Solution Explanation: Finding K-Length Substrings with No Repeated Characters

This problem requires finding the count of substrings of length k within a given string s that contain no repeating characters. The optimal approach uses a sliding window technique combined with a hash table (or dictionary/map) to efficiently track character counts within the window.

Algorithm:

1. Initialization:

   • Create a sliding window of size k.
   • Use a hash table (cnt in the code examples) to store the frequency of each character within the current window. Initially, populate cnt with the first k characters of the string s.
   • Initialize a counter ans to 0. This will store the count of valid substrings.

2. Sliding Window Iteration:

   • Iterate through the string s starting from index k.
   • For each character s[i]:
     • Add: Increment the count of s[i] in the hash table (cnt[s[i]] += 1).
     • Remove (from left): Decrement the count of the character leaving the window (s[i-k]) (cnt[s[i-k]] -= 1). If this count becomes 0, remove the character from the hash table (cnt.pop(s[i-k]) in Python, similar operations in other languages).
     • Check Validity: If the size of the hash table (len(cnt) in Python) is equal to k, it means all characters in the current window are unique. Increment ans.

3. Return: After iterating through the entire string, return ans.

Time Complexity Analysis:

The algorithm iterates through the string s once. All operations within the loop (hash table updates and size checks) take constant time on average. Therefore, the overall time complexity is O(n), where n is the length of the string s.

Space Complexity Analysis:

The space complexity is determined by the size of the hash table cnt. In the worst-case scenario (where all characters in the substring are unique), the hash table will store k entries. Therefore, the space complexity is O(min(k, |Σ|)), where |Σ| represents the size of the character set (26 for lowercase English letters). In practice, it will likely be O(k) because the window size k is not expected to be higher than the alphabet size (26) except in contrived cases.

Code Examples (with explanations):

The provided code examples in Python, Java, C++, Go, TypeScript, and PHP all implement this sliding window approach. The core logic remains consistent across all languages:

• Hash Table/Map: A hash table (Python's Counter, Java's HashMap, C++'s unordered_map, Go's map, TypeScript's Map, PHP's array) is used to efficiently track character frequencies within the window.
• Sliding Window: The loop iterates, adding a new character to the right and removing an old character from the left of the window.
• Uniqueness Check: The size of the hash table is used to determine whether all characters within the window are unique.

The minor variations in code syntax are due to the differences in language features, but the fundamental algorithm remains the same. Each example has detailed comments. Choose the example relevant to your preferred programming language.