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GitHub - tonytonyjan/jaro_winkler: Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string.
Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string. - tonytonyjan/jaro_winkler
Visit SiteGitHub - tonytonyjan/jaro_winkler: Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string.
Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string. - tonytonyjan/jaro_winkler
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jaro_winkler is an implementation of Jaro-Winkler similarity algorithm which is written in C extension and will fallback to pure Ruby version in platforms other than MRI/KRI like JRuby or Rubinius. Both of C and Ruby implementation support any kind of string encoding, such as UTF-8, EUC-JP, Big5, etc.
Installation
gem install jaro_winkler
Usage
require 'jaro_winkler'
# Jaro Winkler Similarity
JaroWinkler.similarity "MARTHA", "MARHTA"
# => 0.9611
JaroWinkler.similarity "MARTHA", "marhta", ignore_case: true
# => 0.9611
JaroWinkler.similarity "MARTHA", "MARHTA", weight: 0.2
# => 0.9778
# Jaro Similarity
JaroWinkler.jaro_similarity "MARTHA", "MARHTA"
# => 0.9444444444444445
There is no JaroWinkler.jaro_winkler_similarity
, it's tediously long.
Options
Name | Type | Default | Note |
---|---|---|---|
ignore_case | boolean | false | All lower case characters are converted to upper case prior to the comparison. |
weight | number | 0.1 | A constant scaling factor for how much the score is adjusted upwards for having common prefixes. |
threshold | number | 0.7 | The prefix bonus is only added when the compared strings have a Jaro similarity above the threshold. |
adj_table | boolean | false | The option is used to give partial credit for characters that may be errors due to known phonetic or character recognition errors. A typical example is to match the letter "O" with the number "0". |
Adjusting Table
Default Table
['A', 'E'], ['A', 'I'], ['A', 'O'], ['A', 'U'], ['B', 'V'], ['E', 'I'], ['E', 'O'], ['E', 'U'], ['I', 'O'], ['I', 'U'],
['O', 'U'], ['I', 'Y'], ['E', 'Y'], ['C', 'G'], ['E', 'F'], ['W', 'U'], ['W', 'V'], ['X', 'K'], ['S', 'Z'], ['X', 'S'],
['Q', 'C'], ['U', 'V'], ['M', 'N'], ['L', 'I'], ['Q', 'O'], ['P', 'R'], ['I', 'J'], ['2', 'Z'], ['5', 'S'], ['8', 'B'],
['1', 'I'], ['1', 'L'], ['0', 'O'], ['0', 'Q'], ['C', 'K'], ['G', 'J'], ['E', ' '], ['Y', ' '], ['S', ' ']
How it works?
Original Formula:
where
m
is the number of matching characters.t
is half the number of transpositions.
With Adjusting Table:
where
s
is the number of nonmatching but similar characters.
Why This?
There is also another similar gem named fuzzy-string-match which both provides C and Ruby version as well.
I reinvent this wheel because of the naming in fuzzy-string-match
such as getDistance
breaks convention, and some weird code like a1 = s1.split( // )
(s1.chars
could be better), furthermore, it's bugged (see tables below).
Compare with other gems
jaro_winkler | fuzzystringmatch | hotwater | amatch | |
---|---|---|---|---|
Encoding Support | Yes | Pure Ruby only | No | No |
Windows Support | Yes | ? | No | Yes |
Adjusting Table | Yes | No | No | No |
Native | Yes | Yes | Yes | Yes |
Pure Ruby | Yes | Yes | No | No |
Speed | 1st | 3rd | 2nd | 4th |
I made a table below to compare accuracy between each gem:
str_1 | str_2 | origin | jaro_winkler | fuzzystringmatch | hotwater | amatch |
---|---|---|---|---|---|---|
"henka" | "henkan" | 0.9667 | 0.9667 | 0.9722 | 0.9667 | 0.9444 |
"al" | "al" | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
"martha" | "marhta" | 0.9611 | 0.9611 | 0.9611 | 0.9611 | 0.9444 |
"jones" | "johnson" | 0.8324 | 0.8324 | 0.8324 | 0.8324 | 0.7905 |
"abcvwxyz" | "cabvwxyz" | 0.9583 | 0.9583 | 0.9583 | 0.9583 | 0.9583 |
"dwayne" | "duane" | 0.84 | 0.84 | 0.84 | 0.84 | 0.8222 |
"dixon" | "dicksonx" | 0.8133 | 0.8133 | 0.8133 | 0.8133 | 0.7667 |
"fvie" | "ten" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
- The "origin" result is from the original C implementation by the author of the algorithm.
- Test data are borrowed from fuzzy-string-match's rspec file.
Benchmark
$ bundle exec rake benchmark
ruby 2.4.1p111 (2017-03-22 revision 58053) [x86_64-darwin16]
# C Extension
Rehearsal --------------------------------------------------------------
jaro_winkler (8c16e09) 0.240000 0.000000 0.240000 ( 0.241347)
fuzzy-string-match (1.0.1) 0.400000 0.010000 0.410000 ( 0.403673)
hotwater (0.1.2) 0.250000 0.000000 0.250000 ( 0.254503)
amatch (0.4.0) 0.870000 0.000000 0.870000 ( 0.875930)
----------------------------------------------------- total: 1.770000sec
user system total real
jaro_winkler (8c16e09) 0.230000 0.000000 0.230000 ( 0.236921)
fuzzy-string-match (1.0.1) 0.380000 0.000000 0.380000 ( 0.381942)
hotwater (0.1.2) 0.250000 0.000000 0.250000 ( 0.254977)
amatch (0.4.0) 0.860000 0.000000 0.860000 ( 0.861207)
# Pure Ruby
Rehearsal --------------------------------------------------------------
jaro_winkler (8c16e09) 0.440000 0.000000 0.440000 ( 0.438470)
fuzzy-string-match (1.0.1) 0.860000 0.000000 0.860000 ( 0.862850)
----------------------------------------------------- total: 1.300000sec
user system total real
jaro_winkler (8c16e09) 0.440000 0.000000 0.440000 ( 0.439237)
fuzzy-string-match (1.0.1) 0.910000 0.010000 0.920000 ( 0.920259)
Todo
- Custom adjusting word table.
Ruby Resources
are all listed below.
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