From 0d6985cf109f2108ef6ce17796266ab25fc88bee Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 13:09:06 +0200
Subject: [PATCH 01/35] Implemented KD-Tree Data Structure

---
 data_structures/kd_tree/__init__.py           |  0
 data_structures/kd_tree/build_kdtree.py       | 19 +++++++++
 .../kd_tree/example/example_usage.py          | 22 ++++++++++
 .../kd_tree/example/hypercube_points.py       |  4 ++
 data_structures/kd_tree/kd_node.py            |  5 +++
 .../kd_tree/nearest_neighbour_search.py       | 41 +++++++++++++++++++
 6 files changed, 91 insertions(+)
 create mode 100644 data_structures/kd_tree/__init__.py
 create mode 100644 data_structures/kd_tree/build_kdtree.py
 create mode 100644 data_structures/kd_tree/example/example_usage.py
 create mode 100644 data_structures/kd_tree/example/hypercube_points.py
 create mode 100644 data_structures/kd_tree/kd_node.py
 create mode 100644 data_structures/kd_tree/nearest_neighbour_search.py

diff --git a/data_structures/kd_tree/__init__.py b/data_structures/kd_tree/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
new file mode 100644
index 000000000000..a1b84b0379a1
--- /dev/null
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -0,0 +1,19 @@
+from .kd_node import KDNode
+
+def build_kdtree(points, depth=0):
+    if not points:
+        return None
+
+    k = len(points[0])  # dimensionality of the points
+    axis = depth % k
+
+    # Sort point list and choose median as pivot element
+    points.sort(key=lambda x: x[axis])
+    median_idx = len(points) // 2
+
+    # 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)
+    )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
new file mode 100644
index 000000000000..74bca1aa4470
--- /dev/null
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -0,0 +1,22 @@
+import numpy as np
+
+from hypercube_points import hypercube_points
+from data_structures.kd_tree.build_kdtree import build_kdtree
+from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
+
+
+num_points = 5000
+cube_size = 10
+num_dimensions = 10
+
+points = hypercube_points(num_points, cube_size, num_dimensions)
+hypercube_kdtree = build_kdtree(points.tolist())
+
+query_point = np.random.rand(num_dimensions).tolist()
+
+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}")
+print(f"Distance: {nearest_dist:.4f}")
+print(f"Nodes visited: {nodes_visited}")
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
new file mode 100644
index 000000000000..3ce930830919
--- /dev/null
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -0,0 +1,4 @@
+import numpy as np
+
+def hypercube_points(num_points, hypercube_size, num_dimensions):
+    return hypercube_size * np.random.rand(num_points, num_dimensions)
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
new file mode 100644
index 000000000000..0748cdd65810
--- /dev/null
+++ b/data_structures/kd_tree/kd_node.py
@@ -0,0 +1,5 @@
+class KDNode:
+    def __init__(self, point, left = None, right = None):
+        self.point = point
+        self.left = left
+        self.right = right
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
new file mode 100644
index 000000000000..59dff5bebe84
--- /dev/null
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -0,0 +1,41 @@
+def nearest_neighbour_search(root, query_point):
+    nearest_point = None
+    nearest_dist = float('inf')
+    nodes_visited = 0
+
+    def search(node, depth=0):
+        nonlocal nearest_point, nearest_dist, nodes_visited
+        if node is None:
+            return
+
+        nodes_visited += 1
+
+        # Calculate the current distance (squared distance)
+        current_point = node.point
+        current_dist = sum((qp - cp) ** 2 for qp, cp 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:
+            nearest_point = current_point
+            nearest_dist = current_dist
+
+        # Determine which subtree to search first (based on axis and query point)
+        k = len(query_point)  # dimensionality of points
+        axis = depth % k
+
+        if query_point[axis] <= current_point[axis]:
+            nearer_subtree = node.left
+            further_subtree = node.right
+        else:
+            nearer_subtree = node.right
+            further_subtree = node.left
+
+        # Search the nearer subtree first
+        search(nearer_subtree, depth + 1)
+
+        # If the further subtree has a closer point
+        if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
+            search(further_subtree, depth + 1)
+
+    search(root, 0)
+    return nearest_point, nearest_dist, nodes_visited

From 6665d23380602b46c6c07e1c8fecc56a5f0b7e92 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 13:16:55 +0200
Subject: [PATCH 02/35] Implemented KD-Tree Data Structure. updated
 DIRECTORY.md.

---
 DIRECTORY.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index 11de569a2c25..645bcea3d28c 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -285,6 +285,12 @@
   * Trie
     * [Radix Tree](data_structures/trie/radix_tree.py)
     * [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) 
+    * [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)
 
 ## Digital Image Processing
   * [Change Brightness](digital_image_processing/change_brightness.py)

From 6b3d47e7e485c6029b9ced7dea5eedf1d5ecf449 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 11:31:38 +0000
Subject: [PATCH 03/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 DIRECTORY.md                                        | 2 +-
 data_structures/kd_tree/build_kdtree.py             | 7 ++++---
 data_structures/kd_tree/example/example_usage.py    | 4 +++-
 data_structures/kd_tree/example/hypercube_points.py | 1 +
 data_structures/kd_tree/kd_node.py                  | 2 +-
 data_structures/kd_tree/nearest_neighbour_search.py | 2 +-
 6 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index 645bcea3d28c..1ca537b991c8 100644
--- a/DIRECTORY.md
+++ b/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)
diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index a1b84b0379a1..71a00df65fcf 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/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),
     )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 74bca1aa4470..96a2492bbb83 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/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}")
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 3ce930830919..429c3579a380 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/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)
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 0748cdd65810..1fa7431ad6ec 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/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
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 59dff5bebe84..9e00c9108bb9 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/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):

From 4203cda76113c401429cd48c0338fa170bf24ad8 Mon Sep 17 00:00:00 2001
From: Ramy <126559907+Ramy-Badr-Ahmed@users.noreply.github.com>
Date: Wed, 28 Aug 2024 14:02:42 +0200
Subject: [PATCH 04/35] Create __init__.py

---
 data_structures/kd_tree/example/__init__.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 data_structures/kd_tree/example/__init__.py

diff --git a/data_structures/kd_tree/example/__init__.py b/data_structures/kd_tree/example/__init__.py
new file mode 100644
index 000000000000..8b137891791f
--- /dev/null
+++ b/data_structures/kd_tree/example/__init__.py
@@ -0,0 +1 @@
+

From 3222bd3d311befaeb6d7ff93658f0663b0fa2117 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 12:03:11 +0000
Subject: [PATCH 05/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/example/__init__.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/data_structures/kd_tree/example/__init__.py b/data_structures/kd_tree/example/__init__.py
index 8b137891791f..e69de29bb2d1 100644
--- a/data_structures/kd_tree/example/__init__.py
+++ b/data_structures/kd_tree/example/__init__.py
@@ -1 +0,0 @@
-

From a41ae5bd2848eec9ee71e7df02137a677104b927 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 19:39:39 +0200
Subject: [PATCH 06/35] Replaced legacy `np.random.rand` call with
 `np.random.Generator` in kd_tree/example_usage.py

---
 data_structures/kd_tree/example/example_usage.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 96a2492bbb83..0ca2019185d8 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -12,7 +12,8 @@
 points = hypercube_points(num_points, cube_size, num_dimensions)
 hypercube_kdtree = build_kdtree(points.tolist())
 
-query_point = np.random.rand(num_dimensions).tolist()
+rng = np.random.default_rng()
+query_point = rng.random(num_dimensions).tolist()
 
 nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
     hypercube_kdtree, query_point

From 1668d73a43abd9c6e6ab4f20c93fbbcd5f8600af Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 19:47:09 +0200
Subject: [PATCH 07/35] Replaced legacy `np.random.rand` call with
 `np.random.Generator` in kd_tree/hypercube_points.py

---
 data_structures/kd_tree/example/hypercube_points.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 429c3579a380..61b2beb9da85 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -2,4 +2,5 @@
 
 
 def hypercube_points(num_points, hypercube_size, num_dimensions):
-    return hypercube_size * np.random.rand(num_points, num_dimensions)
+    rng = np.random.default_rng()
+    return hypercube_size * rng.random((num_points, num_dimensions))

From 81d69176858df202c6004dc1295a6ac05e95bcd4 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 20:02:07 +0200
Subject: [PATCH 08/35] added typehints and docstrings

---
 data_structures/kd_tree/build_kdtree.py       | 18 ++++++--
 .../kd_tree/example/example_usage.py          | 41 ++++++++++++-------
 .../kd_tree/example/hypercube_points.py       | 13 +++++-
 data_structures/kd_tree/kd_node.py            | 21 +++++++++-
 .../kd_tree/nearest_neighbour_search.py       | 30 ++++++++++----
 5 files changed, 95 insertions(+), 28 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 71a00df65fcf..090cef4fdd32 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -1,20 +1,30 @@
+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.
 
