diff --git a/GPy/kern/src/standard_periodic.py b/GPy/kern/src/standard_periodic.py
index e0c76d0c..d45e8d16 100644
--- a/GPy/kern/src/standard_periodic.py
+++ b/GPy/kern/src/standard_periodic.py
@@ -155,11 +155,16 @@ class StdPeriodic(Kern):
         self.period.gradient = 0
         self.lengthscale.gradient = 0
 
-#    def gradients_X(self, dL_dK, X, X2=None):
-#        """derivative of the covariance matrix with respect to X."""
-#
-#        raise NotImplemented("Periodic kernel: dK_dX not implemented")
-#
-#    def gradients_X_diag(self, dL_dKdiag, X):
-#
-#        raise NotImplemented("Periodic kernel: dKdiag_dX not implemented")
+    def gradients_X(self, dL_dK, X, X2=None):
+        K = self.K(X, X2)
+        if X2 is None:
+            dL_dK = dL_dK+dL_dK.T
+            X2 = X
+        dX = -np.pi*((dL_dK*K)[:,:,None]*np.sin(2*np.pi/self.period*(X[:,None,:] - X2[None,:,:]))/(2.*np.square(self.lengthscale)*self.period)).sum(1)
+        return dX
+    
+    def gradients_X_diag(self, dL_dKdiag, X):
+        return np.zeros(X.shape)
+    
+    def input_sensitivity(self, summarize=True):
+        return self.variance*np.ones(self.input_dim)/self.lengthscale**2
\ No newline at end of file
diff --git a/GPy/kern/src/stationary.py b/GPy/kern/src/stationary.py
index 286edcc2..fa7c8bd3 100644
--- a/GPy/kern/src/stationary.py
+++ b/GPy/kern/src/stationary.py
@@ -489,18 +489,21 @@ class RatQuad(Stationary):
         self.link_parameters(self.power)
 
     def K_of_r(self, r):
-        r2 = np.power(r, 2.)
-        return self.variance*np.power(1. + r2/2., -self.power)
+        r2 = np.square(r)
+#         return self.variance*np.power(1. + r2/2., -self.power)
+        return self.variance*np.exp(-self.power*np.log1p(r2/2.))
 
     def dK_dr(self, r):
-        r2 = np.power(r, 2.)
-        return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
+        r2 = np.square(r)
+#         return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
+        return-self.variance*self.power*r*np.exp(-(self.power+1)*np.log1p(r2/2.))
 
     def update_gradients_full(self, dL_dK, X, X2=None):
         super(RatQuad, self).update_gradients_full(dL_dK, X, X2)
         r = self._scaled_dist(X, X2)
-        r2 = np.power(r, 2.)
-        dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
+        r2 = np.square(r)
+#        dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
+        dK_dpow = -self.variance * np.exp(self.power*(np.log(2.)-np.log1p(r2+1)))*np.log1p(r2/2.)
         grad = np.sum(dL_dK*dK_dpow)
         self.power.gradient = grad