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Hash-based data structures and techniques including frequency counting, duplicate detection, and LRU cache implementation.
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| 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)"}} |
Atomic Responsibility: Execute hash-based lookups and data organization.
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
def contains_duplicate(nums: List[int]) -> bool:
"""O(n) time, O(n) space using set."""
return len(nums) != len(set(nums))
def find_duplicates(nums: List[int]) -> List[int]:
"""Find all duplicate elements."""
seen = set()
duplicates = []
for num in nums:
if num in seen:
duplicates.append(num)
else:
seen.add(num)
return duplicates
def find_duplicates_inplace(nums: List[int]) -> List[int]:
"""
Find duplicates with O(1) space using index marking.
Requires: values in range [1, n]
"""
result = []
for num in nums:
index = abs(num) - 1
if nums[index] < 0:
result.append(abs(num))
else:
nums[index] = -nums[index]
return result
from collections import OrderedDict
class LRUCache:
"""
Least Recently Used cache with O(1) operations.
Time: O(1) for get and put
Space: O(capacity)
"""
def __init__(self, capacity: int):
if capacity <= 0:
raise ValueError("Capacity must be positive")
self.cache = OrderedDict()
self.capacity = capacity
def get(self, key: int) -> int:
"""Get value and mark as recently used."""
if key not in self.cache:
return -1
self.cache.move_to_end(key)
return self.cache[key]
def put(self, key: int, value: int) -> None:
"""Add/update value and maintain capacity."""
if key in self.cache:
self.cache.move_to_end(key)
self.cache[key] = value
if len(self.cache) > self.capacity:
self.cache.popitem(last=False)
from collections import defaultdict
def group_anagrams(strs: List[str]) -> List[List[str]]:
"""
Group strings that are anagrams of each other.
Time: O(n * k log k) where k = max string length
Space: O(n * k)
"""
groups = defaultdict(list)
for s in strs:
key = tuple(sorted(s))
groups[key].append(s)
return list(groups.values())
def group_anagrams_count(strs: List[str]) -> List[List[str]]:
"""
Alternative using character count as key.
Time: O(n * k), Space: O(n * k)
"""
groups = defaultdict(list)
for s in strs:
count = [0] * 26
for c in s:
count[ord(c) - ord('a')] += 1
key = tuple(count)
groups[key].append(s)
return list(groups.values())
import pytest
class TestHashingTechniques:
def test_top_k_frequent(self):
assert set(top_k_frequent([1, 1, 1, 2, 2, 3], 2)) == {1, 2}
def test_contains_duplicate(self):
assert contains_duplicate([1, 2, 3, 1]) == True
assert contains_duplicate([1, 2, 3]) == False
def test_lru_cache(self):
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
assert cache.get(1) == 1
cache.put(3, 3)
assert cache.get(2) == -1
def test_group_anagrams(self):
result = group_anagrams(["eat", "tea", "tan", "ate", "nat", "bat"])
assert len(result) == 3
| Issue | Cause | Solution |
|---|---|---|
| KeyError | Missing key access | Use .get() with default |
| TypeError | Unhashable type | Convert list to tuple |
| Memory exceeded | Storing full objects | Store indices only |
□ Key type is hashable?
□ Using get() with default?
□ Handling empty input?
□ Memory efficient storage?