Find the Median of the Uniqueness Array - Practice Coding Problems

Find the Median of the Uniqueness Array - Problem

You are given an integer array nums. The uniqueness array of nums is the sorted array that contains the number of distinct elements of all the subarrays of nums.

In other words, it is a sorted array consisting of distinct(nums[i..j]), for all 0 <= i <= j < nums.length. Here, distinct(nums[i..j]) denotes the number of distinct elements in the subarray that starts at index i and ends at index j.

Return the median of the uniqueness array of nums.

Note that the median of an array is defined as the middle element of the array when it is sorted in non-decreasing order. If there are two choices for a median, the smaller of the two values is taken.

Input & Output

Example 1 — Basic Case

$ Input: nums = [1,2,1,3]

› Output: 2

💡 Note: All subarrays: [1]=1, [1,2]=2, [1,2,1]=2, [1,2,1,3]=3, [2]=1, [2,1]=2, [2,1,3]=3, [1]=1, [1,3]=2, [3]=1. Uniqueness array: [1,1,1,1,2,2,2,2,3,3]. Median is 2.

Example 2 — All Same Elements

$ Input: nums = [3,3,3]

› Output: 1

💡 Note: All subarrays: [3]=1, [3,3]=1, [3,3,3]=1, [3]=1, [3,3]=1, [3]=1. Uniqueness array: [1,1,1,1,1,1]. Median is 1.

Example 3 — All Different Elements

$ Input: nums = [1,2,3]

› Output: 2

💡 Note: All subarrays: [1]=1, [1,2]=2, [1,2,3]=3, [2]=1, [2,3]=2, [3]=1. Uniqueness array: [1,1,1,2,2,3]. Median is 2.

Constraints

1 ≤ nums.length ≤ 10⁵
1 ≤ nums[i] ≤ 10⁵

Visualization

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Understanding the Visualization

Input Array

Given integer array [1,2,1,3]

Generate Uniqueness

Count distinct elements in all subarrays

Find Median

Sort uniqueness array and return median

Key Takeaway

🎯 Key Insight: Binary search on the median value is more efficient than generating all uniqueness counts

Asked in

G Google 15 M Microsoft 12 a Amazon 8

The key insight is to use binary search on the answer space instead of generating all uniqueness values. The optimal approach uses binary search on median value combined with sliding window technique to efficiently count subarrays. Time: O(n² log n), Space: O(n)

Common Approaches

Approach	Time	Space	Notes
✓ Binary Search on Answer	O(n² log n)	O(n)	Binary search the median value while using sliding window to count subarrays
Generate All Uniqueness Values	O(n³)	O(n²)	Generate uniqueness array by checking all subarrays, then sort and find median

Binary Search on Answer — Algorithm Steps

Binary search on possible uniqueness values (1 to n)
For each candidate, use sliding window to count subarrays with uniqueness ≤ candidate
If count ≥ half of total subarrays, try smaller values, otherwise try larger

Visualization

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Step-by-Step Walkthrough

Binary Search

Search median value in range [1, n]

Count Subarrays

Use sliding window to count subarrays ≤ candidate

Adjust Range

Narrow search based on count vs target

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    int key;
    int value;
} HashEntry;

typedef struct {
    HashEntry entries[10000];
    int size;
} HashMap;

int hashGet(HashMap* map, int key) {
    for (int i = 0; i < map->size; i++) {
        if (map->entries[i].key == key) {
            return map->entries[i].value;
        }
    }
    return 0;
}

void hashPut(HashMap* map, int key, int value) {
    for (int i = 0; i < map->size; i++) {
        if (map->entries[i].key == key) {
            map->entries[i].value = value;
            return;
        }
    }
    map->entries[map->size].key = key;
    map->entries[map->size].value = value;
    map->size++;
}

long long countSubarraysWithUniquenessAtMost(int* nums, int numsSize, int k) {
    long long count = 0;
    int left = 0;
    HashMap freq = {0};
    int distinct = 0;
    
    for (int right = 0; right < numsSize; right++) {
        if (hashGet(&freq, nums[right]) == 0) {
            distinct++;
        }
        hashPut(&freq, nums[right], hashGet(&freq, nums[right]) + 1);
        
        while (distinct > k) {
            int leftVal = hashGet(&freq, nums[left]);
            hashPut(&freq, nums[left], leftVal - 1);
            if (leftVal - 1 == 0) {
                distinct--;
            }
            left++;
        }
        
        count += right - left + 1;
    }
    
    return count;
}

int solution(int* nums, int numsSize) {
    long long totalSubarrays = (long long) numsSize * (numsSize + 1) / 2;
    long long target = (totalSubarrays + 1) / 2;
    
    int left = 1, right = numsSize;
    while (left < right) {
        int mid = (left + right) / 2;
        if (countSubarraysWithUniquenessAtMost(nums, numsSize, mid) >= target) {
            right = mid;
        } else {
            left = mid + 1;
        }
    }
    
    return left;
}

int main() {
    char line[10000];
    fgets(line, sizeof(line), stdin);
    line[strcspn(line, "\n")] = 0;
    
    int nums[1000];
    int numsSize = 0;
    char *token = strtok(line + 1, ",]");
    while (token != NULL) {
        nums[numsSize++] = atoi(token);
        token = strtok(NULL, ",]");
    }
    
    int result = solution(nums, numsSize);
    printf("%d\n", result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n² log n)

O(log n) binary search iterations, each requiring O(n²) sliding window counting

⚠ Quadratic Growth

Space Complexity

O(n)

Hash map for tracking distinct elements in sliding window

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Binary Search on Answer — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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