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[jaro_winkler](https://rubygems.org/gems/jaro_winkler) is an implementation of [Jaro-Winkler similarity](http://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) 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
```ruby
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:
%26%5Ctext%7Bothers%7D%5Cend%7Bcases%7D)
where
- `m` is the number of matching characters.
- `t` is half the number of transpositions.
With Adjusting Table:
%26%5Ctext%7Bothers%7D%5Cend%7Bcases%7D)
where
- `s` is the number of nonmatching but similar characters.
# Why This?
There is also another similar gem named [fuzzy-string-match](https://github.com/kiyoka/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](http://web.archive.org/web/20100227020019/http://www.census.gov/geo/msb/stand/strcmp.c).
- Test data are borrowed from [fuzzy-string-match's rspec file](https://github.com/kiyoka/fuzzy-string-match/blob/master/test/basic_pure_spec.rb).
# 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.
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