Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity ...

Last update: Dec 29, 2022

Overview

java-string-similarity

A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented. Check the summary table below for the complete list...

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Overview
Normalized, metric, similarity and distance
Shingles (n-gram) based similarity and distance
Levenshtein
Normalized Levenshtein
Weighted Levenshtein
Damerau-Levenshtein
Optimal String Alignment
Jaro-Winkler
Longest Common Subsequence
Metric Longest Common Subsequence
N-Gram
Shingle (n-gram) based algorithms
Ratcliff-Obershelp
Experimental
- SIFT4
Users

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Using maven:

<dependency>
    <groupId>info.debatty</groupId>
    <artifactId>java-string-similarity</artifactId>
    <version>RELEASE</version>
</dependency>

Or check the releases.

This library requires Java 8 or more recent.

Overview

The main characteristics of each implemented algorithm are presented below. The "cost" column gives an estimation of the computational cost to compute the similarity between two strings of length m and n respectively.

		Normalized?	Metric?	Type	Cost	Typical usage
Levenshtein	distance	No	Yes		O(m*n) ¹
Normalized Levenshtein	distance similarity	Yes	No		O(m*n) ¹
Weighted Levenshtein	distance	No	No		O(m*n) ¹	OCR
Damerau-Levenshtein ³	distance	No	Yes		O(m*n) ¹
Optimal String Alignment ³	distance	No	No		O(m*n) ¹
Jaro-Winkler	similarity distance	Yes	No		O(m*n)	typo correction
Longest Common Subsequence	distance	No	No		O(m*n) ^1,2	diff utility, GIT reconciliation
Metric Longest Common Subsequence	distance	Yes	Yes		O(m*n) ^1,2
N-Gram	distance	Yes	No		O(m*n)
Q-Gram	distance	No	No	Profile	O(m+n)
Cosine similarity	similarity distance	Yes	No	Profile	O(m+n)
Jaccard index	similarity distance	Yes	Yes	Set	O(m+n)
Sorensen-Dice coefficient	similarity distance	Yes	No	Set	O(m+n)
Ratcliff-Obershelp	similarity distance	Yes	No		?

[1] In this library, Levenshtein edit distance, LCS distance and their sibblings are computed using the dynamic programming method, which has a cost O(m.n). For Levenshtein distance, the algorithm is sometimes called Wagner-Fischer algorithm ("The string-to-string correction problem", 1974). The original algorithm uses a matrix of size m x n to store the Levenshtein distance between string prefixes.

If the alphabet is finite, it is possible to use the method of four russians (Arlazarov et al. "On economic construction of the transitive closure of a directed graph", 1970) to speedup computation. This was published by Masek in 1980 ("A Faster Algorithm Computing String Edit Distances"). This method splits the matrix in blocks of size t x t. Each possible block is precomputed to produce a lookup table. This lookup table can then be used to compute the string similarity (or distance) in O(nm/t). Usually, t is choosen as log(m) if m > n. The resulting computation cost is thus O(mn/log(m)). This method has not been implemented (yet).

[2] In "Length of Maximal Common Subsequences", K.S. Larsen proposed an algorithm that computes the length of LCS in time O(log(m).log(n)). But the algorithm has a memory requirement O(m.n²) and was thus not implemented here.

[3] There are two variants of Damerau-Levenshtein string distance: Damerau-Levenshtein with adjacent transpositions (also sometimes called unrestricted Damerau–Levenshtein distance) and Optimal String Alignment (also sometimes called restricted edit distance). For Optimal String Alignment, no substring can be edited more than once.

Normalized, metric, similarity and distance

Although the topic might seem simple, a lot of different algorithms exist to measure text similarity or distance. Therefore the library defines some interfaces to categorize them.

