Prerequisites: DFS, Trees, DSU
Given a tree with of N nodes from value 1 to N and E edges and array arr[] which denotes number associated to each node. You are also given Q queries which contains 2 integers {V, F}. For each query, there is a subtree with vertex V, the task is to check if there exists count of numbers associated with each node in that subtree is F or not. If yes then print True else print False.
Examples:
Input: N = 8, E = { {1, 2}, {1, 3}, {2, 4}, {2, 5}, {5, 8}, {5, 6}, {6, 7} }, arr[] = { 11, 2, 7, 1, -3, -1, -1, -3 }, Q = 3, queries[] = { {2, 3}, {5, 2}, {7, 1} }
Output:
False
True
True
Explanation:
Query 1: No number occurs three times in sub-tree 2
Query 2: Number -1 and -3 occurs 2 times in sub-tree 5
Query 3: Number -1 occurs once in sub-tree 7Input: N = 11, E = { {1, 2}, {1, 3}, {2, 4}, {2, 5}, {4, 9}, {4, 8}, {3, 6}, {3, 7}, {4, 10}, {5, 11} }, arr[] = { 2, -1, -12, -1, 6, 14, 7, -1, -2, 13, 12 }, Q = 2, queries[] = { {2, 2}, {4, 2} }
Output:
False
True
Explanation:
Query 1: No number occurs exactly 2 times in sub-tree 2
Query 2: Number -1 occurs twice in sub-tree 4
Naive Approach: The idea is to traverse the tree using DFS traversal and calculate the frequency of number associated with each vertices of sub-tree, V and store the result in a hashmap. After traversal, we just need to traverse the hashmap to check if the given frequency number exists.
Below is the implementation of the above approach:
Java
// Java program for the above approachimport java.util.ArrayList;import java.util.HashMap;import java.util.Map; @SuppressWarnings("unchecked")public class Main { // To store edges of tree static ArrayList<Integer> adj[]; // To store number associated // with vertex static int num[]; // To store frequency of number static HashMap<Integer, Integer> freq; // Function to add edges in tree static void add(int u, int v) { adj[u].add(v); adj[v].add(u); } // Function returns boolean value // representing is there any number // present in subtree qv having // frequency qc static boolean query(int qv, int qc) { freq = new HashMap<>(); // Start from root int v = 1; // DFS Call if (qv == v) { dfs(v, 0, true, qv); } else dfs(v, 0, false, qv); // Check for frequency for (int fq : freq.values()) { if (fq == qc) return true; } return false; } // Function to implement DFS static void dfs(int v, int p, boolean isQV, int qv) { if (isQV) { // If we are on subtree qv, // then increment freq of // num[v] in freq freq.put(num[v], freq.getOrDefault(num[v], 0) + 1); } // Recursive DFS Call for // adjacency list of node u for (int u : adj[v]) { if (p != u) { if (qv == u) { dfs(u, v, true, qv); } else dfs(u, v, isQV, qv); } } } // Driver Code public static void main(String[] args) { // Given Nodes int n = 8; adj = new ArrayList[n + 1]; for (int i = 1; i <= n; i++) adj[i] = new ArrayList<>(); // Given edges of tree // (root=1) add(1, 2); add(1, 3); add(2, 4); add(2, 5); add(5, 8); add(5, 6); add(6, 7); // Number assigned to each vertex num = new int[] { -1, 11, 2, 7, 1, -3, -1, -1, -3 }; // Total number of queries int q = 3; // Function Call to find each query System.out.println(query(2, 3)); System.out.println(query(5, 2)); System.out.println(query(7, 1)); }} |
false true true
Time Complexity: O(N * Q) Since in each query, tree needs to be traversed.
Auxiliary Space: O(N + E + Q)
Efficient Approach: The idea is to use Extended Disjoint Set Union to the above approach:
- Create an array, size[] to store size of sub-trees.
- Create an array of hashmaps, map[] i.e, map[V][X] = total vertices of number X in sub-tree, V.
