[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 List, Optional
 from .kd_node import KDNode
 
+
 def build_kdtree(points: List[List[float]], depth: int = 0) -> Optional[KDNode]:
     """
     Builds a KD-Tree from a set of k-dimensional 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

@@ -4,6 +4,7 @@
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 
+
 def main() -> None:
     """
     Demonstrates the use of KD-Tree by building it from random points
@@ -32,5 +33,6 @@ 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

@@ -1,7 +1,10 @@
 import numpy as np
 from typing import Union
 
-def hypercube_points(num_points: int, hypercube_size: Union[int, float], num_dimensions: int) -> np.ndarray:
+
+def hypercube_points(
+    num_points: int, hypercube_size: Union[int, 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 List, Optional
 
+
 class KDNode:
     """
     Represents a node in a KD-Tree.
@@ -10,7 +11,12 @@ class KDNode:
         right (Optional[KDNode]): The right subtree of this 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 a point and optional left and right children.
 

data_structures/kd_tree/nearest_neighbour_search.py

@@ -1,7 +1,10 @@
 from typing import Optional, List, Tuple
 from .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.
 
@@ -28,7 +31,10 @@ 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: