I would like to retrieve a particular node in a linked list by iterating a specified number of times; for example, to retrieve the 4th node. By implementing __iter__(), I can iterate with a for loop, but I don't know how get a next() function to work. I've commented out my attempt at a next() function; when it is left in, I still get AttributeError: 'LinkedList' object has no attribute 'next'
EDIT: I would like the getNode(self, loc) to return a node at the specified location by calling next() if possible.
Here is the Node and LinkedList classes:
class Node:
def __init__(self, data = None):
self.data = data
self.next = None
def __repr__(self):
return str(self.data)
class LinkedList:
def __init__(self, nodes = None):
self.head = None
if nodes is not None:
node = Node(data=nodes.pop(0))
self.head = node
for elem in nodes:
node.next = Node(data=elem)
node = node.next
def __repr__(self):
node = self.head
nodes = []
while node is not None:
nodes.append(str(node.data))
node = node.next
nodes.append("None")
return " -> ".join(nodes)
def __iter__(self):
node = self.head
while node is not None:
yield node
node = node.next
# def __next__(self):
# return self.next
def getNode(self,loc):
cnt = 0
for i in self:
if cnt < loc:
cnt += 1
else:
break
return i
ll = LinkedList([1,2,3,4,5])
print(ll)
print(ll.getNode(3))
for i in range(3):
print(ll.next())
[OUTPUT]
I
1 -> 2 -> 3 -> 4 -> 5 -> None
4
Traceback (most recent call last):
File "/Users/jk/_python_source/misc_python/_mymisc/Leetcode work/LinkedList.py", line 53, in <module>
print(ll.next())
AttributeError: 'LinkedList' object has no attribute 'next'
You're doing way more work than you have to. Once you've implemented __iter__, the rest falls into place. You can use it to implement pretty much all your other functions, like get_node, __str__ or __repr__, etc.
class Node:
def __init__(self, value):
self.value = value
self.next = None
def __str__(self):
return str(self.value)
class LinkedList:
def __init__(self):
self.head = None
def add(self, value):
if self.head is None:
self.head = Node(value)
else:
for cursor in self:
pass
cursor.next = Node(value)
return self
def get_node(self, node_index):
for index, node in enumerate(self):
if index == node_index:
break
else:
return None
return node
def __str__(self):
return " -> ".join(map(str, self))
def __iter__(self):
cursor = self.head
while cursor is not None:
yield cursor
cursor = cursor.next
ll = LinkedList().add(1).add(2).add(3)
print(ll)
for node_index in 0, 1, 2, 3:
print("The node at index {} is {}".format(node_index, ll.get_node(node_index)))
Output:
1 -> 2 -> 3
The node at index 0 is 1
The node at index 1 is 2
The node at index 2 is 3
The node at index 3 is None
>>>
ThisgetNode()also works:
def getNode(self,loc):
it = iter(self)
for i in range(loc):
next(it)
return next(it)
I want to return a list of sorted order, provided I'm given a start/stop value for the method. For example, if start=2 and end=8, then i want to return a list within that range, implicitly, of the values in the BST in sorted order.
Since I want it to be in sorted order and not allowed to post sort the list after the method call, I think i should traverse the bst via in order traversal. when I test my implementation, first first doctest return [7,9,11] instead of [5,7,9,11] as intended.
from __future__ import annotations
from typing import Any, List, Optional, Tuple
class BinarySearchTree:
"""Binary Search Tree class.
# === Representation Invariants ===
# - If self._root is None, then so are self._left and self._right.
# This represents an empty BST.
# - If self._root is not None, then self._left and self._right
# are BinarySearchTrees.
# - (BST Property) If self is not empty, then
# all items in self._left are <= self._root, and
# all items in self._right are >= self._root.
"""
def __init__(self, root: Optional[Any]) -> None:
"""Initialize a new BST containing only the given root value.
If <root> is None, initialize an empty tree.
"""
if root is None:
self._root = None
self._left = None
self._right = None
else:
self._root = root
self._left = BinarySearchTree(None)
self._right = BinarySearchTree(None)
def is_empty(self) -> bool:
"""Return True if this BST is empty.
>>> bst = BinarySearchTree(None)
>>> bst.is_empty()
True
>>> bst = BinarySearchTree(10)
>>> bst.is_empty()
False
"""
return self._root is None
def items_in_range(self, start: Any, end: Any) -> List:
"""Return the items in this BST between <start> and <end>, inclusive.
Precondition: all items in this BST can be compared with <start> and
<end>.
The items should be returned in sorted order.
As usual, use the BST property to minimize the number of recursive
calls.
