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# Jaro-Winkler distance

Jaro-Winkler distance is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

The Jaro-Winkler distance is a metric for measuring the edit distance between words. It is similar to the more basic Levenstein distance but the Jaro distance also accounts for transpositions between letters in the words. With the Winkler modification to the Jaro metric, the Jaro-Winkler distance also adds an increase in similarity for words which start with the same letters (prefix).

The Jaro-Winkler distance is a modification of the Jaro similarity metric, which measures the similarity between two strings. The Jaro similarity is 1.0 when strings are identical and 0 when strings have no letters in common. Distance measures such as the Jaro distance or Jaro-Winkler distance, on the other hand, are 0 when strings are identical and 1 when they have no letters in common.

The Jaro similarity between two strings s1 and s2, simj, is defined as

simj = 0     if m is 0.
simj = ( (m / length of s1) + (m / length of s2) + (m - t) / m ) / 3     otherwise.

Where:

• ${\displaystyle m}$   is the number of matching characters (the same character within max(|s1|, |s2|)/2 - 1 of one another);
• ${\displaystyle t}$   is half the number of transpositions (a shared character placed in different positions).

The Winkler modification to Jaro is to check for identical prefixes of the strings.

If we define the number of initial (prefix) characters in common as:

l = the length of a common prefix between strings, up to 4 characters

and, additionally, select a multiplier (Winkler suggested 0.1) for the relative importance of the prefix for the word similarity:

p   =   0.1

The Jaro-Winkler similarity can then be defined as

simw = simj + lp(1 - simj)

Where:

• simj   is the Jaro similarity.
• l   is the number of matching characters at the beginning of the strings, up to 4.
• p   is a factor to modify the amount to which the prefix similarity affects the metric.

Winkler suggested this be 0.1.

The Jaro-Winkler distance between strings, which is 0.0 for identical strings, is then defined as

dw = 1 - simw

String metrics such as Jaro-Winkler distance are useful in applications such as spelling checkers, because letter transpositions are common typing errors and humans tend to misspell the middle portions of words more often than their beginnings. This may help a spelling checker program to generate better alternatives for misspelled word replacement.

Using a dictionary of your choice and the following list of 9 commonly misspelled words:

"accomodate", "definately", "goverment​", "occured", "publically", "recieve​", "seperate", "untill", "wich​"

• Calculate the Jaro-Winkler distance between the misspelled word and words in the dictionary.
• Use this distance to list close alternatives (at least two per word) to the misspelled words.
• Show the calculated distances between the misspelled words and their potential replacements.

## 11l

Translation of: Python
`V WORDS = File(‘linuxwords.txt’).read_lines()V MISSPELLINGS = [‘accomodate’,                  ‘definately’,                  ‘goverment’] F jaro_winkler_distance(=st1, =st2)   I st1.len < st2.len      (st1, st2) = (st2, st1)   V len1 = st1.len   V len2 = st2.len   I len2 == 0      R 0.0   V delta = max(0, len2 I/ 2 - 1)   V flag = (0 .< len2).map(_ -> 0B)   [Char] ch1_match   L(ch1) st1      V idx1 = L.index      L(ch2) st2         V idx2 = L.index         I idx2 <= idx1 + delta & idx2 >= idx1 - delta & ch1 == ch2 & !(flag[idx2])            flag[idx2] = 1B            ch1_match.append(ch1)            L.break   V matches = ch1_match.len   I matches == 0      R 1.0   V transpositions = 0   V idx1 = 0   L(ch2) st2      V idx2 = L.index      I flag[idx2]         transpositions += (ch2 != ch1_match[idx1])         idx1++   V jaro = (Float(matches) / len1 + Float(matches) / len2 + (matches - transpositions / 2) / matches) / 3.0   V commonprefix = 0   L(i) 0 .< min(4, len2)      commonprefix += (st1[i] == st2[i])   R 1.0 - (jaro + commonprefix * 0.1 * (1 - jaro)) F within_distance(maxdistance, stri, maxtoreturn)   V arr = :WORDS.filter(w -> jaro_winkler_distance(@stri, w) <= @maxdistance)   arr.sort(key' x -> jaro_winkler_distance(@stri, x))   R I arr.len <= maxtoreturn {arr} E arr[0 .< maxtoreturn] L(STR) MISSPELLINGS   print("\nClose dictionary words ( distance < 0.15 using Jaro-Winkler distance) to \" "STR" \" are:\n        Word   | Distance")   L(w) within_distance(0.15, STR, 5)      print(‘#14 | #.4’.format(w, jaro_winkler_distance(STR, w)))`
Output:
```Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " accomodate " are:
Word   | Distance
accommodate | 0.0182
accommodated | 0.0333
accommodates | 0.0333
accommodating | 0.0815
accommodation | 0.0815

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " definately " are:
Word   | Distance
definitely | 0.0400
defiantly | 0.0422
define | 0.0800
definite | 0.0850
definable | 0.0872

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " goverment " are:
Word   | Distance
government | 0.0533
govern | 0.0667
governments | 0.0697
movement | 0.0810
governmental | 0.0833
```

## Elm

Author: zh5

`module JaroWinkler exposing (similarity)  commonPrefixLength : List a -> List a -> Int -> IntcommonPrefixLength xs ys counter =    case ( xs, ys ) of        ( x :: xs_, y :: ys_ ) ->            if x == y then                commonPrefixLength xs_ ys_ (counter + 1)             else                counter         _ ->            counter similarity : String -> String -> Floatsimilarity s1 s2 =    let        chars1 =            String.toList s1         chars2 =            String.toList s2         jaroScore =            jaro chars1 chars2         l =            toFloat <| min (commonPrefixLength chars1 chars2 0) 4         p =            0.1    in    jaroScore + (l * p * (1.0 - jaroScore))  containtsInNextN : Int -> a -> List a -> BoolcontaintsInNextN i a items =    case ( i, items ) of        ( 0, _ ) ->            False         ( _, [] ) ->            False         ( _, item :: rest ) ->            if item == a then                True             else                containtsInNextN (i - 1) a rest  exists : Int -> Int -> List a -> a -> Boolexists startAt endAt items i =    if endAt < startAt then        False     else if startAt == 0 then        case items of            first :: rest ->                if i == first then                    True                 else                    exists 0 (endAt - 1) rest i             [] ->                False     else        exists 0 (endAt - startAt) (List.drop startAt items) i  existsInWindow : a -> List a -> Int -> Int -> BoolexistsInWindow item items offset radius =    let        startAt =            max 0 (offset - radius)         endAt =            min (offset + radius) (List.length items - 1)    in    exists startAt endAt items item  transpositions : List a -> List a -> Int -> Inttranspositions xs ys counter =    case ( xs, ys ) of        ( [], _ ) ->            counter         ( _, [] ) ->            counter         ( x :: xs_, y :: ys_ ) ->            if x /= y then                transpositions xs_ ys_ (counter + 1)             else                transpositions xs_ ys_ counter  commonItems : List a -> List a -> Int -> List acommonItems items1 items2 radius =    items1        |> List.indexedMap            (\index item ->                if existsInWindow item items2 index radius then                    [ item ]                 else                    []            )        |> List.concat  jaro : List Char -> List Char -> Floatjaro chars1 chars2 =    let        minLenth =            min (List.length chars1) (List.length chars2)         matchRadius =            minLenth // 2 + (minLenth |> modBy 2)         c1 =            commonItems chars1 chars2 matchRadius         c2 =            commonItems chars2 chars1 matchRadius         c1length =            toFloat (List.length c1)         c2length =            toFloat (List.length c2)         mismatches =            transpositions c1 c2 0         transpositionScore =            (toFloat mismatches + abs (c1length - c2length)) / 2.0         s1length =            toFloat (List.length chars1)         s2length =            toFloat (List.length chars2)         tLength =            max c1length c2length         result =            (c1length / s1length + c2length / s2length + (tLength - transpositionScore) / tLength) / 3.0    in    if isNaN result then        0.0     else        result `

