diff --git a/GPy/core/model.py b/GPy/core/model.py
index 0fdf4800..3863ac42 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -291,7 +291,7 @@ class Model(Parameterized):
             # just check the global ratio
             dx = np.zeros(x.shape)
             dx[transformed_index] = step * (np.sign(np.random.uniform(-1, 1, transformed_index.size)) if transformed_index.size != 2 else 1.)
-
+            
             # evaulate around the point x
             f1 = self._objective(x + dx)
             f2 = self._objective(x - dx)
@@ -303,7 +303,6 @@ class Model(Parameterized):
             denominator = (2 * np.dot(dx, gradient))
             global_ratio = (f1 - f2) / np.where(denominator == 0., 1e-32, denominator)
             global_diff = np.abs(f1 - f2) < tolerance and np.allclose(gradient, 0, atol=tolerance)
-            print self.mpi_comm.rank,global_ratio,global_diff
             if global_ratio is np.nan:
                 global_ratio = 0
             return np.abs(1. - global_ratio) < tolerance or global_diff
diff --git a/GPy/core/sparse_gp_mpi.py b/GPy/core/sparse_gp_mpi.py
index f0465f38..73a37862 100644
--- a/GPy/core/sparse_gp_mpi.py
+++ b/GPy/core/sparse_gp_mpi.py
@@ -46,7 +46,7 @@ class SparseGP_MPI(SparseGP):
         self.add_parameter(self.X, index=0)
         if variational_prior is not None:
             self.add_parameter(variational_prior)
-        self.X.fix()
+#         self.X.fix()
 
         self.mpi_comm = mpi_comm
         # Manage the data (Y) division
diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index 7913b605..4ea4f297 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -70,8 +70,8 @@ class SSGPLVM(SparseGP_MPI):
         X = SpikeAndSlabPosterior(X, X_variance, gamma)
                 
         super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
-        self.X.unfix()
-        self.X.variance.constrain_positive()
+#         self.X.unfix()
+#         self.X.variance.constrain_positive()
                 
         if self.group_spike:
             [self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together