The API reference gives an overview of Geomstats implementation.
The module geometry implements concepts in differential geometry, to perform computations on manifolds and Riemannian metrics, with associated exponential and logarithmic maps, geodesics, and parallel transport.
The module learning implements statistics and learning algorithms for data on manifolds, such as estimation, clustering and dimension reduction. The code is object-oriented and classes inherit from scikit-learn’s base classes and mixins.
In both modules, the operations are vectorized for batch computation and provide support for different execution backends—namely NumPy, Autograd and PyTorch. The module backend implements the operations needed to use Geomstats seamlessly with any backend.