(Normalized) similarity and distance

StringSimilarity : Implementing algorithms define a similarity between strings (0 means strings are completely different).
NormalizedStringSimilarity : Implementing algorithms define a similarity between 0.0 and 1.0, like Jaro-Winkler for example.
StringDistance : Implementing algorithms define a distance between strings (0 means strings are identical), like Levenshtein for example. The maximum distance value depends on the algorithm.
NormalizedStringDistance : This interface extends StringDistance. For implementing classes, the computed distance value is between 0.0 and 1.0. NormalizedLevenshtein is an example of NormalizedStringDistance.

Generally, algorithms that implement NormalizedStringSimilarity also implement NormalizedStringDistance, and similarity = 1 - distance. But there are a few exceptions, like N-Gram similarity and distance (Kondrak)...

Metric distances

The MetricStringDistance interface : A few of the distances are actually metric distances, which means that verify the triangle inequality d(x, y) <= d(x,z) + d(z,y). For example, Levenshtein is a metric distance, but NormalizedLevenshtein is not.

A lot of nearest-neighbor search algorithms and indexing structures rely on the triangle inequality. You can check "Similarity Search, The Metric Space Approach" by Zezula et al. for a survey. These cannot be used with non metric similarity measures.

Read Javadoc for a detailed description

Shingles (n-gram) based similarity and distance

A few algorithms work by converting strings into sets of n-grams (sequences of n characters, also sometimes called k-shingles). The similarity or distance between the strings is then the similarity or distance between the sets.

Some of them, like jaccard, consider strings as sets of shingles, and don't consider the number of occurences of each shingle. Others, like cosine similarity, work using what is sometimes called the profile of the strings, which takes into account the number of occurences of each shingle.

For these algorithms, another use case is possible when dealing with large datasets:

compute the set or profile representation of all the strings
compute the similarity between sets or profiles

Levenshtein

The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.

It is a metric string distance. This implementation uses dynamic programming (Wagner–Fischer algorithm), with only 2 rows of data. The space requirement is thus O(m) and the algorithm runs in O(m.n).

import info.debatty.java.stringsimilarity.*;

public class MyApp {
    
    public static void main (String[] args) {
        Levenshtein l = new Levenshtein();

        System.out.println(l.distance("My string", "My $tring"));
        System.out.println(l.distance("My string", "My $tring"));
        System.out.println(l.distance("My string", "My $tring"));
    }
}

Normalized Levenshtein

This distance is computed as levenshtein distance divided by the length of the longest string. The resulting value is always in the interval [0.0 1.0] but it is not a metric anymore!

The similarity is computed as 1 - normalized distance.

import info.debatty.java.stringsimilarity.*;

public class MyApp {
    
    public static void main (String[] args) {
        NormalizedLevenshtein l = new NormalizedLevenshtein();

        System.out.println(l.distance("My string", "My $tring"));
        System.out.println(l.distance("My string", "My $tring"));
        System.out.println(l.distance("My string", "My $tring"));
    }
}

Weighted Levenshtein

An implementation of Levenshtein that allows to define different weights for different character substitutions.

This algorithm is usually used for optical character recognition (OCR) applications. For OCR, the cost of substituting P and R is lower then the cost of substituting P and M for example because because from and OCR point of view P is similar to R.

It can also be used for keyboard typing auto-correction. Here the cost of substituting E and R is lower for example because these are located next to each other on an AZERTY or QWERTY keyboard. Hence the probability that the user mistyped the characters is higher.

import info.debatty.java.stringsimilarity.*;

public class MyApp {

    public static void main(String[] args) {
        WeightedLevenshtein wl = new WeightedLevenshtein(
                new CharacterSubstitutionInterface() {
                    public double cost(char c1, char c2) {
                        
                        // The cost for substituting 't' and 'r' is considered
                        // smaller as these 2 are located next to each other
                        // on a keyboard
                        if (c1 == 't' && c2 == 'r') {
                            return 0.5;
                        }
                        
                        // For most cases, the cost of substituting 2 characters
                        // is 1.0
                        return 1.0;
                    }
        });
        
        System.out.println(wl.distance("String1", "Srring2"));
    }
}

Damerau-Levenshtein

Similar to Levenshtein, Damerau-Levenshtein distance with transposition (also sometimes calls unrestricted Damerau-Levenshtein distance) is the minimum number of operations needed to transform one string into the other, where an operation is defined as an insertion, deletion, or substitution of a single character, or a transposition of two adjacent characters.

