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This approach uses a hash map to create nodes and establish their parent-child relationships. The root node is determined by finding nodes that haven't been a child in any relationship.
Time Complexity: O(N), where N is the number of descriptions.
Space Complexity: O(V), where V is the number of unique nodes.
1class TreeNode {
2 constructor(val, left = null, right = null) {
3 this.val = val;
4 this.left = left;
5 this.right = right;
6 }
7}
8
9function createBinaryTree(descriptions) {
10 const nodeMap = new Map();
11 const children = new Set();
12
13 for (const [parent, child, isLeft] of descriptions) {
14 if (!nodeMap.has(parent)) {
15 nodeMap.set(parent, new TreeNode(parent));
16 }
17 if (!nodeMap.has(child)) {
18 nodeMap.set(child, new TreeNode(child));
19 }
20
21 if (isLeft === 1) {
22 nodeMap.get(parent).left = nodeMap.get(child);
23 } else {
24 nodeMap.get(parent).right = nodeMap.get(child);
25 }
26 children.add(child);
27 }
28
29 for (const [key, node] of nodeMap) {
30 if (!children.has(key)) {
31 return node;
32 }
33 }
34 return null;
35}
36
The JavaScript solution uses a Map to keep track of each TreeNode instance along with their values. A Set identifies nodes that serve as children. The tree is constructed through relationship mapping, and the root node is determined by checking which node hasn't been listed as a child.
This approach uses a two-pass algorithm: the first pass creates all nodes independently, and the second pass links them based on the descriptive relationships. This avoids simultaneous creation and linking, providing a clearer separation of concerns between node creation and linkage.
Time Complexity: O(N), where N is the number of descriptions.
Space Complexity: O(V), where V is the number of unique nodes.
The Python implementation separates creation and linking stages into two passes. Initial dictionary setup covers all potential nodes while recognizing child nodes, then linkage development iterates through ensuring accurate relationship formation.