## ALGOL 68

Works with: ALGOL 68G version Any - tested with release 2.8.3.win32
Translation of: Wren
- the actual distance routines are translated from the Wren sample, the file reading and asociative arrays etc. are based on similar Algol 68 task solutions.

Uses unixdict.txt - possibly a different version to those used by some other solutions, as this finds a slightly different list of matches for "seperate" (assuming I got the translation correct!).
Prints the 6 closest matches regarddless of their distance (i.e. we don't restrict it to matches closer that 0.15).

`PROC jaro sim = ( STRING sp1, sp2 )REAL:     IF   STRING s1 = sp1[ AT 0 ];          STRING s2 = sp2[ AT 0 ];          INT le1   = ( UPB s1 - LWB s1 ) + 1;          INT le2   = ( UPB s2 - LWB s2 ) + 1;          le1 < 1 AND le2 < 1     THEN # both strings are empty #         1     ELIF le1 < 1 OR  le2 < 1     THEN # one of the strings is empty #    0     ELSE # both strings are non-empty #        INT dist := IF le2 > le1 THEN le2 ELSE le1 FI;        dist OVERAB 2 -:= 1;        [ 0 : le1 ]BOOL matches1; FOR i FROM LWB matches1 TO UPB matches1 DO matches1[ i ] := FALSE OD;        [ 0 : le2 ]BOOL matches2; FOR i FROM LWB matches2 TO UPB matches2 DO matches2[ i ] := FALSE OD;        INT matches  := 0;        INT transpos := 0;        FOR i FROM LWB s1 TO UPB s1 DO            INT start := i - dist;            IF  start < 0 THEN start := 0 FI;            INT end   := i + dist + 1;            IF  end > le2 THEN end := le2 FI;            FOR k FROM start TO end - 1            WHILE IF matches2[ k ]                  THEN TRUE                  ELIF s1[ i ] /= s2[ k ]                  THEN TRUE                  ELSE                      matches2[ k ] := matches1[ i ] := TRUE;                      matches +:= 1;                      FALSE                  FI            DO SKIP OD        OD;        IF matches = 0        THEN 0        ELSE            INT k := 0;            FOR i FROM LWB s1 TO UPB s1 DO                IF matches1[ i ] THEN                    WHILE NOT matches2[ k ] DO k +:= 1 OD;                    IF s1[ i ] /= s2[ k ] THEN transpos +:= 1 FI;                    k +:= 1                FI            OD;            transpos OVERAB 2;            ( ( matches / le1 )            + ( matches / le2 )            + ( ( matches - transpos ) / matches )            ) / 3        FI     FI # jaro sim # ;PROC jaro winkler dist = ( STRING sp, tp )REAL:     BEGIN        STRING s  = sp[ AT 0 ];        STRING t  = tp[ AT 0 ];        INT  ls = ( UPB s - LWB s ) + 1;        INT  lt = ( UPB t - LWB t ) + 1;        INT  l max := IF ls < lt THEN ls ELSE lt FI;        IF   l max > 4 THEN l max := 4 FI;        INT  l := 0;        FOR  i FROM 0 TO l max - 1 DO IF s[ i ] = t[ i ] THEN l +:= 1 FI OD;        REAL js = jaro sim( s, t );        REAL p  = 0.1;        REAL ws = js + ( l * p * ( 1 - js ) );        1 - ws     END # jaro winkler dist # ;# include the Associative Array code #PR read "aArray.a68" PR# test cases #[]STRING misspelt = ( "accomodate", "definately", "goverment", "occured", "publically", "recieve", "seperate", "untill", "wich" );IF  FILE input file;    STRING file name = "unixdict.txt";    open( input file, file name, stand in channel ) /= 0THEN    # failed to open the file #    print( ( "Unable to open """ + file name + """", newline ) )ELSE    # file opened OK #    BOOL at eof := FALSE;    # set the EOF handler for the file #    on logical file end( input file, ( REF FILE f )BOOL:                                     BEGIN                                         # note that we reached EOF on the #                                         # latest read #                                         at eof := TRUE;                                         # return TRUE so processing can continue #                                         TRUE                                     END                       );    REF AARRAY words := INIT LOC AARRAY;    STRING word;    WHILE NOT at eof    DO        STRING word;        get( input file, ( word, newline ) );        words // word := ""    OD;    # close the file #    close( input file );    # look for near matches to the misspelt words #    INT max closest = 6; # max number of closest matches to show #    FOR m pos FROM LWB misspelt TO UPB misspelt DO        [ max closest ]STRING closest word;        [ max closest ]REAL   closest jwd;        FOR i TO max closest DO closest word[ i ] := ""; closest jwd[ i ] := 999 999 999 OD;         REF AAELEMENT e := FIRST words;        WHILE e ISNT nil element DO            STRING word = key OF e;            REAL jwd = jaro winkler dist( misspelt[ m pos ], word );            BOOL found better match := FALSE;            FOR i TO max closest WHILE NOT found better match DO                IF jwd <= closest jwd[ i ] THEN                    # found a new closer match #                    found better match := TRUE;                    # shuffle the others down 1 and insert the new match #                    FOR j FROM max closest BY - 1 TO i + 1 DO                        closest word[ j ] := closest word[ j - 1 ];                        closest jwd[  j ] := closest jwd[  j - 1 ]                    OD;                    closest word[ i ] := word;                    closest jwd[  i ] := jwd                FI            OD;            e := NEXT words        OD;        print( ( "Misspelt word: ", misspelt[ m pos ], ":", newline ) );        FOR i TO max closest DO            print( ( fixed( closest jwd[ i ], -8, 4 ), " ", closest word[ i ], newline ) )        OD;        print( ( newline ) )    ODFI`
Output:
```Misspelt word: accomodate:
0.0182 accommodate
0.1044 accordant
0.1219 acclimate
0.1327 accompanist
0.1333 accost

Misspelt word: definately:
0.0800 define
0.0850 definite
0.0886 defiant
0.1200 definitive
0.1219 designate
0.1267 deflate

Misspelt word: goverment:
0.0667 govern
0.1167 governor
0.1175 governess
0.1330 governance
0.1361 coverlet
0.1367 sovereignty

Misspelt word: occured:
0.0250 occurred
0.0571 occur
0.0952 occurrent
0.1056 occlude
0.1217 concurred
0.1429 cure

Misspelt word: publically:
0.0800 public
0.1325 pullback
0.1327 publication
0.1400 pull
0.1556 pulley
0.1571 publish

Misspelt word: recieve:
0.0667 relieve
0.0762 reeve
0.0852 recessive
0.0852 receptive
0.0905 recipe

Misspelt word: seperate:
0.0708 desperate
0.0917 separate
0.1042 temperate
0.1048 repartee
0.1167 sewerage
0.1167 selenate

Misspelt word: untill:
0.0333 until
0.1111 till
0.1333 huntsville
0.1357 instill
0.1422 unital
0.1511 unilateral

Misspelt word: wich:
0.0533 witch
0.0533 winch
0.0600 which
0.0857 wichita
0.1111 twitch
0.1111 switch
```

