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_k...
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...
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 before getting resultslow_func(1) # already cached - result returned inst...
使用lru_cache 实现缓存/记忆 我在之前的博客中介绍过这一技巧,但我认为它值得用一个简单例子再次进行说明: import functools import time # caching up to 12 different results @functools.lru_cache(maxsize=12) def slow_func(x): time.sleep(2) # Simulate long computation return x slow_func(1) # ...
# 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: ...
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 ...
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 ...
Type:function 可以看出lru_cache使用了LRU算法,在maxsize大小的空间内缓存函数的结果,值得一提的事函数的参数是要可以哈希的,接下来我们利用lru_cache改进我们的递归算法,非常的简单。 代码语言:python 代码运行次数:0 运行 AI代码解释 fromfunctoolsimportlru_cache@lru_cache(100)deffib(n):ifn<2:return1else:re...
>>> return cache['arg'] >>> return wrap 我们也可以对 Fibonacci 函数使用装饰器: >>> @memo >>> def fib(i): >>> if i < 2: return 1 >>> return fib(i-1) + fib(i-2) 这里的关键思想是:增强函数(装饰)函数,记住每个已经计算的Fibonacci值;如果它们在缓存中,就不需要再计算了. ...
To cache the results of an expensive computation, declare it as a global variable. Python Copy CACHED_DATA = None def main(req): global CACHED_DATA if CACHED_DATA is None: CACHED_DATA = load_json() # ... use CACHED_DATA in code Environment variables In Azure Functions, application ...