From eeb5f59fca5936be0eb80a414f67497f52a8f59c Mon Sep 17 00:00:00 2001
From: James Hensman <james.hensman@gmail.com>
Date: Thu, 24 Oct 2013 22:06:07 +0100
Subject: [PATCH] improved docstrings in svigp

---
 GPy/core/svigp.py | 24 +++++-------------------
 1 file changed, 5 insertions(+), 19 deletions(-)

diff --git a/GPy/core/svigp.py b/GPy/core/svigp.py
index c5ea9c6b..9f27f465 100644
--- a/GPy/core/svigp.py
+++ b/GPy/core/svigp.py
@@ -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)
 
     """