On Wed, 31 May 2023, 22:41 Robert Kern, wrote:
> Not sure anyone really uses tanh for serious work.
>
At the risk of derailing the discussion, the case I can think of (but kind
of niche) is using neural networks to approximate differential equations.
Then you need non linearities in the gradien
What about having a np.fastmath module for faster, lower precision
implementations? The error guarantees there would be lower, and possibly
hardware dependent. By default we get the high precision version, but if
the user knows what they are doing, they can get the speed.
/David
On Wed, 31 May 20
On Sat, 25 Feb 2023 at 20:09, Louis Petingi
wrote:
> Thank you for the reply. I am working with the Laplacian matrix of a graph
> which is the Degree matrix minus the adjacency matrix.
> The Laplacian is a symmetric matrix and the smallest eigenvalue is zero.
> As the rows add it to 0, Lx=0x, and
On Tue, 28 Jun 2022, 6:50 pm Ralf Gommers, wrote:
>
>
>> ```
>> kind : {None, 'sort', 'table'}, optional
>>
>
> Regarding the name, `'table'` is an implementation detail. The end user
> should not have to care what the data structure is that is used. I suggest
> to use something like "unsorte
On Wed, 25 May 2022, 4:54 pm Thomas Caswell, wrote:
>
> Stealing some language/concepts from Microsoft (if I recall it correctly),
> we should sort out which entries in that support matrix are Level 1 (CI +
> wheels), Level 2 (CI), Level 3 (we test something that looks like this),
> and Level 4 (
I don't have any opinion on whether this is a good idea or not, but in the
meantime, you can monkey-patch numpy:
np.concat = np.concatenate
And you get your compatibility. And, since modules are singletons, I
believe you only have to do it once and it will propagate across your
modules.
/David
On Tue, 22 Feb 2022, 6:53 am Stefan van der Walt,
wrote:
> On Mon, Feb 21, 2022, at 20:56, Juan Nunez-Iglesias wrote:
>
> > the
> > latter would seem consistent with the "principle of least surprise" when
> moving from a typed language to
> > NumPy work perhaps, though arguably slightly less user
On Sat, 29 May 2021, 4:29 pm Ralf Gommers, wrote:
>
>
> On Fri, May 28, 2021 at 4:58 PM wrote:
>
>> Hi all,
>>
>> Finding topk elements is widely used in several fields, but missed in
>> NumPy.
>> I implement this functionality named as numpy.topk using core numpy
>> functions and open a PR:
>>
On Tue, 23 Feb 2021, 7:41 pm Roman Yurchak, wrote:
> For the first benchmark apparently A.dot(B) with A real and B complex is
> a known issue performance wise https://github.com/numpy/numpy/issues/10468
I splitted B into a vector of size (N, 2) for the real and imaginary part,
and that makes th
