Pattern Matching
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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. -
GNU Grep: Print Lines That Match Patterns Version 3.7, 8 August 2021
GNU Grep: Print lines that match patterns version 3.7, 8 August 2021 Alain Magloire et al. This manual is for grep, a pattern matching engine. Copyright c 1999{2002, 2005, 2008{2021 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the section entitled \GNU Free Documentation License". i Table of Contents 1 Introduction ::::::::::::::::::::::::::::::::::::: 1 2 Invoking grep :::::::::::::::::::::::::::::::::::: 2 2.1 Command-line Options ::::::::::::::::::::::::::::::::::::::::: 2 2.1.1 Generic Program Information :::::::::::::::::::::::::::::: 2 2.1.2 Matching Control :::::::::::::::::::::::::::::::::::::::::: 2 2.1.3 General Output Control ::::::::::::::::::::::::::::::::::: 3 2.1.4 Output Line Prefix Control :::::::::::::::::::::::::::::::: 5 2.1.5 Context Line Control :::::::::::::::::::::::::::::::::::::: 6 2.1.6 File and Directory Selection:::::::::::::::::::::::::::::::: 7 2.1.7 Other Options ::::::::::::::::::::::::::::::::::::::::::::: 9 2.2 Environment Variables:::::::::::::::::::::::::::::::::::::::::: 9 2.3 Exit Status :::::::::::::::::::::::::::::::::::::::::::::::::::: 12 2.4 grep Programs :::::::::::::::::::::::::::::::::::::::::::::::: 13 3 Regular Expressions ::::::::::::::::::::::::::: 14 3.1 Fundamental Structure :::::::::::::::::::::::::::::::::::::::: -
Cygwin User's Guide
Cygwin User’s Guide Cygwin User’s Guide ii Copyright © Cygwin authors Permission is granted to make and distribute verbatim copies of this documentation provided the copyright notice and this per- mission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this documentation under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this documentation into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the Free Software Foundation. Cygwin User’s Guide iii Contents 1 Cygwin Overview 1 1.1 What is it? . .1 1.2 Quick Start Guide for those more experienced with Windows . .1 1.3 Quick Start Guide for those more experienced with UNIX . .1 1.4 Are the Cygwin tools free software? . .2 1.5 A brief history of the Cygwin project . .2 1.6 Highlights of Cygwin Functionality . .3 1.6.1 Introduction . .3 1.6.2 Permissions and Security . .3 1.6.3 File Access . .3 1.6.4 Text Mode vs. Binary Mode . .4 1.6.5 ANSI C Library . .4 1.6.6 Process Creation . .5 1.6.6.1 Problems with process creation . .5 1.6.7 Signals . .6 1.6.8 Sockets . .6 1.6.9 Select . .7 1.7 What’s new and what changed in Cygwin . .7 1.7.1 What’s new and what changed in 3.2 . -
Scons API Docs Version 4.2
SCons API Docs version 4.2 SCons Project July 31, 2021 Contents SCons Project API Documentation 1 SCons package 1 Module contents 1 Subpackages 1 SCons.Node package 1 Submodules 1 SCons.Node.Alias module 1 SCons.Node.FS module 9 SCons.Node.Python module 68 Module contents 76 SCons.Platform package 85 Submodules 85 SCons.Platform.aix module 85 SCons.Platform.cygwin module 85 SCons.Platform.darwin module 86 SCons.Platform.hpux module 86 SCons.Platform.irix module 86 SCons.Platform.mingw module 86 SCons.Platform.os2 module 86 SCons.Platform.posix module 86 SCons.Platform.sunos module 86 SCons.Platform.virtualenv module 87 SCons.Platform.win32 module 87 Module contents 87 SCons.Scanner package 89 Submodules 89 SCons.Scanner.C module 89 SCons.Scanner.D module 93 SCons.Scanner.Dir module 93 SCons.Scanner.Fortran module 94 SCons.Scanner.IDL module 94 SCons.Scanner.LaTeX module 94 SCons.Scanner.Prog module 96 SCons.Scanner.RC module 96 SCons.Scanner.SWIG module 96 Module contents 96 SCons.Script package 99 Submodules 99 SCons.Script.Interactive module 99 SCons.Script.Main module 101 SCons.Script.SConsOptions module 108 SCons.Script.SConscript module 115 Module contents 122 SCons.Tool package 123 Module contents 123 SCons.Variables package 125 Submodules 125 SCons.Variables.BoolVariable module 125 SCons.Variables.EnumVariable module 125 SCons.Variables.ListVariable module 126 SCons.Variables.PackageVariable module 126 SCons.Variables.PathVariable module 127 Module contents 127 SCons.compat package 129 Module contents 129 Submodules 129 SCons.Action -
15-122: Principles of Imperative Computation, Fall 2014 Lab 11: Strings in C
15-122: Principles of Imperative Computation, Fall 2014 Lab 11: Strings in C Tom Cortina(tcortina@cs) and Rob Simmons(rjsimmon@cs) Monday, November 10, 2014 For this lab, you will show your TA your answers once you complete your activities. Autolab is not used for this lab. SETUP: Make a directory lab11 in your private 15122 directory and copy the required lab files to your lab11 directory: cd private/15122 mkdir lab11 cp /afs/andrew.cmu.edu/usr9/tcortina/public/15122-f14/*.c lab11 1 Storing and using strings using C Load the file lab11ex1.c into a text editor. Read through the file and write down what you think the output will be before you run the program. (The ASCII value of 'a' is 97.) Then compile and run the program. Be sure to use all of the required flags for the C compiler. Answer the following questions on paper: Exercise 1. When word is initially printed out character by character, why does only one character get printed? Exercise 2. Change the program so word[3] = 'd'. Recompile and rerun. Explain the change in the output. Run valgrind on your program. How do the messages from valgrind correspond to the change we made? Exercise 3. Change word[3] back. Uncomment the code that treats the four character array word as a 32-bit integer. Compile and run again. Based on the answer, how are bytes of an integer stored on the computer where you are running your code? 2 Arrays of strings Load the file lab11ex2.c into a text editor. -
