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# Python program to demonstrate
# insert operation in binary search tree
# A utility class that represents
# an individual node in a BST
class Node:
def __init__(self, key):
self.left = None
self.right = None
self.val = key
# A utility function to insert
# a new node with the given key
def insert(root, key):
if root is None:
return Node(key)
else:
if root.val == key:
return root
elif root.val < key:
root.right = insert(root.right, key)
else:
root.left = insert(root.left, key)
return root
# A utility function to do inorder tree traversal
def inorder(root):
if root:
inorder(root.left)
print(root.val)
inorder(root.right)
# Driver program to test the above functions
# Let us create the following BST
# 50
# / \
# 30 70
# / \ / \
# 20 40 60 80
r = Node(50)
r = insert(r, 30)
r = insert(r, 20)
r = insert(r, 40)
r = insert(r, 70)
r = insert(r, 60)
r = insert(r, 80)
# Print inoder traversal of the BST
inorder(r)
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class BSTNode:
def __init__(self, key=None):
self.left = None
self.right = None
self.key = key
# Insert method can add a list of nodes to the BST
def insert(self, keyList):
for i in keyList:
self.insertKey(i)
# This insertKey
def insertKey(self, key):
if not self.key:
self.key = key
return
if self.key == key:
return
if key < self.key:
if self.left:
self.left.insertKey(key)
return
self.left = BSTNode(key)
return
if self.right:
self.right.insertKey(key)
return
self.right = BSTNode(key)
def display(self):
lines, *_ = self._display_aux()
for line in lines:
print(line)
def _display_aux(self):
"""Returns list of strings, width, height, and horizontal coordinate of the root."""
# No child.
if self.right is None and self.left is None:
line = '%s' % self.key
width = len(line)
height = 1
middle = width // 2
return [line], width, height, middle
# Only left child.
if self.right is None:
lines, n, p, x = self.left._display_aux()
s = '%s' % self.key
u = len(s)
first_line = (x + 1) * ' ' + (n - x - 1) * '_' + s
second_line = x * ' ' + '/' + (n - x - 1 + u) * ' '
shifted_lines = [line + u * ' ' for line in lines]
return [first_line, second_line] + shifted_lines, n + u, p + 2, n + u // 2
# Only right child.
if self.left is None:
lines, n, p, x = self.right._display_aux()
s = '%s' % self.key
u = len(s)
first_line = s + x * '_' + (n - x) * ' '
second_line = (u + x) * ' ' + '\\' + (n - x - 1) * ' '
shifted_lines = [u * ' ' + line for line in lines]
return [first_line, second_line] + shifted_lines, n + u, p + 2, u // 2
# Two children.
left, n, p, x = self.left._display_aux()
right, m, q, y = self.right._display_aux()
s = '%s' % self.key
u = len(s)
first_line = (x + 1) * ' ' + (n - x - 1) * '_' + s + y * '_' + (m - y) * ' '
second_line = x * ' ' + '/' + (n - x - 1 + u + y) * ' ' + '\\' + (m - y - 1) * ' '
if p < q:
left += [n * ' '] * (q - p)
elif q < p:
right += [m * ' '] * (p - q)
zipped_lines = zip(left, right)
lines = [first_line, second_line] + [a + u * ' ' + b for a, b in zipped_lines]
return lines, n + m + u, max(p, q) + 2, n + u // 2
#Inorder Walk
def inorder(self):
if self.left:
self.left.inorder()
print(self.key)
if self.right:
self.right.inorder()
a = BSTNode()
a.insert([5,7,4,3,5,1,3,6]) #inserting some random numbers in the form of list
a.inorder()
a.display()
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"""
17
/ \
/ \
/ \
4 20
/\ /\
/ \ / \
/ \ / \
1 9 18 23
\
\
\
34
"""
# Binary Search Tree implementation in Python
class BinaryTreeNode():
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def add_child(self, data):
if data == self.data: # check if the of new data exist already in the tree, if yes don't add
return
if data < self.data:
# Add to left subtree
if self.left:
self.left.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.left = BinaryTreeNode(data)
else:
# Add to right subtree
if self.right:
self.right.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.right = BinaryTreeNode(data)
# Visit Left subtree, then Root node and finaly Right subtree
def in_order_traversal(self): # Left - Root - Right
elements = []
# Getting all elements of the Left Subtree
if self.left:
elements += self.left.in_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
elements.append(self.data) # Adding the root node to the list
# Getting all elements of the Right Subtree
if self.right:
elements += self.right.in_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements
# Get all elements from the Root node then the left subtree and finanally the Right subtree
def pre_order_traversal(self): # Root - Left - Right
elements = []
elements.append(self.data)
if self.left:
elements += self.left.pre_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.pre_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements # get the Root node element
# Get all elements from the Right subtree then the left subtree and finally the Root node
def post_order_traversal(self):
elements = []
if self.left:
elements += self.left.post_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.post_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
elements.append(self.data) # Get the Root node element
return elements
def search_element(self, elem): # complexity of log n O(log n)
if self.data == elem:
return True
elif elem < self.data:
# This means if present, element would be on the left
if self.left:
return self.left.search_element(elem)
else:
return False
else:
# This means if present, element would be on the right
if self.right:
return self.right.search_element(elem)
else:
return False
def sum_of_all_elements_in_tree(self):
return sum(self.in_order_traversal())
def max_element_in_tree(self):
return max(self.in_order_traversal())
def min_element_in_tree(self):
return min(self.in_order_traversal())
# Tree Builder helper method
def build_binary_tree(lst_elem: list):
if len(lst_elem) >1:
root_node = BinaryTreeNode(lst_elem[0])
for i in lst_elem[1:]:
root_node.add_child(i)
#root_node.search_element(20)
#print(root_node.in_order_traversal())
return root_node
else:
return print("Insufficient number of elements")
if __name__ == '__main__':
mt = build_binary_tree([17, -5, 4, 1, 20, 9, -1, 23, 18, 0, 34])
print("In Order Traversal", mt.in_order_traversal())
print("Post Order Traversal", mt.post_order_traversal())
print("Pre Order Traversal", mt.pre_order_traversal())
print(mt.search_element(20))
print("Sum of all elemnts in tree", mt.sum_of_all_elements_in_tree())
print("Max element in tree is", mt.max_element_in_tree())
print("Min element in tree is", mt.min_element_in_tree())
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Binary Search Tree at this link:
https://github.com/shreyasvedpathak/Data-Structure-Python/tree/master/BinaryTrees
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def search(node, search_item):
# Base case for recursion:
# The recursion will stop if the search item has been found
if search_item == node.data:
return True
# Check if the search item is greater than the node data
# and there is another node to the right to check
elif search_item > node.data and node.right is not None:
return search(node.right, search_item)
# Check if the search item is less than the node data
# and there is another node to the left to check
elif search_item < node.data and node.left is not None:
return search(node.left, search_item)
# Otherwise the search item does not exist
else:
return False
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Binary Tree implementation at this link:
https://github.com/shreyasvedpathak/Data-Structure-Python/tree/master/BinaryTrees