Binary Search Tree (Python) not showing result - python

I'm somewhat new to Python and need help with an issue I'm facing. I'm trying to make a binary search tree. I've written a code and it works, but won't show any result (printed values). I can't figure out what the issue might be. Here is the entire code:
class Node:
def __init__(self, value):
self.value = value
self.left_child = None
self.right_child = None
class binary_search_tree:
def __init__(self):
self.root_node = None
def insert(self, value, curr_node):
if self.root_node == None:
self.root_node == node(value)
elif self.root_node < value:
if self.right_child == None:
self.right.child = value
else:
curr_node == self.right_child
if curr_node < value:
curr_node.right_child = node(value)
elif curr_node > value:
curr_node.left_child = node(value)
else:
print("Error! Value Already Exists!")
elif self.root_node > value:
if self.left_child == None:
self.left.child = value
else:
curr_node == self.left_child
if curr_node < value:
curr_node.right_child = node(value)
elif curr_node > value:
curr_node.left_child = node(value)
else:
print("Error! Value Already Exists!")
else:
print("Error! Value Already Exists!")
def fill_Tree(tree, num_elems = 100, max_int = 1000):
from random import randint
for x in range (num, elems):
curr_elem = randint(0, max_int)
tree.insert(curr_elem)
return tree
I have made a class Node to handle the nodes and a function insert that helps to insert the values. It check for the root node. If its there, it moves onto the leaf based on the values. If not, it adds the value as the root. The program keeps on checking for values and nodes and their differences (less than, greater than etc), just the ways a tree is supposed to function. The program executes, but nothing shows up. Not sure what I'm doing wrong though.
Any sort of help would be appreciated!
Thanks.

If that is your entire code, and if the input and execution is perfect, it would not show any result, because you are not printing any result.
You don't have an apparent main function to create an object of the class binar_search_tree.
The print statements that you have are only when there is an error. If everything works perfectly, your code doesn't print anything
You would need a method that can display the the tree

Currently, your insertion method is assigning values to the left or right children of the root, not traveling to a depth beyond those two nodes, should a value smaller than the left child or greater than the right child be found. Instead, create one class to store the value, left, and right child, along with the necessary insertion method. To determine if a value exists in the tree, it is cleaner to utilize __getitem__ with recursion:
def check_val(f):
def wrapper(cls, _val):
if _val in cls.__class__.seen:
raise ValueError(f"'{_val}' already in '{cls.__class__.__name__}'")
return f(cls, _val)
return wrapper
class Tree:
seen = []
def __init__(self, value=None):
self.left = None
self.value = value
self.right = None
def __lt__(self, _node):
return self.value < getattr(_node, 'value', _node)
#check_val
def insert_val(self, _val):
if self.value is None:
self.value = _val
self.__class__.seen.append(_val)
else:
if _val < self.value:
if self.left is None:
self.left = Tree(_val)
Tree.seen.append(_val)
else:
self.left.insert_val(_val)
else:
if self.right is None:
self.right = Tree(_val)
Tree.seen.append(_val)
else:
self.right.insert_val(_val)
def __getitem__(self, val):
if self.value == val:
return True
if val < self.value:
return getattr(self.left, '__getitem__', lambda _:False)(val)
return getattr(self.right, '__getitem__', lambda _:False)(val)
#classmethod
def load_tree(cls, size = 10):
_t = cls()
import random
for _ in range(size):
_t.insert_val(random.randint(1, 100))
return _t
To run:
t = Tree.load_tree()
print(t.__class__.seen)
#[82, 94, 33, 59, 73, 72, 96, 14, 58, 67]
for i in t.__class__.seen:
assert t[i]
print('all cases passed')
Output:
'all cases passed'

