[READ-ONLY] Mirror of https://github.com/FoxxMD/string-sameness. Compare the sameness of two strings
foxxmd.github.io/string-sameness/
compare
cosine-similarity
dice-coefficient
levenshtein-distance
sameness
string
text
typescript
Initial commit
+572
+29
.github/workflows/typedoc_deploy.yml
+29
.github/workflows/typedoc_deploy.yml
···
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# https://github.com/TypeStrong/typedoc/issues/1485#issuecomment-889376169
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name: Typedoc Deploy
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on: [push, pull_request]
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jobs:
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build_and_deploy:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout the repository
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uses: actions/checkout@v3
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- name: Setup Node.js
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uses: actions/setup-node@v3
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with:
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node-version: 18.x
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cache: 'yarn'
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- run: yarn install --frozen-lockfile
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- run: yarn run build
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- name: Create the docs directory locally in CI
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run: npx typedoc src/index.ts
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- name: Deploy 🚀
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uses: JamesIves/github-pages-deploy-action@4.1.4
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with:
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branch: gh-pages
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folder: docs
+1
.nvmrc
+1
.nvmrc
···
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lts/hydrogen
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dist/index.d.ts
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dist/index.d.ts
···
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export declare const defaultStrCompareTransformFuncs: ((str: string) => string)[];
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export interface StringComparisonOptions {
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transforms?: ((str: string) => string)[];
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}
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export interface StringSamenessResult {
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scores: {
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dice: number;
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cosine: number;
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leven: number;
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};
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highScore: number;
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highScoreWeighted: number;
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}
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export declare const stringSameness: (valA: string, valB: string, options?: StringComparisonOptions) => StringSamenessResult;
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export default stringSameness;
+54
dist/index.js
+54
dist/index.js
···
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"use strict";
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var __importDefault = (this && this.__importDefault) || function (mod) {
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return (mod && mod.__esModule) ? mod : { "default": mod };
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.stringSameness = exports.defaultStrCompareTransformFuncs = void 0;
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const cosineSimilarity_js_1 = __importDefault(require("./matchingStrategies/cosineSimilarity.js"));
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const levenSimilarity_js_1 = __importDefault(require("./matchingStrategies/levenSimilarity.js"));
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const string_similarity_1 = __importDefault(require("string-similarity"));
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const sentenceLengthWeight = (length) => {
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// thanks jordan :')
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// constants are black magic
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return (Math.log(length) / 0.20) - 5;
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};
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exports.defaultStrCompareTransformFuncs = [
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// lower case to remove case sensitivity
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(str) => str.toLocaleLowerCase(),
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// remove excess whitespace
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(str) => str.trim(),
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// remove non-alphanumeric characters so that differences in punctuation don't subtract from comparison score
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(str) => str.replace(/[^A-Za-z0-9 ]/g, ""),
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// replace all instances of 2 or more whitespace with one whitespace
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(str) => str.replace(/\s{2,}|\n/g, " ")
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];
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const stringSameness = (valA, valB, options) => {
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const { transforms = exports.defaultStrCompareTransformFuncs, } = options || {};
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const cleanA = transforms.reduce((acc, curr) => curr(acc), valA);
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const cleanB = transforms.reduce((acc, curr) => curr(acc), valB);
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const shortest = cleanA.length > cleanB.length ? cleanB : cleanA;
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// Dice's Coefficient
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const dice = string_similarity_1.default.compareTwoStrings(cleanA, cleanB) * 100;
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// Cosine similarity
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const cosine = (0, cosineSimilarity_js_1.default)(cleanA, cleanB) * 100;
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// Levenshtein distance
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const ls = (0, levenSimilarity_js_1.default)(cleanA, cleanB);
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const levenSimilarPercent = ls[1];
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// use shortest sentence for weight
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const weightScore = sentenceLengthWeight(shortest.length);
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// take average score
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const highScore = (dice + cosine + levenSimilarPercent) / 3;
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// weight score can be a max of 15
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const highScoreWeighted = highScore + Math.min(weightScore, 15);
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return {
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scores: {
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dice,
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cosine,
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leven: levenSimilarPercent
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},
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highScore,
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highScoreWeighted,
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};
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};
