On Sep 14, 4:05 pm, Scott David Daniels wrote:
> Steven D'Aprano wrote:
> > On Sun, 13 Sep 2009 17:58:14 -0500, Robert Kern wrote:
> > Exactly -- there are 2**53 distinct floats on most IEEE systems, the vast
> > majority of which might as well be "random". What's the point of caching
> > numbers
Gabriel Genellina wrote:
En Sun, 13 Sep 2009 20:53:26 -0300, Steven D'Aprano
escribió:
There may be something to be said for caching "common" floats, like pi,
small integers (0.0, 1.0, 2.0, ...), 0.5, 0.25 and similar, but I doubt
the memory savings would be worth the extra complexity.
Pi i
Steven D'Aprano wrote:
On Sun, 13 Sep 2009 17:58:14 -0500, Robert Kern wrote:
Exactly -- there are 2**53 distinct floats on most IEEE systems, the vast
majority of which might as well be "random". What's the point of caching
numbers like 2.5209481723210079? Chances are it will never come up aga
En Sun, 13 Sep 2009 20:53:26 -0300, Steven D'Aprano
escribió:
There may be something to be said for caching "common" floats, like pi,
small integers (0.0, 1.0, 2.0, ...), 0.5, 0.25 and similar, but I doubt
the memory savings would be worth the extra complexity.
I've read some time ago, that
On Sep 13, 4:17 pm, Carl Banks wrote:
> On Sep 13, 3:18 pm, John Ladasky wrote:
>
> > In my leisure time, I would like to dig deeper into the issue of why
> > object identities are not guaranteed for elements in numpy arrays...
> > with elements of type "float", at least, I thought this would be
On Sun, 13 Sep 2009 17:58:14 -0500, Robert Kern wrote:
> John Ladasky wrote:
>
>> In my leisure time, I would like to dig deeper into the issue of why
>> object identities are not guaranteed for elements in numpy arrays...
>> with elements of type "float", at least, I thought this would be
>> tri
On Sep 13, 3:18 pm, John Ladasky wrote:
> In my leisure time, I would like to dig deeper into the issue of why
> object identities are not guaranteed for elements in numpy arrays...
> with elements of type "float", at least, I thought this would be
> trivial.
Unlike Python lists, numpy arrays don
John Ladasky wrote:
In my leisure time, I would like to dig deeper into the issue of why
object identities are not guaranteed for elements in numpy arrays...
with elements of type "float", at least, I thought this would be
trivial.
Why do you think that? We would have to keep a reference aroun
John Ladasky wrote:
> OK, so there's a dedicated function in numpy to handle this. Thanks!
>
> I tried "x is NaN" after noting the obvious, that any equality or
> inequality test involving NaN will return False.
>
> In my leisure time, I would like to dig deeper into the issue of why
> object id
Hi Robert,
Thanks for the quick reply.
On Sep 13, 1:22 pm, Robert Kern wrote:
> The problem is that you are trying to use "is" to compare by Python object
> identity. Except for dtype=object arrays, the object identities of the
> individual elements that you extract from numpy arrays are never
John Ladasky wrote:
Hi folks,
I am aware that numpy has its own discussion group, which is hosted at
gmane. Unfortunately, I can't seem to get in to gmane today.
It is not hosted at GMane. It just has a GMane mirror.
http://www.scipy.org/Mailing_Lists
In any case, I'm not sure whether I
Hi folks,
I am aware that numpy has its own discussion group, which is hosted at
gmane. Unfortunately, I can't seem to get in to gmane today.
In any case, I'm not sure whether I have a problem with numpy, or with
my understanding of the Python pickle module, so I'm posting here.
I am pickling n
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