gplib 0.17.3

Python library for Gaussian Process Regression.

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