-def build_kdtree(points, depth=0):
+    Args:
+        points (List[List[float]]): A list of k-dimensional points (each point is a list of floats).
+        depth (int): The current depth in the tree. Used to determine the splitting axis. Defaults to 0.
+
+    Returns:
+        Optional[KDNode]: The root of the KD-Tree or None if the input list is empty.
+    """
     if not points:
         return None
 
-    k = len(points[0])  # dimensionality of the points
+    k = len(points[0])  # Dimensionality of the points
     axis = depth % k
 
     # Sort point list and choose median as pivot element
-    points.sort(key=lambda x: x[axis])
+    points.sort(key=lambda point: point[axis])
     median_idx = len(points) // 2
 
     # 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),
+        right=build_kdtree(points[median_idx + 1:], depth + 1),
     )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 0ca2019185d8..d6e01b4ef953 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -1,25 +1,36 @@
 import numpy as np
-
+from typing import List
 from hypercube_points import hypercube_points
 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
+    in a 10-dimensional hypercube and performing a nearest neighbor search.
+    """
+    num_points: int = 5000
+    cube_size: int = 10
+    num_dimensions: int = 10
 
-num_points = 5000
-cube_size = 10
-num_dimensions = 10
+    # Generate random points within the hypercube
+    points: np.ndarray = hypercube_points(num_points, cube_size, num_dimensions)
+    hypercube_kdtree = build_kdtree(points.tolist())
 
-points = hypercube_points(num_points, cube_size, num_dimensions)
-hypercube_kdtree = build_kdtree(points.tolist())
+    # Generate a random query point within the same space
+    rng = np.random.default_rng()
+    query_point: List[float] = rng.random(num_dimensions).tolist()
 
-rng = np.random.default_rng()
-query_point = rng.random(num_dimensions).tolist()
+    # Perform nearest neighbor search
+    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 the results
+    print(f"Query point: {query_point}")
+    print(f"Nearest point: {nearest_point}")
+    print(f"Distance: {nearest_dist:.4f}")
+    print(f"Nodes visited: {nodes_visited}")
 
-print(f"Query point: {query_point}")
-print(f"Nearest point: {nearest_point}")
-print(f"Distance: {nearest_dist:.4f}")
-print(f"Nodes visited: {nodes_visited}")
+if __name__ == "__main__":
+    main()
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 61b2beb9da85..65548bfe259c 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -1,6 +1,17 @@
 import numpy as np
+from typing import Union
 
+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.
 
-def hypercube_points(num_points, hypercube_size, num_dimensions):
+    Args:
+        num_points (int): The number of random points to generate.
+        hypercube_size (Union[int, float]): The size of the hypercube (side length).
+        num_dimensions (int): The number of dimensions of the hypercube.
+
+    Returns:
+        np.ndarray: An array of shape (num_points, num_dimensions) with the generated points.
+    """
     rng = np.random.default_rng()
     return hypercube_size * rng.random((num_points, num_dimensions))
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 1fa7431ad6ec..d05e1f3da1cf 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,5 +1,24 @@
+from typing import List, Optional
+
 class KDNode:
-    def __init__(self, point, left=None, right=None):
+    """
+    Represents a node in a KD-Tree.
+
+    Attributes:
+        point (List[float]): The k-dimensional point stored in this node.
+        left (Optional[KDNode]): The left subtree of this node.
+        right (Optional[KDNode]): The right subtree of this node.
+    """
+
+    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.
+
+        Args:
+            point (List[float]): The k-dimensional point to be stored in this node.
+            left (Optional[KDNode]): The left subtree of this node. Defaults to None.
+            right (Optional[KDNode]): The right subtree of this node. Defaults to None.
+        """
         self.point = point
         self.left = left
         self.right = right
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 9e00c9108bb9..bbb84fdfb098 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -1,9 +1,25 @@
-def nearest_neighbour_search(root, query_point):
-    nearest_point = None
-    nearest_dist = float("inf")
-    nodes_visited = 0
-
-    def search(node, depth=0):
+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]:
+    """
+    Performs a nearest neighbor search in a KD-Tree for a given query point.
+
+    Args:
+        root (Optional[KDNode]): The root node of the KD-Tree.
+        query_point (List[float]): The point for which the nearest neighbor is being searched.
+
+    Returns:
+        Tuple[Optional[List[float]], float, int]:
+            - The nearest point found in the KD-Tree to the query point.
+            - The squared distance to the nearest point.
+            - The number of nodes visited during the search.
+    """
+    nearest_point: Optional[List[float]] = None
+    nearest_dist: float = float("inf")
+    nodes_visited: int = 0
+
+    def search(node: Optional[KDNode], depth: int = 0) -> None:
         nonlocal nearest_point, nearest_dist, nodes_visited
         if node is None:
             return
@@ -12,7 +28,7 @@ def search(node, depth=0):
 
         # Calculate the current distance (squared distance)
         current_point = node.point
-        current_dist = sum((qp - cp) ** 2 for qp, cp 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:

From 6cddcbdff7696dc4d5528bc759dc2817543e994d Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 18:02:36 +0000
Subject: [PATCH 09/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/build_kdtree.py             |  3 ++-
 data_structures/kd_tree/example/example_usage.py    |  2 ++
 data_structures/kd_tree/example/hypercube_points.py |  5 ++++-
 data_structures/kd_tree/kd_node.py                  |  8 +++++++-
 data_structures/kd_tree/nearest_neighbour_search.py | 10 ++++++++--
 5 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 090cef4fdd32..3d053cee832c 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/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),
     )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index d6e01b4ef953..61264f6a7d83 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/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()
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 65548bfe259c..c1541af22026 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/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.
 
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index d05e1f3da1cf..7a19af9cbf57 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/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.
 
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index bbb84fdfb098..36a4cdc88a69 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/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:

From 8b238d1d4d7639d87dea5102459e289d4abb1532 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 20:08:37 +0200
Subject: [PATCH 10/35] docstring for search()

---
 .../kd_tree/nearest_neighbour_search.py       | 23 ++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index bbb84fdfb098..f0ad2ea5525f 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/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.
 
@@ -20,6 +23,18 @@ def nearest_neighbour_search(root: Optional[KDNode], query_point: List[float]) -
     nodes_visited: int = 0
 
     def search(node: Optional[KDNode], depth: int = 0) -> None:
+        """
+        Recursively searches the KD-Tree to find the nearest point to the query point.
+
+        Args:
+            node (Optional[KDNode]): The current node being examined.
+            depth (int): The current depth of the tree, which determines the axis to split on.
+
+        Updates:
+            nearest_point: The closest point found so far in the KD-Tree.
+            nearest_dist: The squared distance from the query point to the nearest point found.
+            nodes_visited: The number of nodes visited during the search.
+        """
         nonlocal nearest_point, nearest_dist, nodes_visited
         if node is None:
             return
@@ -28,7 +43,9 @@ 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:
@@ -49,7 +66,7 @@ def search(node: Optional[KDNode], depth: int = 0) -> None:
         # Search the nearer subtree first
         search(nearer_subtree, depth + 1)
 
-        # If the further subtree has a closer point
+        # If the further subtree has a closer point, search it
         if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
             search(further_subtree, depth + 1)
 

From ead2838aafd022db6343551bdbf9080d19f0e826 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 18:09:36 +0000
Subject: [PATCH 11/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/nearest_neighbour_search.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index f0ad2ea5525f..2d8acbabf0b0 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -1,9 +1,9 @@
 from typing import Optional, List, Tuple
 from .kd_node import KDNode
 
+
 def nearest_neighbour_search(
-        root: Optional[KDNode],
-        query_point: List[float]
+    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.
@@ -44,7 +44,8 @@ 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)
+            (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

From 543584cb696e5541b63b084d9864f65e370a58e6 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 20:42:43 +0200
Subject: [PATCH 12/35] Added tests. Updated docstrings/typehints

---
 data_structures/kd_tree/build_kdtree.py       | 17 +++--
 .../kd_tree/example/example_usage.py          |  6 +-
 .../kd_tree/example/hypercube_points.py       | 14 ++--
 data_structures/kd_tree/kd_node.py            | 24 +++----
 .../kd_tree/nearest_neighbour_search.py       | 35 ++++------
 data_structures/kd_tree/tests/__init__.py     |  0
 data_structures/kd_tree/tests/test_kdtree.py  | 70 +++++++++++++++++++
 7 files changed, 106 insertions(+), 60 deletions(-)
 create mode 100644 data_structures/kd_tree/tests/__init__.py
 create mode 100644 data_structures/kd_tree/tests/test_kdtree.py

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 3d053cee832c..69811b80050c 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -1,17 +1,16 @@
-from typing import List, Optional
+from typing import Optional
 from .kd_node import KDNode
 
-
-def build_kdtree(points: List[List[float]], depth: int = 0) -> Optional[KDNode]:
+def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
     """
-    Builds a KD-Tree from a set of k-dimensional points.
+    Builds a KD-Tree from a list of points.
 
