diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index b6bc07a0..261d5a49 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -552,27 +552,28 @@ def parametric_mean_function(max_iters=100, optimize=True, plot=True):
 
 def warped_gp_cubic_sine(max_iters=100):
     """
-    A test replicating the sine regression problem from
+    A test replicating the cubic sine regression problem from
     Snelson's paper.
     """
     X = (2 * np.pi) * np.random.random(151) - np.pi
     Y = np.sin(X) + np.random.normal(0,0.2,151)
     Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
-        
+    X = X[:, None]
+    Y = Y[:, None]
+
     warp_k = GPy.kern.RBF(1)
     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])
+    warp_m = GPy.models.WarpedGP(X, Y, kernel=warp_k, warping_function=warp_f)
+    m = GPy.models.GPRegression(X, Y)
     m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
     warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
     print(warp_m)
     print(warp_m['.*warp.*'])
+
     warp_m.predict_in_warped_space = False
     warp_m.plot(title="Warped GP - Latent space")
     warp_m.predict_in_warped_space = True
     warp_m.plot(title="Warped GP - Warped space")
     m.plot(title="Standard GP")
-    #warp_f.plot(X.min()-10, X.max()+10)
     warp_m.plot_warping()
     pb.show()