Related

Binary Search Tree Find minimum not clear

The logic I tried:
def min_tree_value(self):
while self.left:
self.left = self.left.left
return self.data
Actual Python program Logic:
def min_tree_value(self):
if self.left is None:
return self.data
return self.left.min_tree_value()
The actual Python program logic is in recursion form. I tried the same logic in While loop()
I'm not sure whether my logic is correct. Do help me to figure out the incorrect logic and point where I'm Wrong.
Your logic is almost there, but not quite:
def min_tree_value(self):
node = self
while node.left:
# don't change the structure by rebinding node.left,
# but iterate the tree by moving along nodes!
node = node.left
return node.data
Note that in the original code, you never reassign self before returning its value, so you always returned the root value.
First of all, the question asks about finding the minimum element in a binary tree.
The algorithm you used, will find the minimum element in the Binary Search Tree (as the leftmost element is the minimum).
For finding minimum element in a simple Binary Tree, use the following algorithm:
# Returns the min value in a binary tree
def find_min_in_BT(root):
if root is None:
return float('inf')
res = root.data
lres = find_min_in_BT(root.leftChild)
rres = find_min_in_BT(root.rightChild)
if lres < res:
res = lres
if rres < res:
res = rres
return res
Additions to the answer after OP changed the question:
The logic for the algorithm you tried is correct, with a small correction in the implementation: self = self.data. Both of them find the leftmost element.
I have also tested both the functions which return the same output:
class Node:
def __init__(self, data):
self.left = None
self.right = None
self.data = data
def insert(self, data):
if self.data:
if data < self.data:
if self.left is None:
self.left = Node(data)
else:
self.left.insert(data)
elif data > self.data:
if self.right is None:
self.right = Node(data)
else:
self.right.insert(data)
else:
self.data = data
def findval(self, lkpval):
if lkpval < self.data:
if self.left is None:
return str(lkpval)+" Not Found"
return self.left.findval(lkpval)
elif lkpval > self.data:
if self.right is None:
return str(lkpval)+" Not Found"
return self.right.findval(lkpval)
else:
print(str(self.data) + ' is found')
def PrintTree(self):
if self.left:
self.left.PrintTree()
print( self.data),
if self.right:
self.right.PrintTree()
def min_tree_value_original(self):
if self.left is None:
return self.data
return self.left.min_tree_value_original()
def min_tree_value_custom(self):
while self.left:
self = self.left
return self.data
root = Node(12)
root.insert(6)
root.insert(14)
root.insert(3)
root.insert(3)
root.insert(1)
root.insert(0)
root.insert(-1)
root.insert(-2)
print(root.min_tree_value_original())
print(root.min_tree_value_custom())
Output:
-2
-2
Here -2 is the smallest and the leftmost element in the BST.

Issues inserting into arbitrary positions (not beginning or end) of doubly linked list

