levenshtein-distance

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . I want to add timestamps to book sentences, fitting the relevant audiobook. In various languages ideally. Here's an example: Pride and prejudice text from gutenberg project audio from Librivox My idea was to find a voice recognition tool that puts timestamps on sentences (step 1),

问题 I'm sitting here and programmnging some algorithms for my main program in Java (well the first one so far). I programmed the levenshtein algorithm just fine thanks to wiki being so nice with the pseudocode to newbeginners plus a nice tutorial :D I then decided to upgrade to Damerau and added the extra lines but then I read that it's not DL algo but OptimalStringAlignmentDistance instead. I tried reading the actionscript code to understand what more I needed to add to make it to DL but got

问题 I'm trying to implement a "Did you mean?" sort of function for a search. I'm trying to do a query that uses the levenshtein function, which was written in ruby. I was wondering how I could use this function in a sqlite3 query. I was thinking it might be something like this: @results = the_db.where('levenshtein(name, ?) <= 3', searchphrase) But i'm not sure how to get it to work. Could someone help me out? 回答1: Try the editdist3 function: The editdist3 algorithm is a function that computes the

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . I've found a great python library implementing Levenshtein functions (distance, ratio, etc.) at http://code.google.com/p/pylevenshtein/ but the project seems inactive and the documentation is nowhere to be found. I was wondering if anyone knows better than me and can point me to the documentation. 回答1: You won't

问题 According to an answer to another question, in sqlite the Levenshtein distance is implemented in a SQL function called editdist3 . (Compare also the documentation) Now when I try to use it, all I get is an error that it doesn’t exist: ╰┄┄> sqlite3 SQLite version 3.11.1 2016-03-03 16:17:53 Enter ".help" for usage hints. Connected to a transient in-memory database. Use ".open FILENAME" to reopen on a persistent database. sqlite> CREATE TABLE test (col1 TEXT); sqlite> INSERT INTO test VALUES (

问题 I'm posting this in the spirit of answering your own questions. The question I had was: How can I implement the Levenshtein algorithm for calculating edit-distance between two strings, as described here, in Delphi? Just a note on performance: This thing is very fast. On my desktop (2.33 Ghz dual-core, 2GB ram, WinXP), I can run through an array of 100K strings in less than one second. 回答1: function EditDistance(s, t: string): integer; var d : array of array of integer; i,j,cost : integer;

问题 I have a set (2k - 4k) of small strings (3-6 characters) and I want to cluster them. Since I use strings, previous answers on How does clustering (especially String clustering) work?, informed me that Levenshtein distance is good to be used as a distance function for strings. Also, since I do not know in advance the number of clusters, hierarchical clustering is the way to go and not k-means. Although I get the problem in its abstract form, I do not know what is the easie way to actually do

问题 Is there a package that contains Levenshtein distance counting function which is implemented as a C or Fortran code? I have many strings to compare and stringMatch from MiscPsycho is too slow for this. 回答1: levenshteinDist (from the RecordLinkage package) calls compiled C code. Give it a try. 回答2: And stringdist in the stringdist package does it too, even faster than levenshteinDist under certain conditions (1) 回答3: You could try stringDist from Biostrings as well 来源： https://stackoverflow

问题 I just implemented a best match file search algorithm to find the closest match to a string in a dictionary. After profiling my code, I found out that the overwhelming majority of time is spent calculating the distance between the query and the possible results. I am currently implementing the algorithm to calculate the Levenshtein Distance using a 2-D array, which makes the implementation an O(n^2) operation. I was hoping someone could suggest a faster way of doing the same. Here's my

问题 So I have a random javascript array of names... [@larry,@nicholas,@notch] etc. They all start with the @ symbol. I'd like to sort them by the Levenshtein Distance so that the the ones at the top of the list are closest to the search term. At the moment, I have some javascript that uses jQuery's .grep() on it using javascript .match() method around the entered search term on key press: (code edited since first publish) limitArr = $.grep(imTheCallback, function(n){ return n.match(searchy