     Args:
-        points (List[List[float]]): A list of k-dimensional points (each point is a list of floats).
-        depth (int): The current depth in the tree. Used to determine the splitting axis. Defaults to 0.
+        points (list[list[float]]): The list of points to build the KD-Tree from.
+        depth (int): The current depth in the tree (used to determine axis for splitting).
 
     Returns:
-        Optional[KDNode]: The root of the KD-Tree or None if the input list is empty.
+        Optional[KDNode]: The root node of the KD-Tree.
     """
     if not points:
         return None
@@ -27,5 +26,5 @@ 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),
+    )
\ No newline at end of file
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 61264f6a7d83..37b32c9db36a 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -1,5 +1,4 @@
 import numpy as np
-from typing import List
 from hypercube_points import hypercube_points
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
@@ -11,7 +10,7 @@ def main() -> None:
     in a 10-dimensional hypercube and performing a nearest neighbor search.
     """
     num_points: int = 5000
-    cube_size: int = 10
+    cube_size: float = 10.0  # Size of the hypercube (edge length)
     num_dimensions: int = 10
 
     # Generate random points within the hypercube
@@ -20,7 +19,7 @@ def main() -> None:
 
     # Generate a random query point within the same space
     rng = np.random.default_rng()
-    query_point: List[float] = rng.random(num_dimensions).tolist()
+    query_point: list[float] = rng.random(num_dimensions).tolist()
 
     # Perform nearest neighbor search
     nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
@@ -33,6 +32,5 @@ def main() -> None:
     print(f"Distance: {nearest_dist:.4f}")
     print(f"Nodes visited: {nodes_visited}")
 
-
 if __name__ == "__main__":
     main()
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index c1541af22026..7397c4ce6b2e 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -1,20 +1,16 @@
 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: float, num_dimensions: int) -> np.ndarray:
     """
     Generates random points uniformly distributed within an n-dimensional hypercube.
 
     Args:
-        num_points (int): The number of random points to generate.
-        hypercube_size (Union[int, float]): The size of the hypercube (side length).
-        num_dimensions (int): The number of dimensions of the hypercube.
+        num_points (int): Number of points to generate.
+        hypercube_size (float): Size of the hypercube.
+        num_dimensions (int): Number of dimensions of the hypercube.
 
     Returns:
-        np.ndarray: An array of shape (num_points, num_dimensions) with the generated points.
+        np.ndarray: An array of shape (num_points, num_dimensions) with generated points.
     """
     rng = np.random.default_rng()
     return hypercube_size * rng.random((num_points, num_dimensions))
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 7a19af9cbf57..90f1e8e0c334 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,29 +1,23 @@
-from typing import List, Optional
-
+from typing import Optional
 
 class KDNode:
     """
     Represents a node in a KD-Tree.
 
     Attributes:
-        point (List[float]): The k-dimensional point stored in this node.
-        left (Optional[KDNode]): The left subtree of this node.
-        right (Optional[KDNode]): The right subtree of this node.
+        point (list[float]): The point stored in this node.
+        left (Optional[KDNode]): The left child node.
+        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 a point and optional left and right children.
+        Initializes a KDNode with the given point and child nodes.
 
         Args:
-            point (List[float]): The k-dimensional point to be stored in this node.
-            left (Optional[KDNode]): The left subtree of this node. Defaults to None.
-            right (Optional[KDNode]): The right subtree of this node. Defaults to None.
+            point (list[float]): The point stored in this node.
+            left (Optional[KDNode]): The left child node.
+            right (Optional[KDNode]): The right child node.
         """
         self.point = point
         self.left = left
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 2d8acbabf0b0..8258b846aca6 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -1,39 +1,31 @@
-from typing import Optional, List, Tuple
-from .kd_node import KDNode
+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.
 
     Args:
         root (Optional[KDNode]): The root node of the KD-Tree.
-        query_point (List[float]): The point for which the nearest neighbor is being searched.
+        query_point (list[float]): The point for which the nearest neighbor is being searched.
 
     Returns:
-        Tuple[Optional[List[float]], float, int]:
+        tuple[Optional[list[float]], float, int]:
             - The nearest point found in the KD-Tree to the query point.
             - The squared distance to the nearest point.
             - The number of nodes visited during the search.
     """
-    nearest_point: Optional[List[float]] = None
+    nearest_point: Optional[list[float]] = None
     nearest_dist: float = float("inf")
     nodes_visited: int = 0
 
     def search(node: Optional[KDNode], depth: int = 0) -> None:
         """
-        Recursively searches the KD-Tree to find the nearest point to the query point.
+        Recursively searches the KD-Tree for the nearest neighbor.
 
         Args:
-            node (Optional[KDNode]): The current node being examined.
-            depth (int): The current depth of the tree, which determines the axis to split on.
-
-        Updates:
-            nearest_point: The closest point found so far in the KD-Tree.
-            nearest_dist: The squared distance from the query point to the nearest point found.
-            nodes_visited: The number of nodes visited during the search.
+            node (Optional[KDNode]): The current node in the KD-Tree.
+            depth (int): The current depth in the tree.
         """
         nonlocal nearest_point, nearest_dist, nodes_visited
         if node is None:
@@ -43,10 +35,7 @@ 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:
@@ -54,7 +43,7 @@ def search(node: Optional[KDNode], depth: int = 0) -> None:
             nearest_dist = current_dist
 
         # Determine which subtree to search first (based on axis and query point)
-        k = len(query_point)  # dimensionality of points
+        k = len(query_point)  # Dimensionality of points
         axis = depth % k
 
         if query_point[axis] <= current_point[axis]:
@@ -67,7 +56,7 @@ def search(node: Optional[KDNode], depth: int = 0) -> None:
         # Search the nearer subtree first
         search(nearer_subtree, depth + 1)
 
-        # If the further subtree has a closer point, search it
+        # If the further subtree has a closer point
         if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
             search(further_subtree, depth + 1)
 
diff --git a/data_structures/kd_tree/tests/__init__.py b/data_structures/kd_tree/tests/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
new file mode 100644
index 000000000000..a6cc570c8ec9
--- /dev/null
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -0,0 +1,70 @@
+import unittest
+import numpy as np
+from data_structures.kd_tree.build_kdtree import build_kdtree
+from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
+from data_structures.kd_tree.kd_node import KDNode
+from data_structures.kd_tree.example.hypercube_points import hypercube_points
+
+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.kdtree = build_kdtree(self.points.tolist())
+
+    def test_build_kdtree(self):
+        """
+        Test that KD-Tree is built correctly.
+        """
+        # Check if root is not None
+        self.assertIsNotNone(self.kdtree)
+
+        # Check if root has correct dimensions
+        self.assertEqual(len(self.kdtree.point), self.num_dimensions)
+
+        # Check that the tree is balanced to some extent (simplistic check)
+        self.assertIsInstance(self.kdtree, KDNode)
+
+    def test_nearest_neighbour_search(self):
+        """
+        Test the nearest neighbor search function.
+        """
+        rng = np.random.default_rng()
+        query_point = rng.random(self.num_dimensions).tolist()
+
+        nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
+            self.kdtree, query_point
+        )
+
+        # Check that nearest point is not None
+        self.assertIsNotNone(nearest_point)
+
+        # Check that distance is a non-negative number
+        self.assertGreaterEqual(nearest_dist, 0)
+
+        # Check that nodes visited is a non-negative integer
+        self.assertGreaterEqual(nodes_visited, 0)
+
+    def test_edge_cases(self):
+        """
+        Test edge cases such as an empty KD-Tree.
+        """
+        empty_kdtree = build_kdtree([])
+        query_point = [0.0] * self.num_dimensions
+
+        nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
+            empty_kdtree, query_point
+        )
+
+        # With an empty KD-Tree, nearest_point should be None
+        self.assertIsNone(nearest_point)
+        self.assertEqual(nearest_dist, float("inf"))
+        self.assertEqual(nodes_visited, 0)
+
+if __name__ == '__main__':
+    unittest.main()

From ad31f83a1e89b4620bb59e35ec7311bd92fb36e5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 18:43:54 +0000
Subject: [PATCH 13/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/build_kdtree.py             |  5 +++--
 data_structures/kd_tree/example/example_usage.py    |  1 +
 data_structures/kd_tree/example/hypercube_points.py |  5 ++++-
 data_structures/kd_tree/kd_node.py                  |  8 +++++++-
 data_structures/kd_tree/nearest_neighbour_search.py | 10 ++++++++--
 data_structures/kd_tree/tests/test_kdtree.py        |  9 ++++++---
 6 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 69811b80050c..23ff90dac41f 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/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),
-    )
\ No newline at end of file
+        right=build_kdtree(points[median_idx + 1 :], depth + 1),
+    )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 37b32c9db36a..3dfabda4f81b 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/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()
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 7397c4ce6b2e..ae00a3d8b047 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/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.
 
