“Kod do drukowania drzewa wyszukiwania binarnego w Python” Kod odpowiedzi

Kod do drukowania drzewa wyszukiwania binarnego w Python



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()
Poised Panda

Implementacja drzewa wyszukiwania binarnego w Python

"""
	              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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