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59 lines
1.7 KiB
ReStructuredText
59 lines
1.7 KiB
ReStructuredText
GPy - A Gaussian Process (GP) framework in Python
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=================================================
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Introduction
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------------
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`GPy <http://sheffieldml.github.io/GPy/>`_ is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. It includes support for basic GP regression, multiple output GPs (using coregionalization), various noise models, sparse GPs, non-parametric regression and latent variables.
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The `GPy homepage <http://sheffieldml.github.io/GPy/>`_ contains tutorials for users and further information on the project, including installation instructions.
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The documentation hosted here is mostly aimed at developers interacting closely with the code-base.
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Source Code
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-----------
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The code can be found on our `Github project page <https://github.com/SheffieldML/GPy>`_. It is open source and provided under the BSD license.
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Installation
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------------
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Installation instructions can currently be found on our `Github project page <https://github.com/SheffieldML/GPy>`_.
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Tutorials
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---------
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Several tutorials have been developed in the form of `Jupyter Notebooks <https://nbviewer.jupyter.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb>`_.
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.. toctree::
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:maxdepth: 1
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:caption: Architecture
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architecture
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.. toctree::
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:maxdepth: 1
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:caption: For developers
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tuto_creating_new_models
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tuto_creating_new_kernels
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tuto_plotting
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tuto_parameterized
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.. toctree::
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:maxdepth: 1
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:caption: API Documentation
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GPy.core
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GPy.core.parameterization
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GPy.models
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GPy.kern
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GPy.likelihoods
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GPy.mappings
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GPy.examples
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GPy.util
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GPy.plotting
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GPy.inference.optimization
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GPy.inference.latent_function_inference
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GPy.inference.mcmc
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