The Chestnut Machine Learning Library is a suite of machine learning algorithms written in Python with some code written in C for efficiency. Most algorithms are called with a simple, functional API with input data encoded as arrays. Some packages have a MATLAB-like API to enable migration from a MATLAB environment. The class hierarchy is minimal to enable new users to quickly learn and call Chestnut code from their own codes. The first alpha release includes the following packages:
- boosting: AdaBoost, LPBoost, BrownBoost, MADABoost, SoftBoost, TotalBoost (some boosters require CVXOPT)
- hmm: hidden markov models with support for discrete and continuous emission distributions and multiple training sequences.
- cluster: hierarchical, k-means, QT, and shifting means clustering algorithms
- knn: Voronoi tesselations, kd-trees, k-nearest neighbor classifiers
- linear: basic Fisher's linear discriminant analysis
- Changes to previous version:
Initial Announcement on mloss.org.
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