Ok to preface, this is to help with a school assignment. I understand there are unpythonic methods in the following code but this is the format they insist upon. In my insert method I have 4 cases to account for. All of them are dealt with appropriately except for else and im unsure why. For some reason the method insert isn't updating the linked list to include the new_node when this new_node is not placed at the end or start of the list. I'm unsure why this is as at the appropriate position we store the old value of current, we then store its previous and we set current = new_node, we then set new_node's next to be the old value of current and new_node's previous to be the old currents previous. I'm confused as to why this won't work.
class DLinkedListNode:
# An instance of this class represents a node in Doubly-Linked List
def __init__(self,initData,initNext,initPrevious):
self.data = initData
self.next = initNext
self.previous = initPrevious
if initNext != None:
self.next.previous = self
if initPrevious != None:
self.previous.next = self
def getData(self):
return self.data
def setData(self,newData):
self.data = newData
def getNext(self):
return self.next
def getPrevious(self):
return self.previous
def setNext(self,newNext):
self.next = newNext
def setPrevious(self,newPrevious):
self.previous = newPrevious
class DLinkedList:
# An instance of this class represents the Doubly-Linked List
def __init__(self):
self.__head=None
self.__tail=None
self.__size=0
def search(self, item):
current = self.__head
found = False
while current != None and not found:
if current.getData() == item:
found= True
else:
current = current.getNext()
return found
def index(self, item):
current = self.__head
found = False
index = 0
while current != None and not found:
if current.getData() == item:
found= True
else:
current = current.getNext()
index = index + 1
if not found:
index = -1
return index
def add(self, item):
new_node=DLinkedListNode(item,None,None)
new_node.setNext(self.__head)
self.__head=new_node
current=self.__head
new_node.setPrevious(None)
current=current.getNext()
self.__size+=1
def remove(self, item):
# remove the node containing the item from the list
if self.__size == 0:
raise Exception('List is Empty')
current = self.__head
previous = None
found = False
while current != None and not found:
if current.getData() == item:
found = True
else:
previous = current
current = current.getNext()
if not found:
raise Exception('Item not in list')
else:
if previous == None: # the item is in the first node of the list
self.__head = current.getNext()
else: # item is not in the first node
previous.setNext(current.getNext())
self.__size = self.__size -1
def append(self, item):
# adds an item at the end of the list
new_node = DLinkedListNode(item,None,None)
current = self.__head # Start the traversal
if self.__size == 0: # check if list is empty
self.add(item)
else:
while (current.getNext()!=None):
current= current.getNext() # traversing the list
new_node.setNext(None)
new_node.setPrevious(current)
current.setNext(new_node)
self.__size = self.__size +1
def insert(self, pos, item):
# inserts the item at pos
# pos should be a positive number (or zero) of type int
assert type(pos)==int,'Error:pos is not an integer'
assert pos>=0,'Error:pos must be positive'
current=self.__head
new_node= DLinkedListNode(item,None,None)
if pos==0:
self.add(item)
elif pos==self.__size:
self.append(item)
elif pos>self.__size:
raise Exception('Position attempted to enter is larger than the size of linked list.')
else:
current_pos=0
while(current.getNext()!=None):
if (pos)==current_pos:
# storage is a holder variable
storage=current
right=current.getNext()
left=current.getPrevious()
current=new_node
new_node.setPrevious(left)
new_node.setNext(storage)
return True
current=current.getNext()
current_pos+=1
self.__size+=1
# doubly linked list
#Hello(prev)<-->World(store data)-->None
def pop1(self):
current = self.__head
previous = None
while (current.getNext() != None):
previous = current
current = current.getNext()
if (previous == None):
self.__head = None
else:
previous.setNext(None)
self.__size -= 1
return current.getData()
def pop(self, pos=None):
if pos!=None:
assert pos<=self.__size,'Pos must be within list'
assert type(pos)==int,'Pos must be an int'
assert pos>=0,'Pos must not be negative'
current=self.__head
current_pos=0
if pos==(self.getSize()-1) or pos==None:
data_from_method=self.pop1()
return data_from_method
else:
while current.getNext()!=None:
if pos==current_pos:
data=current.getData()
left=current.getPrevious()
right=current.getNext()
left.setNext(right)
right.setPrevious(left)
return data
current_pos+=1
current=current.getNext()
# doubly linked list
#Hello(prev)<-->World(store data)-->None
def searchLarger(self, item):
current=self.__head
current_pos=0
while current.getNext()!=None:
if item<current.getData():
return current_pos
current=current.getNext()
current_pos+=1
return -1
def getSize(self):
return self.__size
def getItem(self, pos):
assert type(pos)==int,'position must be type int'
assert pos<=self.__size,'Position is outside of the list'
current=self.__head
current_pos=0
if pos>=0:
while current!=None:
if current_pos==pos:
return current.getData()
current_pos+=1
current=current.getNext()
else:
current=self.__tail
while current!=None:
if current_pos==pos:
return current.getData()
current_pos-=1
current=current.getPrevious()
def __str__(self):
# returns a string representation of the list
current = self.__head
string = ''
while current != None:
if current.getNext()==None:
string = string + str(current.getData())+''
else:
string=string+str(current.getData())+' '
current = current.getNext()
return string
def test():
new_list=DLinkedList()
for i in range(20):
new_list.insert(i,i)
new_list.insert(1,90)
print(new_list)
test()
You currently have code that links the new node to nodes in the list, but no code that links the existing nodes to the new node. The only gesture you have in that direction is current=new_node, which doesn't achieve anything useful (since current is a local variable and you're about to return from the function).
You probably want something like left.setNext(newnode) and right.setPrevious(newnode), though I'm not sure you've set up both of those variables correctly (one of them should probably be current, unless you're intending to replace an existing node).

