apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-29 15:59:41 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

bugs fixed in tutorial's tests

Browse source
This commit is contained in:
Nicolas 2013-06-05 17:29:46 +01:00
parent 5c91155b1a
commit 73a122362f
2 changed files with 30 additions and 86 deletions
Download patch file Download diff file Expand all files Collapse all files

4
doc/tuto_GP_regression.rst
View file

@ -25,7 +25,7 @@ The first step is to define the covariance kernel we want to use for the model.
kernel = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
The parameter ``D`` stands for the dimension of the input space. The parameters ``variance`` and ``lengthscale`` are optional. Many other kernels are implemented such as:
The parameter ``input_dim`` stands for the dimension of the input space. The parameters ``variance`` and ``lengthscale`` are optional. Many other kernels are implemented such as:
* linear (``GPy.kern.linear``)
* exponential kernel (``GPy.kern.exponential``)
@ -69,7 +69,7 @@ There are various ways to constrain the parameters of the kernel. The most basic
but it is also possible to set a range on to constrain one parameter to be fixed. The parameter of ``m.constrain_positive`` is a regular expression that matches the name of the parameters to be constrained (as seen in ``print m``). For example, if we want the variance to be positive, the lengthscale to be in [1,10] and the noise variance to be fixed we can write::
m.unconstrain('') # Required to remove the previous constrains
m.unconstrain('') # may be used to remove the previous constrains
m.constrain_positive('.*rbf_variance')
m.constrain_bounded('.*lengthscale',1.,10. )
m.constrain_fixed('.*noise',0.0025)