diff --git a/GPy/core/model.py b/GPy/core/model.py
index 1595e347..7feb72b2 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -349,7 +349,7 @@ class Model(Parameterized):
                 numerical_gradient = (f1 - f2) / (2 * step)
                 if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
                 else: ratio = (f1 - f2) / (2 * step * gradient[xind])
-                difference = np.abs((f1 - f2) / 2 / step - gradient[xind])
+                difference = np.abs(numerical_gradient - gradient[xind])
 
                 if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
                     formatted_name = "\033[92m {0} \033[0m".format(names[nind])
diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index e359409e..2a036378 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -699,36 +699,10 @@ class OptimizationHandlable(Indexable):
 
     def _get_params_transformed(self):
         raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
-#         # transformed parameters (apply un-transformation rules)
-#         p = self.param_array.copy()
-#         [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-#         if self.has_parent() and self.constraints[__fixed__].size != 0:
-#             fixes = np.ones(self.size).astype(bool)
-#             fixes[self.constraints[__fixed__]] = FIXED
-#             return p[fixes]
-#         elif self._has_fixes():
-#             return p[self._fixes_]
-#         return p
 #
     def _set_params_transformed(self, p):
         raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
 
-#         """
-#         Set parameters p, but make sure they get transformed before setting.
-#         This means, the optimizer sees p, whereas the model sees transformed(p),
-#         such that, the parameters the model sees are in the right domain.
-#         """
-#         if not(p is self.param_array):
-#             if self.has_parent() and self.constraints[__fixed__].size != 0:
-#                 fixes = np.ones(self.size).astype(bool)
-#                 fixes[self.constraints[__fixed__]] = FIXED
-#                 self.param_array.flat[fixes] = p
-#             elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
-#             else: self.param_array.flat = p
-#         [np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
-#          for c, ind in self.constraints.iteritems() if c != __fixed__]
-#         self._trigger_params_changed()
-
     def _trigger_params_changed(self, trigger_parent=True):
         """
         First tell all children to update,
@@ -736,7 +710,7 @@ class OptimizationHandlable(Indexable):
 
         If trigger_parent is True, we will tell the parent, otherwise not.
         """
-        [p._trigger_params_changed(trigger_parent=False) for p in self.parameters]
+        [p._trigger_params_changed(trigger_parent=False) for p in self.parameters if not p.is_fixed]
         self.notify_observers(None, None if trigger_parent else -np.inf)
 
     def _size_transformed(self):
diff --git a/GPy/kern/_src/add.py b/GPy/kern/_src/add.py
index 12f5d444..ee743f8b 100644
--- a/GPy/kern/_src/add.py
+++ b/GPy/kern/_src/add.py
@@ -10,7 +10,7 @@ class Add(CombinationKernel):
     """
     Add given list of kernels together.
     propagates gradients through.
-    
+
     This kernel will take over the active dims of it's subkernels passed in.
     """
     def __init__(self, subkerns, name='add'):
@@ -40,7 +40,7 @@ class Add(CombinationKernel):
         return reduce(np.add, (p.Kdiag(X) for p in which_parts))
 
     def update_gradients_full(self, dL_dK, X, X2=None):
-        [p.update_gradients_full(dL_dK, X, X2) for p in self.parts]
+        [p.update_gradients_full(dL_dK, X, X2) for p in self.parts if not p.is_fixed]
 
     def update_gradients_diag(self, dL_dK, X):
         [p.update_gradients_diag(dL_dK, X) for p in self.parts]