[GRASS-dev] using rand(x,y) in r.mapcalc (grass7)

[Paulo van Breugel]

On 21-07-14 19:01, Markus Neteler wrote:
> On Sun, Jul 6, 2014 at 12:25 AM, Glynn Clements
> <glynn at gclements.plus.com> wrote:
>>> Glynn Clements <glynn at gclements.plus.com> wrote:
> ...
>> In ticket #2272, I attached a portable implementation of lrand48(). If
>> desired, we could add this to libgis and use that in preference to any
>> implementation-specific PRNG.
> This would be excellent.
>
>>>> If you want a different result each time, set GRASS_RND_SEED to a
>>>> different value each time, e.g.
> IMHO this is not intuitive at all. I would suggest to invert the
> behaviour for GRASS 7:
> - per default generate random numbers which differ,
> - if the user needs reproducability, then have a env var to enable that.
>
>> The main thing is that I believe that
>> reproducibility should be the default.
> I humbly disagree. This is not what the user expects. It is also the
> opposite of how for example R behaves:
>
> R
>> runif(1)
> [1] 0.5624295
>> runif(1)
> [1] 0.1683853
>
> http://en.wikibooks.org/wiki/R_Programming/Random_Number_Generation#Seed
> " If you want to perform an exact replication of your program, you
> have to specify the seed using the function set.seed()."
>
>> If people have to take explicit
>> action to introduce randomness,
> The problem is that most will not even realize the current behaviour of rand().
>
>> they're more likely to consider the
>> issues involved. If randomised seeds are the default, the lack of
>> reproducibility may not be considered until it is too late.
> The R community (and some users here) think the opposite... when you
> ask for rand() then you expect a random number.
And not only the R community I am sure. In all statistical packages I 
have ever worked with  one can see the same behaviour, a random number 
is random (i.e., each time a different seed), unless the seed is 
explicitly defined by the user. And it seems to be the default behaviour 
by python/numpy:

 >>> import numpy as np
 >>> np.random.random()
0.8351426142559701
 >>> np.random.random()
0.4813823441998394
 >>> np.random.random()
0.7279314267025369

> Just to avoid this:
> https://xkcd.com/221/
>
> Markus

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