It does respect triangle inequality, and is thus a metric distance.

This is not to be confused with the optimal string alignment distance, which is an extension where no substring can be edited more than once.

import info.debatty.java.stringsimilarity.*;

public class MyApp {


    public static void main(String[] args) {
        Damerau d = new Damerau();
        
        // 1 substitution
        System.out.println(d.distance("ABCDEF", "ABDCEF"));
        
        // 2 substitutions
        System.out.println(d.distance("ABCDEF", "BACDFE"));
        
        // 1 deletion
        System.out.println(d.distance("ABCDEF", "ABCDE"));
        System.out.println(d.distance("ABCDEF", "BCDEF"));
        System.out.println(d.distance("ABCDEF", "ABCGDEF"));
        
        // All different
        System.out.println(d.distance("ABCDEF", "POIU"));
    }
}

Will produce:

1.0
2.0
1.0
1.0
1.0
6.0

Optimal String Alignment

The Optimal String Alignment variant of Damerau–Levenshtein (sometimes called the restricted edit distance) computes the number of edit operations needed to make the strings equal under the condition that no substring is edited more than once, whereas the true Damerau–Levenshtein presents no such restriction. The difference from the algorithm for Levenshtein distance is the addition of one recurrence for the transposition operations.

Note that for the optimal string alignment distance, the triangle inequality does not hold and so it is not a true metric.

import info.debatty.java.stringsimilarity.*;

public class MyApp {


    public static void main(String[] args) {
        OptimalStringAlignment osa = new OptimalStringAlignment();
        
        System.out.println(osa.distance("CA", "ABC"));;
    }
}

Will produce:

3.0

Jaro-Winkler

Jaro-Winkler is a string edit distance that was developed in the area of record linkage (duplicate detection) (Winkler, 1990). The Jaro–Winkler distance metric is designed and best suited for short strings such as person names, and to detect typos.

Jaro-Winkler computes the similarity between 2 strings, and the returned value lies in the interval [0.0, 1.0]. It is (roughly) a variation of Damerau-Levenshtein, where the substitution of 2 close characters is considered less important then the substitution of 2 characters that a far from each other.

The distance is computed as 1 - Jaro-Winkler similarity.

import info.debatty.java.stringsimilarity.*;

public class MyApp {


    public static void main(String[] args) {
        JaroWinkler jw = new JaroWinkler();
        
        // substitution of s and t
        System.out.println(jw.similarity("My string", "My tsring"));
        
        // substitution of s and n
        System.out.println(jw.similarity("My string", "My ntrisg"));
    }
}

will produce:

0.9740740656852722
0.8962963223457336

Longest Common Subsequence

The longest common subsequence (LCS) problem consists in finding the longest subsequence common to two (or more) sequences. It differs from problems of finding common substrings: unlike substrings, subsequences are not required to occupy consecutive positions within the original sequences.

It is used by the diff utility, by Git for reconciling multiple changes, etc.

The LCS distance between strings X (of length n) and Y (of length m) is n + m - 2 |LCS(X, Y)| min = 0 max = n + m

LCS distance is equivalent to Levenshtein distance when only insertion and deletion is allowed (no substitution), or when the cost of the substitution is the double of the cost of an insertion or deletion.

This class implements the dynamic programming approach, which has a space requirement O(m.n), and computation cost O(m.n).

