This is the vas-string-metrics Reference Manual, generated automatically by Declt version 4.0 beta 2 "William Riker" on Sun Dec 15 08:01:47 2024 GMT+0.
The main system appears first, followed by any subsystem dependency.
vas-string-metrics
Jaro-Winkler and Levenshtein string distance algorithms.
Vladimir Sedach <vsedach@gmail.com>
LLGPLv3
package.lisp
(file).
levenshtein.lisp
(file).
jaro-winkler.lisp
(file).
soerensen-dice.lisp
(file).
Files are sorted by type and then listed depth-first from the systems components trees.
vas-string-metrics/vas-string-metrics.asd
vas-string-metrics/package.lisp
vas-string-metrics/levenshtein.lisp
vas-string-metrics/jaro-winkler.lisp
vas-string-metrics/soerensen-dice.lisp
vas-string-metrics/vas-string-metrics.asd
vas-string-metrics
(system).
vas-string-metrics/levenshtein.lisp
package.lisp
(file).
vas-string-metrics
(system).
levenshtein-distance
(function).
normalized-levenshtein-distance
(function).
vas-string-metrics/jaro-winkler.lisp
package.lisp
(file).
vas-string-metrics
(system).
jaro-distance
(function).
jaro-winkler-distance
(function).
matching-char-list
(function).
prefix-length
(function).
vas-string-metrics/soerensen-dice.lisp
package.lisp
(file).
vas-string-metrics
(system).
soerensen-dice-coefficient
(function).
Packages are listed by definition order.
vas-string-metrics
common-lisp
.
jaro-distance
(function).
jaro-winkler-distance
(function).
levenshtein-distance
(function).
normalized-levenshtein-distance
(function).
soerensen-dice-coefficient
(function).
bigrams
(function).
matching-char-list
(function).
prefix-length
(function).
seq-cdr
(function).
Definitions are sorted by export status, category, package, and then by lexicographic order.
Finds the Jaro distance (measure of similarity) from string s1 to string s2. Returns a value in the range from 0 (no similarity) to 1 (exact match).
Finds the Jaro distance (measure of similarity) from string s1 to string s2. Returns a value in the range from 0 (no similarity) to 1 (exact match).
Finds the Levenshtein distance (minimum number of edits) from string s1 to string s2.
Finds the normalized Levenshtein distance (from 0 for no similarity to 1 for exact match) from string s1 to string s2.
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