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The idea is to identify the type of each node by examining the parent-child relationship. Specifically, to identify:
Steps:
Time Complexity: O(n), where n is the number of nodes, as we traverse through the list of nodes usually once or twice.
Space Complexity: O(n), used by the children count array and the result array.
1import java.util.*;
2
3class Node {
4 int id, p_id;
5 Node(int id, int p_id) { this.id = id; this.p_id = p_id; }
6}
7
8public class TreeNode {
9 public static void classifyNodes(List<Node> nodes) {
10 Map<Integer, Integer> childCount = new HashMap<>();
11 Map<Integer, String> types = new HashMap<>();
12
13 for (Node node : nodes) {
14 if (node.p_id != -1) {
15 childCount.put(node.p_id, childCount.getOrDefault(node.p_id, 0) + 1);
16 }
17 types.put(node.id, "Leaf");
18 }
19
20 for (Node node : nodes) {
21 if (node.p_id == -1) {
22 types.put(node.id, "Root");
23 } else if (childCount.getOrDefault(node.id, 0) > 0) {
24 types.put(node.id, "Inner");
25 }
26 }
27
28 for (Node node : nodes) {
29 System.out.println(node.id + " " + types.get(node.id));
30 }
31 }
32
33 public static void main(String[] args) {
34 List<Node> nodes = Arrays.asList(
35 new Node(1, -1),
36 new Node(2, 1),
37 new Node(3, 1),
38 new Node(4, 2),
39 new Node(5, 2)
40 );
41 classifyNodes(nodes);
42 }
43}
The Java solution organizes the nodes in a List. It applies HashMaps for tracking the count of children. This allows cross-referencing nodes to determine their types as 'Root', 'Inner', or 'Leaf'. Each completed analysis ends with printing the node id and their corresponding type.
This approach centers around identifying nodes type by tracking parent relationships directly:
Steps:
Time Complexity: O(n)
Space Complexity: O(n)
Python's implementation uses a set to distinguish between nodes that are involved as parents. This effectively allows rapid classification of node status, ensuring concise filtering of data.