[BUG] Fix Catch22 TypingError on float32 input with outlier features

aeon/transformations/collection/feature_based/_catch22.py

@@ -1934,7 +1934,10 @@ def _transform_case_numba(
         # typed placeholders; real values are assigned before first use. These
         # must be dtype-fixed (not aliases of series) so the kernel also
         # compiles for integer input arrays.
-        outlier_series = np.empty(0, np.float64)
+        # / 1 applies the same dtype promotion z_normalise_series_with_mean
+        # uses internally (float32 stays float32, everything else becomes
+        # float64), so this placeholder always matches the real value's dtype.
+        outlier_series = series[:0] / 1
         have_outlier = False
         fft = ac_tw[:0]
         have_fft = False

aeon/transformations/collection/feature_based/tests/test_catch22.py

@@ -1521,3 +1521,24 @@ def track_n(s):
     assert abs(np.std(passed_series["p"]) - 1.0) < 1e-10
     assert abs(np.mean(passed_series["n"])) < 1e-10
     assert abs(np.std(passed_series["n"]) - 1.0) < 1e-10
+
+
+def test_catch22_float32_output_dtype():
+    """Test Catch22 does not raise on float32 input with outlier features.
+
+    Regression test: the outlier_series placeholder in _transform_case_numba was
+    hardcoded to float64, which numba's nopython mode could not unify with a
+    float32 z-normalised series produced for features 13/14 (outlier_include /
+    outlier_include_n), raising a TypingError for float32 input.
+    """
+    X = np.random.default_rng(0).standard_normal((6, 1, 40)).astype(np.float32)
+
+    # outlier_norm=True exercises the fixed code path (features 13/14)
+    c22 = Catch22(outlier_norm=True, replace_nans=True)
+    result = c22.fit_transform(X)
+    assert result.shape == (6, 22)
+
+    # outlier_norm=False takes the other branch -- should still work
+    c22_no_norm = Catch22(outlier_norm=False, replace_nans=True)
+    result_no_norm = c22_no_norm.fit_transform(X)
+    assert result_no_norm.shape == (6, 22)