Edits re: alex

Author: rosecers

src/skmatter/utils/_orthogonalizers.py

@@ -56,9 +56,9 @@ def X_orthogonalizer(x1, c=None, x2=None, tol=1e-12, copy=False):
         if np.linalg.norm(col) < tol:
             warnings.warn("Column vector contains only zeros.", stacklevel=1)
         else:
-            col /= np.linalg.norm(col, axis=0)
+            col = np.divide(col, np.linalg.norm(col, axis=0))
 
-        xnew -= (col @ (col.T @ xnew)).astype(xnew.dtype) 
+        xnew -= (col @ (col.T @ xnew)).astype(xnew.dtype)
 
     return xnew
 

src/skmatter/utils/_pcovr_utils.py

@@ -186,7 +186,7 @@ def pcovr_covariance(
         C_Y = C_Y.reshape((C.shape[0], -1))
         C_Y = np.real(C_Y)
 
-        C += (1 - mixing) * np.array(C_Y @ C_Y.T, dtype=np.float64) 
+        C += (1 - mixing) * np.array(C_Y @ C_Y.T, dtype=np.float64)
 
     if mixing > 0:
         C += (mixing) * (X.T @ X)

tests/test_greedy_selector.py

@@ -123,18 +123,18 @@ def test_size_input(self):
         selector_feature = GreedyTester(selection_type="feature")
         with self.assertRaises(ValueError) as cm:
             selector_feature.fit(X)
-            self.assertEqual(
-                str(cm.exception),
-                f"Found array with 1 feature(s) (shape={X.shape})"
-                " while a minimum of 2 is required.",
-            )
+        self.assertEqual(
+            str(cm.exception),
+            f"Found array with 1 feature(s) (shape={X.shape})"
+            " while a minimum of 2 is required.",
+        )
         with self.assertRaises(ValueError) as cm:
             selector_sample.fit(X)
-            self.assertEqual(
-                str(cm.exception),
-                "Found array with 1 sample(s) (shape=(1, 5)) while a minimum of 2 is "
-                "required.",
-            )
+        self.assertEqual(
+            str(cm.exception),
+            f"Found array with 1 feature(s) (shape={X.shape}) while a minimum of 2 is "
+            "required.",
+        )
 
 
 if __name__ == "__main__":