enh(Python): binary tree (#665)
Cem Bağlum
GitHub
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@ -66,5 +66,5 @@
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## Trees
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- [Binary Tree](trees/binary_tree.py)
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- [Binary Search Tree](trees/binary-search-tree.py)
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- [Binary Search Tree](trees/binary_search_tree.py)
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@ -1,8 +1,5 @@
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# author: @hashfx
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# Binary Tree Data Structure
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# Data is stored in hierarchical form where a parent node can have at most 2 child nodes
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#
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# A
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# ___|___
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# B C
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@ -10,49 +7,56 @@
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# D E
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# / \ \
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# F G H
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#
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# Here, A is "ROOT NODE" and B, C are "CHILD NODE"
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# (B-D-F-G), (B-E-H) is a sub-tree
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# B is 'ROOT NODE' for D, E & D is 'ROOT NODE' for F, G & E is root node for H
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# Those nodes [C, F, G, H] who do not have any child node are "LEAF NODE"
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#
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# Rules for Binary Search Tree:
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# > All nodes are unique
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# > Right sub-tree > Left sub-tree ===== Left sub-tree < Right sub-tree
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# [Value(B<A AND C>A), Value(D<B AND E>B), Value(F<D AND G>D), Value(H>E)]
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# > One parent node can not have more than 2 child nodes
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# > Elements are not duplicated
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# Searching in Binary Tree:
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# Suppose we want to search E in the Tree:
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# > At Root Node(A) :: IF A>E THEN element would be at Left sub-Tree
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# > At Left sub-Tree(B) :: IF B<E THEN element would be at Right of the sub-tree
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#
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# Significance of BST:
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# With every iteration, search space is reduced by 1/2 (half)
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# Let no of nodes in a tree (n) be 8 then:
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# n = 8 [8->4->2->1] {Search completed in 3 iterations}
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# 3 compared to 8 is log(2)8 = 2
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# Search Complexity : O(log n)
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# Insertion Complexity : O(log n)
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#
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#
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# Types of BST:
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# Breadth First Search
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#
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#
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# Depth First Search
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# order here means base node
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# > In Order Traversal : first visit left sub-tree >> root node >> right sub-tree [F-D-G-B-H-E-A-C]
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# {Root node in between left and right tree}
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# > Pre Order Traversal : root node >> left sub-tree >> right sub-tree [A-B-D-F-G-E-H-C]
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# {Root node before left and right tree}
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# > Post Order Traversal : left sub-tree >> right sub-tree >> root node [F-G-D-H-E-B-C-A]
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# {Root node after left and right tree}
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""" Binary Tree Data Structure
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Data is stored in hierarchical form where a parent node can have at most 2 child nodes
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Here, A is "ROOT NODE" and B, C are "CHILD NODE"
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(B-D-F-G), (B-E-H) is a sub-tree
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B is 'ROOT NODE' for D, E & D is 'ROOT NODE' for F, G & E is root node for H
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Those nodes [C, F, G, H] who do not have any child node are "LEAF NODE"
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Rules for Binary Search Tree:
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> All nodes are unique
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> Right sub-tree > Left sub-tree ===== Left sub-tree < Right sub-tree
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[Value(B<A AND C>A), Value(D<B AND E>B), Value(F<D AND G>D), Value(H>E)]
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> One parent node can not have more than 2 child nodes
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> Elements are not duplicated
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Searching in Binary Tree:
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Suppose we want to search E in the Tree:
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> At Root Node(A) :: IF A>E THEN element would be at Left sub-Tree
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> At Left sub-Tree(B) :: IF B<E THEN element would be at Right of the sub-tree
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Significance of BST:
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With every iteration, search space is reduced by 1/2 (half)
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Let no of nodes in a tree (n) be 8 then:
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n = 8 [8->4->2->1] {Search completed in 3 iterations}
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3 compared to 8 is log(2)8 = 2
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Search Complexity : O(log n)
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Insertion Complexity : O(log n)
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Types of BST:
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Breadth First Search
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Depth First Search
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order here means base node
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> In Order Traversal :
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first visit left sub-tree >> root node >> right sub-tree [F-D-G-B-H-E-A-C]
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{Root node in between left and right tree}
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> Pre Order Traversal :
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root node >> left sub-tree >> right sub-tree [A-B-D-F-G-E-H-C]
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{Root node before left and right tree}
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> Post Order Traversal :
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left sub-tree >> right sub-tree >> root node [F-G-D-H-E-B-C-A]
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{Root node after left and right tree}"""
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class Node:
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# constructor
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""" constructor """
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def __init__(self, data):
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""" constructor """
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self.data = data
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self.left = None
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self.right = None
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@ -63,11 +67,12 @@ class Node:
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# checking if entered data is already present
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if data == self.data:
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return
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return None
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# if tree is empty means no node(root) at tree else incoming data will be treated as node(root(tree))
