Count subarrays having total distinct elements same as original array Last Updated : 09 Mar, 2023 Comments Improve Suggest changes Like Article Like Report Try it on GfG Practice Given an array of n integers. Count the total number of sub-arrays having total distinct elements, the same as that of the total distinct elements of the original array. Examples: Input : arr[] = {2, 1, 3, 2, 3} Output : 5 Total distinct elements in array is 3 Total sub-arrays that satisfy the condition are: Subarray from index 0 to 2 Subarray from index 0 to 3 Subarray from index 0 to 4 Subarray from index 1 to 3 Subarray from index 1 to 4 Input : arr[] = {2, 4, 5, 2, 1} Output : 2 Input : arr[] = {2, 4, 4, 2, 4} Output : 9 Recommended PracticeEquivalent Sub-ArraysTry It! A Naive approach is to run a loop one inside another and consider all sub-arrays and, for every sub-array, count all distinct elements by using hashing and compare them with the total distinct elements of the original array. Initialise an unordered set unst1 to count distinct elements.Initialise a variable totalDist for total number of distinct elements in given array.Generate all the subarray and for every element count the distinct element in that subarray.Check if the number of distinct elements of the current subarray is equal to totalDist then increment the count by 1.Finally, return count. Below is the implementation of the above approach: C++ // C++ program Count total number of sub-arrays // having total distinct elements same as that // original array. #include <bits/stdc++.h> using namespace std; // Function to calculate distinct sub-array int countDistictSubarray(int arr[], int n) { unordered_set<int> unst1; for (int i = 0; i < n; i++) unst1.insert(arr[i]); int totalDist = unst1.size(); int count = 0; for (int i = 0; i < n; i++) { unordered_set<int> unst; for (int j = i; j < n; j++) { unst.insert(arr[j]); if (unst.size() == totalDist) count++; } } return count; } // Driver code int main() { int arr[] = { 2, 1, 3, 2, 3 }; int n = sizeof(arr) / sizeof(arr[0]); cout << countDistictSubarray(arr, n) << endl; return 0; } // This code is contributed by hkdass001 Java import java.util.*; // Function to calculate distinct sub-array public class Gfg { public static int countDistictSubarray(int[] arr, int n) { Set<Integer> unst1 = new HashSet<>(); for (int i = 0; i < n; i++) unst1.add(arr[i]); int totalDist = unst1.size(); int count = 0; for (int i = 0; i < n; i++) { Set<Integer> unst = new HashSet<>(); for (int j = i; j < n; j++) { unst.add(arr[j]); if (unst.size() == totalDist) count++; } } return count; } // Driver code public static void main(String[] args) { int[] arr = { 2, 1, 3, 2, 3 }; int n = arr.length; System.out.println(countDistictSubarray(arr, n)); } } Python3 # Python3 program to count total number of sub-arrays # having total distinct elements same as that # original array. # Function to calculate distinct sub-array def countDistictSubarray(arr, n): unst1 = set(arr) totalDist = len(unst1) count = 0 for i in range(n): unst = set() for j in range(i, n): unst.add(arr[j]) if len(unst) == totalDist: count += 1 return count # Driver code arr = [2, 1, 3, 2, 3] n = len(arr) print(countDistictSubarray(arr, n)) # This code is contributed by Prajwal Kandekar C# using System; using System.Collections.Generic; class Gfg { public static int countDistictSubarray(int[] arr, int n) { HashSet<int> unst1 = new HashSet<int>(); for (int i = 0; i < n; i++) unst1.Add(arr[i]); int totalDist = unst1.Count; int count = 0; for (int i = 0; i < n; i++) { HashSet<int> unst = new HashSet<int>(); for (int j = i; j < n; j++) { unst.Add(arr[j]); if (unst.Count == totalDist) count++; } } return count; } // Driver code public static void Main(string[] args) { int[] arr = { 2, 1, 3, 2, 3 }; int n = arr.Length; Console.WriteLine(countDistictSubarray(arr, n)); } } // This code is contributed by divya_p123. JavaScript // Javascript program Count total number of sub-arrays // having total distinct elements same as that // original array. // Function to calculate distinct sub-array function countDistinctSubarray(arr, n) { const unst1 = new Set(arr); const totalDist = unst1.size; let count = 0; for (let i = 0; i < n; i++) { const unst = new Set(); for (let j = i; j < n; j++) { unst.add(arr[j]); if (unst.size === totalDist) { count += 1; } } } return count; } // Driver code const arr = [2, 1, 3, 2, 3]; const n = arr.length; console.log(countDistinctSubarray(arr, n)); Output5 Time Complexity: O(n*n)Auxiliary Space: O(n) An efficient approach is to use a sliding window to count all distinct elements in one iteration. Find the number of distinct elements in the entire array. Let this number be k <= N. Initialize Left = 0, Right = 0 and window = 0. Increment right until the number of distinct elements in the range [Left=0, Right] is equal to k(or window size would not equal to k), let this right be R1. Now, since the sub-array [Left = 0, R1] has k distinct elements, so all the sub-arrays starting at Left = 0 and ending after R1 will also have k distinct elements. Thus, add N-R1+1 to the answer because [Left.. R1], [Left.. R1+1], [Left.. R1+2] ... [Left.. N-1] contains all the distinct numbers. Now keeping R1 same, increment left. Decrease the frequency of the previous element i.e., arr[0], and if its frequency becomes 0, decrease the window size. Now, the sub-array is [Left = 1, Right = R1]. Repeat the same process from step 2 for other values of Left and Right till Left < N. Implementation: C++ // C++ program Count total number of sub-arrays // having total distinct elements same as that // original array. #include<bits/stdc++.h> using namespace std; // Function to calculate distinct sub-array int countDistictSubarray(int arr[], int n) { // Count distinct elements in whole array unordered_map<int, int> vis; for (int i = 0; i < n; ++i) vis[arr[i]] = 1; int k = vis.size(); // Reset the container by removing all elements vis.clear(); // Use sliding window concept to find // count of subarrays having k distinct // elements. int ans = 0, right = 0, window = 0; for (int left = 0; left < n; ++left) { while (right < n && window < k) { ++vis[ arr[right] ]; if (vis[ arr[right] ] == 1) ++window; ++right; } // If window size equals to array distinct // element size, then update answer if (window == k) ans += (n - right + 1); // Decrease the frequency of previous element // for next sliding window --vis[ arr[left] ]; // If frequency is zero then decrease the // window size if (vis[ arr[left] ] == 0) --window; } return ans; } // Driver code int main() { int arr[] = {2, 1, 3, 2, 3}; int n = sizeof(arr) / sizeof(arr[0]); cout << countDistictSubarray(arr, n) <<"n"; return 0; } Java // Java program Count total number of sub-arrays // having total distinct elements same as that // original array. import java.util.HashMap; class Test { // Method to calculate distinct sub-array static int countDistictSubarray(int arr[], int n) { // Count distinct elements in whole array HashMap<Integer, Integer> vis = new HashMap<Integer,Integer>(){ @Override public Integer get(Object key) { if(!containsKey(key)) return 0; return super.get(key); } }; for (int i = 0; i < n; ++i) vis.put(arr[i], 1); int k = vis.size(); // Reset the container by removing all elements vis.clear(); // Use sliding window concept to find // count of subarrays having k distinct // elements. int ans = 0, right = 0, window = 0; for (int left = 0; left < n; ++left) { while (right < n && window < k) { vis.put(arr[right], vis.get(arr[right]) + 1); if (vis.get(arr[right])== 1) ++window; ++right; } // If window size equals to array distinct // element size, then update answer if (window == k) ans += (n - right + 1); // Decrease the frequency of previous element // for next sliding window vis.put(arr[left], vis.get(arr[left]) - 1); // If frequency is zero then decrease the // window size if (vis.get(arr[left]) == 0) --window; } return ans; } // Driver method public static void main(String args[]) { int arr[] = {2, 1, 3, 2, 3}; System.out.println(countDistictSubarray(arr, arr.length)); } } Python3 # Python3 program Count total number of # sub-arrays having total distinct elements # same as that original array. # Function to calculate distinct sub-array def countDistictSubarray(arr, n): # Count distinct elements in whole array vis = dict() for i in range(n): vis[arr[i]] = 1 k = len(vis) # Reset the container by removing # all elements vid = dict() # Use sliding window concept to find # count of subarrays having k distinct # elements. ans = 0 right = 0 window = 0 for left in range(n): while (right < n and