Levenshtein | Javascript implementation of the L-diggity
kandi X-RAY | Levenshtein Summary
kandi X-RAY | Levenshtein Summary
Levenshtein string difference in Javascript.
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Levenshtein Key Features
Levenshtein Examples and Code Snippets
Lines of Code : 20
License : No License
Copyconst levenshteinDistance = (s, t) => {
if (!s.length) return t.length;
if (!t.length) return s.length;
const arr = [];
for (let i = 0; i <= t.length; i++) {
arr[i] = [i];
for (let j = 1; j <= s.length; j++) {
arr[i][j] def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure th function minkowskiDistance (x, lx, y, ly, p) {
var d;
var i;
if (lx !== ly) {
throw 'Both vectors should have same dimension';
}
if (isNaN(p)) {
throw 'The order "p" must be a number';
}
if function levenshteinDistance (s, ls, t, lt) {
var memo = [];
var currRowMemo;
var i;
var k;
for (k = 0; k <= lt; k += 1) {
memo[k] = k;
}
for (i = 1; i <= ls; i += 1) {
currRowMemo = [ Community Discussions
Trending Discussions on Levenshtein
QUESTION
I have a list of floats that I want to compare to other lists and get the similarity ratio in python :
The list that I want to compare:
...ANSWER
Answered 2021-May-31 at 15:32The question is no exactly clear in my oppinion, nevertheless you could see if the following approach helps you:
QUESTION
Hi i am getting error in upload function i have another file index html file where user uploads the file and
...ANSWER
Answered 2021-May-29 at 06:56your uploader function has the decotrator @route. when this decorator applies to a function that function will expect an active request object when its being called meaning if you call that function it should be a http/https request. since you are calling it from your .py file and it doesn't create any request while calling the function therefore it will fail.
you can manually test your code without any active request. for more help refer to this flask documentation for more info on request context and how you can achieve what you want. FLASK DOCS REQUEST
QUESTION
I am trying to deploy my Python app on Heroku, but have been unsuccessful. It seems that a problem is occurring with the PyICU package, which I'm unsure how to correct. I've confirmed that this is the only issue with my deployment; when I remove PyICU from my requirements file, everything works. But of course my site can't work without it.
Can anyone please guide me in how to correctly install this package on Heroku? I've tried various methods, including downloading the .whl file and then adding that to my requirements file, but then I get another error:
ERROR: PyICU-2.7.3-cp38-cp38m-win_amd64.whl is not a supported wheel on this platform. I don't understand why - it's the correct Python and os version.
Here are the relevant excerpts from the build log:
...ANSWER
Answered 2021-May-26 at 15:55Why are you using the windows wheel (PyICU-2.7.3-cp38-cp38m-win_amd64.whl)? You probably need a manylinux wheel.
You can also try pyicu-binary package.
QUESTION
I have data in list like this:
...ANSWER
Answered 2021-Apr-16 at 15:23You can do a certain amount of 'canonicalization' by removing punctuation and words like "Inc", "Corp" (see partial example below), and by removing parentheticals but ultimately this is a very hard problem because of (i) abbreviations; (ii) location specifiers (East, North, ...); (iii) corporate taxonomy: is it a subsidiary, a branch, a franchisee, or a separate company?
Ultimately a list of synonyms may be the best approach together with some light canonicalization to remove common corporate entity type designators.
QUESTION
I am currently trying to cluster a list of sequences based on their similarity using python.
ex:
DFKLKSLFD
DLFKFKDLD
LDPELDKSL
...
The way I pre process my data is by computing the pairwise distances using for example the Levenshtein distance. After calculating all the pairwise distances and creating the distance matrix, I want to use it as input for the clustering algorithm.
I have already tried using Affinity Propagation, but convergence is a bit unpredictable and I would like to go around this problem.
Does anyone have any suggestions regarding other suitable clustering algorithms for this case?
Thank you!!
...ANSWER
Answered 2021-Apr-01 at 22:38QUESTION
I am trying to compare two sentences ("Kat" and input "Spat") using Levenshtein Distance. If the sentences are similar enough, I want a "correct" text to appear. The script, which I have copied, works fine, but I am having troubles with the If-statement. I want a "correct" text to appear, if the Levenshtein distance is measured to "2" (As is the case with "Kat" and "Spat"), but I don't know which variable should be set as equal to "2".
...ANSWER
Answered 2021-Mar-25 at 10:59Just read the value
QUESTION
Is the a quantitative descriptor of similarity between two words based on how they sound/are pronounced, analogous to Levenshtein distance?
I know soundex gives same id to similar sounding words, but as far as I undestood it is not a quantitative descriptor of difference between the words.
...ANSWER
Answered 2021-Mar-19 at 23:42You could combine phonetic encoding and string comparison algorithm. As a matter of fact jellyfish supplies both.
Setting up the libraries examples
QUESTION
So I have successfully implemented the Levenshtein (edit minimum distance) algorithm with the help of Wikipedia and this Needleman tutorial, whereby custom, insertion and deletion cost 1, and substitution/replacement cost 2.
Executable gist https://gist.github.com/axelmukwena/8696ec4ec72849d3cf384f5d97321407
...ANSWER
Answered 2021-Mar-15 at 11:04It should not be min(a,b,c). You should select the node that minimizes the score of the other node plus cost of operation from the other node to the current one.
QUESTION
If I have a set of sentences and I would like to extract the duplicates, I should work like in the following example:
...ANSWER
Answered 2021-Feb-20 at 12:13TLDR: Possibly you can use bag of word (BoW) representation and convert these sentences to vectors. Then, simply check out the correlations and eliminate the ones if their correlation are too high with another.
Bag of words
Let's think about the following sentence:
- Jack is a handsome, handsome man
and assume our entire universe of words is in that sentence. Then, we can simply create a vector for the number of words occurring in this sentence (which is 1 per word) which is a vector with 5 features (Jack, is, a, handsome, man).
Then, the corresponding BoW representation is: [1, 1, 1, 2, 1].
Another sentence in this universe could be,
- Jack Jack handsome handsome man
Again, we could use our 5 featured vector to represent this sentence.
[2, 0, 0, 2, 1].
Then, you can calculate this sentences' similarity in R.
QUESTION
I am using the following to compare several strings to each other. It's the fastest method I've been able to devise, but it results in a very large 2D array. which I can look at and see what I want. Ideally, I would like to set a threshold and pull the index(es) for each value over that number. To make matters more complicated, I don't want the index comparing the string to itself, and it's possible the string might be duplicated elsewhere so I would want to know if that's the case, so I can't just ignore 1's.
...ANSWER
Answered 2021-Mar-04 at 14:18I'm not entirely sure I read your post correctly, but I believe this should get you started:
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