from functools import lru_cache不允许参数是list,fromfunctoolsimportlru_cache不允许参数是list
5)functools.lru_cache(maxsize=128,typed=False) lru 最近最少使用,cache缓存。 如果maxsize设置的是none,所以禁用了LRU功能,并且缓存可以无限制的增长,当maxsize是2的次幂的时候,LRU执行良好。 如果typed设置为True,则不同类型的函数参数将单独缓存,例如;f(3)和f(3.0)视为不同结果的不同调用。 import functo...
lru_cache是Python标准库functools中的一个函数装饰器,用于缓存函数的结果。当被装饰的函数再次被调用时,如果其参数与之前某次调用完全相同,则直接从缓存中返回结果,而不是重新执行函数体。这样可以显著提高程序的执行效率,特别是对于那些计算成本较高或IO密集型的函数。lru_cache使用最近最少使用(Least Recently Used,...
# in memory caching decorator: ref https://dbader.org/blog/python-memoization @functools.lru_cache(1) def getSampleInfoList(raw_data_path): sample_list = [] with open(str(raw_data_path) + '/labels.txt', mode = 'r') as f: reader = csv.reader(f, delimiter = ' ') for i, row...
import functools import importlib import inspect from typing import Dict, Callable, Union @functools.lru_cache(maxsize=None) def import_class(class_string) -> Callable: class_string = class_string.split('.') module_name = '.'.join(class_string[:-1]).strip() class_name = class_string[-...
@functools.lru_cache(maxsize=None) def _removals(version: tuple[int, ...]) -> Mapping[str, set[str]]: ret = {} for k, v in REMOVALS.items(): if k <= version: ret.update(v) return ret def _remove_import(i: int, tokens: list[Token]) -> None: del tokens[i:find_end(toke...
但是,在Python中,标准工具包functools实现了一种名为LRU(Least Recently Used)的缓存策略,可以通过传入参数,来设定缓存最近多少次的计算结果,如果传入参数为None,那么则不缓存。 现在,为了让大家更加容易理解,先来举一个例子, import time as tt def func(): ...
from functools import lru_cache from pandas import Timestamp from time import sleep @lru_cache # 第二次不再执行 包括print 直接输出 def foo(var=None): res = Timestamp.now() print(res, "print") sleep(1) return res if __name__ == "__main__": ...
from functools import cache, lru_cache, partial 2 changes: 1 addition & 1 deletion 2 scipy/spatial/__init__.py Original file line numberDiff line numberDiff line change @@ -108,7 +108,7 @@ """ # noqa: E501 from ._kdtree import * from ._ckdtree import * from ._ckdtree import...
backports.functools_lru_cache 1.6.4 pyhd3eb1b0_0 backports.tempfile 1.0 pyhd3eb1b0_1 backports.weakref 1.0.post1 py_1 bcrypt 3.2.0 py311h80987f9_1 beautifulsoup4 4.12.2 py311hca03da5_0 binaryornot 0.4.4 pyhd3eb1b0_1 black 23.11.0 py311hca03da5_0 ...