recursive insert only inserts first element python bst

I'm trying to implement insert function of bst but my recursive approach only inserts the first value. My approach was to traverse until the empty child and set that node to new value.
class BSTNode(object):
def __init__(self, val):
self.val = val
self.left = None
self.right = None
def insert(self, val):
if self.root is None:
self.root = BSTNode(val)
else:
self.insertNode(self.root,val)
def insertNode(self,node, val):
if node is None:
node = BSTNode(val)
return
elif val <= node.val:
node.left = self.insertNode(node.left, val)
elif val > node.val:
node.right = self.insertNode(node.right, val)
bst = new BST()
bst.insert(5)
bst.insert(10)
You're not returning BSTNode in insertNode (so you create a node but return None). You should do this instead:
if node is None:
return BSTNode(val)
So the full method becomes:
def insertNode(self, node, val):
if node is None:
return BSTNode(val)
elif val <= node.val:
node.left = self.insertNode(node.left, val)
elif val > node.val:
node.right = self.insertNode(node.right, val)
return node
It might be helpful to first create a node class... it's easier to think of a node alone than it is to think about a Tree.
class Node:
def __init__(self,value):
self.value = value
self.left = None
self.right = None
def insert_value(self,value):
# there are 3 cases
if not self.value: # in one case this node has no value
self.value = value
elif value < self.value: # if value is less than this nodes value
# then insert it to the left
if not self.left:
self.left = Node(value)
else:
self.left.insert_value(value)
else: # otherwise if value is greater than or equal to this nodes value
# then insert it to the right
if not self.right:
self.right = Node(value)
else:
self.right.insert_value(value)
Once you have this Node class the tree implementation becomes trivial.
class BST:
def __init__(self,root_value=None):
self.root = Node(root_value)
def insert_value(self,value):
self.root.insert_value(value)
bst = BST()
bst.insert_value(5)
bst.insert_value(10)
print(bst.root)
Now you certainly do not have to solve this in this fashion ... but it makes it easier to reason about (at least for me).

Why does my min function always return None?

I'm trying to write a min method for a binary search tree, but for some reason the output is always None!
I understand that I always have to go left in order to find the minimum and that's what my code is doing but I don't understand why I always get None as an output!
class Tree_node():
def __init__(self, key, val):
self.key = key
self.val = val
self.left = None
self.right = None
class BSearch_tree():
def __init__(self):
self.root=None
def insert(self, key, val):
if self.root==None:
self.root=Tree_node(key,val)
elif key == self.root.key:
self.val=val
elif key < self.root.key:
left = BSearch_tree()
left.root = self.root.left
self.root.left=BSearch_tree.insert(left,key,val)
elif key > self.root.key:
right = BSearch_tree()
right.root=self.root.right
self.root.right=BSearch_tree.insert(right,key,val)
def min(self):
if self.root==None:
return None
current= self
while current.root.left!=None:
current=current.root.left
return current

How to implement a binary search tree in Python?

