From e5fbfe19ee40a59434667dc5b55b63c3d5cf4051 Mon Sep 17 00:00:00 2001 From: Nicolas Date: Thu, 31 Jan 2013 10:47:24 +0000 Subject: [PATCH] rst files from documentation --- doc/GPy.kern.rst | 28 ++++++++++++++++++++++------ doc/GPy.models.rst | 8 ++++++++ doc/GPy.rst | 1 + doc/tuto_GP_regression.rst | 4 ++-- 4 files changed, 33 insertions(+), 8 deletions(-) diff --git a/doc/GPy.kern.rst b/doc/GPy.kern.rst index 95943178..d6593939 100644 --- a/doc/GPy.kern.rst +++ b/doc/GPy.kern.rst @@ -89,18 +89,34 @@ kern Package :undoc-members: :show-inheritance: -:mod:`linear_ARD` Module ------------------------- +:mod:`periodic_Matern32` Module +------------------------------- -.. automodule:: GPy.kern.linear_ARD +.. automodule:: GPy.kern.periodic_Matern32 :members: :undoc-members: :show-inheritance: -:mod:`rbf-testing` Module -------------------------- +:mod:`periodic_Matern52` Module +------------------------------- -.. automodule:: GPy.kern.rbf-testing +.. automodule:: GPy.kern.periodic_Matern52 + :members: + :undoc-members: + :show-inheritance: + +:mod:`periodic_exponential` Module +---------------------------------- + +.. automodule:: GPy.kern.periodic_exponential + :members: + :undoc-members: + :show-inheritance: + +:mod:`product_orthogonal` Module +-------------------------------- + +.. automodule:: GPy.kern.product_orthogonal :members: :undoc-members: :show-inheritance: diff --git a/doc/GPy.models.rst b/doc/GPy.models.rst index 47af78ab..b0a7a298 100644 --- a/doc/GPy.models.rst +++ b/doc/GPy.models.rst @@ -9,6 +9,14 @@ models Package :undoc-members: :show-inheritance: +:mod:`BGPLVM` Module +-------------------- + +.. automodule:: GPy.models.BGPLVM + :members: + :undoc-members: + :show-inheritance: + :mod:`GPLVM` Module ------------------- diff --git a/doc/GPy.rst b/doc/GPy.rst index 61a4242c..d3c1e843 100644 --- a/doc/GPy.rst +++ b/doc/GPy.rst @@ -15,6 +15,7 @@ Subpackages .. toctree:: GPy.core + GPy.examples GPy.inference GPy.kern GPy.models diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst index 7b2af232..7d1a43df 100644 --- a/doc/tuto_GP_regression.rst +++ b/doc/tuto_GP_regression.rst @@ -3,7 +3,7 @@ Gaussian process regression tutorial ************************************* -We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process 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. We first import the libraries we will need: :: @@ -61,7 +61,7 @@ gives the following output: :: GP regression model before optimization of the parameters. The shaded region corresponds to 95% confidence intervals (ie +/- 2 standard deviation). -The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy: +The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is to find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy: * Constrain the parameters of the kernel to ensure the kernel will always be a valid covariance structure (For example, we don\'t want some variances to be negative!). * Run the optimization