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rfind(string,substring) returns integer Description Returns the position of the start of the last occurrence of substring in string. If the substring is not found most of these routines return an invalid index value – -1 where indexes are 0-based, 0 where they are 1-based – or some value to be interpreted as Boolean FALSE. Related instr
Java Apache java.util.regex Java's User manual: Java GNU GPLv2 with Classpath exception jEdit: JRegex JRegex: Java BSD MATLAB: Regular Expressions: MATLAB Language: Proprietary Oniguruma: Kosako: C BSD Atom, Take Command Console, Tera Term, TextMate, Sublime Text, SubEthaEdit, EmEditor, jq, Ruby: Pattwo Stevesoft Java (compatible with Java 1.0 ...
Then if P is shifted to k 2 such that its left end is between c and k 1, in the next comparison phase a prefix of P must match the substring T[(k 2 - n)..k 1]. Thus if the comparisons get down to position k 1 of T, an occurrence of P can be recorded without explicitly comparing past k 1. In addition to increasing the efficiency of Boyer–Moore ...
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
However, it can make a regular expression much more concise—eliminating a single complement operator can cause a double exponential blow-up of its length. [26] [27] [28] Regular expressions in this sense can express the regular languages, exactly the class of languages accepted by deterministic finite automata. There is, however, a ...
A fuzzy Mediawiki search for "angry emoticon" has as a suggested result "andré emotions" In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly).
In computer science, the Knuth–Morris–Pratt algorithm (or KMP algorithm) is a string-searching algorithm that searches for occurrences of a "word" W within a main "text string" S by employing the observation that when a mismatch occurs, the word itself embodies sufficient information to determine where the next match could begin, thus bypassing re-examination of previously matched characters.
Naively computing the hash value for the substring s[i+1..i+m] requires O(m) time because each character is examined. Since the hash computation is done on each loop, the algorithm with a naive hash computation requires O(mn) time, the same complexity as a straightforward string matching algorithm. For speed, the hash must be computed in ...