From ed84c0722c772c348ae70ba1eb6b1448017e5913 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Fri, 8 Feb 2013 22:57:01 +0000 Subject: [PATCH] Removed ipython code from tuto --- doc/tuto_GP_regression.rst | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst index a78929b3..92b25bc0 100644 --- a/doc/tuto_GP_regression.rst +++ b/doc/tuto_GP_regression.rst @@ -2,28 +2,6 @@ Gaussian process regression tutorial ************************************* - -.. ipython:: python - - import numpy as np - import GPy as gpy - - """run a simple demonstration of a standard gaussian process fitting it to data sampled from an rbf covariance.""" - data = gpy.util.datasets.toy_rbf_1d() - - # create simple gp model - m = gpy.models.GP_regression(data['X'],data['Y']) - - # optimize - m.ensure_default_constraints() - m.optimize() - - print(m) - - # plot - m.plot() - - 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: ::