Memoization effectively refers to remembering ("memoization" ? "memorandum" ? to be remembered) results of method calls based on the method inputs and then returning the remembered result rather than computing the result again. You can think of it as a cache for method results. For further details, see page 387 for the definition in Introduction To Algorithms (3e), Cormen et al.
A simple example for computing factorials using memoization in Python would be something like this:
factorial_memo = {}
def factorial(k):
if k < 2: return 1
if k not in factorial_memo:
factorial_memo[k] = k * factorial(k-1)
return factorial_memo[k]
You can get more complicated and encapsulate the memoization process into a class:
class Memoize:
def __init__(self, f):
self.f = f
self.memo = {}
def __call__(self, *args):
if not args in self.memo:
self.memo[args] = self.f(*args)
#Warning: You may wish to do a deepcopy here if returning objects
return self.memo[args]
Then:
def factorial(k):
if k < 2: return 1
return k * factorial(k - 1)
factorial = Memoize(factorial)
A feature known as "decorators" was added in Python 2.4 which allow you to now simply write the following to accomplish the same thing:
@Memoize
def factorial(k):
if k < 2: return 1
return k * factorial(k - 1)
The Python Decorator Library has a similar decorator called memoized
that is slightly more robust than the Memoize
class shown here.