On Sun, 7 Jun 2015 11:35 pm, Peter Otten wrote:
> Steven D'Aprano wrote:
>
>
>>> I wrote a very simple function to test random:
>>> def test_random(length, multiplier = 10000):
>>> number_list = length * [0]
>>> for i in range(length * multiplier):
>>> number_list[random.randint(0, length - 1)] += 1
>>> minimum = min(number_list)
>>> maximum = max(number_list)
>>> return (minimum, maximum, minimum / maximum)
>>
>> Putting aside the timing aspects, your frequency calculations are not
>> done in a very Pythonic manner.
>
> I would agree if the frequency table were sparse, i. e. many indices with
>
> number_list[index] == 0
>
> but that's not the case with on average 10000 hits per index.
>
>> A better way might be:
>
> I'm too lazy to measure, but this will likely be a tad slower. Did you
> mean to convey this by "Putting aside the timing aspects"?
No, I meant that using a Counter to count stuff is more Pythonic than using
a list.
>> from collections import Counter
>> from random import randint
>>
>> def test_random(length, multiplier = 10000):
>> freq = Counter(
>> randint(0, length - 1) for i in range(length * multiplier)
>> )
>> minimum = min(freq[i] for i in range(length))
>
> How about
>
> if len(freq) < length:
> minimum = 0
> else:
> minimum = min(freq.values())
That's quite nice, and it's extensible to the case where I return the
counter:
if len(freq) != length:
# At least one element never showed up at all.
freq.subtract(dict.fromkeys(range(length), 0)
> Not as an optimisation (replacing randint() with randrange() is probably
> more effective in that department), but because it's closer to being self-
> explanatory.
I thought about replacing randint with randrange, but they are different
functions with different implementations, and could exhibit different
biases. So one might wish to test them separately for statistical
properties.
--
Steven
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