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# if tree is empty means no node(root) at tree
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# else incoming data will be treated as node(root(tree))
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if self.data:
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''' check if data(right) > data(left) & node(parent)'''
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# check if data(right) > data(left) & node(parent)
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if data < self.data: # data is smaller than data of node(parent)
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if self.left is None: # and if no element is present at left of node
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self.left = Node(data) # insert data at left
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@ -82,23 +87,27 @@ class Node:
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else:
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self.data = data # if tree is empty; treat incoming data as root of the tree
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return None
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def InOrderTraversal(self):
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def in_order_traversal(self):
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""" constructor """
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elements = [] # list to be filled with all elements of BST in specific order
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# In-order-Traversal : left sub-tree >> root node >> right sub-tree
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if self.left: # put elements of left sub-tree in list[elements]
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elements += self.left.InOrderTraversal()
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elements += self.left.in_order_traversal()
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elements.append(self.data) # put root node data in list[elements]
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if self.right: # put elements of right sub-tree in list[elements]
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elements += self.right.InOrderTraversal()
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elements += self.right.in_order_traversal()
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return elements # return list[elements]
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def PreOrderTraversal(self):
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def pre_order_traversal(self):
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""" constructor """
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elements = []
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# Pre-Order-Traversal : root node >> left sub-tree >> right sub-tree
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@ -106,23 +115,24 @@ class Node:
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elements.append(self.data) # put root node data in list[elements]
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if self.left: # put elements of left sub-tree in list[elements]
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elements += self.left.InOrderTraversal()
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elements += self.left.in_order_traversal()
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if self.right: # put elements of right sub-tree in list[elements]
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elements += self.right.InOrderTraversal()
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elements += self.right.in_order_traversal()
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return elements # return list[elements]
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def PostOrderTraversal(self):
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def post_order_traversal(self):
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""" constructor """
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elements = []
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# Pre-Order-Traversal : left sub-tree >> right sub-tree >> root node
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if self.left: # put elements of left sub-tree in list[elements]
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elements += self.left.InOrderTraversal()
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elements += self.left.in_order_traversal()
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if self.right: # put elements of right sub-tree in list[elements]
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elements += self.right.InOrderTraversal()
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elements += self.right.in_order_traversal()
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elements.append(self.data) # put root node data in list[elements]
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@ -137,22 +147,21 @@ class Node:
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# search for val in left sub-tree
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if self.left:
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return self.left.search(val)
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else:
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return False
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return False
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if val > self.data:
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# search for val in right sub-tree
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if self.right:
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return self.right.search(val)
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else:
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return False
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return False
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return None
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def max(self):
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'''Maximum element of tree: keep searching on right sub-tree to find maximum element '''
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if self.right is None: # leaf node
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return self.data
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return self.right.max()
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'''Maximum element of tree: keep searching on right sub-tree to find maximum element '''
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if self.right is None: # leaf node
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return self.data
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return self.right.max()
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def min(self):
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''' Minimum element of tree: keep searching on left sub-tree to find minimum element '''
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@ -162,6 +171,7 @@ class Node:
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def delete(self, val):
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""" constructor """
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if val < self.data: # search for element in left sub-tree
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if self.left: # check if there is any left sub-tree
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self.left = self.left.delete(val) # delete recursion
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@ -193,13 +203,13 @@ class Node:
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def build_tree(elements):
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""" constructor """
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root = Node(elements[0])
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for i in range(1, len(elements)):
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root.add_child(elements[i])
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return root
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# If build_tree() is not used
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# root = Node(4)
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# root.add_child(6)
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# root.add_child(3)
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# root.add_child(8)
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# root.add_child(5)
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''' smaller elements will be displayed at left/top of root node <--> greater elements will be displayed at
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right/bottom of root node '''
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# root.display()
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# main method
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if __name__ == '__main__':
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# Numeric BST
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num_list = [20, 18, 37, 15, 7, 5, 9, 18, 24, 0] # repeated elements are removed
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list_tree = build_tree(num_list)
