[我一直在使用以下记忆修饰器（来自很棒的书《 Python Algorithms：掌握Python语言中的基本算法...喜欢它，顺便说一句）。
def memo(func):
cache = {}
@ wraps(func)
def wrap(*args):
if args not in cache:
cache[args] = func(*args)
return cache[args]
return wrap
此装饰器的问题在于，基于字典的缓存意味着我所有的参数都必须是可哈希的。
有人有没有允许使用不可散列的参数（例如字典）的实现（或对此方法进行了调整）？
我知道缺少哈希值意味着“是否在缓存中？”这一问题。变得不平凡，但我只是想问一下。
===编辑以提供上下文===
我正在研究一个函数，该函数在给定模块字典的情况下返回Parnas样式的“使用层次”：依赖项。这是设置：
def uses_hierarchy(requirements):
"""
uses_hierarchy(requirements)
Arguments:
requirements - a dictionary of the form {mod: list of dependencies, }
Return value:
A dictionary of the form {level: list of mods, ...}
Assumptions:
- No cyclical requirements (e.g. if a requires b, b cannot require a).
- Any dependency not listed as a mod assumed to be level 0.
"""
levels = dict([(mod, _level(mod, requirements))
for mod in requirements.iterkeys()])
reversed = dict([(value, []) for value in levels.itervalues()])
for k, v in levels.iteritems():
reversed[v].append(k)
return reversed
def _level(mod, requirements):
if not requirements.has_key(mod):
return 0
dependencies = requirements[mod]
if not dependencies:
return 0
else:
return max([_level(dependency, requirements)
for dependency in dependencies]) + 1
这样：
>>> requirements = {'a': [],
... 'b': [],
... 'c': ['a'],
... 'd': ['a','b'],
... 'e': ['c','d'],
... 'f': ['e']
... }
>>> uses_hierarchy(requirements)
{0: ['a', 'b'], 1: ['c', 'd'], 2: ['e'], 3: ['f']}
_ level是我要记住的功能，以使此设置更具可伸缩性。在没有记忆的情况下实现时，它会多次计算依赖级别（例如，我在上面的示例中认为“ a”被计算了8次）。
谢谢，
麦克
这里是Alex Martelli Python Cookbook中的示例，该示例显示了如何使用cPickle创建带有可变参数的函数的备忘录装饰器（原始版本：
import cPickle
class MemoizeMutable:
def __init__(self, fn):
self.fn = fn
self.memo = {}
def __call__(self, *args, **kwds):
import cPickle
str = cPickle.dumps(args, 1)+cPickle.dumps(kwds, 1)
if not self.memo.has_key(str):
print "miss" # DEBUG INFO
self.memo[str] = self.fn(*args, **kwds)
else:
print "hit" # DEBUG INFO
return self.memo[str]
这里是link。
EDIT：使用您提供的代码和此备注修饰符：
_level = MemoizeMutable(_level)
equirements = {'a': [],
'b': [],
'c': ['a'],
'd': ['a','b'],
'e': ['c','d'],
'f': ['e']
}
print uses_hierarchy(equirements)
我能够重现此内容：
miss
miss
hit
miss
miss
hit
miss
hit
hit
hit
miss
hit
{0: ['a', 'b'], 1: ['c', 'd'], 2: ['e'], 3: ['f']}
从技术上讲，您可以通过将dict（或list或set）变成一个元组来解决此问题。例如：
key = tuple(the_dict.iteritems())
key = tuple(the_list)
key = tuple(sorted(the_set))
cache[key] = func( .. )
但是我不会在memo中执行此操作，我希望更改要在其上使用备忘录的功能-例如，与其接受dict，他们应该只接受(key, value)对，而不是接受列出或设置它们应该只取*args。
未经严格测试，但似乎可以工作：
from functools import wraps
def memo(func):
cache = []
argslist = []
@ wraps(func)
def wrap(*args):
try:
result = cache[argslist.index(args)]
print 'cache hit'
return result
except ValueError:
argslist.append(args)
cache.append(func(*args))
print 'cache miss'
return cache[-1]
return wrap
d1 = { 'a':3, 'b':42 }
d2 = { 'c':7, 'd':19 }
d3 = { 'e':34, 'f':67 }
@memo
def func(d):
return sum(d.values())
print func(d1)
# cache miss
# 45
print func(d2)
# cache miss
# 26
print func(d3)
# cache miss
# 101
print func(d2)
# cache hit
# 26
由于没有其他人提到过它，因此Python Wiki拥有一个装饰器库，其中包含许多memoizing decorator patterns。
我个人的偏好是最后一个，它允许调用代码将方法简单地视为延迟评估的属性，而不是方法。但我更喜欢here的实现。
class lazy_property(object):
'''Decorator: Enables the value of a property to be lazy-loaded.
From Mercurial's util.propertycache
Apply this decorator to a no-argument method of a class and you
will be able to access the result as a lazy-loaded class property.
The method becomes inaccessible, and the property isn't loaded
until the first time it's called. Repeated calls to the property
don't re-run the function.
This takes advantage of the override behavior of Descriptors -
__get__ is only called if an attribute with the same name does
not exist. By not setting __set__ this is a non-data descriptor,
and "If an instance's dictionary has an entry with the same name
as a non-data descriptor, the dictionary entry takes precedence."
- http://users.rcn.com/python/download/Descriptor.htm
To trigger a re-computation, 'del' the property - the value, not
this class, will be deleted, and the value will be restored upon
the next attempt to access the property.
'''
def __init__(self,func):
self.func = func
self.name = func.__name__
def __get__(self, obj, type=None):
result = self.func(obj)
setattr(obj, self.name, result)
return result
在上面链接的同一文件中，还有一个lazy_dict装饰器，使您可以将函数视为具有延迟计算值的字典。