scikit-learn
diff --git a/‎sklearn/cluster/bicluster.py
Lines changed: 6 additions & 6 deletions b/‎sklearn/cluster/bicluster.py
Lines changed: 6 additions & 6 deletions
diff --git a/‎sklearn/tests/test_common.py
Lines changed: 1 addition & 1 deletion b/‎sklearn/tests/test_common.py
Lines changed: 1 addition & 1 deletion
@@ -38,13 +38,13 @@ def _scale_normalize(X):
     row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1)))
     if row_diag.shape[0] != 1:
         row_diag = row_diag.squeeze()
-    
+
     col_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=0)))
-    if col_diag.ndim == 1 and col_diag.shape[0]!=1 :
+    if col_diag.ndim == 1 and col_diag.shape[0] != 1:
         col_diag = col_diag.squeeze()
-    if col_diag.ndim == 2 and col_diag.shape[0]==1 and col_diag.shape[1]!=1 :
+    if col_diag.ndim == 2 and col_diag.shape[1] != 1:
         col_diag = col_diag.squeeze()
-    
+
     row_diag = np.where(np.isnan(row_diag), 0, row_diag)
     col_diag = np.where(np.isnan(col_diag), 0, col_diag)
 
@@ -296,7 +296,7 @@ def _fit(self, X):
 
         z = np.vstack((row_diag[:, np.newaxis] * u,
                        col_diag[:, np.newaxis] * v))
-        
+
         _, labels = self._k_means(z, self.n_clusters)
 
         n_rows = X.shape[0]
@@ -493,7 +493,7 @@ def _fit(self, X):
                                   for _ in range(n_row_clusters)
                                   for label in range(n_col_clusters))
         return self
-            
+
     def _fit_best_piecewise(self, vectors, n_best, n_clusters):
         """Find the ``n_best`` vectors that are best approximated by piecewise
         constant vectors.
 
@@ -22,7 +22,6 @@
 from sklearn.utils.testing import ignore_warnings
 
 import sklearn
-from sklearn.cluster.bicluster import BiclusterMixin
 from sklearn.decomposition import ProjectedGradientNMF
 
 from sklearn.linear_model.base import LinearClassifierMixin
@@ -220,6 +219,7 @@ def test_transformer_n_iter():
             else:
                 yield check_transformer_n_iter, name, estimator
 
+
 def test_get_params_invariance():
     # Test for estimators that support get_params, that
     # get_params(deep=False) is a subset of get_params(deep=True)