Hi,
On Wed, Mar 26, 2014 at 11:34 AM, Julian Taylor
wrote:
> On 26.03.2014 16:27, Olivier Grisel wrote:
>> Hi Carl,
>>
>> I installed Python 2.7.6 64 bits on a windows server instance from
>> rackspace cloud and then ran get-pip.py and then could successfully
>> install the numpy and scipy wheel
===
Announcing Numexpr 2.4 RC1
===
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It wears mu
Matthew Brett wrote:
> Julian - do you have any opinion on using gotoBLAS instead of OpenBLAS
> for the Windows binaries?
That is basically OpenBLAS too, except with more bugs and no AVX support.
Sturla
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Hi,
On Sun, Apr 6, 2014 at 11:47 AM, Sturla Molden wrote:
> Matthew Brett wrote:
>
>> Julian - do you have any opinion on using gotoBLAS instead of OpenBLAS
>> for the Windows binaries?
>
> That is basically OpenBLAS too, except with more bugs and no AVX support.
I know that OpenBLAS is a fork
MKL BLAS LAPACK has issues as well:
http://software.intel.com/en-us/articles/intel-mkl-110-bug-fixes .
In case of OpenBLAS or GOTOBLAS what precisly is the problem you identify
as showstopper?
Regards
Carl
2014-04-06 20:59 GMT+02:00 Matthew Brett :
> Hi,
>
> On Sun, Apr 6, 2014 at 11:47 AM,
hi,
numpy.random is largely built from a cython file. Up to know numpy git
included generated c sources for this one file.
It is troublesome to have merge 20k line changes for one line bugfixes,
so it is proposed to remove the generated sources from the master branch
in this PR:
https://github.com/
On 4/6/2014 3:51 AM, Francesc Alted wrote:
===
Announcing Numexpr 2.4 RC1
===
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doin
On Tue, Apr 1, 2014 at 4:13 PM, Charles R Harris
wrote:
>
>
>
> On Mon, Mar 24, 2014 at 6:33 PM, Nathaniel Smith wrote:
>>
>> On Mon, Mar 24, 2014 at 11:58 PM, Charles R Harris
>> wrote:
>> > On Mon, Mar 24, 2014 at 5:56 PM, Nathaniel Smith wrote:
>> >>
>> >> On Sat, Mar 22, 2014 at 6:13 PM, Na
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
https
Yaroslav Halchenko 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
> numbers are based on
> a = np.random.bytes(4*n).view(dtype='> 5).astype(np.int32)
b = (np.random.bytes(4*n).view(dtype='> 6).astype(np.int32)
r = (a * 67108864.0 + b) / 9007199254740992.0
Sturla
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Matthew Brett wrote:
> Put another way - does anyone know what bugs in gotoBLAS2 do arise for
> Windows / Intel builds?
http://www.openblas.net/Changelog.txt
There are some bug fixes for x86_64 here.
GotoBLAS (and GotoBLAS2) were the de facto BLAS on many HPC systems, and
are well proven. But
Carl Kleffner wrote:
> MKL BLAS LAPACK has issues as well:
> href="http://software.intel.com/en-us/articles/intel-mkl-110-bug-fixes";>http://software.intel.com/en-us/articles/intel-mkl-110-bug-fixes
> .
> In case of OpenBLAS or GOTOBLAS what precisly is the problem you identify
> as showstopper?
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 generat
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