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ml-distance-euclidean\n\n\u003ch3 align=\"center\"\u003e\n  \n  \u003ca href=\"https://www.zakodium.com\"\u003e\n   \u003cimg src=\"https://www.zakodium.com/brand/zakodium-logo-white.svg\" width=\"50\" alt=\"Zakodium logo\" /\u003e\n  \u003c/a\u003e\n  \n  \u003cp\u003e\n    Maintained by \u003ca href=\"https://www.zakodium.com\"\u003eZakodium\u003c/a\u003e\n  \u003c/p\u003e\n  \n[![NPM version][npm-image]][npm-url]\n[![build status][travis-image]][travis-url]\n[![npm download][download-image]][download-url]\n  \n\u003c/h3\u003e\n\nCompute the euclidean distance between two vectors\n\n## Installation\n\n`$ npm install ml-distance-euclidean`\n\n## API\n\n```js\nconst { euclidean, squaredEuclidean } = require(\"ml-distance-euclidean\");\n\neuclidean([0, 1, 4, 6, 2], [3, 6, 9, 4, 3]); // 8\n```\n\n### euclidean(p, q)\n\nReturns the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance#n_dimensions) between vectors p and q.\n\n### squaredEuclidean(p, q)\n\nReturns the [squared Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance#Squared_Euclidean_distance) between vectors p and q.\n\n## License\n\n[MIT](./LICENSE)\n\n[npm-image]: https://img.shields.io/npm/v/ml-distance-euclidean.svg?style=flat-square\n[npm-url]: https://npmjs.org/package/ml-distance-euclidean\n[travis-image]: https://img.shields.io/travis/mljs/distance-euclidean/master.svg?style=flat-square\n[travis-url]: https://travis-ci.org/mljs/distance-euclidean\n[download-image]: https://img.shields.io/npm/dm/ml-distance-euclidean.svg?style=flat-square\n[download-url]: https://npmjs.org/package/ml-distance-euclidean\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmljs%2Fdistance-euclidean","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmljs%2Fdistance-euclidean","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmljs%2Fdistance-euclidean/lists"}