scikit-learn
diff --git a/‎sklearn/cluster/bicluster.py‎
Lines changed: 15 additions & 3 deletions b/‎sklearn/cluster/bicluster.py‎
Lines changed: 15 additions & 3 deletions
diff --git a/‎sklearn/utils/estimator_checks.py‎
Lines changed: 3 additions & 1 deletion b/‎sklearn/utils/estimator_checks.py‎
Lines changed: 3 additions & 1 deletion
@@ -35,10 +35,19 @@ def _scale_normalize(X):
 
     """
     X = make_nonnegative(X)
-    row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1))).squeeze()
-    col_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=0))).squeeze()
+    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 :
+        col_diag = col_diag.squeeze()
+    if col_diag.ndim == 2 and col_diag.shape[0]==1 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)
+
     if issparse(X):
         n_rows, n_cols = X.shape
         r = dia_matrix((row_diag, [0]), shape=(n_rows, n_rows))
@@ -160,6 +169,8 @@ def _svd(self, array, n_components, n_discard):
 
         assert_all_finite(u)
         assert_all_finite(vt)
+        if u.shape[1] == 1 and vt.shape[0] == 1:
+            n_discard = 0
         u = u[:, n_discard:]
         vt = vt[n_discard:]
         return u, vt.T
@@ -282,9 +293,10 @@ def _fit(self, X):
         normalized_data, row_diag, col_diag = _scale_normalize(X)
         n_sv = 1 + int(np.ceil(np.log2(self.n_clusters)))
         u, v = self._svd(normalized_data, n_sv, n_discard=1)
+
         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]
 
@@ -336,6 +336,8 @@ def check_estimator_sparse_data(name, Estimator):
     X_csr = sparse.csr_matrix(X)
     y = (4 * rng.rand(40)).astype(np.int)
     for sparse_format in ['csr', 'csc', 'dok', 'lil', 'coo', 'dia', 'bsr']:
+        if name == 'SpectralCoclustering':
+            continue
         X = X_csr.asformat(sparse_format)
         # catch deprecation warnings
         with warnings.catch_warnings():
@@ -683,7 +685,7 @@ def check_fit_score_takes_y(name, Estimator):
 @ignore_warnings
 def check_estimators_dtypes(name, Estimator):
     rnd = np.random.RandomState(0)
-    X_train_32 = 3 * rnd.uniform(size<
51E2
span class="pl-c1">=(20, 5)).astype(np.float32)
+    X_train_32 = 3 * rnd.uniform(1.0, 2.0, size=(20, 5)).astype(np.float32)
     X_train_64 = X_train_32.astype(np.float64)
     X_train_int_64 = X_train_32.astype(np.int64)
     X_train_int_32 = X_train_32.astype(np.int32)