window < k): if arr[right] in vid.keys(): vid[ arr[right] ] += 1 else: vid[ arr[right] ] = 1 if (vid[ arr[right] ] == 1): window += 1 right += 1 # If window size equals to array distinct # element size, then update answer if (window == k): ans += (n - right + 1) # Decrease the frequency of previous # element for next sliding window vid[ arr[left] ] -= 1 # If frequency is zero then decrease # the window size if (vid[ arr[left] ] == 0): window -= 1 return ans # Driver code arr = [2, 1, 3, 2, 3] n = len(arr) print(countDistictSubarray(arr, n)) # This code is contributed by # mohit kumar 29 C# // C# program Count total number of sub-arrays // having total distinct elements same as that // original array. using System; using System.Collections.Generic; class Test { // Method to calculate distinct sub-array static int countDistictSubarray(int []arr, int n) { // Count distinct elements in whole array Dictionary<int, int> vis = new Dictionary<int,int>(); for (int i = 0; i < n; ++i) if(!vis.ContainsKey(arr[i])) vis.Add(arr[i], 1); int k = vis.Count; // Reset the container by removing all elements vis.Clear(); // Use sliding window concept to find // count of subarrays having k distinct // elements. int ans = 0, right = 0, window = 0; for (int left = 0; left < n; ++left) { while (right < n && window < k) { if(vis.ContainsKey(arr[right])) vis[arr[right]] = vis[arr[right]] + 1; else vis.Add(arr[right], 1); if (vis[arr[right]] == 1) ++window; ++right; } // If window size equals to array distinct // element size, then update answer if (window == k) ans += (n - right + 1); // Decrease the frequency of previous element // for next sliding window if(vis.ContainsKey(arr[left])) vis[arr[left]] = vis[arr[left]] - 1; // If frequency is zero then decrease the // window size if (vis[arr[left]] == 0) --window; } return ans; } // Driver method public static void Main(String []args) { int []arr = {2, 1, 3, 2, 3}; Console.WriteLine(countDistictSubarray(arr, arr.Length)); } } // This code is contributed by PrinciRaj1992 JavaScript <script> // Javascript program Count total number of sub-arrays // having total distinct elements same as that // original array. // Method to calculate distinct sub-array function countDistictSubarray(arr,n) { // Count distinct elements in whole array let vis = new Map(); for (let i = 0; i < n; ++i) vis.set(arr[i], 1); let k = vis.size; // Reset the container by removing all elements let vid=new Map(); // Use sliding window concept to find // count of subarrays having k distinct // elements. let ans = 0, right = 0, window = 0; for (let left = 0; left < n; left++) { while (right < n && window < k) { if(vid.has(arr[right])) vid.set(arr[right], vid.get(arr[right]) + 1); else vid.set(arr[right], 1); if (vid.get(arr[right])== 1) window++; right++; } // If window size equals to array distinct // element size, then update answer if (window == k) ans += (n - right + 1); // Decrease the frequency of previous element // for next sliding window if(vid.has(arr[left])) vid.set(arr[left], vid.get(arr[left])- 1); // If frequency is zero then decrease the // window size if (vid.get(arr[left]) == 0) --window; } return ans; } // Driver method let arr=[2, 1, 3, 2, 3]; document.write(countDistictSubarray(arr, arr.length)); // This code is contributed by patel2127 </script> Output5n Time complexity: O(n) Auxiliary space: O(n) Comment More infoAdvertise with us Next Article Count subarrays having total distinct elements same as original array S Shubham Bansal Improve Article Tags : Hash DSA Arrays sliding-window Practice Tags : ArraysHashsliding-window Similar Reads Hashing in Data Structure Hashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. 