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Eric Kalosa-Kenyon 2020-01-14 11:57:41 -08:00
parent 3c80c6e30f
commit 1d9bbaf751
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17
doc/source/tuto_creating_new_kernels.rst
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@ -73,13 +73,13 @@ automatically.
The implementation of this function is optional.
This functions deals as a callback for each optimization iteration. If
one optimization step was successfull and the parameters (added by
This functions is called as a callback upon each successful change to the parameters. If
one optimization step was successfull and the parameters (linked by
:py:func:`~GPy.core.parameterization.parameterized.Parameterized.link_parameters`
``(*parameters)``) this callback function will be called to be able to
update any precomputations for the kernel. Do not implement the
gradient updates here, as those are being done by the model enclosing
the kernel::
``(*parameters)``) are changed, this callback function will be called. This callback may be used to
update precomputations for the kernel. Do not implement the
gradient updates here, as gradient updates are performed by the model enclosing
the kernel. In this example, we issue a no-op::
def parameters_changed(self):
# nothing todo here
@ -92,8 +92,9 @@ the kernel::
The implementation of this function in mandatory.
This function is used to compute the covariance matrix associated with
the inputs X, X2 (np.arrays with arbitrary number of line (say
:math:`n_1`, :math:`n_2`) and ``self.input_dim`` columns). ::
the inputs X, X2 (np.arrays with arbitrary number of lines,
:math:`n_1`, :math:`n_2`, corresponding to the number of samples over which to calculate covariance)
and ``self.input_dim`` columns. ::
def K(self,X,X2):
if X2 is None: X2 = X