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.. -
Aspera CLI User Guide
Aspera Command- Line Interface Guide 3.7.7 Mac OS X Revision: 74 Generated: 09/25/2018 16:52 Contents Introduction............................................................................................................... 3 System Requirements............................................................................................... 3 Installation................................................................................................................. 3 Installing the Aspera CLI.....................................................................................................................................3 Configuring for Faspex.........................................................................................................................................4 Configuring for Aspera on Cloud........................................................................................................................ 4 Uninstalling........................................................................................................................................................... 5 aspera: The Command-Line Transfer Client........................................................ 5 About the Command-Line Client.........................................................................................................................5 Prerequisites.......................................................................................................................................................... 6 aspera Command Reference................................................................................................................................ -
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. -
Bash Guide for Beginners
Bash Guide for Beginners Machtelt Garrels Garrels BVBA <tille wants no spam _at_ garrels dot be> Version 1.11 Last updated 20081227 Edition Bash Guide for Beginners Table of Contents Introduction.........................................................................................................................................................1 1. Why this guide?...................................................................................................................................1 2. Who should read this book?.................................................................................................................1 3. New versions, translations and availability.........................................................................................2 4. Revision History..................................................................................................................................2 5. Contributions.......................................................................................................................................3 6. Feedback..............................................................................................................................................3 7. Copyright information.........................................................................................................................3 8. What do you need?...............................................................................................................................4 9. Conventions used in this -
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"). -
Unibasic 9.3 Reference Guide
Unibasic - Dynamic Concepts Wiki 6/19/17, 1244 PM Unibasic From Dynamic Concepts Wiki Contents 1 UniBasic 2 About this Guide 2.1 Conventions 3 Installation & Configuration 3.1 Configuring Unix for UniBasic 3.1.1 Number of Processes 3.1.2 Number of Open Files 3.1.3 Number of Open i-nodes 3.1.4 Number of Locks 3.1.5 Message Queues 3.2 Unix Accounting & Protection System 3.3 Creating a Unix Account for UniBasic 3.4 UniBasic Security & Licensing 3.4.1 Software Licensing 3.4.2 Hardware Licensing 3.5 Loading the Installation File 3.5.1 Loading the UniBasic Installation File 3.5.2 Loading the UniBasic Development File 3.6 ubinstall - Installing UniBasic Packages 3.6.1 Errors During Installation 3.7 Configuring a UniBasic Environment 3.7.1 Directories and Paths 3.7.2 Filenames and Pathnames 3.7.3 Organizing Logical Units and Packnames 3.7.4 Environment Variables 3.7.5 Setting up .profile for Multiple Users 3.8 Command Line Interpreter 3.9 Launching UniBasic From Unix 3.10 Terminating a UniBasic Process 3.11 Licensing a New Installation 3.12 Changing the SSN Activation Key 3.13 Launching UniBasic Ports at Startup 3.14 Configuring Printer Drivers 3.15 Configuring Serial Printers 3.16 Configuring Terminal Drivers 3.17 Creating a Customized Installation Media 4 Introduction To UniBasic 4.1 Data 4.1.1 Numeric Data 4.1.1.1 Numeric Precision 4.1.1.2 Special Notes on %3 and %6 Numerics https://engineering.dynamic.com/mediawiki/index.php?title=Unibasic&printable=yes Page 1 of 397 Unibasic - Dynamic Concepts Wiki 6/19/17, 1244 PM 4.1.1.3 Integers Stored in Floating-Point