A python library for Gaussian Process Regression.

Setup GPlib

  • Create and activate virtualenv (for python2) or venv (for python3)

# for python3
python3 -m venv .env
# or for python2
python2 -m virtualenv .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.cov.SquaredExponential(),
  likelihood_function=gplib.lik.Gaussian(),
  inference_method=gplib.inf.ExactGaussian()
)

  • 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
cd gplib
git config user.email 'MAIL'
git config user.name 'NAME'
git config credential.helper 'cache --timeout=300'
git config push.default simple

  • Update API documentation

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

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.19.2)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ibaidev from gravatar.com ibaidev

Credits

Author: Ibai Roman

License

GNU General Public License v3 (GPLv3) (GPLv3)

Tags

Gaussian Process

Classifiers

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