Hey Steven,
Thank you for having taken the time to comment qualitatively my response.
For the first part :
I think there is a way to map x <- [1; + inf] to y <- [0;1] by putting y =
1/x
I didn't pay attention of the fact stated by @Richard Damon
that the shape of distribution won't be
Senhaji Rhazi hamza writes:
> I think there is a way to map x <- [1; + inf] to y <- [0;1] by
> putting y = 1/x
I don't think that's the point. I'll put money on a poor choice of
value for the random parameter in the example: the OP chose the Pareto
variate because that's what he mentioned
On Aug 18, 2019, at 06:20, Senhaji Rhazi hamza
wrote:
>
> Hey Steven,
>
> I think there is a way to map x <- [1; + inf] to y <- [0;1] by putting y = 1/x
Well, that gives you a distribution from (0, 1] instead of [0. 1), which is
technically not legal as a substitute for random, even though
> On Aug 18, 2019, at 9:20 AM, Senhaji Rhazi hamza
> wrote:
>
> Hey Steven,
>
> I think there is a way to map x <- [1; + inf] to y <- [0;1] by putting y = 1/x
>
One big issue with using that mapping is it doesn’t preserve many of the
characteristics of the initial distribution. If the x
Hey Steven,
I think there is a way to map x <- [1; + inf] to y <- [0;1] by putting y =
1/x
But i think there is another issue to consider :
for example if we want to sort :
random.choice(['a', 'b', 'c'], random=lambda: random.paretovariate(1.75)),
we should have a mapping
between the elements
On Sat, Aug 17, 2019 at 09:02:54AM +0200, Patryk Gałczyński wrote:
> Hi!
>
> Recently I encountered a situation when I needed to pick a random
> population sample but with specific distribution function - paretovariate in
> this particular case.
Let me see if I understand... you have a
On Aug 17, 2019, at 00:02, Patryk Gałczyński wrote:
>
> One thing I'm concern about is how it would all relate to _randommodule.c (I
> have little knowledge about C part)
You probably don’t need to change the C code at all. I’m pretty sure none of
these functions are implemented in C, they
