Releases#
Version 0.3 (2025-07-15)#
Improve UMAP via direct gradient computation and edge masking PR #198.
Support for torch.compile PR #194.
Automatically handle duplicates PR #188.
Standardize logging PR #187.
Make affinity_out optional in AffinityMatcher PR #186.
Implement PHATE algorithm PR #185.
Implement PACMAP algorithm PR #182.
COSNE support for hyperbolic embeddings PR #178.
Allow for any Torch optimizer or scheduler PR #174.
Ensure compatibility with python 3.8+ PR #173.
Version 0.2 (2025-02-07)#
FAISS support for KNN PR #160.
CIFAR examples with DINOv2 features PR #158.
Fast linter and formatter with Ruff PR #151.
Pre-commit hooks added for code quality and consistency checks PR #147.
Incremental PCA PR #137.
Clean citation style via sphinxcontrib-bibtex PR #143.
Functionality to switch to keops backend if it is installed and an out-of-memory error is raised PR #130.
Code of conduct PR #127.
Pull request template PR #125.
Version 0.1 (2024-09-17)#
Multiple basic affinities, including scalar product, Gaussian, and Student kernels.
Affinities based on k-NN normalizations such as Self-tuning affinities and MAGIC.
Doubly stochastic affinities with entropic and quadratic projections.
Adaptive affinities with entropy control (entropic affinity) and its symmetric version.
Input and output affinities of UMAP.
A template object AffinityMatcher to solve DR with gradient descent and any input and output affinities.
Neighbor embedding methods like SNE, t-SNE, t-SNEkhorn, UMAP, LargeVis, and InfoTSNE.
Template objects for neighbor embedding methods.
Spectral embeddings via eigendecomposition of the input affinity matrix (when applicable).
KeOps compatibility for all components, except spectral embeddings.
Silhouette score.