public Class NormalizedLevenshtein

Class Inheritance

extends Object
implements NormalizedStringDistance, NormalizedStringSimilarity

java.lang.Object
info.debatty.java.stringsimilarity.NormalizedLevenshtein

All Implemented Interfaces

info.debatty.java.stringsimilarity.interfaces.NormalizedStringSimilarity, info.debatty.java.stringsimilarity.interfaces.StringSimilarity, java.io.Serializable, info.debatty.java.stringsimilarity.interfaces.NormalizedStringDistance, info.debatty.java.stringsimilarity.interfaces.StringDistance

Annotations

@Immutable

Imports

info.debatty.java.stringsimilarity.interfaces.NormalizedStringSimilarity, .NormalizedStringDistance, net.jcip.annotations.Immutable

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.

Author: Thibault Debatty

Field Summary

Modifier and Type	Field and Description
private final Levenshtein	l

Constructor Summary

Access	Constructor and Description
public	NormalizedLevenshtein()

Method Summary

Modifier and Type	Method and Description
public final double	Returns: The computed distance in the range [0, 1] distance(final String The first string to compare. s1, final String The second string to compare. s2) Implements info.debatty.java.stringsimilarity.interfaces.StringDistance.distance. Compute distance as Levenshtein(s1, s2) / max(\|s1\|, \|s2\|).
public final double	Returns: 1.0 - the computed distance similarity(final String The first string to compare. s1, final String The second string to compare. s2) Implements info.debatty.java.stringsimilarity.interfaces.StringSimilarity.similarity. Return 1 - distance.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

l	back to summary
private final Levenshtein l

Constructor Detail

NormalizedLevenshtein	back to summary
public NormalizedLevenshtein()

Method Detail

distance	back to summary
public final double distance(final String s1, final String s2) Implements info.debatty.java.stringsimilarity.interfaces.StringDistance.distance. Compute distance as Levenshtein(s1, s2) / max(\|s1\|, \|s2\|). Parameters s1:String The first string to compare. s2:String The second string to compare. Returns:double The computed distance in the range [0, 1] Exceptions NullPointerException: if s1 or s2 is null.

distance

back to summary

public final double distance(final String s1, final String s2)

Implements info.debatty.java.stringsimilarity.interfaces.StringDistance.distance.

Compute distance as Levenshtein(s1, s2) / max(|s1|, |s2|).

Parameters

s1:String: The first string to compare.
s2:String: The second string to compare.

Returns:double

The computed distance in the range [0, 1]

Exceptions

NullPointerException:: if s1 or s2 is null.

similarity	back to summary
public final double similarity(final String s1, final String s2) Implements info.debatty.java.stringsimilarity.interfaces.StringSimilarity.similarity. Return 1 - distance. Parameters s1:String The first string to compare. s2:String The second string to compare. Returns:double 1.0 - the computed distance Exceptions NullPointerException: if s1 or s2 is null.

similarity

back to summary

public final double similarity(final String s1, final String s2)

Implements info.debatty.java.stringsimilarity.interfaces.StringSimilarity.similarity.

Return 1 - distance.

Parameters

s1:String: The first string to compare.
s2:String: The second string to compare.

Returns:double

1.0 - the computed distance

Exceptions

NullPointerException:: if s1 or s2 is null.