geomstats/geomstats

Health v2 · maintenance only

Adoption not part of the score

1513 stars · 295 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Geomstats is an open-source Python package for computations, statistics, and machine learning on manifolds with geometric structures. It provides differential geometry operations (manifolds, Lie groups, Riemannian metrics) and geometric learning algorithms with a Scikit-Learn-like API, supporting NumPy, Autograd, and PyTorch backends.

Use cases

• compute statistics on rotation matrices
• perform machine learning on manifold-structured data
• compute geodesics and Riemannian metrics
• cluster data lying on a Lie group like SO(3)
• learn geometric deep learning models with PyTorch backend
• analyze shape spaces of biological or natural shapes
• learn differential geometry hands-on with code examples

When to choose

• your data lives on nonlinear manifolds such as rotations, SPD matrices, or shape spaces
• you need geometrically consistent statistics like Fréchet means instead of naive Euclidean averages
• you want a Scikit-Learn-style API for Riemannian learning algorithms
• you need to switch between NumPy, Autograd, and PyTorch backends

When to avoid

• your data is standard Euclidean vectors and ordinary scikit-learn suffices
• you need a general-purpose deep learning framework rather than geometry-aware tools
• you are not comfortable with differential geometry concepts and have no need for them

Facets

library · maturity active

machine-learning deep-learning data-science math simulation machine-learning mathematics data-science education python cross-platform riemannian-geometry manifold-learning lie-groups geometric-statistics geodesics differential-geometry numpy pytorch scikit-learn-api algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="geomstats/geomstats")