This is what I've got so far but it is not working:
class Node:
rChild,lChild,data = None,None,None
def __init__(self,key):
self.rChild = None
self.lChild = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
def main():
t = Tree()
t.root = Node(4)
t.root.rchild = Node(5)
print t.root.data #this works
print t.root.rchild.data #this works too
t = Tree()
t.insert(t.root,4)
t.insert(t.root,5)
print t.root.data #this fails
print t.root.rchild.data #this fails too
if __name__ == '__main__':
main()
Here is a quick example of a binary insert:
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
if root is None:
root = node
else:
if root.data > node.data:
if root.l_child is None:
root.l_child = node
else:
binary_insert(root.l_child, node)
else:
if root.r_child is None:
root.r_child = node
else:
binary_insert(root.r_child, node)
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print root.data
in_order_print(root.r_child)
def pre_order_print(root):
if not root:
return
print root.data
pre_order_print(root.l_child)
pre_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
3
/ \
1 7
/
5
print "in order:"
in_order_print(r)
print "pre order"
pre_order_print(r)
in order:
1
3
5
7
pre order
3
1
7
5
class Node:
rChild,lChild,data = None,None,None
This is wrong - it makes your variables class variables - that is, every instance of Node uses the same values (changing rChild of any node changes it for all nodes!). This is clearly not what you want; try
class Node:
def __init__(self, key):
self.rChild = None
self.lChild = None
self.data = key
now each node has its own set of variables. The same applies to your definition of Tree,
class Tree:
root,size = None,0 # <- lose this line!
def __init__(self):
self.root = None
self.size = 0
Further, each class should be a "new-style" class derived from the "object" class and should chain back to object.__init__():
class Node(object):
def __init__(self, data, rChild=None, lChild=None):
super(Node,self).__init__()
self.data = data
self.rChild = rChild
self.lChild = lChild
class Tree(object):
def __init__(self):
super(Tree,self).__init__()
self.root = None
self.size = 0
Also, main() is indented too far - as shown, it is a method of Tree which is uncallable because it does not accept a self argument.
Also, you are modifying the object's data directly (t.root = Node(4)) which kind of destroys encapsulation (the whole point of having classes in the first place); you should be doing something more like
def main():
t = Tree()
t.add(4) # <- let the tree create a data Node and insert it
t.add(5)
class Node:
rChild,lChild,parent,data = None,None,None,0
def __init__(self,key):
self.rChild = None
self.lChild = None
self.parent = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,someNumber):
self.size = self.size+1
if self.root is None:
self.root = Node(someNumber)
else:
self.insertWithNode(self.root, someNumber)
def insertWithNode(self,node,someNumber):
if node.lChild is None and node.rChild is None:#external node
if someNumber > node.data:
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
else: #not external
if someNumber > node.data:
if node.rChild is not None:
self.insertWithNode(node.rChild, someNumber)
else: #if empty node
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
if node.lChild is not None:
self.insertWithNode(node.lChild, someNumber)
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
def printTree(self,someNode):
if someNode is None:
pass
else:
self.printTree(someNode.lChild)
print someNode.data
self.printTree(someNode.rChild)
def main():
t = Tree()
t.insert(5)
t.insert(3)
t.insert(7)
t.insert(4)
t.insert(2)
t.insert(1)
t.insert(6)
t.printTree(t.root)
if __name__ == '__main__':
main()
My solution.
class BST:
def __init__(self, val=None):
self.left = None
self.right = None
self.val = val
def __str__(self):
return "[%s, %s, %s]" % (self.left, str(self.val), self.right)
def isEmpty(self):
return self.left == self.right == self.val == None
def insert(self, val):
if self.isEmpty():
self.val = val
elif val < self.val:
if self.left is None:
self.left = BST(val)
else:
self.left.insert(val)
else:
if self.right is None:
self.right = BST(val)
else:
self.right.insert(val)
a = BST(1)
a.insert(2)
a.insert(3)
a.insert(0)
print a
The Op's Tree.insert method qualifies for the "Gross Misnomer of the Week" award -- it doesn't insert anything. It creates a node which is not attached to any other node (not that there are any nodes to attach it to) and then the created node is trashed when the method returns.
For the edification of #Hugh Bothwell:
>>> class Foo(object):
... bar = None
