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Python/data_structures/binary_tree/binary_search_tree.py
Maxim Smolskiy 53b2926704
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r"""
A binary search Tree
Example
8
/ \
3 10
/ \ \
1 6 14
/ \ /
4 7 13
>>> t = BinarySearchTree().insert(8, 3, 6, 1, 10, 14, 13, 4, 7)
>>> print(" ".join(repr(i.value) for i in t.traversal_tree()))
8 3 1 6 4 7 10 14 13
>>> tuple(i.value for i in t.traversal_tree(inorder))
(1, 3, 4, 6, 7, 8, 10, 13, 14)
>>> tuple(t)
(1, 3, 4, 6, 7, 8, 10, 13, 14)
>>> t.find_kth_smallest(3, t.root)
4
>>> tuple(t)[3-1]
4
>>> print(" ".join(repr(i.value) for i in t.traversal_tree(postorder)))
1 4 7 6 3 13 14 10 8
>>> t.remove(20)
Traceback (most recent call last):
...
ValueError: Value 20 not found
>>> BinarySearchTree().search(6)
Traceback (most recent call last):
...
IndexError: Warning: Tree is empty! please use another.
Other example:
>>> testlist = (8, 3, 6, 1, 10, 14, 13, 4, 7)
>>> t = BinarySearchTree()
>>> for i in testlist:
... t.insert(i) # doctest: +ELLIPSIS
BinarySearchTree(root=8)
BinarySearchTree(root={'8': (3, None)})
BinarySearchTree(root={'8': ({'3': (None, 6)}, None)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, None)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, 10)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, {'10': (None, 14)})})
BinarySearchTree(root={'8': ({'3': (1, 6)}, {'10': (None, {'14': (13, None)})})})
BinarySearchTree(root={'8': ({'3': (1, {'6': (4, None)})}, {'10': (None, {'14': ...
BinarySearchTree(root={'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, ...
Prints all the elements of the list in order traversal
>>> print(t)
{'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, None)})})}
Test existence
>>> t.search(6) is not None
True
>>> 6 in t
True
>>> t.search(-1) is not None
False
>>> -1 in t
False
>>> t.search(6).is_right
True
>>> t.search(1).is_right
False
>>> t.get_max().value
14
>>> max(t)
14
>>> t.get_min().value
1
>>> min(t)
1
>>> t.empty()
False
>>> not t
False
>>> for i in testlist:
... t.remove(i)
>>> t.empty()
True
>>> not t
True
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
from typing import Any, Self
@dataclass
class Node:
value: int
left: Node | None = None
right: Node | None = None
parent: Node | None = None # Added in order to delete a node easier
def __iter__(self) -> Iterator[int]:
"""
>>> list(Node(0))
[0]
>>> list(Node(0, Node(-1), Node(1), None))
[-1, 0, 1]
"""
yield from self.left or []
yield self.value
yield from self.right or []
def __repr__(self) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value)
return pformat({f"{self.value}": (self.left, self.right)}, indent=1)
@property
def is_right(self) -> bool:
return bool(self.parent and self is self.parent.right)
@dataclass
class BinarySearchTree:
root: Node | None = None
def __bool__(self) -> bool:
return bool(self.root)
def __iter__(self) -> Iterator[int]:
yield from self.root or []
def __str__(self) -> str:
"""
Return a string of all the Nodes using in order traversal
"""
return str(self.root)
def __reassign_nodes(self, node: Node, new_children: Node | None) -> None:
if new_children is not None: # reset its kids
new_children.parent = node.parent
if node.parent is not None: # reset its parent
if node.is_right: # If it is the right child
node.parent.right = new_children
else:
node.parent.left = new_children
else:
self.root = new_children
def empty(self) -> bool:
"""
Returns True if the tree does not have any element(s).
False if the tree has element(s).
