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

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

Author: pre-commit-ci[bot]

DIRECTORY.md

@@ -287,7 +287,7 @@
     * [Trie](data_structures/trie/trie.py)
   * KD Tree
     * [KD Tree Node](data_structures/kd_tree/kd_node.py)
-    * [Build KD Tree](data_structures/kd_tree/build_kdtree.py) 
+    * [Build KD Tree](data_structures/kd_tree/build_kdtree.py)
     * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
     * [Hypercibe Points](data_structures/kd_tree/example/hypercube_points.py)
     * [Example Usage](data_structures/kd_tree/example/example_usage.py)

data_structures/kd_tree/build_kdtree.py

@@ -1,5 +1,6 @@
 from .kd_node import KDNode
 
+
 def build_kdtree(points, depth=0):
     if not points:
         return None
@@ -13,7 +14,7 @@ def build_kdtree(points, depth=0):
 
     # Create node and construct subtrees
     return KDNode(
-        point = points[median_idx],
-        left = build_kdtree(points[:median_idx], depth + 1),
-        right = build_kdtree(points[median_idx + 1:], depth + 1)
+        point=points[median_idx],
+        left=build_kdtree(points[:median_idx], depth + 1),
+        right=build_kdtree(points[median_idx + 1 :], depth + 1),
     )

data_structures/kd_tree/example/example_usage.py

@@ -14,7 +14,9 @@
 
 query_point = np.random.rand(num_dimensions).tolist()
 
-nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(hypercube_kdtree, query_point)
+nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
+    hypercube_kdtree, query_point
+)
 
 print(f"Query point: {query_point}")
 print(f"Nearest point: {nearest_point}")

data_structures/kd_tree/example/hypercube_points.py

@@ -1,4 +1,5 @@
 import numpy as np
 
+
 def hypercube_points(num_points, hypercube_size, num_dimensions):
     return hypercube_size * np.random.rand(num_points, num_dimensions)

data_structures/kd_tree/kd_node.py

@@ -1,5 +1,5 @@
 class KDNode:
-    def __init__(self, point, left = None, right = None):
+    def __init__(self, point, left=None, right=None):
         self.point = point
         self.left = left
         self.right = right

data_structures/kd_tree/nearest_neighbour_search.py

@@ -1,6 +1,6 @@
 def nearest_neighbour_search(root, query_point):
     nearest_point = None
-    nearest_dist = float('inf')
+    nearest_dist = float("inf")
     nodes_visited = 0
 
     def search(node, depth=0):