diff --git a/.gitattributes b/.gitattributes
index e280dc95e78ac..7985aa57bc658 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -18,7 +18,7 @@
 /sklearn/utils/arrayfuncs.c -diff
 /sklearn/utils/graph_shortest_path.c -diff
 /sklearn/utils/lgamma.c -diff
-sklearn/utils/_logistic_sigmoid.c -diff
+/sklearn/utils/_logistic_sigmoid.c -diff
 /sklearn/utils/murmurhash.c -diff
 /sklearn/utils/seq_dataset.c -diff
 /sklearn/utils/sparsefuncs.c -diff
diff --git a/sklearn/neural_network/tests/test_rbm.py b/sklearn/neural_network/tests/test_rbm.py
index 9d6535b1dd186..791fdc4990d74 100644
--- a/sklearn/neural_network/tests/test_rbm.py
+++ b/sklearn/neural_network/tests/test_rbm.py
@@ -101,15 +101,12 @@ def test_gibbs_smoke():
 
 
 def test_pseudo_likelihood_no_clipping():
-    """
-    checks that the pseudo likelihood is computed without clipping,
-    which happened until commit 52d1b778ca7164ac04ea9f8ba39077054954b77a
-    the new implementation (as of commit
-    52d1b778ca7164ac04ea9f8ba39077054954b77a ) follows:
+    """Check that the pseudo likelihood is computed without clipping.
+
     http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/
     """
     rng = np.random.RandomState(42)
-    X = np.array([[0. for i in xrange(1000)], [1. for i in xrange(1000)]])
+    X = np.vstack([np.zeros(1000), np.ones(1000)])
     rbm1 = BernoulliRBM(n_components=10, batch_size=2,
                         n_iter=10, random_state=rng)
     rbm1.fit(X)