Problem
You are given an array of integers nums
of length n
and a positive integer k
.
The power of an array is defined as:
- Its maximum element if all of its elements are consecutive and sorted in ascending order.
- -1 otherwise.
You need to find the power of all
subarrays
of nums
of size k
.
Return an integer array results
of size n - k + 1
, where results[i]
is the power of nums[i..(i + k - 1)]
.
Examples
Example 1:
Input: nums = [1,2,3,4,3,2,5], k = 3
Output: [3,4,-1,-1,-1]
Explanation:
There are 5 subarrays of `nums` of size 3:
- `[1, 2, 3]` with the maximum element 3.
- `[2, 3, 4]` with the maximum element 4.
- `[3, 4, 3]` whose elements are **not** consecutive.
- `[4, 3, 2]` whose elements are **not** sorted.
- `[3, 2, 5]` whose elements are **not** consecutive.
Example 2:
Input: nums = [2,2,2,2,2], k = 4
Output: [-1,-1]
Example 3:
Input: nums = [3,2,3,2,3,2], k = 2
Output: [-1,3,-1,3,-1]
Solution
Method 1 - Sliding Window
Here is the approach:
- Sliding window: We’ll use a sliding window of size
k
to examine each subarray fromnums
. - Check consecutiveness: For each subarray, check if the elements are consecutive and sorted in ascending order.
- Determine power: If the subarray elements satisfy the consecutive and ascending condition, find the maximum value of that subarray; otherwise, return
-1
.
Code
Java
class Solution {
public int[] resultsArray(int[] nums, int k) {
int n = nums.length;
int[] ans = new int[n - k + 1];
for (int i = 0; i <= n - k; i++) {
boolean isConsecutive = true;
for (int j = i + 1; j < i + k; j++) {
if (nums[j] != nums[j - 1] + 1) {
isConsecutive = false;
break;
}
}
if (isConsecutive) {
ans[i] = nums[i + k - 1];
} else {
ans[i] = -1;
}
}
return ans;
}
}
Python
class Solution:
def results_array(self, nums: List[int], k: int) -> List[int]:
n = len(nums)
ans = [0] * (n - k + 1)
for i in range(n - k + 1):
is_consecutive = True
for j in range(i + 1, i + k):
if nums[j] != nums[j - 1] + 1:
is_consecutive = False
break
if is_consecutive:
ans[i] = nums[i + k - 1]
else:
ans[i] = -1
return ans
Complexity
- ⏰ Time complexity:
O(n * k)
, wheren
is the length of thenums
array andk
is the subarray size. - 🧺 Space complexity:
O(n - k + 1)
, which is the size of the result array.