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If k is more than the number of distinct elements, print -1.Examples:Input: arr[] = {1, 2, 1, 3, 4, 2}, k = 2Output: 7 min read Intermediate problems on HashingFind Itinerary from a given list of ticketsGiven a list of tickets, find the itinerary in order using the given list.Note: It may be assumed that the input list of tickets is not cyclic and there is one ticket from every city except the final destination.Examples:Input: "Chennai" -> "Bangalore" "Bombay" -> "Delhi" "Goa" -> "Chennai" 11 min read Find number of Employees Under every ManagerGiven a 2d matrix of strings arr[][] of order n * 2, where each array arr[i] contains two strings, where the first string arr[i][0] is the employee and arr[i][1] is his manager. The task is to find the count of the number of employees under each manager in the hierarchy and not just their direct rep 9 min read Longest Subarray With Sum Divisible By KGiven an arr[] containing n integers and a positive integer k, he problem is to find the longest subarray's length with the sum of the elements divisible by k.Examples:Input: arr[] = [2, 7, 6, 1, 4, 5], k = 3Output: 4Explanation: The subarray [7, 6, 1, 4] has sum = 18, which is divisible by 3.Input: 10 min read Longest Subarray with 0 Sum Given an array arr[] of size n, the task is to find the length of the longest subarray with sum equal to 0.Examples:Input: arr[] = {15, -2, 2, -8, 1, 7, 10, 23}Output: 5Explanation: The longest subarray with sum equals to 0 is {-2, 2, -8, 1, 7}Input: arr[] = {1, 2, 3}Output: 0Explanation: There is n 10 min read Longest Increasing consecutive subsequenceGiven N elements, write a program that prints the length of the longest increasing consecutive subsequence. Examples: Input : a[] = {3, 10, 3, 11, 4, 5, 6, 7, 8, 12} Output : 6 Explanation: 3, 4, 5, 6, 7, 8 is the longest increasing subsequence whose adjacent element differs by one. Input : a[] = {6 10 min read Count Distinct Elements In Every Window of Size KGiven an array arr[] of size n and an integer k, return the count of distinct numbers in all windows of size k. Examples: Input: arr[] = [1, 2, 1, 3, 4, 2, 3], k = 4Output: [3, 4, 4, 3]Explanation: First window is [1, 2, 1, 3], count of distinct numbers is 3. Second window is [2, 1, 3, 4] count of d 10 min read Design a data structure that supports insert, delete, search and getRandom in constant timeDesign a data structure that supports the following operations in O(1) time.insert(x): Inserts an item x to the data structure if not already present.remove(x): Removes item x from the data structure if present. search(x): Searches an item x in the data structure.getRandom(): Returns a random elemen 5 min read Subarray with Given Sum - Handles Negative NumbersGiven an unsorted array of integers, find a subarray that adds to a given number. If there is more than one subarray with the sum of the given number, print any of them.Examples: Input: arr[] = {1, 4, 20, 3, 10, 5}, sum = 33Output: Sum found between indexes 2 and 4Explanation: Sum of elements betwee 13 min read Implementing our Own Hash Table with Separate Chaining in JavaAll data structure has their own special characteristics, for example, a BST is used when quick searching of an element (in log(n)) is required. A heap or a priority queue is used when the minimum or maximum element needs to be fetched in constant time. Similarly, a hash table is used to fetch, add 10 min read Implementing own Hash Table with Open Addressing Linear ProbingIn Open Addressing, all elements are stored in the hash table itself. So at any point, size of table must be greater than or equal to total number of keys (Note that we can increase table size by copying old data if needed).Insert(k) - Keep probing until an empty slot is found. Once an empty slot is 13 min read Maximum possible difference of two subsets of an arrayGiven an array of n-integers. The array may contain repetitive elements but the highest frequency of any element must not exceed two. You have to make two subsets such that the difference of the sum of their elements is maximum and both of them jointly contain all elements of the given array along w 15+ min read Sorting using trivial hash functionWe have read about various sorting algorithms such as heap sort, bubble sort, merge sort and others. Here we will see how can we sort N elements using a hash array. But this algorithm has a limitation. We can sort only those N elements, where the value of elements is not large (typically not above 1 15+ min read Smallest subarray with k distinct numbersWe are given an array consisting of n integers and an integer k. We need to find the smallest subarray [l, r] (both l and r are inclusive) such that there are exactly k different numbers. If no such subarray exists, print -1 and If multiple subarrays meet the criteria, return the one with the smalle 14 min read Like