Merge branch 'master' of github.com:SheffieldML/GPy

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James Hensman 2013-03-11 11:51:39 +00:00
commit 401b4f2775
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List of implemented kernels
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The :math:`\checkmark` symbol represents the functions that have been implemented for each kernel.
.. |tick|
.. |tick| image:: tick.png
====== =========== === ======= =========== =============== ======= =========== ====== ====== =======
NAME get/set K Kdiag dK_dtheta dKdiag_dtheta dK_dX dKdiag_dX psi0 psi1 psi2
====== =========== === ======= =========== =============== ======= =========== ====== ====== =======
rbf \\checkmark y
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Gaussian process regression tutorial
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We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process regression model, also known as a kriging model.
We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process regression model, also known as a kriging model. The code shown in this tutorial can be found without the comments at GPy/examples/tuto_GP_regression.py.
We first import the libraries we will need: ::

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tutorial : A kernel overview
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The aim of this tutorial is to give a better understanding of the kernel objects in GPy and to list the ones that are already implemented. The code shown in this tutorial can be found without the comments at GPy/examples/tuto_kernel_overview.py.
First we import the libraries we will need ::
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Implemented kernels
===================
Many kernels are already implemented in GPy. Here is a summary of most of them:
Many kernels are already implemented in GPy. A comprehensive list can be found `here <kernel_implementation.html>`_ . The following figure gives a summary of most of them:
.. figure:: Figures/tuto_kern_overview_allkern.png
:align: center