On Thu, Mar 13, 2014 at 1:03 AM, Alan G Isaac wrote:
> On 3/12/2014 6:04 PM, Nathaniel Smith wrote:
>>https://github.com/numpy/numpy/pull/4351
>
> The Semantics section still begins with 0d, then 2d, then 1d, then nd.
> Given the context of the proposal, the order should be:
>
> 2d (the core n
On 3/12/2014 6:04 PM, Nathaniel Smith wrote:
>https://github.com/numpy/numpy/pull/4351
The Semantics section still begins with 0d, then 2d, then 1d, then nd.
Given the context of the proposal, the order should be:
2d (the core need expressed in the proposal)
nd (which generalizes via broadca
Hi all,
The proposal to add an infix operator to Python for matrix
multiplication is nearly ready for its debut on python-ideas; so if
you want to look it over first, just want to check out where it's
gone, then now's a good time:
https://github.com/numpy/numpy/pull/4351
The basic idea here is
On Wed, Mar 12, 2014 at 11:12 AM, Leo Mao wrote:
> Hi Aron,
>
> Previously mentioned by Julian, Yeppp may be a good candidate.
> As for selecting a good library, I will consider the performance and the
> API of the library.
> The integration of the library should improve the performance of numpy
Hi Aron,
Previously mentioned by Julian, Yeppp may be a good candidate.
As for selecting a good library, I will consider the performance and the
API of the library.
The integration of the library should improve the performance of numpy and
also not make the source too complicated to maintain.
And
Hi Leo,
Out of curiosity, which vector math libraries did you have in mind as
likely candidates for inclusion? How are you planning on selecting the
library to integrate?
Cheers,
Aron
On Wed, Mar 12, 2014 at 12:52 PM, Leo Mao wrote:
> Hi,
> The attachment is my draft of proposal. The project
Hi,
The attachment is my draft of proposal. The project is "vector math library
integration".
I think I need some feedback to make it solider.
Any comment will be appreciated.
Thanks in advance.
Regards,
Leo Mao
<<< text/html; charset=US-ASCII; name="proposal.html": Unrecognized >>>
__
I'm pleased to announce the release of Biggus version 0.5.0.
Biggus is a pure Python library for handling virtual n-dimensional arrays
of arbitrary size, and providing lazy/deferred evaluation of arithmetic and
statistical operations. Biggus works with your n-dimensional array data in
whatever for