import functoolsimport time# caching up to 12 different results@functools.lru_cache(maxsize=12)defslow_func(x): time.sleep(2) # Simulate long computation return xslow_func(1) # ... waiting for 2 sec befor
然后我们使用Python的内置functools的lru_cache函数。 # Example of efficient code # Using Python's functools' lru_cache function import functools @functools.lru_cache() def fibonacci_v2(n): ifn == 0: return0 elifn == 1: return1 returnfib...
使用Python的内置functools的lru_cache函数使用Memoization加速57x。 lru_cache函数是如何实现的? “LRU”是“Least Recently Used”的缩写。lru_cache是一个装饰器,可以应用于函数以启用memoization。它将最近函数调用的结果存储在缓存中,当再次出现相同的输入时,可以提供缓存的结果,从而节省了计算时间。lru_cache函数,当...
~ $ python3.8 -m cProfile -s time slow_program.py 1297 function calls (1272 primitive calls) in 11.081 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 3 11.079 3.693 11.079 3.693 slow_program.py:4(exp) 1 0.000 0.000 0.002 0.002 {built-...
cache[key] = (time.time(), original_function(*args, **kwargs)) return cache[key][1] return wrapper return decorator @cache_decorator(ttl=30) def expensive_computation(a, b): # 假设这是一个耗时较长的计算 time.sleep(1) return a * b ...
First add a @cache decorator to your module:Python decorators.py import functools # ... def cache(func): """Keep a cache of previous function calls""" @functools.wraps(func) def wrapper_cache(*args, **kwargs): cache_key = args + tuple(kwargs.items()) if cache_key not in ...
import functools from decorators import count_calls def cache(func): """Keep a cache of previous function calls""" @functools.wraps(func) def wrapper_cache(*args, **kwargs): cache_key = args + tuple(kwargs.items()) if cache_key not in wrapper_cache.cache: wrapper_cache.cache[cache_...
# Using Python's functools' lru_cache function importfunctools @functools.lru_cache() deffibonacci_v2(n): ifn==0: return0 elifn==1: return1 returnfibonacci_v2(n-1)+fibonacci_v2(n-2) def_test_10_v1(numbers): output=[] foriinnumbers: ...
The lru_cache decorator is a built-in tool in Python that caches the results of expensive function calls. This improves performance by avoiding redundant calculations for repeated inputs. Example: from functools import lru_cache @lru_cache(maxsize=128) def fibonacci(n): if n < 2: return n...
fromfunctoolsimportlru_cacheimportcallgraph.decoratorascallgraph@callgraph()@lru_cache()defnchoosek(n,k):ifk==0:return1ifn==k:return1returnnchoosek(n-1,k-1)+nchoosek(n-1,k)nchoosek(5,2)nchoosek.__callgraph__.view() See theAPI documentationfor additional documentation. ...