## C++

Translation of: Swift
`#include <algorithm>#include <cstdlib>#include <fstream>#include <iomanip>#include <iostream>#include <string>#include <vector> auto load_dictionary(const std::string& path) {    std::ifstream in(path);    if (!in)        throw std::runtime_error("Cannot open file " + path);    std::string line;    std::vector<std::string> words;    while (getline(in, line))        words.push_back(line);    return words;} double jaro_winkler_distance(std::string str1, std::string str2) {    size_t len1 = str1.size();    size_t len2 = str2.size();    if (len1 < len2) {        std::swap(str1, str2);        std::swap(len1, len2);    }    if (len2 == 0)        return len1 == 0 ? 0.0 : 1.0;    size_t delta = std::max(size_t(1), len1/2) - 1;    std::vector<bool> flag(len2, false);    std::vector<char> ch1_match;    ch1_match.reserve(len1);    for (size_t idx1 = 0; idx1 < len1; ++idx1) {        char ch1 = str1[idx1];        for (size_t idx2 = 0; idx2 < len2; ++idx2) {            char ch2 = str2[idx2];            if (idx2 <= idx1 + delta && idx2 + delta >= idx1                && ch1 == ch2 && !flag[idx2]) {                flag[idx2] = true;                ch1_match.push_back(ch1);                break;            }        }    }    size_t matches = ch1_match.size();    if (matches == 0)        return 1.0;    size_t transpositions = 0;    for (size_t idx1 = 0, idx2 = 0; idx2 < len2; ++idx2) {        if (flag[idx2]) {            if (str2[idx2] != ch1_match[idx1])                ++transpositions;            ++idx1;        }    }    double m = matches;    double jaro = (m/len1 + m/len2 + (m - transpositions/2.0)/m)/3.0;    size_t common_prefix = 0;    len2 = std::min(size_t(4), len2);    for (size_t i = 0; i < len2; ++i) {        if (str1[i] == str2[i])            ++common_prefix;    }    return 1.0 - (jaro + common_prefix * 0.1 * (1.0 - jaro));} auto within_distance(const std::vector<std::string>& words,                     double max_distance, const std::string& str,                     size_t max_to_return) {    using pair = std::pair<std::string, double>;    std::vector<pair> result;    for (const auto& word : words) {        double jaro = jaro_winkler_distance(word, str);        if (jaro <= max_distance)            result.emplace_back(word, jaro);    }    std::stable_sort(result.begin(), result.end(),        [](const pair& p1, const pair& p2) { return p1.second < p2.second; });    if (result.size() > max_to_return)        result.resize(max_to_return);    return result;} int main() {    try {        auto words(load_dictionary("linuxwords.txt"));        std::cout << std::fixed << std::setprecision(4);        for (auto str : {"accomodate", "definately", "goverment",                            "occured", "publically", "recieve",                            "seperate", "untill", "wich"}) {            std::cout << "Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to '"                << str << "' are:\n        Word   |  Distance\n";            for (const auto& pair : within_distance(words, 0.15, str, 5)) {                std::cout << std::setw(14) << pair.first << " | "                    << std::setw(6) << pair.second << '\n';            }            std::cout << '\n';        }    } catch (const std::exception& ex) {        std::cerr << ex.what() << '\n';        return EXIT_FAILURE;    }    return EXIT_SUCCESS;}`
Output:
```Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
Word   |  Distance
accommodate | 0.0182
accommodated | 0.0333
accommodates | 0.0333
accommodating | 0.0815
accommodation | 0.0815

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
Word   |  Distance
definitely | 0.0400
defiantly | 0.0422
define | 0.0800
definite | 0.0850
definable | 0.0872

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
Word   |  Distance
government | 0.0533
govern | 0.0667
governments | 0.0697
movement | 0.0810
governmental | 0.0833

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
Word   |  Distance
occurred | 0.0250
occur | 0.0571
occupied | 0.0786
occurs | 0.0905
accursed | 0.0917

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
Word   |  Distance
publicly | 0.0400
public | 0.0800
publicity | 0.1044
publication | 0.1327
biblically | 0.1400

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
Word   |  Distance
relieve | 0.0667

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
Word   |  Distance
desperate | 0.0708
separate | 0.0917
temperate | 0.1042
separated | 0.1144
separates | 0.1144

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
Word   |  Distance
until | 0.0333
untie | 0.1067
untimely | 0.1083
till | 0.1111
Antilles | 0.1264

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
Word   |  Distance
witch | 0.0533
which | 0.0600
switch | 0.1111
twitch | 0.1111
witches | 0.1143

```

## F#

This task uses Jaro Distance (F#)

` // Calculate Jaro-Winkler Similarity of 2 Strings. Nigel Galloway: August 7th., 2020let Jw P n g=let L=float(let i=Seq.map2(fun n g->n=g) n g in (if Seq.length i>4 then i|>Seq.take 4 else i)|>Seq.takeWhile id|>Seq.length)             let J=J n g in J+P*L*(1.0-J) let dict=System.IO.File.ReadAllLines("linuxwords.txt")let fN g=let N=Jw 0.1 g in dict|>Array.map(fun n->(n,1.0-(N n)))|>Array.sortBy snd["accomodate";"definately";"goverment";"occured";"publically";"recieve";"seperate";"untill";"wich"]|>  List.iter(fun n->printfn "%s" n;fN n|>Array.take 5|>Array.iter(fun n->printf "%A" n);printfn "\n") `
Output:
```accomodate
("accommodate", 0.01818181818)("accommodated", 0.03333333333)("accommodates", 0.03333333333)("accommodation", 0.08153846154)("accommodating", 0.08153846154)

definately
("definitely", 0.04)("defiantly", 0.04222222222)("define", 0.08)("definite", 0.085)("definable", 0.08722222222)

goverment
("government", 0.05333333333)("govern", 0.06666666667)("governments", 0.0696969697)("governmental", 0.08333333333)("governs", 0.09523809524)

occured
("occurred", 0.025)("occur", 0.05714285714)("occupied", 0.07857142857)("occurs", 0.09047619048)("cured", 0.09523809524)

publically
("publicly", 0.04)("public", 0.08)("publicity", 0.1044444444)("publication", 0.1327272727)("politically", 0.1418181818)

recieve

seperate
("desperate", 0.0787037037)("separate", 0.09166666667)("separated", 0.1143518519)("separates", 0.1143518519)("temperate", 0.1157407407)

untill
("until", 0.03333333333)("untie", 0.1066666667)("untimely", 0.1083333333)("till", 0.1111111111)("Huntsville", 0.1333333333)

wich
("witch", 0.05333333333)("which", 0.06)("switch", 0.1111111111)("twitch", 0.1111111111)("witches", 0.1142857143)
```

## Go

This uses unixdict and borrows code from the Jaro_distance#Go task. Otherwise it is a translation of the Wren entry.

`package main import (    "bytes"    "fmt"    "io/ioutil"    "log"    "sort") func jaroSim(str1, str2 string) float64 {    if len(str1) == 0 && len(str2) == 0 {        return 1    }    if len(str1) == 0 || len(str2) == 0 {        return 0    }    match_distance := len(str1)    if len(str2) > match_distance {        match_distance = len(str2)    }    match_distance = match_distance/2 - 1    str1_matches := make([]bool, len(str1))    str2_matches := make([]bool, len(str2))    matches := 0.    transpositions := 0.    for i := range str1 {        start := i - match_distance        if start < 0 {            start = 0        }        end := i + match_distance + 1        if end > len(str2) {            end = len(str2)        }        for k := start; k < end; k++ {            if str2_matches[k] {                continue            }            if str1[i] != str2[k] {                continue            }            str1_matches[i] = true            str2_matches[k] = true            matches++            break        }    }    if matches == 0 {        return 0    }    k := 0    for i := range str1 {        if !str1_matches[i] {            continue        }        for !str2_matches[k] {            k++        }        if str1[i] != str2[k] {            transpositions++        }        k++    }    transpositions /= 2    return (matches/float64(len(str1)) +        matches/float64(len(str2)) +        (matches-transpositions)/matches) / 3} func jaroWinklerDist(s, t string) float64 {    ls := len(s)    lt := len(t)    lmax := lt    if ls < lt {        lmax = ls    }    if lmax > 4 {        lmax = 4    }    l := 0    for i := 0; i < lmax; i++ {        if s[i] == t[i] {            l++        }    }    js := jaroSim(s, t)    p := 0.1    ws := js + float64(l)*p*(1-js)    return 1 - ws} type wd struct {    word string    dist float64} func main() {    misspelt := []string{        "accomodate", "definately", "goverment", "occured", "publically",        "recieve", "seperate", "untill", "wich",    }    b, err := ioutil.ReadFile("unixdict.txt")    if err != nil {        log.Fatal("Error reading file")    }    words := bytes.Fields(b)    for _, ms := range misspelt {        var closest []wd        for _, w := range words {            word := string(w)            if word == "" {                continue            }            jwd := jaroWinklerDist(ms, word)            if jwd < 0.15 {                closest = append(closest, wd{word, jwd})            }        }        fmt.Println("Misspelt word:", ms, ":")        sort.Slice(closest, func(i, j int) bool { return closest[i].dist < closest[j].dist })        for i, c := range closest {            fmt.Printf("%0.4f %s\n", c.dist, c.word)            if i == 5 {                break            }        }        fmt.Println()    }}`
Output:
```Misspelt word: accomodate :
0.0182 accommodate
0.1044 accordant
0.1219 acclimate
0.1327 accompanist
0.1333 accord

Misspelt word: definately :
0.0800 define
0.0850 definite
0.0886 defiant
0.1200 definitive
0.1219 designate
0.1267 deflate

Misspelt word: goverment :
0.0667 govern
0.1167 governor
0.1175 governess
0.1330 governance
0.1361 coverlet
0.1367 sovereignty

Misspelt word: occured :
0.0250 occurred
0.0571 occur
0.0952 occurrent
0.1056 occlude
0.1217 concurred
0.1429 cure

Misspelt word: publically :
0.0800 public
0.1327 publication
0.1400 pull
0.1492 pullback

Misspelt word: recieve :
0.0667 relieve
0.0762 reeve
0.0852 receptive
0.0852 recessive
0.0905 recife

Misspelt word: seperate :
0.0708 desperate
0.0917 separate
0.1042 temperate
0.1167 selenate
0.1167 sewerage
0.1167 sept

Misspelt word: untill :
0.0333 until
0.1111 till
0.1333 huntsville
0.1357 instill
0.1422 unital

Misspelt word: wich :
0.0533 winch
0.0533 witch
0.0600 which
0.0857 wichita
0.1111 switch
0.1111 twitch
```

