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 (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 docstrings and other documentation pages,
introduced 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 new banner for README.md and for our landing page, courtesy of @aterenin,
example notebooks are now ran 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 PyPI package,
LAB is now a lightweight dependency, thanks to @wesselb,
kernels are now normalized to have unit outputscale by default.
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:
spaces.Hyperbolic,spaces.SymmetricPositiveDefiniteMatrices,spaces.SpecialOrthogonal.spaces.SpecialUnitary.New package
geometric_kernels.feature_mapsfor (approximate) finite-dimensional feature maps. Mostly by @stoprightthere.New small package
geometric_kernels.samplingfor efficient sampling from geometric Gaussian process priors. Based on (approximate) finite-dimensional feature maps. Mostly by @stoprightthere.Examples/Tutorials improvements, mostly by @vabor112:
Graph.ipynb,Hyperbolic.ipynb,Hypersphere.ipynb,Mesh.ipynb,SPD.ipynb,SpecialOrthogonal.ipynb,SpecialUnitary.ipynb,Torus.ipynbfeaturing tutorials on all the spaces in the library,backends/JAX_Graph.ipynb,backends/PyTorch_Graph.ipynb,backends/TensorFlow_Graph.ipynbshowcasing how to use all the backends supported by the library,frontends/GPflow.ipynb,frontends/GPJax.ipynb,frontends/GPyTorch.ipynbshowcasing how to use all the frontends supported by the library,other/folder.Documentation improvements, mostly by @vabor112:
docs/theory,Other:
mypy,spaces.ProductDiscreteSpectrumSpaceandkernels.ProductGeometricKernel,README.mdand for our landing page, courtesy of @aterenin,