Yaroslav Halchenko li...@onerussian.com wrote:
so I would assume that the devil is indeed in R post-processing and would look
into it (if/when get a chance).
I tried to look into the R source code. It's the worst mess I have ever
seen. I couldn't even find their Mersenne twister.
Sturla
On Mon, 07 Apr 2014, Sturla Molden wrote:
so I would assume that the devil is indeed in R post-processing and would
look
into it (if/when get a chance).
I tried to look into the R source code. It's the worst mess I have ever
seen. I couldn't even find their Mersenne twister.
it is in
On Apr 7, 2014 3:59 AM, Yaroslav Halchenko li...@onerussian.com wrote:
so I would assume that the devil is indeed in R post-processing and would
look
into it (if/when get a chance).
The devil here is the pigeon and the holes problem. Mersenne Twister
generates random integers in a certain
On Mon, Apr 7, 2014 at 3:16 PM, Daπid davidmen...@gmail.com wrote:
On Apr 7, 2014 3:59 AM, Yaroslav Halchenko li...@onerussian.com wrote:
so I would assume that the devil is indeed in R post-processing and would
look
into it (if/when get a chance).
The devil here is the pigeon and the holes
Yaroslav Halchenko li...@onerussian.com wrote:
it is in src/main/RNG.c (ack is nice ;) )... from visual inspection looks
matching
I see... It's a rather vanilla Mersenne Twister, and it just use 32 bits of
randomness. An signed int32 is multiplied by 2.3283064365386963e-10 to
scale it to
Hi NumPy gurus,
We wanted to test some of our code by comparing to results of R
implementation which provides bootstrapped results.
R, Python std library, numpy all have Mersenne Twister RNG implementation. But
all of them generate different numbers. This issue was previously discussed in
Yaroslav Halchenko li...@onerussian.com wrote:
R, Python std library, numpy all have Mersenne Twister RNG implementation.
But
all of them generate different numbers. This issue was previously discussed
in
https://github.com/numpy/numpy/issues/4530 : In Python, and numpy generated
a = np.random.bytes(4*n).view(dtype='u4')
If you for example want a post-processing that just use 32 bits of
randomness per deviate, you can e.g. do something like this:
r =
np.random.bytes(4*n).view(dtype='u4').astype(float)/float(0x+1)
I have no idea what R does, though.
NumPy's
On Sun, 06 Apr 2014, Sturla Molden wrote:
R, Python std library, numpy all have Mersenne Twister RNG implementation.
But
all of them generate different numbers. This issue was previously
discussed in
https://github.com/numpy/numpy/issues/4530 : In Python, and numpy generated
