Comparing simple handrolled memoization to @cache decorator from functools

memoizers.py

# curiosity piqued by this note: https://substack.com/@pythonclcoding/note/c-107253391

from functools import cache

@cache
def fib_cache(n):
    if n <= 1:
        return n
    return fib_cache(n-1)+fib_cache(n-2)

my_cache = {}

def fib_memo(n):
    if n <= 1:
        return n
    if n in my_cache:
        return my_cache[n]
    fib_n = fib_memo(n-1)+fib_memo(n-2)
    my_cache[n] = fib_n
    return fib_n

>>> from test_cache import fib_cache, fib_memo
>>> from timeit import timeit
>>> timeit(lambda: fib_cache(200), number=9001)
0.0011600839789025486
>>> timeit(lambda: fib_memo(200), number=9001)
0.0016002089832909405