Hi Scott,
may I ask you which kind of vector / matrix operation in extended precision
(np.longdouble) is supported in 'pint' ? It can't be backed by the
underlying blas library as extended precision functions are not supported
there.
Carl
Am Di., 6. Dez. 2022 um 15:24 Uhr schrieb Charles R Harri
Hi,
Maybe a short introduction like this one:
https://albertcthomas.github.io/good-practices-random-number-generators/
could help to get the idea.
Cheers
Carl
Am Fr., 15. Okt. 2021 um 13:51 Uhr schrieb Ilhan Polat :
> As a cosmic coincidence this happened to me yesterday. My goal: generate
> n
The stackoverflow link above contains a simple testcase:
>>> from scipy.linalg import get_blas_funcs>>> gemm = get_blas_funcs("gemm",
>>> [X, Y])>>> np.all(gemm(1, X, Y) == np.dot(X, Y))True
It would be of interest to benchmark gemm against np.dot. Maybe np.dot
doesn't use blas at al for whateve
https://stackoverflow.com/questions/19839539/how-to-get-faster-code-than-numpy-dot-for-matrix-multiplication
maybe C_CONTIGUOUS vs F_CONTIGUOUS?
Carl
Am Di., 23. Feb. 2021 um 19:52 Uhr schrieb Neal Becker :
> One suspect is that it seems the numpy version was multi-threading.
> This isn't usef
2018-03-09 2:06 GMT+01:00 Nathaniel Smith :
> On Thu, Mar 8, 2018 at 1:52 AM, Gregor Thalhammer
> wrote:
> >
> > Hi,
> >
> > long time ago I wrote a wrapper to to use optimised and parallelized math
> > functions from Intels vector math library
> > geggo/uvml: Provide vectorized math function (MK
Does this still apply:
https://scipy.github.io/old-wiki/pages/License_Compatibility.html
Carl
2017-06-24 22:07 GMT+02:00 Sebastian Berg :
> On Sat, 2017-06-24 at 15:47 -0400, josef.p...@gmail.com wrote:
> >
> >
> > On Sat, Jun 24, 2017 at 3:16 PM, Nathaniel Smith
> > wrote:
> > > On Jun 24, 201
