Can you help me with this memoization simple example?
MRAB
python at mrabarnett.plus.com
Sat Mar 30 20:39:09 EDT 2024
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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