On Mon, Feb 8, 2021 at 12:10 PM Sebastian Berg
wrote:
>
> This type of change should be in the release notes undoubtedly and
> likely a `.. versionchanged::` directive in the docstring.
>
> Maybe the best thing would be to create a single, prominent but brief,
> changelog listing all (or almost a
On Sat, Feb 6, 2021 at 2:32 AM wrote:
> I tried to implement a different implementation of the ziggurat method for
> generating standard normal distributions that is about twice as fast and
> uses 2/3 of the memory than the old one.
> I tested the implementation separately and am very confident i
ruary 8, 2021 4:06 PM
> To: Discussion of Numerical Python
> Subject: Re: [Numpy-discussion] Question about optimizing
> random_standard_normal
>
> On Mon, Feb 8, 2021 at 10:53 AM Kevin Sheppard <
> kevin.k.shepp...@gmail.com> wrote:
> > My reading is that the first 4 are p
On Mon, Feb 8, 2021 at 11:38 AM Kevin Sheppard
wrote:
> That is good news indeed. Seems like a good upgrade, especially given the
> breadth of application of normals and the multiple appearances within
> distributions.c (e.g., Cauchy). Is there a deprecation for a change like
> this? Or is it ju
this is the first time a substantially new algo has replaced an existing one. Kevin From: Robert KernSent: Monday, February 8, 2021 4:06 PMTo: Discussion of Numerical PythonSubject: Re: [Numpy-discussion] Question about optimizing random_standard_normal On Mon, Feb 8, 2021 at 10:53 AM Kevin Sheppard
On Mon, Feb 8, 2021 at 10:53 AM Kevin Sheppard
wrote:
> My reading is that the first 4 are pure C, presumably using the standard
> practice of inclining so as to make the tightest loop possible, and to
> allow the compiler to make other optimizations. The final line is what
> happens when you re
PythonSubject: Re: [Numpy-discussion] Question about optimizing random_standard_normal On Mon, Feb 8, 2021 at 3:05 AM Kevin Sheppard <kevin.k.shepp...@gmail.com> wrote:If I understand correctly, there is no gain when applying this patch to Generator. I'm not that surprised that this
On Mon, Feb 8, 2021 at 3:05 AM Kevin Sheppard
wrote:
> If I understand correctly, there is no gain when applying this patch to
> Generator. I'm not that surprised that this is the case since the compiler
> is much more limited in when it can do in Generator than what it can when
> filling a larg
If I understand correctly, there is no gain when applying this patch to
Generator. I'm not that surprised that this is the case since the compiler
is much more limited in when it can do in Generator than what it can when
filling a large array directly with functions available for inlining and
unro
‐‐‐ Original Message ‐‐‐
On Saturday, February 6, 2021 3:29 PM, Robert Kern
wrote:
> On Sat, Feb 6, 2021 at 7:27 AM wrote:
>
>>> Well, I can tell you why it needs to be backward compatible! I use random
>>> numbers fairly frequently, and to unit test them I set a specific seed and
>>>
> Well, I can tell you why it needs to be backward compatible! I use random
> numbers fairly frequently, and to unit test them I set a specific seed and
> then make sure I get the same answers.
Hmm, I guess that makes sense. I tried to adjust my algorithms to do the same
thing with the same bit
On Sat, Feb 6, 2021 at 7:27 AM wrote:
> Well, I can tell you why it needs to be backward compatible! I use random
> numbers fairly frequently, and to unit test them I set a specific seed and
> then make sure I get the same answers.
>
> Hmm, I guess that makes sense. I tried to adjust my algorith
On Sat, Feb 6, 2021 at 5:27 AM wrote:
> Well, I can tell you why it needs to be backward compatible! I use random
> numbers fairly frequently, and to unit test them I set a specific seed and
> then make sure I get the same answers.
>
> Hmm, I guess that makes sense. I tried to adjust my algorith
I tried to implement a different implementation of the ziggurat method for
generating standard normal distributions that is about twice as fast and uses
2/3 of the memory than the old one.
I tested the implementation separately and am very confident it's correct, but
it does fail 28 test in cove
Have you benchmarked it using the generator interface? The structure of
this as a no monolithic generator makes it a good deal slower than
generating in straight C (with everything inline). While I'm not sure a
factor of 2 is enough to justify a change (for me 10x, 1.2x is not but I
don't know whe
Well, I can tell you why it needs to be backward compatible! I use random
numbers fairly frequently, and to unit test them I set a specific seed and
then make sure I get the same answers.
If your change went in (and I were using numpy normal distributions, which
I am not) then my tests would brea
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