From 1d6885f6d99d344cb30d86442243d05215baeb7d Mon Sep 17 00:00:00 2001 From: Nicolas Date: Thu, 7 Feb 2013 16:09:58 +0000 Subject: [PATCH] small changes in tutorial --- doc/tuto_GP_regression.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst index 17284707..3527e86f 100644 --- a/doc/tuto_GP_regression.rst +++ b/doc/tuto_GP_regression.rst @@ -45,6 +45,7 @@ By default, some observation noise is added to the modle. The functions ``print` m.plot() gives the following output: :: + Marginal log-likelihood: -4.479e+00 Name | Value | Constraints | Ties | Prior ----------------------------------------------------------------- @@ -78,7 +79,7 @@ Once the constrains have been imposed, the model can be optimized:: m.optimize() -If we want to perform some restarts to try to improve the result of the optimization, we can use the optimize_restart function:: +If we want to perform some restarts to try to improve the result of the optimization, we can use the ``optimize_restart`` function:: m.optimize_restarts(Nrestarts = 10) @@ -128,7 +129,7 @@ Here is a 2 dimensional example:: m.plot() print(m) -The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic). The output of the last 2 lines is:: +The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic). The output of the last two lines is:: Marginal log-likelihood: 6.682e+01 Name | Value | Constraints | Ties | Prior