diff --git a/GPy/core/__init__.py b/GPy/core/__init__.py
index 142eccbf..1c6ebea6 100644
--- a/GPy/core/__init__.py
+++ b/GPy/core/__init__.py
@@ -7,6 +7,6 @@ from .parameterization.param import Param, ParamConcatenation
 from .parameterization.observable_array import ObsAr
 
 from .gp import GP
-from .svgp import SVGP
+#from .svgp import SVGP
 from .sparse_gp import SparseGP
 from .mapping import *
diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index f33e7715..a9585c86 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -161,6 +161,16 @@ class NormalPosterior(VariationalPosterior):
         from ...plotting.matplot_dep import variational_plots
         return variational_plots.plot(self, *args, **kwargs)
 
+    def KL(self, other):
+        """Compute the KL divergence to another NormalPosterior Object. This only holds, if the two NormalPosterior objects have the same shape, as we do computational tricks for the multivariate normal KL divergence.
+        """
+        return .5*(
+            np.sum(self.variance/other.variance) 
+            + ((other.mean-self.mean)**2/other.variance).sum() 
+            - self.num_data * self.input_dim
+            + np.sum(np.log(other.variance)) - np.sum(np.log(self.variance))
+            )
+    
 class SpikeAndSlabPosterior(VariationalPosterior):
     '''
     The SpikeAndSlab distribution for variational approximations.
diff --git a/GPy/inference/latent_function_inference/__init__.py b/GPy/inference/latent_function_inference/__init__.py
index 6754000d..f927aba4 100644
--- a/GPy/inference/latent_function_inference/__init__.py
+++ b/GPy/inference/latent_function_inference/__init__.py
@@ -69,7 +69,7 @@ from .expectation_propagation_dtc import EPDTC
 from .dtc import DTC
 from .fitc import FITC
 from .var_dtc_parallel import VarDTC_minibatch
-from .svgp import SVGP
+#from .svgp import SVGP
 
 # class FullLatentFunctionData(object):
 #
diff --git a/GPy/inference/optimization/stochastics.py b/GPy/inference/optimization/stochastics.py
index 2151c39c..645c5f64 100644
--- a/GPy/inference/optimization/stochastics.py
+++ b/GPy/inference/optimization/stochastics.py
@@ -72,10 +72,10 @@ class SparseGPStochastics(StochasticStorage):
             bdict = {}
             for d in self.d:
                 inan = np.isnan(self.Y[:, d])
-                arr_str = np.array2string(inan, 
+                arr_str = int(np.array2string(inan, 
                                           np.inf, 0, 
                                           True, '', 
-                                          formatter={'bool':lambda x: '1' if x else '0'})
+                                          formatter={'bool':lambda x: '1' if x else '0'}), 2)
                 try:
                     bdict[arr_str][0].append(d)
                 except:
diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py
index 98f5b6b7..5a62e5d8 100644
--- a/GPy/plotting/matplot_dep/variational_plots.py
+++ b/GPy/plotting/matplot_dep/variational_plots.py
@@ -43,7 +43,7 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
         if i < means.shape[1] - 1:
             a.set_xticklabels('')
     pb.draw()
-    fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
+    a.figure.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
     return dict(lines=lines, fills=fills, bg_lines=bg_lines)
 
 def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_side=True):