- Calculate the size of each subtree using DFS traversal by calling dfsSize().
- Using DFS traversal by calling dfs(V, p), calculate the value of map[V].
- In the traversal, to calculate map[V], choose the adjacent vertex of V having maximum size ( bigU ) except parent vertex, p.
- For join operation pass the reference of map[bigU] to map[V] i.e, map[V] = map[bigU].
- And atlast merge maps of all adjacent vertices, u to map[V], except parent vertex, p and bigU vertex.
- Now, check if map[V] contains frequency F or not. If yes then print True else print False.
Below is the implementation of the efficient approach:
Java
// Java program for the above approachimport java.awt.*;import java.util.ArrayList;import java.util.HashMap;import java.util.Map;import java.util.Map.Entry; @SuppressWarnings("unchecked")public class Main { // To store edges static ArrayList<Integer> adj[]; // num[v] = number assigned // to vertex v static int num[]; // map[v].get(c) = total vertices // in subtree v having number c static Map<Integer, Integer> map[]; // size[v]=size of subtree v static int size[]; static HashMap<Point, Boolean> ans; static ArrayList<Integer> qv[]; // Function to add edges static void add(int u, int v) { adj[u].add(v); adj[v].add(u); } // Function to find subtree size // of every vertex using dfsSize() static void dfsSize(int v, int p) { size[v]++; // Traverse dfsSize recursively for (int u : adj[v]) { if (p != u) { dfsSize(u, v); size[v] += size[u]; } } } // Function to implement DFS Traversal static void dfs(int v, int p) { int mx = -1, bigU = -1; // Find adjacent vertex with // maximum size for (int u : adj[v]) { if (u != p) { dfs(u, v); if (size[u] > mx) { mx = size[u]; bigU = u; } } } if (bigU != -1) { // Passing referencing map[v] = map[bigU]; } else { // If no adjacent vertex // present initialize map[v] map[v] = new HashMap<Integer, Integer>(); } // Update frequency of current number map[v].put(num[v], map[v].getOrDefault(num[v], 0) + 1); // Add all adjacent vertices // maps to map[v] for (int u : adj[v]) { if (u != bigU && u != p) { for (Entry<Integer, Integer> pair : map[u].entrySet()) { map[v].put( pair.getKey(), pair.getValue() + map[v] .getOrDefault( pair.getKey(), 0)); } } } // Store all queries related // to vertex v for (int freq : qv[v]) { ans.put(new Point(v, freq), map[v].containsValue(freq)); } } // Function to find answer for // each queries static void solveQuery(Point queries[], int N, int q) { // Add all queries to qv // where i<qv[v].size() qv = new ArrayList[N + 1]; for (int i = 1; i <= N; i++) qv[i] = new ArrayList<>(); for (Point p : queries) { qv[p.x].add(p.y); } // Get sizes of all subtrees size = new int[N + 1]; // calculate size[] dfsSize(1, 0); // Map will be used to store // answers for current vertex // on dfs map = new HashMap[N + 1]; // To store answer of queries ans = new HashMap<>(); // DFS Call dfs(1, 0); for (Point p : queries) { // Print answer for each query System.out.println(ans.get(p)); } } // Driver Code public static void main(String[] args) { int N = 8; adj = new ArrayList[N + 1]; for (int i = 1; i <= N; i++) adj[i] = new ArrayList<>(); // Given edges (root=1) add(1, 2); add(1, 3); add(2, 4); add(2, 5); add(5, 8); add(5, 6); add(6, 7); // Store number given to vertices // set num[0]=-1 because // there is no vertex 0 num = new int[] { -1, 11, 2, 7, 1, -3, -1, -1, -3 }; // Queries int q = 3; // To store queries Point queries[] = new Point[q]; // Given Queries queries[0] = new Point(2, 3); queries[1] = new Point(5, 2); queries[2] = new Point(7, 1); // Function Call solveQuery(queries, N, q); }} |
false true true
Time Complexity: O(N*logN2)
Auxiliary Space: O(N + E + Q)
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