Find length of longest subarray with contiguous ones after replacement
Problem Statement
Given an array containing 0s and 1s, if you are allowed to replace no more than ‘k’ 0s with 1s, find the length of the longest contiguous subarray having all 1s.
Example 1: Input: Array=[0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1], k=2 Output: 6 Explanation: Replace the ‘0’ at index 5 and 8 to have the longest contiguous subarray of 1s having length 6.Example 2: Input: Array=[0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1], k=3 Output: 9 Explanation: Replace the ‘0’ at index 6, 9, and 10 to have the longest contiguous subarray of 1s having length 9.
Solution
This problem follows the Sliding Window pattern and is quite similar to Longest Substring with same Letters after Replacement. The only difference is that, in the problem, we only have two characters (1s and 0s) in the input arrays.
Following a similar approach, we’ll iterate through the array to add one number at a time in the window. We’ll also keep track of the maximum number of repeating 1s in the current window (let’s call it maxOnesCount). So at any time, we know that we can have a window which has 1s repeating maxOnesCount time, so we should try to replace the remaining 0s. If we have more than ‘k’ remaining 0s, we should shrink the window as we are not allowed to replace more than ‘k’ 0s.
Below is how the algorithm will look like in Javascript:
<script>
function length_of_longest_substring(arr, k) {
let windowStart = 0,
maxLength = 0,
maxOnesCount = 0;
// Try to extend the range [windowStart, windowEnd]
for (windowEnd = 0; windowEnd < arr.length; windowEnd++) {
if (arr[windowEnd] === 1) {
maxOnesCount += 1;
}
// Current window size is from windowStart to windowEnd, overall we have a maximum of 1s
// repeating 'maxOnesCount' times, this means we can have a window with 'maxOnesCount' 1s
// and the remaining are 0s which should replace with 1s.
// now, if the remaining 1s are more than 'k', it is the time to shrink the window as we
// are not allowed to replace more than 'k' 0s
if ((windowEnd - windowStart + 1 - maxOnesCount) > k) {
if (arr[windowStart] === 1) {
maxOnesCount -= 1;
}
windowStart += 1;
}
maxLength = Math.max(maxLength, windowEnd - windowStart + 1);
}
return maxLength;
}
document.write(length_of_longest_substring([0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1], 2)+'<br/>');
document.write(length_of_longest_substring([0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1], 3)+'<br/>');
</script>
Time Complexity
The time complexity of the above algorithm will be O(N) where ‘N’ is the count of numbers in the input array.
Space Complexity
The algorithm runs in constant space O(1).
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