## Julia

`# download("http://users.cs.duke.edu/~ola/ap/linuxwords", "linuxwords.txt")const words = read("linuxwords.txt", String) |> split .|> strip function jarowinklerdistance(s1, s2)    if length(s1) < length(s2)        s1, s2 = s2, s1    end    len1, len2 = length(s1), length(s2)    len2 == 0 && return 0.0    delta = max(0, len2 ÷ 2 - 1)    flag = zeros(Bool, len2)  # flags for possible transpositions, begin as false    ch1_match = eltype(s1)[]    for (i, ch1) in enumerate(s1)        for (j, ch2) in enumerate(s2)            if (j <= i + delta) && (j >= i - delta) && (ch1 == ch2) && !flag[j]                flag[j] = true                push!(ch1_match, ch1)                break            end        end    end    matches = length(ch1_match)    matches == 0 && return 1.0    transpositions, i = 0, 0    for (j, ch2) in enumerate(s2)        if flag[j]            i += 1            transpositions += (ch2 != ch1_match[i])        end    end    jaro = (matches / len1 + matches / len2 + (matches - transpositions/2) / matches) / 3.0    commonprefix = count(i -> s1[i] == s2[i], 1:min(len2, 4))    return 1 - (jaro + commonprefix * 0.1 * (1 - jaro))end function closewords(s, maxdistance, maxtoreturn)    jw = 0.0    arr = [(w, jw) for w in words if (jw = jarowinklerdistance(s, w)) <= maxdistance]    sort!(arr, lt=(x, y) -> x[2] < y[2])    return length(arr) <= maxtoreturn ? arr : arr[1:maxtoreturn]end for s in ["accomodate", "definately", "goverment", "occured", "publically",    "recieve", "seperate", "untill", "wich"]    println("\nClose dictionary words ( distance < 0.15 using Jaro-Winkler distance) to '\$s' are:")    println("    Word      |  Distance")    for (w, jw) in closewords(s, 0.15, 5)        println(rpad(w, 14), "| ", Float16(jw))    endend `
Output:
```Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
Word      |  Distance
accommodate   | 0.01819
accommodated  | 0.03333
accommodates  | 0.03333
accommodating | 0.08154
accommodation | 0.08154

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
Word      |  Distance
definitely    | 0.04
defiantly     | 0.04224
define        | 0.08
definite      | 0.085
definable     | 0.0872

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
Word      |  Distance
government    | 0.05334
govern        | 0.06665
governments   | 0.0697
movement      | 0.081
governmental  | 0.0833

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
Word      |  Distance
occurred      | 0.025
occur         | 0.05713
occupied      | 0.07855
occurs        | 0.09045
accursed      | 0.0917

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
Word      |  Distance
publicly      | 0.04
public        | 0.08
publicity     | 0.10443
publication   | 0.1327
biblically    | 0.14

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
Word      |  Distance
relieve       | 0.06665

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
Word      |  Distance
desperate     | 0.07086
separate      | 0.0917
temperate     | 0.1042
separated     | 0.1144
separates     | 0.1144

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
Word      |  Distance
until         | 0.03333
untie         | 0.1067
untimely      | 0.10834
Antilles      | 0.1263
untidy        | 0.1333

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
Word      |  Distance
witch         | 0.05334
which         | 0.06
witches       | 0.11426
rich          | 0.11664
wick          | 0.11664
```

## Perl

`use strict;use warnings;use List::Util qw(min max head); sub jaro_winkler {    my(\$s, \$t) = @_;    my(@s_matches, @t_matches, \$matches);     return 0 if \$s eq \$t;     my \$s_len = length \$s; my @s = split //, \$s;    my \$t_len = length \$t; my @t = split //, \$t;     my \$match_distance = int (max(\$s_len,\$t_len)/2) - 1;     for my \$i (0 .. \$#s) {        my \$start = max(0, \$i - \$match_distance);        my \$end   = min(\$i + \$match_distance, \$t_len - 1);        for my \$j (\$start .. \$end) {            next if \$t_matches[\$j] or \$s[\$i] ne \$t[\$j];            (\$s_matches[\$i], \$t_matches[\$j]) = (1, 1);            \$matches++ and last;        }    }    return 1 unless \$matches;     my(\$k, \$transpositions) = (0, 0);     for my \$i (0 .. \$#s) {        next unless \$s_matches[\$i];        \$k++ until  \$t_matches[\$k];        \$transpositions++ if \$s[\$i] ne \$t[\$k];        \$k++;    }     my \$prefix = 0;    \$s[\$_] eq \$t[\$_] and ++\$prefix for 0 .. -1 + min 5, \$s_len, \$t_len;     my \$jaro = (\$matches / \$s_len + \$matches / \$t_len +        ((\$matches - \$transpositions / 2) / \$matches)) / 3;     1 - (\$jaro + \$prefix * .1 * ( 1 - \$jaro) )} my @words = split /\n/, `cat ./unixdict.txt`; for my \$word (<accomodate definately goverment occured publically recieve seperate untill wich>) {    my %J;    \$J{\$_} = jaro_winkler(\$word, \$_) for @words;    print "\nClosest 5 dictionary words with a Jaro-Winkler distance < .15 from '\$word':\n";    printf "%15s : %0.4f\n", \$_, \$J{\$_}         for head 5, sort { \$J{\$a} <=> \$J{\$b} or \$a cmp \$b } grep { \$J{\$_} < 0.15 } keys %J;}`
Output:
```Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'accomodate':
accommodate : 0.0152
accordant : 0.1044
accompanist : 0.1106
accompaniment : 0.1183

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'definately':
define : 0.0667
definite : 0.0708
defiant : 0.0886
definitive : 0.1000
designate : 0.1219

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'goverment':
govern : 0.0556
governor : 0.0972
governess : 0.0979
governance : 0.1108
coverlet : 0.1167

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'occured':
occurred : 0.0208
occur : 0.0476
occurrent : 0.0794
occlude : 0.1056
occurring : 0.1217

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'publically':
public : 0.0667
publication : 0.1106
publish : 0.1310
pull : 0.1400
pullback : 0.1492

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'recieve':
relieve : 0.0571
reeve : 0.0667
receptive : 0.0852
recessive : 0.0852

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'seperate':
desperate : 0.0708
separate : 0.0786
sewerage : 0.1000
repartee : 0.1083
repeater : 0.1083

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'untill':
until : 0.0278
till : 0.1111
tilt : 0.1111
huntsville : 0.1333
instill : 0.1357

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from 'wich':
winch : 0.0533
witch : 0.0533
which : 0.0600
wichita : 0.0857
switch : 0.1111```

## Phix

Uses jaro() from Jaro_distance#Phix and unixdict.txt

`function jaroWinklerDist(string s, t)    integer lm = min({length(s),length(t),4}),            l = sum(sq_eq(s[1..lm],t[1..lm]))    return (1-jaro(s, t))*(1-l*0.1)end function constant mispelt = {"accomodate", "definately", "goverment", "occured",                     "publically", "recieve", "seperate", "untill", "wich"},         words = get_text(join_path({"demo","unixdict.txt"}),GT_LF_STRIPPED),         nom = length(mispelt),         now = length(words)sequence jwds = repeat(0,now)for m=1 to nom do    string ms = mispelt[m]    printf(1,"\nMisspelt word: %s :\n", ms)    for w=1 to now do        jwds[w] = jaroWinklerDist(ms,words[w])    end for    sequence tags = custom_sort(jwds,tagset(now))    for j=1 to 6 do        integer tj = tags[j]--      if jwds[tj]>0.15 then exit end if        printf(1,"%0.4f %s\n", {jwds[tj], words[tj]})    end forend for`

Output identical to Go/Wren Algol68

## Python

`"""Test Jaro-Winkler distance metric.linuxwords.txt is from http://users.cs.duke.edu/~ola/ap/linuxwords""" WORDS = [s.strip() for s in open("linuxwords.txt").read().split()]MISSPELLINGS = [    "accomodate​",    "definately​",    "goverment",    "occured",    "publically",    "recieve",    "seperate",    "untill",    "wich",] def jaro_winkler_distance(st1, st2):    """    Compute Jaro-Winkler distance between two strings.    """    if len(st1) < len(st2):        st1, st2 = st2, st1    len1, len2 = len(st1), len(st2)    if len2 == 0:        return 0.0    delta = max(0, len2 // 2 - 1)    flag = [False for _ in range(len2)]  # flags for possible transpositions    ch1_match = []    for idx1, ch1 in enumerate(st1):        for idx2, ch2 in enumerate(st2):            if idx2 <= idx1 + delta and idx2 >= idx1 - delta and ch1 == ch2 and not flag[idx2]:                flag[idx2] = True                ch1_match.append(ch1)                break     matches = len(ch1_match)    if matches == 0:        return 1.0    transpositions, idx1 = 0, 0    for idx2, ch2 in enumerate(st2):        if flag[idx2]:            transpositions += (ch2 != ch1_match[idx1])            idx1 += 1     jaro = (matches / len1 + matches / len2 + (matches - transpositions/2) / matches) / 3.0    commonprefix = 0    for i in range(min(4, len2)):        commonprefix += (st1[i] == st2[i])     return 1.0 - (jaro + commonprefix * 0.1 * (1 - jaro)) def within_distance(maxdistance, stri, maxtoreturn):    """    Find words in WORDS of closeness to stri within maxdistance, return up to maxreturn of them.    """    arr = [w for w in WORDS if jaro_winkler_distance(stri, w) <= maxdistance]    arr.sort(key=lambda x: jaro_winkler_distance(stri, x))    return arr if len(arr) <= maxtoreturn else arr[:maxtoreturn] for STR in MISSPELLINGS:    print('\nClose dictionary words ( distance < 0.15 using Jaro-Winkler distance) to "',          STR, '" are:\n        Word   |  Distance')    for w in within_distance(0.15, STR, 5):        print('{:>14} | {:6.4f}'.format(w, jaro_winkler_distance(STR, w))) `
Output:
```Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " accomodate " are:
Word   |  Distance
accommodate | 0.0182
accommodated | 0.0333
accommodates | 0.0333
accommodating | 0.0815
accommodation | 0.0815