In "Length of Maximal Common Subsequences", K.S. Larsen proposed an algorithm that computes the length of LCS in time O(log(m).log(n)). But the algorithm has a memory requirement O(m.n²) and was thus not implemented here.

import info.debatty.java.stringsimilarity.*;

public class MyApp {
    public static void main(String[] args) {
        LongestCommonSubsequence lcs = new LongestCommonSubsequence();

        // Will produce 4.0
        System.out.println(lcs.distance("AGCAT", "GAC"));
        
        // Will produce 1.0
        System.out.println(lcs.distance("AGCAT", "AGCT"));
    }
}

Metric Longest Common Subsequence

Distance metric based on Longest Common Subsequence, from the notes "An LCS-based string metric" by Daniel Bakkelund. http://heim.ifi.uio.no/~danielry/StringMetric.pdf

The distance is computed as 1 - |LCS(s1, s2)| / max(|s1|, |s2|)

public class MyApp {

        public static void main(String[] args) {

        info.debatty.java.stringsimilarity.MetricLCS lcs = 
                new info.debatty.java.stringsimilarity.MetricLCS();

        String s1 = "ABCDEFG";   
        String s2 = "ABCDEFHJKL";
        // LCS: ABCDEF => length = 6
        // longest = s2 => length = 10
        // => 1 - 6/10 = 0.4
        System.out.println(lcs.distance(s1, s2));

        // LCS: ABDF => length = 4
        // longest = ABDEF => length = 5
        // => 1 - 4 / 5 = 0.2
        System.out.println(lcs.distance("ABDEF", "ABDIF"));
    }
}

N-Gram

Normalized N-Gram distance as defined by Kondrak, "N-Gram Similarity and Distance", String Processing and Information Retrieval, Lecture Notes in Computer Science Volume 3772, 2005, pp 115-126.

http://webdocs.cs.ualberta.ca/~kondrak/papers/spire05.pdf

The algorithm uses affixing with special character '\n' to increase the weight of first characters. The normalization is achieved by dividing the total similarity score the original length of the longest word.

In the paper, Kondrak also defines a similarity measure, which is not implemented (yet).

import info.debatty.java.stringsimilarity.*;

public class MyApp {

    public static void main(String[] args) {
        
        // produces 0.583333
        NGram twogram = new NGram(2);
        System.out.println(twogram.distance("ABCD", "ABTUIO"));
        
        // produces 0.97222
        String s1 = "Adobe CreativeSuite 5 Master Collection from cheap 4zp";
        String s2 = "Adobe CreativeSuite 5 Master Collection from cheap d1x";
        NGram ngram = new NGram(4);
        System.out.println(ngram.distance(s1, s2));
    }
}

Shingle (n-gram) based algorithms

The cost for computing these similarities and distances is mainly domnitated by k-shingling (converting the strings into sequences of k characters). Therefore there are typically two use cases for these algorithms:

Directly compute the distance between strings:

import info.debatty.java.stringsimilarity.*;

public class MyApp {
    
    public static void main(String[] args) {
        QGram dig = new QGram(2);
        
        // AB BC CD CE
        // 1  1  1  0
        // 1  1  0  1
        // Total: 2

        System.out.println(dig.distance("ABCD", "ABCE"));
    }
}

Or, for large datasets, pre-compute the profile of all strings. The similarity can then be computed between profiles:

import info.debatty.java.stringsimilarity.KShingling;
import info.debatty.java.stringsimilarity.StringProfile;


/**
 * Example of computing cosine similarity with pre-computed profiles.
 */
public class PrecomputedCosine {

    public static void main(String[] args) throws Exception {
        String s1 = "My first string";
        String s2 = "My other string...";

        // Let's work with sequences of 2 characters...
        Cosine cosine = new Cosine(2);

        // Pre-compute the profile of strings
        Map<String, Integer> profile1 = cosine.getProfile(s1);
        Map<String, Integer> profile2 = cosine.getProfile(s2);

        // Prints 0.516185
        System.out.println(cosine.similarity(profile1, profile2));
    }
}

Pay attention, this only works if the same KShingling object is used to parse all input strings !

