stefanosele/GPfY
Gaussian processes with spherical harmonic features in JAX
This is a specialized tool for machine learning researchers and practitioners working with data that lies on a sphere, such as astronomical observations or climate data. It takes spherical data points as input and uses advanced statistical modeling to make predictions or understand patterns across the spherical surface. Researchers who need to model complex, curved relationships in their data will find this useful.
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Use this if you need to build accurate predictive models for data sampled from a sphere, such as geographical or celestial coordinates, using a Gaussian process framework.
Not ideal if your data is not inherently spherical or if you need a general-purpose machine learning library for Euclidean data.
Stars
15
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 24, 2025
Commits (30d)
0
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