gplib 0.13.2

Python library for Gaussian Process Regression.

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