Q-Gram

Q-gram distance, as defined by Ukkonen in "Approximate string-matching with q-grams and maximal matches" http://www.sciencedirect.com/science/article/pii/0304397592901434

The distance between two strings is defined as the L1 norm of the difference of their profiles (the number of occurences of each n-gram): SUM( |V1_i - V2_i| ). Q-gram distance is a lower bound on Levenshtein distance, but can be computed in O(m + n), where Levenshtein requires O(m.n)

Cosine similarity

The similarity between the two strings is the cosine of the angle between these two vectors representation, and is computed as V1 . V2 / (|V1| * |V2|)

Distance is computed as 1 - cosine similarity.

Jaccard index

Like Q-Gram distance, the input strings are first converted into sets of n-grams (sequences of n characters, also called k-shingles), but this time the cardinality of each n-gram is not taken into account. Each input string is simply a set of n-grams. The Jaccard index is then computed as |V1 inter V2| / |V1 union V2|.

Distance is computed as 1 - similarity. Jaccard index is a metric distance.

Sorensen-Dice coefficient

Similar to Jaccard index, but this time the similarity is computed as 2 * |V1 inter V2| / (|V1| + |V2|).

Distance is computed as 1 - similarity.

Ratcliff-Obershelp

Ratcliff/Obershelp Pattern Recognition, also known as Gestalt Pattern Matching, is a string-matching algorithm for determining the similarity of two strings. It was developed in 1983 by John W. Ratcliff and John A. Obershelp and published in the Dr. Dobb's Journal in July 1988

Ratcliff/Obershelp computes the similarity between 2 strings, and the returned value lies in the interval [0.0, 1.0].

The distance is computed as 1 - Ratcliff/Obershelp similarity.

import info.debatty.java.stringsimilarity.*;

public class MyApp {


    public static void main(String[] args) {
        RatcliffObershelp ro = new RatcliffObershelp();
        
        // substitution of s and t
        System.out.println(ro.similarity("My string", "My tsring"));
        
        // substitution of s and n
        System.out.println(ro.similarity("My string", "My ntrisg"));
    }
}

will produce:

0.8888888888888888
0.7777777777777778

Experimental

SIFT4

SIFT4 is a general purpose string distance algorithm inspired by JaroWinkler and Longest Common Subsequence. It was developed to produce a distance measure that matches as close as possible to the human perception of string distance. Hence it takes into account elements like character substitution, character distance, longest common subsequence etc. It was developed using experimental testing, and without theoretical background.

import info.debatty.java.stringsimilarity.experimental.Sift4;

public class MyApp {

    public static void main(String[] args) {
        String s1 = "This is the first string";
        String s2 = "And this is another string";
        Sift4 sift4 = new Sift4();
        sift4.setMaxOffset(5);
        double expResult =  11.0;
        double result = sift4.distance(s1, s2);
        assertEquals(expResult, result, 0.0);
    }
}

Users

StringSimilarity.NET a .NET port of java-string-similarity
OrientDB string-metrics wraps java-string-similarity to provide different string similarity and distance measures as SQL functions in OrientDB

Use java-string-similarity in your project and want it to be mentioned here? Don't hesitate to drop me a line!

Security & stability

Comments

sift4

I've made a java port of the sift4 common algorithm. https://siderite.blogspot.com/2014/11/super-fast-and-accurate-string-distance.html

Is there any interest in adding it here?

opened by nrktkt 16
Resolves #28: null and empty value handling

This PR resolves #28 for handling empty strings, but also handles null values as well, treating null values effectively as empty strings. This should avoid any unexpected NullPointerExceptions from users of the library. Additional tests have been added for these cases.

In the case of normalized similarity/distance: two null or empty values are treated as similar/zero-distance, while if one of them is null or empty it is treated as dissimilar/completely distant.

