Memoizing decorator. Has the same API as the functools.lru_cache() in Py3.2 but without the LRU feature, so it takes less memory, runs faster, and doesn't need locks to keep the dictionary in a consistent state.
Python, 76 lines
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from functools import wraps
_CacheInfo = namedtuple("CacheInfo", "hits misses maxsize currsize")
def cache():
"""Memoizing cache decorator.
Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses maxsize, size) with
f.cache_info(). Clear the cache and statistics with f.cache_clear().
"""
def decorating_function(user_function,
tuple=tuple, sorted=sorted, len=len, KeyError=KeyError):
cache = dict()
hits = misses = 0
kwd_mark = object() # separates positional and keyword args
@wraps(user_function)
def wrapper(*args, **kwds):
nonlocal hits, misses
key = args
if kwds:
key += (kwd_mark,) + tuple(sorted(kwds.items()))
try:
result = cache[key]
hits += 1
except KeyError:
result = user_function(*args, **kwds)
cache[key] = result
misses += 1
return result
def cache_info():
"""Report cache statistics"""
return _CacheInfo(hits, misses, None, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
nonlocal hits, misses
cache.clear()
hits = misses = 0
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return wrapper
return decorating_function
# ----- Example ----------------------------------------------------------------
if __name__ == '__main__':
@cache()
def fib(n):
if n < 2:
return 1
return fib(n-1) + fib(n-2)
from random import shuffle
inputs = list(range(30))
shuffle(inputs)
results = sorted(fib(n) for n in inputs)
print(results)
print(fib.cache_info())
expected_output = '''[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,
233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657,
46368, 75025, 121393, 196418, 317811, 514229, 832040]
CacheInfo(hits=56, misses=30, maxsize=None, currsize=30)
'''
|
Fast, lightweight alternative to the LRU cache in Py3.2. Use the LRU version for long running processes that need to free-up memory. Use this for whenever cumulative cache growth isn't an issue.
The @cache() syntax is used instead of @cache to keep the API as close as possible to the LRU cache.
Tags: cache, memoization
Isn't that the same thing as " functools.lru_cache(maxsize=None) " ?