3

我想要的是一个记忆装饰器：

我已经调整了我看到的一个例子，并提出了以下内容： 

import functools

class Memoized(object):
  """Decorator that caches a function's return value each time it is called.
  If called later with the same arguments, the cached value is returned, and
  not re-evaluated.
  """

  __cache = {}

  def __init__(self, func):
    self.func = func
    self.key = (func.__module__, func.__name__)

  def __call__(self, *args):
    try:
      return Memoized.__cache[self.key][args]
    except KeyError:
      value = self.func(*args)
      Memoized.__cache[self.key] = {args : value}
      return value
    except TypeError:
      # uncachable -- for instance, passing a list as an argument.
      # Better to not cache than to blow up entirely.
      return self.func(*args)

  def __get__(self, obj, objtype):
    """Support instance methods."""
    return functools.partial(self.__call__, obj)

  @staticmethod
  def reset():
    Memoized.__cache = {}

我的问题是缓存部分似乎涉及很多开销（例如，对于递归函数）。以下面的函数为例，我可以用非记忆版本调用 fib(30) 十次，比记忆版本更短。

def fib(n):

   if n in (0, 1):
      return n
   return fib(n-1) + fib(n-2)

谁能建议一个更好的方法来编写这个装饰器？（或将我指向一个更好（即更快）的装饰器，它可以满足我的需求）。我对保留方法签名或帮助自省工具“了解”有关装饰函数的任何内容不感兴趣。

谢谢。

PS使用python 2.7

4

2 回答 2

13

您实际上并没有缓存任何数据，因为每次设置新的缓存值时都会覆盖以前的值：

Memoized.__cache[self.key] = {args : value}

例如。

import functools

class Memoized(object):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """

    cache = {}

    def __init__(self, func):
        self.func = func
        self.key = (func.__module__, func.__name__)
        self.cache[self.key] = {}

    def __call__(self, *args):
      try:
          return Memoized.cache[self.key][args]
      except KeyError:
          value = self.func(*args)
          Memoized.cache[self.key][args] = value
          return value
      except TypeError:
          # uncachable -- for instance, passing a list as an argument.
          # Better to not cache than to blow up entirely.
          return self.func(*args)

    def __get__(self, obj, objtype):
        """Support instance methods."""
        return functools.partial(self.__call__, obj)

    @staticmethod
    def reset():
        Memoized.cache = {}
  • fib(30) 没有缓存：2.86742401123
  • fib(30) 带缓存：0.000198125839233

其他一些注意事项：

  • Don't use __prefix; there's no reason to here and it only uglifies the code.
  • Instead of using a single, monolithic, class-attribute dict, give each instance of Memoized its own dict, and keep a registry of Memoized objects. This improves encapsulation, and removes the oddity of depending on the module and function names.

.

import functools
import weakref

class Memoized(object):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.

    >>> counter = 0
    >>> @Memoized
    ... def count():
    ...     global counter
    ...     counter += 1
    ...     return counter

    >>> counter = 0
    >>> Memoized.reset()
    >>> count()
    1
    >>> count()
    1
    >>> Memoized.reset()
    >>> count()
    2

    >>> class test(object):
    ...     @Memoized
    ...     def func(self):
    ...         global counter
    ...         counter += 1
    ...         return counter
    >>> testobject = test()
    >>> counter = 0
    >>> testobject.func()
    1
    >>> testobject.func()
    1
    >>> Memoized.reset()
    >>> testobject.func()
    2
    """

    caches = weakref.WeakSet()

    def __init__(self, func):
        self.func = func
        self.cache = {}
        Memoized.caches.add(self)

    def __call__(self, *args):
      try:
          return self.cache[args]
      except KeyError:
          value = self.func(*args)
          self.cache[args] = value
          return value
      except TypeError:
          # uncachable -- for instance, passing a list as an argument.
          # Better to not cache than to blow up entirely.
          return self.func(*args)

    def __get__(self, obj, objtype):
        """Support instance methods."""
        return functools.partial(self.__call__, obj)

    @staticmethod
    def reset():
        for memo in Memoized.caches:
            memo.cache = {}

if __name__ == '__main__':
    import doctest
    doctest.testmod()

Edited: add tests, and use weakref.WeakSet. Note that WeakSet is only available in 2.7 (which the OP is using); for a version that works in 2.6, see the edit history.

于 2011-02-07T22:39:51.670 回答
2

这是一个明显更快的版本。不幸的是reset，实际上不能再完全清除缓存，因为所有实例都在存储它们自己对每个函数字典的引用的本地副本。虽然你可以让它工作：

import functools

class Memoized(object):
  """Decorator that caches a function's return value each time it is called.
  If called later with the same arguments, the cached value is returned, and
  not re-evaluated.
  """

  __cache = {}

  def __init__(self, func):
    self.func = func
    key = (func.__module__, func.__name__)
    if key not in self.__cache:
      self.__cache[key] = {}
    self.mycache = self.__cache[key]

  def __call__(self, *args):
    try:
      return self.mycache[args]
    except KeyError:
      value = self.func(*args)
      self.mycache[args] = value
      return value
    except TypeError:
      # uncachable -- for instance, passing a list as an argument.
      # Better to not cache than to blow up entirely.
      return self.func(*args)

  def __get__(self, obj, objtype):
    """Support instance methods."""
    return functools.partial(self.__call__, obj)

  @classmethod
  def reset(cls):
    for v in cls.__cache.itervalues():
      v.clear()
于 2011-02-07T22:32:47.740 回答