added initial test for warped gps using identity function

GPy/models/warped_gp.py

@@ -64,7 +64,6 @@ class WarpedGP(GP):
     def log_likelihood(self):
         ll = GP.log_likelihood(self)
         jacobian = self.warping_function.fgrad_y(self.Y_untransformed)
-        print np.log(jacobian)
         return ll + np.log(jacobian).sum()
 
     def plot_warping(self):

GPy/testing/model_tests.py

@@ -205,7 +205,8 @@ class MiscTests(unittest.TestCase):
 
     def test_warped_gp(self):
         k = GPy.kern.RBF(1)
-        m = GPy.models.WarpedGP(self.X, self.Y, kernel=k)
+        warp = GPy.util.warping_functions.IdentityFunction()
+        m = GPy.models.WarpedGP(self.X, self.Y, kernel=k, warping_function=warp)
         m.randomize()
         m.optimize()
         print(m)

GPy/util/warping_functions.py

@@ -301,17 +301,26 @@ class IdentityFunction(WarpingFunction):
     and should not be used in practice.
     """
     def __init__(self):
-        self.num_parameters = 0
+        self.num_parameters = 4
+        self.psi = Param('psi', np.zeros((1,3)))
+        self.d = Param('%s' % ('d'), 1.0, Logexp())
         super(IdentityFunction, self).__init__(name='identity')
+        self.link_parameter(self.psi)
+        self.link_parameter(self.d)
+
         
     def f(self, y):
         return y
 
     def fgrad_y(self, y):
-        return 1.0
+        return np.ones(y.shape)
 
-    def fgrad_y_psi(self,y):
-        return 1.0
+    def fgrad_y_psi(self, y, return_covar_chain=False):
+        gradients = np.ones((y.shape[0], y.shape[1], len(self.psi), 4))
+        if return_covar_chain:
+            return gradients, gradients
+        return gradients
+        
 
     def f_inv(self,z):
         return z