https://github.com/mzed/ofxrapidlib
openFrameworks wrapper for the RapidLib machine learning library
https://github.com/mzed/ofxrapidlib
machine-learning openframeworks signal-processing
Last synced: 10 months ago
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openFrameworks wrapper for the RapidLib machine learning library
- Host: GitHub
- URL: https://github.com/mzed/ofxrapidlib
- Owner: mzed
- License: bsd-3-clause
- Created: 2017-07-07T11:45:36.000Z (over 8 years ago)
- Default Branch: main
- Last Pushed: 2022-03-29T21:23:28.000Z (almost 4 years ago)
- Last Synced: 2025-03-27T13:11:20.437Z (11 months ago)
- Topics: machine-learning, openframeworks, signal-processing
- Language: C++
- Size: 7.61 MB
- Stars: 30
- Watchers: 2
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
[](https://github.com/mzed/ofxRapidLib/blob/master/LICENSE)
# ofxRapidLib
ofxRapidLib 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.
RapidLib is an open source, cross-platform project and is avaiable under a BSD license.
### The master branch of ofxRapidLib has been tested with:
- ofx_0.10.1, 0.11.0
- MacOS 10.14 with XCode 10
- Windows 10 with Visual Studio 2017, 2019
# Documentation
### [RapidLib DOxygen documentation](https://mzed.github.io/RapidLib/doxygen/annotated.html)
### Interactive machine learning
The interactive machine learning API has the following classes:
- classification (k-Nearest Neighbor)
- regression (Neural Network)
- seriesClassification (Dynamic Time Warping)
There are also two classes for holding the data that are used to train machine learning models:
- trainingExample
- trainingSeries
### Basic signal processing
In 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:
- rapidStream.velocity() (aka first-order difference)
- rapidStream.acceleration() (aka second-order difference)
- rapidStream.minimum() _The smallest value in the buffer_
- rapidStream.maximum() _The largest value in the buffer_
- rapidStream.sum() _sum of all buffered values_
- rapidStream.mean()
- rapidStream.standardDeviation()
- rapidStream.rms() _root mean square of values in the buffer_
- rapidStream.bayesfilter(input) _Bayesian filter for EMG envelope detection_
- rapidStream.minVelocity()
- rapidStream.maxVelocity()
- rapidStream.minAcceleration()
- rapidStream.maxAcceleration()
# Examples
Description of examples
# JavaScript
RapidLib has been ported to JavaScript. A node module is maintained [here](https://www.npmjs.com/package/rapidlib) Add it to your node app with:
```
npm install rapidlib
```
The RapidLib JavaScript library also runs client side. It is extensively documented on CodeCircle. Search for the tag "#RapidLib"
- [RapidLib_001: Basic](https://live.codecircle.com/d/wiCgiE7ogQXFgMEMt)