A Text Pattern-Matching Tool Based on Parsing Expression Grammars
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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. -
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.. -
On the Relation Between Context-Free Grammars And
On the Relation between Context-Free Grammars and Parsing Expression Grammars Fabio Mascarenhas Department of Computer Science – UFRJ – Rio de Janeiro – Brazil S´ergio Medeiros School of Science and Technology – UFRN – Natal – Brazil Roberto Ierusalimschy Department of Computer Science – PUC-Rio – Rio de Janeiro – Brazil Abstract Context-Free Grammars (CFGs) and Parsing Expression Grammars (PEGs) have several similarities and a few differences in both their syntax and seman- tics, but they are usually presented through formalisms that hinder a proper comparison. In this paper we present a new formalism for CFGs that highlights the similarities and differences between them. The new formalism borrows from PEGs the use of parsing expressions and the recognition-based semantics. We show how one way of removing non-determinism from this formalism yields a formalism with the semantics of PEGs. We also prove, based on these new formalisms, how LL(1) grammars define the same language whether interpreted as CFGs or as PEGs, and also show how strong-LL(k), right-linear, and LL- regular grammars have simple language-preserving translations from CFGs to PEGs. Once these classes of CFGs can be automatically translated to equivalent PEGs, we can reuse classic top-down grammars in PEG-based tools. Keywords: Context-free grammars, parsing expression grammars, parsing, LL(1), LL(k), LL-regular, right-linear grammars, natural semantics arXiv:1304.3177v2 [cs.FL] 13 Feb 2014 1. Introduction Context-Free Grammars (CFGs) are the formalism of choice for describing the syntax of programming languages. A CFG describes a language as the set Email addresses: [email protected] (Fabio Mascarenhas), [email protected] (S´ergio Medeiros), [email protected] (Roberto Ierusalimschy) Preprint submitted to Science of Computer Programming March 19, 2018 of strings generated from the grammar’s initial symbol by a sequence of rewrit- ing steps. -
Parsing Expression Grammars: a Recognition-Based Syntactic Foundation
Parsing Expression Grammars: A Recognition-Based Syntactic Foundation Bryan Ford Massachusetts Institute of Technology Cambridge, MA [email protected] Abstract 1 Introduction For decades we have been using Chomsky’s generative system of Most language syntax theory and practice is based on generative grammars, particularly context-free grammars (CFGs) and regu- systems, such as regular expressions and context-free grammars, in lar expressions (REs), to express the syntax of programming lan- which a language is defined formally by a set of rules applied re- guages and protocols. The power of generative grammars to ex- cursively to generate strings of the language. A recognition-based press ambiguity is crucial to their original purpose of modelling system, in contrast, defines a language in terms of rules or predi- natural languages, but this very power makes it unnecessarily diffi- cates that decide whether or not a given string is in the language. cult both to express and to parse machine-oriented languages using Simple languages can be expressed easily in either paradigm. For CFGs. Parsing Expression Grammars (PEGs) provide an alterna- example, fs 2 a∗ j s = (aa)ng is a generative definition of a trivial tive, recognition-based formal foundation for describing machine- language over a unary character set, whose strings are “constructed” oriented syntax, which solves the ambiguity problem by not intro- by concatenating pairs of a’s. In contrast, fs 2 a∗ j (jsj mod 2 = 0)g ducing ambiguity in the first place. Where CFGs express nondeter- is a recognition-based definition of the same language, in which a ministic choice between alternatives, PEGs instead use prioritized string of a’s is “accepted” if its length is even. -
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. -
A Programming Language Where the Syntax and Semantics Are Mutable at Runtime
DEPARTMENT OF COMPUTER SCIENCE A Programming Language Where the Syntax and Semantics Are Mutable at Runtime Christopher Graham Seaton A dissertation submitted to the University of Bristol in accordance with the requirements of the degree of Master of Enginnering in the Faculty of Engineering May 2007 CSMENG-07 Declaration A dissertation submitted to the University of Bristol in accordance with the requirements of the degree of Master of Enginnering in the Faculty of Engineering. It has not been submitted for any other degree or diploma of any examining body. Except where specifically acknowledged, it is all the work of the Author. Christopher Graham Seaton, May 2007 SUMMARY Katahdin is a programming language where the syntax and semantics are mu- table at runtime. The Katahdin interpreter parses and executes programs ac- cording to a language definition that can be modified by the running program. Commands to modify the language definition are similar to those that define new types and functions in other languages, and can be collected into lan- guage definition modules. Katahdin can execute programs written in any language which it has a defini- tion module for. The definition of more than one language can be composed allowing more than one language to be used in the same program, the same file, and even the same function. Data and code is shared between all lan- guages, with a function defined in one language callable from another with no binding layer. Traditional language development involves using a parser generator tool to generate a static parser from the language grammar. Katahdin’s approach is to just-in-time compile a parser from the grammar as the program runs. -
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................................... -
ANTLR Reference Manual ANTLR Reference Manual ANTLR Jguru Credits Project Lead Terence Parr
ANTLR Reference Manual ANTLR Reference Manual ANTLR jGuru Credits Project Lead Terence Parr Support from jGuru.com Your View of the Java Universe Help with initial coding John Lilly, Empathy Software C++ code generator by Peter Wells and Ric Klaren Sather code generator by Mika Illouz Infrastructure support from Perforce: The world's best source code control system Substantial intellectual effort donated by John Mitchell Scott Stanchfield Jim Coker Monty Zukowski (Streams, translation) Chapman Flack (UNICODE, streams) ParseView parser debugger by Scott Stanchfield ANTLR Version 2.7.1 Version: $Id: //depot/code/org.antlr/main/main/doc/index.html#8 $ October 1, 2000 Check out the ANTLR FAQ at jguru.com! Check out the Bug Database (ok, so it's out of date) Check out http://www.ANTLR.org ParseView debugger is available. (for prior versions) http://www.antlr.org/doc/ (1 of 7) [8/10/2001 10:45:13 AM] ANTLR Reference Manual If you are looking for the previous main version (PCCTS 1.33) of ANTLR rather than the Java/C++/Sather version (2.x), see Getting started with PCCTS. Download ANTLR 2.7.1. ANTLR 2.7.1 release notes ANTLR Meta-Language ● Meta-Language Vocabulary ● Header Section ● Parser Class Definitions ● Lexical Analyzer Class Definitions ● Tree-parser Class Definitions ● Options Section ● Tokens Section ● Grammar Inheritance ● Rule Definitions ● Atomic Production elements ● Simple Production elements ● Production Element Operators ● Token Classes ● Predicates ● Element Labels ● EBNF Rule Elements ● Interpretation Of Semantic Actions ● Semantic