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 90f1e8e0c334..e14ec39c24c0 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/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.
 
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 8258b846aca6..eb80638d4923 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/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:
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index a6cc570c8ec9..dcf0edec6cfc 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -5,8 +5,8 @@
 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.
@@ -14,7 +14,9 @@ def setUp(self):
         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()

From 1322921f9283782abcdee4bb34f49c2d9ba0da2b Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Wed, 28 Aug 2024 21:24:46 +0200
Subject: [PATCH 14/35] updated tests and used | for type annotations

---
 data_structures/kd_tree/build_kdtree.py       |  17 +--
 .../kd_tree/example/example_usage.py          |   2 +-
 .../kd_tree/example/hypercube_points.py       |   6 +-
 data_structures/kd_tree/kd_node.py            |   1 -
 .../kd_tree/nearest_neighbour_search.py       |  26 +++--
 data_structures/kd_tree/tests/test_kdtree.py  | 103 +++++++++---------
 6 files changed, 78 insertions(+), 77 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 23ff90dac41f..1a0a27bb5ced 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -1,8 +1,8 @@
-from typing import Optional
-from .kd_node import KDNode
+from data_structures.kd_tree.kd_node import KDNode
 
-
-def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
+def build_kdtree(
+        points: list[list[float]], depth: int = 0
+) -> KDNode | None:
     """
     Builds a KD-Tree from a list of points.
 
@@ -11,7 +11,7 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
         depth (int): The current depth in the tree (used to determine axis for splitting).
 
     Returns:
-        Optional[KDNode]: The root node of the KD-Tree.
+        KDNode | None: The root node of the KD-Tree, or None if no points are provided.
     """
     if not points:
         return None
@@ -24,8 +24,11 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> Optional[KDNode]:
     median_idx = len(points) // 2
 
     # Create node and construct subtrees
+    left_points = points[:median_idx]
+    right_points = points[median_idx + 1:]
+
     return KDNode(
         point=points[median_idx],
-        left=build_kdtree(points[:median_idx], depth + 1),
-        right=build_kdtree(points[median_idx + 1 :], depth + 1),
+        left=build_kdtree(left_points, depth + 1),
+        right=build_kdtree(right_points, depth + 1),
     )
diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 3dfabda4f81b..c0d76405e9d6 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -1,5 +1,5 @@
 import numpy as np
-from hypercube_points import hypercube_points
+from data_structures.kd_tree.example.hypercube_points import hypercube_points
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index ae00a3d8b047..6e844a836f4e 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -1,8 +1,7 @@
 import numpy as np
 
-
 def hypercube_points(
-    num_points: int, hypercube_size: float, num_dimensions: int
+        num_points: int, hypercube_size: float, num_dimensions: int
 ) -> np.ndarray:
     """
     Generates random points uniformly distributed within an n-dimensional hypercube.
@@ -16,4 +15,5 @@ def hypercube_points(
         np.ndarray: An array of shape (num_points, num_dimensions) with generated points.
     """
     rng = np.random.default_rng()
-    return hypercube_size * rng.random((num_points, num_dimensions))
+    shape = (num_points, num_dimensions)
+    return hypercube_size * rng.random(shape)
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index e14ec39c24c0..f2b0ff1d2b10 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,6 +1,5 @@
 from typing import Optional
 
-
 class KDNode:
     """
     Represents a node in a KD-Tree.
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index eb80638d4923..1a946c342da1 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -1,34 +1,36 @@
-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]:
+        root: KDNode | None,
+        query_point: list[float]
+) -> tuple[list[float] | None, float, int]:
     """
     Performs a nearest neighbor search in a KD-Tree for a given query point.
 
     Args:
-        root (Optional[KDNode]): The root node of the KD-Tree.
+        root (KDNode | None): The root node of the KD-Tree.
         query_point (list[float]): The point for which the nearest neighbor is being searched.
 
     Returns:
-        tuple[Optional[list[float]], float, int]:
-            - The nearest point found in the KD-Tree to the query point.
+        tuple[list[float] | None, float, int]:
+            - The nearest point found in the KD-Tree to the query point, or None if no point is found.
             - The squared distance to the nearest point.
             - The number of nodes visited during the search.
     """
-    nearest_point: Optional[list[float]] = None
+    nearest_point: list[float] | None = None
     nearest_dist: float = float("inf")
     nodes_visited: int = 0
 
-    def search(node: Optional[KDNode], depth: int = 0) -> None:
+    def search(
+            node: KDNode | None,
+            depth: int = 0
+    ) -> None:
         """
-        Recursively searches the KD-Tree for the nearest neighbor.
+        Recursively searches for the nearest neighbor in the KD-Tree.
 
         Args:
-            node (Optional[KDNode]): The current node in the KD-Tree.
-            depth (int): The current depth in the tree.
+            node (KDNode | None): The current node in the KD-Tree.
+            depth (int): The current depth in the KD-Tree.
         """
         nonlocal nearest_point, nearest_dist, nodes_visited
         if node is None:
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index dcf0edec6cfc..8927afbaa619 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -1,73 +1,70 @@
-import unittest
 import numpy as np
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 from data_structures.kd_tree.kd_node import KDNode
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 
+def test_build_kdtree():
+    """
+    Test that KD-Tree is built correctly.
+    """
+    num_points = 10
+    cube_size = 10.0
+    num_dimensions = 2
+    points = hypercube_points(num_points, cube_size, num_dimensions)
+    kdtree = build_kdtree(points.tolist())
 
-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.kdtree = build_kdtree(self.points.tolist())
+    # Check if root is not None
+    assert kdtree is not None
 
-    def test_build_kdtree(self):
-        """
-        Test that KD-Tree is built correctly.
-        """
-        # Check if root is not None
-        self.assertIsNotNone(self.kdtree)
+    # Check if root has correct dimensions
+    assert len(kdtree.point) == num_dimensions
 
-        # Check if root has correct dimensions
-        self.assertEqual(len(self.kdtree.point), self.num_dimensions)
+    # Check that the tree is balanced to some extent (simplistic check)
+    assert isinstance(kdtree, KDNode)
 
-        # Check that the tree is balanced to some extent (simplistic check)
-        self.assertIsInstance(self.kdtree, KDNode)
+def test_nearest_neighbour_search():
+    """
+    Test the nearest neighbor search function.
+    """
+    num_points = 10
+    cube_size = 10.0
+    num_dimensions = 2
+    points = hypercube_points(num_points, cube_size, num_dimensions)
+    kdtree = build_kdtree(points.tolist())
 
-    def test_nearest_neighbour_search(self):
-        """
-        Test the nearest neighbor search function.
-        """
-        rng = np.random.default_rng()
-        query_point = rng.random(self.num_dimensions).tolist()
+    rng = np.random.default_rng()
+    query_point = rng.random(num_dimensions).tolist()
 
-        nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
-            self.kdtree, query_point
-        )
+    nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
+        kdtree, query_point
+    )
 
-        # Check that nearest point is not None
-        self.assertIsNotNone(nearest_point)
+    # Check that nearest point is not None
+    assert nearest_point is not None
 
-        # Check that distance is a non-negative number
-        self.assertGreaterEqual(nearest_dist, 0)
+    # Check that distance is a non-negative number
+    assert nearest_dist >= 0
 
-        # Check that nodes visited is a non-negative integer
-        self.assertGreaterEqual(nodes_visited, 0)
+    # Check that nodes visited is a non-negative integer
+    assert nodes_visited >= 0
 
-    def test_edge_cases(self):
-        """
-        Test edge cases such as an empty KD-Tree.
-        """
-        empty_kdtree = build_kdtree([])
-        query_point = [0.0] * self.num_dimensions
+def test_edge_cases():
+    """
+    Test edge cases such as an empty KD-Tree.
+    """
+    empty_kdtree = build_kdtree([])
+    query_point = [0.0] * 2  # Using a default 2D query point
 
-        nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
-            empty_kdtree, query_point
-        )
-
-        # With an empty KD-Tree, nearest_point should be None
-        self.assertIsNone(nearest_point)
-        self.assertEqual(nearest_dist, float("inf"))
-        self.assertEqual(nodes_visited, 0)
+    nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
+        empty_kdtree, query_point
+    )
 
+    # With an empty KD-Tree, nearest_point should be None
+    assert nearest_point is None
+    assert nearest_dist == float("inf")
+    assert nodes_visited == 0
 
 if __name__ == "__main__":
-    unittest.main()
+    import pytest
+    pytest.main()

From 4608a9f8c2ba3a770d28cbd604987a7e4909123c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 28 Aug 2024 19:25:51 +0000
Subject: [PATCH 15/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/build_kdtree.py             | 7 +++----
 data_structures/kd_tree/example/hypercube_points.py | 3 ++-
 data_structures/kd_tree/kd_node.py                  | 1 +
 data_structures/kd_tree/nearest_neighbour_search.py | 9 +++------
 data_structures/kd_tree/tests/test_kdtree.py        | 5 +++++
 5 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 1a0a27bb5ced..138ecbf1cd92 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -1,8 +1,7 @@
 from data_structures.kd_tree.kd_node import KDNode
 
-def build_kdtree(
-        points: list[list[float]], depth: int = 0
-) -> KDNode | None:
+
+def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
     """
     Builds a KD-Tree from a list of points.
 