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print(list_tree.InOrderTraversal()) # return list in sorted order
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print(list_tree.PreOrderTraversal()) # return list in sorted order
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print(list_tree.PostOrderTraversal()) # return list in sorted order
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print(list_tree.in_order_traversal()) # return list in sorted order
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print(list_tree.pre_order_traversal()) # return list in sorted order
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print(list_tree.post_order_traversal()) # return list in sorted order
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# list_tree.display() # display tree using display function
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print(list_tree.search(20)) # True
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print(list_tree.search(4)) # False
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list_tree.delete(20)
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print("Deleted element: ", list_tree.InOrderTraversal())
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print("Deleted element: ", list_tree.in_order_traversal())
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# String BST
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country = ["India", "Australia", "France", "Japan", "Sweden"]
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country_tree = build_tree(country)
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print(country_tree.InOrderTraversal()) # return list in sorted order
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print(country_tree.in_order_traversal()) # return list in sorted order
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print(country_tree.search("UK")) # False
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print(country_tree.search("Japan")) # True
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# Author: github.com/Mo-Shakib
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""" The binary tree data structure implementation """
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class Node:
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""" node class """
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def __init__(self, data = None):
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""" initializing function """
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self.left = None
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self.right = None
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self.data = data
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# for setting left node
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def setLeft(self, node):
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def set_left(self, node):
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""" for setting left node """
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self.left = node
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# for setting right node
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def setRight(self, node):
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def set_right(self, node):
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""" for setting right node """
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self.right = node
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# for getting the left node
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def getLeft(self):
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def get_left(self):
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""" for getting the left node """
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return self.left
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# for getting right node
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def getRight(self):
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def get_right(self):
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""" for getting right node """
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return self.right
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# for setting data of a node
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def setData(self, data):
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def set_data(self, data):
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""" for setting data of a node """
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self.data = data
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# for getting data of a node
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def getData(self):
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def get_data(self):
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""" for getting data of a node """
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return self.data
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def inorder(tree):
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""" in this we traverse first to the leftmost node,
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then print its data and then traverse for rightmost node """
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if tree:
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inorder(tree.get_left())
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print(tree.get_data(), end = ' ')
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inorder(tree.get_right())
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# in this we traverse first to the leftmost node, then print its data and then traverse for rightmost node
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def inorder(Tree):
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if Tree:
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inorder(Tree.getLeft())
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print(Tree.getData(), end = ' ')
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inorder(Tree.getRight())
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return
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def preorder(tree):
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""" in this we first print the root node
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and then traverse towards leftmost node and then to the rightmost node """
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if tree:
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print(tree.get_data(), end = ' ')
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preorder(tree.get_left())
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preorder(tree.get_right())
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# in this we first print the root node and then traverse towards leftmost node and then to the rightmost node
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def preorder(Tree):
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if Tree:
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print(Tree.getData(), end = ' ')
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preorder(Tree.getLeft())
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preorder(Tree.getRight())
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return
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# in this we first traverse to the leftmost node and then to the rightmost node and then print the data
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def postorder(Tree):
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if Tree:
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postorder(Tree.getLeft())
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postorder(Tree.getRight())
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print(Tree.getData(), end = ' ')
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return
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def postorder(tree):
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""" in this we first traverse to the leftmost node
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and then to the rightmost node and then print the data """
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if tree:
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postorder(tree.get_left())
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postorder(tree.get_right())
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print(tree.get_data(), end = ' ')
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if __name__ == '__main__':
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root = Node(1)
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root.setLeft(Node(2))
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root.setRight(Node(3))
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root.left.setLeft(Node(4))
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root.set_left(Node(2))
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root.set_right(Node(3))
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root.left.set_left(Node(4))
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print('Inorder Traversal:')
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inorder(root)
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