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exports.stringSameness = stringSameness;
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exports.default = exports.stringSameness;
+2
dist/matchingStrategies/CosineSimilarity.d.ts
+2
dist/matchingStrategies/CosineSimilarity.d.ts
+52
dist/matchingStrategies/CosineSimilarity.js
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dist/matchingStrategies/CosineSimilarity.js
···
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"use strict";
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// reproduced from https://github.com/sumn2u/string-comparison/blob/master/jscosine.js
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// https://sumn2u.medium.com/string-similarity-comparision-in-js-with-examples-4bae35f13968
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Object.defineProperty(exports, "__esModule", { value: true });
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function termFreqMap(str) {
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var words = str.split(' ');
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var termFreq = {};
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words.forEach(function (w) {
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termFreq[w] = (termFreq[w] || 0) + 1;
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});
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return termFreq;
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}
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function addKeysToDict(map, dict) {
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for (var key in map) {
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dict[key] = true;
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}
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}
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function termFreqMapToVector(map, dict) {
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var termFreqVector = [];
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for (var term in dict) {
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termFreqVector.push(map[term] || 0);
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}
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return termFreqVector;
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}
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function vecDotProduct(vecA, vecB) {
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var product = 0;
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for (var i = 0; i < vecA.length; i++) {
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product += vecA[i] * vecB[i];
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}
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return product;
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}
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function vecMagnitude(vec) {
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var sum = 0;
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for (var i = 0; i < vec.length; i++) {
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sum += vec[i] * vec[i];
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}
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return Math.sqrt(sum);
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}
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function cosineSimilarity(vecA, vecB) {
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return vecDotProduct(vecA, vecB) / (vecMagnitude(vecA) * vecMagnitude(vecB));
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}
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const calculateCosineSimilarity = function textCosineSimilarity(strA, strB) {
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var termFreqA = termFreqMap(strA);
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var termFreqB = termFreqMap(strB);
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var dict = {};
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addKeysToDict(termFreqA, dict);
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addKeysToDict(termFreqB, dict);
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var termFreqVecA = termFreqMapToVector(termFreqA, dict);
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var termFreqVecB = termFreqMapToVector(termFreqB, dict);
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return cosineSimilarity(termFreqVecA, termFreqVecB);
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};
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exports.default = calculateCosineSimilarity;
+2
dist/matchingStrategies/cosineSimilarity.d.ts
+2
dist/matchingStrategies/cosineSimilarity.d.ts
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dist/matchingStrategies/cosineSimilarity.js
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dist/matchingStrategies/cosineSimilarity.js
···
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"use strict";
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// reproduced from https://github.com/sumn2u/string-comparison/blob/master/jscosine.js
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// https://sumn2u.medium.com/string-similarity-comparision-in-js-with-examples-4bae35f13968
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Object.defineProperty(exports, "__esModule", { value: true });
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function termFreqMap(str) {
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var words = str.split(' ');
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var termFreq = {};
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words.forEach(function (w) {
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termFreq[w] = (termFreq[w] || 0) + 1;
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});
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return termFreq;
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}
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function addKeysToDict(map, dict) {
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for (var key in map) {
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dict[key] = true;
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}
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}
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function termFreqMapToVector(map, dict) {
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var termFreqVector = [];
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for (var term in dict) {
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termFreqVector.push(map[term] || 0);
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}
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return termFreqVector;
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}
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function vecDotProduct(vecA, vecB) {
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var product = 0;
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for (var i = 0; i < vecA.length; i++) {
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product += vecA[i] * vecB[i];
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}
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return product;
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}
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function vecMagnitude(vec) {
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var sum = 0;
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for (var i = 0; i < vec.length; i++) {
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sum += vec[i] * vec[i];
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}
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return Math.sqrt(sum);
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}
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function cosineSimilarity(vecA, vecB) {
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return vecDotProduct(vecA, vecB) / (vecMagnitude(vecA) * vecMagnitude(vecB));
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}
