geometric_kernels - v0.3.1

What's Changed

• Example Applications by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/157

Full Changelog: https://github.com/geometric-kernels/GeometricKernels/compare/v0.3.0...v0.3.1

- Python
Published by vabor112 9 months ago

geometric_kernels - v0.3.0

What's Changed

• Implementation of kernels on the edge space of graphs or simplicial complexes by @cookbook-ms in https://github.com/geometric-kernels/GeometricKernels/pull/139

New Contributors

• @cookbook-ms made their first contribution in https://github.com/geometric-kernels/GeometricKernels/pull/139

Full Changelog: https://github.com/geometric-kernels/GeometricKernels/compare/v0.2.3...v0.3

- Python
Published by vabor112 about 1 year ago

geometric_kernels - v0.2.3

Constraint version of plum-dispatch because of https://github.com/wesselb/lab/issues/23

Full Changelog: https://github.com/geometric-kernels/GeometricKernels/compare/v0.2.2...v0.2.3

- Python
Published by vabor112 about 1 year ago

geometric_kernels - v0.2.2

What's Changed

• Replace opt_einsum's contract with lab's einsum for better backend-independence by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/145
• Hypersphere space small improvements by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/142
• The Hypercube space for binary vectors and labeled unweighted graphs by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/141
• Fix algorithm selecting signatures and add precomputed characters for SO(9), SU(7), SU(8), SU(9) by @imbirik in https://github.com/geometric-kernels/GeometricKernels/pull/151
• Revise tests and numerous fixes by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/149

- Python
Published by vabor112 over 1 year ago

geometric_kernels - v0.2.1

What's Changed

• Add "If you have a question" section to README.md by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/131
• Github cosmetics by @stoprightthere in https://github.com/geometric-kernels/GeometricKernels/pull/133
• Replace all references to "gpflow" organization with "geometric-kernels" organization by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/134
• Use fitgpytorchmodel or fit.fitgpytorchmll depening on the botorсh version by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/137
• Add a missing type cast and fix a typo in kernels/karhunen_loeve.py by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/136
• Minor documentation improvements by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/135
• Add citation to the preprint of the GeometricKernels paper by @vabor112 in https://github.com/geometric-kernels/GeometricKernels/pull/138
• Add citation file by @aterenin in https://github.com/geometric-kernels/GeometricKernels/pull/140
• Fix dependencies (Version 0.2.1) by @stoprightthere in https://github.com/geometric-kernels/GeometricKernels/pull/143

Full Changelog: https://github.com/geometric-kernels/GeometricKernels/compare/v0.2...v0.2.1

- Python
Published by vabor112 over 1 year ago

geometric_kernels - v0.2

New geometric kernel that just works, kernels.MaternGeometricKernel. Relies on (hopefully) sensible defaults we defined. Mostly by @stoprightthere.

New spaces, based on Azangulov et al. (2022, 2023), mostly by @imbirik and @stoprightthere: - hyperbolic spaces $\mathbb{H}_n$ in spaces.Hyperbolic, - manifolds of symmetric positive definite matrices $\mathrm{SPD}(n)$ endowed with the affine-invariant Riemannian metric in spaces.SymmetricPositiveDefiniteMatrices, - special orthogonal groups $\mathrm{SO}(n)$ in spaces.SpecialOrthogonal. - special unitary groups $\mathrm{SU}(n)$ in spaces.SpecialUnitary.

New package geometric_kernels.feature_maps for (approximate) finite-dimensional feature maps. Mostly by @stoprightthere.

New small package geometric_kernels.sampling for efficient sampling from geometric Gaussian process priors. Based on the (approximate) finite-dimensional feature maps. Mostly by @stoprightthere.

Examples/Tutorials improvements, mostly by @vabor112: - new Jupyter notebooks Graph.ipynb, Hyperbolic.ipynb, Hypersphere.ipynb, Mesh.ipynb, SPD.ipynb, SpecialOrthogonal.ipynb, SpecialUnitary.ipynb, Torus.ipynb featuring tutorials on all the spaces in the library, - new Jupyter notebooks backends/JAX_Graph.ipynb, backends/PyTorch_Graph.ipynb, backends/TensorFlow_Graph.ipynb showcasing how to use all the backends supported by the library, - new Jupyter notebooks frontends/GPflow.ipynb, frontends/GPJax.ipynb, frontends/GPyTorch.ipynb showcasing how to use all the frontends supported by the library, - other notebooks updated and grouped together in other/ folder.

Documentation improvements, mostly by @vabor112: - all docstrings throughout the library revised, - added new documentation pages describing the basic theoretical concepts, in docs/theory, - notebooks are now rendered as part of the documentation, you can refer to them from the docstrings and other documentation pages, - introduced a more or less unified style for docstrings.

Other: - refactoring and bug fixes, - added type hints throughout the library and enabled mypy, - updated frontends (with limited suppot for GPJax due to conflicting dependencies), - improved spaces.ProductDiscreteSpectrumSpace and kernels.ProductGeometricKernel, - filtered out or fixed some annoying external warnings, - added a new banner for README.md and for our landing page, courtesy of @aterenin, - example notebooks are now run as tests, - we now support Python 3.8, 3.9, 3.10, 3.11 and have test workflows for all the supported Python versions, - we now provide a PyPI package, - LAB is now a lightweight dependency, thanks to @wesselb, - kernels are now normalized to have unit outputscale by default.

- Python
Published by stoprightthere almost 2 years ago

geometric_kernels - Alpha release

GeometricKernels alpha release.

- Python
Published by stoprightthere over 3 years ago