apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-27 22:06:22 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Tutorial update due to some changes in GPy

Browse source
This commit is contained in:
Nicolas 2013-02-21 10:07:42 +00:00
parent 37e7e11b5b
commit fc86b8e1ac
4 changed files with 27 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

14
doc/tuto_kernel_overview.rst
View file

@ -13,17 +13,25 @@ First we import the libraries we will need ::
For most kernels, the dimension is the only mandatory parameter to define a kernel object. However, it is also possible to specify the values of the parameters. For example, the three following commands are valid for defining a squared exponential kernel (ie rbf or Gaussian) ::
ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
ker2 = GPy.kern.rbf(D=1, variance = 1.5, lengthscale=2.)
ker2 = GPy.kern.rbf(D=1, variance = .75, lengthscale=2.)
ker3 = GPy.kern.rbf(1, .5, .5)
A `plot` and a `print` functions are implemented to represent kernel objects ::
A ``print`` and a ``plot`` functions are implemented to represent kernel objects. The commands ::
print ker1
print ker2
ker1.plot()
ker2.plot()
ker3.plot()
should return::
Name | Value | Constraints | Ties
-------------------------------------------------------
rbf_variance | 1.0000 | |
rbf_lengthscale | 1.0000 | |
.. figure:: Figures/tuto_kern_overview_basicdef.png
:align: center
:height: 350px