[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

data_structures/kd_tree/build_kdtree.py

@@ -1,6 +1,7 @@
 from typing import Optional
 from .kd_node import KDNode
 
+
 def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
     """
     Builds a KD-Tree from a list of points.
@@ -26,5 +27,5 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
     return KDNode(
         point=points[median_idx],
         left=build_kdtree(points[:median_idx], depth + 1),
-        right=build_kdtree(points[median_idx + 1:], depth + 1),
-    )
+        right=build_kdtree(points[median_idx + 1 :], depth + 1),
+    )

data_structures/kd_tree/example/example_usage.py

@@ -32,5 +32,6 @@ def main() -> None:
     print(f"Distance: {nearest_dist:.4f}")
     print(f"Nodes visited: {nodes_visited}")
 
+
 if __name__ == "__main__":
     main()

data_structures/kd_tree/example/hypercube_points.py

@@ -1,6 +1,9 @@
 import numpy as np
 
-def hypercube_points(num_points: int, hypercube_size: float, num_dimensions: int) -> np.ndarray:
+
+def hypercube_points(
+    num_points: int, hypercube_size: float, num_dimensions: int
+) -> np.ndarray:
     """
     Generates random points uniformly distributed within an n-dimensional hypercube.
 

data_structures/kd_tree/kd_node.py

@@ -1,5 +1,6 @@
 from typing import Optional
 
+
 class KDNode:
     """
     Represents a node in a KD-Tree.
@@ -10,7 +11,12 @@ class KDNode:
         right (Optional[KDNode]): The right child node.
     """
 
-    def __init__(self, point: list[float], left: Optional['KDNode'] = None, right: Optional['KDNode'] = None) -> None:
+    def __init__(
+        self,
+        point: list[float],
+        left: Optional["KDNode"] = None,
+        right: Optional["KDNode"] = None,
+    ) -> None:
         """
         Initializes a KDNode with the given point and child nodes.
 

data_structures/kd_tree/nearest_neighbour_search.py

@@ -1,7 +1,10 @@
 from typing import Optional
 from data_structures.kd_tree.kd_node import KDNode
 
-def nearest_neighbour_search(root: Optional[KDNode], query_point: list[float]) -> tuple[Optional[list[float]], float, int]:
+
+def nearest_neighbour_search(
+    root: Optional[KDNode], query_point: list[float]
+) -> tuple[Optional[list[float]], float, int]:
     """
     Performs a nearest neighbor search in a KD-Tree for a given query point.
 
@@ -35,7 +38,10 @@ def search(node: Optional[KDNode], depth: int = 0) -> None:
 
         # Calculate the current distance (squared distance)
         current_point = node.point
-        current_dist = sum((query_coord - point_coord) ** 2 for query_coord, point_coord in zip(query_point, current_point))
+        current_dist = sum(
+            (query_coord - point_coord) ** 2
+            for query_coord, point_coord in zip(query_point, current_point)
+        )
 
         # Update nearest point if the current node is closer
         if nearest_point is None or current_dist < nearest_dist:

data_structures/kd_tree/tests/test_kdtree.py

@@ -5,16 +5,18 @@
 from data_structures.kd_tree.kd_node import KDNode
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 
-class TestKDTree(unittest.TestCase):
 
+class TestKDTree(unittest.TestCase):
     def setUp(self):
         """
         Set up test data.
         """
         self.num_points = 10
         self.cube_size = 10.0
         self.num_dimensions = 2
-        self.points = hypercube_points(self.num_points, self.cube_size, self.num_dimensions)
+        self.points = hypercube_points(
+            self.num_points, self.cube_size, self.num_dimensions
+        )
         self.kdtree = build_kdtree(self.points.tolist())
 
     def test_build_kdtree(self):
@@ -66,5 +68,6 @@ def test_edge_cases(self):
         self.assertEqual(nearest_dist, float("inf"))
         self.assertEqual(nodes_visited, 0)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     unittest.main()