On 2024-03-31 09:04, marc nicole wrote:
Thanks for the first comment which I incorporated
but when you say "You can't use a list as a key, but you can use a
tuple as a key,
provided that the elements of the tuple are also immutable."
does it mean the result of sum of the array is not convenient to use
as key as I do?
Which tuple I should use to refer to the underlying list value as you
suggest?
I was suggesting using `tuple` on the argument:
def memoize(f):
cache = {}
def g(*args):
key = tuple(args[0]), args[1]
if key not in cache:
cache[key] = f(args[0], args[1])
return cache[key]
return g
Anything else is good in my code ?
Thanks
Le dim. 31 mars 2024 à 01:44, MRAB via Python-list
<python-list@python.org> a écrit :
On 2024-03-31 00:09, marc nicole via Python-list wrote:
> I am creating a memoization example with a function that adds up
/ averages
> the elements of an array and compares it with the cached ones to
retrieve
> them in case they are already stored.
>
> In addition, I want to store only if the result of the function
differs
> considerably (passes a threshold e.g. 500000 below).
>
> I created an example using a decorator to do so, the results
using the
> decorator is slightly faster than without the memoization which
is OK, but
> is the logic of the decorator correct ? anybody can tell me ?
>
> My code is attached below:
>
>
>
> import time
>
>
> def memoize(f):
> cache = {}
>
> def g(*args):
> if args[1] == "avg":
> sum_key_arr = sum(list(args[0])) / len(list(args[0]))
'list' will iterate over args[0] to make a list, and 'sum' will
iterate
over that list.
It would be simpler to just let 'sum' iterate over args[0].
> elif args[1] == "sum":
> sum_key_arr = sum(list(args[0]))
> if sum_key_arr not in cache:
> for (
> key,
> value,
> ) in (
> cache.items()
> ): # key in dict cannot be an array so I use the
sum of the
> array as the key
You can't use a list as a key, but you can use a tuple as a key,
provided that the elements of the tuple are also immutable.
> if (
> abs(sum_key_arr - key) <= 500000
> ): # threshold is great here so that all
values are
> approximated!
> # print('approximated')
> return cache[key]
> else:
> # print('not approximated')
> cache[sum_key_arr] = f(args[0], args[1])
> return cache[sum_key_arr]
>
> return g
>
>
> @memoize
> def aggregate(dict_list_arr, operation):
> if operation == "avg":
> return sum(list(dict_list_arr)) / len(list(dict_list_arr))
> if operation == "sum":
> return sum(list(dict_list_arr))
> return None
>
>
> t = time.time()
> for i in range(200, 15000):
> res = aggregate(list(range(i)), "avg")
>
> elapsed = time.time() - t
> print(res)
> print(elapsed)
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