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " definately " are:
Word   |  Distance
definitely | 0.0400
defiantly | 0.0422
define | 0.0800
definite | 0.0850
definable | 0.0872

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " goverment " are:
Word   |  Distance
government | 0.0533
govern | 0.0667
governments | 0.0697
movement | 0.0810
governmental | 0.0833

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " occured " are:
Word   |  Distance
occurred | 0.0250
occur | 0.0571
occupied | 0.0786
occurs | 0.0905
accursed | 0.0917

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " publically " are:
Word   |  Distance
publicly | 0.0400
public | 0.0800
publicity | 0.1044
publication | 0.1327
biblically | 0.1400

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " recieve " are:
Word   |  Distance
relieve | 0.0667

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " seperate " are:
Word   |  Distance
desperate | 0.0708
separate | 0.0917
temperate | 0.1042
separated | 0.1144
separates | 0.1144

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " untill " are:
Word   |  Distance
until | 0.0333
untie | 0.1067
untimely | 0.1083
Antilles | 0.1264
untidy | 0.1333

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " wich " are:
Word   |  Distance
witch | 0.0533
which | 0.0600
witches | 0.1143
rich | 0.1167
wick | 0.1167
```

## Raku

Works with: Rakudo version 2020.07

A minor modification of the Jaro distance task entry.

using the unixdict.txt file from www.puzzlers.org

`sub jaro-winkler (\$s, \$t) {     return 0 if \$s eq \$t;     my \$s_len = + my @s = \$s.comb;    my \$t_len = + my @t = \$t.comb;     my \$match_distance = (\$s_len max \$t_len) div 2 - 1;     my @s_matches;    my @t_matches;    my \$matches = 0;     for ^@s -> \$i {         my \$start = 0 max \$i - \$match_distance;        my \$end = \$i + \$match_distance min (\$t_len - 1);         for \$start .. \$end -> \$j {            @t_matches[\$j] and next;            @s[\$i] eq @t[\$j] or next;            @s_matches[\$i] = 1;            @t_matches[\$j] = 1;            \$matches++;            last;        }    }     return 1 if \$matches == 0;     my \$k              = 0;    my \$transpositions = 0;     for ^@s -> \$i {        @s_matches[\$i] or next;        until @t_matches[\$k] { ++\$k }        @s[\$i] eq @t[\$k] or ++\$transpositions;        ++\$k;    }     my \$prefix = 0;     ++\$prefix if @s[\$_] eq @t[\$_] for ^(min 4, \$s_len, \$t_len);     my \$jaro = (\$matches / \$s_len + \$matches / \$t_len +        ((\$matches - \$transpositions / 2) / \$matches)) / 3;     1 - (\$jaro + \$prefix * .1 * ( 1 - \$jaro) )}  my @words =  './unixdict.txt'.IO.slurp.words; for <accomodate definately goverment occured publically recieve seperate untill wich>   -> \$word {    my %result = @words.race.map: { \$_ => jaro-winkler(\$word, \$_) };    say "\nClosest 5 dictionary words with a Jaro-Winkler distance < .15 from \$word:";    printf "%15s : %0.4f\n", .key, .value for %result.grep({ .value < .15 }).sort({+.value, ~.key}).head(5);}`
Output:
```Closest 5 dictionary words with a Jaro-Winkler distance < .15 from accomodate:
accommodate : 0.0182
accordant : 0.1044
acclimate : 0.1219
accompanist : 0.1327

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from definately:
define : 0.0800
definite : 0.0850
defiant : 0.0886
definitive : 0.1200
designate : 0.1219

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from goverment:
govern : 0.0667
governor : 0.1167
governess : 0.1175
governance : 0.1330
coverlet : 0.1361

