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Fixed more errors in docs 2

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James McMurray 2013-09-20 17:46:23 +01:00
parent 7f2472fa22
commit aa5ebcbd8f
15 changed files with 99 additions and 84 deletions
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37
GPy/kern/constructors.py
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@ -80,29 +80,30 @@ def gibbs(input_dim,variance=1., mapping=None):
.. math::
r = sqrt((x_i - x_j)'*(x_i - x_j))
r = \\sqrt{((x_i - x_j)'*(x_i - x_j))}
k(x_i, x_j) = \sigma^2*Z*exp(-r^2/(l(x)*l(x) + l(x')*l(x')))
k(x_i, x_j) = \\sigma^2*Z*exp(-r^2/(l(x)*l(x) + l(x')*l(x')))
Z = \sqrt{2*l(x)*l(x')/(l(x)*l(x) + l(x')*l(x')}
Z = \\sqrt{2*l(x)*l(x')/(l(x)*l(x) + l(x')*l(x')}
where :math:`l(x)` is a function giving the length scale as a function of space.
This is the non stationary kernel proposed by Mark Gibbs in his 1997
thesis. It is similar to an RBF but has a length scale that varies
with input location. This leads to an additional term in front of
the kernel.
Where :math:`l(x)` is a function giving the length scale as a function of space.
The parameters are :math:`\sigma^2`, the process variance, and the parameters of l(x) which is a function that can be specified by the user, by default an multi-layer peceptron is used is used.
This is the non stationary kernel proposed by Mark Gibbs in his 1997
thesis. It is similar to an RBF but has a length scale that varies
with input location. This leads to an additional term in front of
the kernel.
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param mapping: the mapping that gives the lengthscale across the input space.
:type mapping: GPy.core.Mapping
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one weight variance parameter \sigma^2_w), otherwise there is one weight variance parameter per dimension.
:type ARD: Boolean
:rtype: Kernpart object
The parameters are :math:`\\sigma^2`, the process variance, and the parameters of l(x) which is a function that can be specified by the user, by default an multi-layer peceptron is used is used.
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance :math:`\\sigma^2`
:type variance: float
:param mapping: the mapping that gives the lengthscale across the input space.
:type mapping: GPy.core.Mapping
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one weight variance parameter :math:`\\sigma^2_w`), otherwise there is one weight variance parameter per dimension.
:type ARD: Boolean
:rtype: Kernpart object
"""
part = parts.gibbs.Gibbs(input_dim,variance,mapping)

3
GPy/kern/kern.py
View file

@ -222,7 +222,8 @@ class kern(Parameterized):
def prod(self, other, tensor=False):
"""
multiply two kernels (either on the same space, or on the tensor product of the input space).
Multiply two kernels (either on the same space, or on the tensor product of the input space).
:param other: the other kernel to be added
:type other: GPy.kern
:param tensor: whether or not to use the tensor space (default is false).