levenshtein-distance

问题 I have found some Python implementations of the Levenshtein distance. I am wondering though how these algorithms can be efficiently modified so that they break if the Levenshtein distance is greater than n (e.g. 3) instead of running until the end? So essentially I do not want to let the algorithm run for too long to calculate the final distance if I simply want to know if the distance is greater than a threshold or not. I have found some relevant posts here: Modifying Levenshtein Distance

问题 Suppose I have a list:- person_name = ['zakesh', 'oldman LLC', 'bikash', 'goldman LLC', 'zikash','rakesh'] I am trying to group the list in such a way so the Levenshtein distance between two strings is maximum. For finding out the ratio between two words, I am using a python package fuzzywuzzy. Examples :- >>> from fuzzywuzzy import fuzz >>> combined_list = ['rakesh', 'zakesh', 'bikash', 'zikash', 'goldman LLC', 'oldman LLC'] >>> fuzz.ratio('goldman LLC', 'oldman LLC') 95 >>> fuzz.ratio(

问题 Have this code , i want to have levenshtein distance between two list of numbers. import textdistance S1=[1,2,3,7,9,15,19,20] S2=[1,2,3,7,8,14,20] #convert lists to string Str1=‘’.join(str(e) for e in S1) Str2=‘’.join(str(e) for e in S2) textdistance.levenshtein.similarity(Str1,Str2) textdistance.levenshtein.distance(Str1,Str2) The above code gives similarity of : 7 Which is wrong , the correct is 5 . And shows distance value of 4 , which wrong also, the correct distance is 3. How to

问题 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

问题 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

问题 I have been trying to cluster multiple datasets of URLs (around 1 million each), to find the original and the typos of each URL. I decided to use the levenshtein distance as a similarity metric, along with dbscan as the clustering algorithm as k-means algorithms won't work because I do not know the number of clusters. I am facing some problems using Scikit-learn's implementation of dbscan. This snippet below works on small datasets in the format I an using, but since it is precomputing the

问题 Currently, I use https://jsfiddle.net/MartinThoma/h9kL6zox/1/ (see this answer) to highlight changes from one string (< 255 chars) to another string (<255 chars). I can only add highlighting code to one of them. There are three types of changes which I would like to highlight: C1: Insertions C2: Deletions C3: Changes Here is the current code: highlight($("#new"), $("#old")); function highlight(newElem, oldElem){ var newText = newElem.text(); var oldText = oldElem.text(); var text = ""; var

问题 Table a has around 8,000 rows and table b has around 250,000 rows. Without the levenshtein function the query takes just under 2 seconds. With the function included it is taking about 25 minutes. SELECT * FROM library a, classifications b WHERE a.`release_year` = b.`year` AND a.`id` IS NULL AND levenshtein_ratio(a.title, b.title) > 82 回答1: I'm assuming that levenshtein_ratio is a function that you wrote (or maybe included from somewhere else). If so, the database server would not be able to

问题 I have a list of words: lst = ['dog', 'cat', 'mate', 'mouse', 'zebra', 'lion'] I also have a pandas dataframe: df = pd.DataFrame({'input': ['dog', 'kat', 'leon', 'moues'], 'suggested_class': ['a', 'a', 'a', 'a']}) input suggested_class dog a kat a leon a moues a I would like to populate the suggested_class column with the value from lst that has the highest levenshtein distance to a word in the input column. I am using the fuzzywuzzy package to calculate that. The expected output would be:

问题 I have the following two strings: a = 'bjork gudmundsdottir' b = 'b. gudmundsson gunnar' The Levenshtein distance between the two is 12 . When I use the following formula for Levenshtein distance, I get a discrepancy of 0.01 with the python-Levenshtein library: >>> Ldist / max(len( a ), len( b )) >>> float(12)/21 0.5714285714285714 # python-Levenshtein Levenshtein.ratio(a,b) 0.5853658536585366 # difflib >>> seq=difflib.SequenceMatcher(a=a,b=b) >>> seq.ratio() 0.5853658536585366 What accounts