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const calculateCosineSimilarity = function textCosineSimilarity(strA, strB) {
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var termFreqA = termFreqMap(strA);
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var termFreqB = termFreqMap(strB);
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var dict = {};
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addKeysToDict(termFreqA, dict);
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addKeysToDict(termFreqB, dict);
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var termFreqVecA = termFreqMapToVector(termFreqA, dict);
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var termFreqVecB = termFreqMapToVector(termFreqB, dict);
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return cosineSimilarity(termFreqVecA, termFreqVecB);
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};
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exports.default = calculateCosineSimilarity;
+2
dist/matchingStrategies/levenSimilarity.d.ts
+2
dist/matchingStrategies/levenSimilarity.d.ts
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dist/matchingStrategies/levenSimilarity.js
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dist/matchingStrategies/levenSimilarity.js
···
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"use strict";
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var __importDefault = (this && this.__importDefault) || function (mod) {
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return (mod && mod.__esModule) ? mod : { "default": mod };
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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const leven_1 = __importDefault(require("leven"));
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const levenSimilarity = (valA, valB) => {
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let longer;
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let shorter;
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if (valA.length > valB.length) {
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longer = valA;
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shorter = valB;
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}
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else {
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longer = valB;
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shorter = valA;
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}
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const distance = (0, leven_1.default)(longer, shorter);
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const diff = (distance / longer.length) * 100;
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return [distance, 100 - diff];
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};
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exports.default = levenSimilarity;
+37
package.json
+37
package.json
···
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{
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"name": "@foxxmd/string-sameness",
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"version": "0.1.0",
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"description": "determine how closely the same two strings are",
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"repository": "https://github.com/foxxmd/string-sameness",
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"author": "FoxxMD",
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"license": "MIT",
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"private": false,
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"engines": {
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"node": ">=18.0.0",
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"npm": ">=9.3.0"
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},
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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"files": [
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"/dist"
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],
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"scripts": {
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"typedoc": "typedoc",
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"build": "tsc"
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},
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"exports": {
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".": "./dist/index.js",
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"./strategies/cosine": "./dist/cosineSimilarity.js",
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"./strategies/leven": "./dist/levenSimilarity.js"
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},
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"devDependencies": {
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"@tsconfig/node16": "^1.0.3",
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"@types/string-similarity": "^4.0.0",
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"typedoc": "^0.23.28",
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"typescript": "^4.9.5"
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},
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"dependencies": {
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"leven": "^4.0.0",
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"string-similarity": "^4.0.4"
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}
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}
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src/index.ts
+73
src/index.ts
···
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import calculateCosineSimilarity from "./matchingStrategies/cosineSimilarity.js";
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import levenSimilarity from "./matchingStrategies/levenSimilarity.js";
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import stringSimilarity from 'string-similarity';
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const sentenceLengthWeight = (length: number) => {
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// thanks jordan :')
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// constants are black magic
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return (Math.log(length) / 0.20) - 5;
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+
}
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+
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export const defaultStrCompareTransformFuncs = [
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+
// lower case to remove case sensitivity
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+
(str: string) => str.toLocaleLowerCase(),
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+
// remove excess whitespace
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+
(str: string) => str.trim(),
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+
// remove non-alphanumeric characters so that differences in punctuation don't subtract from comparison score
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(str: string) => str.replace(/[^A-Za-z0-9 ]/g, ""),
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+
// replace all instances of 2 or more whitespace with one whitespace
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(str: string) => str.replace(/\s{2,}|\n/g, " ")
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];
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export interface StringComparisonOptions {
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transforms?: ((str: string) => string)[]
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}
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+
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export interface StringSamenessResult {
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scores: {
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dice: number
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cosine: number
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leven: number
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},