...
>>> a = Foo()
>>> b = Foo()
>>> a.bar
>>> a.bar = 42
>>> b.bar
>>> b.bar = 666
>>> a.bar
42
>>> b.bar
666
>>>
The accepted answer neglects to set a parent attribute for each node inserted, without which one cannot implement a successor method which finds the successor in an in-order tree walk in O(h) time, where h is the height of the tree (as opposed to the O(n) time needed for the walk).
Here is an implementation based on the pseudocode given in Cormen et al., Introduction to Algorithms, including assignment of a parent attribute and a successor method:
class Node(object):
def __init__(self, key):
self.key = key
self.left = None
self.right = None
self.parent = None
class Tree(object):
def __init__(self, root=None):
self.root = root
def insert(self, z):
y = None
x = self.root
while x is not None:
y = x
if z.key < x.key:
x = x.left
else:
x = x.right
z.parent = y
if y is None:
self.root = z # Tree was empty
elif z.key < y.key:
y.left = z
else:
y.right = z
#staticmethod
def minimum(x):
while x.left is not None:
x = x.left
return x
#staticmethod
def successor(x):
if x.right is not None:
return Tree.minimum(x.right)
y = x.parent
while y is not None and x == y.right:
x = y
y = y.parent
return y
Here are some tests to show that the tree behaves as expected for the example given by DTing:
import pytest
#pytest.fixture
def tree():
t = Tree()
t.insert(Node(3))
t.insert(Node(1))
t.insert(Node(7))
t.insert(Node(5))
return t
def test_tree_insert(tree):
assert tree.root.key == 3
assert tree.root.left.key == 1
assert tree.root.right.key == 7
assert tree.root.right.left.key == 5
def test_tree_successor(tree):
assert Tree.successor(tree.root.left).key == 3
assert Tree.successor(tree.root.right.left).key == 7
if __name__ == "__main__":
pytest.main([__file__])
Just something to help you to start on.
A (simple idea of) binary tree search would be quite likely be implement in python according the lines:
def search(node, key):
if node is None: return None # key not found
if key< node.key: return search(node.left, key)
elif key> node.key: return search(node.right, key)
else: return node.value # found key
Now you just need to implement the scaffolding (tree creation and value inserts) and you are done.
I find the solutions a bit clumsy on the insert part. You could return the root reference and simplify it a bit:
def binary_insert(root, node):
if root is None:
return node
if root.data > node.data:
root.l_child = binary_insert(root.l_child, node)
else:
root.r_child = binary_insert(root.r_child, node)
return root
its easy to implement a BST using two classes, 1. Node and 2. Tree
Tree class will be just for user interface, and actual methods will be implemented in Node class.
class Node():
def __init__(self,val):
self.value = val
self.left = None
self.right = None
def _insert(self,data):
if data == self.value:
return False
elif data < self.value:
if self.left:
return self.left._insert(data)
else:
self.left = Node(data)
return True
else:
if self.right:
return self.right._insert(data)
else:
self.right = Node(data)
return True
def _inorder(self):
if self:
if self.left:
self.left._inorder()
print(self.value)
if self.right:
self.right._inorder()
class Tree():
def __init__(self):
self.root = None
def insert(self,data):
if self.root:
return self.root._insert(data)
else:
self.root = Node(data)
return True
def inorder(self):
if self.root is not None:
return self.root._inorder()
else:
return False
if __name__=="__main__":
a = Tree()
a.insert(16)
a.insert(8)
a.insert(24)
a.insert(6)
a.insert(12)
a.insert(19)
a.insert(29)
a.inorder()
Inorder function for checking whether BST is properly implemented.
Another Python BST with sort key (defaulting to value)
LEFT = 0
RIGHT = 1
VALUE = 2
SORT_KEY = -1
class BinarySearchTree(object):
def __init__(self, sort_key=None):
self._root = []
self._sort_key = sort_key
self._len = 0
def insert(self, val):
if self._sort_key is None:
sort_key = val // if no sort key, sort key is value
else:
sort_key = self._sort_key(val)
node = self._root
while node:
if sort_key < node[_SORT_KEY]:
node = node[LEFT]
else:
node = node[RIGHT]
if sort_key is val:
node[:] = [[], [], val]
else:
node[:] = [[], [], val, sort_key]
self._len += 1
def minimum(self):
return self._extreme_node(LEFT)[VALUE]
def maximum(self):
return self._extreme_node(RIGHT)[VALUE]
def find(self, sort_key):
return self._find(sort_key)[VALUE]
def _extreme_node(self, side):
if not self._root:
raise IndexError('Empty')
node = self._root
while node[side]:
node = node[side]
return node
def _find(self, sort_key):
node = self._root
while node:
