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Python library for Gaussian Process Regression.

Project description

GPlib

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.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

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