apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 12:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

docstrings in kern.py

Browse source
This commit is contained in:
James Hensman 2014-02-26 08:23:46 +00:00
parent 80acca640f
commit e9957ed896
1 changed files with 19 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

23
GPy/kern/_src/kern.py
View file

@ -26,27 +26,41 @@ class Kern(Parameterized):
raise NotImplementedError
def Kdiag(self, Xa):
raise NotImplementedError
def psi0(self,Z,variational_posterior):
def psi0(self, Z, variational_posterior):
raise NotImplementedError
def psi1(self,Z,variational_posterior):
def psi1(self, Z, variational_posterior):
raise NotImplementedError
def psi2(self,Z,variational_posterior):
def psi2(self, Z, variational_posterior):
raise NotImplementedError
def gradients_X(self, dL_dK, X, X2):
raise NotImplementedError
def gradients_X_diag(self, dL_dK, X):
raise NotImplementedError
def update_gradients_full(self, dL_dK, X, X2):
"""Set the gradients of all parameters when doing full (N) inference."""
raise NotImplementedError
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
"""
Set the gradients of all parameters when doing inference with
uncertain inputs, using expectations of the kernel.
The esential maths is
dL_d{theta_i} = dL_dpsi0 * dpsi0_d{theta_i} +
dL_dpsi1 * dpsi1_d{theta_i} +
dL_dpsi2 * dpsi2_d{theta_i}
"""
raise NotImplementedError
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
"""
Returns the derivative of the objective wrt Z, using the chain rule
through the expectation variables.
"""
raise NotImplementedError
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
"""
Compute the gradients wrt the parameters of the variational
@ -106,7 +120,8 @@ 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