node_key = node[SORT_KEY]
if sort_key < node_key:
node = node[LEFT]
elif sort_key > node_key:
node = node[RIGHT]
else:
return node
raise KeyError("%r not found" % sort_key)
Here is a compact, object oriented, recursive implementation:
class BTreeNode(object):
def __init__(self, data):
self.data = data
self.rChild = None
self.lChild = None
def __str__(self):
return (self.lChild.__str__() + '<-' if self.lChild != None else '') + self.data.__str__() + ('->' + self.rChild.__str__() if self.rChild != None else '')
def insert(self, btreeNode):
if self.data > btreeNode.data: #insert left
if self.lChild == None:
self.lChild = btreeNode
else:
self.lChild.insert(btreeNode)
else: #insert right
if self.rChild == None:
self.rChild = btreeNode
else:
self.rChild.insert(btreeNode)
def main():
btreeRoot = BTreeNode(5)
print 'inserted %s:' %5, btreeRoot
btreeRoot.insert(BTreeNode(7))
print 'inserted %s:' %7, btreeRoot
btreeRoot.insert(BTreeNode(3))
print 'inserted %s:' %3, btreeRoot
btreeRoot.insert(BTreeNode(1))
print 'inserted %s:' %1, btreeRoot
btreeRoot.insert(BTreeNode(2))
print 'inserted %s:' %2, btreeRoot
btreeRoot.insert(BTreeNode(4))
print 'inserted %s:' %4, btreeRoot
btreeRoot.insert(BTreeNode(6))
print 'inserted %s:' %6, btreeRoot
The output of the above main() is:
inserted 5: 5
inserted 7: 5->7
inserted 3: 3<-5->7
inserted 1: 1<-3<-5->7
inserted 2: 1->2<-3<-5->7
inserted 4: 1->2<-3->4<-5->7
inserted 6: 1->2<-3->4<-5->6<-7
Here is a working solution.
class BST:
def __init__(self,data):
self.root = data
self.left = None
self.right = None
def insert(self,data):
if self.root == None:
self.root = BST(data)
elif data > self.root:
if self.right == None:
self.right = BST(data)
else:
self.right.insert(data)
elif data < self.root:
if self.left == None:
self.left = BST(data)
else:
self.left.insert(data)
def inordertraversal(self):
if self.left != None:
self.left.inordertraversal()
print (self.root),
if self.right != None:
self.right.inordertraversal()
t = BST(4)
t.insert(1)
t.insert(7)
t.insert(3)
t.insert(6)
t.insert(2)
t.insert(5)
t.inordertraversal()
A simple, recursive method with only 1 function and using an array of values:
class TreeNode(object):
def __init__(self, value: int, left=None, right=None):
super().__init__()
self.value = value
self.left = left
self.right = right
def __str__(self):
return str(self.value)
def create_node(values, lower, upper) -> TreeNode:
if lower > upper:
return None
index = (lower + upper) // 2
value = values[index]
node = TreeNode(value=value)
node.left = create_node(values, lower, index - 1)
node.right = create_node(values, index + 1, upper)
return node
def print_bst(node: TreeNode):
if node:
# Simple pre-order traversal when printing the tree
print("node: {}".format(node))
print_bst(node.left)
print_bst(node.right)
if __name__ == '__main__':
vals = [0, 1, 2, 3, 4, 5, 6]
bst = create_node(vals, lower=0, upper=len(vals) - 1)
print_bst(bst)
As you can see, we really only need 1 method, which is recursive: create_node. We pass in the full values array in each create_node method call, however, we update the lower and upper index values every time that we make the recursive call.
Then, using the lower and upper index values, we calculate the index value of the current node and capture it in value. This value is the value for the current node, which we use to create a node.
From there, we set the values of left and right by recursively calling the function, until we reach the end state of the recursion call when lower is greater than upper.
Important: we update the value of upper when creating the left side of the tree. Conversely, we update the value of lower when creating the right side of the tree.
Hopefully this helps!
The following code is basic on #DTing‘s answer and what I learn from class, which uses a while loop to insert (indicated in the code).
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
y = None
x = root
z = node
#while loop here
while x is not None:
y = x
if z.data < x.data:
x = x.l_child
else:
x = x.r_child
z.parent = y
if y == None:
root = z
elif z.data < y.data:
y.l_child = z
else:
y.r_child = z
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print(root.data)
in_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
in_order_print(r)
The problem, or at least one problem with your code is here:-
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
You see the statement "if node.data > someNumber:" and the associated "else:" statement both have the same code after them. i.e you do the same thing whether the if statement is true or false.