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from occured:
occurred : 0.0250
occur : 0.0571
occurrent : 0.0952
occlude : 0.1056
concurred : 0.1217

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from publically:
public : 0.0800
publication : 0.1327
pull : 0.1400
pullback : 0.1492

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from recieve:
relieve : 0.0667
reeve : 0.0762
receptive : 0.0852
recessive : 0.0852

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from seperate:
desperate : 0.0708
separate : 0.0917
temperate : 0.1042
selenate : 0.1167
sept : 0.1167

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from untill:
until : 0.0333
till : 0.1111
huntsville : 0.1333
instill : 0.1357
unital : 0.1422

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from wich:
winch : 0.0533
witch : 0.0533
which : 0.0600
wichita : 0.0857
switch : 0.1111```

## Rust

Translation of: Python
`use std::fs::File;use std::io::{self, BufRead}; fn load_dictionary(filename: &str) -> std::io::Result<Vec<String>> {    let file = File::open(filename)?;    let mut dict = Vec::new();    for line in io::BufReader::new(file).lines() {        dict.push(line?);    }    Ok(dict)} fn jaro_winkler_distance(string1: &str, string2: &str) -> f64 {    let mut st1 = string1;    let mut st2 = string2;    let mut len1 = st1.chars().count();    let mut len2 = st2.chars().count();    if len1 < len2 {        std::mem::swap(&mut st1, &mut st2);        std::mem::swap(&mut len1, &mut len2);    }    if len2 == 0 {        return if len1 == 0 { 0.0 } else { 1.0 };    }    let delta = std::cmp::max(1, len1 / 2) - 1;    let mut flag = vec![false; len2];    let mut ch1_match = vec![];    for (idx1, ch1) in st1.chars().enumerate() {        for (idx2, ch2) in st2.chars().enumerate() {            if idx2 <= idx1 + delta && idx2 + delta >= idx1 && ch1 == ch2 && !flag[idx2] {                flag[idx2] = true;                ch1_match.push(ch1);                break;            }        }    }    let matches = ch1_match.len();    if matches == 0 {        return 1.0;    }    let mut transpositions = 0;    let mut idx1 = 0;    for (idx2, ch2) in st2.chars().enumerate() {        if flag[idx2] {            transpositions += (ch2 != ch1_match[idx1]) as i32;            idx1 += 1;        }    }    let m = matches as f64;    let jaro =        (m / (len1 as f64) + m / (len2 as f64) + (m - (transpositions as f64) / 2.0) / m) / 3.0;    let mut commonprefix = 0;    for (c1, c2) in st1.chars().zip(st2.chars()).take(std::cmp::min(4, len2)) {        commonprefix += (c1 == c2) as i32;    }    1.0 - (jaro + commonprefix as f64 * 0.1 * (1.0 - jaro))} fn within_distance<'a>(    dict: &'a Vec<String>,    max_distance: f64,    stri: &str,    max_to_return: usize,) -> Vec<(&'a String, f64)> {    let mut arr: Vec<(&String, f64)> = dict        .iter()        .map(|w| (w, jaro_winkler_distance(stri, w)))        .filter(|x| x.1 <= max_distance)        .collect();    // The trait std::cmp::Ord is not implemented for f64, otherwise    // we could just do this:    // arr.sort_by_key(|x| x.1);    let compare_distance = |d1, d2| {        use std::cmp::Ordering;        if d1 < d2 {            Ordering::Less        } else if d1 > d2 {            Ordering::Greater        } else {            Ordering::Equal        }    };    arr.sort_by(|x, y| compare_distance(x.1, y.1));    arr[0..std::cmp::min(max_to_return, arr.len())].to_vec()} fn main() {    match load_dictionary("linuxwords.txt") {        Ok(dict) => {            for word in &[                "accomodate",                "definately",                "goverment",                "occured",                "publically",                "recieve",                "seperate",                "untill",                "wich",            ] {                println!("Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to '{}' are:", word);                println!("        Word   |  Distance");                for (w, dist) in within_distance(&dict, 0.15, word, 5) {                    println!("{:>14} | {:6.4}", w, dist)                }                println!();            }        }        Err(error) => eprintln!("{}", error),    }}`
Output:
```Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
Word   |  Distance
accommodate | 0.0182
accommodated | 0.0333
accommodates | 0.0333
accommodating | 0.0815
accommodation | 0.0815

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
Word   |  Distance
definitely | 0.0400
defiantly | 0.0422
define | 0.0800
definite | 0.0850
definable | 0.0872

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
Word   |  Distance
government | 0.0533
govern | 0.0667
governments | 0.0697
movement | 0.0810
governmental | 0.0833

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
Word   |  Distance
occurred | 0.0250
occur | 0.0571
occupied | 0.0786
occurs | 0.0905
accursed | 0.0917

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
Word   |  Distance
publicly | 0.0400
public | 0.0800
publicity | 0.1044
publication | 0.1327
biblically | 0.1400

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
Word   |  Distance
relieve | 0.0667

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
Word   |  Distance
desperate | 0.0708
separate | 0.0917
temperate | 0.1042
separated | 0.1144
separates | 0.1144

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
Word   |  Distance
until | 0.0333
untie | 0.1067
untimely | 0.1083
till | 0.1111
Antilles | 0.1264

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
Word   |  Distance
witch | 0.0533
which | 0.0600
switch | 0.1111
twitch | 0.1111
witches | 0.1143

