Implement the BSTIterator class that represents an iterator over the in-order traversal of a binary search tree (BST):
BSTIterator(TreeNode root) Initializes an object of the BSTIterator class. The root of the BST is given as part of the constructor. The pointer should be initialized to a non-existent number smaller than any element in the BST.boolean hasNext() Returns true if there exists a number in the traversal to the right of the pointer, otherwise returns false.int next() Moves the pointer to the right, then returns the number at the pointer.Notice that by initializing the pointer to a non-existent smallest number, the first call to next() will return the smallest element in the BST.
You may assume that next() calls will always be valid. That is, there will be at least a next number in the in-order traversal when next() is called.
Example 1:
Input: ["BSTIterator","next","next","hasNext","next","hasNext","next","hasNext","next","hasNext"], [[[7,3,15,null,null,9,20]],[],[],[],[],[],[],[],[],[]]
Output: [null,3,7,true,9,true,15,true,20,false]Explanation:
BSTIterator bSTIterator = new BSTIterator([7, 3, 15, null, null, 9, 20]);
bSTIterator.next(); // return 3
bSTIterator.next(); // return 7
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 9
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 15
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 20
bSTIterator.hasNext(); // return False
Constraints:
1 <= The number of nodes in the tree <= 100,000.0 <= Node.val <= 1,000,000100,000 calls will be made to hasNext, and next.Follow up:
next() and hasNext() to run in average O(1) time and use O(h) memory, where h is the height of the tree?Before attempting this problem, you should be comfortable with:
The simplest approach is to perform an inorder traversal of the BST upfront and store all values in an array. Since inorder traversal of a BST visits nodes in ascending order, the array will be sorted. Then, next() simply returns the next element from the array, and hasNext() checks if there are more elements remaining.
0.next(), return the value at the current pointer and increment it.hasNext(), check if the pointer is less than the array length.# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class BSTIterator:
def __init__(self, root: Optional[TreeNode]):
self.arr = []
self.itr = 0
def dfs(node):
if not node:
return
dfs(node.left)
self.arr.append(node.val)
dfs(node.right)
dfs(root)
def next(self) -> int:
val = self.arr[self.itr]
self.itr += 1
return val
def hasNext(self) -> bool:
return self.itr < len(self.arr)This is the same approach as the recursive version, but implemented iteratively using an explicit stack. We simulate the recursion by pushing nodes onto the stack, going left as far as possible, then processing nodes and going right. The result is the same sorted array of values.
next(), return the value at the current pointer and increment it.hasNext(), check if the pointer is less than the array length.# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class BSTIterator:
def __init__(self, root: Optional[TreeNode]):
self.arr = []
self.itr = 0
stack = []
while root or stack:
while root:
stack.append(root)
root = root.left
root = stack.pop()
self.arr.append(root.val)
root = root.right
def next(self) -> int:
val = self.arr[self.itr]
self.itr += 1
return val
def hasNext(self) -> bool:
return self.itr < len(self.arr)Instead of flattening the entire tree upfront, we can save memory by only keeping track of the path from the root to the current position. We initialize the stack with all nodes along the leftmost path. When next() is called, we pop the top node, and if it has a right child, we push all nodes along the leftmost path of the right subtree. This way, the stack always contains the ancestors needed to continue the traversal.
next(), pop the top node from the stack as the result.hasNext(), return true if the stack is not empty.# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class BSTIterator:
def __init__(self, root: Optional[TreeNode]):
self.stack = []
while root:
self.stack.append(root)
root = root.left
def next(self) -> int:
res = self.stack.pop()
cur = res.right
while cur:
self.stack.append(cur)
cur = cur.left
return res.val
def hasNext(self) -> bool:
return bool(self.stack)Where is the number of nodes and is the height of the given tree.
This is a slight variation of the previous approach. Instead of initializing the stack in the constructor, we defer the leftward traversal to the next() method. We keep a pointer to the current node and only push nodes onto the stack when next() is called. This makes the constructor O(1) but the logic is essentially the same.
next(), push all nodes from the current node down to its leftmost descendant onto the stack.hasNext(), return true if either the current node is not null or the stack is not empty.# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class BSTIterator:
def __init__(self, root: Optional[TreeNode]):
self.cur = root
self.stack = []
def next(self) -> int:
while self.cur:
self.stack.append(self.cur)
self.cur = self.cur.left
node = self.stack.pop()
self.cur = node.right
return node.val
def hasNext(self) -> bool:
return bool(self.cur) or bool(self.stack)Where is the number of nodes and is the height of the given tree.
BST iterator must return elements in ascending order, which requires inorder traversal (left, root, right). Using preorder or postorder traversal produces elements in the wrong order.
When using the stack-based approach, after popping a node and returning its value, you must push the leftmost path of its right subtree onto the stack. Forgetting this step causes right subtrees to be skipped entirely.
# Wrong: just pop and return
node = stack.pop()
return node.val
# Correct: also handle right subtree
node = stack.pop()
cur = node.right
while cur:
stack.append(cur)
cur = cur.left
return node.valThe hasNext() method must return True if there are more elements to iterate. For the stack-based approach, this means checking if either the stack is non-empty OR the current pointer is not null (depending on the implementation variant).