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From: MRAB <python@mrabarnett.plus.com>
Newsgroups: comp.lang.python
Subject: Re: Can you help me with this memoization simple example?
Date: Sun, 31 Mar 2024 00:39:09 +0000
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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)