@@ -25,7 +24,7 @@ def build_kdtree(
 
     # Create node and construct subtrees
     left_points = points[:median_idx]
-    right_points = points[median_idx + 1:]
+    right_points = points[median_idx + 1 :]
 
     return KDNode(
         point=points[median_idx],
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 6e844a836f4e..31073e1c8df2 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -1,7 +1,8 @@
 import numpy as np
 
+
 def hypercube_points(
-        num_points: int, hypercube_size: float, num_dimensions: int
+    num_points: int, hypercube_size: float, num_dimensions: int
 ) -> np.ndarray:
     """
     Generates random points uniformly distributed within an n-dimensional hypercube.
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index f2b0ff1d2b10..e14ec39c24c0 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,5 +1,6 @@
 from typing import Optional
 
+
 class KDNode:
     """
     Represents a node in a KD-Tree.
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 1a946c342da1..1dd28e3b0119 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -1,8 +1,8 @@
 from data_structures.kd_tree.kd_node import KDNode
 
+
 def nearest_neighbour_search(
-        root: KDNode | None,
-        query_point: list[float]
+    root: KDNode | None, query_point: list[float]
 ) -> tuple[list[float] | None, float, int]:
     """
     Performs a nearest neighbor search in a KD-Tree for a given query point.
@@ -21,10 +21,7 @@ def nearest_neighbour_search(
     nearest_dist: float = float("inf")
     nodes_visited: int = 0
 
-    def search(
-            node: KDNode | None,
-            depth: int = 0
-    ) -> None:
+    def search(node: KDNode | None, depth: int = 0) -> None:
         """
         Recursively searches for the nearest neighbor in the KD-Tree.
 
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index 8927afbaa619..b55d32a30e13 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -4,6 +4,7 @@
 from data_structures.kd_tree.kd_node import KDNode
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 
+
 def test_build_kdtree():
     """
     Test that KD-Tree is built correctly.
@@ -23,6 +24,7 @@ def test_build_kdtree():
     # Check that the tree is balanced to some extent (simplistic check)
     assert isinstance(kdtree, KDNode)
 
+
 def test_nearest_neighbour_search():
     """
     Test the nearest neighbor search function.
@@ -49,6 +51,7 @@ def test_nearest_neighbour_search():
     # Check that nodes visited is a non-negative integer
     assert nodes_visited >= 0
 
+
 def test_edge_cases():
     """
     Test edge cases such as an empty KD-Tree.
@@ -65,6 +68,8 @@ def test_edge_cases():
     assert nearest_dist == float("inf")
     assert nodes_visited == 0
 
+
 if __name__ == "__main__":
     import pytest
+
     pytest.main()

From 7c1aa7ea9dc290889827da4f0d9d8ae81ccde196 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Thu, 29 Aug 2024 13:07:13 +0200
Subject: [PATCH 16/35] E501 for build_kdtree.py, hypercube_points.py,
 nearest_neighbour_search.py

---
 data_structures/kd_tree/build_kdtree.py             | 6 ++++--
 data_structures/kd_tree/example/hypercube_points.py | 3 ++-
 data_structures/kd_tree/nearest_neighbour_search.py | 6 ++++--
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 138ecbf1cd92..7850eb9019f1 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -7,10 +7,12 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
 
     Args:
         points (list[list[float]]): The list of points to build the KD-Tree from.
-        depth (int): The current depth in the tree (used to determine axis for splitting).
+        depth (int): The current depth in the tree
+                     (used to determine axis for splitting).
 
     Returns:
-        KDNode | None: The root node of the KD-Tree, or None if no points are provided.
+        KDNode | None: The root node of the KD-Tree,
+                       or None if no points are provided.
     """
     if not points:
         return None
diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 31073e1c8df2..d7c3937c6a17 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -13,7 +13,8 @@ def hypercube_points(
         num_dimensions (int): Number of dimensions of the hypercube.
 
     Returns:
-        np.ndarray: An array of shape (num_points, num_dimensions) with generated points.
+        np.ndarray: An array of shape (num_points, num_dimensions)
+                    with generated points.
     """
     rng = np.random.default_rng()
     shape = (num_points, num_dimensions)
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index 1dd28e3b0119..b37a811ee19a 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -9,11 +9,13 @@ def nearest_neighbour_search(
 
     Args:
         root (KDNode | None): The root node of the KD-Tree.
-        query_point (list[float]): The point for which the nearest neighbor is being searched.
+        query_point (list[float]): The point for which the nearest neighbor
+                                    is being searched.
 
     Returns:
         tuple[list[float] | None, float, int]:
-            - The nearest point found in the KD-Tree to the query point, or None if no point is found.
+            - The nearest point found in the KD-Tree to the query point,
+              or None if no point is found.
             - The squared distance to the nearest point.
             - The number of nodes visited during the search.
     """

From ba24e755eb95365097072a655ab9fd8572811b67 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Thu, 29 Aug 2024 13:15:27 +0200
Subject: [PATCH 17/35] I001 for example_usage.py and test_kdtree.py

---
 data_structures/kd_tree/example/example_usage.py | 2 +-
 data_structures/kd_tree/tests/test_kdtree.py     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index c0d76405e9d6..14355ee83d73 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -1,6 +1,6 @@
 import numpy as np
-from data_structures.kd_tree.example.hypercube_points import hypercube_points
 from data_structures.kd_tree.build_kdtree import build_kdtree
+from data_structures.kd_tree.example.hypercube_points import hypercube_points
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 
 
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index b55d32a30e13..feb99102e269 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -1,8 +1,8 @@
 import numpy as np
 from data_structures.kd_tree.build_kdtree import build_kdtree
-from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
-from data_structures.kd_tree.kd_node import KDNode
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
+from data_structures.kd_tree.kd_node import KDNode
+from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 
 
 def test_build_kdtree():

From 05975a34ad0aac93e81fb256efee6a455fb836bd Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Thu, 29 Aug 2024 13:18:39 +0200
Subject: [PATCH 18/35] I001 for example_usage.py and test_kdtree.py

---
 data_structures/kd_tree/example/example_usage.py | 1 +
 data_structures/kd_tree/tests/test_kdtree.py     | 1 +
 2 files changed, 2 insertions(+)

diff --git a/data_structures/kd_tree/example/example_usage.py b/data_structures/kd_tree/example/example_usage.py
index 14355ee83d73..e270f0cdd245 100644
--- a/data_structures/kd_tree/example/example_usage.py
+++ b/data_structures/kd_tree/example/example_usage.py
@@ -1,4 +1,5 @@
 import numpy as np
+
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index feb99102e269..ee3451f222f7 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -1,4 +1,5 @@
 import numpy as np
+
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 from data_structures.kd_tree.kd_node import KDNode

From 31782d1806202d95403b20074df9f3583140a331 Mon Sep 17 00:00:00 2001
From: Ramy <126559907+Ramy-Badr-Ahmed@users.noreply.github.com>
Date: Tue, 3 Sep 2024 11:55:45 +0200
Subject: [PATCH 19/35] Update data_structures/kd_tree/build_kdtree.py

Co-authored-by: Christian Clauss <cclauss@me.com>
---
 data_structures/kd_tree/build_kdtree.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 7850eb9019f1..5fc9a406a791 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -11,7 +11,7 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
                      (used to determine axis for splitting).
 
