gplib 0.6.5

Python library for Gaussian Process Regression.

GPlib

A python library for Gaussian Process Regression.

Setup GPlib

• The following packages must be installed before installing GPlib

# for ptyhon3
apt-get install python3-tk
# or for python2
apt-get install python-tk

• Create and activate virtualenv (for python2) or venv (for ptyhon3)

# for ptyhon3
python3 -m venv --system-site-packages .env
# or for python2
virtualenv --system-site-packages .env

source .env/bin/activate

• Upgrade pip

# for ptyhon3
python3 -m pip install --upgrade pip
# or for python2
python -m pip install --upgrade pip

• Install GPlib package

python -m pip install gplib

Use GPlib

• 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]
}

• 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.Constant(data),
  covariance_function=gplib.cov.Sum([
      gplib.cov.SquaredExponential(data, is_ard=False),
      gplib.cov.WhiteNoise(data)
  ]),
  likelihood_function=gplib.lik.Gaussian(),
  inference_method=gplib.inf.ExactGaussian()
)

• Plot the GP and the data.

gplib.plot.gp_1d(gp, data)

• 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)

• There are more examples in examples/ directory. Check them out!

Develop GPlib

• Download the repository using git

git clone https://github.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