通常，復制一個對象應該創(chuàng)建一個精確的克隆：如果它有緩存的值，也應該復制它們。如果不這樣做，這通常是對深度復制的速度優(yōu)化，不會有明顯的副作用。在

如果您正在制作某個內(nèi)容的副本，并且希望清除該副本中的緩存值，則應顯式清除該緩存。在

如果您確實希望對象的副本不復制緩存，那么定義__copy__或{}方法來控制復制。(請注意，這通常用于復制底層資源，如文件描述符和句柄)。在

這里有兩個例子。在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.

"""

def __init__(self, func):

self.func = func

self.cache = {}

def __copy__(self):

"""

Don't copy the cache in a copy.

"""

return memoized(self.func)

def __deepcopy__(self, memo):

"""

Don't copy the cache in a deep copy.

"""

return memoized(self.func)

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 __repr__(self):

"""Return the function's docstring."""

return self.func.__doc__

def __get__(self, obj, objtype):

"""Support instance methods."""

return functools.partial(self.__call__, obj)

def clear_cache(self):

self.cache = {}

@memoized

def fibonacci(n):

"Return the nth fibonacci number."

if n in (0, 1):

return n

return fibonacci(n-1) + fibonacci(n-2)

fibonacci(12)

print fibonacci.cache

fibonacci.clear_cache()

print fibonacci.cache

fibonacci(12)

print fibonacci.cache

import copy

f = copy.deepcopy(fibonacci)

print f.cache

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