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highScore: number
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highScoreWeighted: number
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}
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export const stringSameness = (valA: string, valB: string, options?: StringComparisonOptions): StringSamenessResult => {
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const {
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transforms = defaultStrCompareTransformFuncs,
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} = options || {};
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const cleanA = transforms.reduce((acc, curr) => curr(acc), valA);
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const cleanB = transforms.reduce((acc, curr) => curr(acc), valB);
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const shortest = cleanA.length > cleanB.length ? cleanB : cleanA;
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+
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// Dice's Coefficient
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const dice = stringSimilarity.compareTwoStrings(cleanA, cleanB) * 100;
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// Cosine similarity
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const cosine = calculateCosineSimilarity(cleanA, cleanB) * 100;
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// Levenshtein distance
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const ls = levenSimilarity(cleanA, cleanB);
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const levenSimilarPercent = ls[1];
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// use shortest sentence for weight
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const weightScore = sentenceLengthWeight(shortest.length);
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// take average score
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const highScore = (dice + cosine + levenSimilarPercent) / 3;
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// weight score can be a max of 15
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const highScoreWeighted = highScore + Math.min(weightScore, 15);
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return {
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scores: {
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dice,
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cosine,
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leven: levenSimilarPercent
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},
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highScore,
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highScoreWeighted,
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}
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}
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export default stringSameness;
+3
src/matchingStrategies/cosineSimilarity.d.ts
+3
src/matchingStrategies/cosineSimilarity.d.ts
+70
src/matchingStrategies/cosineSimilarity.ts
+70
src/matchingStrategies/cosineSimilarity.ts
···
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// reproduced from https://github.com/sumn2u/string-comparison/blob/master/jscosine.js
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// https://sumn2u.medium.com/string-similarity-comparision-in-js-with-examples-4bae35f13968
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interface StrMap {
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[key: string]: number
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}
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interface BoolMap {
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[key: string]: boolean
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}
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function termFreqMap(str: string) {
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var words = str.split(' ');
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var termFreq: StrMap = {};
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words.forEach(function(w) {
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termFreq[w] = (termFreq[w] || 0) + 1;
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});
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return termFreq;
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}
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function addKeysToDict(map: StrMap, dict: BoolMap) {
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for (var key in map) {
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dict[key] = true;
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}
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}
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function termFreqMapToVector(map: StrMap, dict: StrMap): number[] {
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var termFreqVector = [];
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for (var term in dict) {
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termFreqVector.push(map[term] || 0);
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}
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return termFreqVector;
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}
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+
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function vecDotProduct(vecA: number[], vecB: number[]) {
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var product = 0;
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for (var i = 0; i < vecA.length; i++) {
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product += vecA[i] * vecB[i];
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}
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return product;
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}
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+
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function vecMagnitude(vec: number[]) {
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var sum = 0;
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for (var i = 0; i < vec.length; i++) {
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sum += vec[i] * vec[i];
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}
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return Math.sqrt(sum);
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}
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+
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function cosineSimilarity(vecA: number[], vecB: number[]) {
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return vecDotProduct(vecA, vecB) / (vecMagnitude(vecA) * vecMagnitude(vecB));
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+
}
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+
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const calculateCosineSimilarity = function textCosineSimilarity(strA: string, strB: string) {
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var termFreqA = termFreqMap(strA);
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var termFreqB = termFreqMap(strB);
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+
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var dict = {};
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addKeysToDict(termFreqA, dict);
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addKeysToDict(termFreqB, dict);
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+
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var termFreqVecA = termFreqMapToVector(termFreqA, dict);
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var termFreqVecB = termFreqMapToVector(termFreqB, dict);
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+
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return cosineSimilarity(termFreqVecA, termFreqVecB);
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}
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export default calculateCosineSimilarity;
+3
src/matchingStrategies/levenSimilarity.d.ts
