Metadata-Version: 2.1
Name: gplib
Version: 0.15.1
Summary: Python library for Gaussian Process Regression.
Home-page: https://gitlab.com/ibaidev/gplib
Author: Ibai Roman
Author-email: ibaidev@protonmail.com
License: GPLv3
Keywords: Gaussian Process
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: matplotlib


GPlib
=====

A python library for Gaussian Process Regression.

Setup GPlib
-----------

- Create and activate virtualenv (for python2) or
  venv (for python3)

.. code-block:: bash

  # for python3
  python3 -m venv .env
  # or for python2
  python2 -m virtualenv .env

  source .env/bin/activate

- Upgrade pip

.. code-block:: bash

  python -m pip install --upgrade pip

- Install GPlib package

.. code-block:: bash

  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.

.. code-block:: python

  import gplib

- Initialize the GP with the desired modules.

.. code-block:: python

  gp = gplib.GP(
    mean_function=gplib.mea.Fixed(),
    covariance_function=gplib.cov.SquaredExponential()
  )

- Plot the GP.

.. code-block:: python

  gplib.plot.gp_1d(gp, n_samples=10)

- Generate some random data.

.. code-block:: python

  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.

.. code-block:: python

  posterior_gp = gp.get_posterior(data)

- Finally plot the posterior GP.

.. code-block:: python

  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

.. code-block:: bash

  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

.. code-block:: bash

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


