apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-08 15:05:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

improved docstrings in svigp

Browse source
This commit is contained in:
James Hensman 2013-10-24 22:06:07 +01:00
parent 683f45366b
commit eeb5f59fca
1 changed files with 5 additions and 19 deletions
Download patch file Download diff file Expand all files Collapse all files

24
GPy/core/svigp.py
View file

@ -18,30 +18,16 @@ class SVIGP(GPBase):
Stochastic Variational inference in a Gaussian Process
:param X: inputs
:type X: np.ndarray (N x Q)
:type X: np.ndarray (num_data x num_inputs)
:param Y: observed data
:type Y: np.ndarray of observations (N x D)
:param batchsize: the size of a h
Additional kwargs are used as for a sparse GP. They include:
:type Y: np.ndarray of observations (num_data x output_dim)
:param batchsize: the size of a minibatch
:param q_u: canonical parameters of the distribution squasehd into a 1D array
:type q_u: np.ndarray
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:param kernel: the kernel/covariance function. See link kernels
:type kernel: a GPy kernel
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
:type X_uncertainty: np.ndarray (N x Q) | None
:param Zslices: slices for the inducing inputs (see slicing TODO: link)
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:param beta: noise precision. TODO: ignore beta if doing EP
:type beta: float
:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
:param Z: inducing inputs
:type Z: np.ndarray (num_inducing x num_inputs)
"""