters, characters with special meanings within the regex, allows extremely robust searches. Before jumping in, the reader would do well to read over ‘An Introduction to Perl Regular Expressions in SAS 9’, referencing page 3 in particular (Cody, 2004). -
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. -
855 Symbols & (Ampersand), 211 && (AND Operator), 608 `` (Backquotes/Backticks), 143 & (Bitwise AND) Operator, 1
index.fm Page 855 Wednesday, October 25, 2006 1:28 PM Index Symbols A & (ampersand), 211 abs() function, 117 && (AND operator), 608 Access methods, 53, 758–760 `` (backquotes/backticks), 143 ACTION attribute, 91 & (bitwise AND) operator, 140 action attribute, 389–390, 421 ~ (bitwise NOT) operator, 141 addcslashes() function, 206–208, 214 | (bitwise OR) operator, 140 Addition (+), 112 ^ (bitwise XOR) operator, 140 addslashes() function, 206–208, 214 ^ (caret metacharacter), 518, 520–521 admin_art_edit.php, 784, 793–796 " (double quote), 211 admin_art_list.php, 784 @ (error control) operator, 143–145 admin_artist_edit.php, 784, 788–789 > (greater than), 211, 608, 611-612 admin_artist_insert.php, 784, 791–792 << (left shift) operator, 141 admin_artist_list.php, 784, 786–787, < (less than), 211, 608, 611-612 796 * metacharacter, 536–538 admin_footer.php, 784 !, NOT operator, 608 admin_header.php, 784, 803 < operator, 608, 611–612 admin_login.php, 784, 797, 803 <= operator, 608 Advisory locking, 474 <>, != operator, 608–609 Aliases, 630–631 = operator, 608–609 Alphabetic sort of, 299–300 > operator, 608, 611–612 Alphanumeric word characters, metasymbols -> operator, 743–744 representing, 531–533 >= operator, 608 ALTER command, 631 || (OR operator), 130–132, 608 ALTER TABLE statement, 620, 631–633 >> (right shift) operator, 141 Alternation, metacharacters for, 543 ' (single quote), 211, 352–353, 609 Anchoring metacharacters, 520–523 % wildcard, 613–614 beginning-of-line anchor, 520–523 >>> (zero-fill right shift) operator, end-of-line anchor, -
Perl Regular Expressions 102
NESUG 2006 Posters Perl Regular Expressions 102 Kenneth W. Borowiak, Howard M. Proskin & Associates, Inc., Rochester, NY ABSTRACT Perl regular expressions were made available in SAS® in Version 9 through the PRX family of functions and call routines. The SAS community has already generated some literature on getting started with these often-cryptic, but very powerful character functions. The goal of this paper is to build upon the existing literature by exposing some of the pitfalls and subtleties of writing regular expressions. Using a fictitious clinical trial adverse event data set, concepts such as zero-width assertions, anchors, non-capturing buffers and greedy quantifiers are explored. This paper is targeted at those who already have a basic knowledge of regular expressions. Keywords: word boundary, negative lookaheads, positive lookbehinds, zero-width assertions, anchors, non-capturing buffers, greedy quantifiers INTRODUCTION Regular expressions enable you to generally characterize a pattern for subsequent matching and manipulation of text fields. If you have you ever used a text editor’s Find (-and Replace) capability of literal strings then you are already using regular expressions, albeit in the most strict sense. In SAS Version 9, Perl regular expressions were made available through the PRX family of functions and call routines. Though the Programming Extract and Reporting Language is itself a programming language, it is the regular expression capabilities of Perl that have been implemented in SAS. The SAS community of users and developers has already generated some literature on getting started with these often- cryptic, but very powerful character functions. Introductory papers by Cassell [2005], Cody [2006], Pless [2005] and others are referenced at the end of this paper. -
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"). -
Quick Tips and Tricks: Perl Regular Expressions in SAS® Pratap S
Paper 4005-2019 Quick Tips and Tricks: Perl Regular Expressions in SAS® Pratap S. Kunwar, Jinson Erinjeri, Emmes Corporation. ABSTRACT Programming with text strings or patterns in SAS® can be complicated without the knowledge of Perl regular expressions. Just knowing the basics of regular expressions (PRX functions) will sharpen anyone's programming skills. Having attended a few SAS conferences lately, we have noticed that there are few presentations on this topic and many programmers tend to avoid learning and applying the regular expressions. Also, many of them are not aware of the capabilities of these functions in SAS. In this presentation, we present quick tips on these expressions with various applications which will enable anyone learn this topic with ease. INTRODUCTION SAS has numerous character (string) functions which are very useful in manipulating character fields. Every SAS programmer is generally familiar with basic character functions such as SUBSTR, SCAN, STRIP, INDEX, UPCASE, LOWCASE, CAT, ANY, NOT, COMPARE, COMPBL, COMPRESS, FIND, TRANSLATE, TRANWRD etc. Though these common functions are very handy for simple string manipulations, they are not built for complex pattern matching and search-and-replace operations. Regular expressions (RegEx) are both flexible and powerful and are widely used in popular programming languages such as Perl, Python, JavaScript, PHP, .NET and many more for pattern matching and translating character strings. Regular expressions skills can be easily ported to other languages like SQL., However, unlike SQL, RegEx itself is not a programming language, but simply defines a search pattern that describes text. Learning regular expressions starts with understanding of character classes and metacharacters. -
Context-Free Grammar for the Syntax of Regular Expression Over the ASCII
Context-free Grammar for the syntax of regular expression over the ASCII character set assumption : • A regular expression is to be interpreted a Haskell string, then is used to match against a Haskell string. Therefore, each regexp is enclosed inside a pair of double quotes, just like any Haskell string. For clarity, a regexp is highlighted and a “Haskell input string” is quoted for the examples in this document. • Since ASCII character strings will be encoded as in Haskell, therefore special control ASCII characters such as NUL and DEL are handled by Haskell. context-free grammar : BNF notation is used to describe the syntax of regular expressions defined in this document, with the following basic rules: • <nonterminal> ::= choice1 | choice2 | ... • Double quotes are used when necessary to reflect the literal meaning of the content itself. <regexp> ::= <union> | <concat> <union> ::= <regexp> "|" <concat> <concat> ::= <term><concat> | <term> <term> ::= <star> | <element> <star> ::= <element>* <element> ::= <group> | <char> | <emptySet> | <emptyStr> <group> ::= (<regexp>) <char> ::= <alphanum> | <symbol> | <white> <alphanum> ::= A | B | C | ... | Z | a | b | c | ... | z | 0 | 1 | 2 | ... | 9 <symbol> ::= ! | " | # | $ | % | & | ' | + | , | - | . | / | : | ; | < | = | > | ? | @ | [ | ] | ^ | _ | ` | { | } | ~ | <sp> | \<metachar> <sp> ::= " " <metachar> ::= \ | "|" | ( | ) | * | <white> <white> ::= <tab> | <vtab> | <nline> <tab> ::= \t <vtab> ::= \v <nline> ::= \n <emptySet> ::= Ø <emptyStr> ::= "" Explanations : 1. Definition of <metachar> in our definition of regexp: Symbol meaning \ Used to escape a metacharacter, \* means the star char itself | Specifies alternatives, y|n|m means y OR n OR m (...) Used for grouping, giving the group priority * Used to indicate zero or more of a regexp, a* matches the empty string, “a”, “aa”, “aaa” and so on Whi tespace char meaning \n A new line character \t A horizontal tab character \v A vertical tab character 2. -
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.