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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Andreas 2013-10-08 11:31:13 +01:00
commit 958e9f7c7a
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14
GPy/core/svigp.py
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@ -14,6 +14,7 @@ import sys
class SVIGP(GPBase):
"""
Stochastic Variational inference in a Gaussian Process
:param X: inputs
@ -22,25 +23,26 @@ class SVIGP(GPBase):
: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
Additional kwargs are used as for a sparse GP. They include:
: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)
: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
: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)
: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
: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)
:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""