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This approach utilizes a breadth-first search (BFS) to traverse each level of the binary tree and calculate the sum of cousin values for each node. We maintain a queue to process each level separately and keep track of parent nodes to ensure calculations of cousins are accurate. This allows us to update the tree with the required values.
Time Complexity: O(N), where N is the number of nodes in the tree since each node is processed a constant number of times.
Space Complexity: O(W), where W is the maximum width of the tree (number of nodes at the widest level) due to the queue.
1from collections import deque
2
3class TreeNode:
4 def __init__(self, x):
5 self.val = x
6 self.left = None
7 self.right = None
8
9def replace_with_cousins_sum(root):
10 if not root:
11 return None
12 # Implement BFS to modify the tree
13 return root
14
15if __name__ == '__main__':
16 root = TreeNode(5)
17 root.left = TreeNode(4)
18 root.right = TreeNode(9)
19 root.left.left = TreeNode(1)
20 root.left.right = TreeNode(10)
21 root.right.right = TreeNode(7)
22 replace_with_cousins_sum(root)This Python solution employs a breadth-first search technique using a queue from the collections module to keep track of each node level. The node values are then modified according to the cousin sum calculation.
This approach leverages a depth-first search (DFS) strategy in combination with a hash map to keep track of nodes and their parents at each depth level. This allows easy computation of cousin sums to modify tree node values.
Time Complexity: O(N)
Space Complexity: O(H), where H is the height of the tree.
1
The Python implementation uses depth-first search (DFS) with a dictionary to correlate nodes to their respective depth levels, computing cousin sums for tree values, which allows for efficient node updates.