     Returns:
-        KDNode | None: The root node of the KD-Tree,
+        The root node of the KD-Tree,
                        or None if no points are provided.
     """
     if not points:

From 6a9b3e16cbf1a84a6d00da01bf7a0090c8de7abf Mon Sep 17 00:00:00 2001
From: Ramy <126559907+Ramy-Badr-Ahmed@users.noreply.github.com>
Date: Tue, 3 Sep 2024 11:56:09 +0200
Subject: [PATCH 20/35] Update
 data_structures/kd_tree/example/hypercube_points.py

Co-authored-by: Christian Clauss <cclauss@me.com>
---
 data_structures/kd_tree/example/hypercube_points.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index d7c3937c6a17..04d529a2a6a6 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -8,9 +8,9 @@ def hypercube_points(
     Generates random points uniformly distributed within an n-dimensional hypercube.
 
     Args:
-        num_points (int): Number of points to generate.
-        hypercube_size (float): Size of the hypercube.
-        num_dimensions (int): Number of dimensions of the hypercube.
+        num_points: Number of points to generate.
+        hypercube_size: Size of the hypercube.
+        num_dimensions: Number of dimensions of the hypercube.
 
     Returns:
         np.ndarray: An array of shape (num_points, num_dimensions)

From 2fd24d400422123ddc7878d613dca34dc58e7a8a Mon Sep 17 00:00:00 2001
From: Ramy <126559907+Ramy-Badr-Ahmed@users.noreply.github.com>
Date: Tue, 3 Sep 2024 11:56:57 +0200
Subject: [PATCH 21/35] Update
 data_structures/kd_tree/example/hypercube_points.py

Co-authored-by: Christian Clauss <cclauss@me.com>
---
 data_structures/kd_tree/example/hypercube_points.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/example/hypercube_points.py b/data_structures/kd_tree/example/hypercube_points.py
index 04d529a2a6a6..2d8800ac9338 100644
--- a/data_structures/kd_tree/example/hypercube_points.py
+++ b/data_structures/kd_tree/example/hypercube_points.py
@@ -13,7 +13,7 @@ def hypercube_points(
         num_dimensions: Number of dimensions of the hypercube.
 
     Returns:
-        np.ndarray: An array of shape (num_points, num_dimensions)
+        An array of shape (num_points, num_dimensions)
                     with generated points.
     """
     rng = np.random.default_rng()

From 2cf9d9289657164dd78e8b79ccb0107260444adb Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Tue, 3 Sep 2024 12:40:31 +0200
Subject: [PATCH 22/35] Added new test cases requested in Review. Refactored
 the test_build_kdtree() to include various checks.

---
 data_structures/kd_tree/tests/test_kdtree.py | 43 ++++++++++++++------
 1 file changed, 31 insertions(+), 12 deletions(-)

diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index ee3451f222f7..516ad46adb2b 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 
 from data_structures.kd_tree.build_kdtree import build_kdtree
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
@@ -6,24 +7,42 @@
 from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
 
 
-def test_build_kdtree():
+@pytest.mark.parametrize(
+    "num_points, cube_size, num_dimensions, depth, expected_result",
+    [
+        (0, 10.0, 2, 0, None),      # Empty points list
+        (10, 10.0, 2, 2, KDNode),   # Depth = 2, 2D points
+        (10, 10.0, 3, -2, KDNode),  # Depth = -2, 3D points
+    ],
+)
+def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_result):
     """
     Test that KD-Tree is built correctly.
+
+    Cases:
+        - Empty points list.
+        - Positive depth value.
+        - Negative depth value.
     """
-    num_points = 10
-    cube_size = 10.0
-    num_dimensions = 2
-    points = hypercube_points(num_points, cube_size, num_dimensions)
-    kdtree = build_kdtree(points.tolist())
+    points = hypercube_points(num_points, cube_size, num_dimensions).tolist() \
+        if num_points > 0 \
+        else []
+
+    kdtree = build_kdtree(points, depth = depth)
 
-    # Check if root is not None
-    assert kdtree is not None
+    if expected_result is None:
+        # Empty points list case
+        assert kdtree is None, f"Expected None for empty points list, got {kdtree}"
+    else:
+        # Check if root node is not None
+        assert kdtree is not None, "Expected a KDNode, got None"
 
-    # Check if root has correct dimensions
-    assert len(kdtree.point) == num_dimensions
+        # Check if root has correct dimensions
+        assert len(kdtree.point) == num_dimensions, \
+            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
 
-    # Check that the tree is balanced to some extent (simplistic check)
-    assert isinstance(kdtree, KDNode)
+        # Check that the tree is balanced to some extent (simplistic check)
+        assert isinstance(kdtree, KDNode), f"Expected KDNode instance, got {type(kdtree)}"
 
 
 def test_nearest_neighbour_search():

From a3803ee251ae13fc55f4e37d18f49f4afc32b762 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 3 Sep 2024 10:41:47 +0000
Subject: [PATCH 23/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/tests/test_kdtree.py | 21 ++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index 516ad46adb2b..36ed791121f9 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -10,8 +10,8 @@
 @pytest.mark.parametrize(
     "num_points, cube_size, num_dimensions, depth, expected_result",
     [
-        (0, 10.0, 2, 0, None),      # Empty points list
-        (10, 10.0, 2, 2, KDNode),   # Depth = 2, 2D points
+        (0, 10.0, 2, 0, None),  # Empty points list
+        (10, 10.0, 2, 2, KDNode),  # Depth = 2, 2D points
         (10, 10.0, 3, -2, KDNode),  # Depth = -2, 3D points
     ],
 )
@@ -24,11 +24,13 @@ def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_res
         - Positive depth value.
         - Negative depth value.
     """
-    points = hypercube_points(num_points, cube_size, num_dimensions).tolist() \
-        if num_points > 0 \
+    points = (
+        hypercube_points(num_points, cube_size, num_dimensions).tolist()
+        if num_points > 0
         else []
+    )
 
-    kdtree = build_kdtree(points, depth = depth)
+    kdtree = build_kdtree(points, depth=depth)
 
     if expected_result is None:
         # Empty points list case
@@ -38,11 +40,14 @@ def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_res
         assert kdtree is not None, "Expected a KDNode, got None"
 
         # Check if root has correct dimensions
-        assert len(kdtree.point) == num_dimensions, \
-            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
+        assert (
+            len(kdtree.point) == num_dimensions
+        ), f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
 
         # Check that the tree is balanced to some extent (simplistic check)
-        assert isinstance(kdtree, KDNode), f"Expected KDNode instance, got {type(kdtree)}"
+        assert isinstance(
+            kdtree, KDNode
+        ), f"Expected KDNode instance, got {type(kdtree)}"
 
 
 def test_nearest_neighbour_search():

From f1f5862c836c1a440fdcc331a4d2386c3880c70e Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Tue, 3 Sep 2024 12:46:30 +0200
Subject: [PATCH 24/35] Considered ruff errors

---
 data_structures/kd_tree/tests/test_kdtree.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index 516ad46adb2b..b53bc2496bef 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -8,7 +8,7 @@
 
 
 @pytest.mark.parametrize(
-    "num_points, cube_size, num_dimensions, depth, expected_result",
+    ("num_points", "cube_size", "num_dimensions", "depth", "expected_result"),
     [
         (0, 10.0, 2, 0, None),      # Empty points list
         (10, 10.0, 2, 2, KDNode),   # Depth = 2, 2D points
@@ -42,7 +42,8 @@ def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_res
             f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
 
         # Check that the tree is balanced to some extent (simplistic check)
-        assert isinstance(kdtree, KDNode), f"Expected KDNode instance, got {type(kdtree)}"
+        assert isinstance(kdtree, KDNode), \
+            f"Expected KDNode instance, got {type(kdtree)}"
 
 
 def test_nearest_neighbour_search():

From 5c07a1a4851654e1030786cb619873c6e1492800 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Tue, 3 Sep 2024 12:49:32 +0200
Subject: [PATCH 25/35] Considered ruff errors

---
 data_structures/kd_tree/tests/test_kdtree.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/tests/test_kdtree.py b/data_structures/kd_tree/tests/test_kdtree.py
index 36ed791121f9..81f2cc990074 100644
--- a/data_structures/kd_tree/tests/test_kdtree.py
+++ b/data_structures/kd_tree/tests/test_kdtree.py
@@ -8,7 +8,7 @@
 
 
 @pytest.mark.parametrize(
-    "num_points, cube_size, num_dimensions, depth, expected_result",
+    ("num_points", "cube_size", "num_dimensions", "depth", "expected_result"),
     [
         (0, 10.0, 2, 0, None),  # Empty points list
         (10, 10.0, 2, 2, KDNode),  # Depth = 2, 2D points

From 3c09ac1dd793f3ee43003427e1475d5f303b6a22 Mon Sep 17 00:00:00 2001
From: Christian Clauss <cclauss@me.com>
Date: Tue, 3 Sep 2024 13:58:34 +0200
Subject: [PATCH 26/35] Apply suggestions from code review

---
 data_structures/kd_tree/build_kdtree.py             |  4 ++--
 data_structures/kd_tree/kd_node.py                  | 13 +++++--------
 data_structures/kd_tree/nearest_neighbour_search.py |  4 ++--
 3 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/data_structures/kd_tree/build_kdtree.py b/data_structures/kd_tree/build_kdtree.py
index 5fc9a406a791..c5b800a2c992 100644
--- a/data_structures/kd_tree/build_kdtree.py
+++ b/data_structures/kd_tree/build_kdtree.py
@@ -6,8 +6,8 @@ def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
     Builds a KD-Tree from a list of points.
 
