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2958. Length of Longest Subarray With at Most K Frequency

Description

You are given an integer array nums and an integer k.

The frequency of an element x is the number of times it occurs in an array.

An array is called good if the frequency of each element in this array is less than or equal to k.

Return the length of the longest good subarray of nums.

A subarray is a contiguous non-empty sequence of elements within an array.

 

Example 1:

Input: nums = [1,2,3,1,2,3,1,2], k = 2
Output: 6
Explanation: The longest possible good subarray is [1,2,3,1,2,3] since the values 1, 2, and 3 occur at most twice in this subarray. Note that the subarrays [2,3,1,2,3,1] and [3,1,2,3,1,2] are also good.
It can be shown that there are no good subarrays with length more than 6.

Example 2:

Input: nums = [1,2,1,2,1,2,1,2], k = 1
Output: 2
Explanation: The longest possible good subarray is [1,2] since the values 1 and 2 occur at most once in this subarray. Note that the subarray [2,1] is also good.
It can be shown that there are no good subarrays with length more than 2.

Example 3:

Input: nums = [5,5,5,5,5,5,5], k = 4
Output: 4
Explanation: The longest possible good subarray is [5,5,5,5] since the value 5 occurs 4 times in this subarray.
It can be shown that there are no good subarrays with length more than 4.

 

Constraints:

  • 1 <= nums.length <= 105
  • 1 <= nums[i] <= 109
  • 1 <= k <= nums.length

Solutions

Solution 1: Two Pointers

We can use two pointers $j$ and $i$ to represent the left and right endpoints of the subarray, initially both pointers point to the first element of the array.

Next, we iterate over each element $x$ in the array $nums$. For each element $x$, we increment the occurrence count of $x$, then check if the current subarray meets the requirements. If the current subarray does not meet the requirements, we move the pointer $j$ one step to the right, and decrement the occurrence count of $nums[j]$, until the current subarray meets the requirements. Then we update the answer $ans = \max(ans, i - j + 1)$. Continue the iteration until $i$ reaches the end of the array.

The time complexity is $O(n)$, and the space complexity is $O(n)$. Here, $n$ is the length of the array $nums$.

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class Solution:
    def maxSubarrayLength(self, nums: List[int], k: int) -> int:
        cnt = defaultdict(int)
        ans = j = 0
        for i, x in enumerate(nums):
            cnt[x] += 1
            while cnt[x] > k:
                cnt[nums[j]] -= 1
                j += 1
            ans = max(ans, i - j + 1)
        return ans

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class Solution {
    public int maxSubarrayLength(int[] nums, int k) {
        Map<Integer, Integer> cnt = new HashMap<>();
        int ans = 0;
        for (int i = 0, j = 0; i < nums.length; ++i) {
            cnt.merge(nums[i], 1, Integer::sum);
            while (cnt.get(nums[i]) > k) {
                cnt.merge(nums[j++], -1, Integer::sum);
            }
            ans = Math.max(ans, i - j + 1);
        }
        return ans;
    }
}

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class Solution {
public:
    int maxSubarrayLength(vector<int>& nums, int k) {
        unordered_map<int, int> cnt;
        int ans = 0;
        for (int i = 0, j = 0; i < nums.size(); ++i) {
            ++cnt[nums[i]];
            while (cnt[nums[i]] > k) {
                --cnt[nums[j++]];
            }
            ans = max(ans, i - j + 1);
        }
        return ans;
    }
};

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func maxSubarrayLength(nums []int, k int) (ans int) {
    cnt := map[int]int{}
    for i, j, n := 0, 0, len(nums); i < n; i++ {
        cnt[nums[i]]++
        for ; cnt[nums[i]] > k; j++ {
            cnt[nums[j]]--
        }
        ans = max(ans, i-j+1)
    }
    return
}

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function maxSubarrayLength(nums: number[], k: number): number {
    const cnt: Map<number, number> = new Map();
    let ans = 0;
    for (let i = 0, j = 0; i < nums.length; ++i) {
        cnt.set(nums[i], (cnt.get(nums[i]) ?? 0) + 1);
        for (; cnt.get(nums[i])! > k; ++j) {
            cnt.set(nums[j], cnt.get(nums[j])! - 1);
        }
        ans = Math.max(ans, i - j + 1);
    }
    return ans;
}

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