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Merge branch 'master' of github.com:SheffieldML/GPy

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andreas 2013-03-11 19:19:49 +00:00
commit 9d97887c7e
3 changed files with 15 additions and 15 deletions
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2
GPy/kern/rbf.py
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@ -12,7 +12,7 @@ class rbf(kernpart):
.. math:: .. math::
k(r) = \sigma^2 \exp(- \frac{1}{2}r^2) \ \ \ \ \ \\text{ where } r^2 = \sum_{i=1}^d \frac{ (x_i-x^\prime_i)^2}{\ell_i^2}} k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \ \ \ \ \ \\text{ where } r^2 = \sum_{i=1}^d \\frac{ (x_i-x^\prime_i)^2}{\ell_i^2}
where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input. where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.

2
doc/tuto_creating_new_kernels.rst
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@ -22,7 +22,7 @@ We advise the reader to start with copy-pasting an existing kernel and to modify
**Header** **Header**
The header is similar to all kernels:: The header is similar to all kernels: ::
from kernpart import kernpart from kernpart import kernpart
import numpy as np import numpy as np

4
doc/tuto_kernel_overview.rst
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@ -133,7 +133,7 @@ Various constrains can be applied to the parameters of a kernel
* ``constrain_fixed`` to fix the value of a parameter (the value will not be modified during optimisation) * ``constrain_fixed`` to fix the value of a parameter (the value will not be modified during optimisation)
* ``constrain_positive`` to make sure the parameter is greater than 0. * ``constrain_positive`` to make sure the parameter is greater than 0.
* ``constrain_bounded`` to impose the parameter to be in a given range. * ``constrain_bounded`` to impose the parameter to be in a given range.
* ``tie_param`` to impose the value of two (or more) parameters to be equal. * ``tie_params`` to impose the value of two (or more) parameters to be equal.
When calling one of these functions, the parameters to constrain can either by specified by a regular expression that matches its name or by a number that corresponds to the rank of the parameter. Here is an example :: When calling one of these functions, the parameters to constrain can either by specified by a regular expression that matches its name or by a number that corresponds to the rank of the parameter. Here is an example ::
@ -146,7 +146,7 @@ When calling one of these functions, the parameters to constrain can either by s
k.constrain_positive('var') k.constrain_positive('var')
k.constrain_fixed(np.array([1]),1.75) k.constrain_fixed(np.array([1]),1.75)
k.tie_param('len') k.tie_params('len')
k.unconstrain('white') k.unconstrain('white')
k.constrain_bounded('white',lower=1e-5,upper=.5) k.constrain_bounded('white',lower=1e-5,upper=.5)
print k print k