levenshtein-distance | 易学教程

Difference between Jaro-Winkler and Levenshtein distance? [closed]

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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 5 years ago . I have a use case where I need to do fuzzy matching of millions of records from multiple files. I identified two algorithms for that: Jaro-Winkler and Levenshtein edit distance. When I started exploring both, I was not able to understand what the exact difference is between the

Fast Levenshtein distance in R?

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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. George Dontas levenshteinDist (from the RecordLinkage package) calls compiled C code. Give it a try. And stringdist in the stringdist package does it too, even faster than levenshteinDist under certain conditions ( 1 ) Aaron Statham You could try stringDist from Biostrings as well 来源： https://stackoverflow.com/questions/3182091/fast-levenshtein-distance-in-r

Levenshtein type algorithm with numeric vectors

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问题 I have two vectors with numeric values. Such as v1 <- c(1, 3, 4, 5, 6, 7, 8) v2 <- c(54, 23, 12, 53, 7, 8) I would like to compute the number of insertions , deletions and replacements that I need to turn one vector into the other with certain costs per operation c1 c2 and c3 respectively. I am aware that the function adist on the base package does this for strings but I have no knowledge of the equivalent function with numbers. I thought about referencing each number with a letter but I have

Levenshtein Distance Algorithm better than O(n*m)?

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I have been looking for an advanced levenshtein distance algorithm, and the best I have found so far is O(n*m) where n and m are the lengths of the two strings. The reason why the algorithm is at this scale is because of space, not time, with the creation of a matrix of the two strings such as this one: Is there a publicly-available levenshtein algorithm which is better than O(n*m)? I am not averse to looking at advanced computer science papers & research, but haven't been able to find anything. I have found one company, Exorbyte, which supposedly has built a super-advanced and super-fast

R: String Fuzzy Matching using jarowinkler

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问题 I have two vector of type character in R. I want to be able to compare the reference list to the raw character list using jarowinkler and assign a % similarity score. So for example if i have 10 reference items and twenty raw data items, i want to be able to get the best score for the comparison and what the algorithm matched it to (so 2 vectors of 10). If i have raw data of size 8 and 10 reference items, i should only end up with a 2 vector result of 8 items with the best match and score per

String similarity metrics in Python

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I want to find string similarity between two strings. This page has examples of some of them. Python has an implemnetation of Levenshtein algorithm . Is there a better algorithm, (and hopefully a python library), under these contraints. I want to do fuzzy matches between strings. eg matches('Hello, All you people', 'hello, all You peopl') should return True False negatives are acceptable, False positives, except in extremely rare cases are not. This is done in a non realtime setting, so speed is not (much) of concern. [Edit] I am comparing multi word strings. Would something other than

Edit distance such as Levenshtein taking into account proximity on keyboard

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问题 Is there an edit distance such as Levenshtein which takes into account distance for substitutions? For example, if we would consider if words are equal, typo and tylo are really close ( p and l are physically close on the keyboard), while typo and tyqo are far apart. I'd like to allocate a smaller distance to more likely typos. There must be a metric that takes this kind of promixity into account? 回答1: the kind of distance you ask is not included in levenshtein - but you should use a helper

Most efficient way to calculate Levenshtein distance

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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 implementation: public int calculate(String root, String query) { int arr[][] = new int[root.length() + 2]

How to sort an array by similarity in relation to an inputted word.

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问题 I have on PHP array, for example: $arr = array("hello", "try", "hel", "hey hello"); Now I want to do rearrange of the array which will be based on the most nearly close words between the array and my $search var. How can I do that? 回答1: This could be a quick solution by using http://php.net/manual/en/function.similar-text.php: This calculates the similarity between two strings as described in Programming Classics: Implementing the World's Best Algorithms by Oliver (ISBN 0-131-00413-1). Note

String similarity -> Levenshtein distance

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问题 I'm using the Levenshtein algorithm to find the similarity between two strings. This is a very important part of the program I'm making, so it needs to be effective. The problem is that the algorithm doesn't find the following examples as similar: CONAIR AIRCON The algorithm will give a distance of 6. So for this word of 6 letters (You look at the word with the highest amount of letters), the difference is of 100% => the similarity is 0%. I need to find a way to find the similarities between