diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index c7849a46..f969bbe4 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -163,7 +163,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
     data['Y'] = data['Y'] - np.mean(data['Y'])
 
     lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
-    pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)
+    pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)  # @UndefinedVariable
     ax = pb.gca()
     pb.xlabel('length scale')
     pb.ylabel('log_10 SNR')
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index d9928295..2dc943bf 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -8,6 +8,7 @@ from parts.kernpart import Kernpart
 import itertools
 from parts.prod import Prod as prod
 from matplotlib.transforms import offset_copy
+import GPy
 
 class kern(Parameterized):
     def __init__(self, input_dim, parts=[], input_slices=None):
diff --git a/GPy/kern/parts/kernpart.py b/GPy/kern/parts/kernpart.py
index edbdc091..2d3d1191 100644
--- a/GPy/kern/parts/kernpart.py
+++ b/GPy/kern/parts/kernpart.py
@@ -1,5 +1,6 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
+import numpy
 
 
 class Kernpart(object):
@@ -72,5 +73,5 @@ class Kernpart_inner(Kernpart):
         Kernpart.__init__(self, input_dim)
 
         # initialize cache
-        self._Z, self._mu, self._S = np.empty(shape=(3, 1))
-        self._X, self._X2, self._params = np.empty(shape=(3, 1))
+        self._Z, self._mu, self._S = numpy.empty(shape=(3, 1))
+        self._X, self._X2, self._params = numpy.empty(shape=(3, 1))
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index 8afd1470..d6f879ec 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -518,7 +518,7 @@ def simulation_BGPLVM():
     Y = np.array(mat_data['Y'], dtype=float)
     S = np.array(mat_data['initS'], dtype=float)
     mu = np.array(mat_data['initMu'], dtype=float)
-    return data_details_return({'S': S, 'Y': Y, 'mu': mu}, data_set)
+    return data_details_return({'S': S, 'Y': Y, 'mu': mu}, mat_data)
     return {'Y': Y, 'S': S,
             'mu' : mu,
             'info': "Simulated test dataset generated in MATLAB to compare BGPLVM between python and MATLAB"}