fixed

aeon/classification/distance_based/_time_series_neighbors.py

@@ -163,11 +163,11 @@ def _predict(self, X):
         """
         self._check_is_fitted()
 
-        indexes = self.kneighbors(X, return_distance=False, n_jobs=self.n_jobs)[:, 0]
+        indexes = self.kneighbors(X, return_distance=False)[:, 0]
         return self.classes_[self.y_[indexes]]
 
     @threaded
-    def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1):
+    def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
         """Find the K-neighbors of a point.
 
         Returns indices of and distances to the neighbors of each point.
@@ -184,10 +184,6 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1):
             passed to the constructor.
         return_distance : bool, default=True
             Whether or not to return the distances.
-        n_jobs : int, default=1
-            The number of jobs to run in parallel. If -1, then the number of jobs is set
-            to the number of CPU cores. If 1, then the function is executed in a single
-            thread. If greater than 1, then the function is executed in parallel.
 
         Returns
         -------
@@ -220,7 +216,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1):
             X,
             self.X_ if not query_is_train else None,
             method=self.distance,
-            n_jobs=n_jobs,
+            n_jobs=self.n_jobs,
             **self._distance_params,
         )