info.debatty.java.stringsimilarity

Class SorensenDice

java.lang.Object

info.debatty.java.stringsimilarity.SorensenDice

All Implemented Interfaces:

NormalizedStringDistance, NormalizedStringSimilarity, StringDistance, StringSimilarity, Serializable

@Immutable
public class SorensenDice
extends Object
implements NormalizedStringDistance, NormalizedStringSimilarity

Similar to Jaccard index, but this time the similarity is computed as 2 * |V1 inter V2| / (|V1| + |V2|). Distance is computed as 1 - cosine similarity.

Author:

Thibault Debatty

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SorensenDice() Sorensen-Dice coefficient, aka Sørensen index, Dice's coefficient or Czekanowski's binary (non-quantitative) index.
SorensenDice(int k) Sorensen-Dice coefficient, aka Sørensen index, Dice's coefficient or Czekanowski's binary (non-quantitative) index.

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(String s1, String s2) Returns 1 - similarity.
int	getK() Return k, the length of k-shingles (aka n-grams).
Map<String,Integer>	getProfile(String string) Compute and return the profile of s, as defined by Ukkonen "Approximate string-matching with q-grams and maximal matches".
double	similarity(String s1, String s2) Similarity is computed as 2 * |A inter B| / (|A| + |B|).

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SorensenDice

public SorensenDice(int k)

Sorensen-Dice coefficient, aka Sørensen index, Dice's coefficient or Czekanowski's binary (non-quantitative) index. The strings are first converted to boolean sets of k-shingles (sequences of k characters), then the similarity is computed as 2 * |A inter B| / (|A| + |B|). Attention: Sorensen-Dice distance (and similarity) does not satisfy triangle inequality.

Parameters:

k -

SorensenDice

public SorensenDice()

Sorensen-Dice coefficient, aka Sørensen index, Dice's coefficient or Czekanowski's binary (non-quantitative) index. The strings are first converted to boolean sets of k-shingles (sequences of k characters), then the similarity is computed as 2 * |A inter B| / (|A| + |B|). Attention: Sorensen-Dice distance (and similarity) does not satisfy triangle inequality. Default k is 3.

Method Detail

similarity

public final double similarity(String s1,
                String s2)

Similarity is computed as 2 * |A inter B| / (|A| + |B|).

Specified by:

similarity in interface StringSimilarity

Parameters:

s1 - The first string to compare.

s2 - The second string to compare.

Returns:

The computed Sorensen-Dice similarity.

Throws:

NullPointerException - if s1 or s2 is null.

distance

public double distance(String s1,
              String s2)

Returns 1 - similarity.

Specified by:

distance in interface StringDistance

Parameters:

s1 - The first string to compare.

s2 - The second string to compare.

Returns:

1.0 - the computed similarity

Throws:

NullPointerException - if s1 or s2 is null.

getK

public int getK()

Return k, the length of k-shingles (aka n-grams).

Returns:

The length of k-shingles.

getProfile

public final Map<String,Integer> getProfile(String string)

Compute and return the profile of s, as defined by Ukkonen "Approximate string-matching with q-grams and maximal matches". https://www.cs.helsinki.fi/u/ukkonen/TCS92.pdf The profile is the number of occurrences of k-shingles, and is used to compute q-gram similarity, Jaccard index, etc. Pay attention: the memory requirement of the profile can be up to k * size of the string

Parameters:

string -

Returns:

the profile of this string, as an unmodifiable Map