Dear Robert,
indeed, this is the difference ! Thanks ! Seeding numpy with 4357
gives identical sequences...
Giovanni
On 05 Nov 2008, at 19:01, Robert Kern wrote:
> On Wed, Nov 5, 2008 at 08:05, Giovanni Samaey
> <[EMAIL PROTECTED]> wrote:
>>>
>>> Hi,
>>> how about other seed values ? I th
On Wed, Nov 5, 2008 at 08:05, Giovanni Samaey
<[EMAIL PROTECTED]> wrote:
>>
>> Hi,
>> how about other seed values ? I thought seed=0, is (often) used to
>> mean a "random", i.e. current time or alike, seed value ... !?
>
> Not in this case: I always get the same sequence with seed=0
> (different f
On Wed, Nov 05, 2008 at 03:19:09PM +0100, Matthieu Brucher wrote:
> > Not in this case: I always get the same sequence with seed=0
> > (different for both implementation, but the same each time I run it.)
> > I got around it by installing pygsl and taking random numbers from
> > there instead of fr
> Not in this case: I always get the same sequence with seed=0
> (different for both implementation, but the same each time I run it.)
> I got around it by installing pygsl and taking random numbers from
> there instead of from numpy.
>
> But I still find it strange to get two different sequences f
>
> Hi,
> how about other seed values ? I thought seed=0, is (often) used to
> mean a "random", i.e. current time or alike, seed value ... !?
Not in this case: I always get the same sequence with seed=0
(different for both implementation, but the same each time I run it.)
I got around it by ins
Sebastian Haase wrote:
>
> Hi,
> how about other seed values ? I thought seed=0, is (often) used to
> mean a "random", i.e. current time or alike, seed value ... !?
>
Not really. A fixed seed means you will always get the exact same serie
of numbers. The seed is the initial condition of your r
On Wed, Nov 5, 2008 at 11:30 AM, Giovanni Samaey
<[EMAIL PROTECTED]> wrote:
> Hi all,
> I have a question concerning the Mersenne Twister random number generation
> in numpy: when I seed it with 0, I get a different sequence of numbers in
> numpy, compared to GSL.
> In numpy:
> r = numpy.Random.Ra
Hi all,
I have a question concerning the Mersenne Twister random number
generation in numpy: when I seed it with 0, I get a different
sequence of numbers in numpy, compared to GSL.
In numpy:
r = numpy.Random.RandomState(seed=0)
r.uniform(size=5) > array([ 0.5488135 , 0.71518937,
