This approach involves using a frequency array to count the number of times each character appears. We then use a set to track frequencies that we have already seen, and decrement frequencies until they are unique.
Time Complexity: O(N), where N is the length of the string. Space Complexity: O(1) because the number of unique characters is limited (26 lowercase English letters).
1def minDeletions(s: str) -> int:
2 from collections import Counter
3
4 freq = Counter(s)
5 used_freqs = set()
6 deletions = 0
7
8 for count in freq.values():
9 # Reduce the frequency until it is unique
10 while count > 0 and count in used_freqs:
11 count -= 1
12 deletions += 1
13 used_freqs.add(count)
14 return deletionsIn this implementation, we use Python's Counter from the collections module to count character frequencies. We also maintain a set to keep track of unique frequencies we have seen. For each character frequency, if it's already in the set, we decrement the frequency until it's unique.
This approach uses a priority queue (or max heap) to manage frequencies, adjusting each frequency to maintain uniqueness from the highest to the lowest.
Time Complexity: O(N log N) due to priority queue operations. Space Complexity: O(1), constrained space regarding character set size.
1function minDeletions(s) {
2 const freq = new Array(26).fill(0);
3 for (let ch of s) {
4 freq[ch.charCodeAt() - 97]++;
5 }
6
7 const maxHeap = [];
8 for (let f of freq) {
9 if (f > 0) maxHeap.push(f);
10 }
11 maxHeap.sort((a, b) => b - a);
12
13 let deletions = 0;
14 while (maxHeap.length > 1) {
15 let top = maxHeap.shift();
16 if (top === maxHeap[0]) {
17 if (top - 1 > 0) maxHeap.push(top - 1);
18 deletions++;
19 }
20 maxHeap.sort((a, b) => b - a);
21 }
22 return deletions;
23}The JavaScript solution mirrors the priority queue logic using a sorted array as a pseudo-max heap. The top element modification and reinserting after decrement mimics the behavior of a max heap.