```

## Swift

Translation of: Rust
`import Foundation func loadDictionary(_ path: String) throws -> [String] {    let contents = try String(contentsOfFile: path, encoding: String.Encoding.ascii)    return contents.components(separatedBy: "\n")} func jaroWinklerDistance(string1: String, string2: String) -> Double {    var st1 = Array(string1)    var st2 = Array(string2)    var len1 = st1.count    var len2 = st2.count    if len1 < len2 {        swap(&st1, &st2)        swap(&len1, &len2)    }    if len2 == 0 {        return len1 == 0 ? 0.0 : 1.0    }    let delta = max(1, len1 / 2) - 1    var flag = Array(repeating: false, count: len2)    var ch1Match: [Character] = []    ch1Match.reserveCapacity(len1)    for idx1 in 0..<len1 {        let ch1 = st1[idx1]        for idx2 in 0..<len2 {            let ch2 = st2[idx2]            if idx2 <= idx1 + delta && idx2 + delta >= idx1 && ch1 == ch2 && !flag[idx2] {                flag[idx2] = true                ch1Match.append(ch1)                break            }        }    }    let matches = ch1Match.count    if matches == 0 {        return 1.0    }    var transpositions = 0    var idx1 = 0    for idx2 in 0..<len2 {        if flag[idx2] {            if st2[idx2] != ch1Match[idx1] {                transpositions += 1            }            idx1 += 1        }    }    let m = Double(matches)    let jaro =        (m / Double(len1) + m / Double(len2) + (m - Double(transpositions) / 2.0) / m) / 3.0    var commonPrefix = 0    for i in 0..<min(4, len2) {        if st1[i] == st2[i] {            commonPrefix += 1        }    }    return 1.0 - (jaro + Double(commonPrefix) * 0.1 * (1.0 - jaro))} func withinDistance(words: [String], maxDistance: Double, string: String,                    maxToReturn: Int) -> [(String, Double)] {    var arr = Array(words.map{(\$0, jaroWinklerDistance(string1: string, string2: \$0))}        .filter{\$0.1 <= maxDistance})    arr.sort(by: { x, y in return x.1 < y.1 })    return Array(arr[0..<min(maxToReturn, arr.count)])} func pad(string: String, width: Int) -> String {    if string.count >= width {        return string    }    return String(repeating: " ", count: width - string.count) + string} do {    let dict = try loadDictionary("linuxwords.txt")    for word in ["accomodate", "definately", "goverment", "occured",                 "publically", "recieve", "seperate", "untill", "wich"] {        print("Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to '\(word)' are:")        print("        Word   |  Distance")        for (w, dist) in withinDistance(words: dict, maxDistance: 0.15,                                        string: word, maxToReturn: 5) {            print("\(pad(string: w, width: 14)) | \(String(format: "%6.4f", dist))")        }        print()    }} catch {    print(error.localizedDescription)}`
Output:
```Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
Word   |  Distance
accommodate | 0.0182
accommodated | 0.0333
accommodates | 0.0333
accommodating | 0.0815
accommodation | 0.0815

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
Word   |  Distance
definitely | 0.0400
defiantly | 0.0422
define | 0.0800
definite | 0.0850
definable | 0.0872

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
Word   |  Distance
government | 0.0533
govern | 0.0667
governments | 0.0697
movement | 0.0810
governmental | 0.0833

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
Word   |  Distance
occurred | 0.0250
occur | 0.0571
occupied | 0.0786
occurs | 0.0905
accursed | 0.0917

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
Word   |  Distance
publicly | 0.0400
public | 0.0800
publicity | 0.1044
publication | 0.1327
biblically | 0.1400

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
Word   |  Distance
relieve | 0.0667

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
Word   |  Distance
desperate | 0.0708
separate | 0.0917
temperate | 0.1042
separated | 0.1144
separates | 0.1144

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
Word   |  Distance
until | 0.0333
untie | 0.1067
untimely | 0.1083
till | 0.1111
Antilles | 0.1264

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
Word   |  Distance
witch | 0.0533
which | 0.0600
switch | 0.1111
twitch | 0.1111
witches | 0.1143

```

## Wren

Library: Wren-fmt
Library: Wren-sort

This uses unixdict and borrows code from the Jaro_distance#Wren task.

`import "io" for Fileimport "/fmt" for Fmtimport "/sort" for Sort var jaroSim = Fn.new { |s1, s2|    var le1 = s1.count    var le2 = s2.count    if (le1 == 0 && le2 == 0) return 1    if (le1 == 0 || le2 == 0) return 0    var dist = (le2 > le1) ? le2 : le1    dist = (dist/2).floor - 1    var matches1 = List.filled(le1, false)    var matches2 = List.filled(le2, false)    var matches = 0    var transpos = 0    for (i in 0...s1.count) {        var start = i - dist        if (start < 0) start = 0        var end = i + dist + 1        if (end > le2) end = le2        var k = start        while (k < end) {            if (!(matches2[k] || s1[i] != s2[k])) {                matches1[i] = true                matches2[k] = true                matches = matches + 1                break            }            k = k + 1        }    }    if (matches == 0) return 0    var k = 0    for (i in 0...s1.count) {        if (matches1[i]) {            while(!matches2[k]) k = k + 1            if (s1[i] != s2[k]) transpos = transpos + 1            k = k + 1        }    }    transpos = transpos / 2    return (matches/le1 + matches/le2 + (matches - transpos)/matches) / 3} var jaroWinklerDist = Fn.new { |s, t|    var ls = s.count    var lt = t.count    var lmax = (ls < lt) ? ls : lt    if (lmax > 4) lmax = 4    var l = 0    for (i in 0...lmax) {        if (s[i] == t[i]) l = l + 1    }    var js = jaroSim.call(s, t)    var p = 0.1    var ws = js + l*p*(1 - js)    return 1 - ws} var misspelt = ["accomodate", "definately", "goverment", "occured", "publically", "recieve", "seperate", "untill", "wich"]var words = File.read("unixdict.txt").split("\n").map { |w| w.trim() }.where { |w| w != "" }for (ms in misspelt) {    var closest = []    for (word in words) {       var jwd = jaroWinklerDist.call(ms, word)       if (jwd < 0.15) closest.add([word, jwd])    }    System.print("Misspelt word: %(ms):")    var cmp = Fn.new { |n1, n2| (n1[1]-n2[1]).sign }    Sort.insertion(closest, cmp)    for (c in closest.take(6)) Fmt.print("\$0.4f \$s", c[1], c[0])    System.print()}`
Output:
```Misspelt word: accomodate:
0.0182 accommodate
0.1044 accordant
0.1219 acclimate
0.1327 accompanist
0.1333 accord

Misspelt word: definately:
0.0800 define
0.0850 definite
0.0886 defiant
0.1200 definitive
0.1219 designate
0.1267 deflate

Misspelt word: goverment:
0.0667 govern
0.1167 governor
0.1175 governess
0.1330 governance
0.1361 coverlet
0.1367 sovereignty

Misspelt word: occured:
0.0250 occurred
0.0571 occur
0.0952 occurrent
0.1056 occlude
0.1217 concurred
0.1429 cure

Misspelt word: publically:
0.0800 public
0.1327 publication
0.1400 pull
0.1492 pullback

Misspelt word: recieve:
0.0667 relieve
0.0762 reeve
0.0852 receptive
0.0852 recessive
0.0905 recife

Misspelt word: seperate:
0.0708 desperate
0.0917 separate
0.1042 temperate
0.1167 selenate
0.1167 sept
0.1167 sewerage

Misspelt word: untill:
0.0333 until
0.1111 till
0.1333 huntsville
0.1357 instill
0.1422 unital

Misspelt word: wich:
0.0533 winch
0.0533 witch
0.0600 which
0.0857 wichita
0.1111 switch
0.1111 twitch
```