diff --git a/GPy/testing/inference_tests.py b/GPy/testing/inference_tests.py
index 786e5794..92def798 100644
--- a/GPy/testing/inference_tests.py
+++ b/GPy/testing/inference_tests.py
@@ -13,72 +13,39 @@ import GPy
 class InferenceXTestCase(unittest.TestCase):
 
     def genData(self):
-        np.random.seed(1)
-        D1,D2,N = 12,12,50
+        np.random.seed(1111)
+        Ylist = GPy.examples.dimensionality_reduction._simulate_matern(5, 1, 1, 10, 3, False)[0]
+        return Ylist[0]
 
-        x = np.linspace(0, 4 * np.pi, N)[:, None]
-        s1 = np.vectorize(lambda x: np.sin(x))
-        s2 = np.vectorize(lambda x: np.cos(x)**2)
-        s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
-        sS = np.vectorize(lambda x: np.cos(x))
-
-        s1 = s1(x)
-        s2 = s2(x)
-        s3 = s3(x)
-        sS = sS(x)
-
-        s1 -= s1.mean(); s1 /= s1.std(0)
-        s2 -= s2.mean(); s2 /= s2.std(0)
-        s3 -= s3.mean(); s3 /= s3.std(0)
-        sS -= sS.mean(); sS /= sS.std(0)
-
-        S1 = np.hstack([s1, sS])
-        S2 = np.hstack([s3, sS])
-
-        P1 = np.random.randn(S1.shape[1], D1)
-        P2 = np.random.randn(S2.shape[1], D2)
-
-        Y1 = S1.dot(P1)
-        Y2 = S2.dot(P2)
-
-        Y1 += .01 * np.random.randn(*Y1.shape)
-        Y2 += .01 * np.random.randn(*Y2.shape)
-
-        Y1 -= Y1.mean(0)
-        Y2 -= Y2.mean(0)
-        Y1 /= Y1.std(0)
-        Y2 /= Y2.std(0)
-
-        slist = [s1, s2, s3, sS]
-        slist_names = ["s1", "s2", "s3", "sS"]
-        Ylist = [Y1, Y2]
-
-        return Ylist
-
-    def test_inferenceX_BGPLVM(self):
+    def test_inferenceX_BGPLVM_Linear(self):
         Ys = self.genData()
-        m = GPy.models.BayesianGPLVM(Ys[0],5,kernel=GPy.kern.Linear(5,ARD=True))
-
-        x,mi = m.infer_newX(m.Y, optimize=False)
-        self.assertTrue(mi.checkgrad())
-
-        m.optimize(max_iters=10000)
-        x, mi = m.infer_newX(m.Y)
-
-        print(m.X.mean - mi.X.mean)
+        m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
+        m.optimize()
+        x, mi = m.infer_newX(m.Y, optimize=True)
         np.testing.assert_array_almost_equal(m.X.mean, mi.X.mean, decimal=2)
         np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
 
-    def test_inferenceX_GPLVM(self):
+    def test_inferenceX_BGPLVM_RBF(self):
         Ys = self.genData()
-        m = GPy.models.GPLVM(Ys[0],3,kernel=GPy.kern.RBF(3,ARD=True))
+        m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
+        m.optimize()
+        x, mi = m.infer_newX(m.Y, optimize=True)
+        np.testing.assert_array_almost_equal(m.X.mean, mi.X.mean, decimal=2)
+        np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
 
-        x,mi = m.infer_newX(m.Y, optimize=False)
-        self.assertTrue(mi.checkgrad())
+    def test_inferenceX_GPLVM_Linear(self):
+        Ys = self.genData()
+        m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
+        m.optimize()
+        x, mi = m.infer_newX(m.Y, optimize=True)
+        np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
 
-#         m.optimize(max_iters=10000)
-#         x,mi = m.infer_newX(m.Y)
-#         self.assertTrue(np.allclose(m.X, x))
+    def test_inferenceX_GPLVM_RBF(self):
+        Ys = self.genData()
+        m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
+        m.optimize()
+        x, mi = m.infer_newX(m.Y, optimize=True)
+        np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
 
 
 if __name__ == "__main__":