>>> BinarySearchTree().empty()
True
>>> BinarySearchTree().insert(1).empty()
False
>>> BinarySearchTree().insert(8, 3, 6, 1, 10, 14, 13, 4, 7).empty()
False
"""
return not self.root
def __insert(self, value) -> None:
"""
Insert a new node in Binary Search Tree with value label
"""
new_node = Node(value) # create a new Node
if self.empty(): # if Tree is empty
self.root = new_node # set its root
else: # Tree is not empty
parent_node = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
parent_node.left = new_node # We insert the new node in a leaf
break
else:
parent_node = parent_node.left
elif parent_node.right is None:
parent_node.right = new_node
break
else:
parent_node = parent_node.right
new_node.parent = parent_node
def insert(self, *values) -> Self:
for value in values:
self.__insert(value)
return self
def search(self, value) -> Node | None:
"""
>>> tree = BinarySearchTree().insert(10, 20, 30, 40, 50)
>>> tree.search(10)
{'10': (None, {'20': (None, {'30': (None, {'40': (None, 50)})})})}
>>> tree.search(20)
{'20': (None, {'30': (None, {'40': (None, 50)})})}
>>> tree.search(30)
{'30': (None, {'40': (None, 50)})}
>>> tree.search(40)
{'40': (None, 50)}
>>> tree.search(50)
50
>>> tree.search(5) is None # element not present
True
>>> tree.search(0) is None # element not present
True
>>> tree.search(-5) is None # element not present
True
>>> BinarySearchTree().search(10)
Traceback (most recent call last):
...
IndexError: Warning: Tree is empty! please use another.
"""
if self.empty():
raise IndexError("Warning: Tree is empty! please use another.")
else:
node = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
node = node.left if value < node.value else node.right
return node
def get_max(self, node: Node | None = None) -> Node | None:
"""
We go deep on the right branch
>>> BinarySearchTree().insert(10, 20, 30, 40, 50).get_max()
50
>>> BinarySearchTree().insert(-5, -1, 0.1, -0.3, -4.5).get_max()
{'0.1': (-0.3, None)}
>>> BinarySearchTree().insert(1, 78.3, 30, 74.0, 1).get_max()
{'78.3': ({'30': (1, 74.0)}, None)}
>>> BinarySearchTree().insert(1, 783, 30, 740, 1).get_max()
{'783': ({'30': (1, 740)}, None)}
"""
if node is None:
if self.root is None:
return None
node = self.root
if not self.empty():
while node.right is not None:
node = node.right
return node
def get_min(self, node: Node | None = None) -> Node | None:
"""
We go deep on the left branch
>>> BinarySearchTree().insert(10, 20, 30, 40, 50).get_min()
{'10': (None, {'20': (None, {'30': (None, {'40': (None, 50)})})})}
>>> BinarySearchTree().insert(-5, -1, 0, -0.3, -4.5).get_min()
{'-5': (None, {'-1': (-4.5, {'0': (-0.3, None)})})}
>>> BinarySearchTree().insert(1, 78.3, 30, 74.0, 1).get_min()
{'1': (None, {'78.3': ({'30': (1, 74.0)}, None)})}
>>> BinarySearchTree().insert(1, 783, 30, 740, 1).get_min()
{'1': (None, {'783': ({'30': (1, 740)}, None)})}
"""
if node is None:
node = self.root
if self.root is None:
return None
if not self.empty():
node = self.root
while node.left is not None:
node = node.left
return node
def remove(self, value: int) -> None:
# Look for the node with that label
node = self.search(value)
if node is None:
msg = f"Value {value} not found"
raise ValueError(msg)
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(node, None)
elif node.left is None: # Has only right children
self.__reassign_nodes(node, node.right)
elif node.right is None: # Has only left children
self.__reassign_nodes(node, node.left)
else:
predecessor = self.get_max(
node.left
) # Gets the max value of the left branch
self.remove(predecessor.value) # type: ignore[union-attr]
node.value = (
predecessor.value # type: ignore[union-attr]
) # Assigns the value to the node to delete and keep tree structure
def preorder_traverse(self, node: Node | None) -> Iterable:
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left)
yield from self.preorder_traverse(node.right)
def traversal_tree(self, traversal_function=None) -> Any:
"""
This function traversal the tree.
You can pass a function to traversal the tree as needed by client code
"""
if traversal_function is None:
return self.preorder_traverse(self.root)
else:
return traversal_function(self.root)
def inorder(self, arr: list, node: Node | None) -> None:
"""Perform an inorder traversal and append values of the nodes to
a list named arr"""
if node:
self.inorder(arr, node.left)
arr.append(node.value)
self.inorder(arr, node.right)
def find_kth_smallest(self, k: int, node: Node) -> int:
"""Return the kth smallest element in a binary search tree"""
arr: list[int] = []
self.inorder(arr, node) # append all values to list using inorder traversal
return arr[k - 1]
def inorder(curr_node: Node | None) -> list[Node]:
"""
inorder (left, self, right)
"""
node_list = []
if curr_node is not None:
node_list = [*inorder(curr_node.left), curr_node, *inorder(curr_node.right)]
return node_list
def postorder(curr_node: Node | None) -> list[Node]:
"""
postOrder (left, right, self)
"""
node_list = []
if curr_node is not None:
node_list = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
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