https://github.com/swyoon/pytorch-minimal-gaussian-process

A minimal implementation of Gaussian process regression in PyTorch
https://github.com/swyoon/pytorch-minimal-gaussian-process

gaussian-process gaussian-process-regression gaussian-processes pytorch

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A minimal implementation of Gaussian process regression in PyTorch

• Host: GitHub
• URL: https://github.com/swyoon/pytorch-minimal-gaussian-process
• Owner: swyoon
• Created: 2021-12-30T12:11:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-08T01:53:59.000Z (almost 2 years ago)
• Last Synced: 2024-08-03T09:08:53.880Z (4 months ago)
• Topics: gaussian-process, gaussian-process-regression, gaussian-processes, pytorch
• Language: Jupyter Notebook
• Homepage:
• Size: 492 KB
• Stars: 59
• Watchers: 1
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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)](http://www.gaussianprocess.org/gpml/).

* Author: [Sangwoong Yoon](https://swyoon.github.io/), [Hyeokjun Kwon](https://www.linkedin.com/in/hyeokjun-kwon-24b992210)

## 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.

## Updates

* 2022-01-01: Bugfix in predictive variance computation

* 2023-01-03: Implement binary Laplace Gaussian process regression

## Dependency

* Numpy

* PyTorch

* PyTest

* Matplotlib (for demo)

## How to Use

### Gaussian process regression

```python

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)

```

### Gaussian process classification

```python

from gp import GP

# generate data

X = torch.randn(100,1)

f = torch.sin(X * 3 * np.pi / 4)

y = (f > 0.).int() * 2 - 1

grid = torch.linspace(-5, 5, 200)[:,None]

# run GP

gp = BinaryLaplaceGPC()  # you may specify initial hyperparameters using keyword arguments

gp.fit(X, y)

mu, var, pi = gp.forward(grid)

```

## Unittesting

```

$ pytest

```

## See also

* [GPyTorch](https://gpytorch.ai/): A full-featured Gaussian process package based on PyTorch.