RankFind does not use Levenshtein distance to build results

[colelawrence]

RankFind still uses plain Find to determine initial results, then measures Levenshtein distance afterwards. Maybe we can have another RankFindLevenshtein that uses Levenshtein distance to determine results.

[lithammer]

I actually played around with something like that for RankFind at one point. What I found was that unless you set a high threshold, most longer targets would never match.

Say you search for "aaa" in "aaabbbbbbbbbbbbbbbbbbb" and your threshold is 15 it wouldn't match. Perhaps that's exactly what you wanted, but I think it might have to be a bit smarter and take length into consideration as well or something... This is basically when I went with the current solution of RankFind 😃

Another approach would be to create a RankFindFunc(s, t string, fn func(t, s string) bool) where you can define your own criteria.

[lithammer]

Then you'd have something like this

const threshold = 15

func predicate(s, t string) bool {
    distance := LevenshteinDistance(s, t)  // 19
    return distance < threshold
}

fuzzy.RankFindFunc("aaa", "aaabbbbbbbbbbbbbbbbbbb", predicate)  // false

[colelawrence]

Interesting, thank you for the explanations!

On Wed, Sep 9, 2015, 6:08 AM Peter Renström [email protected]
wrote:

Then you'd have something like this

const threshold = 15
func predicate(s, t string) bool {
distance := LevenshteinDistance(s, t)
return distance < threshold
}
RankFindFunc("aaa", "aaabbbbbbbbbbbbbbbbbbb", predicate)

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[lithammer]

Yeah, so I'm not sure the distance alone is enough to determine a match. Having "a" match "ab" but not "abc" is confusing, it should give at least as many hits as a plain old substring search.

Did you have a good use-case for this? Perhaps I'm just missing something here 😄

[colelawrence]

Yeah, so I use it to create a search engine for my GoCourseSort project, and the reason I use Levenshtein instead of simple match, is so a word in the search query matches a keyword from the database of strings.

I think it is vaguely similar to the way BigTables work (don't quote me).

Imagine book titles: "Gone with the Wind", "Gone Girl", and "The Girls", we create an index of key words with references:

gonegirl := &Book{
  title: "Gone Girl",
}
gonewiththewind := &Book{
  title: "Gone with the Wind",
}
thegirls := &Book{
  title: "The Girls",
}
indexByKeywords := map[string][]*Book {
  "girls": { thegirls },
  "girl": { gonegirl },
  "gone": { gonegirl, gonewiththewind },
  "with": { gonewiththewind },
  "the": { gonewiththewind, thegirls },
  "wind": { gonewiththewind },
}

Now you enter the search term "the girls"

Then I'm Levenshtein ranking "the" against the keys of indexByKeywords, and "girls" against the keys of indexByKeywords, then using a ranking formula based on Levenshtein distance, index of word in search, and index of word in title, for each list of references I get back per search term.

This is important because in my search engine, I don't want order of words to be important in any way, but I need "CS" to match "CSC" and "131" to match "121", because I'm using it for a college course catalog.

[elazarl]

You might want to look at my implementation and the fuzzy find