diff --git a/GPy/testing/inference_tests.py b/GPy/testing/inference_tests.py
index ac92c519..d5039049 100644
--- a/GPy/testing/inference_tests.py
+++ b/GPy/testing/inference_tests.py
@@ -11,39 +11,38 @@ import GPy
 
 
 class InferenceXTestCase(unittest.TestCase):
-    
+
     def genData(self):
         D1,D2,N = 12,12,50
-        np.random.seed(1234)
-    
+
         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)
@@ -52,33 +51,34 @@ class InferenceXTestCase(unittest.TestCase):
         slist = [s1, s2, s3, sS]
         slist_names = ["s1", "s2", "s3", "sS"]
         Ylist = [Y1, Y2]
-        
+
         return Ylist
-    
+
     def test_inferenceX_BGPLVM(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)
 
-        self.assertTrue(np.allclose(m.X.mean, mi.X.mean))
-        self.assertTrue(np.allclose(m.X.variance, mi.X.variance))
+        m.optimize(max_iters=10000)
+        x, mi = m.infer_newX(m.Y)
+
+        print m.X.mean - mi.X.mean
+        self.assertTrue(np.allclose(m.X.mean, mi.X.mean, rtol=1e-4, atol=1e-4))
+        self.assertTrue(np.allclose(m.X.variance, mi.X.variance, rtol=1e-4, atol=1e-4))
 
     def test_inferenceX_GPLVM(self):
         Ys = self.genData()
         m = GPy.models.GPLVM(Ys[0],3,kernel=GPy.kern.RBF(3,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)
 #         self.assertTrue(np.allclose(m.X, x))
-        
+
 
 if __name__ == "__main__":
     unittest.main()