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Python library for Gaussian Process Regression.

Project description

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

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