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Rabbitzzc\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.","_id":"string-comparison@1.3.0","dist":{"shasum":"345c872d5c1046a067f4d0aacff38c7cb2cb0411","integrity":"sha512-46aD+slEwybxAMPRII83ATbgMgTiz5P8mVd7Z6VJsCzSHFjdt1hkAVLeFxPIyEb11tc6ihpJTlIqoO0MCF6NPw==","tarball":"https://registry.npmjs.org/string-comparison/-/string-comparison-1.3.0.tgz","fileCount":10,"unpackedSize":33786,"signatures":[{"keyid":"SHA256:jl3bwswu80PjjokCgh0o2w5c2U4LhQAE57gj9cz1kzA","sig":"MEQCIB351FNGD/LTU6kYYaQot/g1uSs2CkWnRcO+gP8qni4YAiB1rn7cy9kZYINYXEM/hLu3Thzn/L25a/ibLTibrB16BQ=="}]},"_npmUser":{"name":"simida","email":"zzclovelcs@gmail.com"},"directories":{},"maintainers":[{"name":"simida","email":"zzclovelcs@gmail.com"}],"_npmOperationalInternal":{"host":"s3://npm-registry-packages","tmp":"tmp/string-comparison_1.3.0_1701251623463_0.6734072780032092"},"_hasShrinkwrap":false}},"time":{"created":"2019-10-16T12:36:58.973Z","1.0.0":"2019-10-16T12:36:59.099Z","modified":"2023-11-29T09:53:43.815Z","1.0.1":"2019-10-16T13:12:11.006Z","1.0.2":"2019-10-17T01:35:19.545Z","1.0.3":"2019-10-17T02:33:48.324Z","1.0.4":"2019-10-17T02:34:21.408Z","1.0.5":"2019-10-18T10:58:20.131Z","1.0.6":"2019-10-18T11:09:04.028Z","1.0.7":"2019-10-18T11:10:42.791Z","1.0.8":"2020-09-28T10:14:46.257Z","1.0.9":"2020-09-28T10:24:19.077Z","1.1.0":"2022-02-18T10:34:42.791Z","1.2.0":"2023-08-29T12:21:44.409Z","1.3.0":"2023-11-29T09:53:43.641Z"},"maintainers":[{"name":"simida","email":"zzclovelcs@gmail.com"}],"description":"A library implementing different string similarity","keywords":["strings","compare similarity","similarity","Dice's Coefficient","Cosine","Jaccard Index","Levenshtein","Longest Common Subsequence","Metric Longest Common Subsequence","difference","compare","comparision","similar","distance","match","sort match"],"repository":{"type":"git","url":"https://github.com/Rabbitzzc/js-string-comparision"},"author":{"name":"Rabbitzzc","email":"zzclovelcs@gmail.com"},"license":"MIT","readme":"\n# string-comparison\n\n\n[](https://github.com/Rabbitzzc/js-string-comparison/stargazers)\n[](https://github.com/Rabbitzzc/js-string-comparison/blob/master/LICENCE)\n\n**JavaScript implementation of [tdebatty/java-string-similarity](https://github.com/tdebatty/java-string-similarity)**\n\nA library implementing different string similarity, distance and sortMatch measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Longest Common Subsequence, cosine similarity etc.) are currently implemented. Check the summary table below for the complete list...\n\n- [string-comparison](#string-comparison)\n - [Download \\& Usage](#download--usage)\n - [OverView](#overview)\n - [Normalized, metric, similarity and distance](#normalized-metric-similarity-and-distance)\n - [(Normalized) similarity and distance](#normalized-similarity-and-distance)\n - [Levenshtein](#levenshtein)\n - [Longest Common Subsequence](#longest-common-subsequence)\n - [Metric Longest Common Subsequence](#metric-longest-common-subsequence)\n - [Cosine similarity](#cosine-similarity)\n - [Sorensen-Dice coefficient](#sorensen-dice-coefficient)\n - [Jaro-Winkler similarity](#jaro-winkler-similarity)\n - [API](#api)\n - [Methods](#methods)\n - [similarity](#similarity)\n - [params](#params)\n - [return](#return)\n - [distance](#distance)\n - [params](#params-1)\n - [return](#return-1)\n - [sortMatch](#sortmatch)\n - [params](#params-2)\n - [return](#return-2)\n - [CHANGELOG](#changelog)\n - [MIT](#mit)\n\n\n## Download & Usage\n\ndownload\n\n```shell\nnpm install string-comparison --save\nyarn add string-comparison\npnpm add string-comparison\n```\nusage\n\n```js\nlet stringComparison = require('string-comparison')\n// or import stringComparison from 'string-comparison'\n\nconst Thanos = 'healed'\nconst Rival = 'sealed'\nconst Avengers = ['edward', 'sealed', 'theatre']\n\n// use by cosine\nlet cos = stringComparison.Cosine\n\nconsole.log(cos.similarity(Thanos, Rival))\nconsole.log(cos.distance(Thanos, Rival))\nconsole.log(cos.sortMatch(Thanos, Avengers))\n\n```\n\n## OverView\n\nThe 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.\n\n| | Measure(s) | Normalized? | Metric? | Type | Cost | Typical usage |\n| ------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------- | ----------- | ------- | ------- | ------ | --------------- |\n| [Jaccard index](https://github.com/luozhouyang/python-string-similarity/blob/master/README.md#jaccard-index) | similarity
distance
sortMatch | Yes | Yes | Set | O(m+n) | |\n| [Cosine similarity](https://github.com/luozhouyang/python-string-similarity/blob/master/README.md#cosine-similarity) | similarity
distance
sortMatch | Yes | No | Profile | O(m+n) | |\n| [Sorensen-Dice coefficient](https://github.com/luozhouyang/python-string-similarity/blob/master/README.md#sorensen-dice-coefficient) | similarity
distance
sortMatch | Yes | No | Set | O(m+n) | |\n| [Levenshtein](https://github.com/luozhouyang/python-string-similarity/blob/master/README.md#levenshtein) | similarity
distance
sortMatch | No | Yes | | O(m*n) | |\n| [Jaro-Winkler](https://github.com/luozhouyang/python-string-similarity/blob/master/README.md#jaro-winkler) | similarity distance
sortMatch | Yes | No | | O(m*n) | typo correction |\n\n## Normalized, metric, similarity and distance\n\nAlthough 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.\n\n### (Normalized) similarity and distance\n\n- StringSimilarity : Implementing algorithms define a similarity between strings (0 means strings are completely different).\n- NormalizedStringSimilarity : Implementing algorithms define a similarity between 0.0 and 1.0, like Jaro-Winkler for example.\n- 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.\n- 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.\n\n## Levenshtein\n\nThe 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.\n\nIt 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).\n\n```js\nimport { levenshtein } from \"string-comparison\"\nimport type {SortMatchResultType} from \"string-comparison\"\n\nconst Thanos = 'healed'\nconst Rival = 'sealed'\nconst Avengers = ['edward', 'sealed', 'theatre']\n\nconsole.log(levenshtein.similarity(Thanos, Rival))\nconsole.log(levenshtein.distance(Thanos, Rival))\nconsole.log(levenshtein.sortMatch(Thanos, Avengers) as SortMatchResultType)\n\n// output\n0.8333333333333334\n1\n[\n { member: 'edward', index: 0, rating: 0.16666666666666663 },\n { member: 'theatre', index: 2, rating: 0.4285714285714286 },\n { member: 'sealed', index: 1, rating: 0.8333333333333334 }\n]\n```\n\n\n## Longest Common Subsequence\n\nThe 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.\n\nIt is used by the diff utility, by Git for reconciling multiple changes, etc.\n\nThe LCS distance between strings X (of length n) and Y (of length m) is n + m - 2 |LCS(X, Y)|\nmin = 0\nmax = n + m\n\nLCS 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.\n\nThis class implements the dynamic programming approach, which has a space requirement O(m.n), and computation cost O(m.n).