hashing-techniques

name	hashing-techniques
description	Hash-based data structures and techniques including frequency counting, duplicate detection, and LRU cache implementation.
sasmp_version	1.3.0
bonded_agent	06-hash-tables
bond_type	PRIMARY_BOND
atomic_responsibility	hash_based_operations
version	2.0.0
parameter_validation	{"strict":true,"rules":[{"name":"input_data","type":"any","required":true},{"name":"capacity","type":"integer","required":false}]}
retry_logic	{"max_attempts":3,"backoff_ms":[100,200,400],"retryable_errors":["memory_exceeded","key_error"]}
logging_hooks	{"on_start":true,"on_complete":true,"on_error":true,"log_format":"[HSH-SKILL] {timestamp} | {operation} | {status}"}
complexity_annotations	{"frequency_count":{"time":"O(n)","space":"O(k) unique elements"},"lru_cache":{"time":"O(1) all operations","space":"O(capacity)"},"group_anagrams":{"time":"O(n * k log k)","space":"O(n * k)"}}

Hashing Techniques Skill

Atomic Responsibility: Execute hash-based lookups and data organization.

Frequency Counting Pattern

from typing import List
from collections import Counter
import heapq

def top_k_frequent(nums: List[int], k: int) -> List[int]:
    """
    Find k most frequent elements.

    Time: O(n log k), Space: O(n)
    """
    count = Counter(nums)
    return heapq.nlargest(k, count.keys(), key=count.get)


def top_k_frequent_bucket(nums: List[int], k: int) -> List[int]:
    """
    Bucket sort approach for O(n) time.

    Time: O(n), Space: O(n)
    """
    count = Counter(nums)
    buckets = [[] for _ in range(len(nums) + 1)]

    for num, freq in count.items():
        buckets[freq].append(num)

    result = []
    for i in range(len(buckets) - 1, -1, -1):
        for num in buckets[i]:
            result.append(num)
            if len(result) == k:
                return result

    return result