GPlib

A python library for Gaussian Process Regression.

Setup GPlib

Create and activate venv

python3 -m venv .env

source .env/bin/activate

Upgrade pip
```
python -m pip install --upgrade pip
```
Install GPlib package
```
python -m pip install gplib
```
Matplotlib requires to install a backend to work interactively

(See https://matplotlib.org/faq/virtualenv_faq.html). The easiest solution is to install the Tk framework, which can be found as python-tk (or python3-tk) on certain Linux distributions.

Use GPlib

Import GPlib to use it in your python script.
```
import gplib
```

Initialize the GP with the desired modules.

gp = gplib.GP(
    mean_function=gplib.mea.Fixed(),
    covariance_function=gplib.ker.SquaredExponential()
)

Plot the GP.
```
gplib.plot.gp_1d(gp, n_samples=10)
```

Generate some random data.

import numpy as np
data = {
    'X': np.arange(3, 8, 1.0)[:, None],
    'Y': np.random.uniform(0, 2, 5)[:, None]
}

Get the posterior GP given the data.
```
posterior_gp = gp.get_posterior(data)
```

Finally plot the posterior GP.

gplib.plot.gp_1d(posterior_gp, data, n_samples=10)

There are more examples in examples/ directory. Check them out!

Develop GPlib

Download the repository using git

git clone https://gitlab.com/ibaidev/gplib.git

Update API documentation

source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../gplib

Metadata

Release files for gplib 0.19.2

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Source distribution for gplib 0.19.2
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gplib-0.19.2-py3-none-any.whl 89.6 kB Jun 2, 2026 Without provenance	Python 3	none	any	Details

Total release size: 133.7 kB

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Download URL	gplib-0.19.2.tar.gz
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Uploaded via	`twine/6.2.0 CPython/3.14.5`

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