scikit-learn
diff --git a/‎sklearn/metrics/cluster/tests/test_unsupervised.py
Lines changed: 1 addition & 0 deletions b/‎sklearn/metrics/cluster/tests/test_unsupervised.py
Lines changed: 1 addition & 0 deletions
diff --git a/‎sklearn/metrics/cluster/unsupervised.py
Lines changed: 3 additions & 43 deletions b/‎sklearn/metrics/cluster/unsupervised.py
Lines changed: 3 additions & 43 deletions
@@ -3,6 +3,7 @@
 
 from sklearn import datasets
 from sklearn.metrics.cluster.unsupervised import silhouette_score
+from sklearn.metrics.cluster.unsupervised import silhouette_sample
 from sklearn.metrics import pairwise_distances
 from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import assert_almost_equal
 
@@ -161,50 +161,8 @@ def silhouette_samples(X, labels, metric='euclidean', **kwds):
        <http://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
 
     """
-<<<<<<< HEAD
     le = LabelEncoder()
     labels = le.fit_transform(labels)
-=======
-    distances = pairwise_distances(X, metric=metric, **kwds)
-    n = labels.shape[0]
-    A = np.array([_intra_cluster_distance(distances[i], labels, i)
-                  for i in range(n)])
-    B = np.array([_nearest_cluster_distance(distances[i], labels, i)
-                  for i in range(n)])
-    sil_samples = (B - A) / np.maximum(A, B)
-    # nan values are for clusters of size 1, and should be 0
-    return np.nan_to_num(sil_samples)
-
-
-def _intra_cluster_distance(distances_row, labels, i):
-    """Calculate the mean intra-cluster distance for sample i.
-
-    Parameters
-    ----------
-    distances_row : array, shape = [n_samples]
-        Pairwise distance matrix between sample i and each sample.
-
-    labels : array, shape = [n_samples]
-        label values for each sample
-
-    i : int
-        Sample index being calculated. It is excluded from calculation and
-        used to determine the current label
-
-    Returns
-    -------
-    a : float
-        Mean intra-cluster distance for sample i
-    """
-    mask = labels == labels[i]
-    mask[i] = False
-    if not np.any(mask):
-        # cluster of size 1
-        return 0
-    a = np.mean(distances_row[mask])
-    return a
-
->>>>>>> reverted the comment
 
     distances = pairwise_distances(X, metric=metric, **kwds)
     unique_labels = le.classes_
@@ -242,4 +200,6 @@ def _intra_cluster_distance(distances_row, labels, i):
 
     sil_samples = inter_clust_dists - intra_clust_dists
     sil_samples /= np.maximum(intra_clust_dists, inter_clust_dists)
-    return np.nan_to_num(sil_samples)
+
+    # nan values are for clusters of size 1, and should be 0
+    return np.nan_to_num(sil_samples)