{"id":17211106,"url":"https://github.com/mzed/ofxrapidlib","last_synced_at":"2025-04-13T22:33:03.644Z","repository":{"id":199305222,"uuid":"96533345","full_name":"mzed/ofxRapidLib","owner":"mzed","description":"openFrameworks wrapper for the RapidLib machine learning library","archived":false,"fork":false,"pushed_at":"2022-03-29T21:23:28.000Z","size":7980,"stargazers_count":30,"open_issues_count":3,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T13:11:20.437Z","etag":null,"topics":["machine-learning","openframeworks","signal-processing"],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mzed.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-07-07T11:45:36.000Z","updated_at":"2025-03-16T19:14:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"62be9adf-1165-4cfe-97f9-d8d9b2c3203e","html_url":"https://github.com/mzed/ofxRapidLib","commit_stats":null,"previous_names":["mzed/ofxrapidlib"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mzed%2FofxRapidLib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mzed%2FofxRapidLib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mzed%2FofxRapidLib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mzed%2FofxRapidLib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mzed","download_url":"https://codeload.github.com/mzed/ofxRapidLib/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248790821,"owners_count":21162092,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","openframeworks","signal-processing"],"created_at":"2024-10-15T02:56:16.560Z","updated_at":"2025-04-13T22:32:58.601Z","avatar_url":"https://github.com/mzed.png","language":"C++","readme":"![alt text](ofxaddons_thumbnail.png \"rapidmix\")  \n\n[![GitHub license](https://img.shields.io/badge/License-BSD%203--Clause-brightgreen.svg)](https://github.com/mzed/ofxRapidLib/blob/master/LICENSE)\n\n# ofxRapidLib\nofxRapidLib is an [openFrameworks](http://openframeworks.cc/) wrapper for the [RapidLib](https://mzed.github.io/RapidLib/) library. [RapidLib](https://mzed.github.io/RapidLib/) is a lightweight, interactive machine learning library intended to be used in interactive music and visual projects. It was directly inspired by Rebecca Fiebrink's [Wekinator](http://www.wekinator.org/), and was written in collaboration with her at Goldsmiths, University of London, as part of the [RAPID-MIX](http://rapidmix.goldsmithsdigital.com/) project.\n\nRapidLib is an open source, cross-platform project and is avaiable under a BSD license.\n\n### The master branch of ofxRapidLib has been tested with:\n- ofx_0.10.1, 0.11.0\n- MacOS 10.14 with XCode 10\n- Windows 10 with Visual Studio 2017, 2019\n\n# Documentation\n### [RapidLib DOxygen documentation](https://mzed.github.io/RapidLib/doxygen/annotated.html)\n\n### Interactive machine learning\n\nThe interactive machine learning API has the following classes:\n- classification (k-Nearest Neighbor)\n- regression (Neural Network)\n- seriesClassification (Dynamic Time Warping)\n\nThere are also two classes for holding the data that are used to train machine learning models:\n- trainingExample\n- trainingSeries\n\n### Basic signal processing\n\nIn addition to machine learning, ofxRapidLib provides users with some basic signal processing algorithms for pre-processing incoming sensor data. This is centered around a buffer class, called **rapidStream**. It offers the following functions:\n- rapidStream.velocity() (aka first-order difference)\n- rapidStream.acceleration() (aka second-order difference)\n- rapidStream.minimum() _The smallest value in the buffer_\n- rapidStream.maximum() _The largest value in the buffer_\n- rapidStream.sum() _sum of all buffered values_\n- rapidStream.mean()\n- rapidStream.standardDeviation()\n- rapidStream.rms() _root mean square of values in the buffer_\n- rapidStream.bayesfilter(input) _Bayesian filter for EMG envelope detection_\n- rapidStream.minVelocity()\n- rapidStream.maxVelocity()\n- rapidStream.minAcceleration()\n- rapidStream.maxAcceleration()\n\n# Examples  \nDescription of examples  \n\n# JavaScript\nRapidLib has been ported to JavaScript. A node module is maintained [here](https://www.npmjs.com/package/rapidlib) Add it to your node app with:\n```\nnpm install rapidlib\n```\nThe RapidLib JavaScript library also runs client side.  It is extensively documented on CodeCircle. Search for the tag \"#RapidLib\"\n- [RapidLib_001: Basic](https://live.codecircle.com/d/wiCgiE7ogQXFgMEMt)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmzed%2Fofxrapidlib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmzed%2Fofxrapidlib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmzed%2Fofxrapidlib/lists"}