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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 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(
  • 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
  • Update API documentation

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

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