From 02988dbe1ab88d71d11bec98ffba6f79a98e732b Mon Sep 17 00:00:00 2001
From: tanishy7777 <tanishyelgoe777@gmail.com>
Date: Sun, 13 Apr 2025 18:30:35 +0530
Subject: [PATCH 1/2] Fixes sbd distances for multivariate case

---
 aeon/distances/_distance.py                   |  2 +-
 aeon/distances/_sbd.py                        | 23 ++++++++++++++-----
 .../expected_distance_results.py              |  2 +-
 3 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py
index 33f9141440..dccf1e0bd5 100644
--- a/aeon/distances/_distance.py
+++ b/aeon/distances/_distance.py
@@ -859,7 +859,7 @@ class DistanceType(Enum):
         "pairwise_distance": sbd_pairwise_distance,
         "type": DistanceType.CROSS_CORRELATION,
         "symmetric": True,
-        "unequal_support": True,
+        "unequal_support": False,
     },
     {
         "name": "shift_scale",
diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py
index 1097f27b5a..52cb5e5aa3 100644
--- a/aeon/distances/_sbd.py
+++ b/aeon/distances/_sbd.py
@@ -104,11 +104,14 @@ def sbd_distance(x: np.ndarray, y: np.ndarray, standardize: bool = True) -> floa
             return _univariate_sbd_distance(_x, _y, standardize)
         else:
             # independent (time series should have the same number of channels!)
-            nchannels = min(x.shape[0], y.shape[0])
-            distance = 0.0
+            if x.shape[0] != y.shape[0]:
+                raise ValueError("x and y must have the same number of channels ")
+            nchannels = x.shape[0]  # both x and y have the same number of channels
+            norm = np.linalg.norm(x) * np.linalg.norm(y)
+            distance = np.zeros((2 * x.shape[1] - 1,))
             for i in range(nchannels):
-                distance += _univariate_sbd_distance(x[i], y[i], standardize)
-            return distance / nchannels
+                distance += _helper_sbd(x[i], y[i], standardize)
+            return np.abs(1 - np.max(distance) / norm)
 
     raise ValueError("x and y must be 1D or 2D")
 
@@ -240,8 +243,16 @@ def _univariate_sbd_distance(x: np.ndarray, y: np.ndarray, standardize: bool) ->
         x = (x - np.mean(x)) / np.std(x)
         y = (y - np.mean(y)) / np.std(y)
 
-    with objmode(a="float64[:]"):
-        a = correlate(x, y, method="fft")
+    a = _helper_sbd(x, y, standardize)
 
     b = np.sqrt(np.dot(x, x) * np.dot(y, y))
     return np.abs(1.0 - np.max(a / b))
+
+
+@njit(cache=True, fastmath=True)
+def _helper_sbd(x, y, standardize):
+
+    with objmode(a="float64[:]"):
+        a = correlate(x, y, method="fft")
+
+    return a
diff --git a/aeon/testing/expected_results/expected_distance_results.py b/aeon/testing/expected_results/expected_distance_results.py
index 7126c5c624..2145a3208f 100644
--- a/aeon/testing/expected_results/expected_distance_results.py
+++ b/aeon/testing/expected_results/expected_distance_results.py
@@ -115,7 +115,7 @@
         0.6617308353925114,
         0.6617308353925114,
         0.5750093257763462,
-        0.5263609881742105,
+        None,
         0.0,
     ],
     "shift_scale": [

From 18542e0bbe9be8dec67554afff917b47fc3ce1d0 Mon Sep 17 00:00:00 2001
From: tanishy7777 <tanishyelgoe777@gmail.com>
Date: Sun, 13 Apr 2025 19:10:25 +0530
Subject: [PATCH 2/2] Fixes doctest and changes expected distance with params

---
 aeon/distances/_sbd.py                                     | 4 +++-
 aeon/testing/expected_results/expected_distance_results.py | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py
index 52cb5e5aa3..e4860fe07f 100644
--- a/aeon/distances/_sbd.py
+++ b/aeon/distances/_sbd.py
@@ -107,7 +107,9 @@ def sbd_distance(x: np.ndarray, y: np.ndarray, standardize: bool = True) -> floa
             if x.shape[0] != y.shape[0]:
                 raise ValueError("x and y must have the same number of channels ")
             nchannels = x.shape[0]  # both x and y have the same number of channels
-            norm = np.linalg.norm(x) * np.linalg.norm(y)
+            norm = np.linalg.norm(x.astype(np.float64)) * np.linalg.norm(
+                y.astype(np.float64)
+            )
             distance = np.zeros((2 * x.shape[1] - 1,))
             for i in range(nchannels):
                 distance += _helper_sbd(x[i], y[i], standardize)
diff --git a/aeon/testing/expected_results/expected_distance_results.py b/aeon/testing/expected_results/expected_distance_results.py
index 2145a3208f..953b8682cd 100644
--- a/aeon/testing/expected_results/expected_distance_results.py
+++ b/aeon/testing/expected_results/expected_distance_results.py
@@ -185,7 +185,7 @@
         [8.602610210695161, 8.645028399102344],
         [1.750534284134988, 12.516745017325773],
     ],
-    "sbd": [[0.2435580798173309, 0.18613277150939772]],
+    "sbd": [[0.2435580798173309, 0.21430477859140418]],
     "shift_scale": [
         [0.8103639073457298, 5.535457073146429],
         [0.6519267432870345, 5.491208968546096],