Tree Sort

Tree sort is an efficient sorting algorithm that uses a binary tree data structure to sort data. It is a comparison-based sorting algorithm, meaning it compares two elements at a time to determine their relative order. Tree sort is a stable sorting algorithm, meaning it preserves the relative order of equal elements. Tree sort has a time complexity of O(n log n) on average and in the worst case, and a space complexity of O(n) in the worst case.

What is a Binary Tree?

A binary tree is a data structure that stores data in nodes. A node has two children, a left child and a right child. The left child is always less than the parent node and the right child is always greater than the parent node. This allows the tree to be sorted in an efficient manner.

How Tree Sort Works

Tree sort works by inserting the elements of an unsorted list into a binary tree. The tree is then traversed in-order to retrieve the elements in sorted order. Tree sort is an efficient sorting algorithm as it only requires one comparison per item, making it faster than other comparison-based sorting algorithms such as quicksort and merge sort.

Python Code

The following Python code shows how tree sort works:

# Define a Node class
class Node:

def __init__(self, value):
    self.value = value
    self.left = None
    self.right = None

# Define a Tree class
class Tree:

def __init__(self):
    self.root = None

# Insert a new node into the tree
def insert(self, value):
    new_node = Node(value)
    if self.root == None:
        self.root = new_node
    else:
        curr_node = self.root
        while True:
            if value <= curr_node.value:
                if curr_node.left == None:
                    curr_node.left = new_node
                    break
                else:
                    curr_node = curr_node.left
            else:
                if curr_node.right == None:
                    curr_node.right = new_node
                    break
                else:
                    curr_node = curr_node.right

# Traverse the tree in-order to retrieve the elements 
# in sorted order
def in_order_traversal(self, node):
    if node != None:
        self.in_order_traversal(node.left)
        print(node.value)
        self.in_order_traversal(node.right)

# Sort the elements of an unsorted list 
# using tree sort
def tree_sort(unsorted_list):
    tree = Tree()
    for item in unsorted_list:
        tree.insert(item)
    sorted_list = []
    tree.in_order_traversal(tree.root, sorted_list)
    return sorted_list

Time Complexity

Tree sort has a time complexity of O(n log n) in the average and worst case. This is because the tree must be traversed in-order to retrieve the elements in sorted order, which requires O(n log n) time.

Space Complexity

Tree sort has a space complexity of O(n) in the worst case. This is because the tree data structure requires O(n) space to store the elements.

Conclusion

Tree sort is an efficient sorting algorithm that uses a binary tree data structure to sort data. It is a comparison-based sorting algorithm, meaning it compares two elements at a time to determine their relative order. Tree sort has a time complexity of O(n log n) in the average and worst case, and a space complexity of O(n) in the worst case.

Exercises

def tree_sort(unsorted_list):
    tree = Tree()
    for item in unsorted_list:
        tree.insert(item)
    sorted_list = []
    tree.in_order_traversal(tree.root, sorted_list)
    return sorted_list

def pre_order_traversal(self, node):
    if node != None:
        print(node.value)
        self.pre_order_traversal(node.left)
        self.pre_order_traversal(node.right)

def post_order_traversal(self, node):
    if node != None:
        self.post_order_traversal(node.left)
        self.post_order_traversal(node.right)
        print(node.value)

def deepest_node(root):
    if root is None:
        return None
    queue = [root]
    deepest_node = None
    while queue:
        node = queue.pop(0)
        if node.left:
            queue.append(node.left)
        if node.right:
            queue.append(node.right)
        deepest_node = node
    return deepest_node

def height(root):
    if root is None:
        return 0
    else:
        left_height = height(root.left)
        right_height = height(root.right)
        if left_height > right_height:
            return left_height + 1
        else:
            return right_height + 1