In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare bone sometimes. Here is a minimal implementation of Gaussian process regression in PyTorch.
The implementation generally follows Algorithm 2.1 in Gaussian Process for Machine Learning (Rassmussen and Williams, 2006).
- Author: Sangwoong Yoon
- Gaussian process regression with squared exponential kernel.
- Hyperparameter optimization via marginal likelihood maximization using Pytorch built-in autograd functionality. (See
demo.ipynb
) - Unittesting using Pytest.
- 2022-01-01: Bugfix in predictive variance computation
- Numpy
- PyTorch
- PyTest
- Matplotlib (for demo)
from gp import GP
# generate data
X = torch.randn(100,1)
y = torch.sin(X * 2 * np.pi /4). + torch.randn(100, 1) * 0.1
grid = torch.linspace(-5, 5, 200)[:,None]
# run GP
gp = GP() # you may specify initial hyperparameters using keyword arguments
gp.fit(X, y)
mu, var = gp.forward(grid)
$ pytest
- GPyTorch: A full-featured Gaussian process package based on PyTorch.