diff --git a/GPy/models/warped_gp.py b/GPy/models/warped_gp.py
index 3e5733eb..d35c1a15 100644
--- a/GPy/models/warped_gp.py
+++ b/GPy/models/warped_gp.py
@@ -5,7 +5,7 @@ import numpy as np
 from ..util.warping_functions import *
 from ..core import GP
 from .. import likelihoods
-from GPy.util.warping_functions import TanhWarpingFunction
+from GPy.util.warping_functions import TanhFunction
 from GPy import kern
 
 class WarpedGP(GP):
@@ -17,7 +17,7 @@ class WarpedGP(GP):
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
         if warping_function == None:
-            self.warping_function = TanhWarpingFunction(warping_terms)
+            self.warping_function = TanhFunction(warping_terms)
             self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1) * 1)
         else:
             self.warping_function = warping_function
diff --git a/GPy/testing/model_tests.py b/GPy/testing/model_tests.py
index 3ced78f2..ab88d011 100644
--- a/GPy/testing/model_tests.py
+++ b/GPy/testing/model_tests.py
@@ -319,7 +319,7 @@ class MiscTests(unittest.TestCase):
         
         import matplotlib.pyplot as plt
         warp_k = GPy.kern.RBF(1)
-        warp_f = GPy.util.warping_functions.TanhWarpingFunction(n_terms=2)
+        warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
         warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
 
         m = GPy.models.GPRegression(X[:, None], Y[:, None])
diff --git a/GPy/util/warping_functions.py b/GPy/util/warping_functions.py
index dd41e5ee..5bccbd91 100644
--- a/GPy/util/warping_functions.py
+++ b/GPy/util/warping_functions.py
@@ -49,7 +49,7 @@ class WarpingFunction(Parameterized):
         plt.show()
 
 
-class TanhWarpingFunction(WarpingFunction):
+class TanhFunction(WarpingFunction):
 
     def __init__(self, n_terms=3, initial_y=None):
         """
@@ -59,7 +59,7 @@ class TanhWarpingFunction(WarpingFunction):
         self.num_parameters = 3 * self.n_terms + 1
         self.psi = np.ones((self.n_terms, 3))
 
-        super(TanhWarpingFunction, self).__init__(name='warp_tanh')
+        super(TanhFunction, self).__init__(name='warp_tanh')
         self.psi = Param('psi', self.psi)
         self.psi[:, :2].constrain_positive()