     Args:
-        points (list[list[float]]): The list of points to build the KD-Tree from.
-        depth (int): The current depth in the tree
+        points: The list of points to build the KD-Tree from.
+        depth: The current depth in the tree
                      (used to determine axis for splitting).
 
     Returns:
diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index e14ec39c24c0..276773ee595c 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,21 +1,18 @@
-from typing import Optional
-
-
 class KDNode:
     """
     Represents a node in a KD-Tree.
 
     Attributes:
-        point (list[float]): The point stored in this node.
-        left (Optional[KDNode]): The left child node.
-        right (Optional[KDNode]): The right child node.
+        point: The point stored in this node.
+        left: The left child node.
+        right: The right child node.
     """
 
     def __init__(
         self,
         point: list[float],
-        left: Optional["KDNode"] = None,
-        right: Optional["KDNode"] = None,
+        left: KDNode | None = None,
+        right: KDNode | None  = None,
     ) -> None:
         """
         Initializes a KDNode with the given point and child nodes.
diff --git a/data_structures/kd_tree/nearest_neighbour_search.py b/data_structures/kd_tree/nearest_neighbour_search.py
index b37a811ee19a..d9727736f21c 100644
--- a/data_structures/kd_tree/nearest_neighbour_search.py
+++ b/data_structures/kd_tree/nearest_neighbour_search.py
@@ -28,8 +28,8 @@ def search(node: KDNode | None, depth: int = 0) -> None:
         Recursively searches for the nearest neighbor in the KD-Tree.
 
         Args:
-            node (KDNode | None): The current node in the KD-Tree.
-            depth (int): The current depth in the KD-Tree.
+            node: The current node in the KD-Tree.
+            depth: The current depth in the KD-Tree.
         """
         nonlocal nearest_point, nearest_dist, nodes_visited
         if node is None:

From bab43e75d3fc2eadc9d0c68e86069da504d4a721 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 3 Sep 2024 11:58:57 +0000
Subject: [PATCH 27/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/kd_node.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 276773ee595c..11e21c34efe0 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -12,7 +12,7 @@ def __init__(
         self,
         point: list[float],
         left: KDNode | None = None,
-        right: KDNode | None  = None,
+        right: KDNode | None = None,
     ) -> None:
         """
         Initializes a KDNode with the given point and child nodes.

From a10ff1507ae14b505d5f8516a43b4d7f040a8695 Mon Sep 17 00:00:00 2001
From: Christian Clauss <cclauss@me.com>
Date: Tue, 3 Sep 2024 14:00:37 +0200
Subject: [PATCH 28/35] Update kd_node.py

---
 data_structures/kd_tree/kd_node.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 11e21c34efe0..c76e77fdf3bf 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -11,8 +11,8 @@ class KDNode:
     def __init__(
         self,
         point: list[float],
-        left: KDNode | None = None,
-        right: KDNode | None = None,
+        left: "KDNode" | None = None,
+        right: "KDNode" | None = None,
     ) -> None:
         """
         Initializes a KDNode with the given point and child nodes.

From d77a2859bb7bcf58098d350a56f35ca138541625 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Tue, 3 Sep 2024 14:29:51 +0200
Subject: [PATCH 29/35] imported annotations from __future__

---
 data_structures/kd_tree/kd_node.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index c76e77fdf3bf..0bd8cfee6855 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+
 class KDNode:
     """
     Represents a node in a KD-Tree.
@@ -11,8 +14,8 @@ class KDNode:
     def __init__(
         self,
         point: list[float],
-        left: "KDNode" | None = None,
-        right: "KDNode" | None = None,
+        left:  KDNode | None = None,
+        right: KDNode | None = None,
     ) -> None:
         """
         Initializes a KDNode with the given point and child nodes.

From 042680694e9d181a6c401aefdcb8696074d00401 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 3 Sep 2024 12:30:25 +0000
Subject: [PATCH 30/35] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/kd_tree/kd_node.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_structures/kd_tree/kd_node.py b/data_structures/kd_tree/kd_node.py
index 0bd8cfee6855..e1011027938d 100644
--- a/data_structures/kd_tree/kd_node.py
+++ b/data_structures/kd_tree/kd_node.py
@@ -14,7 +14,7 @@ class KDNode:
     def __init__(
         self,
         point: list[float],
-        left:  KDNode | None = None,
+        left: KDNode | None = None,
         right: KDNode | None = None,
     ) -> None:
         """

From 9c4cbd475311e6ace73e565162a374cd78549b75 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Sat, 7 Sep 2024 20:54:08 +0200
Subject: [PATCH 31/35] Implementation of the suffix tree data structure

---
 data_structures/suffix_tree/__init__.py       |  0
 .../suffix_tree/build_suffix_tree.py          | 58 +++++++++++++++++++
 .../suffix_tree/example/__init__.py           |  0
 .../suffix_tree/example/example_usage.py      | 29 ++++++++++
 .../suffix_tree/suffix_tree_node.py           | 26 +++++++++
 data_structures/suffix_tree/tests/__init__.py |  0
 .../suffix_tree/tests/test_suffix_tree.py     | 42 ++++++++++++++
 7 files changed, 155 insertions(+)
 create mode 100644 data_structures/suffix_tree/__init__.py
 create mode 100644 data_structures/suffix_tree/build_suffix_tree.py
 create mode 100644 data_structures/suffix_tree/example/__init__.py
 create mode 100644 data_structures/suffix_tree/example/example_usage.py
 create mode 100644 data_structures/suffix_tree/suffix_tree_node.py
 create mode 100644 data_structures/suffix_tree/tests/__init__.py
 create mode 100644 data_structures/suffix_tree/tests/test_suffix_tree.py

