levenshtein-distance

Damerau - Levenshtein Distance, adding a threshold

由 戏子无情 提交于 2019-12-05 01:34:07
问题 I have the following implementation, but I want to add a threshold, so if the result is going to be greater than it, just stop calculating and return. How would I go about that? EDIT: Here is my current code, threshold is not yet used...the goal is that it is used public static int DamerauLevenshteinDistance(string string1, string string2, int threshold) { // Return trivial case - where they are equal if (string1.Equals(string2)) return 0; // Return trivial case - where one is empty if

Matching an approximate string in a Core Data store

由 ≡放荡痞女 提交于 2019-12-05 00:21:41
I have a small problem with the core data application i'm currently writing. I have two differents models, contexts and peristent stores. One is for my app data, the other one is for a website with relevant infos to me. Most of the time, I match exactly one record from my app to another record from the other source. Sometimes however, I have to fallback to fuzzy string matching to link the two records. I'm trying to match song titles. My local title could be the (made up) "The French Idealist is in your pensée" and the remote song title could be "01 - 10 - French idealist in in you're pensee,

How to install python-levenshtein on Windows?

由 好久不见. 提交于 2019-12-04 23:41:40
After searching for days I'm about ready to give up finding precompiled binaries for Python 2.7 (Windows 64-bit) of the Python Levenshtein library , so not I'm attempting to compile it myself. I've installed the most recent version of MinGW32 (version 0.5-beta-20120426-1) and set it as the default compiler in distutils . Here we go: C:\Users\tomas>pip install python-levenshtein Downloading/unpacking python-levenshtein Running setup.py egg_info for package python-levenshtein warning: no files found matching '*' under directory 'docs' warning: no previously-included files matching '*pyc' found

Damerau–Levenshtein distance (Edit Distance with Transposition) c implementation

由 偶尔善良 提交于 2019-12-04 18:01:49
问题 I implemented the Damerau–Levenshtein distance in c++ but it does not give correct o/p for the input (pantera,aorta) the correct o/p is 4 but my code gives 5..... int editdist(string s,string t,int n,int m) { int d1,d2,d3,cost; int i,j; for(i=0;i<=n;i++) { for(j=0;j<=m;j++) { if(s[i+1]==t[j+1]) cost=0; else cost=1; d1=d[i][j+1]+1; d2=d[i+1][j]+1; d3=d[i][j]+cost; d[i+1][j+1]=minimum(d1,d2,d3); if(i>0 && j>0 && s[i+1]==t[j] && s[i]==t[j+1] ) //transposition { d[i+1][j+1]=min(d[i+1][j+1],d[i-1]

Complexity of edit distance (Levenshtein distance) recursion top down implementation

由 霸气de小男生 提交于 2019-12-04 15:01:27
I have been working all day with a problem which I can't seem to get a handle on. The task is to show that a recursive implementation of edit distance has the time complexity Ω(2 max(n,m) ) where n & m are the length of the words being measured. The implementation is comparable to this small python example def lev(a, b): if("" == a): return len(b) # returns if a is an empty string if("" == b): return len(a) # returns if b is an empty string return min(lev(a[:-1], b[:-1])+(a[-1] != b[-1]), lev(a[:-1], b)+1, lev(a, b[:-1])+1) From: http://www.clear.rice.edu/comp130/12spring/editdist/ I have

URL path similarity/string similarity algorithm

由 為{幸葍}努か 提交于 2019-12-04 14:49:55
My problem is that I need to compare URL paths and deduce if they are similar. Below I provide example data to process: # GROUP 1 /robots.txt # GROUP 2 /bot.html # GROUP 3 /phpMyAdmin-2.5.6-rc1/scripts/setup.php /phpMyAdmin-2.5.6-rc2/scripts/setup.php /phpMyAdmin-2.5.6/scripts/setup.php /phpMyAdmin-2.5.7-pl1/scripts/setup.php /phpMyAdmin-2.5.7/scripts/setup.php /phpMyAdmin-2.6.0-alpha/scripts/setup.php /phpMyAdmin-2.6.0-alpha2/scripts/setup.php # GROUP 4 //phpMyAdmin/ I tried Levenshtein distance to compare, but for me is not enough accurate. I do not need 100% accurate algorithm, but I think

How can I create a threshold for similar strings using Levenshtein distance and account for typos?

由 杀马特。学长 韩版系。学妹 提交于 2019-12-04 11:29:59
We recently encountered an interesting problem at work where we discovered duplicate user submitted data in our database. We realized that the Levenshtein distance between most of this data was simply the difference between the 2 strings in question. That indicates that if we simply add characters from one string into the other then we end up with the same string, and for most things this seems like the best way for us to account for items that are duplicate. We also want to account for typos. So we started to think about on average how often do people make typos online per word, and try to

LevensteinDistance - Commons Lang 3.0 API

由 回眸只為那壹抹淺笑 提交于 2019-12-04 11:02:47
问题 With Commons Lang api I can calculate the similarity between two strings through the LevensteinDistance. The result is the number of changes needed to change one string into another. I wish the result was within the range from 0 to 1, where it would be easier to identify the similarity between the strings. The result would be closer to 0 great similarity. Is it possible? Below the example I'm using: public class TesteLevenstein { public static void main(String[] args) { int distance1 =

Edit Distance Algorithm

由 喜你入骨 提交于 2019-12-04 10:04:12
I have a dictionary of 'n' words given and there are 'm' Queries to respond to. I want to output the number of words in dictionary which are edit distance 1 or 2. I want to optimize the result set given that n and m are roughly 3000. Edit added from answer below: I will try to word it differently. Initially there are 'n' words given as a set of Dictionary words. Next 'm' words are given which are query words and for each query word, I need to find if the word already exists in Dictionary (Edit Distance '0') or the total count of words in dictionary which are at edit distance 1 or 2 from the

two whole texts similarity using levenshtein distance [closed]

由 送分小仙女□ 提交于 2019-12-04 08:53:10
I have two text files which I'd like to compare. What I did is: I've split both of them into sentences. I've measured levenshtein distance between each of the sentences from one file with each of the sentences from second file. I'd like to calculate average similarity between those two text files, however I have trouble to deliver any meaningful value - obviously arithmetic mean (sum of all the distances [normalized] divided by number of comparisions) is a bad idea. How to interpret such results? edit: Distance values are normalized. The levenshtein distances has a maximum value, i.e. the max.
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