gpvecchia
Vecchia approximation routines for Gaussian Process and Data Assimilation. This code is a lightweight adaption of code from this package.
Please see the examples for particular use cases.
Documentation
Documentation is available on read the docs.
Installation
pip
pip install gpvecchia
To install a local development version
pip install -e ./
To install latest from github
pip install git+https://github.com/tide-itrh/gpvecchia.git
Features
- Vecchia-approximated Gaussian Process likelihood, prediction, prior/posterior sampling and conditional sampling, scaling to large datasets by conditioning each point on a small set of nearest neighbours instead of the full covariance matrix.
- A small library of covariance kernels (Matern 1/2, 3/2, 5/2, squared-exponential, cosine) in
gpvecchia.cov, plus anisotropic coordinate scaling and rotation. - Numba-jitted core routines, parallelised across CPU cores.
Quick Usage
import numpy as np
from gpvecchia import GPtideVecchia
from gpvecchia.cov import matern32
# Training data locations and observed values
xd = np.linspace(0, 100, 1000)[:, None]
yd = np.sin(xd[:, 0] / 5) + 0.1 * np.random.randn(1000)
# covparams = (marginal std dev, length scale)
covparams = (1.0, 5.0)
noise = 0.1
GP = GPtideVecchia(
xd, xd, noise, matern32, covparams,
order_func=np.random.permutation, order_params=len(xd),
)
mean, std = GP(yd)
See the examples for anisotropic/scaled kernels, MLE and MCMC parameter estimation, and conditional sampling.
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