I'd suggest you probably intended to do different things here, perhaps one of these should say self.insert(node.lchild, someNumber) ?
Another Python BST solution
class Node(object):
def __init__(self, value):
self.left_node = None
self.right_node = None
self.value = value
def __str__(self):
return "[%s, %s, %s]" % (self.left_node, self.value, self.right_node)
def insertValue(self, new_value):
"""
1. if current Node doesnt have value then assign to self
2. new_value lower than current Node's value then go left
2. new_value greater than current Node's value then go right
:return:
"""
if self.value:
if new_value < self.value:
# add to left
if self.left_node is None: # reached start add value to start
self.left_node = Node(new_value)
else:
self.left_node.insertValue(new_value) # search
elif new_value > self.value:
# add to right
if self.right_node is None: # reached end add value to end
self.right_node = Node(new_value)
else:
self.right_node.insertValue(new_value) # search
else:
self.value = new_value
def findValue(self, value_to_find):
"""
1. value_to_find is equal to current Node's value then found
2. if value_to_find is lower than Node's value then go to left
3. if value_to_find is greater than Node's value then go to right
"""
if value_to_find == self.value:
return "Found"
elif value_to_find < self.value and self.left_node:
return self.left_node.findValue(value_to_find)
elif value_to_find > self.value and self.right_node:
return self.right_node.findValue(value_to_find)
return "Not Found"
def printTree(self):
"""
Nodes will be in sequence
1. Print LHS items
2. Print value of node
3. Print RHS items
"""
if self.left_node:
self.left_node.printTree()
print(self.value),
if self.right_node:
self.right_node.printTree()
def isEmpty(self):
return self.left_node == self.right_node == self.value == None
def main():
root_node = Node(12)
root_node.insertValue(6)
root_node.insertValue(3)
root_node.insertValue(7)
# should return 3 6 7 12
root_node.printTree()
# should return found
root_node.findValue(7)
# should return found
root_node.findValue(3)
# should return Not found
root_node.findValue(24)
if __name__ == '__main__':
main()
def BinaryST(list1,key):
start = 0
end = len(list1)
print("Length of List: ",end)
for i in range(end):
for j in range(0, end-i-1):
if(list1[j] > list1[j+1]):
temp = list1[j]
list1[j] = list1[j+1]
list1[j+1] = temp
print("Order List: ",list1)
mid = int((start+end)/2)
print("Mid Index: ",mid)
if(key == list1[mid]):
print(key," is on ",mid," Index")
elif(key > list1[mid]):
for rindex in range(mid+1,end):
if(key == list1[rindex]):
print(key," is on ",rindex," Index")
break
elif(rindex == end-1):
print("Given key: ",key," is not in List")
break
else:
continue
elif(key < list1[mid]):
for lindex in range(0,mid):
if(key == list1[lindex]):
print(key," is on ",lindex," Index")
break
elif(lindex == mid-1):
print("Given key: ",key," is not in List")
break
else:
continue
size = int(input("Enter Size of List: "))
list1 = []
for e in range(size):
ele = int(input("Enter Element in List: "))
list1.append(ele)
key = int(input("\nEnter Key for Search: "))
print("\nUnorder List: ",list1)
BinaryST(list1,key)
class TreeNode:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
class BinaryTree:
def __init__(self, root=None):
self.root = root
def add_node(self, node, value):
"""
Node points to the left of value if node > value; right otherwise,
BST cannot have duplicate values
"""
if node is not None:
if value < node.value:
if node.left is None:
node.left = TreeNode(value)
else:
self.add_node(node.left, value)
else:
if node.right is None:
node.right = TreeNode(value)
else:
self.add_node(node.right, value)
else:
self.root = TreeNode(value)
def search(self, value):
"""
Value will be to the left of node if node > value; right otherwise.
"""
node = self.root
while node is not None:
if node.value == value:
return True # node.value
if node.value > value:
node = node.left
else:
node = node.right
return False
def traverse_inorder(self, node):
"""
Traverse the left subtree of a node as much as possible, then traverse
the right subtree, followed by the parent/root node.
"""
if node is not None:
self.traverse_inorder(node.left)
print(node.value)
self.traverse_inorder(node.right)
def main():
binary_tree = BinaryTree()
binary_tree.add_node(binary_tree.root, 200)
binary_tree.add_node(binary_tree.root, 300)
binary_tree.add_node(binary_tree.root, 100)
binary_tree.add_node(binary_tree.root, 30)
binary_tree.traverse_inorder(binary_tree.root)
print(binary_tree.search(200))
if __name__ == '__main__':
main()

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