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strsim

Finds degree of similarity between two strings, based on Dice's Coefficient.

Table of Contents

Usage

Install using:

go get -u github.com/hbakhtiyor/strsim

In your code:

import "github.com/hbakhtiyor/strsim"

similarity := strsim.Compare("healed", "sealed")

matches := strsim.FindBestMatch("healed", []string{"edward", "sealed", "theatre")

API

Requiring the module gives an object with two methods:

Compare(a, b string) float64

Returns a fraction between 0 and 1, which indicates the degree of similarity between the two strings. 0 indicates completely different strings, 1 indicates identical strings. The comparison is case-sensitive.

Arguments
  1. a (string): The first string
  2. b (string): The second string

Order does not make a difference.

Returns

(float64): A fraction from 0 to 1, both inclusive. Higher number indicates more similarity.

Examples
strsim.Compare("healed", "sealed")
// → 0.8

strsim.Compare("Olive-green table for sale, in extremely good condition.", 
  "For sale: table in very good  condition, olive green in colour.")
// → 0.6060606060606061

strsim.Compare("Olive-green table for sale, in extremely good condition.", 
  "For sale: green Subaru Impreza, 210,000 miles")
// → 0.2558139534883721

strsim.Compare("Olive-green table for sale, in extremely good condition.", 
  "Wanted: mountain bike with at least 21 gears.")
// → 0.1411764705882353

FindBestMatch(s string, targets []string) *MatchResult

Compares s against each string in targets.

Arguments
  1. s (string): The string to match each target string against.
  2. targets ([]string): Each string in this array will be matched against the main string.
Returns

(MatchResult): An object with a Matches field, which gives a similarity score for each target string, a BestMatch field, which specifies which target string was most similar to the main string, and a BestMatchIndex field, which specifies the index of the BestMatch in the targets array.

Examples
strsim.FindBestMatch("Olive-green table for sale, in extremely good condition.", []string{
  "For sale: green Subaru Impreza, 210,000 miles", 
  "For sale: table in very good condition, olive green in colour.", 
  "Wanted: mountain bike with at least 21 gears.",
});
// → 
MatchResult {
  Matches: []Match {
    { Target: "For sale: green Subaru Impreza, 210,000 miles",
      Score: 0.2558139534883721 },
    { Target: "For sale: table in very good condition, olive green in colour.",
      Score: 0.6060606060606061 },
    { Target: "Wanted: mountain bike with at least 21 gears.",
      Score: 0.1411764705882353 } },
  BestMatch: Match
    { Target: "For sale: table in very good condition, olive green in colour.",
      Score: 0.6060606060606061 },
  BestMatchIndex: 1 
}

Benchmark

BenchmarkCompare-4         	   20000	     82479 ns/op	   15921 B/op	      51 allocs/op
BenchmarkFindBestMatch-4   	   30000	     60800 ns/op	   11707 B/op	      41 allocs/op
BenchmarkSortedByScore-4   	 2000000	       638 ns/op	     128 B/op	       4 allocs/op
Hardware used
  • Intel® Core™ i3-2310M CPU @ 2.10GHz × 4
  • 4Gb RAM
Version
  • Go 1.11.2
  • Ubuntu 18.04.01 LTS x86_64 OS
  • 4.15.0-39-generic kernel

Credit

https://github.com/aceakash/string-similarity