diff --git a/data_structures/suffix_tree/__init__.py b/data_structures/suffix_tree/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/data_structures/suffix_tree/build_suffix_tree.py b/data_structures/suffix_tree/build_suffix_tree.py
new file mode 100644
index 000000000000..1044b7f0a768
--- /dev/null
+++ b/data_structures/suffix_tree/build_suffix_tree.py
@@ -0,0 +1,58 @@
+from data_structures.suffix_tree.suffix_tree_node import SuffixTreeNode
+
+
+class SuffixTree:
+    def __init__(self, text: str) -> None:
+        """
+        Initializes the suffix tree with the given text.
+
+        Args:
+            text (str): The text for which the suffix tree is to be built.
+        """
+        self.text: str = text
+        self.root: SuffixTreeNode = SuffixTreeNode()
+        self.build_suffix_tree()
+
+    def build_suffix_tree(self) -> None:
+        """
+        Builds the suffix tree for the given text by adding all suffixes.
+        """
+        text = self.text
+        n = len(text)
+        for i in range(n):
+            suffix = text[i:]
+            self._add_suffix(suffix, i)
+
+    def _add_suffix(self, suffix: str, index: int) -> None:
+        """
+        Adds a suffix to the suffix tree.
+
+        Args:
+            suffix (str): The suffix to add.
+            index (int): The starting index of the suffix in the original text.
+        """
+        node = self.root
+        for char in suffix:
+            if char not in node.children:
+                node.children[char] = SuffixTreeNode()
+            node = node.children[char]
+        node.is_end_of_string = True
+        node.start = index
+        node.end = index + len(suffix) - 1
+
+    def search(self, pattern: str) -> bool:
+        """
+        Searches for a pattern in the suffix tree.
+
+        Args:
+            pattern (str): The pattern to search for.
+
+        Returns:
+            bool: True if the pattern is found, False otherwise.
+        """
+        node = self.root
+        for char in pattern:
+            if char not in node.children:
+                return False
+            node = node.children[char]
+        return True
diff --git a/data_structures/suffix_tree/example/__init__.py b/data_structures/suffix_tree/example/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/data_structures/suffix_tree/example/example_usage.py b/data_structures/suffix_tree/example/example_usage.py
new file mode 100644
index 000000000000..c2563cd59f05
--- /dev/null
+++ b/data_structures/suffix_tree/example/example_usage.py
@@ -0,0 +1,29 @@
+from data_structures.suffix_tree.build_suffix_tree import SuffixTree
+
+
+def main() -> None:
+    """
+    Demonstrate the usage of the SuffixTree class.
+
+    - Initializes a SuffixTree with a predefined text.
+    - Defines a list of patterns to search for within the suffix tree.
+    - Searches for each pattern in the suffix tree.
+
+    Patterns tested:
+        - "ana" (found) --> True
+        - "ban" (found) --> True
+        - "na" (found) --> True
+        - "xyz" (not found) --> False
+        - "mon" (found) --> True
+    """
+    text = "monkey banana"
+    suffix_tree = SuffixTree(text)
+
+    patterns = ["ana", "ban", "na", "xyz", "mon"]
+    for pattern in patterns:
+        found = suffix_tree.search(pattern)
+        print(f"Pattern '{pattern}' found: {found}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data_structures/suffix_tree/suffix_tree_node.py b/data_structures/suffix_tree/suffix_tree_node.py
new file mode 100644
index 000000000000..b845280e4cc8
--- /dev/null
+++ b/data_structures/suffix_tree/suffix_tree_node.py
@@ -0,0 +1,26 @@
+from __future__ import annotations
+from typing import Dict, Optional
+
+
+class SuffixTreeNode:
+    def __init__(self,
+                 children: Dict[str, 'SuffixTreeNode'] = None,
+                 is_end_of_string: bool = False,
+                 start: int | None = None,
+                 end: int | None = None,
+                 suffix_link: SuffixTreeNode | None = None) -> None:
+        """
+        Initializes a suffix tree node.
+
+        Parameters:
+            children (Dict[str, SuffixTreeNode], optional): The children of this node. Defaults to an empty dictionary.
+            is_end_of_string (bool, optional): Indicates if this node represents the end of a string. Defaults to False.
+            start (int | None, optional): The start index of the suffix in the text. Defaults to None.
+            end (int | None, optional): The end index of the suffix in the text. Defaults to None.
+            suffix_link (SuffixTreeNode | None, optional): Link to another suffix tree node. Defaults to None.
+        """
+        self.children = children or {}
+        self.is_end_of_string = is_end_of_string
+        self.start = start
+        self.end = end
+        self.suffix_link = suffix_link
diff --git a/data_structures/suffix_tree/tests/__init__.py b/data_structures/suffix_tree/tests/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/data_structures/suffix_tree/tests/test_suffix_tree.py b/data_structures/suffix_tree/tests/test_suffix_tree.py
new file mode 100644
index 000000000000..fe8a73551ff9
--- /dev/null
+++ b/data_structures/suffix_tree/tests/test_suffix_tree.py
@@ -0,0 +1,42 @@
+import unittest
+from data_structures.suffix_tree.build_suffix_tree import SuffixTree
+
+
+class TestSuffixTree(unittest.TestCase):
+    def setUp(self) -> None:
+        """Set up the initial conditions for each test."""
+        self.text = "banana"
+        self.suffix_tree = SuffixTree(self.text)
+
+    def test_search_existing_patterns(self):
+        """Test searching for patterns that exist in the suffix tree."""
+        patterns = ["ana", "ban", "na"]
+        for pattern in patterns:
+            with self.subTest(pattern = pattern):
+                self.assertTrue(self.suffix_tree.search(pattern), f"Pattern '{pattern}' should be found.")
+
+    def test_search_non_existing_patterns(self):
+        """Test searching for patterns that do not exist in the suffix tree."""
+        patterns = ["xyz", "apple", "cat"]
+        for pattern in patterns:
+            with self.subTest(pattern = pattern):
+                self.assertFalse(self.suffix_tree.search(pattern), f"Pattern '{pattern}' should not be found.")
+
+    def test_search_empty_pattern(self):
+        """Test searching for an empty pattern."""
+        self.assertTrue(self.suffix_tree.search(""), "An empty pattern should be found.")
+
+    def test_search_full_text(self):
+        """Test searching for the full text."""
+        self.assertTrue(self.suffix_tree.search(self.text), "The full text should be found in the suffix tree.")
+
+    def test_search_substrings(self):
+        """Test searching for substrings of the full text."""
+        substrings = ["ban", "ana", "a", "na"]
+        for substring in substrings:
+            with self.subTest(substring = substring):
+                self.assertTrue(self.suffix_tree.search(substring), f"Substring '{substring}' should be found.")
+
+
+if __name__ == "__main__":
+    unittest.main()

From 95ae328460789adc7b7946a11eebb4b7ced28964 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Sat, 7 Sep 2024 21:00:46 +0200
Subject: [PATCH 32/35] Adding data to DIRECTORY.md

---
 DIRECTORY.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index 1ca537b991c8..fd71075f97b9 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -289,8 +289,12 @@
     * [KD Tree Node](data_structures/kd_tree/kd_node.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)
+    * [Hypercube Points](data_structures/kd_tree/example/hypercube_points.py)
     * [Example Usage](data_structures/kd_tree/example/example_usage.py)
+  * Suffix Tree
+    * [Suffix Tree Node](data_structures/suffix_tree/suffix_tree_node.py)
+    * [Build Suffix Tree](data_structures/suffix_tree/build_suffix_tree.py)
+    * [Example Usage](data_structures/suffix_tree/example/example_usage.py)
 
 ## Digital Image Processing
   * [Change Brightness](digital_image_processing/change_brightness.py)

From 1454bb22bb9c28ba7a7c989ce4aaa0c67582518e Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Sat, 7 Sep 2024 21:23:33 +0200
Subject: [PATCH 33/35] Minor file renaming

---
 data_structures/suffix_tree/example/example_usage.py            | 2 +-
 .../suffix_tree/{build_suffix_tree.py => suffix_tree.py}        | 0
 data_structures/suffix_tree/tests/test_suffix_tree.py           | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename data_structures/suffix_tree/{build_suffix_tree.py => suffix_tree.py} (100%)

diff --git a/data_structures/suffix_tree/example/example_usage.py b/data_structures/suffix_tree/example/example_usage.py
index c2563cd59f05..d097cb6e33f3 100644
--- a/data_structures/suffix_tree/example/example_usage.py
+++ b/data_structures/suffix_tree/example/example_usage.py
@@ -1,4 +1,4 @@
-from data_structures.suffix_tree.build_suffix_tree import SuffixTree
+from data_structures.suffix_tree.suffix_tree import SuffixTree
 
 
 def main() -> None:
diff --git a/data_structures/suffix_tree/build_suffix_tree.py b/data_structures/suffix_tree/suffix_tree.py
similarity index 100%
rename from data_structures/suffix_tree/build_suffix_tree.py
rename to data_structures/suffix_tree/suffix_tree.py
diff --git a/data_structures/suffix_tree/tests/test_suffix_tree.py b/data_structures/suffix_tree/tests/test_suffix_tree.py
index fe8a73551ff9..cef147a3d41c 100644
--- a/data_structures/suffix_tree/tests/test_suffix_tree.py
+++ b/data_structures/suffix_tree/tests/test_suffix_tree.py
@@ -1,5 +1,5 @@
 import unittest
-from data_structures.suffix_tree.build_suffix_tree import SuffixTree
+from data_structures.suffix_tree.suffix_tree import SuffixTree
 
 
 class TestSuffixTree(unittest.TestCase):

From a2b3a8677da1f1f40b574306ff25b631f4f74fb9 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Sat, 7 Sep 2024 21:36:05 +0200
Subject: [PATCH 34/35] minor correction

---
 DIRECTORY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index fd71075f97b9..4554ab669506 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -289,7 +289,7 @@
     * [KD Tree Node](data_structures/kd_tree/kd_node.py)
     * [Build KD Tree](data_structures/kd_tree/build_kdtree.py)
     * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
-    * [Hypercube Points](data_structures/kd_tree/example/hypercube_points.py)
+    * [Hypercibe Points](data_structures/kd_tree/example/hypercube_points.py)
     * [Example Usage](data_structures/kd_tree/example/example_usage.py)
   * Suffix Tree
     * [Suffix Tree Node](data_structures/suffix_tree/suffix_tree_node.py)

From 51832af53fa56b3075c7b30826abeeadf359edf4 Mon Sep 17 00:00:00 2001
From: Ramy-Badr-Ahmed <ramy.badr@hotmail.com>
Date: Sat, 7 Sep 2024 21:54:06 +0200
Subject: [PATCH 35/35] renaming in DIRECTORY.md

---
 DIRECTORY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index 4554ab669506..272b55510d46 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -293,7 +293,7 @@
     * [Example Usage](data_structures/kd_tree/example/example_usage.py)
   * Suffix Tree
     * [Suffix Tree Node](data_structures/suffix_tree/suffix_tree_node.py)
-    * [Build Suffix Tree](data_structures/suffix_tree/build_suffix_tree.py)
+    * [Suffix Tree](data_structures/suffix_tree/suffix_tree.py)
     * [Example Usage](data_structures/suffix_tree/example/example_usage.py)
 
 ## Digital Image Processing