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90 lines
2.9 KiB
ReStructuredText
90 lines
2.9 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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Architecture
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------------
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GPy is a big, powerful package, with many features. The concept of how to use GPy in general terms is roughly as follows. A model (:py:class:`GPy.models`) is created - this is at the heart of GPy from a user perspective. A kernel (:py:class:`GPy.kern`), data and, usually, a representation of noise are assigned to the model. Specific models require, or can make use of, additional information. The kernel and noise are controlled by hyperparameters - calling the optimize (:py:class:`GPy.core.gp.GP.optimize`) method against the model invokes an iterative process which seeks optimal hyperparameter values. The model object can be used to make plots and predictions (:py:class:`GPy.core.gp.GP.predict`).
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.. graphviz::
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digraph GPy_Arch {
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rankdir=LR
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node[shape="rectangle" style="rounded,filled" fontname="Arial"]
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edge [color="#006699" len=2.5]
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Data->Model
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Hyperparameters->Kernel
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Hyperparameters->Noise
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Kernel->Model
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Noise->Model
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Model->Optimize
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Optimize->Hyperparameters
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Model->Predict
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Model->Plot
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Optimize [shape="ellipse"]
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Predict [shape="ellipse"]
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Plot [shape="ellipse"]
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subgraph cluster_0 {
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Data
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Kernel
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Noise
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}
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}
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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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