diff --git a/GPy/models/gp_coregionalized_regression.py b/GPy/models/gp_coregionalized_regression.py
index be5b9ac3..f8228807 100644
--- a/GPy/models/gp_coregionalized_regression.py
+++ b/GPy/models/gp_coregionalized_regression.py
@@ -36,7 +36,9 @@ class GPCoregionalizedRegression(GP):
 
         #Kernel
         if kernel is None:
-            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
+            kernel = kern.RBF(X.shape[1]-1)
+            
+        kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=1,name=kernel_name)
 
         #Likelihood
         likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)
diff --git a/GPy/models/sparse_gp_coregionalized_regression.py b/GPy/models/sparse_gp_coregionalized_regression.py
index 1e5049e2..207bc665 100644
--- a/GPy/models/sparse_gp_coregionalized_regression.py
+++ b/GPy/models/sparse_gp_coregionalized_regression.py
@@ -42,7 +42,9 @@ class SparseGPCoregionalizedRegression(SparseGP):
 
         #Kernel
         if kernel is None:
-            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
+            kernel = kern.RBF(X.shape[1]-1)
+            
+        kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=1,name=kernel_name)
 
         #Likelihood
         likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)
diff --git a/GPy/testing/gp_tests.py b/GPy/testing/gp_tests.py
index 63345c18..b8cd89e2 100644
--- a/GPy/testing/gp_tests.py
+++ b/GPy/testing/gp_tests.py
@@ -22,7 +22,7 @@ class Test(unittest.TestCase):
 
     def test_setxy_bgplvm(self):
         k = GPy.kern.RBF(1)
-        m = GPy.models.BayesianGPLVM(self.Y, 2, kernel=k)
+        m = GPy.models.BayesianGPLVM(self.Y, 1, kernel=k)
         mu, var = m.predict(m.X)
         X = m.X.copy()
         Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
@@ -32,10 +32,11 @@ class Test(unittest.TestCase):
         mu2, var2 = m.predict(m.X)
         np.testing.assert_allclose(mu, mu2)
         np.testing.assert_allclose(var, var2)
+        
 
     def test_setxy_gplvm(self):
         k = GPy.kern.RBF(1)
-        m = GPy.models.GPLVM(self.Y, 2, kernel=k)
+        m = GPy.models.GPLVM(self.Y, 1, kernel=k)
         mu, var = m.predict(m.X)
         X = m.X.copy()
         Xnew = X[:10].copy()
diff --git a/GPy/testing/pickle_tests.py b/GPy/testing/pickle_tests.py
index f7f161c4..4c3ecd52 100644
--- a/GPy/testing/pickle_tests.py
+++ b/GPy/testing/pickle_tests.py
@@ -11,7 +11,7 @@ import tempfile
 from GPy.examples.dimensionality_reduction import mrd_simulation
 from GPy.core.parameterization.variational import NormalPosterior
 from GPy.models.gp_regression import GPRegression
-from functools import reduce
+import GPy
 from nose import SkipTest
 
 def toy_model():
@@ -33,7 +33,7 @@ class ListDictTestCase(unittest.TestCase):
 class Test(ListDictTestCase):
     @SkipTest
     def test_load_pickle(self):
-        import os, GPy
+        import os
         m = GPy.load(os.path.join(os.path.abspath(os.path.split(__file__)[0]), 'pickle_test.pickle'))
         self.assertTrue(m.checkgrad())
         self.assertEqual(m.log_likelihood(), -4.7351019830022087)