diff --git a/GPy/inference/optimization.py b/GPy/inference/optimization.py
index 2c5e2fba..9bc44fd4 100644
--- a/GPy/inference/optimization.py
+++ b/GPy/inference/optimization.py
@@ -170,12 +170,12 @@ class opt_rasm(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "Rasmussen's Conjugate Gradient"
 
-    def opt(self):
+    def opt(self, f_fp = None, f = None, fp = None):
         """
         Run Rasmussen's Conjugate Gradient optimizer
         """
 
-        assert self.f_fp != None, "Rasmussen's minimizer requires f_fp"
+        assert f_fp != None, "Rasmussen's minimizer requires f_fp"
         statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached',
                 'NaNs in optimization']
 
@@ -187,8 +187,8 @@ class opt_rasm(Optimizer):
         if self.gtol is not None:
             print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
 
-        opt_result = rasm.minimize(self.x_init, self.f_fp, (), messages = self.messages,
-                maxnumfuneval = self.max_f_eval)
+        opt_result = rasm.minimize(self.x_init, f_fp, (), messages = self.messages,
+                                   maxnumfuneval = self.max_f_eval)
         self.x_opt = opt_result[0]
         self.f_opt = opt_result[1][-1]
         self.funct_eval = opt_result[2]
diff --git a/GPy/models/warped_GP.py b/GPy/models/warped_GP.py
index 9a1bcbe1..bf5af21f 100644
--- a/GPy/models/warped_GP.py
+++ b/GPy/models/warped_GP.py
@@ -22,7 +22,7 @@ class warpedGP(GP_regression):
         if warping_function == None:
             self.warping_function = TanhWarpingFunction(warping_terms)
             # self.warping_params = np.random.randn(self.warping_function.n_terms, 3)
-            self.warping_params = np.ones((self.warping_function.n_terms, 3))*1.0 # TODO better init
+            self.warping_params = np.ones((self.warping_function.n_terms, 3))*0.0 # TODO better init
             self.warp_params_shape = (self.warping_function.n_terms, 3) # todo get this from the subclass
 
         self.Z = Y.copy()