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version](https://img.shields.io/npm/v/ml-distance-euclidean.svg)](https://www.npmjs.com/package/ml-distance-euclidean)\n[![npm download](https://img.shields.io/npm/dm/ml-distance-euclidean.svg)](https://www.npmjs.com/package/ml-distance-euclidean)\n[![test coverage](https://img.shields.io/codecov/c/github/mljs/spectra-processing.svg)](https://codecov.io/gh/mljs/spectra-processing)\n[![license](https://img.shields.io/npm/l/ml-distance-euclidean.svg)](https://github.com/mljs/spectra-processing/blob/main/LICENSE)\n\n</h3>\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","readmeFilename":"README.md"}