A minimal implementation of Gaussian process regression in PyTorch

Related tags

Deep Learning pytorch-minimal-gaussian-process
Overview

pytorch-minimal-gaussian-process

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).

Features

  • 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.

Dependency

  • Numpy
  • PyTorch
  • PyTest
  • Matplotlib (for demo)

How to Use

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)

Unittesting

$ pytest

See also

  • GPyTorch: A full-featured Gaussian process package based on PyTorch.
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Comments
  • [Feat] Gaussian process classifier

    [Feat] Gaussian process classifier

    What has changed

    1. Binary classification에서 Laplace approximation을 사용한 Gaussian process classification이 추가되었습니다.
    2. 데모와 test도 함께 추가되었습니다.
    3. Readme도 업데이트 되었습니다.

    Additional info

    Before submitting

    • [X] Did you list all the changes introduced by this PR?
    • [X] Did you verify tests pass locally with your changes?
    • [X] Did you update the sample configs and documentations with your changes?
    opened by emckwon 1
Owner
Sangwoong Yoon
PhD student at Seoul National University, studying machine learning
Sangwoong Yoon
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