Text completion: Difference between revisions

[https://github.com/dwyl/english-words Github Repo]<br>

[https://raw.githubusercontent.com/dwyl/english-words/master/words.txt Raw Text, Save as .txt file]<br>

[https://dev.to/lefebvre/the-soundex-algorithm-5el1 SoundEx Algorithm]<br>

[http://www.catalysoft.com/articles/StrikeAMatch.html Dice's Coefficient]<br>

;Possible Output:

<pre>

=={{header|C++}}==

{{trans|Julia}}

#include <fstream>

#include <iostream>

}

return EXIT_SUCCESS;

 

{{out}}

{{Trans|Wren}}

Using '''unixdict.txt'''.

program Text_Completion;

 

end.

 

 

{{out}}

{{trans|Julia}}

{{works with|Factor|0.99 2020-07-03}}

math.ranges namespaces prettyprint.config sequences splitting ;

 

dup [ similarity ] [ drop word ] [ word swap lev-dist-of ] tri

"Words at Levenshtein distance of %d (%.1f%% similarity) from %u:\n%u\n\n" printf

{{out}}

<pre>

{ "collection" "combination" "commission" "comparison" "compensation" "competing" "competitive" "complaint" "complete" "completed" "completely" "complexity" "compliance" "compliant" "compression" "computing" "conclusion" "conditions" "connection" "convention" "conviction" "cooperation" "corporation" "correction" "correlation" "corruption" "nomination" "opinion" "opposition" "option" "pollution" "population" "position" "simulation" "solution" }

</pre>

 

=={{header|Go}}==

{{trans|Wren}}

 

import (

fmt.Println()

}

 

{{out}}

=={{header|Java}}==

[https://github.com/dwyl/english-words Github Repo Uses dependencies given].

import java.io.File;

import java.io.IOException;

}

}

;Output

<pre>

 

Process finished with exit code 0

</pre>

 

=={{header|Julia}}==

See https://en.wikipedia.org/wiki/Levenshtein_distance, the number of one character edits to obtain one word from another.

 

const fname = download("https://www.mit.edu/~ecprice/wordlist.10000", "wordlist10000.txt")

levdistof(n, wrd), "\n")

end

<pre>

Words at Levenshtein distance of 1 (90% similarity) from "complition":

["collection", "combination", "commission", "comparison", "compensation", "competing", "competitive", "complaint", "complete", "completed", "completely", "complexity", "compliance", "compliant", "compression", "computing", "conclusion", "conditions", "connection", "convention", "conviction", "cooperation", "corporation", "correction", "correlation", "corruption", "nomination", "opinion", "opposition", "option", "pollution", "population", "position", "simulation", "solution"]

</pre>

 

=={{header|Nim}}==

We use the function <code>editDistance</code> from the <code>std/editdistance</code> module to get the Levenshtein distance (computed by considering in Unicode codepoints).

 

 

let search = "complition"

echo &"Words which are {similarity:4.1f}% similar:"

echo lev[i].join(" ")

 

{{out}}

=={{header|Perl}}==

Inspired by Raku Sorenson-Dice implementation (doesn't handle Unicode, but module <code>Text::Dice</code> can).

use warnings;

use feature 'say';

use Path::Tiny;

 

 

sub score {

push @ranked, sprintf "%.3f $_", $scored{$_} for sort { $scored{$b} <=> $scored{$a} } keys %scored;

say "\n$word:\n" . join("\n", head 10, @ranked);

{{out}}

<pre>complition:

 

=={{header|Phix}}==

{{out}}

(matches Delphi/Go/Wren)

===Hamming distance===

{{Trans|Java}}

my @s1 = $user_word.comb;

my @listed = gather for $filename.IO.lines -> $line {

 

say "{.[0].fmt('%.2f')}% {.[1]}" for @listed;

{{out}}

<pre>80.00% compaction

Using unixdict.txt from www.puzzlers.org

 

my $match = bigram $phrase;

%hash.race.map: { [(2 * ($match ∩ .value) / ($match + .value)).round(.001), .key] }

say "\n$w:";

.say for sorenson($w, %hash).grep(*.[0] >= .55).sort({-.[0],~.[1]}).head(10);

 

{{out}}

 

No attempt was made to change (by any method) three letters or more.

parse arg what iFID . /*obtain optional arguments from the CL*/

if what==''|what=="," then what= 'complition' /*Not specified? Then use the default.*/

if @.z\==. then $= $ z; if r==1 then call recur

end /*k*/

{{out|output|text=&nbsp; when using the default inputs:}}

<pre>

(80%) compaction

(80%) complexion

</pre>

 

{{libheader|Wren-fmt}}

This uses 'unixdict' and the Levenshtein distance algorithm to test for similarity.

 

var levenshtein = Fn.new { |s, t|

System.print(lev[i])

System.print()

 

{{out}}