Automatic pre-commit fixes

Author: samay2504

aeon/segmentation/_hidalgo.py

@@ -170,7 +170,7 @@ def _get_neighbourhood_params(self, X):
             n_neighbors=q + 1, algorithm="ball_tree", metric=metric
         ).fit(X)
         distances, Iin = nbrs.kneighbors(X)
-        
+
         # Add numerical stability: prevent division by zero when duplicate points exist
         # Use epsilon to handle cases where r1 (distances[:, 1]) is zero or near-zero
         eps = 1e-12
@@ -362,10 +362,10 @@ def sample_p(K, p, pp, c1, _rng):
                     eps = 1e-12
                     denom = max(c1[k] - 1 + c1[K - 1] - 1, eps)
                     rmax = (c1[k] - 1) / denom
-                    
+
                     # Prevent division by zero when rmax is 0 or 1
                     rmax = np.clip(rmax, eps, 1.0 - eps)
-                    
+
                     frac = ((r1 / rmax) ** (c1[k] - 1)) * (
                         ((1 - r1) / (1 - rmax)) ** (c1[K - 1] - 1)
                     )

aeon/segmentation/tests/test_hidalgo.py

@@ -17,24 +17,28 @@ def test_partition_function():
 def test_hidalgo_zero_distance_stability():
     """
     Test Hidalgo segmenter with duplicate/near-duplicate points.
-    
+
     Regression test for issue #3068: AssertionError when data contains
     identical rows, causing zero distances in nearest neighbor search.
     This should not crash but handle duplicates gracefully.
     """
     # Create data with exact duplicates (causes zero distances)
-    X = np.array([[0.1, 0.2, 0.3],
-                  [0.1, 0.2, 0.3],  # Exact duplicate
-                  [0.4, 0.5, 0.6],
-                  [0.7, 0.8, 0.9],
-                  [0.7, 0.8, 0.9]])  # Another duplicate
-    
+    X = np.array(
+        [
+            [0.1, 0.2, 0.3],
+            [0.1, 0.2, 0.3],  # Exact duplicate
+            [0.4, 0.5, 0.6],
+            [0.7, 0.8, 0.9],
+            [0.7, 0.8, 0.9],
+        ]
+    )  # Another duplicate
+
     # This should not raise AssertionError or divide-by-zero warnings
     hidalgo = HidalgoSegmenter(K=2, q=2, n_iter=100, burn_in=0.5)
-    
+
     # Should complete without errors
     result = hidalgo.fit_predict(X, axis=0)
-    
+
     # Basic sanity checks
     assert result is not None
     assert len(result) >= 0  # May return empty array if no changepoints
@@ -44,17 +48,16 @@ def test_hidalgo_zero_distance_stability():
 def test_hidalgo_normal_data():
     """
     Test Hidalgo segmenter with normal random data.
-    
+
     Verifies that the fix doesn't break normal operation.
     """
     # Random data without duplicates
     rng = np.random.RandomState(42)
     X = rng.rand(50, 3)
-    
+
     hidalgo = HidalgoSegmenter(K=3, q=3, n_iter=200, burn_in=0.8)
     result = hidalgo.fit_predict(X, axis=0)
-    
+
     # Should work as before
     assert result is not None
     assert isinstance(result, np.ndarray)
-