\n\nIn \"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.\n\n```js\nimport { longestCommonSubsequence } from \"string-comparison\"\nor \nimport { lcs } from \"string-comparison\"\n\n\nconst Thanos = 'healed'\nconst Rival = 'sealed'\nconst Avengers = ['edward', 'sealed', 'theatre']\n\nconsole.log(lcs.similarity(Thanos, Rival))\nconsole.log(lcs.distance(Thanos, Rival))\nconsole.log(lcs.sortMatch(Thanos, Avengers))\n\n// output\n0.8333333333333334\n2\n[\n { member: 'edward', index: 0, rating: 0.5 },\n { member: 'theatre', index: 2, rating: 0.6153846153846154 },\n { member: 'sealed', index: 1, rating: 0.8333333333333334 }\n]\n```\n\n## Metric Longest Common Subsequence\n\nDistance metric based on Longest Common Subsequence, from the notes \"An LCS-based string metric\" by Daniel Bakkelund.\nhttp://heim.ifi.uio.no/~danielry/StringMetric.pdf\n\nThe distance is computed as 1 - |LCS(s1, s2)| / max(|s1|, |s2|)\n\n```js\nimport { metricLcs } from \"string-comparison\"\nor \nimport { mlcs } from \"string-comparison\"\n\nconst Thanos = 'healed'\nconst Rival = 'sealed'\nconst Avengers = ['edward', 'sealed', 'theatre']\n\nconsole.log(metricLcs.similarity(Thanos, Rival))\nconsole.log(metricLcs.distance(Thanos, Rival))\nconsole.log(metricLcs.sortMatch(Thanos, Avengers))\n\n// output\n0.8333333333333334\n0.16666666666666663\n[\n { member: 'edward', index: 0, rating: 0.5 },\n { member: 'theatre', index: 2, rating: 0.5714285714285714 },\n { member: 'sealed', index: 1, rating: 0.8333333333333334 }\n]\n```\n\n## Cosine similarity\n\nLike 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|.\n\nDistance is computed as 1 - similarity.\nJaccard index is a metric distance.\n\n```js\nimport { cosine } from \"string-comparison\"\n```\n\n## Sorensen-Dice coefficient\n\nSimilar to Jaccard index, but this time the similarity is computed as 2 * |V1 inter V2| / (|V1| + |V2|).\n\nDistance is computed as 1 - similarity.\n\n```js\nimport { diceCoefficient } from \"string-comparison\"\n```\n\n## Jaro-Winkler similarity\n\nThe Jaro-Winkler similarity is a string metric measuring edit distance between two strings. Jaro – Winkler Similarity is much similar to Jaro Similarity. They both differ when the prefix of two string match. Jaro – Winkler Similarity uses a prefix scale ‘p’ which gives a more accurate answer when the strings have a common prefix up to a defined maximum length l. \n\n```js\nimport { jaroWinkler } from \"string-comparison\"\n```\n\n## API\n\n* `cosine`\n* `diceCoefficient`\n* `jaccardIndex`\n* `levenshtein`\n* `lcs` = `longestCommonSubsequence`\n* `mlcs` = `metricLcs`\n* `jaroWinkler`\n\n\n## Methods\n* `similarity`.\n* `distance`.\n* `sortMatch`\n\n### similarity\n\nImplementing algorithms define a similarity between strings\n\n#### params\n\n1. thanos [String]\n2. rival [String]\n\n#### return\n\nReturn a similarity between 0.0 and 1.0\n\n### distance\n\nImplementing algorithms define a distance between strings (0 means strings are identical)\n\n#### params\n\n1. `thanos` [String]\n2. `rival` [String]\n\n#### return\n\nReturn a number\n\n### sortMatch\n\n#### params\n\n1. thanos [String]\n2. avengers [...String]\n\n#### return\n\nReturn an array of objects - `SortMatchResultType` ex:\n```js\n[\n { member: 'edward', rating: 0.16666666666666663 },\n { member: 'theatre', rating: 0.4285714285714286 },\n { member: 'mailed', rating: 0.5 },\n { member: 'sealed', rating: 0.8333333333333334 }\n]\n```\n\n## CHANGELOG\n[CHANGELOG](./CHANGELOG.md)\n\n\n## MIT\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}