>>> bst = BinarySearchTree(7)
>>> left = BinarySearchTree(3)
>>> left._left = BinarySearchTree(2)
>>> left._right = BinarySearchTree(5)
>>> right = BinarySearchTree(11)
>>> right._left = BinarySearchTree(9)
>>> right._right = BinarySearchTree(13)
>>> bst._left = left
>>> bst._right = right
>>> bst.items_in_range(4, 11)
[5, 7, 9, 11]
>>> bst.items_in_range(10, 13)
[11, 13]
"""
if self.is_empty():
return []
else:
#use helper here
if end >= self._root >= start:
return (self._left._helper_items_in_range_left(start)
+ [self._root]
+ self._right._helper_item_in_range_right(end))
elif self._root > end:
return self._left.items_in_range(start,end)
elif self._root < start:
return self._right.items_in_range(start,end)
else:
pass
def _helper_items_in_range_left(self, start):
if self.is_empty():
return []
elif self._root < start:
return []
else:
return self._left._helper_items_in_range_left(start) +\
[self._root] + self._right._helper_items_in_range_left(start)
def _helper_item_in_range_right(self, end):
if self.is_empty():
return []
elif self._root > end:
return []
else:
return self._left._helper_item_in_range_right(end) + [self._root] +\
self._right._helper_item_in_range_right(end)
You could use something like this. Note that I tested it using a different tree structure.
import itertools
from collections import deque
class BSTIterator(object):
def __init__(self, root):
# Constructor takes in a tree root
self.stack = deque()
self._get_min(root)
def _get_min(self, root):
# We need to create our stack, i.e. dive down the left
curr = root
while curr != None:
self.stack.append(curr)
curr = curr.left
def __iter__(self): # Return self as the iterator
return self
def __next__(self): # Every time `next` is called return our data.
try:
curr = self.stack.pop()
self._get_min(curr.right)
return curr.data
except IndexError:
raise StopIteration
Tree type used:
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
Tested with:
root = Node(8)
root.insert(3)
root.insert(10)
root.insert(1)
root.insert(7)
root.insert(12)
root.insert(121)
root.insert(23)
root.insert(19)
root.insert(9)
b_iter = BSTIterator(root)
# root.print_tree()
# Since we now have an iterator we can for loop over it
# such as
# y = [x for x in b_iter]
# or we can slice it like
y = [x for x in itertools.islice(b_iter, 2, 5)]
print(y)
Prints:
[7, 8, 9]
That is how I would define a method returning a list of nodes from a given range (inclusive, non-decreasing order).
class Tree:
def __init__(self, root):
self.root = root
def nodes_in_range(self, start, end):
def search_range(node):
if node is not None:
if start <= node.value:
yield from search_range(node.left)
if start <= node.value <= end:
yield node.value
if end >= node.value:
yield from search_range(node.right)
return list(search_range(self.root))
I'm trying to create 2 single linked lists and find the intersection between them. I'm getting errors such as NameError: obj is not defined for the LinkedList line and would like a working solution. How do I make this work? What am I doing wrong? Am I even close? What is the meaning of life? This is in python.
class IntersectSolution:
def intersect(sll_a, sll_b):
b_x_node = sll_b
while sll_b and not sll_a.search(sll_b.get_data()):
sll_b.get_next()
b_x_node = sll_b
if b_x_node == None:
print("No intersections between nodes.")
print("Intersection node is: {}".format(b_x_node))
class Node:
def __init__(self, data = None, next_node = None):
self.data = data
self.next_node = next_node
def get_data(self):
return self.data
def get_next(self):
return self.next_node
def set_next(self, new_node):
self.next_node = new_node
class LinkedList(obj):
def __init__(self, head = None):
self.head = head
def insert(self, data):
new_node = Node(data)
new_node.set_next(self.head)
self.head = new_node
def size(self):
current = self.head
count = 0
while current:
count += 1
current = current.get_next
return count
def search(self, data):
current = self.head
found = False
while current and found is False:
if current.get_data() == data:
found = True
else:
current = current.get_next()
if current is None:
raise ValueError("Data not in list")
return current
def delete(self, data):
current = self.head
previous = None
found = False
while current and found is False:
if current.get_data() == data:
found = True
else:
previous = current
current = current.get_next()
if current is None:
raise ValueError("Data not in list")
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
a = LinkedList(Node)
b = LinkedList(Node)
for i in range(1, 15, 2):
a.insert(i)
for j in range(23, 8, -3):
b.insert(j)
ISoln = IntersectSolution
ISoln.intersect(a,b)
You can concatenate both linked-lists by implementing a custom __add__ method and then finding the values in the concatenated result that exist in both original lists:
class LinkedList:
def __init__(self, _val=None):
self.val = _val
self._next = None
def insert(self, _val):
if self.val is None:
self.val = _val
else:
getattr(self._next, 'insert', lambda x:setattr(self, '_next', LinkedList(x)))(_val)
def __iter__(self): #iterate over all values in list
yield self.val
yield from [[], self._next][bool(self._next)]
def __add__(self, _list): #concatenate two linkedlists
_l = self.__class__()
for i in _list:
_l.insert(i)
for i in self:
_l.insert(i)
return _l
def __contains__(self, _val): #check if a value exists in the list
if self.val is None:
return False
return True if self.val == _val else getattr(self._next, '__contains__', lambda _:False)(_val)
#classmethod
def intersection(cls, _a, _b):
_result = cls()
for i in (_a+_b):
if i in _a and i in _b and i not in _result:
_result.insert(i)
return _result
l = LinkedList()
for i in range(10):
l.insert(i)
l1 = LinkedList()
for i in range(6, 14):
l1.insert(i)
_intersection = LinkedList.intersection(l, l1)
print([i for i in _intersection])
Output:
[6, 7, 8, 9]
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()