ml-lib

ml-lib is a library of machine learning externals for Max and Pure Data. ml-lib is primarily based on the Gesture Recognition Toolkit by Nick Gillian ml-lib is designed to work on a variety of platforms including OS X, Windows, Linux, on Intel and ARM architectures.

The goal of ml-lib is to provide a simple, consistent interface to a wide range of machine learning techniques in Max and Pure Data. The canonical NIME 2015 paper on ml-lib can be found here.

Full class documentation can be found here.

Bug Reports and Discussion

Please use the GitHub Issue Tracker for all bug reports and feature requests.

Development Status

ML-LIB IS CURRENTLY IN OPEN BETA

This means the software is considered feature complete, but has not been extensively tested and should not be used in production environments.

The library has currently been tested on Mac OS X with Max 5.x and 6.x and on Mac OS X and Linux on i386 and armv6 architectures using Pure Data.

Bugs should be reported via the issues page.

Installation

• Download the latest release from the GitHub releases page
• For Max, add the ml.*.mxo externals and the ml-lib helpfiles to your Max Search Path
• For Pd, add ml.*.pd_* to your Pd search path and add the "ml" library to your startup flags by opening up the application Preferences and adding "-lib ml" to the Startup Flags

Compiling from source

Instructions for compiling ml-lib from source can be found here

Library structure

ml-lib objects follow the naming convention ml.* where “*” is an abbreviated form of the algorithm implemented by the object.

A full list of all objects and their parameters can be found here.

For more detailed descriptions of the underlying algorithms, see links below.

Objects fall into one of five categories:

• Pre-processing: pre-process data prior to used as input to a classification or regression object
• Post-processing: post-process data after being output from a classification or regression object
• Feature extraction: extract “features” from control data. Feature vectors can be used as input to classification or regression objects
• Classification: take feature vectors as input, and output a value representing the class of the input. For example an object detecting hand position might output 0 for left, 1 for right, 2 for top and 3 for bottom.
• Regression: perform an M x N mapping between an input vector and an output vector with one or more dimensions. For example an object may map x and y dimensions of hand position to a single dimension representing the distance from origin (0, 0).

Object list

Pre-processing

No objects currently implemented

Post-processing

No objects currently implemented

Feature extraction

• ml.peak: output detected peaks from a continues stream of input values
• ml.minmax: output a vector of minima and maxima locations (peaks) from an input vector

Classification

• ml.adaboost: Adaptive Boosting
• ml.dtw: Dynamic Time Warping
• ml.gmm: Gaussian Mixture Model
• ml.hmm: Hidden Markov Models
• ml.knn: k’s Nearest Neighbour
• ml.mindist:Minimum Distance
• ml.randforest: Random Decision Forest
• ml.softmax: Softmax
• ml.svm: Support Vector Machines

Regression

• ml.linreg: Linear Regression
• ml.logreg: Logistic Regression
• ml.mlp: Multi-layer Perceptron Artificial Neural Networks (ANN)

See the help file for each component for further details about operation and usage.

Credits

This software has been designed and developed by Ali Momeni and Jamie Bullock. The Gesture Recognition Toolkit is developed by Nick Gillian

ml-lib is supported by Art Fab, the College of Fine Arts and The Frank-Ratchye STUDIO for Creative Inquiry at Carnegie Mellon University

License

ml-lib is copyright (c) 2014 Carnegie Mellon University.

ml-lib is distributed under the GNU General Public License version 2. A copy of this is available in the accompanying LICENSE file. See also http://www.gnu.org/licenses/.