For metric and non-metric distance algorithms, all of them appeared to use the max length in the case of empty strings already, so I took that approach for all of them. I'm not 100% confident that this is correct, so let me know if any of the algorithms don't work with that approach.

Had to modify .gitignore to exclude the IntelliJ-generated files.

opened by paulirwin 14
Couldn't build the library

Have I tried to build the library after downloading on my machine using maven, it failed and there an error in NGram.java distance() about overriding. I am not sure how to resolve this. Let me know how to execute the example you mentioned in read.me. Thank you.

opened by avidudi 6
Feature Request: Ability to store and specify profiles
Use-case: Searching for a best match

Given a string Q, and a list of strings L, I want to find the distance between Q and every element of L and then pick the element with the shortest distance.

Current solution

The current solution is to call a distance function with two String parameters. This function assumes nothing about the Strings and hence some information is recomputed every time (for example, profiles of a string).

Proposal

Two additional APIs can be provided to improve performance:

Profile getProfile(String) double distance(Profile p1, Profile p2)

For convenience, a third API would also be useful:

double distance(Profile p, String s) { return distance(p, getProfile(s)); }

This can be used like this:

String query = "alex"; QGram qg = new QGram(2); Profile queryProfile = qg.getProfile(query); list forEach { element -> println(qg.distance(queryProfile, element)); }

Further, if the list is going to be persistent, it could also be possible to serialize the profile of each element of the list into the persistent store. Then, both the query string and the list element's profile need not be recomputed every time!

If the overall idea sounds good to you, I will write more about how the Profile type could be made type safe across the different implementations of StringSimilarityInterface.
opened by hrj 6
Implementation of Ratcliff-Obershelp algorithm

Ported from .Net code by Ligi https://github.com/dxpux (as a patch for fuzzystring) I've edited the README.md as well, but since I'm not an expert to it, so rework probably is needed.

opened by denmase 4
WeightedLevenshtein ins/del weights.

Extend WeightedLevenshtein to have customizable insert / deletion weights. Previously, insert / deletion weights were hardcoded at 1.0. Customizing them allows the caller to under-weight the insertion of a thin letter like I or l to reflect the likelihood of OCR errors (for example).

This adds a new interface, CharacterInsDelInterface, which is an adjunct to CharacterSubstitutionInterface. The old behavior is preserved if the caller does not provide a CharacterSubstitutionInterface subclass.

This also adds insert / deletion tests to the old WeightedLevenshteinTest.testDistance, and adds a new testDistanceCharacterInsDelInterface test.

opened by ewanmellor 4
Jaro-Winkler handling empty strings

Hi,

When using jaro-winkler method I see that when comparing empty strings '' and '' the score return is 1, indicating a complete mismatch.

here is an example below implemented in scala

val jaro = new JaroWinkler() println(jaro.distance("","")) println(jaro.distance("match","match"))

Can this be altered so that when comparing empty strings it returns an exact match of 0?

Thanks

opened by dodgy99 4
Prevent divide by zero errors.

MetricLCS and NormalizedLevenshtein both divide by the max string length to produce distances. If two empty strings are used then a division by zero occurs and NaN is returned.

This PR checks the max string length and returns a distance of 0 if the max string length is 0.

opened by emmettu 4
Add a limit parameter to the {Weighted,}Levenshtein distance.

Add a limit parameter to Levenshtein and WeightedLevenshtein's distance methods. This causes the calculation to exit early if the limit is reached. This means that if the caller only cares about strings with a small distance, they can terminate early if the strings are found to be very different.

opened by ewanmellor 3
@override annotation in distance

@override annotation in NGram.java shows error in Java 6 and above. It should be removed as the method is not being overridden. It is just being implemented.

opened by amriteya 3
Optimisation: use a compiled regex pattern

This speeds up the qgram computation by 2x (after the jvm is warmed up) in my tests.

