diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 76142241..a7dac56e 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -9,7 +9,8 @@ from ..util.plot import gpplot, Tango, x_frame1D, x_frame2D
 from model import Model
 from parameterization import ObservableArray
 from .. import likelihoods
-from GPy.likelihoods.gaussian import Gaussian
+from ..likelihoods.gaussian import Gaussian
+from ..inference.latent_function_inference import exact_gaussian_inference
 
 class GP(Model):
     """
@@ -26,7 +27,7 @@ class GP(Model):
 
     """
     def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp'):
-        super(GPBase, self).__init__(name)
+        super(GP, self).__init__(name)
 
         assert X.ndim == 2
         self.X = ObservableArray(X)
@@ -50,13 +51,13 @@ class GP(Model):
         else:
             inference_method = expectation_propagation
             print "defaulting to ", inference_method, "for latent function inference"
-
-        super(GP, self).__init__(X, Y, kernel, likelihood, inference_method, name)
-        self.parameters_changed()
+        self.inference_method = inference_method
 
         self.add_parameter(self.kern, gradient=self.dL_dtheta_K)
         self.add_parameter(self.likelihood, gradient=lambda:self.posterior.dL_dtheta_lik)
 
+        self.parameters_changed()
+
     def parameters_changed(self):
         super(GP, self).parameters_changed()
         self.K = self.kern.K(self.X)
@@ -241,8 +242,7 @@ class GP(Model):
 
             #define the frame on which to plot
             resolution = resolution or 200
-            Xu = self.X * self._Xscale + self._Xoffset #NOTE self.X are the normalized values now
-            Xnew, xmin, xmax = x_frame1D(Xu[:,free_dims], plot_limits=plot_limits)
+            Xnew, xmin, xmax = x_frame1D(self.X[:,free_dims], plot_limits=plot_limits)
             Xgrid = np.empty((Xnew.shape[0],self.input_dim))
             Xgrid[:,free_dims] = Xnew
             for i,v in fixed_inputs:
@@ -259,7 +259,7 @@ class GP(Model):
                 Y = self.Y
             for d in which_data_ycols:
                 gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
-                ax.plot(Xu[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
+                ax.plot(self.X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
 
             #optionally plot some samples
             if samples: #NOTE not tested with fixed_inputs
@@ -279,8 +279,7 @@ class GP(Model):
 
             #define the frame for plotting on
             resolution = resolution or 50
-            Xu = self.X * self._Xscale + self._Xoffset #NOTE self.X are the normalized values now
-            Xnew, _, _, xmin, xmax = x_frame2D(Xu[:,free_dims], plot_limits, resolution)
+            Xnew, _, _, xmin, xmax = x_frame2D(self.X[:,free_dims], plot_limits, resolution)
             Xgrid = np.empty((Xnew.shape[0],self.input_dim))
             Xgrid[:,free_dims] = Xnew
             for i,v in fixed_inputs: