apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-26 13:26:22 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

X caching is not yet done, parameter caching working fine. X cache must be adjusted to update at the right times

Browse source
This commit is contained in:
Max Zwiessele 2013-10-27 17:04:46 +00:00
parent d3721b76a8
commit d8151eee61
12 changed files with 249 additions and 109 deletions
Download patch file Download diff file Expand all files Collapse all files

77
doc/tuto_creating_new_kernels.rst
View file

@ -35,45 +35,72 @@ The implementation of this function in mandatory.
For all kernparts the first parameter ``input_dim`` corresponds to the dimension of the input space, and the following parameters stand for the parameterization of the kernel.
The following attributes are compulsory: ``self.input_dim`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.num_params`` (number of parameters, integer). ::
You have to call ``super(<class_name>, self).__init__(input_dim,
name)`` to make sure the input dimension and name of the kernel are
stored in the right place. These attributes are available as
``self.input_dim`` and ``self.name`` at runtime.
.. The following attributes are compulsory: ``self.input_dim`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.num_params`` (number of parameters, integer). ::
Parameterization is done by adding
:py:class:``GPy.core.parameter.Param`` objects to ``self`` and use
them as normal numpy ``array-like``s in yout code. The parameters have
to be added by calling
:py:function:``GPy.core.parameterized:Parameterized.add_parameters``
with the :py:class:``GPy.core.parameter.Param`` objects as arguments.
def __init__(self,input_dim,variance=1.,lengthscale=1.,power=1.):
assert input_dim == 1, "For this kernel we assume input_dim=1"
self.input_dim = input_dim
self.num_params = 3
self.name = 'rat_quad'
self.variance = variance
self.lengthscale = lengthscale
self.power = power
super(RationalQuadratic, self).__init__(input_dim, 'rat_quad')
assert input_dim == 1, "For this kernel we assume input_dim=1"
self.variance = Param('variance', variance)
self.lengthscale = Param('lengtscale', lengthscale)
self.power = Param('power', power)
self.add_parameters(self.variance, self.lengthscale, self.power)
**_get_params(self)**
From now on you can use the parameters ``self.variance,
self.lengthscale, self.power`` as normal numpy ``array-like``s in your
code. Updates from the optimization routine will be done
automatically.
The implementation of this function in mandatory.
**parameters_changed(self)**
This function returns a one dimensional array of length ``self.num_params`` containing the value of the parameters. ::
The implementation of this function is optional.
def _get_params(self):
return np.hstack((self.variance,self.lengthscale,self.power))
This functions deals as a callback for each optimization iteration. If
one optimization step was successfull and the parameters (added by
:py:function:``GPy.core.parameterized:Parameterized.add_parameters``)
this callback function will be called to be able to update any
precomputations for the kernel.
**_set_params(self,x)**
def parameters_changed(self):
# nothing todo here
The implementation of this function in mandatory.
.. **_get_params(self)**
The input is a one dimensional array of length ``self.num_params`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...). ::
.. The implementation of this function in mandatory.
def _set_params(self,x):
self.variance = x[0]
self.lengthscale = x[1]
self.power = x[2]
.. This function returns a one dimensional array of length ``self.num_params`` containing the value of the parameters. ::
**_get_param_names(self)**
.. def _get_params(self):
.. return np.hstack((self.variance,self.lengthscale,self.power))
The implementation of this function in mandatory.
.. **_set_params(self,x)**
It returns a list of strings of length ``self.num_params`` corresponding to the parameter names. ::
.. The implementation of this function in mandatory.
def _get_param_names(self):
return ['variance','lengthscale','power']
.. The input is a one dimensional array of length ``self.num_params`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...). ::
.. def _set_params(self,x):
.. self.variance = x[0]
.. self.lengthscale = x[1]
.. self.power = x[2]
.. **_get_param_names(self)**
.. The implementation of this function in mandatory.
.. It returns a list of strings of length ``self.num_params`` corresponding to the parameter names. ::
.. def _get_param_names(self):
.. return ['variance','lengthscale','power']
**K(self,X,X2,target)**