Compare similarity algorithms

Expanding on my wiki-walk comment in the errata and noting some of the ground-floor literature on the comparability of algorithms that apply to similar problem spaces, let’s explore the applicability of these algorithms before we determine if they’re numerically comparable. From Wikipedia, Jaro-Winkler: In computer science and statistics, the Jaro–Winkler distance (Winkler, 1990) is a … Read more

How to compare almost similar Strings in Java? (String distance measure) [closed]

The following Java libraries offer multiple compare algorithms (Levenshtein,Jaro Winkler,…): Apache Commons Lang 3: https://commons.apache.org/proper/commons-lang/ Simmetrics: http://sourceforge.net/projects/simmetrics/ Both libraries have a java documentation (Apache Commons Lang Javadoc,Simmetrics Javadoc). //Usage of Apache Commons Lang 3 import org.apache.commons.lang3.StringUtils; public double compareStrings(String stringA, String stringB) { return StringUtils.getJaroWinklerDistance(stringA, stringB); } //Usage of Simmetrics import uk.ac.shef.wit.simmetrics.similaritymetrics.JaroWinkler public double compareStrings(String … Read more

How to create simple fuzzy search with PostgreSQL only?

Postgres provides a module with several string comparsion functions such as soundex and metaphone. But you will want to use the levenshtein edit distance function. Example: test=# SELECT levenshtein(‘GUMBO’, ‘GAMBOL’); levenshtein ————- 2 (1 row) The 2 is the edit distance between the two words. When you apply this against a number of words and … Read more

Implementation of Levenshtein distance for mysql/fuzzy search?

In order to efficiently search using levenshtein distance, you need an efficient, specialised index, such as a bk-tree. Unfortunately, no database system I know of, including MySQL, implements bk-tree indexes. This is further complicated if you’re looking for full-text search, instead of just a single term per row. Off-hand, I can’t think of any way … Read more

String similarity metrics in Python [duplicate]

I realize it’s not the same thing, but this is close enough: >>> import difflib >>> a=”Hello, All you people” >>> b = ‘hello, all You peopl’ >>> seq=difflib.SequenceMatcher(a=a.lower(), b=b.lower()) >>> seq.ratio() 0.97560975609756095 You can make this as a function def similar(seq1, seq2): return difflib.SequenceMatcher(a=seq1.lower(), b=seq2.lower()).ratio() > 0.9 >>> similar(a, b) True >>> similar(‘Hello, world’, … Read more

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