Quick Python: Memoization
There is often a trade-off when it comes to efficiency of CPU vs memory usage. In this post, I will show how the
lru_cache decorator can cache results of a function call for quicker future lookup.
from functools import lru_cache @lru_cache(maxsize=2**7) def fib(n): if n == 1: return 0 if n == 2: return 1 return f(n - 1) + f(n - 2)
In the code above,
maxsize indicates the number of calls to store. Setting it to
None will make it so that there is no upper bound. The documentation recommends setting it equal to a power of two.
Do note though that
lru_cache does not make the execution of the lines in the function faster. It only stores the results of the function in a dictionary.