[ENH] k-nearest neighbors regressor: support for non-brute algorithms and non-precomputed mode to improve memory efficiency

sktime/regression/distance_based/_time_series_neighbors.py

@@ -246,3 +246,40 @@ def _predict(self, X):
         y_pred = self.knn_estimator_.predict(dist_mat)
 
         return y_pred
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return ``"default"`` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default={}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
+            ``create_test_instance`` uses the first (or only) dictionary in ``params``.
+        """
+        param1 = {
+            "n_neighbors": 1,
+            "weights": "uniform",
+            "algorithm": "auto",
+            "distance": "euclidean",
+            "distance_params": None,
+            "n_jobs": None,
+        }
+        param2 = {
+            "n_neighbors": 3,
+            "weights": "distance",
+            "algorithm": "ball_tree",
+            "distance": "dtw",
+            "distance_params": {"window": 0.5},
+            "n_jobs": -1,
+        }
+        return [param1, param2]