2b:null 2c:["$","title",null,{"children":"Reusable Euclidean Distance Function | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:[] 2f:{"id":"9907c0223c41a513","slug":null,"title":"Reusable Euclidean Distance Function","thumbnail":"df6d6832e16be65d7e9f35086311a267cdf6af741ab4e16c3f79d69e0a597305","access_level":"public","create_time":"$D2025-04-22T05:35:17.339Z","update_time":"$D2025-04-22T10:27:32.149Z","view_time":null,"first_public_time":"$D2025-04-22T05:43:03.400Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":25,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"bio":""}],"members":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Reusable Euclidean Distance Function","cells":[{"id":0,"value":"# Reusable Euclidean Distance Function","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"The distance function computes the Euclidean distance between two objects across 'n' dimensions by applying the Pythagorean theorem in multidimensional space. Internally, it uses JavaScript’s Math.hypot to calculate the square root of the sum of squares of differences, which simplifies and optimizes the computation for any number of dimensions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"## How to use?\n\n1. Import the function using - import {distance} from \"9907c0223c41a513\"\n\n2. The function takes 3 attributes (a, b, attrs):\n - 'a' -> An object with some attributes.\n - 'b' -> An object with some attributes (ideally the same as object 'a').\n - 'attrs' -> A list of numeric attributes to compute the euclidean distance on.\n\n3. Once you have the 3 required parameters use the function as such - distance(a, b, attrs).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"// Function to calculate Euclidean Distance\ndistance = (a, b, attrs) => {\n return Math.hypot(...attrs.map(k => a[k] - b[k]));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"1"}} 30:"$2f:body"