PS: I am playing with this library to match fuzzy match entries in a database application. Compared to some other libraries, this seems to be very fast as it is working with string tokens. There seems to be lot of scope for optimising further; I will investigate later. Do you have any plans for optimising too?

opened by hrj 3
Jaro winkler similarity on Empty strings

I am using jaro wrinkler similarity to check similarities between names. In one of the use case, i found this issue. s1 = "SOME NAME" - s2 = "" -> similarity = 1 Why is the output "1". shouldn't "1" be for exact matches ? please help

version details : java-string-similarity -> 2.0.0

opened by santhosh6328 1
Kotlin Multiplatform port

Similar to #59 and #19, I decided to make a pure Kotlin port of this library.

Although on the server side, Kotlin has compat with Java libraries, I wanted to make it a Multiplatform lib because why not.

Currently there is no README, but when I finish it, I'm going to mention this repo in the README.

The port is here, under kt-string-similarity: https://github.com/solo-studios/kt-fuzzy

opened by solonovamax 0
There is no such package on npm, and I am trying to do this!

I have not found such perfect package on npm, so, could I translate this package from Java to TypeScript?

This is the semi-finished repo. Please look it over: https://github.com/hellojayjay/string-metric

Will you allow me to do so?

opened by hellojayjay 2
Travis-ci: added support for ppc64le

Hi, I have added support for ppc64le build on travis-ci in the branch . The travis-ci build log can be tracked on the link :https://travis-ci.com/github/sanjaymsh/java-string-similarity/builds/191872664 . I believe it is ready for the final review and merge. Please have a look on it.

Thanks !!

opened by sanjaymsh 2

Any problem using singleton? [Question]

Hi Team,

Can anyone imagine or already get any problem to use a that lib (RatcliffObershelp specificly) in a Singleton class?

My use :

import info.debatty.java.stringsimilarity.RatcliffObershelp;

public class StringSimilarityCalculator {

    private static StringSimilarityCalculator stringSimilarityCalculator;
    private static RatcliffObershelp calculo = new RatcliffObershelp();
    private static final double minSimilaridadeEndereco = 0.90;

    private StringSimilarityCalculator(){}

    public static StringSimilarityCalculator getInstance() {

        if(stringSimilarityCalculator == null){

            stringSimilarityCalculator = new StringSimilarityCalculator();
        }

        return stringSimilarityCalculator;
    }

    public boolean isStringEnderecoSimilar(String enderecoPagador, String enderecoPagadorBanco){

        return calculo.similarity(enderecoPagador.toLowerCase(),
                enderecoPagadorBanco.toLowerCase()) >= minSimilaridadeEndereco;
    }
}

Thanks !!

opened by raizoor 0

N-Gram Example Comment Incorrect?
This issue was reported to my team's .NET port of your library but I confirmed that it is an issue here as well.

The example on the README shows that the N-Gram code expects values of 0.416666 and 0.97222. However, different results are given when the code is actually ran. I am not sure if this is a bug in the code, or that the README comment is outdated/incorrect.

I created a unit test for the README example, and sure enough it fails:

@Test public void exampleFromReadme() { // produces 0.416666 NGram twogram = new NGram(2); assertEquals(0.416666, twogram.distance("ABCD", "ABTUIO"), 0.001); // produces 0.97222 String s1 = "Adobe CreativeSuite 5 Master Collection from cheap 4zp"; String s2 = "Adobe CreativeSuite 5 Master Collection from cheap d1x"; NGram ngram = new NGram(4); assertEquals(0.97222, ngram.distance(s1, s2), 0.001); }

Results:

java.lang.AssertionError: Expected :0.416666 Actual :0.5833333134651184

This result (0.583) is the same that we get on the .NET side of things. As I am not an expert in these algorithms, I am unsure if this is a code bug or a need to update the README.
opened by paulirwin 2