+3
src/matchingStrategies/levenSimilarity.d.ts
+19
src/matchingStrategies/levenSimilarity.ts
+19
src/matchingStrategies/levenSimilarity.ts
···
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+
import leven from "leven";
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+
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const levenSimilarity = (valA: string, valB: string) => {
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let longer: string;
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+
let shorter: string;
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if (valA.length > valB.length) {
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+
longer = valA;
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+
shorter = valB;
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+
} else {
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longer = valB;
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shorter = valA;
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+
}
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+
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const distance = leven(longer, shorter);
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const diff = (distance / longer.length) * 100;
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return [distance, 100 - diff];
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}
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+
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export default levenSimilarity;
+17
tsconfig.json
+17
tsconfig.json
···
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{
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"extends": "@tsconfig/node16/tsconfig.json",
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"compilerOptions": {
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"module": "commonjs",
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"declaration": true,
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"outDir": "./dist"
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},
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"typeRoots": [
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"./src/types.d.ts"
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],
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"include": [
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"src/**/*"
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],
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"exclude": [
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"node_modules"
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]
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}
+17
typedoc.json
+17
typedoc.json
···
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{
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+
"$schema": "https://typedoc.org/schema.json",
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+
"name": "FoxxMD Common Libs",
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+
"entryPoints": ["./src"],
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+
"sort": ["source-order"],
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+
"categorizeByGroup": false,
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"searchGroupBoosts": {
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"Functions": 1.5
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},
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+
"navigationLinks": {
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"Docs": "https://github.com/foxxmd/common-libs",
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"GitHub": "https://github.com/foxxmd/common-libs"
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+
},
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"sidebarLinks": {
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"API": "https://typedoc.org/api"
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+
}
17
+
}
+97
yarn.lock
+97
yarn.lock
···
1
+
# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
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+
# yarn lockfile v1
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4
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"@tsconfig/node16@^1.0.3":
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version "1.0.3"
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integrity sha512-yOlFc+7UtL/89t2ZhjPvvB/DeAr3r+Dq58IgzsFkOAvVC6NMJXmCGjbptdXdR9qsX7pKcTL+s87FtYREi2dEEQ==
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version "4.0.0"
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resolved "https://registry.yarnpkg.com/@types/string-similarity/-/string-similarity-4.0.0.tgz#8cc03d5d1baad2b74530fe6c7d849d5768d391ad"
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ansi-sequence-parser@^1.1.0:
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version "1.1.0"
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resolved "https://registry.yarnpkg.com/ansi-sequence-parser/-/ansi-sequence-parser-1.1.0.tgz#4d790f31236ac20366b23b3916b789e1bde39aed"
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balanced-match@^1.0.0:
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version "1.0.2"
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resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
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brace-expansion@^2.0.1:
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version "2.0.1"
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resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-2.0.1.tgz#1edc459e0f0c548486ecf9fc99f2221364b9a0ae"
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dependencies:
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balanced-match "^1.0.0"
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jsonc-parser@^3.2.0:
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version "3.2.0"
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leven@^4.0.0:
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version "4.0.0"
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lunr@^2.3.9:
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version "2.3.9"
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marked@^4.2.12:
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minimatch@^7.1.3:
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brace-expansion "^2.0.1"
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shiki@^0.14.1:
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vscode-textmate "^8.0.0"
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string-similarity@^4.0.4:
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71
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typedoc@^0.23.28:
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dependencies:
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lunr "^2.3.9"
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marked "^4.2.12"
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minimatch "^7.1.3"
82
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shiki "^0.14.1"
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84
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typescript@^4.9.5:
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version "4.9.5"
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vscode-oniguruma@^1.7.0:
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version "1.7.0"
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resolved "https://registry.yarnpkg.com/vscode-oniguruma/-/vscode-oniguruma-1.7.0.tgz#439bfad8fe71abd7798338d1cd3dc53a8beea94b"
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integrity sha512-L9WMGRfrjOhgHSdOYgCt/yRMsXzLDJSL7BPrOZt73gU0iWO4mpqzqQzOz5srxqTvMBaR0XZTSrVWo4j55Rc6cA==
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vscode-textmate@^8.0.0:
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version "8.0.0"
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resolved "https://registry.yarnpkg.com/vscode-textmate/-/vscode-textmate-8.0.0.tgz#2c7a3b1163ef0441097e0b5d6389cd5504b59e5d"
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