<p>通常,复制一个对象应该创建一个精确的克隆:如果它有缓存的值,也应该复制它们。如果不这样做,这通常是对深度复制的速度优化,不会有明显的副作用。在</p>
<p>如果您正在制作某个内容的副本,并且希望清除该副本中的缓存值,则应显式清除该缓存。在</p>
<p>如果您确实希望对象的副本不复制缓存,那么定义<code>__copy__</code>或{<cd2>}方法来控制复制。(请注意,这通常用于复制底层资源,如文件描述符和句柄)。在</p>
<p>这里有两个例子。在</p>
<pre><code>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
</code></pre>